how to do research analysis

Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Quantitative data analysis is one of those things that often strikes fear in students. It’s totally understandable – quantitative analysis is a complex topic, full of daunting lingo , like medians, modes, correlation and regression. Suddenly we’re all wishing we’d paid a little more attention in math class…

The good news is that while quantitative data analysis is a mammoth topic, gaining a working understanding of the basics isn’t that hard , even for those of us who avoid numbers and math . In this post, we’ll break quantitative analysis down into simple , bite-sized chunks so you can approach your research with confidence.

Overview: Quantitative Data Analysis 101

• What (exactly) is quantitative data analysis?
• When to use quantitative analysis
• How quantitative analysis works

The two “branches” of quantitative analysis

• Descriptive statistics 101
• Inferential statistics 101
• How to choose the right quantitative methods
• Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

• Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
• Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
• And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

• Mean – this is simply the mathematical average of a range of numbers.
• Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
• Mode – this is simply the most commonly occurring number in the data set.
• In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
• Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
• Skewness . As the name suggests, skewness indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones.

But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

• Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
• Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
• And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Don’t be a sucker – give your descriptive statistics the love and attention they deserve!

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

• Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
• And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

Of course, when you’re working with inferential statistics, the composition of your sample is really important. In other words, if your sample doesn’t accurately represent the population you’re researching, then your findings won’t necessarily be very useful.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post .

What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other.

Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

Here’s a scatter plot demonstrating the correlation (relationship) between weight and height. Intuitively, we’d expect there to be some relationship between these two variables, which is what we see in this scatter plot. In other words, the results tend to cluster together in a diagonal line from bottom left to top right.

As I mentioned, these are are just a handful of inferential techniques – there are many, many more. Importantly, each statistical method has its own assumptions and limitations .

For example, some methods only work with normally distributed (parametric) data, while other methods are designed specifically for non-parametric data. And that’s exactly why descriptive statistics are so important – they’re the first step to knowing which inferential techniques you can and can’t use.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

• The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
• Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Why does this matter?

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

This is another reminder of why descriptive statistics are so important – they tell you all about the shape of your data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

• Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
• The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
• Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
• Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
• To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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A Step-by-Step Guide to the Data Analysis Process

Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it’s important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.

In this post, we’ll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we’ll also use examples and highlight a few tools to make the journey easier. When you’re done, you’ll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.

Here are the steps we’ll take you through:

• Defining the question
• Collecting the data
• Cleaning the data
• Analyzing the data
• Sharing your results
• Embracing failure

On popular request, we’ve also developed a video based on this article. Scroll further along this article to watch that.

Ready? Let’s get started with step one.

1. Step one: Defining the question

The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’.

Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as: “Why are we losing customers?” It’s possible, though, that this doesn’t get to the core of the problem. A data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.

Let’s say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, “Why are we losing customers?” but, “Which factors are negatively impacting the customer experience?” or better yet: “How can we boost customer retention while minimizing costs?”

Now you’ve defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you’ve noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don’t come back? Which sources of data will help you answer this question?

Tools to help define your objective

Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you’ll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and DashThis . However, you’ll also find open-source software like Grafana , Freeboard , and Dashbuilder . These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.

2. Step two: Collecting the data

Once you’ve established your objective, you’ll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let’s explore each one.

What is first-party data?

First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company’s customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.

What is second-party data?

To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.

What is third-party data?

Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data .

Tools to help you collect data

Once you’ve devised a data strategy (i.e. you’ve identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you’ll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP , SAS , and the data integration platform, Xplenty . If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm .

Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics .

3. Step three: Cleaning the data

Once you’ve collected your data, the next step is to get it ready for analysis. This means cleaning, or ‘scrubbing’ it, and is crucial in making sure that you’re working with high-quality data . Key data cleaning tasks include:

• Removing major errors, duplicates, and outliers —all of which are inevitable problems when aggregating data from numerous sources.
• Removing unwanted data points —extracting irrelevant observations that have no bearing on your intended analysis.
• Bringing structure to your data —general ‘housekeeping’, i.e. fixing typos or layout issues, which will help you map and manipulate your data more easily.
• Filling in major gaps —as you’re tidying up, you might notice that important data are missing. Once you’ve identified gaps, you can go about filling them.

A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one…so don’t rush it! You’ll find a step-by-step guide to data cleaning here . You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.

Carrying out an exploratory analysis

Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let’s use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning’s clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.

Tools to help you clean your data

Cleaning datasets manually—especially large ones—can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine , are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder , which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?

4. Step four: Analyzing the data

Finally, you’ve cleaned your data. Now comes the fun bit—analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.

Descriptive analysis

Descriptive analysis identifies what has already happened . It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let’s refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they’ll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.

Learn more: What is descriptive analytics?

Diagnostic analysis

Diagnostic analytics focuses on understanding why something has happened . It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. Remember TopNotch Learning’s business problem? ‘Which factors are negatively impacting the customer experience?’ A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let’s imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they’re losing customers because they lack expertise in this sector. And that’s a useful insight!

Predictive analysis

Predictive analysis allows you to identify future trends based on historical data . In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn’t stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they’ll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.

Prescriptive analysis

Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It’s also the most complex. This is because it incorporates aspects of all the other analyses we’ve described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.

Learn more:  What are the different types of data analysis?

5. Step five: Sharing your results

You’ve finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization’s stakeholders!) This is more complex than simply sharing the raw results of your work—it involves interpreting the outcomes, and presenting them in a manner that’s digestible for all types of audiences. Since you’ll often present information to decision-makers, it’s very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.

How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That’s why it’s very important to provide all the evidence that you’ve gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it’s important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!

Tools for interpreting and sharing your findings

There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts , Tableau , Datawrapper , and Infogram . If you’re familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly , Seaborn , and Matplotlib . Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!

You can learn more about storytelling with data in this free, hands-on tutorial .  We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it’s really like to work as a data analyst!

6. Step six: Embrace your failures

The last ‘step’ in the data analytics process is to embrace your failures. The path we’ve described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you’d never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.

While these pitfalls can feel like failures, don’t be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What’s important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn’t be as interesting. Use the steps we’ve outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.

In this post, we’ve covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst’s work:

• Define the question —What business problem are you trying to solve? Frame it as a question to help you focus on finding a clear answer.
• Collect data —Create a strategy for collecting data. Which data sources are most likely to help you solve your business problem?
• Clean the data —Explore, scrub, tidy, de-dupe, and structure your data as needed. Do whatever you have to! But don’t rush…take your time!
• Analyze the data —Carry out various analyses to obtain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
• Share your results —How best can you share your insights and recommendations? A combination of visualization tools and communication is key.
• Embrace your mistakes —Mistakes happen. Learn from them. This is what transforms a good data analyst into a great one.

What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we’ve described, you can create a tailored technique that works for you.

To learn more, check out our free, 5-day data analytics short course . You might also be interested in the following:

• These are the top 9 data analytics tools
• 10 great places to find free datasets for your next project
• How to build a data analytics portfolio

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

• Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
• SQL : A programming language used to manage and manipulate relational databases.
• R : An open-source programming language and software environment for statistical computing and graphics.
• Python : A general-purpose programming language that is widely used in data analysis and machine learning.
• Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
• SAS : A statistical analysis software used for data management, analysis, and reporting.
• SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
• Matlab : A numerical computing software that is widely used in scientific research and engineering.
• RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

• Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
• Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
• Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
• Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
• Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
• Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
• Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
• Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

• Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
• Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
• Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
• Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
• Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
• Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
• Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

• To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
• To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
• To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
• To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
• To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

• Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
• Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
• Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
• Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
• Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
• Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
• Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

• Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
• Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
• Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
• Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
• Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
• Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
• Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
• Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

• Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
• Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
• Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
• Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
• Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
• Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
• Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
• Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

• Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
• Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
• Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
• Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
• Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
• Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
• Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Home Market Research

Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
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How to conduct a meta-analysis in eight steps: a practical guide

• Open access
• Published: 30 November 2021
• Volume 72 , pages 1–19, ( 2022 )

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• Christopher Hansen 1 ,
• Holger Steinmetz 2 &
• Jörn Block 3 , 4 , 5  

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1 Introduction

“Scientists have known for centuries that a single study will not resolve a major issue. Indeed, a small sample study will not even resolve a minor issue. Thus, the foundation of science is the cumulation of knowledge from the results of many studies.” (Hunter et al. 1982 , p. 10)

Meta-analysis is a central method for knowledge accumulation in many scientific fields (Aguinis et al. 2011c ; Kepes et al. 2013 ). Similar to a narrative review, it serves as a synopsis of a research question or field. However, going beyond a narrative summary of key findings, a meta-analysis adds value in providing a quantitative assessment of the relationship between two target variables or the effectiveness of an intervention (Gurevitch et al. 2018 ). Also, it can be used to test competing theoretical assumptions against each other or to identify important moderators where the results of different primary studies differ from each other (Aguinis et al. 2011b ; Bergh et al. 2016 ). Rooted in the synthesis of the effectiveness of medical and psychological interventions in the 1970s (Glass 2015 ; Gurevitch et al. 2018 ), meta-analysis is nowadays also an established method in management research and related fields.

The increasing importance of meta-analysis in management research has resulted in the publication of guidelines in recent years that discuss the merits and best practices in various fields, such as general management (Bergh et al. 2016 ; Combs et al. 2019 ; Gonzalez-Mulé and Aguinis 2018 ), international business (Steel et al. 2021 ), economics and finance (Geyer-Klingeberg et al. 2020 ; Havranek et al. 2020 ), marketing (Eisend 2017 ; Grewal et al. 2018 ), and organizational studies (DeSimone et al. 2020 ; Rudolph et al. 2020 ). These articles discuss existing and trending methods and propose solutions for often experienced problems. This editorial briefly summarizes the insights of these papers; provides a workflow of the essential steps in conducting a meta-analysis; suggests state-of-the art methodological procedures; and points to other articles for in-depth investigation. Thus, this article has two goals: (1) based on the findings of previous editorials and methodological articles, it defines methodological recommendations for meta-analyses submitted to Management Review Quarterly (MRQ); and (2) it serves as a practical guide for researchers who have little experience with meta-analysis as a method but plan to conduct one in the future.

2 Eight steps in conducting a meta-analysis

2.1 step 1: defining the research question.

The first step in conducting a meta-analysis, as with any other empirical study, is the definition of the research question. Most importantly, the research question determines the realm of constructs to be considered or the type of interventions whose effects shall be analyzed. When defining the research question, two hurdles might develop. First, when defining an adequate study scope, researchers must consider that the number of publications has grown exponentially in many fields of research in recent decades (Fortunato et al. 2018 ). On the one hand, a larger number of studies increases the potentially relevant literature basis and enables researchers to conduct meta-analyses. Conversely, scanning a large amount of studies that could be potentially relevant for the meta-analysis results in a perhaps unmanageable workload. Thus, Steel et al. ( 2021 ) highlight the importance of balancing manageability and relevance when defining the research question. Second, similar to the number of primary studies also the number of meta-analyses in management research has grown strongly in recent years (Geyer-Klingeberg et al. 2020 ; Rauch 2020 ; Schwab 2015 ). Therefore, it is likely that one or several meta-analyses for many topics of high scholarly interest already exist. However, this should not deter researchers from investigating their research questions. One possibility is to consider moderators or mediators of a relationship that have previously been ignored. For example, a meta-analysis about startup performance could investigate the impact of different ways to measure the performance construct (e.g., growth vs. profitability vs. survival time) or certain characteristics of the founders as moderators. Another possibility is to replicate previous meta-analyses and test whether their findings can be confirmed with an updated sample of primary studies or newly developed methods. Frequent replications and updates of meta-analyses are important contributions to cumulative science and are increasingly called for by the research community (Anderson & Kichkha 2017 ; Steel et al. 2021 ). Consistent with its focus on replication studies (Block and Kuckertz 2018 ), MRQ therefore also invites authors to submit replication meta-analyses.

2.2 Step 2: literature search

2.2.1 search strategies.

Similar to conducting a literature review, the search process of a meta-analysis should be systematic, reproducible, and transparent, resulting in a sample that includes all relevant studies (Fisch and Block 2018 ; Gusenbauer and Haddaway 2020 ). There are several identification strategies for relevant primary studies when compiling meta-analytical datasets (Harari et al. 2020 ). First, previous meta-analyses on the same or a related topic may provide lists of included studies that offer a good starting point to identify and become familiar with the relevant literature. This practice is also applicable to topic-related literature reviews, which often summarize the central findings of the reviewed articles in systematic tables. Both article types likely include the most prominent studies of a research field. The most common and important search strategy, however, is a keyword search in electronic databases (Harari et al. 2020 ). This strategy will probably yield the largest number of relevant studies, particularly so-called ‘grey literature’, which may not be considered by literature reviews. Gusenbauer and Haddaway ( 2020 ) provide a detailed overview of 34 scientific databases, of which 18 are multidisciplinary or have a focus on management sciences, along with their suitability for literature synthesis. To prevent biased results due to the scope or journal coverage of one database, researchers should use at least two different databases (DeSimone et al. 2020 ; Martín-Martín et al. 2021 ; Mongeon & Paul-Hus 2016 ). However, a database search can easily lead to an overload of potentially relevant studies. For example, key term searches in Google Scholar for “entrepreneurial intention” and “firm diversification” resulted in more than 660,000 and 810,000 hits, respectively. Footnote 1 Therefore, a precise research question and precise search terms using Boolean operators are advisable (Gusenbauer and Haddaway 2020 ). Addressing the challenge of identifying relevant articles in the growing number of database publications, (semi)automated approaches using text mining and machine learning (Bosco et al. 2017 ; O’Mara-Eves et al. 2015 ; Ouzzani et al. 2016 ; Thomas et al. 2017 ) can also be promising and time-saving search tools in the future. Also, some electronic databases offer the possibility to track forward citations of influential studies and thereby identify further relevant articles. Finally, collecting unpublished or undetected studies through conferences, personal contact with (leading) scholars, or listservs can be strategies to increase the study sample size (Grewal et al. 2018 ; Harari et al. 2020 ; Pigott and Polanin 2020 ).

2.2.2 Study inclusion criteria and sample composition

Next, researchers must decide which studies to include in the meta-analysis. Some guidelines for literature reviews recommend limiting the sample to studies published in renowned academic journals to ensure the quality of findings (e.g., Kraus et al. 2020 ). For meta-analysis, however, Steel et al. ( 2021 ) advocate for the inclusion of all available studies, including grey literature, to prevent selection biases based on availability, cost, familiarity, and language (Rothstein et al. 2005 ), or the “Matthew effect”, which denotes the phenomenon that highly cited articles are found faster than less cited articles (Merton 1968 ). Harrison et al. ( 2017 ) find that the effects of published studies in management are inflated on average by 30% compared to unpublished studies. This so-called publication bias or “file drawer problem” (Rosenthal 1979 ) results from the preference of academia to publish more statistically significant and less statistically insignificant study results. Owen and Li ( 2020 ) showed that publication bias is particularly severe when variables of interest are used as key variables rather than control variables. To consider the true effect size of a target variable or relationship, the inclusion of all types of research outputs is therefore recommended (Polanin et al. 2016 ). Different test procedures to identify publication bias are discussed subsequently in Step 7.

In addition to the decision of whether to include certain study types (i.e., published vs. unpublished studies), there can be other reasons to exclude studies that are identified in the search process. These reasons can be manifold and are primarily related to the specific research question and methodological peculiarities. For example, studies identified by keyword search might not qualify thematically after all, may use unsuitable variable measurements, or may not report usable effect sizes. Furthermore, there might be multiple studies by the same authors using similar datasets. If they do not differ sufficiently in terms of their sample characteristics or variables used, only one of these studies should be included to prevent bias from duplicates (Wood 2008 ; see this article for a detection heuristic).

In general, the screening process should be conducted stepwise, beginning with a removal of duplicate citations from different databases, followed by abstract screening to exclude clearly unsuitable studies and a final full-text screening of the remaining articles (Pigott and Polanin 2020 ). A graphical tool to systematically document the sample selection process is the PRISMA flow diagram (Moher et al. 2009 ). Page et al. ( 2021 ) recently presented an updated version of the PRISMA statement, including an extended item checklist and flow diagram to report the study process and findings.

2.3 Step 3: choice of the effect size measure

2.3.1 types of effect sizes.

