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Qualitative designs and methodologies for business, management, and organizational research.

• Robert P. Gephart Robert P. Gephart Alberta School of Business, University of Alberta
•  and  Rohny Saylors Rohny Saylors Carson College of Business, Washington State University
• https://doi.org/10.1093/acrefore/9780190224851.013.230
• Published online: 28 September 2020

Qualitative research designs provide future-oriented plans for undertaking research. Designs should describe how to effectively address and answer a specific research question using qualitative data and qualitative analysis techniques. Designs connect research objectives to observations, data, methods, interpretations, and research outcomes. Qualitative research designs focus initially on collecting data to provide a naturalistic view of social phenomena and understand the meaning the social world holds from the point of view of social actors in real settings. The outcomes of qualitative research designs are situated narratives of peoples’ activities in real settings, reasoned explanations of behavior, discoveries of new phenomena, and creating and testing of theories.

A three-level framework can be used to describe the layers of qualitative research design and conceptualize its multifaceted nature. Note, however, that qualitative research is a flexible and not fixed process, unlike conventional positivist research designs that are unchanged after data collection commences. Flexibility provides qualitative research with the capacity to alter foci during the research process and make new and emerging discoveries.

The first or methods layer of the research design process uses social science methods to rigorously describe organizational phenomena and provide evidence that is useful for explaining phenomena and developing theory. Description is done using empirical research methods for data collection including case studies, interviews, participant observation, ethnography, and collection of texts, records, and documents.

The second or methodological layer of research design offers three formal logical strategies to analyze data and address research questions: (a) induction to answer descriptive “what” questions; (b) deduction and hypothesis testing to address theory oriented “why” questions; and (c) abduction to understand questions about what, how, and why phenomena occur.

The third or social science paradigm layer of research design is formed by broad social science traditions and approaches that reflect distinct theoretical epistemologies—theories of knowledge—and diverse empirical research practices. These perspectives include positivism, interpretive induction, and interpretive abduction (interpretive science). There are also scholarly research perspectives that reflect on and challenge or seek to change management thinking and practice, rather than producing rigorous empirical research or evidence based findings. These perspectives include critical research, postmodern research, and organization development.

Three additional issues are important to future qualitative research designs. First, there is renewed interest in the value of covert research undertaken without the informed consent of participants. Second, there is an ongoing discussion of the best style to use for reporting qualitative research. Third, there are new ways to integrate qualitative and quantitative data. These are needed to better address the interplay of qualitative and quantitative phenomena that are both found in everyday discourse, a phenomenon that has been overlooked.

• qualitative methods
• research design
• methods and methodologies
• interpretive induction
• interpretive science
• critical theory
• postmodernism
• organization development

Introduction

Qualitative research uses linguistic symbols and stories to describe and understand actual behavior in real settings (Denzin & Lincoln, 1994 ). Understanding requires describing “specific instances of social phenomena” (Van Maanen, 1998 , p. xi) to determine what this behavior means to lay participants and to scientific researchers. This process produces “narratives-non-fiction division that link events to events in storied or dramatic fashion” to uncover broad social science principles at work in specific cases (p. xii).

A research design and/or proposal is often created at the outset of research to act as a guide. But qualitative research is not a rule-governed process and “no one knows” the rules to write memorable and publishable qualitative research (Van Maanen, 1998 , p. xxv). Thus qualitative research “is anything but standardized, or, more tellingly, impersonal” (p. xi). Design is emergent and is often created as it is being done.

Qualitative research is also complex. This complexity is addressed by providing a framework with three distinct layers of knowledge creation resources that are assembled during qualitative research: the methods layer, the logic layer, and the paradigmatic layer. Research methods are addressed first because “there is no necessary connection between research strategies and methods of data collection and analysis” (Blaikie, 2010 , p. 227). Research methods (e.g., interviews) must be adapted for use with the specific logical strategies and paradigmatic assumptions in mind.

The first, or methods, layer uses qualitative methods to “collect data.” That is, to observe phenomena and record written descriptions of observations, often through field notes. Established methods for description include participant and non-participant observation, ethnography, focus groups, individual interviews, and collection of documentary data. The article explains how established methods have been adapted and used to answer a range of qualitative research questions.