The two most common meta-analytical effect size measures in management studies are (z-transformed) correlation coefficients and standardized mean differences (Aguinis et al. 2011a ; Geyskens et al. 2009 ). However, meta-analyses in management science and related fields may not be limited to those two effect size measures but rather depend on the subfield of investigation (Borenstein 2009 ; Stanley and Doucouliagos 2012 ). In economics and finance, researchers are more interested in the examination of elasticities and marginal effects extracted from regression models than in pure bivariate correlations (Stanley and Doucouliagos 2012 ). Regression coefficients can also be converted to partial correlation coefficients based on their t-statistics to make regression results comparable across studies (Stanley and Doucouliagos 2012 ). Although some meta-analyses in management research have combined bivariate and partial correlations in their study samples, Aloe ( 2015 ) and Combs et al. ( 2019 ) advise researchers not to use this practice. Most importantly, they argue that the effect size strength of partial correlations depends on the other variables included in the regression model and is therefore incomparable to bivariate correlations (Schmidt and Hunter 2015 ), resulting in a possible bias of the meta-analytic results (Roth et al. 2018 ). We endorse this opinion. If at all, we recommend separate analyses for each measure. In addition to these measures, survival rates, risk ratios or odds ratios, which are common measures in medical research (Borenstein 2009 ), can be suitable effect sizes for specific management research questions, such as understanding the determinants of the survival of startup companies. To summarize, the choice of a suitable effect size is often taken away from the researcher because it is typically dependent on the investigated research question as well as the conventions of the specific research field (Cheung and Vijayakumar 2016 ).

2.3.2 Conversion of effect sizes to a common measure

After having defined the primary effect size measure for the meta-analysis, it might become necessary in the later coding process to convert study findings that are reported in effect sizes that are different from the chosen primary effect size. For example, a study might report only descriptive statistics for two study groups but no correlation coefficient, which is used as the primary effect size measure in the meta-analysis. Different effect size measures can be harmonized using conversion formulae, which are provided by standard method books such as Borenstein et al. ( 2009 ) or Lipsey and Wilson ( 2001 ). There also exist online effect size calculators for meta-analysis. Footnote 2

2.4 Step 4: choice of the analytical method used

Choosing which meta-analytical method to use is directly connected to the research question of the meta-analysis. Research questions in meta-analyses can address a relationship between constructs or an effect of an intervention in a general manner, or they can focus on moderating or mediating effects. There are four meta-analytical methods that are primarily used in contemporary management research (Combs et al. 2019 ; Geyer-Klingeberg et al. 2020 ), which allow the investigation of these different types of research questions: traditional univariate meta-analysis, meta-regression, meta-analytic structural equation modeling, and qualitative meta-analysis (Hoon 2013 ). While the first three are quantitative, the latter summarizes qualitative findings. Table 1 summarizes the key characteristics of the three quantitative methods.

2.4.1 Univariate meta-analysis

In its traditional form, a meta-analysis reports a weighted mean effect size for the relationship or intervention of investigation and provides information on the magnitude of variance among primary studies (Aguinis et al. 2011c ; Borenstein et al. 2009 ). Accordingly, it serves as a quantitative synthesis of a research field (Borenstein et al. 2009 ; Geyskens et al. 2009 ). Prominent traditional approaches have been developed, for example, by Hedges and Olkin ( 1985 ) or Hunter and Schmidt ( 1990 , 2004 ). However, going beyond its simple summary function, the traditional approach has limitations in explaining the observed variance among findings (Gonzalez-Mulé and Aguinis 2018 ). To identify moderators (or boundary conditions) of the relationship of interest, meta-analysts can create subgroups and investigate differences between those groups (Borenstein and Higgins 2013 ; Hunter and Schmidt 2004 ). Potential moderators can be study characteristics (e.g., whether a study is published vs. unpublished), sample characteristics (e.g., study country, industry focus, or type of survey/experiment participants), or measurement artifacts (e.g., different types of variable measurements). The univariate approach is thus suitable to identify the overall direction of a relationship and can serve as a good starting point for additional analyses. However, due to its limitations in examining boundary conditions and developing theory, the univariate approach on its own is currently oftentimes viewed as not sufficient (Rauch 2020 ; Shaw and Ertug 2017 ).

2.4.2 Meta-regression analysis

Meta-regression analysis (Hedges and Olkin 1985 ; Lipsey and Wilson 2001 ; Stanley and Jarrell 1989 ) aims to investigate the heterogeneity among observed effect sizes by testing multiple potential moderators simultaneously. In meta-regression, the coded effect size is used as the dependent variable and is regressed on a list of moderator variables. These moderator variables can be categorical variables as described previously in the traditional univariate approach or (semi)continuous variables such as country scores that are merged with the meta-analytical data. Thus, meta-regression analysis overcomes the disadvantages of the traditional approach, which only allows us to investigate moderators singularly using dichotomized subgroups (Combs et al. 2019 ; Gonzalez-Mulé and Aguinis 2018 ). These possibilities allow a more fine-grained analysis of research questions that are related to moderating effects. However, Schmidt ( 2017 ) critically notes that the number of effect sizes in the meta-analytical sample must be sufficiently large to produce reliable results when investigating multiple moderators simultaneously in a meta-regression. For further reading, Tipton et al. ( 2019 ) outline the technical, conceptual, and practical developments of meta-regression over the last decades. Gonzalez-Mulé and Aguinis ( 2018 ) provide an overview of methodological choices and develop evidence-based best practices for future meta-analyses in management using meta-regression.

2.4.3 Meta-analytic structural equation modeling (MASEM)

MASEM is a combination of meta-analysis and structural equation modeling and allows to simultaneously investigate the relationships among several constructs in a path model. Researchers can use MASEM to test several competing theoretical models against each other or to identify mediation mechanisms in a chain of relationships (Bergh et al. 2016 ). This method is typically performed in two steps (Cheung and Chan 2005 ): In Step 1, a pooled correlation matrix is derived, which includes the meta-analytical mean effect sizes for all variable combinations; Step 2 then uses this matrix to fit the path model. While MASEM was based primarily on traditional univariate meta-analysis to derive the pooled correlation matrix in its early years (Viswesvaran and Ones 1995 ), more advanced methods, such as the GLS approach (Becker 1992 , 1995 ) or the TSSEM approach (Cheung and Chan 2005 ), have been subsequently developed. Cheung ( 2015a ) and Jak ( 2015 ) provide an overview of these approaches in their books with exemplary code. For datasets with more complex data structures, Wilson et al. ( 2016 ) also developed a multilevel approach that is related to the TSSEM approach in the second step. Bergh et al. ( 2016 ) discuss nine decision points and develop best practices for MASEM studies.

2.4.4 Qualitative meta-analysis

While the approaches explained above focus on quantitative outcomes of empirical studies, qualitative meta-analysis aims to synthesize qualitative findings from case studies (Hoon 2013 ; Rauch et al. 2014 ). The distinctive feature of qualitative case studies is their potential to provide in-depth information about specific contextual factors or to shed light on reasons for certain phenomena that cannot usually be investigated by quantitative studies (Rauch 2020 ; Rauch et al. 2014 ). In a qualitative meta-analysis, the identified case studies are systematically coded in a meta-synthesis protocol, which is then used to identify influential variables or patterns and to derive a meta-causal network (Hoon 2013 ). Thus, the insights of contextualized and typically nongeneralizable single studies are aggregated to a larger, more generalizable picture (Habersang et al. 2019 ). Although still the exception, this method can thus provide important contributions for academics in terms of theory development (Combs et al., 2019 ; Hoon 2013 ) and for practitioners in terms of evidence-based management or entrepreneurship (Rauch et al. 2014 ). Levitt ( 2018 ) provides a guide and discusses conceptual issues for conducting qualitative meta-analysis in psychology, which is also useful for management researchers.

2.5 Step 5: choice of software

Software solutions to perform meta-analyses range from built-in functions or additional packages of statistical software to software purely focused on meta-analyses and from commercial to open-source solutions. However, in addition to personal preferences, the choice of the most suitable software depends on the complexity of the methods used and the dataset itself (Cheung and Vijayakumar 2016 ). Meta-analysts therefore must carefully check if their preferred software is capable of performing the intended analysis.

Among commercial software providers, Stata (from version 16 on) offers built-in functions to perform various meta-analytical analyses or to produce various plots (Palmer and Sterne 2016 ). For SPSS and SAS, there exist several macros for meta-analyses provided by scholars, such as David B. Wilson or Andy P. Field and Raphael Gillet (Field and Gillett 2010 ). Footnote 3 Footnote 4 For researchers using the open-source software R (R Core Team 2021 ), Polanin et al. ( 2017 ) provide an overview of 63 meta-analysis packages and their functionalities. For new users, they recommend the package metafor (Viechtbauer 2010 ), which includes most necessary functions and for which the author Wolfgang Viechtbauer provides tutorials on his project website. Footnote 5 Footnote 6 In addition to packages and macros for statistical software, templates for Microsoft Excel have also been developed to conduct simple meta-analyses, such as Meta-Essentials by Suurmond et al. ( 2017 ). Footnote 7 Finally, programs purely dedicated to meta-analysis also exist, such as Comprehensive Meta-Analysis (Borenstein et al. 2013 ) or RevMan by The Cochrane Collaboration ( 2020 ).

2.6 Step 6: coding of effect sizes

2.6.1 coding sheet.

The first step in the coding process is the design of the coding sheet. A universal template does not exist because the design of the coding sheet depends on the methods used, the respective software, and the complexity of the research design. For univariate meta-analysis or meta-regression, data are typically coded in wide format. In its simplest form, when investigating a correlational relationship between two variables using the univariate approach, the coding sheet would contain a column for the study name or identifier, the effect size coded from the primary study, and the study sample size. However, such simple relationships are unlikely in management research because the included studies are typically not identical but differ in several respects. With more complex data structures or moderator variables being investigated, additional columns are added to the coding sheet to reflect the data characteristics. These variables can be coded as dummy, factor, or (semi)continuous variables and later used to perform a subgroup analysis or meta regression. For MASEM, the required data input format can deviate depending on the method used (e.g., TSSEM requires a list of correlation matrices as data input). For qualitative meta-analysis, the coding scheme typically summarizes the key qualitative findings and important contextual and conceptual information (see Hoon ( 2013 ) for a coding scheme for qualitative meta-analysis). Figure  1 shows an exemplary coding scheme for a quantitative meta-analysis on the correlational relationship between top-management team diversity and profitability. In addition to effect and sample sizes, information about the study country, firm type, and variable operationalizations are coded. The list could be extended by further study and sample characteristics.

Exemplary coding sheet for a meta-analysis on the relationship (correlation) between top-management team diversity and profitability

2.6.2 Inclusion of moderator or control variables

It is generally important to consider the intended research model and relevant nontarget variables before coding a meta-analytic dataset. For example, study characteristics can be important moderators or function as control variables in a meta-regression model. Similarly, control variables may be relevant in a MASEM approach to reduce confounding bias. Coding additional variables or constructs subsequently can be arduous if the sample of primary studies is large. However, the decision to include respective moderator or control variables, as in any empirical analysis, should always be based on strong (theoretical) rationales about how these variables can impact the investigated effect (Bernerth and Aguinis 2016 ; Bernerth et al. 2018 ; Thompson and Higgins 2002 ). While substantive moderators refer to theoretical constructs that act as buffers or enhancers of a supposed causal process, methodological moderators are features of the respective research designs that denote the methodological context of the observations and are important to control for systematic statistical particularities (Rudolph et al. 2020 ). Havranek et al. ( 2020 ) provide a list of recommended variables to code as potential moderators. While researchers may have clear expectations about the effects for some of these moderators, the concerns for other moderators may be tentative, and moderator analysis may be approached in a rather exploratory fashion. Thus, we argue that researchers should make full use of the meta-analytical design to obtain insights about potential context dependence that a primary study cannot achieve.

2.6.3 Treatment of multiple effect sizes in a study

A long-debated issue in conducting meta-analyses is whether to use only one or all available effect sizes for the same construct within a single primary study. For meta-analyses in management research, this question is fundamental because many empirical studies, particularly those relying on company databases, use multiple variables for the same construct to perform sensitivity analyses, resulting in multiple relevant effect sizes. In this case, researchers can either (randomly) select a single value, calculate a study average, or use the complete set of effect sizes (Bijmolt and Pieters 2001 ; López-López et al. 2018 ). Multiple effect sizes from the same study enrich the meta-analytic dataset and allow us to investigate the heterogeneity of the relationship of interest, such as different variable operationalizations (López-López et al. 2018 ; Moeyaert et al. 2017 ). However, including more than one effect size from the same study violates the independency assumption of observations (Cheung 2019 ; López-López et al. 2018 ), which can lead to biased results and erroneous conclusions (Gooty et al. 2021 ). We follow the recommendation of current best practice guides to take advantage of using all available effect size observations but to carefully consider interdependencies using appropriate methods such as multilevel models, panel regression models, or robust variance estimation (Cheung 2019 ; Geyer-Klingeberg et al. 2020 ; Gooty et al. 2021 ; López-López et al. 2018 ; Moeyaert et al. 2017 ).

2.7 Step 7: analysis

2.7.1 outlier analysis and tests for publication bias.

Before conducting the primary analysis, some preliminary sensitivity analyses might be necessary, which should ensure the robustness of the meta-analytical findings (Rudolph et al. 2020 ). First, influential outlier observations could potentially bias the observed results, particularly if the number of total effect sizes is small. Several statistical methods can be used to identify outliers in meta-analytical datasets (Aguinis et al. 2013 ; Viechtbauer and Cheung 2010 ). However, there is a debate about whether to keep or omit these observations. Anyhow, relevant studies should be closely inspected to infer an explanation about their deviating results. As in any other primary study, outliers can be a valid representation, albeit representing a different population, measure, construct, design or procedure. Thus, inferences about outliers can provide the basis to infer potential moderators (Aguinis et al. 2013 ; Steel et al. 2021 ). On the other hand, outliers can indicate invalid research, for instance, when unrealistically strong correlations are due to construct overlap (i.e., lack of a clear demarcation between independent and dependent variables), invalid measures, or simply typing errors when coding effect sizes. An advisable step is therefore to compare the results both with and without outliers and base the decision on whether to exclude outlier observations with careful consideration (Geyskens et al. 2009 ; Grewal et al. 2018 ; Kepes et al. 2013 ). However, instead of simply focusing on the size of the outlier, its leverage should be considered. Thus, Viechtbauer and Cheung ( 2010 ) propose considering a combination of standardized deviation and a study’s leverage.

Second, as mentioned in the context of a literature search, potential publication bias may be an issue. Publication bias can be examined in multiple ways (Rothstein et al. 2005 ). First, the funnel plot is a simple graphical tool that can provide an overview of the effect size distribution and help to detect publication bias (Stanley and Doucouliagos 2010 ). A funnel plot can also support in identifying potential outliers. As mentioned above, a graphical display of deviation (e.g., studentized residuals) and leverage (Cook’s distance) can help detect the presence of outliers and evaluate their influence (Viechtbauer and Cheung 2010 ). Moreover, several statistical procedures can be used to test for publication bias (Harrison et al. 2017 ; Kepes et al. 2012 ), including subgroup comparisons between published and unpublished studies, Begg and Mazumdar’s ( 1994 ) rank correlation test, cumulative meta-analysis (Borenstein et al. 2009 ), the trim and fill method (Duval and Tweedie 2000a , b ), Egger et al.’s ( 1997 ) regression test, failsafe N (Rosenthal 1979 ), or selection models (Hedges and Vevea 2005 ; Vevea and Woods 2005 ). In examining potential publication bias, Kepes et al. ( 2012 ) and Harrison et al. ( 2017 ) both recommend not relying only on a single test but rather using multiple conceptionally different test procedures (i.e., the so-called “triangulation approach”).

2.7.2 Model choice

After controlling and correcting for the potential presence of impactful outliers or publication bias, the next step in meta-analysis is the primary analysis, where meta-analysts must decide between two different types of models that are based on different assumptions: fixed-effects and random-effects (Borenstein et al. 2010 ). Fixed-effects models assume that all observations share a common mean effect size, which means that differences are only due to sampling error, while random-effects models assume heterogeneity and allow for a variation of the true effect sizes across studies (Borenstein et al. 2010 ; Cheung and Vijayakumar 2016 ; Hunter and Schmidt 2004 ). Both models are explained in detail in standard textbooks (e.g., Borenstein et al. 2009 ; Hunter and Schmidt 2004 ; Lipsey and Wilson 2001 ).

In general, the presence of heterogeneity is likely in management meta-analyses because most studies do not have identical empirical settings, which can yield different effect size strengths or directions for the same investigated phenomenon. For example, the identified studies have been conducted in different countries with different institutional settings, or the type of study participants varies (e.g., students vs. employees, blue-collar vs. white-collar workers, or manufacturing vs. service firms). Thus, the vast majority of meta-analyses in management research and related fields use random-effects models (Aguinis et al. 2011a ). In a meta-regression, the random-effects model turns into a so-called mixed-effects model because moderator variables are added as fixed effects to explain the impact of observed study characteristics on effect size variations (Raudenbush 2009 ).

2.8 Step 8: reporting results

2.8.1 reporting in the article.

The final step in performing a meta-analysis is reporting its results. Most importantly, all steps and methodological decisions should be comprehensible to the reader. DeSimone et al. ( 2020 ) provide an extensive checklist for journal reviewers of meta-analytical studies. This checklist can also be used by authors when performing their analyses and reporting their results to ensure that all important aspects have been addressed. Alternative checklists are provided, for example, by Appelbaum et al. ( 2018 ) or Page et al. ( 2021 ). Similarly, Levitt et al. ( 2018 ) provide a detailed guide for qualitative meta-analysis reporting standards.

For quantitative meta-analyses, tables reporting results should include all important information and test statistics, including mean effect sizes; standard errors and confidence intervals; the number of observations and study samples included; and heterogeneity measures. If the meta-analytic sample is rather small, a forest plot provides a good overview of the different findings and their accuracy. However, this figure will be less feasible for meta-analyses with several hundred effect sizes included. Also, results displayed in the tables and figures must be explained verbally in the results and discussion sections. Most importantly, authors must answer the primary research question, i.e., whether there is a positive, negative, or no relationship between the variables of interest, or whether the examined intervention has a certain effect. These results should be interpreted with regard to their magnitude (or significance), both economically and statistically. However, when discussing meta-analytical results, authors must describe the complexity of the results, including the identified heterogeneity and important moderators, future research directions, and theoretical relevance (DeSimone et al. 2019 ). In particular, the discussion of identified heterogeneity and underlying moderator effects is critical; not including this information can lead to false conclusions among readers, who interpret the reported mean effect size as universal for all included primary studies and ignore the variability of findings when citing the meta-analytic results in their research (Aytug et al. 2012 ; DeSimone et al. 2019 ).

2.8.2 Open-science practices

Another increasingly important topic is the public provision of meta-analytical datasets and statistical codes via open-source repositories. Open-science practices allow for results validation and for the use of coded data in subsequent meta-analyses ( Polanin et al. 2020 ), contributing to the development of cumulative science. Steel et al. ( 2021 ) refer to open science meta-analyses as a step towards “living systematic reviews” (Elliott et al. 2017 ) with continuous updates in real time. MRQ supports this development and encourages authors to make their datasets publicly available. Moreau and Gamble ( 2020 ), for example, provide various templates and video tutorials to conduct open science meta-analyses. There exist several open science repositories, such as the Open Science Foundation (OSF; for a tutorial, see Soderberg 2018 ), to preregister and make documents publicly available. Furthermore, several initiatives in the social sciences have been established to develop dynamic meta-analyses, such as metaBUS (Bosco et al. 2015 , 2017 ), MetaLab (Bergmann et al. 2018 ), or PsychOpen CAMA (Burgard et al. 2021 ).

3 Conclusion

This editorial provides a comprehensive overview of the essential steps in conducting and reporting a meta-analysis with references to more in-depth methodological articles. It also serves as a guide for meta-analyses submitted to MRQ and other management journals. MRQ welcomes all types of meta-analyses from all subfields and disciplines of management research.

Gusenbauer and Haddaway ( 2020 ), however, point out that Google Scholar is not appropriate as a primary search engine due to a lack of reproducibility of search results.

One effect size calculator by David B. Wilson is accessible via: https://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php .

The macros of David B. Wilson can be downloaded from: http://mason.gmu.edu/~dwilsonb/ .

The macros of Field and Gillet ( 2010 ) can be downloaded from: https://www.discoveringstatistics.com/repository/fieldgillett/how_to_do_a_meta_analysis.html .

The tutorials can be found via: https://www.metafor-project.org/doku.php .

Metafor does currently not provide functions to conduct MASEM. For MASEM, users can, for instance, use the package metaSEM (Cheung 2015b ).

The workbooks can be downloaded from: https://www.erim.eur.nl/research-support/meta-essentials/ .

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Data Analysis Techniques in Research – Methods, Tools & Examples

Varun Saharawat is a seasoned professional in the fields of SEO and content writing. With a profound knowledge of the intricate aspects of these disciplines, Varun has established himself as a valuable asset in the world of digital marketing and online content creation.

Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

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Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

• Inspecting : Initial examination of data to understand its structure, quality, and completeness.
• Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
• Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
• Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

• Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
• Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
• Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

• Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
• Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
• Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

• Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
• ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

• Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
• Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

• Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
• Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

• Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
• Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
• Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
• Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
• Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

• Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
• Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

• Calculate the mean, median, and mode of academic scores for both groups.
• Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

• Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
• Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

• Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
• Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

• Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
• Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

• Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
• Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
• Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

• Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
• Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
• Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

• Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
• Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

• Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
• Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

• Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
• Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

• Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
• Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

• Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
• Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

• Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
• Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

• Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

• Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
• Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

• Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
• Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

• Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
• Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

• Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
• Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

• Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
• Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

• Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
• Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

• Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
• Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

• Description: An open-source programming language specifically designed for statistical computing and data visualization.
• Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

• Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
• Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

• Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
• Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

• Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
• Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

• Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
• Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

• Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
• Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

• Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
• Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

• Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
• Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

• Description: A data mining software application used for building predictive models and conducting advanced analytics.
• Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

• Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
• Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
• Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
• In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
• Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
• In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
• In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
• Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

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Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

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Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

• Interview transcripts
• Surveys with open-ended questions
• Contact center transcripts
• Texts and documents
• Audio and video recordings
• Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

• Gathering and collecting your qualitative data
• Organizing and connecting into your qualitative data
• Coding your qualitative data
• Analyzing the qualitative data for insights
• Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues.  It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope,  helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered.  Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis.  Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them.  It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations.  The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

• Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
• Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
• Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
• Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
• Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
• Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

• Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
• Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
• Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
• Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
• Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
• Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend, and review analysis is a great place to start. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.  

The benefits of using computer-assisted qualitative data analysis software:

• Assists in the organizing of your data
• Opens you up to exploring different interpretations of your data analysis
• Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:  

• Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations ( conversational analytics ), and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
• EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably.  For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

• Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
• Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
• Keep repeating step 2, adding new codes and revising the code description as often as necessary.  Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
• Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
• Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

• CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
• Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
• IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
• Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy.  Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which  customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

• Demographic
• And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

• Calculate overall NPS
• Calculate NPS in the subset of responses that do not contain that theme
• Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

The visualization could look like this:

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs.  This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.  

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable.  Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding.  Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places.  And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.  

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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How to Analyze Data in 2023 – A Step-by-Step Guide & Expert Tips

Table of contents

To see what Databox can do for you, including how it helps you track and visualize your performance data in real-time, check out our home page. Click here .

Do you roll your eyes when you see you’ve been assigned the task to “analyze data” or “create a report”?

Research has shown  that analyzing data doesn’t come naturally to most people.

Creating awesome marketing campaigns? Great!

But when it comes to analyzing whether that campaign was a success, it’s where most companies fall short.

We wanted to help solve that problem–especially because data-driven companies are  three times more likely  to report significant improvements in decision-making.

So, how do you overcome the fear (or struggle) of analyzing data?

In this guide, we’ll share the results of our survey that helped us understand how difficult data analysis is to master, along with some pro tips from 30+ experts on the subject.

What is Data Analysis?

Why is it important to analyze your data, 5 data analysis types, how companies analyze and report on data, how to analyze data in 6 steps, choosing the tools you need for data analysis, best ways to analyze data effectively, make data analysis easy with databox.

Data analysis refers to the process of collecting, cleaning, defining, and processing raw data to uncover valuable and actionable insights that will enable you (and your team) to make better-informed decisions, backed by facts rather than assumptions.

Collecting data alone doesn’t amount to much unless you take the time to dig through and interpret it.

By analyzing data consistently, you can drastically improve your business’ performance, but it’s necessary that all company departments participate.

While the term “data analysis” might send shivers down the spine for most people, this skill can be learned, even if you’re not a natural number person.

And speaking of numbers, a great example of what data analysis is can be found in the popular TV show “Numb3rs”

In the show, Charlie Eppes, a math genius, helps the FBI solve cases through data analysis techniques like predictive modeling and pattern recognition. It’s a great example of how data analysis can be used in real-world situations to make sense of complex data and uncover hidden patterns and connections.

Data analysis is pivotal for business success both in the short and long term.

On a deeper level, analyzing your data makes it easier for you to determine the ROI of your marketing and sales efforts, understand customer behavior patterns and market trends, make data-driven decisions, and more.

 Here are some of the top reasons why you should analyze data:

• Improved customer experience : Analyzing your data helps you understand your customers better (behavior and actions), their needs, and how you can deliver better and more personalized customer support.
• Better decision-making : Data analysis helps boost your confidence as a business owner and make better-informed, data-driven decisions. By analyzing data you’ll be able to get a snapshot of all aspects of your business, including what’s working and what’s not, the risks, potential opportunities for improvement, and much more.
• Understand customer behavior : Stay up to speed with everything that pertains to your customers with data analysis. Learn and easily predict customer behavior based on data, follow up by taking action or making changes if necessary promptly. 
• Helps with competitor analysis : Data analysis makes it easy to conduct competitor analysis. It provides you with all the information you need to know about your competitors, including insights into their strengths, weaknesses, sales tactics, and marketing strategies.

Related : Data Insights: Best Practices for Extracting Insights from Data

To properly understand how data analysis works, you’ll first need to learn about its different types and what they encompass.

Here are the five main data analysis types that most companies focus on:

• Text Analysis
• Statistical Analysis
• Diagnostic Analysis
• Predictive Analysis
• Prescriptive Analysis

1. Text Analysis

Text analysis (aka data mining ) refers to the process of transforming large sets of raw data into actionable business data. It’s essentially rearranging textual data so it’s easier to manage and filter.

When done properly, this type of analysis lets you extract the insights that are relevant to your specific industry and use them to develop future strategies.

Nowadays, most companies use modern tools to perform text analysis and streamline the entire process.

These tools can even be used for sentiment analysis – an advanced analytical process that lets you understand the specific emotion behind a text (positive, negative, or neutral) and then scores it based on several factors relevant to your organization.

For instance, you can use the tool to go through your company’s social media comments on an Instagram post that introduces your new product.

It will show you the overall sentiment by analyzing keywords like “great” and “awesome” for positive sentiment or “disappointed” and “frustrated” for negative sentiment.

In most cases, text analysis is used for data from product reviews, articles, surveys, social media information, and any other word-based source.

2. Statistical Analysis

Statistical analysis relies on statistical techniques to examine and summarize data, draw conclusions, and make predictions.

This type of analysis helps businesses make better (and more informed) decisions since they’ll have a better understanding of key business metrics and previous trends.

For example, a business might use statistical analysis to understand customer behavior and which products are most popular and why, or to predict future sales and demand for its products.

One popular example of statistical analysis can be found in Brad Pitt’s movie “Moneyball”.

Brad plays Billie Beane, the general manager of a professional baseball team with a limited budget, who uses statistical analysis to build a winning team by focusing on undervalued players that are overlooked by other teams.

For instance, he looks for players who have a high on-base percentage, a measurement of how often a player gets on base by any means (usually undervalued by other teams).

3. Diagnostic Analysis

Diagnostic analysis is one of the most commonly used techniques in modern business – it’s used to identify data anomalies and show you why something happened the way it did.

In diagnostic analysis, data from various sources is collected, analyzed, and interpreted to identify the underlying causes of problems or issues within a business.

The goal of diagnostic analysis is to provide insight into the factors that are contributing to problems or challenges within a business, so that appropriate action can be taken to address them.

However, aside from fixing problems, you can also use diagnostic analysis to see what’s driving positive outcomes and apply those same tactics to other strategies.

Let’s say that a retail store is seeing a decline in sales. The manager wants to see what’s happening, so he conducts a diagnostic analysis.

He collects data on a variety of factors that could be causing the decline, such as the store’s location, product prices, types of products, local competition, etc.

With diagnostic analysis, the manager identifies key patterns and trends that showcase the relationship between sales and these different factors.

In the end, he discovers that the sales decline is due to the store’s location. For instance, the store might be surrounded by a huge number of competitors or it’s not easily accessible to customers.

Either way, the manager now knows which issue he has to find a solution for (e.g. he will move the store to a new location).

4. Predictive Analysis

Predictive analysis is the technique used for seeing what’s most likely to happen in the future, based on historical data from previous trends and patterns.

It can be applied to a wide range of business scenarios – from predicting customer behavior and forecasting market trend to identifying potential risks and opportunities.

There are also lots of different techniques used within predictive analysis, such as regression analysis, decision trees, and neural networks.

To better explain predictive analysis, we’ll use another movie example.

In the sci-fi movie “Blade Runner 2049”, Ryan Gosling plays K, a member of a special police unit that hunts down rogue robots.

One of K’s main advantages over these robots is that he uses predictive analysis to analyze the robots’ past behavior (basically historical data) and make predictions about what they’re most likely to do next.

With this information, K is able to identify potential threats and take preventive action quickly.

5. Prescriptive Analysis

Prescriptive analysis is a type of data analysis that’s used to determine the best course of action to take in a given situation.

It involves using data and advanced algorithms to identify the actions that will have the greatest impact on a business’s performance and help it achieve its goals.

For instance, a retailer can use prescriptive analytics to determine the best way to allocate inventory across different stores.

By analyzing customer demand, store locations, and similar data, the retailer can identify which actions will improve inventory management and maximize sales in the long run.

This is just one example; this technique can be applied to a wide range of other business scenarios, such as improving supply chain efficiency, enhancing customer experience, and more.

Related : 6 Key Differences Between Data Analysis and Reporting

Want to know what the data analysis process looks like in other companies?

So did we, which is why it’s one of the aspects we focused on when conducting our 2023 State of Business Reporting that had 314 respondents.

One of the first things we wanted to check was who is primarily responsible for creating data analysis reports in companies.

It turns out that people in charge of making reports are mostly managers (only 12% of surveyed companies stated they have analysts making reports) – so we can conclude that at least managers are data proficient enough to read and analyze data.

This is also shown in another research about data literacy (65 respondents), where respondents stated that management is the most data-proficient sector in most companies. 

And, while management seems to be the most involved around data analysis and reporting, companies estimate data literacy across their organization highly.

Respondents stated that 53% of their employees are data literate enough to make reports and analyze data.

But Nevena Rudan, one of the A-list data analysts here at Databox, reminds us that “being able to read and understand data is not the same as being able to put that data in context and derive actionable insights from it.”

“At one point, companies became obsessed with numbers so much, and forgot to include common sense and practice their observation skills.

“There is a big difference between making data-driven and data-informed decisions. The most successful businesses make data-informed and data-inspired decisions, and that approach allows them to grow.”

Nevena Rudan

Marketing Research Analyst at Databox

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Lastly, we asked the respondents whether they rely on any external consultants or outsource data reporting in any way.

We found out that most companies rely on their own resources when it comes to making reports for most of their business operations.

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Here is an example of what your dashboard can look like… (just imagine your data populating here)

And here’s another one…

We get it. You may not have the time to build out the perfect dashboard before your next meeting.

Luckily, we do.

Connect with someone on our team, share the metrics or areas that you need to track, and we’ll build your dashboards for you in just 24 hours.

Learn more about our  free dashboard setup here , reach out for assistance via email or chat, or book a call.

Now that you’re familiar with the fundamentals, let’s move on to the exact step-by-step guide you can follow to analyze your data properly.