The second, or logic, layer involves selecting a research strategy—a “logic, or set of procedures, for answering research questions” (Blaikie, 2010 , p. 18). Research strategies link research objectives, data collection methods, and logics of analysis. The three logical strategies used in qualitative organizational research are inductive logic, deductive logic and abductive logic (Blaikie, 2010 , p. 79). 1 Each logical strategy makes distinct assumptions about the nature of knowledge (epistemology), the nature of being (ontology), and how logical strategies and assumptions are used in data collection and analysis. The task is to describe important methods suitable for each logical strategy, factors to consider when selecting methods (Blaikie, 2010 ), and illustrates how data collection and analysis methods are adapted to ensure for consistency with specific logics and paradigms.

The third, or paradigms, layer of research design addresses broad frameworks and scholarly traditions for understanding research findings. Commitment to a paradigm or research tradition entails commitments to theories, research strategies, and methods. Three paradigms that do empirical research and seek scientific knowledge are addressed first: positivism, interpretive induction, and interpretive abduction. Then, three scholarly and humanist approaches that critique conventional research and practice to encourage organizational change are discussed: critical theory and research, postmodern perspectives, and organization development (OD). Paradigms or traditions provide broad scholarly contexts that make specific studies comprehensible and meaningful. Lack of grounding in an intellectual tradition limits the ability of research to contribute: contributions always relate to advancing the state of knowledge in specific unfolding research traditions that also set norms for assessing research quality. The six research designs are explained to show how consistency in design levels can be achieved for each of the different paradigms. Further, qualitative research designs must balance the need for a clear plan to achieve goals with the need for adaptability and flexibility to incorporate insights and overcome obstacles that emerge during research.

Our general goal has been to provide a practical guide to inspire and assist readers to better understand, design, implement, and publish qualitative research. We conclude by addressing future challenges and trends in qualitative research.

The Substance of Research Design

A research design is a written text that can be prepared prior to the start of a research project (Blaikie, 2010 , p. 4) and shared or used as “a private working document.” Figure 1 depicts the elements of a qualitative research design and research process. Interest in a topic or problem leads researchers to pose questions and select relevant research methods to fulfill research purposes. Implementation of the methods requires use of logical strategies in conjunction with paradigms of research to specify concepts, theories, and models. The outcomes, depending on decisions made during research, are scientific knowledge, scholarly (non-scientific) knowledge, or applied knowledge useful for practice.

Figure 1. Elements of qualitative research design.

Research designs describe a problem or research question and explain how to use specific qualitative methods to collect and analyze qualitative data that answer a research question. The purposes of design are to describe and justify the decisions made during the research process and to explain how the research outcomes can be produced. Designs are thus future-oriented plans that specify research activities, connect activities to research goals and objectives, and explain how to interpret the research outcomes using paradigms and theories.

In contrast, a research proposal is “a public document that is used to obtain necessary approvals for a research proposal to proceed” (Blaikie, 2010 , p. 4). Research designs are often prepared prior to creating a research proposal, and research proposals often require the inclusion of research designs. Proposals also require greater formality when they are the basis for a legal contract between a researcher and a funding agency. Thus, designs and proposals are mutually relevant and have considerable overlap but are addressed to different audiences. Table 1 provides the specific features of designs and proposals. This discussion focuses on designs.

Table 1. Decisions Necessitated by Research Designs and Proposals

Source: Based on Blaikie ( 2010 ), pp. 12–34.

The “real starting point” for a research design (or proposal) is “the formulation of the research question” (Blaikie, 2010 , p. 17). There are three types of research questions: “what” questions seek descriptions; “why” questions seek answers and understanding; and “how” questions address conditions where certain events occur, underlying mechanisms, and conditions necessary for change interventions (p. 17). It is useful to start with research questions rather than goals, and to explain what the research is intended to achieve (p. 17) in a technical way.

The process of finding a topic and formulating a useful research question requires several considerations (Silverman, 2014 , pp. 31–33, 34–40). Researchers must avoid settings where data collection will be difficult (pp. 31–32); specify an appropriate scope for the topic—neither too wide or too narrow—that can be addressed (pp. 35–36); fit research questions into a relevant theory (p. 39); find the appropriate level of theory to address (p. 42); select appropriate designs and research methods (pp. 42–44); ensure the volume of data can be handled (p. 48); and do an effective literature review (p. 48).

A literature review is an important way to link the proposed research to current knowledge in the field, and to explain what was previously known or what theory suggests to be the case (Blaikie, 2010 , p. 17). Research questions can used to bound and frame the literature review while the literature review often inspires research questions. The review may also provide bases for creating new hypotheses and for answering some of the initial research questions (Blaikie, 2010 , p. 18).

Layers of Research Design

There are three layers of research design. The first layer focuses on research methods for collecting data. The second layer focuses on the logical frameworks used for analyzing data. The third layer focuses on the paradigm used to create a coherent worldview from research methods and logical frameworks.

Layer One: Design as Research Methods

Qualitative research addresses the meanings people have for phenomena. It collects narratives of organizational activity, uses analytical induction to create coherent representations of the truths and meanings in organizational contexts, and then creates explanations of this conduct and its prevalence (Van Maanan, 1998 , pp. xi–xii). Thus qualitative research involves “doing research with words” (Gephart, 2013 , title) in order to describe the linguistic symbols and stories that members use in specific settings.

There are four general methods for collecting qualitative data and creating qualitative descriptions (see Table 2 ). The in-depth case study approach provides a history of an event or phenomenon over time using multiple data sources. Observational strategies use the researcher to observe and describe behavior in actual settings. Interview strategies use a format where a researcher asks questions of an informant. And documentary research collects texts, documents, official records, photographs, and videos as data—formally written or visually recorded evidence that can be replayed and reviewed (Creswell, 2014 , p. 190). These methods are adapted to fit the needs of specific projects.

Table 2. Qualitative Data Collection Methods

The In-Depth Case Study Method

The in-depth case study is a key strategy for qualitative research (Piekkari & Welch, 2012 ). It was the most common qualitative method used during the formative years of the field, from 1956 to 1965 , when 48% of qualitative papers published in the Administrative Science Quarterly used the case study method (Van Maanen, 1998 , p. xix). The case design uses one or more data collection strategies to describe in detail how a single event or phenomenon, selected by a researcher, has changed over time. This provides an understanding of the processes that underlie changes to the phenomenon. In-depth case study methods use observations, documents, records, and interviews that describe the events in the case unfolded and their implications. Case studies contextualize phenomena by studying them in actual situations. They provide rich insights into multiple dimensions of a single phenomenon (Campbell, 1975 ); offer empirical insights into what, how, and why questions related to phenomena; and assist in the creation of robust theory by providing diverse data collected over time (Gephart & Richardson, 2008 , p. 36).

Maniha and Perrow ( 1965 ) provide an example of a case study concerned with organizational goal displacement, an important issue in early organizational theorizing that proposed organizations emerge from rational goals. Organizational rationality was becoming questioned at the time that the authors studied a Youth Commission with nine members in a city of 70,000 persons (Maniha & Perrow, 1965 ). The organization’s activities were reconstructed from interviews with principals and stakeholders of the organization, minutes from Youth Commission meetings, documents, letters, and newspaper accounts (Maniha & Perrow, 1965 ).

The account that emerged from the data analysis is a history of how a “reluctant organization” with “no goals to guide it” was used by other aggressive organizations for their own ends. It ultimately created its own mission (Maniha & Perrow, 1965 ). Thus, an organization that initially lacked rational goals developed a mission through the irrational process of goal slippage or displacement. This finding challenged prevailing thinking at the time.

Observational Strategies

Observational strategies involve a researcher present in a situation who observes and records, the activities and conversations that occur in the setting, usually in written field notes. The three observational strategies in Table 2 —participant observation, ethnography, and systematic self-observation—differ in terms of the role of the researcher and in the data collection approach.