Step 1: Define your goals and the question you need to answer Step 2: Determine how to measure set goals Step 3: Collect your data Step 4: Clean the data Step 5: Analyze your data Step 6: Visualize and interpret results

Define Your Goals and the Question You Need to Answer

Before you do anything else, you’ll first need to define what you want to achieve through data analysis.

This is crucial because it puts you on the right track in terms of collecting the right data and using the appropriate tools and techniques.

Also, it helps you avoid collecting unnecessary data or performing irrelevant analysis, a waste of both time and resources.

Furthermore, defining your goals will help you evaluate the results of your analysis and determine whether your findings are relevant and useful.

Let’s say you’re the marketing manager for an eCommerce company and you want to understand why sales have been declining over the past few months.

You will probably define your goals something like this:

• Identify which factors are contributing to the decline in sales
• Recommend actions that the company can take to improve sales

Now, based on these goals, the questions that will guide your data analysis will probably look like this:

• Are there any trends or patterns in our sales data over the past few months that stick out?
• How do current trends compare to historical ones?
• Are there any changes in customer behavior?

Once you have clear goals and focused questions in place, you’ll be able to collect the proper data, perform the appropriate analysis, and identify potential solutions to the problem.

Branko Kral of Chosen Data also emphasizes the importance of asking a specific question since it will “keep you focused”.

“It is very easy to get lost in the analytics tools, such as Google Analytics, if you open them without a specific question in mind. It is desirable to dig around and explore new reports or report modifications, but you want to keep coming back to the main motivation for the analysis.”

Kral’s team put this into action when they experienced a drop in organic traffic: “The main question was – what caused the drop and what can we do bring the traffic back up?”

“There were some nuances in the data, but overall, we discovered that organic traffic was affected site-wide, as well as without us making any major changes to the site’s SEO qualities for at least a few weeks before the drop. That gave us the confidence to state that the cause for the drop was external.”

“We researched SEO news and learned that the early June algorithm update favors big publishers. We’ve also been noticing the external factor of featured snippets pushing page 1 results further down,” Kral adds.

Related : 7 Data Analysis Questions to Improve Your Business Reporting Process

Determine How to Measure Set Goals

After you have your goals laid out, you’ll need to determine how to measure them. This includes identifying the appropriate metrics and KPIs.

For example, if your goal is to increase sales, you’ll need to track metrics such as revenue, number of sales, or average order value.

Or, if your objective is to increase the efficiency of your customer support, you’ll probably want to track individual agent efficiency and check how satisfied your clients are with the overall service.

Collect Your Data

This step involves gathering data from a variety of internal and external sources that are relevant to your overall goal.

There are essentially two broad types of data – quantitative and qualitative. For best results, you should aim to collect both.

Quantitative data is numerical data that can be measured, compared, and counted. This includes things like revenue, sales figures, business expenses, department performance metrics, and more.

Qualitative data is non-numerical data that describes attributes or characteristics. Unlike quantitative data, it’s not easily measured or counted. This includes customer feedback, competitor analysis, market research, brand reputation, employee satisfaction surveys, and more.

Since qualitative data can’t be found in spreadsheets per se, you’ll probably have to dig through the tools your company uses on a daily basis.

For instance, some internal sources can be company emails, social media comments, and customer support conversations.

As for external resources, a good idea can be to check out specific industry reports, government data, and market research studies.

Clean the Data

Making sure your data is accurate and consistent can make a huge difference in your findings, which is why it’s important that you properly clean it before the analysis.

Some of the most common methods are:

• Correcting errors – This may involve checking for typos, inconsistencies, or missing values, and making the appropriate corrections.
• Using standardized data formats – Make sure that all the data is in the same format. For instance, this could mean converting data from different formats (such as dates or currencies) into a standard one.
• Removing duplicate or irrelevant data : The data set should only include relevant and unique data. Check whether there are any duplicates or data that isn’t relevant for your specific analysis.
• Consolidating data : In some cases, the data may need to be consolidated. This involves combining data from multiple sources or summarizing the data to create relevant summary statistics.

Naturally, smaller data sets are a lot easier to handle and you can even review them manually. Just make sure that you follow the same steps for each data set.

However, sometimes that data set might be smaller but it contains a lot of variables, making the process a lot more complex than it first meets the eye. In this situation, it’s best to use a specialized tool.

On the other hand, larger data sets are pretty much always complex and require a lot of time to go through. This is why it’s standard practice to use specialized software to go through them.

While cleaning data is generally considered the most “tedious” part of the process, it’s a necessary step in making sure your analysis yields the most useful insights and information.

Eve Lyons-Berg of  Data Leaders Brief says that this is probably the most important step because “Data analysis is built on the fundamental assumption that the data you’re analyzing is trustworthy.”

“If you’re looking at unreliable data, or insignificant (i.e. too small) data, or even just inconsistent data (ie a metric that’s usually measured daily, but with several week-long gaps at random intervals), your results won’t be reliable.”  

Analyze Your Data

After you have defined your goals, collected the data, and cleaned it properly, you’ll finally be ready for the analysis.

As for the exact way you should go about analyzing it, the best answer is it depends .

It depends on what you’ve defined as your goal, what type of data you’re dealing with, which resources are available to you, etc.

Another important thing you’ll have to determine is which data analysis technique suits the situation best (text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis).

We’ve covered all of them above so make sure you go through them and the examples once again before making a decision.

No matter which technique you go with, the overall goal here is to understand your data better and use it to make informed decisions.

Related : 12 Tips for Developing a Successful Data Analytics Strategy

Visualize and Interpret Results

Once you finish analyzing the data, the best way to understand it and build a story around is to visualize your findings .

Data visualization involves creating graphical representations of the data, such as bar charts, line charts, heat maps, scatter plots, and dashboards.  

For the latter, you can use free dashboard software like Databox.

Databox helps you tell a compelling story with your data and you’ll be able to transform your findings into stunning visuals in literally a few clicks of a button.

Instead of logging into multiple tools, you can connect your data source ( 100+ integrations available) and drag all of your key findings into one comprehensive dashboard.

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While Excel and other spreadsheet-based tools are the most popular for storing and analyzing data sets, they aren’t always practical.

For instance, if you’re dealing with qualitative (non-numerical) data like social media comments and customer support conversations, organizing it in a spreadsheet is pretty much impossible.

Or, if you’re dealing with larger sets of data with a lot of complex variables, you’ll want to have more specialized tools by your side that will reduce the chance of human error and automate the process.

Let’s go through some of the most popular types of data analysis tools:

Spreadsheets

Business intelligence tools, predictive analysis tools, data modeling tools.

• Analytics tools (Department-Specific)

Spreadsheet tools like Excel are one of the most flexible solutions if you’re dealing with small or medium data sets.

You typically don’t need to be tech-savvy to operate these tools and the interfaces tend to be very user-friendly.

Related : Create an Excel Dashboard from Scratch in 8 Steps (or Just 3 with Databox)

Business intelligence (BI) tools are specifically created to help organizations analyze larger data sets and identify key trends and patterns.

They have powerful data processing capabilities and can quickly handle large amounts of data from multiple sources.

Another advantage of BI tools is that they also offer visualization features and make it easy for users to create charts, graphs, and other visualizations that help reveal data insights and patterns.

Related : 7 Business Intelligence Report Examples to Inspire Your Own (Sourced by 17 Pros)

Predictive analysis tools use data mining, machine learning, and other advanced analytics techniques to identify patterns and trends in data sets and to generate predictions based on those patterns.

In other words, you can use these tools to see what’s scenario is most likely to occur in the future and how it will impact your organization.

Data modeling tools help you create a visual representation of your database and make it more understandable and easier to work with.

They allow you to create diagrams that show how your data is organized and related, which saves time and makes the building and maintenance process a lot easier.

Analytics Tools (Department-Specific)

Department-specific analytics tools are designed to support data analysis in specific departments or areas of an organization.

In other words, they’re tailored to the specific needs of a particular department, such as marketing, finance, or human resources.

For example, a marketing analytics tool could include features for analyzing customer data, tracking marketing campaign efficiency, and identifying sales data trends.

Similarly, a finance analytics tool could include features for analyzing financial data, creating budgets and forecasts, and identifying financial performance trends.

There’s no “one-size-fits-all” way to analyze data and each company has its own modus operandi of doing things (probably even several).

This is because there are so many variables that you need to consider to devise the perfect analysis strategy.

However, there are some practices that are pretty much universal among all organizations.

These include:

Look for Patterns and Trends

Compare current data against historical trends, look for any data that goes against your expectations, pull data from various sources, determine the next steps.

Once your data is filtered and you’ve prepared the appropriate analysis tool, it’s time to start drawing up patterns.

If you’re mostly dealing with quantitative data, spotting patterns is relatively simple and you can charts and similar visualizations to help you out.

However, it can get a bit more complicated with qualitative data like emails or customer support chats.

In this situation, you can try out the following:

• Text analysis – We talked about this technique earlier in detail. It’s a great choice when you need to extract insights from unstructured data like emails, chats, comments, etc.
• Sentiment analysis – This method relies on natural language processing to determine whether the unstructured data represents positive or negative emotions. One of the most popular use cases among companies is to use sentiment analysis for assessing brand perception.
• Topic analysis – You can use this analysis to extract the main topics from larger data sets. For instance, you can use it to analyze customer feedback or product reviews. The main goal is to check out the underlying sentiment in the data set.
• Cohort analysis – The technique used for grouping customers into similar categories (cohorts) based on common characteristics or behaviors. Companies use this analysis to understand their customer base better and make informed decisions.

The most important thing in this practice is not to make any assumptions .

For instance, if your Facebook Ad campaigns are getting a lot of clicks and there’s a spike in product sales, that doesn’t necessarily mean that the two are connected.

Just like you wouldn’t assume that a positive correlation between an increase in ice cream sales and robberies in the same town means that one’s causing the other.

This mistake is often called false causality, and it’s very common among beginner data analysts. Make sure you always have enough evidence to support the causation before sharing any insights with your team.

Comparing your current data with previous trends provides you with a broader perspective and puts the data into context.

A lot of valuable insights can be extracted once you start identifying the changes in data over a set period of time.

For instance, you might notice that your company sees a huge spike in sales each year around Christmas time.

With this information, you can prepare a more aggressive marketing campaign a few weeks before Christmas to try and take advantage of that momentum.

Related : Data Trend Analysis in Google Analytics: 7 Best Practices for Measuring Your Marketing Performance

Naturally, finding the insights that are related to the goals you set at the beginning and looking for trends that support your existing assumptions is the first thing you’ll do post-analysis.

But make sure you also look for data that goes in the opposite direction of your expectation, so you don’t get a bad case of confirmation bias .

If you do notice some data anomalies, keep on investigating them until you see why they’ve appeared. More often than not, the explanation will be simple, but you’ll want to rule out any major concerns.

Lauren Pope of G2 agrees with this and adds that you “shouldn’t follow data blindly but trust your gut instead”.

“Listening to the data is important, but it’s not infallible. If the data is suddenly telling you something VERY different from what it did just a week ago, take the time to see if everything is running the way it should.

There’s a chance that a module has been turned off, a UTM code has been corrupted, or something else has gone wrong.”

It’s a tactic also used by the team at  Web Canopy Studio , as Kenny Lange explains: “I find it most helpful to drill down into anomalies – even if they’re small. It’s easy to rationalize the change in the patterns and assume that whatever you’re seeing isn’t statistically significant.”

“In addition to drilling down into anomalies, always be asking ‘why?’ I know up and to the right is good, but if you never understand what levers are controlling your growth, you’ll be unable to fix them when they break.”

Pulling data from multiple sources can help you acquire the bigger picture and it provides a broader perspective on the trends and patterns that are being analyzed.

For example, if a company is analyzing sales data, pulling data from multiple sources such as sales reports, customer feedback, and market research reports can provide a more comprehensive view of the overall sales performance.

This later helps you make more informed business decisions and improves the overall quality of the data analysis.

Giselle Bardwell of Kiwi Creative is one of the respondents that emphasize this practice because it “provides a much deeper understanding of the data”.

“Leverage a platform, like Databox, to combine multiple sources and metrics to tell a full story of how marketing and sales are performing (or not!) Bringing all the data together makes it easier to find correlations, similarities, and areas to improve.”

We recently looked at overall engagement on our blog in terms of the initial landing page, interactions with various calls-to-action (CTAs) on the page, and the journey the user takes through the website after reading a post.

Looking at the difference between user interactions on the blog versus sales-specific pages helped us to revise our content strategy to include more relevant CTAs to boost lead growth.”

Here’s a question for you – what do you plan to do with the insights you extracted from the data analysis?

Extracting insights is great and all, but you also need to have a plan on what you’ll use them for.

Some examples where you can put your findings to work are:

• Use your current performance data to set realistic targets and KPIs
• Use the insights to make better-informed business decisions
• Improve your customer satisfaction (if the data gave you a better understanding of what they want or need)
• Investigate any unexpected insights
• Share the most important insights with company shareholders and department leaders
• Try to identify new revenue streams
• Optimize company operations

These are just some examples of how you can utilize your findings.

Remember, even though you’ll probably feel relieved after wrapping up the data analysis, the analysis itself isn’t the end goal.

The primary reason you’re analyzing all that data is so you can help the company make better decisions moving forward and come up with more efficient strategies in all departments.

For most people, data analysis is as exciting as watching paint dry.

Not only are you working with complex and raw information, but you also have to spend hours (if not days) collecting it, cleaning it, filtering it… you get the idea.

After you go through all these steps and finish the analysis, you’ll need to present that data in a clear and concise manner, both for you and the stakeholders.

This includes selecting the right visualization type, then manually creating different bars and graphs, and putting the data you analyzed into perspective.

However, most employees burn out during the first part of the process, so this last step generally takes them a lot more time to complete than it should.

What’s more, they won’t be able to put the same amount of energy into it, which can sometimes even lead to misleading insights.

With Databox, you can make sure this never happens.

Here’s how easy data analysis reporting is with our software:

• Connect your data source
• Drag and drop the metrics you want to track
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This process will take literally minutes… and it’s not even the fastest solution we offer.

You also have the option to contact our customer support team for a free setup and explain what you want your dashboard to include and how you want it structured, and we’ll have it ready in less than 24 hours.

Sign up for a free trial and never worry about impressing your shareholders with a phenomenal data analysis report again.

Filip Stojanovic is a content writer who studies Business and Political Sciences. Also, I am a huge tennis enthusiast. Although my dream is to win a Grand Slam, working as a content writer is also interesting.

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The Beginner’s Guide to Analyzing Data

15 Minute Read

Data comes in many forms, which makes the challenge of analyzing data unique to each dataset and its purpose. There isn’t a “one size fits all” approach for analyzing data. Knowing how to analyze, interpret, and apply findings to your data analysis can be the difference between successful and accurate insights… and leaving information on the table, still hidden in raw data but just out of reach.

By delving into data analysis techniques, we can begin to build a toolkit to reliably call upon to find meaning in data no matter the occasion or objective.

In this beginner’s guide, we’ll tackle the basics of data analysis for beginners and answer some of the most common questions people have, like how to analyze data and ways to apply data in research.

 Let’s start with the fundamentals.

What Is Data Analysis?

Data analysis is the process of gathering, organizing, and interpreting information. This can be anything from open-ended survey responses, transcripts , or notes. Data analysis goes a step beyond simply putting information in one place and organizing it. An analysis process helps you create models to visualize the information, find patterns, see tension, draw stronger conclusions, and even forecast potential outcomes.

All data analysis starts with “raw data.” This is unfiltered, uncategorized information. It can be something a person wrote, feedback they provided, or comments made in a remote user interview.

Data analysis helps you highlight the most relevant details, find similarities among data clusters (groups) and even break it down into different categories based on classifications using tags. 

How Data Is Analyzed in Research

There are numerous ways to analyze data, but the easiest method is to use a flexible platform that centralizes all of the steps. With greater insight and statistical evidence, you minimize risks that inherently accompany the decision-making process. Rather than hope you’ve gotten the best information, you can weigh both sides of a hypothesis and make the best call based on factual conclusions.

Research platforms like Notably help you import and analyze data in both structured and visual ways. With an open and spatial canvas, shifting data visually gives you the context of your entire dataset to see connections and identify relationships you may not have considered without a bird’s eye view of your data.