Participant observation . This is one of the earliest qualitative methods (McCall & Simmons, 1969 ). One gains access to a setting and an informant holding an appropriate social role, for example, client, customer, volunteer, or researcher. One then observes and records what occurs in the setting using field notes. Many features or topics in a setting can become a focus for participant observers. And observations can be conducted using continuum of different roles from the complete participant, observer as participant, and participant observer, to the complete observer who observes without participation (Creswell, 2014 , Table 9.2, p. 191).

Ethnography . An ethnography is “a written representation of culture” (Van Maanen, 1988 ) produced after extended participation in a culture. Ethnography is a form of participant observation that focuses on the cultural aspects of the group or organization under study (Van Maanen, 1988 , 2010 ). It involves prolonged and close contact with group members in a role where the observer becomes an apprentice to an informant to learn about a culture (Agar, 1980 ; McCurdy, Spradley, & Shandy, 2005 ; Spradley, 1979 ).

Ethnography produces fine-grained descriptions of a micro-culture, based on in-depth cultural participation (McCurdy et al., 2005 ; Spradley, 1979 , 2016 ). Ethnographic observations seek to capture cultural members’ worldviews (see Perlow, 1997 ; Van Maanen, 1988 ; Watson, 1994 ). Ethnographic techniques for interviewing informants have been refined into an integrated developmental research strategy—“the ethno-semantic method”—for undertaking qualitative research (Spradley, 1979 , 2016 ; Van Maanen, 1981 ). The ethnosemantic method uses a structured approach to uncover and confirm key cultural features, themes, and cultural reasoning processes (McCurdy et al., 2005 , Table 3 ; Spradley, 1979 ).

Systematic Self-Observation . Systematic self-observation (SSO) involves “training informants to observe and record a selected feature of their own everyday experience” (Rodrigues & Ryave, 2002 , p. 2; Rodriguez, Ryave, & Tracewell, 1998 ). Once aware that they are experiencing the target phenomenon, informants “immediately write a field report on their observation” (Rodrigues & Ryave, 2002 , p. 2) describing what was said and done, and providing background information on the context, thoughts, emotions, and relationships of people involved. SSO generates high-quality field notes that provide accurate descriptions of informants’ experiences (pp. 4–5). SSO allows informants to directly provide descriptions of their personal experiences including difficult to capture emotions.

Interview Strategies

Interviews are conversations between researchers and research participants—termed “subjects” in positivist research and informants in “interpretive research.” Interviews can be conducted as individual face-to-face interactions (Creswell, 2014 , p. 190) or by telephone, email, or through computer-based media. Two broad types of interview strategies are (a) the individual interview and (b) the group interview or focus group (Morgan, 1997 ). Interviews elicit informants’ insights into their culture and background information, and obtain answers and opinions. Interviews typically address topics and issues that occur outside the interview setting and at previous times. Interview data are thus reconstructions or undocumented descriptions of action in past settings (Creswell, 2014 , p. 191) that provide descriptions that are less accurate and valid descriptions than direct, real-time observations of settings.

Structured and unstructured interviews. Structured interviews pose a standardized set of fixed, closed-ended questions (Easterby-Smith, Thorpe, & Jackson, 2012 ) to respondents whose responses are recorded as factual information. Responses may be forced choice or open ended. However, most qualitative research uses unstructured or partially structured interviews that pose open-ended questions in a flexible order that can be adapted. Unstructured interviews allow for detailed responses and clarification of statements (Easterby-Smith et al., 2012 ; McLeod, 2014 )and the content and format can be tailored to the needs and assumptions of specific research projects (Gephart & Richardson, 2008 , p. 40).

The informant interview (Spradley, 1979 ) poses questions to informants to elicit and clarify background information about their culture, and to validate ethnographic observations. In interviews, informants teach the researcher their culture (Spradley, 1979 , pp. 24–39). The informant interview is part of a developmental research sequence (McCurdy et al., 2005 ; Spradley, 1979 ) that begins with broad “grand tour” questions that ask an informant to describe an important domain in their culture. The questions later narrow to focus on details of cultural domains and members’ folk concepts. This process uncovers semantic relationships among concepts of members and deeper cultural themes (McCurdy et al., 2005 ; Spradley, 1979 ).