Before starting with the determined process for analyzing data, let’s review the most common types of data analysis, which are:

• Qualitative data analysis
• Quantitative data analysis
• Evaluative data analysis

What Is Quantitative Data Analysis?

Quantitative data deals with numbers, so it reflects any information pertaining to figures. This could be calculating the average age of a group, looking at grades and GPAs, discerning averages, and so on.

Quantitative data analysis is strictly mathematical, but its actual numbers can be derived from factual data. For example, let’s imagine you wanted to identify key differences between a target group. Rather than measure each criteria individually, you could assign a number to each factor, e.g. language, location and gender could each be given their own number.

U.S. visitors would fall under “1” while someone from Europe may fall under “2.” These numbers allow you to easily group information without losing any of the important meaning behind them. Rather than dilute information into statistics, you use number-based operations to draw more concrete, clear conclusions about your audience.

What Is Qualitative Data Analysis?

Qualitative data spans beyond numbers to look more closely at experiences, emotions, and human characteristics. It allows you to delve deeper into a research question, explore the implications of your hypothesis, and even form new hypotheses based on the data you gather.

Notably features for data analysis are rooted in qualitative data collection; by drawing key points from video transcripts or notes, you can unpack all sorts on inquiries through interview analysis , user testing, and more.

Let’s look at an example that differentiates quantitative vs. qualitative data.

Quantitative data: A company sent out an email to 500 subscribers and 150 readers opened the email. This represents a 30% open rate out of their mailing list.

Qualitative data: In an open-response survey, participants revealed that they often did not have time to read long emails during their usual active hours. Some reported that they felt emails often pertained to irrelevant information, or they found the subject headings misleading.

While quantitative data can capture immediate facts based on numbers, qualitative data gives a deeper level of understanding into a person’s experience. You learn, through their own narrative, what their core challenges are and what problems they’re facing. It allows researchers to incorporate feelings and wants into analysis without becoming too subjective.

Evaluative Data Analysis

Evaluative analysis, or evaluative research , helps businesses determine whether their end goals have been met and if a product or application is delivering its desired results. It’s a fundamental aspect of UX, where the end-user’s experience ultimately determines an effort’s success.

At Notably, we allow our curiosity to guide us, and evaluative research helps us build new questions after we’ve completed a project’s lifecycle. Evaluate analysis is integral to innovation and ongoing improvement. It’s what helps companies separate the distance between themselves and their consumers to tap into core needs and deliver more effective solutions in the future.

Rather than asking “Did this work?”, evaluative data helps you answer questions that reach deeper, like “Did this work as well as we intended, and did it create new and unexpected challenges that we can grow from?”

Why Is Rigorous Data Analysis Important?

Proper data analysis ensures you only get the most relevant information from your audience. It helps remove your ego and bias from the equation to look more closely at the real experience behind human users.

While many misconstrue data analysis as a cold, detached process, in reality, it ensures that people, their wants, needs, or ethics, are always put first in research.

How Can You Make Business Decisions From Data Analysis?

Once you’ve answered, “How do you analyze data?”, it’s time to go a step further and ask, “What can I do with what I’ve gathered?”

Data collection and analysis is just one part of the picture. Analysis is really a tool that propels companies forward through educated and personalized insight. Using data analysis, businesses can run hypothetical scenarios, test hypotheses, and lower risks by taking the most educated path toward their goals.

Moreover, data allows you to prioritize objectives and refine strategies to reflect user needs. It can make the shift from business-centric to customer-centric more practical without sacrificing the bottom line.

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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organisations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organise and summarise the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalise your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarise your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, frequently asked questions about statistics.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

• Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
• Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
• Null hypothesis: Parental income and GPA have no relationship with each other in college students.
• Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

• In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
• In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
• In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

• In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
• In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
• In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
• Experimental
• Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalise your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

• Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
• Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

• Probability sampling: every member of the population has a chance of being selected for the study through random selection.
• Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalisable findings, you should use a probability sampling method. Random selection reduces sampling bias and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to be biased, they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

• your sample is representative of the population you’re generalising your findings to.
• your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalise your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialised, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalised in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

• Will you have resources to advertise your study widely, including outside of your university setting?
• Will you have the means to recruit a diverse sample that represents a broad population?
• Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

• Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
• Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
• Expected effect size : a standardised indication of how large the expected result of your study will be, usually based on other similar studies.
• Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarise them.

Inspect your data

There are various ways to inspect your data, including the following:

• Organising data from each variable in frequency distribution tables .
• Displaying data from a key variable in a bar chart to view the distribution of responses.
• Visualising the relationship between two variables using a scatter plot .

By visualising your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

• Mode : the most popular response or value in the data set.
• Median : the value in the exact middle of the data set when ordered from low to high.
• Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

• Range : the highest value minus the lowest value of the data set.
• Interquartile range : the range of the middle half of the data set.
• Standard deviation : the average distance between each value in your data set and the mean.
• Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

• Estimation: calculating population parameters based on sample statistics.
• Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

• A point estimate : a value that represents your best guess of the exact parameter.
• An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

• A test statistic tells you how much your data differs from the null hypothesis of the test.
• A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

• Comparison tests assess group differences in outcomes.
• Regression tests assess cause-and-effect relationships between variables.
• Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

• A simple linear regression includes one predictor variable and one outcome variable.
• A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

• A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
• A z test is for exactly 1 or 2 groups when the sample is large.
• An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

• If you have only one sample that you want to compare to a population mean, use a one-sample test .
• If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
• If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
• If you expect a difference between groups in a specific direction, use a one-tailed test .
• If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

• a t value (test statistic) of 3.00
• a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

• a t value of 3.08
• a p value of 0.001

The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimise the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasises null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Statistical analysis is the main method for analyzing quantitative research data . It uses probabilities and models to test predictions about a population from sample data.

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• Central Limit Theorem | Formula, Definition & Examples
• Central Tendency | Understanding the Mean, Median & Mode
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• Descriptive Statistics | Definitions, Types, Examples
• How to Calculate Standard Deviation (Guide) | Calculator & Examples
• How to Calculate Variance | Calculator, Analysis & Examples
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• How to Find the Mean | Definition, Examples & Calculator
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• How to Find the Range of a Data Set | Calculator & Formula
• Inferential Statistics | An Easy Introduction & Examples
• Levels of measurement: Nominal, ordinal, interval, ratio
• Missing Data | Types, Explanation, & Imputation
• Normal Distribution | Examples, Formulas, & Uses
• Null and Alternative Hypotheses | Definitions & Examples
• Poisson Distributions | Definition, Formula & Examples
• Skewness | Definition, Examples & Formula
• T-Distribution | What It Is and How To Use It (With Examples)
• The Standard Normal Distribution | Calculator, Examples & Uses
• Type I & Type II Errors | Differences, Examples, Visualizations
• Understanding Confidence Intervals | Easy Examples & Formulas
• Variability | Calculating Range, IQR, Variance, Standard Deviation
• What is Effect Size and Why Does It Matter? (Examples)
• What Is Interval Data? | Examples & Definition
• What Is Nominal Data? | Examples & Definition
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Reference management. Clean and simple.

How to do a content analysis

What is content analysis?

Why would you use a content analysis, types of content analysis, conceptual content analysis, relational content analysis, reliability and validity, reliability, the advantages and disadvantages of content analysis, a step-by-step guide to conducting a content analysis, step 1: develop your research questions, step 2: choose the content you’ll analyze, step 3: identify your biases, step 4: define the units and categories of coding, step 5: develop a coding scheme, step 6: code the content, step 7: analyze the results, frequently asked questions about content analysis, related articles.

In research, content analysis is the process of analyzing content and its features with the aim of identifying patterns and the presence of words, themes, and concepts within the content. Simply put, content analysis is a research method that aims to present the trends, patterns, concepts, and ideas in content as objective, quantitative or qualitative data , depending on the specific use case.

As such, some of the objectives of content analysis include:

• Simplifying complex, unstructured content.
• Identifying trends, patterns, and relationships in the content.
• Determining the characteristics of the content.
• Identifying the intentions of individuals through the analysis of the content.
• Identifying the implied aspects in the content.

Typically, when doing a content analysis, you’ll gather data not only from written text sources like newspapers, books, journals, and magazines but also from a variety of other oral and visual sources of content like:

• Voice recordings, speeches, and interviews.
• Web content, blogs, and social media content.
• Films, videos, and photographs.

One of content analysis’s distinguishing features is that you'll be able to gather data for research without physically gathering data from participants. In other words, when doing a content analysis, you don't need to interact with people directly.

The process of doing a content analysis usually involves categorizing or coding concepts, words, and themes within the content and analyzing the results. We’ll look at the process in more detail below.

Typically, you’ll use content analysis when you want to:

• Identify the intentions, communication trends, or communication patterns of an individual, a group of people, or even an institution.
• Analyze and describe the behavioral and attitudinal responses of individuals to communications.
• Determine the emotional or psychological state of an individual or a group of people.
• Analyze the international differences in communication content.
• Analyzing audience responses to content.

Keep in mind, though, that these are just some examples of use cases where a content analysis might be appropriate and there are many others.

The key thing to remember is that content analysis will help you quantify the occurrence of specific words, phrases, themes, and concepts in content. Moreover, it can also be used when you want to make qualitative inferences out of the data by analyzing the semantic meanings and interrelationships between words, themes, and concepts.

In general, there are two types of content analysis: conceptual and relational analysis . Although these two types follow largely similar processes, their outcomes differ. As such, each of these types can provide different results, interpretations, and conclusions. With that in mind, let’s now look at these two types of content analysis in more detail.

With conceptual analysis, you’ll determine the existence of certain concepts within the content and identify their frequency. In other words, conceptual analysis involves the number of times a specific concept appears in the content.

Conceptual analysis is typically focused on explicit data, which means you’ll focus your analysis on a specific concept to identify its presence in the content and determine its frequency.

However, when conducting a content analysis, you can also use implicit data. This approach is more involved, complicated, and requires the use of a dictionary, contextual translation rules, or a combination of both.

No matter what type you use, conceptual analysis brings an element of quantitive analysis into a qualitative approach to research.

Relational content analysis takes conceptual analysis a step further. So, while the process starts in the same way by identifying concepts in content, it doesn’t focus on finding the frequency of these concepts, but rather on the relationships between the concepts, the context in which they appear in the content, and their interrelationships.

Before starting with a relational analysis, you’ll first need to decide on which subcategory of relational analysis you’ll use:

• Affect extraction: With this relational content analysis approach, you’ll evaluate concepts based on their emotional attributes. You’ll typically assess these emotions on a rating scale with higher values assigned to positive emotions and lower values to negative ones. In turn, this allows you to capture the emotions of the writer or speaker at the time the content is created. The main difficulty with this approach is that emotions can differ over time and across populations.
• Proximity analysis: With this approach, you’ll identify concepts as in conceptual analysis, but you’ll evaluate the way in which they occur together in the content. In other words, proximity analysis allows you to analyze the relationship between concepts and derive a concept matrix from which you’ll be able to develop meaning. Proximity analysis is typically used when you want to extract facts from the content rather than contextual, emotional, or cultural factors.
• Cognitive mapping: Finally, cognitive mapping can be used with affect extraction or proximity analysis. It’s a visualization technique that allows you to create a model that represents the overall meaning of content and presents it as a graphic map of the relationships between concepts. As such, it’s also commonly used when analyzing the changes in meanings, definitions, and terms over time.

Now that we’ve seen what content analysis is and looked at the different types of content analysis, it’s important to understand how reliable it is as a research method . We’ll also look at what criteria impact the validity of a content analysis.

There are three criteria that determine the reliability of a content analysis:

• Stability . Stability refers to the tendency of coders to consistently categorize or code the same data in the same way over time.
• Reproducibility . This criterion refers to the tendency of coders to classify categories membership in the same way.
• Accuracy . Accuracy refers to the extent to which the classification of content corresponds to a specific standard.

Keep in mind, though, that because you’ll need to code or categorize the concepts you’ll aim to identify and analyze manually, you’ll never be able to eliminate human error. However, you’ll be able to minimize it.

In turn, three criteria determine the validity of a content analysis:

• Closeness of categories . This is achieved by using multiple classifiers to get an agreed-upon definition for a specific category by using either implicit variables or synonyms. In this way, the category can be broadened to include more relevant data.
• Conclusions . Here, it’s crucial to decide what level of implication will be allowable. In other words, it’s important to consider whether the conclusions are valid based on the data or whether they can be explained using some other phenomena.
• Generalizability of the results of the analysis to a theory . Generalizability comes down to how you determine your categories as mentioned above and how reliable those categories are. In turn, this relies on how accurately the categories are at measuring the concepts or ideas that you’re looking to measure.

Considering everything mentioned above, there are definite advantages and disadvantages when it comes to content analysis:

Let’s now look at the steps you’ll need to follow when doing a content analysis.

The first step will always be to formulate your research questions. This is simply because, without clear and defined research questions, you won’t know what question to answer and, by implication, won’t be able to code your concepts.

Based on your research questions, you’ll then need to decide what content you’ll analyze. Here, you’ll use three factors to find the right content:

• The type of content . Here you’ll need to consider the various types of content you’ll use and their medium like, for example, blog posts, social media, newspapers, or online articles.
• What criteria you’ll use for inclusion . Here you’ll decide what criteria you’ll use to include content. This can, for instance, be the mentioning of a certain event or advertising a specific product.
• Your parameters . Here, you’ll decide what content you’ll include based on specified parameters in terms of date and location.

The next step is to consider your own pre-conception of the questions and identify your biases. This process is referred to as bracketing and allows you to be aware of your biases before you start your research with the result that they’ll be less likely to influence the analysis.

Your next step would be to define the units of meaning that you’ll code. This will, for example, be the number of times a concept appears in the content or the treatment of concept, words, or themes in the content. You’ll then need to define the set of categories you’ll use for coding which can be either objective or more conceptual.

Based on the above, you’ll then organize the units of meaning into your defined categories. Apart from this, your coding scheme will also determine how you’ll analyze the data.

The next step is to code the content. During this process, you’ll work through the content and record the data according to your coding scheme. It’s also here where conceptual and relational analysis starts to deviate in relation to the process you’ll need to follow.

As mentioned earlier, conceptual analysis aims to identify the number of times a specific concept, idea, word, or phrase appears in the content. So, here, you’ll need to decide what level of analysis you’ll implement.

In contrast, with relational analysis, you’ll need to decide what type of relational analysis you’ll use. So, you’ll need to determine whether you’ll use affect extraction, proximity analysis, cognitive mapping, or a combination of these approaches.

Once you’ve coded the data, you’ll be able to analyze it and draw conclusions from the data based on your research questions.

Content analysis offers an inexpensive and flexible way to identify trends and patterns in communication content. In addition, it’s unobtrusive which eliminates many ethical concerns and inaccuracies in research data. However, to be most effective, a content analysis must be planned and used carefully in order to ensure reliability and validity.

The two general types of content analysis: conceptual and relational analysis . Although these two types follow largely similar processes, their outcomes differ. As such, each of these types can provide different results, interpretations, and conclusions.

In qualitative research coding means categorizing concepts, words, and themes within your content to create a basis for analyzing the results. While coding, you work through the content and record the data according to your coding scheme.

Content analysis is the process of analyzing content and its features with the aim of identifying patterns and the presence of words, themes, and concepts within the content. The goal of a content analysis is to present the trends, patterns, concepts, and ideas in content as objective, quantitative or qualitative data, depending on the specific use case.

Content analysis is a qualitative method of data analysis and can be used in many different fields. It is particularly popular in the social sciences.

It is possible to do qualitative analysis without coding, but content analysis as a method of qualitative analysis requires coding or categorizing data to then analyze it according to your coding scheme in the next step.