The long interview (McCracken, 1988 ) involves a lengthy, quasi-structured interview sessions with informants to acquire rapid and efficient access to cultural themes and issues in a group. Long interviews differ ethnographic interviews by using a “more efficient and less obtrusive format” (p. 7). This creates a “sharply focused, rapid and highly intense interview process” that avoids indeterminate and redundant questions and pre-empts the need for observation or involvement in a culture. There are four stages in the long interview: (a) review literature to uncover analytical categories and design the interview; (b) review cultural categories to prepare the interview guide; (c) construct the questionnaire; and (d) analyze data to discover analytical categories (p. 30, fig. 1 ).

The active interview is a dynamic process where the researcher and informant co-construct and negotiate interview responses (Holstein & Gubrium, 1995 ). The goal is to uncover the subjective meanings that informants hold for phenomenon, and to understand how meaning is produced through communication. The active approach is common in interpretive, critical, and postmodern research that assumes a negotiated order. For example, Richardson and McKenna ( 2000 ) explored how ex-patriate British faculty members themselves interpreted and explained their expatriate experience. The researchers viewed the interview setting as one where the researchers and informants negotiated meanings between themselves, rather than a setting where prepared questions and answers were shared.

Documentary, Photographic, and Video Records as Data

Documents, records, artifacts, photographs, and video recordings are physically enduring forms of data that are separable from their producers and provide mute evidence with no inherent meaning until they are read, written about, and discussed (Hodder, 1994 , p. 393). Records (e.g., marriage certificate) attest to a formal transaction, are associated with formal governmental institutions, and may have legally restricted access. In contrast, documents are texts prepared for personal reasons with fewer legal restrictions but greater need for contextual interpretation. Several approaches to documentary and textual data analysis have been developed (see Table 3 ). Documents that researchers have found useful to collect include public documents and minutes of meetings; detailed transcripts of public hearings; corporate and government press releases; annual reports and financial documents; private documents such as diaries of informants; and news media reports.

Photographs and videos are useful for capturing “accurate” visual images of physical phenomena (Ray & Smith, 2012 ) that can be repeatedly reexamined and used as evidence to substantiate research claims (LeBaron, Jarzabkowski, Pratt, & Fetzer, 2018 ). Photos taken from different positions in space may also reveal different features of phenomena. Videos show movement and reveal activities as processes unfolding over time and space. Both photos and videos integrate and display the spatiotemporal contexts of action.

Layer Two: Design as Logical Frameworks

The second research design layer links data collection and analysis methods (Tables 2 and 3 ) to three logics of enquiry that answer specific questions: inductive, deductive, and abductive logical strategies (see Table 4 ). Each logical strategy focuses on producing different types of knowledge using distinctive research principles, processes, and types of research questions they can address.

Table 3. Data Analysis and Integrated Data Collection and Analysis Strategies

Table 4. Logical Strategies for Answering Qualitative Research Questions with Evidence

Based in part on Blaikie ( 1993 ), ch. 5 & 6; Blaikie ( 2010 ), p. 84, table 4.1

The Inductive Strategy

Induction is the scientific method for many scholars (Blaikie, 1993 , p. 134), and an essential logic for qualitative management research (Pratt, 2009 , p. 856). Inductive strategies ask “what” questions to explore a domain to discover unknown features of a phenomenon (Blaikie, 2010 , p. 83). There are four stages to the inductive strategy: (a) observe and record all facts without selection or anticipating their importance; (b) analyze, compare, and classify facts without employing hypotheses; (c) develop generalizations inductively based on the analyses; and (d) subject generalizations to further testing (Blaikie, 1993 , p. 137).

Inductive research assumes a real world outside human thought that can be directly sensed and described (Blaikie, 2010 ). Principles of inductive research reflect a realist and objectivist ontology. The selection, definition, and measurement of characteristics to be studied are developed from an objective, scientific point of view. Facts about organizational features need to be obtained using unbiased measurement. Further, the elimination method is used to find “the characteristics present in all the positive cases, which are absent in all the negative cases, and which vary in appropriate degrees” (Blaikie, 1993 , p. 135). This requires data collection methods that provide unbiased evidence of the objective facts without pre-supposing their importance.

Induction can establish limited generalizations about phenomena based solely on the observations collected. Generalizations need to be based on the entire sample of data, not on selected observations from large data sets, to establish their validity. The scope of generalization is limited to the sample of data itself. Induction creates evidence to increase our confidence in a conclusion, but the conclusions do not logically follow from premises (Blaikie, 1993 , p. 164). Indeed, inferences from induction cannot be extended beyond the original set of observations and no logical or formal process exists to establish the universality of inferences.

Key data collection methods for inductive designs include observational strategies that allow the researcher to view behavior without making a priori hypotheses, to describe behavior that occurs “naturally” in settings, and to record non-impressionistic descriptions of behavior. Interviews can also elicit descriptions of settings and behavior for inductive qualitative research. Data analysis methods need to describe actual interactions in real settings including discourse among members. These methods include ethnosemantic analysis to uncover key terms and validate actual meanings used by members; analyses of conversational practices that show how meaning is negotiated through sequential turn taking in discourse; and grounded theory-based concept coding and theory development that use the constant comparative method.

Facts or descriptions of events can be compared to one another and generalizations can be made about the world using induction (Blaikie, 2010 ). Outcomes from inductive analysis include descriptions of features in a limited domain of social action that are inferred to exist in other similar settings. Propositions and broader insights can be developed inductively from these descriptions.

The Deductive Strategy

Deductive logic (Blaikie, 1993 , 2010 ) addresses “why” questions to explain associations between concepts that represent phenomena of interest. Researchers can use induction, abduction, or any means, to develop then test the hypotheses to see if they are valid. Hypotheses that are not rejected are temporarily corroborated. The outcomes from deduction are tested hypotheses. Researchers can thus be very creative in hypothesis construction but they cannot discover new phenomena with deduction that is based only on phenomena known in advance (Blaikie, 2010 ). And there is also no purely logical or mechanical process to establish “the validity of [inductively constructed] universal statements from a set of singular statements” from which deductive hypotheses were formed (Hempel, 1966 , p. 15 cited in Blaikie, 1993 , p. 140).

The deductive strategy uses a realist and objectivist ontology and imitates natural science methods. Useful data collection methods include observation, interviewing, and collection of documents that contain facts. Deduction addresses the assumedly objective features of settings and interactions. Appropriate data analysis methods include content coding to identify different types, features, and frequencies of observed phenomena; grounded theory coding and analytical induction to create categories in data, determine how categories are interrelated, and induce theory from observations; and pattern recognition to compare current data to prior models and samples. Content analysis and non-parametric statistics can be used to quantify qualitative data and make it more amenable to analysis, although quantitative analysis of qualitative data is not, strictly speaking, qualitative research (Gephart, 2004 ).

The Abductive Strategy

Abduction is “the process used to produce social scientific accounts of social life by drawing on the concepts and meanings used by social actors, and the activities in which they engage” (Blaikie, 1993 , p. 176). Abductive reasoning assumes that the socially meaningful world is the world experienced by members. The first abductive task is to discover the insider view that is basic to the actions of social actors (p. 176) by uncovering the subjective meanings held by social actors. Subjective meaning (Schutz, 1973a , 1973b ) refers to the meaning that actions hold for the actors themselves and that they can express verbally. Subjective meaning is not inexpressible ideas locked in one’s mind. Abduction starts with lay descriptions of social life, then moves to technical, scientific descriptions of social life (Blaikie, 1993 , p. 177) (see Table 4 ). Abduction answers “what” questions with induction, why questions with deduction, and “how” questions with hypothesized processes that explain how, and under what conditions, phenomena occur. Abduction involves making a logical leap that infers an explanatory process to explain an outcome in an oscillating logic. Deductive, inductive, and inferential processes move recursively from actors’ accounts to social science accounts and back again in abduction (Gephart, 2018 ). This process enables all theory and second-order scientific concepts to be grounded in actors’ first-order meanings.