15 Steps to Good Research

• Define and articulate a research question (formulate a research hypothesis). How to Write a Thesis Statement (Indiana University)
• Identify possible sources of information in many types and formats. Georgetown University Library's Research & Course Guides
• Judge the scope of the project.
• Reevaluate the research question based on the nature and extent of information available and the parameters of the research project.
• Select the most appropriate investigative methods (surveys, interviews, experiments) and research tools (periodical indexes, databases, websites).
• Plan the research project. Writing Anxiety (UNC-Chapel Hill) Strategies for Academic Writing (SUNY Empire State College)
• Retrieve information using a variety of methods (draw on a repertoire of skills).
• Refine the search strategy as necessary.
• Write and organize useful notes and keep track of sources. Taking Notes from Research Reading (University of Toronto) Use a citation manager: Zotero or Refworks
• Evaluate sources using appropriate criteria. Evaluating Internet Sources
• Synthesize, analyze and integrate information sources and prior knowledge. Georgetown University Writing Center
• Revise hypothesis as necessary.
• Use information effectively for a specific purpose.
• Understand such issues as plagiarism, ownership of information (implications of copyright to some extent), and costs of information. Georgetown University Honor Council Copyright Basics (Purdue University) How to Recognize Plagiarism: Tutorials and Tests from Indiana University
• Cite properly and give credit for sources of ideas. MLA Bibliographic Form (7th edition, 2009) MLA Bibliographic Form (8th edition, 2016) Turabian Bibliographic Form: Footnote/Endnote Turabian Bibliographic Form: Parenthetical Reference Use a citation manager: Zotero or Refworks

Adapted from the Association of Colleges and Research Libraries "Objectives for Information Literacy Instruction" , which are more complete and include outcomes. See also the broader "Information Literacy Competency Standards for Higher Education."

• How it works

Thematic Analysis – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 29, 2023

Thematic analysis is one of the most important types of analysis used for qualitative data . When researchers have to analyse audio or video transcripts, they give preference to thematic analysis. A researcher needs to look keenly at the content to identify the context and the message conveyed by the speaker.

Moreover, with the help of this analysis, data can be simplified.  

Importance of Thematic Analysis

Thematic analysis has so many unique and dynamic features, some of which are given below:

Thematic analysis is used because:

• It is flexible.
• It is best for complex data sets.
• It is applied to qualitative data sets.
• It takes less complexity compared to other theories of analysis.

Intellectuals and researchers give preference to thematic analysis due to its effectiveness in the research.

How to Conduct a Thematic Analysis?

While doing any research , if your data and procedure are clear, it will be easier for your reader to understand how you concluded the results . This will add much clarity to your research.

Understand the Data

This is the first step of your thematic analysis. At this stage, you have to understand the data set. You need to read the entire data instead of reading the small portion. If you do not have the data in the textual form, you have to transcribe it.

Example: If you are visiting an adult dating website, you have to make a data corpus. You should read and re-read the data and consider several profiles. It will give you an idea of how adults represent themselves on dating sites. You may get the following results:

I am a tall, single(widowed), easy-going, honest, good listener with a good sense of humor. Being a handyperson, I keep busy working around the house, and I also like to follow my favourite hockey team on TV or spoil my two granddaughters when I get the chance!! Enjoy most music except Rap! I keep fit by jogging, walking, and bicycling (at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times, and adventures together

I enjoy photography, lapidary & seeking collectibles in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception.

Development of Initial Coding:

At this stage, you have to do coding. It’s the essential step of your research . Here you have two options for coding. Either you can do the coding manually or take the help of any tool. A software named the NOVIC is considered the best tool for doing automatic coding.

For manual coding, you can follow the steps given below:

• Please write down the data in a proper format so that it can be easier to proceed.
• Use a highlighter to highlight all the essential points from data.
• Make as many points as possible.
• Take notes very carefully at this stage.
• Apply themes as much possible.
• Now check out the themes of the same pattern or concept.
• Turn all the same themes into the single one.

Example: For better understanding, the previously explained example of Step 1 is continued here. You can observe the coded profiles below:

Make Themes

At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.

Extracted Data Review

Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.

For better understanding, a mind-mapping example is given here:

Reviewing all the Themes Again

You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation. 

You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.

Corpus Data

Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.

When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:

Define all the Themes here

Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.

The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.

Does your Research Methodology Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.

Make a Report

You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.

While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.  

Frequently Asked Questions

What is meant by thematic analysis.

Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.

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Sampling methods are used to to draw valid conclusions about a large community, organization or group of people, but they are based on evidence and reasoning.

A survey includes questions relevant to the research topic. The participants are selected, and the questionnaire is distributed to collect the data.

A meta-analysis is a formal, epidemiological, quantitative study design that uses statistical methods to generalise the findings of the selected independent studies.

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• How It Works

How to Do Market Research: The Complete Guide

Learn how to do market research with this step-by-step guide, complete with templates, tools and real-world examples.

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Market research is the systematic process of gathering, analyzing and interpreting information about a specific market or industry.

What are your customers’ needs? How does your product compare to the competition? What are the emerging trends and opportunities in your industry? If these questions keep you up at night, it’s time to conduct market research.

Market research plays a pivotal role in your ability to stay competitive and relevant, helping you anticipate shifts in consumer behavior and industry dynamics. It involves gathering these insights using a wide range of techniques, from surveys and interviews to data analysis and observational studies.

In this guide, we’ll explore why market research is crucial, the various types of market research, the methods used in data collection, and how to effectively conduct market research to drive informed decision-making and success.

What is market research?

The purpose of market research is to offer valuable insight into the preferences and behaviors of your target audience, and anticipate shifts in market trends and the competitive landscape. This information helps you make data-driven decisions, develop effective strategies for your business, and maximize your chances of long-term growth.

Why is market research important? 

By understanding the significance of market research, you can make sure you’re asking the right questions and using the process to your advantage. Some of the benefits of market research include:

• Informed decision-making: Market research provides you with the data and insights you need to make smart decisions for your business. It helps you identify opportunities, assess risks and tailor your strategies to meet the demands of the market. Without market research, decisions are often based on assumptions or guesswork, leading to costly mistakes.
• Customer-centric approach: A cornerstone of market research involves developing a deep understanding of customer needs and preferences. This gives you valuable insights into your target audience, helping you develop products, services and marketing campaigns that resonate with your customers.
• Competitive advantage: By conducting market research, you’ll gain a competitive edge. You’ll be able to identify gaps in the market, analyze competitor strengths and weaknesses, and position your business strategically. This enables you to create unique value propositions, differentiate yourself from competitors, and seize opportunities that others may overlook.
• Risk mitigation: Market research helps you anticipate market shifts and potential challenges. By identifying threats early, you can proactively adjust their strategies to mitigate risks and respond effectively to changing circumstances. This proactive approach is particularly valuable in volatile industries.
• Resource optimization: Conducting market research allows organizations to allocate their time, money and resources more efficiently. It ensures that investments are made in areas with the highest potential return on investment, reducing wasted resources and improving overall business performance.
• Adaptation to market trends: Markets evolve rapidly, driven by technological advancements, cultural shifts and changing consumer attitudes. Market research ensures that you stay ahead of these trends and adapt your offerings accordingly so you can avoid becoming obsolete. 

As you can see, market research empowers businesses to make data-driven decisions, cater to customer needs, outperform competitors, mitigate risks, optimize resources and stay agile in a dynamic marketplace. These benefits make it a huge industry; the global market research services market is expected to grow from $76.37 billion in 2021 to $108.57 billion in 2026 . Now, let’s dig into the different types of market research that can help you achieve these benefits.

Types of market research 

• Qualitative research
• Quantitative research
• Exploratory research
• Descriptive research
• Causal research
• Cross-sectional research
• Longitudinal research

Despite its advantages, 23% of organizations don’t have a clear market research strategy. Part of developing a strategy involves choosing the right type of market research for your business goals. The most commonly used approaches include:

1. Qualitative research

Qualitative research focuses on understanding the underlying motivations, attitudes and perceptions of individuals or groups. It is typically conducted through techniques like in-depth interviews, focus groups and content analysis — methods we’ll discuss further in the sections below. Qualitative research provides rich, nuanced insights that can inform product development, marketing strategies and brand positioning.

2. Quantitative research

Quantitative research, in contrast to qualitative research, involves the collection and analysis of numerical data, often through surveys, experiments and structured questionnaires. This approach allows for statistical analysis and the measurement of trends, making it suitable for large-scale market studies and hypothesis testing. While it’s worthwhile using a mix of qualitative and quantitative research, most businesses prioritize the latter because it is scientific, measurable and easily replicated across different experiments.

3. Exploratory research

Whether you’re conducting qualitative or quantitative research or a mix of both, exploratory research is often the first step. Its primary goal is to help you understand a market or problem so you can gain insights and identify potential issues or opportunities. This type of market research is less structured and is typically conducted through open-ended interviews, focus groups or secondary data analysis. Exploratory research is valuable when entering new markets or exploring new product ideas.

4. Descriptive research

As its name implies, descriptive research seeks to describe a market, population or phenomenon in detail. It involves collecting and summarizing data to answer questions about audience demographics and behaviors, market size, and current trends. Surveys, observational studies and content analysis are common methods used in descriptive research. 

5. Causal research

Causal research aims to establish cause-and-effect relationships between variables. It investigates whether changes in one variable result in changes in another. Experimental designs, A/B testing and regression analysis are common causal research methods. This sheds light on how specific marketing strategies or product changes impact consumer behavior.

6. Cross-sectional research

Cross-sectional market research involves collecting data from a sample of the population at a single point in time. It is used to analyze differences, relationships or trends among various groups within a population. Cross-sectional studies are helpful for market segmentation, identifying target audiences and assessing market trends at a specific moment.

7. Longitudinal research

Longitudinal research, in contrast to cross-sectional research, collects data from the same subjects over an extended period. This allows for the analysis of trends, changes and developments over time. Longitudinal studies are useful for tracking long-term developments in consumer preferences, brand loyalty and market dynamics.

Each type of market research has its strengths and weaknesses, and the method you choose depends on your specific research goals and the depth of understanding you’re aiming to achieve. In the following sections, we’ll delve into primary and secondary research approaches and specific research methods.

Primary vs. secondary market research

Market research of all types can be broadly categorized into two main approaches: primary research and secondary research. By understanding the differences between these approaches, you can better determine the most appropriate research method for your specific goals.

Primary market research 

Primary research involves the collection of original data straight from the source. Typically, this involves communicating directly with your target audience — through surveys, interviews, focus groups and more — to gather information. Here are some key attributes of primary market research:

• Customized data: Primary research provides data that is tailored to your research needs. You design a custom research study and gather information specific to your goals.
• Up-to-date insights: Because primary research involves communicating with customers, the data you collect reflects the most current market conditions and consumer behaviors.
• Time-consuming and resource-intensive: Despite its advantages, primary research can be labor-intensive and costly, especially when dealing with large sample sizes or complex study designs. Whether you hire a market research consultant, agency or use an in-house team, primary research studies consume a large amount of resources and time.

Secondary market research 

Secondary research, on the other hand, involves analyzing data that has already been compiled by third-party sources, such as online research tools, databases, news sites, industry reports and academic studies.

Here are the main characteristics of secondary market research:

• Cost-effective: Secondary research is generally more cost-effective than primary research since it doesn’t require building a research plan from scratch. You and your team can look at databases, websites and publications on an ongoing basis, without needing to design a custom experiment or hire a consultant. 
• Leverages multiple sources: Data tools and software extract data from multiple places across the web, and then consolidate that information within a single platform. This means you’ll get a greater amount of data and a wider scope from secondary research.
• Quick to access: You can access a wide range of information rapidly — often in seconds — if you’re using online research tools and databases. Because of this, you can act on insights sooner, rather than taking the time to develop an experiment. 

So, when should you use primary vs. secondary research? In practice, many market research projects incorporate both primary and secondary research to take advantage of the strengths of each approach.

One rule of thumb is to focus on secondary research to obtain background information, market trends or industry benchmarks. It is especially valuable for conducting preliminary research, competitor analysis, or when time and budget constraints are tight. Then, if you still have knowledge gaps or need to answer specific questions unique to your business model, use primary research to create a custom experiment. 

Market research methods

• Surveys and questionnaires
• Focus groups
• Observational research
• Online research tools
• Experiments
• Content analysis
• Ethnographic research

How do primary and secondary research approaches translate into specific research methods? Let’s take a look at the different ways you can gather data: 

1. Surveys and questionnaires

Surveys and questionnaires are popular methods for collecting structured data from a large number of respondents. They involve a set of predetermined questions that participants answer. Surveys can be conducted through various channels, including online tools, telephone interviews and in-person or online questionnaires. They are useful for gathering quantitative data and assessing customer demographics, opinions, preferences and needs. On average, customer surveys have a 33% response rate , so keep that in mind as you consider your sample size.

2. Interviews

Interviews are in-depth conversations with individuals or groups to gather qualitative insights. They can be structured (with predefined questions) or unstructured (with open-ended discussions). Interviews are valuable for exploring complex topics, uncovering motivations and obtaining detailed feedback. 

3. Focus groups

The most common primary research methods are in-depth webcam interviews and focus groups. Focus groups are a small gathering of participants who discuss a specific topic or product under the guidance of a moderator. These discussions are valuable for primary market research because they reveal insights into consumer attitudes, perceptions and emotions. Focus groups are especially useful for idea generation, concept testing and understanding group dynamics within your target audience.

4. Observational research

Observational research involves observing and recording participant behavior in a natural setting. This method is particularly valuable when studying consumer behavior in physical spaces, such as retail stores or public places. In some types of observational research, participants are aware you’re watching them; in other cases, you discreetly watch consumers without their knowledge, as they use your product. Either way, observational research provides firsthand insights into how people interact with products or environments.

5. Online research tools

You and your team can do your own secondary market research using online tools. These tools include data prospecting platforms and databases, as well as online surveys, social media listening, web analytics and sentiment analysis platforms. They help you gather data from online sources, monitor industry trends, track competitors, understand consumer preferences and keep tabs on online behavior. We’ll talk more about choosing the right market research tools in the sections that follow.

6. Experiments

Market research experiments are controlled tests of variables to determine causal relationships. While experiments are often associated with scientific research, they are also used in market research to assess the impact of specific marketing strategies, product features, or pricing and packaging changes.

7. Content analysis

Content analysis involves the systematic examination of textual, visual or audio content to identify patterns, themes and trends. It’s commonly applied to customer reviews, social media posts and other forms of online content to analyze consumer opinions and sentiments.

8. Ethnographic research

Ethnographic research immerses researchers into the daily lives of consumers to understand their behavior and culture. This method is particularly valuable when studying niche markets or exploring the cultural context of consumer choices.

How to do market research

• Set clear objectives
• Identify your target audience
• Choose your research methods
• Use the right market research tools
• Collect data
• Analyze data 
• Interpret your findings
• Identify opportunities and challenges
• Make informed business decisions
• Monitor and adapt

Now that you have gained insights into the various market research methods at your disposal, let’s delve into the practical aspects of how to conduct market research effectively. Here’s a quick step-by-step overview, from defining objectives to monitoring market shifts.

1. Set clear objectives

When you set clear and specific goals, you’re essentially creating a compass to guide your research questions and methodology. Start by precisely defining what you want to achieve. Are you launching a new product and want to understand its viability in the market? Are you evaluating customer satisfaction with a product redesign? 

Start by creating SMART goals — objectives that are specific, measurable, achievable, relevant and time-bound. Not only will this clarify your research focus from the outset, but it will also help you track progress and benchmark your success throughout the process. 

You should also consult with key stakeholders and team members to ensure alignment on your research objectives before diving into data collecting. This will help you gain diverse perspectives and insights that will shape your research approach.

2. Identify your target audience

Next, you’ll need to pinpoint your target audience to determine who should be included in your research. Begin by creating detailed buyer personas or stakeholder profiles. Consider demographic factors like age, gender, income and location, but also delve into psychographics, such as interests, values and pain points.

The more specific your target audience, the more accurate and actionable your research will be. Additionally, segment your audience if your research objectives involve studying different groups, such as current customers and potential leads.

If you already have existing customers, you can also hold conversations with them to better understand your target market. From there, you can refine your buyer personas and tailor your research methods accordingly.

3. Choose your research methods

Selecting the right research methods is crucial for gathering high-quality data. Start by considering the nature of your research objectives. If you’re exploring consumer preferences, surveys and interviews can provide valuable insights. For in-depth understanding, focus groups or observational research might be suitable. Consider using a mix of quantitative and qualitative methods to gain a well-rounded perspective. 

You’ll also need to consider your budget. Think about what you can realistically achieve using the time and resources available to you. If you have a fairly generous budget, you may want to try a mix of primary and secondary research approaches. If you’re doing market research for a startup , on the other hand, chances are your budget is somewhat limited. If that’s the case, try addressing your goals with secondary research tools before investing time and effort in a primary research study. 