The abductive strategy contains four layers: (a) everyday concepts and meanings of actors, used for (b) social interaction, from which (c) actors provide accounts, from which (d) social scientific descriptions are made, or theories are generated and applied, to interpret phenomena (Blaikie, 1993 , p. 177). The multifaceted research process, described in Table 4 , requires locating and comprehending members’ important everyday concepts and theories before observing or creating disruptions that force members to explain the unstated knowledge behind their action. The researcher then integrates members’ first-order concepts into a general, second-order scientific theory that makes first-order understandings recoverable.

Abduction emerged from Weber’s interpretive sociology ( 1978 ) and Peirce’s ( 1936 ) philosophy. But Alfred Schutz ( 1973a , 1973b ) is the contemporary scholar who did the most to extend our understanding of abduction, although he never used the term “abduction” (Blaikie, 1993 , 2010 ; Gephart, 2018 ). Schutz conceived abduction as an approach to verifiable interpretive knowledge that is scientific and rigorous (Blaikie, 1993 ; Gephart, 2018 ). Abduction is appropriate for research that seeks to go beyond description to explanation and prediction (Blaikie, 1993 , p. 163) and discovery (Gephart, 2018 ). It employs an interpretive ontology (Schutz, 1973a , 1973b ) and social constructionist epistemology (Berger & Luckmann, 1966 ), using qualitative methods to discover “why people do what they do” (Blaikie, 1993 ).

Dynamic data collection methods are needed for abductive research to capture descriptions of interactions in actual settings and their meanings to members. Observational and interview approaches that elicit members’ concepts and theories are particularly relevant to abductive understanding (see Table 2 ). Data analysis methods must analyze situated, first-order (common sense) discourse as it unfolds in real settings and then systematically develop second-order concepts or theories from data. Relevant approaches to produce and validate findings include ethnography, ethnomethodology, and grounded theorizing (see Table 3 ). The combination of what, why, and how questions used in abduction produces a broader understanding of phenomena than do what and why deductive and inductive questions.

Layer Three: Paradigms of Research

Scholarly paradigms integrate methods, logics, and intellectual worldviews into coherent theoretical perspectives and form the most abstract level of research design. Six paradigms are widely used in management research (Burrell & Morgan, 1979 ; Cunliffe, 2011 ; Gephart, 2004 , 2013 ; Gephart & Richardson, 2008 ; Hassard, 1993 ). The first three perspectives—positivism, interpretive induction, and interpretive abduction—build on logics of design and seek to produce rigorous empirical research that constitutes evidence (see Table 5 ). Three additional perspectives pursue philosophical, critical, and practical knowledge: critical theory, postmodernism, and organization development (see Table 6 ). Tables 5 and 6 describe important features of each research design to show similarities and differences in the processes through which theoretical meaning is bestowed on research results in management and organization studies.

Table 5. Paradigms, Logical Strategies, and Methodologies for Empirical Research

Sources: Based on and adapted and extended from Blaikie ( 1993 , pp. 137, 145, & 152); Blaikie ( 2010 , Table 4.1, p. 84); Gephart ( 2013 , Table 9.1, p. 291) and Gephart ( 2018 , Table 3.1, pp. 38–39).

Table 6. Alternative Paradigms, Logical Strategies, and Methodologies

Based in part on Gephart ( 2004 , 2013 , 2018 ).

The Positivist Approach

The qualitative positivist approach makes assumptions equivalent to those of quantitative research (Gephart, 2004 , 2018 ). It assumes the world is objectively describable and comprehensible using inductive and deductive logics. And rigor is important and achieved by reliability, validity, and generalizability of findings (Kirk & Miller, 1986 ; Malterud, 2001 ). Qualitative positivism mimics natural science logics and methods using data recorded as words and talk rather than numerals.