4. Use the right market research tools

Whether you’re conducting primary or secondary research, you’ll need to choose the right tools. These can help you do anything from sending surveys to customers to monitoring trends and analyzing data. Here are some examples of popular market research tools:

• Market research software: Crunchbase is a platform that provides best-in-class company data, making it valuable for market research on growing companies and industries. You can use Crunchbase to access trusted, first-party funding data, revenue data, news and firmographics, enabling you to monitor industry trends and understand customer needs.

• Survey and questionnaire tools: SurveyMonkey is a widely used online survey platform that allows you to create, distribute and analyze surveys. Google Forms is a free tool that lets you create surveys and collect responses through Google Drive.
• Data analysis software: Microsoft Excel and Google Sheets are useful for conducting statistical analyses. SPSS is a powerful statistical analysis software used for data processing, analysis and reporting.
• Social listening tools: Brandwatch is a social listening and analytics platform that helps you monitor social media conversations, track sentiment and analyze trends. Mention is a media monitoring tool that allows you to track mentions of your brand, competitors and keywords across various online sources.
• Data visualization platforms: Tableau is a data visualization tool that helps you create interactive and shareable dashboards and reports. Power BI by Microsoft is a business analytics tool for creating interactive visualizations and reports.

5. Collect data

There’s an infinite amount of data you could be collecting using these tools, so you’ll need to be intentional about going after the data that aligns with your research goals. Implement your chosen research methods, whether it’s distributing surveys, conducting interviews or pulling from secondary research platforms. Pay close attention to data quality and accuracy, and stick to a standardized process to streamline data capture and reduce errors. 

6. Analyze data

Once data is collected, you’ll need to analyze it systematically. Use statistical software or analysis tools to identify patterns, trends and correlations. For qualitative data, employ thematic analysis to extract common themes and insights. Visualize your findings with charts, graphs and tables to make complex data more understandable.

If you’re not proficient in data analysis, consider outsourcing or collaborating with a data analyst who can assist in processing and interpreting your data accurately.

7. Interpret your findings

Interpreting your market research findings involves understanding what the data means in the context of your objectives. Are there significant trends that uncover the answers to your initial research questions? Consider the implications of your findings on your business strategy. It’s essential to move beyond raw data and extract actionable insights that inform decision-making.

Hold a cross-functional meeting or workshop with relevant team members to collectively interpret the findings. Different perspectives can lead to more comprehensive insights and innovative solutions.

8. Identify opportunities and challenges

Use your research findings to identify potential growth opportunities and challenges within your market. What segments of your audience are underserved or overlooked? Are there emerging trends you can capitalize on? Conversely, what obstacles or competitors could hinder your progress?

Lay out this information in a clear and organized way by conducting a SWOT analysis, which stands for strengths, weaknesses, opportunities and threats. Jot down notes for each of these areas to provide a structured overview of gaps and hurdles in the market.

9. Make informed business decisions

Market research is only valuable if it leads to informed decisions for your company. Based on your insights, devise actionable strategies and initiatives that align with your research objectives. Whether it’s refining your product, targeting new customer segments or adjusting pricing, ensure your decisions are rooted in the data.

At this point, it’s also crucial to keep your team aligned and accountable. Create an action plan that outlines specific steps, responsibilities and timelines for implementing the recommendations derived from your research. 

10. Monitor and adapt

Market research isn’t a one-time activity; it’s an ongoing process. Continuously monitor market conditions, customer behaviors and industry trends. Set up mechanisms to collect real-time data and feedback. As you gather new information, be prepared to adapt your strategies and tactics accordingly. Regularly revisiting your research ensures your business remains agile and reflects changing market dynamics and consumer preferences.

Online market research sources

As you go through the steps above, you’ll want to turn to trusted, reputable sources to gather your data. Here’s a list to get you started:

• Crunchbase: As mentioned above, Crunchbase is an online platform with an extensive dataset, allowing you to access in-depth insights on market trends, consumer behavior and competitive analysis. You can also customize your search options to tailor your research to specific industries, geographic regions or customer personas.

• Academic databases: Academic databases, such as ProQuest and JSTOR , are treasure troves of scholarly research papers, studies and academic journals. They offer in-depth analyses of various subjects, including market trends, consumer preferences and industry-specific insights. Researchers can access a wealth of peer-reviewed publications to gain a deeper understanding of their research topics.
• Government and NGO databases: Government agencies, nongovernmental organizations and other institutions frequently maintain databases containing valuable economic, demographic and industry-related data. These sources offer credible statistics and reports on a wide range of topics, making them essential for market researchers. Examples include the U.S. Census Bureau , the Bureau of Labor Statistics and the Pew Research Center .
• Industry reports: Industry reports and market studies are comprehensive documents prepared by research firms, industry associations and consulting companies. They provide in-depth insights into specific markets, including market size, trends, competitive analysis and consumer behavior. You can find this information by looking at relevant industry association databases; examples include the American Marketing Association and the National Retail Federation .
• Social media and online communities: Social media platforms like LinkedIn or Twitter (X) , forums such as Reddit and Quora , and review platforms such as G2 can provide real-time insights into consumer sentiment, opinions and trends. 

Market research examples

At this point, you have market research tools and data sources — but how do you act on the data you gather? Let’s go over some real-world examples that illustrate the practical application of market research across various industries. These examples showcase how market research can lead to smart decision-making and successful business decisions.

Example 1: Apple’s iPhone launch

Apple ’s iconic iPhone launch in 2007 serves as a prime example of market research driving product innovation in tech. Before the iPhone’s release, Apple conducted extensive market research to understand consumer preferences, pain points and unmet needs in the mobile phone industry. This research led to the development of a touchscreen smartphone with a user-friendly interface, addressing consumer demands for a more intuitive and versatile device. The result was a revolutionary product that disrupted the market and redefined the smartphone industry.

Example 2: McDonald’s global expansion

McDonald’s successful global expansion strategy demonstrates the importance of market research when expanding into new territories. Before entering a new market, McDonald’s conducts thorough research to understand local tastes, preferences and cultural nuances. This research informs menu customization, marketing strategies and store design. For instance, in India, McDonald’s offers a menu tailored to local preferences, including vegetarian options. This market-specific approach has enabled McDonald’s to adapt and thrive in diverse global markets.

Example 3: Organic and sustainable farming

The shift toward organic and sustainable farming practices in the food industry is driven by market research that indicates increased consumer demand for healthier and environmentally friendly food options. As a result, food producers and retailers invest in sustainable sourcing and organic product lines — such as with these sustainable seafood startups — to align with this shift in consumer values. 

The bottom line? Market research has multiple use cases and is a critical practice for any industry. Whether it’s launching groundbreaking products, entering new markets or responding to changing consumer preferences, you can use market research to shape successful strategies and outcomes.

Market research templates

You finally have a strong understanding of how to do market research and apply it in the real world. Before we wrap up, here are some market research templates that you can use as a starting point for your projects:

• Smartsheet competitive analysis templates : These spreadsheets can serve as a framework for gathering information about the competitive landscape and obtaining valuable lessons to apply to your business strategy.
• SurveyMonkey product survey template : Customize the questions on this survey based on what you want to learn from your target customers.
• HubSpot templates : HubSpot offers a wide range of free templates you can use for market research, business planning and more.
• SCORE templates : SCORE is a nonprofit organization that provides templates for business plans, market analysis and financial projections.
• SBA.gov : The U.S. Small Business Administration offers templates for every aspect of your business, including market research, and is particularly valuable for new startups. 

Strengthen your business with market research

When conducted effectively, market research is like a guiding star. Equipped with the right tools and techniques, you can uncover valuable insights, stay competitive, foster innovation and navigate the complexities of your industry.

Throughout this guide, we’ve discussed the definition of market research, different research methods, and how to conduct it effectively. We’ve also explored various types of market research and shared practical insights and templates for getting started. 

Now, it’s time to start the research process. Trust in data, listen to the market and make informed decisions that guide your company toward lasting success.

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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2024 Kamala Harris v. Donald Trump battleground-state estimates

By Kabir Khanna

Updated on: August 18, 2024 / 9:02 PM EDT / CBS News

Here are CBS News' latest estimates of Kamala Harris and Donald Trump's support in the most competitive states in the country leading up to 2024 presidential election. This is where races stand today —  the numbers are updated regularly.

We take a state-by-state approach, because the presidency is determined in the Electoral College, not by national popular vote. We produce estimates of current support using a statistical model  that incorporates all the data we've collected up to this point.

That includes tens of thousands of registered voters who respond to our surveys. We poll voters in every state, but concentrate our efforts in the battlegrounds, which we expect to be more competitive. Our model combines this survey data with voter files and recent election results to anchor estimates.

CBS has a strong track record employing similar models over the past few years . Read more about the Battleground Tracker methodology here .

• Opinion Poll
• Kamala Harris
• Donald Trump
• 2024 Elections

Kabir Khanna, Ph.D., is Deputy Director, Elections & Data Analytics at CBS News. He conducts surveys, develops statistical models, and projects races at the network Decision Desk. His scholarly research centers on political behavior and methodology. He holds a Ph.D. in political science from Princeton University.

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The Integrity of Psychological Research: Uncovering Statistical Reporting Inconsistencies

• Advances in Methods and Practices in Psychological Science
• Statistical Analysis

Accurate reporting in psychological science is vital for ensuring reliable results. Are there statistical inconsistencies in scientific articles?  

In this episode, APS’s Özge Gürcanlı Fischer Baum speaks with Michele Nuijten from Tilburg University to examine how overlooked errors in statistical reporting can undermine the credibility of research findings. Together, they discuss Nuijten’s research published in Advances in Methods and Practices in Psychological Science and examine practical strategies to enhance the quality of psychological research. 

Send us your thoughts and questions at [email protected]  

Unedited transcript

[00:00:10.160] – APS’s Özge Gürcanlı Fischer Baum  

Statistical reporting is a core part of writing scholarly articles. Many conclusions in scientific reports rely on null hypothesis significance testing, making accurate reporting essential for robust findings. What if there are inconsistencies in academic journals? How would it affect our field? If we cannot trust the numbers reported, the reliability of the conclusions is at stake. I am Özge Gürcanlı Fischer Baum with the Association for Psychological Science. Today, I have the pleasure of talking to Michele Nuijten from the Tilburg University. Michele recently published an article on Statistical Reporting Inconsistencies in APS’s Journal: Advances in Methods and Practices in Psychological Science. Join us as we explore the impact of these inconsistencies and discuss potential solutions to enhance the credibility of psychological research. Michele, welcome to Under the Cortex. 

[00:01:09.810] – Michele Nuijten  

Thank you very much. I’m honored to be here. 

[00:01:12.520] – APS’s Özge Gürcanlı Fischer Baum  

Please tell us about yourself first. What type of psychologist are you? 

[00:01:17.420] – Michele Nuijten  

I guess you could classify me as a methodologist. I have a background in psychological methods. That is what I studied. But right now, I think I would call myself a meta-scientist, so someone who researchers’ research. I’m really focusing on trying to detect problems in the way that we do science in psychology and related fields. If I uncover any issues, to also think about pragmatic solutions to make sure that we can move forward in our field in a better and more solid way. 

[00:01:49.360] – APS’s Özge Gürcanlı Fischer Baum  

Yeah, that is fantastic. How did you first get interested in becoming a meta researcher and studying statistical inconsistencies? 

[00:01:59.980] – Michele Nuijten  

It’s actually quite some time ago now. I was still in my master’s program, which is over 10 years ago now, I think. I was a master student in a very interesting time, I think, around 2011, 2012, which is also often marked as the start of the replication crisis, as it’s often called in psychology. We had the massive fraud case of Diederik Stapel, who, coincidentally, was a researcher at the university I’m working now. But there was also an article that seemingly proved that we could look into the future. That was the article that proved that if you have so much flexibility in data analysis, you can inflate your type 1 error to 50%. A lot of these things were happening. Around that time, I also got interested in reporting inconsistencies, mainly out of a more technical interest, I guess. People around me were working on these inconsistencies. Together with a friend, Sacha Epskamp, we thought, Well, this seems like a problem that you can automate. Maybe we can write a program to help us detect problems in articles. 

[00:03:06.220] – APS’s Özge Gürcanlı Fischer Baum  

Yeah, I will come to that. But it is interesting that we are contemporary. During that replication crisis, I was in grad school as well. I was a senior graduate student then. Us also had some replication problems in my field in developmental side. I totally hear you. I’m glad it created a research program for you. Now we have a tool called called statcheck. Can you tell our listeners what it is? 

[00:03:34.480] – Michele Nuijten  

I think the easiest way to explain what Stat Check is is to compare it to a spell checker for statistics. Instead of finding typos in your words, you find typos in your statistical results. What it does is it takes an article, it searches through the text for, as you mentioned, null hypothesis significance tests, so effectively tests with P values, and it tries to use the numbers to recalculate that P value. An equivalent would be if you would write down 2 plus 4 equals 5, then you know when you read that, that something is off, like these numbers don’t match up. Stat Checkdoes a similar thing. It searches for these test results, which often consists of a test statistic, degrees of freedom, and a P-Value. It uses two of these numbers, the test statistic and the degrees of freedom, to recalculate the P-Value and then see if these numbers match or not. 

[00:04:29.850] – APS’s Özge Gürcanlı Fischer Baum  

Yeah. This is an important tool because what I read from your study is that before Stat Check, your earlier work shows about 50% of articles with statistical results contained at least one P-value that didn’t match what the test statistic and the degrees of freedom would indicate it should be. How did you first notice there were problems in statistical reporting? 

[00:04:56.250] – Michele Nuijten  

This was actually a project that some of my colleagues were working on at the time, back when I was still a student, so my colleagues Marion Bacher and Jelte Wicherts, who are, incidentally, my colleagues still now at a different university, they noticed that one high-profile paper contained such inconsistency. They noticed just by looking at the numbers like, Hey, something doesn’t add up here. When they went through this particular paper, they thought, Well, if such a high-profile paper published in a high-quality journal already has some of these just visible errors in it, How much does this occur in the general literature? They actually went to the painstaking process of going through, I think, over a thousand P values by hand to see how often this occurred. When I and my friend saw that this was such a painstaking process, and ironically, also an error-prone process. You can imagine if you have to do this by hand, then we thought, Well, this seems like a thing that you can automate. That’s how we got on this path of looking at reporting inconsistencies in statistics. 

[00:06:04.350] – APS’s Özge Gürcanlı Fischer Baum  

Yeah, let’s talk about how it works then. I have this tool. Could you describe our listeners? What are the steps? We go to this website, and then what happens? 

[00:06:16.110] – Michele Nuijten  

Well, for the side of the user, it’s very straightforward. You go to the web application, which is called statcheck.io. There’s literally one button you can click on. The button is upload your paper. You upload a paper in a Word format or HTML or PDF. Nothing gets saved in the back-end. It only gets scanned by Stat Check, and you get back a nice table with all the results that Stat Checkwas able to find and a list of whether or not it was flagged as consistent. 

[00:06:49.010] – APS’s Özge Gürcanlı Fischer Baum  

In your work, you must have seen a lot of examples. What are some of the worst examples that you saw? 

[00:06:57.330] – Michele Nuijten  

Yeah, that’s a difficult question because Stat Check, in a way, it’s not an AI or something. It merely just looks at numbers and says, Well, these numbers don’t appear to add up. What it does is it recalculates the P-value because we had to choose a number to recalculate calculate, and given the enormous focus on P values in our field, that seemed like the most logical choice. But just as with my earlier example, if you say 2 plus 4 equals 5, the 5 could be incorrect, but the 2 or the 4 could also be incorrect. You don’t know. This also means that sometimes Stat Check flags an inconsistency where the reported P value is smaller than 0.001, but the recomputed P value is 0.80 or something. Which might look like a blatant error and a really dramatic difference. But it could be the case that there is a typo in the test statistic. For instance, if you write down that your T-value is 1.5, but you meant 10.5, it’s only a typo, but it seems as if it would have huge influence on your results. Those type of inconsistencies look dramatic, but might not be. At the other side, you also have types of errors that might, at first glance, seem inconsequential that might have big consequences. 

[00:08:21.260] – Michele Nuijten  

For instance, we have a lot of focus on this P must be smaller than 0.05 criterion to decide whether something is statistically significant. I do sometimes come across cases where the reported P-value is smaller than 0.05, and if I recompute it, it’s 0.06. In absolute terms, this is a very small difference, and you could argue that the statistical evidence that this P-value represents does not differ much. But I think it could signal a bigger underlying problem that people might round down P-values in order to increase their chances to get published. This is something that Stat Check cannot tell you. It only flags these numbers don’t seem to add up. It’s very hard to pinpoint what exactly the reason is. But with these particular types of inconsistencies, I do get a little bit suspicious, like what might What is going on here? 

[00:09:17.050] – APS’s Özge Gürcanlı Fischer Baum  

Yeah. One of the other things I noticed in your report is that there are a few articles that could be 100% wrong in a way that up to 100% of the reported results are inconsistent. How do you think that happens? 

[00:09:36.440] – Michele Nuijten  

Yeah, this signals a problem, or not really a problem, but a difficulty in reporting such prevalence of inconsistencies. Because at what level do you display this? The problem is that different articles have a different number of P-values they report. Sometimes articles only report one P-value. If they report it incorrectly, then they have a 100% inconsistency rate. But it could also be the case that people report 100 P values and 10 of them are wrong. The inconsistency rate would be different, but which one is worse? In absolute sense, you have more errors than the other, but yet you can also argue, Well, if you report a lot of P values, it’s easier to make at least one mistake. It’s hard to come up with a summarizing statistic that fairly reflects what is going on. Yeah. 

[00:10:30.580] – APS’s Özge Gürcanlı Fischer Baum  

Why did you decide to try to fix it in our field? 

[00:10:36.920] – Michele Nuijten  

It seems like such low-hanging fruit. I mean, it’s an issue that technically could be spotted by anyone. It’s just in the paper. It’s right there. Peer reviewers could spot it, but it turns out that they don’t, which makes a lot of sense as well, because we are all very busy. We’re often not that trained in statistics, especially not seeing inconsistencies with the naked eye. But I do think these type of errors or inconsistencies are important because, as you also mentioned at the start, if you cannot trust the numbers that a conclusion is based on, how can you trust the conclusion is correct at all? I think this type of reproducibility, I would call this. If I have the same data and I do the same analysis, I should get the same results. If you spot If I have an inconsistency in a paper, I can already tell you that that result is not reproducible. I cannot get to an inconsistent result based on your raw data. It’s very hard to judge to what extent the data is then trustworthy or the conclusions are It’s worth it. There’s quite a lot of issues going on right now in psychology, things that have been flagged as potential problems. 

[00:11:53.130] – Michele Nuijten  

I think this seems like one of the easiest things that we can solve. If we have a spell checker like this and we can just quickly We quickly run our manuscript through it before we submit it, we save both ourselves and the readers and the editors and everyone involved a lot of pain if we just managed to get out these errors beforehand and we don’t have to get into this annoying world of issuing corrections or just leaving the errors in there. 

[00:12:20.130] – APS’s Özge Gürcanlı Fischer Baum  

I’m really glad you said it is like spell check because I wrote down grammar check for statistical reports. This is what the tool does. Now, do you think journal editors use it or are they allowed to use it? 

[00:12:34.750] – Michele Nuijten  

It’s completely free and everyone is allowed to use it. I would encourage everyone to use it. It’s an R package underneath it. You can use the R package if you have research intentions, if you want to have larger sets of articles to scan. But if you just want to scan a single paper, go to the web app, go through it. It’s free. Within a second, you have your results. I would definitely encourage editors to use it. There are a few that do. For instance, psychological science, if we’re talking about APS journals. I’m not sure, but I think maybe Amps also mentioned something about it. I don’t have a curated list. People or editors that start using Stat Check usually don’t notify me. By the way, if you are an editor interested in using it, feel free to notify me or ask for help. I’m more than happy to assist in any way I can. But I think It’s a great use of the tool. 

[00:13:31.670] – APS’s Özge Gürcanlı Fischer Baum  

Yeah, that’s exactly why I’m asking it, to encourage people that they should use it. It is not AI. I’m glad you clarified that point. It is just a check. If we do grammar check or spelling check for text, we should be able to do it for numbers. This is a great tool that everybody can use and it is free. Let’s take a step back. What was the process of making stat check like? How long did it What was your team like? 

[00:14:03.470] – Michele Nuijten  

Well, I don’t think there will ever be an ending to this. It’s an ongoing project. I’ve been working on this for 10 years now. But I think the initial framework was set up in… Well, I think as it goes with tools like this, the first version is usually done within a day or within an hour by someone. In this case, Sasha Epskamp was the person who developed the first version of Stat Check. After that, I ran with it for the next 10 years to develop it further. There have been many, many updates, mainly behind the scenes. I learned a lot about software development in the process. I learned about unit testing. I learned about best practices on how to use GitHub and branches and all these terms that were new to me. That was a lot of fun to do. During the years, I’ve had many people contributing interesting ideas of people writing code for me. But mainly, I’ve kept it quite close because sometimes tools like this that point out mistakes feel a bit tricky. I have very I very much want to present Stat Check as something that can help improve everybody’s work as something that you can use yourself. 

[00:15:23.240] – Michele Nuijten  

Sometimes people don’t always see it like that. I’m a bit afraid to give it away to have more people develop to develop on it because I’m afraid that maybe mistakes will be introduced. This is a very big pitfall of mine. I really need to learn to let go and invite more people to work on it, especially because I think that many people will be a lot better at it than I am. But this is one of the things I’ve been struggling with a little bit. 

[00:15:52.630] – APS’s Özge Gürcanlı Fischer Baum  

Yeah, but it’s your baby. 

[00:15:54.580] – Michele Nuijten  

I know, yeah. 

[00:15:55.780] – APS’s Özge Gürcanlı Fischer Baum  

You want it to work better and better every single day. No, this is a great resource for everyone involved in our field. Thank you for all the hard work you put into that. Michele, is there anything else that you would like to share with our listeners? 

[00:16:16.370] – Michele Nuijten  

I think more in general about just improving practices in our field, because I think what I really like about Stat Checkand about the type of projects I usually take is I try to focus on things that are pragmatic, that are small steps towards a better science. I sometimes feel like it can be a bit overwhelming. The good news is there are so many initiatives to improve our field. I can imagine that, especially if you’re an early career researcher, that you don’t know where to start. I think that with these small tools like Stat Check, but many other initiatives are similar, just cherry-pick your favorite. Try one, see what happens. I think Christina Bergman calls this the buffet approach. You have this entire table full of open science practices, but you cannot eat them all at once. Just take some samples, try some stuff out, see what works for you and your paper, and in that way, get involved with the new developments. 

[00:17:26.150] – APS’s Özge Gürcanlı Fischer Baum  

Yeah. Thank you very much, Michele. This was a pleasure. Thank Thank you so much for joining Under the Cortex. 

[00:17:33.090] – Michele Nuijten  

Thank you for having me. 

[00:17:34.790] – APS’s Özge Gürcanlı Fischer Baum  

This is Özge Gürcanlı Fischer Baum with APS, and I have been speaking to Michele Nuijten from the Tilburg University. If you want to know more about this research, visit psychologicalscience.org. 

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Fact-Checking Biden’s Speech and More: Day 1 of the Democratic National Convention

We followed the developments and fact-checked the speakers, providing context and explanation.

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President Biden praised his administration’s accomplishments and declared his vice president a worthy successor on the first night of the Democratic National Convention on Monday.

Mr. Biden’s speech capped a night in which Democratic lawmakers and party stalwarts praised Vice President Kamala Harris, warned repeatedly that former President Donald J. Trump was unfit for office and celebrated Mr. Biden’s legacy.

Here’s a look at some of their claims.

“While schools closed and dead bodies filled morgues, Donald Trump downplayed the virus. He told us to inject bleach into our bodies. He peddled conspiracy theories across the country. We lost hundreds of thousands of Americans, and our economy collapsed.”

— Representative Robert Garcia of California

This is exaggerated.

Mr. Trump’s comments, in April 2020, about the efficacy of disinfectants and light as treatments for the coronavirus elicited uproar and confusion . He did not literally instruct people to inject bleach, but raised the suggestion as an “interesting” concept to test out.

At the April 2020 news conference , a member of Mr. Trump’s coronavirus task force said that the virus dies under direct sunlight and that applying bleach in indoor spaces kills the virus in five minutes and isopropyl alcohol does so in 30 seconds.

Mr. Trump responded: “Supposing we hit the body with a tremendous — whether it’s ultraviolet or just very powerful light — and I think you said that that hasn’t been checked, but you’re going to test it. And then I said, supposing you brought the light inside the body, which you can do either through the skin or in some other way, and I think you said you’re going to test that too.”

He added: “And then I see the disinfectant, where it knocks it out in a minute. One minute. And is there a way we can do something like that, by injection inside or almost a cleaning? Because you see it gets in the lungs and it does a tremendous number on the lungs. So it would be interesting to check that.”

Jeanna Smialek

“Trump talks big about bringing back manufacturing jobs, but you know who actually did it? President Biden and Vice President Kamala Harris.”

— Gov. Kathy Hochul of New York

This needs context .

It is true that manufacturing employment is up sharply under the Biden administration, but much of the gains are simply a recovery from job losses early in the coronavirus pandemic. Manufacturing employment is just slightly above its 2019 level. And factory employment also climbed somewhat from when Donald J. Trump took office in early 2017 and the onset of the pandemic in 2020.

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“Thanks to Joe Biden and Kamala Harris, we reopened our schools.”

— Representative James E. Clyburn, Democrat of South Carolina

This needs context.

President Donald J. Trump and President Biden took different approaches to school reopenings during the coronavirus pandemic, with Mr. Trump encouraging schools to stay open and Mr. Biden emphasizing the need to contain the virus before reopening classroom doors. While they could signal policy preferences, developments in how the virus spread and how states and school districts reacted were sometimes out of their control.

The Centers for Disease Control and Prevention warned schools to prepare for disruption in February 2020, and a high school in Washington State became the first to close its doors that month . More schools across the country followed in adopting online instruction, but by the fall of 2020, some schools — often in states with Republican governors — returned to in-person instruction.

One audit found that by the fall of 2020 more schools had reverted to a traditional or hybrid model than remained virtual. A C.D.C. study found that school closures peaked in 2021, under the Biden administration, when the Omicron variant spread. By the fall of 2021, though, 98 percent of public schools were offering in-person instruction full time, according to the Education Department .

“Donald Trump wants to put our 1787 constitution through his Project 2025 paper shredder.”

— Representative Jasmine Crockett, Democrat of Texas

Project 2025, a set of conservative policy proposals assembled by a Washington think tank for a Republican presidential administration, does not directly come from Mr. Trump or his campaign.

Still, CNN documented instances where 140 people who worked for the Trump administration had a role in Project 2025. Some were top advisers to Mr. Trump in his first term and a re all but certain to step into prominent posts should he win a second term.

Mr. Trump has also supported some of the proposals, with even some overlap between Project 2025 and his own campaign plans. Among the similarities: undercutting the independence of the Justice Department and pressing to end diversity, equity and inclusion programs. And he enacted other initiatives mentioned in Project 2025 in his first term, such as levying tariffs on China and making it easier to fire federal workers.

But Mr. Trump has criticized some elements as “absolutely ridiculous and abysmal” though he has not specified which proposals he opposes. When the director of the project departed the think tank, Mr. Trump’s campaign released a statement that stated: “Reports of Project 2025’s demise would be greatly welcomed and should serve as notice to anyone or any group trying to misrepresent their influence with President Trump and his campaign — it will not end well for you.”

“JD Vance says women should stay in violent marriages and pregnancies resulting from rape are simply inconvenient.”

— Gov. Andy Beshear of Kentucky

Mr. Beshear was referring to comments Mr. Vance made during his 2022 campaign for Senate. Mr. Vance has rejected such interpretations.

In remarks to a Christian high school in California in September 2021, Mr. Vance spoke of his grandparents’ marriage, which he described in his memoir as violent.

“This is one of the great tricks that I think the sexual revolution pulled on the American populace, which is the idea that like, ‘Well, OK, these marriages were fundamentally, you know, they were maybe even violent, but certainly they were unhappy. And so getting rid of them and making it easier for people to shift spouses like they change their underwear, that’s going to make people happier in the long term,” he said .

Asked by Vice News about his remarks in 2022, Mr. Vance said, “Any fair person would recognize I was criticizing the progressive frame on this issue, not embracing it.”

He also told Fox News that Democrats had “twisted my words here” and that “it’s not what I believe, it’s not what I said.”

And regarding pregnancies resulting from rape, Mr. Vance told Fox News that he was criticizing the view that such pregnancies are “inconvenient.”

In a 2021 interview , Mr. Vance was asked whether abortion bans should have exceptions for rape or incest. He responded, “At the end of the day, we’re talking about an unborn baby. What kind of society do we want to have? A society that looks at unborn babies as inconveniences to be discarded?”

“Instead of paying $400 a month for insulin, seniors with diabetes will pay $35 a month.”

— President Biden

Mr. Biden signed a law that places a cap of $35 a month on insulin for all Medicare Part D beneficiaries. But he is overstating the average cost before the law.

Patients’ out-of-pocket spending on insulin was $434 on average for all of 2019 — not per month — and $449 per year for Medicare enrollees, according to the Health and Human Services Department .

“The smallest racial wealth gap in 20 years.”

As a percentage of wealth held by white families, Black and Latino families did grow to the largest amounts in 2022 in two decades. But the disparity in absolute dollar value actually increased.

“He called them ‘suckers and losers.’”

The claim that, as president, Donald J. Trump called veterans “suckers” and “losers” stems from a 2020 article in The Atlantic about his relationship to the military.

The article relied on anonymous sources, but many of the accounts have been corroborated by other outlets, including The New York Times, and by John F. Kelly, a retired four-star Marine general who served as Mr. Trump’s White House chief of staff. Mr. Trump has emphatically denied making the remarks since the article was published. Here’s a breakdown .

“Trump wants to cut Social Security and Medicare.”

This is misleading..

Mr. Trump has said repeatedly during his 2024 presidential campaign that he would not cut Social Security or Medicare, though he had previously shown brief and vague support for such proposals.

Asked about his position on the programs in relation to the national debt, Mr. Trump told CNBC in March, “There is a lot you can do in terms of entitlements in terms of cutting and in terms of also the theft and the bad management of entitlements.”

But Mr. Trump and his campaign clarified that he would not seek to cut the programs. Mr. Trump told the website Breitbart , “I will never do anything that will jeopardize or hurt Social Security or Medicare.” And during a July rally in Minnesota, he again vowed, “I will not cut one penny from Social Security or Medicare, and I will not raise the retirement age by one day, not by one day.”

Still, Mr. Trump has not outlined a clear plan for keeping the programs solvent. During his time in office, Mr. Trump did propose some cuts to Medicare — though experts said the cost reductions would not have significantly affected benefits — and to Social Security’s programs for people with disabilities. They were not enacted by Congress.

“He created the largest debt any president had in four years with his two trillion dollars tax cut for the wealthy.”

Looking at a single presidential term, Donald J. Trump’s administration did rack up more debt than any other in raw dollars — about $7.9 trillion . But the debt rose more under President Barack Obama’s eight years than under Mr. Trump’s four years. Also, when viewed as a percentage increase, the national debt rose more under President George H.W. Bush’s single term than under Mr. Trump’s.

The Congressional Budget Office estimated that Mr. Trump’s tax cuts — which passed in December 2017 with no Democrats in support — roughly added another $1 trillion to the federal deficit from 2018 to 2021, even after factoring in economic growth spurred by the tax cuts. But other drivers of the deficit include several sweeping measures that had bipartisan approval. The first coronavirus stimulus package , which received near unanimous support in Congress, added $2 trillion to the deficit over the next two fiscal years. Three additional spending measures contending with the coronavirus pandemic and its economic ramifications added another $1.4 trillion.

It is also important to note that presidents do not hold unilateral responsibility for the debt increase under their time in office. Policies from previous administrations — and programs such as Social Security and Medicare — continue to drive up debt, as do unexpected circumstances.