Positivist research (Bitektine, 2008 ; Su, 2018 ) starts with a hypothesis. This can, but need not, be based in data or inductive theory. The research process, aimed at publication in peer-reviewed journals, requires researchers to (a) identify variables to measure, (b) develop operational definitions of the variables, (c) measure (describe) the variables and their inter-relationships, (d) pose hypotheses to test relationships among variables, then (e) compare observations to hypotheses for testing (Blaikie, 2010 ). When data are consistent with theory, theory passes the test. Otherwise the theory fails. This theory is also assessed for its logical correctness and value for knowledge. The positivist approach can assess deductive and inductive generalizations and provide evidence concerning why something occurs—if proposed hypotheses are not rejected.

Positivists view qualitative research as highly subject to biases that must be prevented to ensure rigor, and 23 methodological steps are recommended to enhance rigor and prevent bias (Gibbert & Ruigrok, 2010 , p. 720). Replicability is another concern because methodology descriptions in qualitative publications “insufficiently describe” how methods are used (Lee, Mitchell, & Sablynski, 1999 , p. 182) and thereby prevent replication. To ensure replicability, a qualitative “article’s description of the method must be sufficiently detailed to allow a reader . . . to replicate that reported study either in a hypothetical or actual manner.”

Qualitative research allows positivists to observe naturally unfolding behavior in real settings and allow “the real world” of work to inform research and theory (Locke & Golden-Biddle, 2004 ). Encounters with the actual world provide insights into meaning construction by members that cannot be captured with outsider (etic) approaches. For example, past quantitative research provided inconsistent findings on the importance of pre- and post-recruitment screening interviews for job choices of recruits. A deeper investigation was thus designed to examine how recruitment impacts job selection (Rynes, Bretz, & Gerhart, 1991 ). To do so, students undergoing recruitment were asked to “tell us in their own words” how their recruiting and decision processes unfolded (Rynes et al., 1991 , p. 399). Using qualitative evidence, the researchers found that, in contrast to quantitative findings, “people do make choices based on how they are treated” (p. 509), and the choices impact recruitment outcomes. Rich descriptions of actual behavior can disconfirm quantitative findings and produce new findings that move the field forward.

An important limitation of positivism is its common emphasis on outsiders’ or scientific observers’ objective conceptions of the world. This limits the attention positivist research gives to members’ knowledge and allows positivist research to impose outsiders’ meanings on members’ everyday behavior, leading to a lack of understanding of what the behavior means to members. Another limitation is that no formal, logical, or proven techniques exist to assess the strength of “relationships” among qualitative variables, although such assessments can be formally done using well-formed quantitative data and techniques. Thus, qualitative positivists often provide ambiguous or inexplicit quantitative depictions of variable relations (e.g., “strong relationship”). Alternatively, the analysts quantify qualitative data by assigning numeric codes to categories (Greckhamer, Misngyi, Elms, & Lacey, 2008 ), using non-parametric statistics, or quantitative content analysis (Sonpar & Golden-Biddle, 2008 ) to create numerals that depict associations among variables.

An illustrative example of positivist research . Cole ( 1985 ) studied why and how organizations change their working structures from bureaucratic forms to small, self-supervised work teams that allow for worker participation in shop floor activities. Cole found that existing research on workplace change focused on the micropolitical level of organizations. He hypothesized that knowledge could be advanced differently, by examining the macropolitical change in industries or nations. Next, a testable conclusion was deduced: a macro analysis of the politics of change can better predict the success of work team implementation, measured as the spread of small group work structures, than an examination of the micropolitics of small groups ( 1985 ). Three settings were selected for the research: Japan, Sweden, and the United States. Japanese data were collected from company visits and interviews with employment officials and union leaders. Swedish documentary data on semiautonomous work groups were used and supplemented by interviews at Volvo and Saab, and prior field research in Sweden. U.S. data were collected through direct observations and a survey of early quality circle adopters.

Extensive change was observed in Sweden and Japan but changes to small work groups were limited in the United States (Cole, 1985 ). This conclusion was verified using records of the experiences of the three nations in work reform, compared across four dimensions: timing and scope of changes, managerial incentives to innovate, characteristics of mobilization, and political dimensions of change. Data revealed the United States had piecemeal experimentation and resistance to reform through the 1970s; diffusion emerged in Japan in the early 1960s and became extensive; and Swedish workplace reform started in the 1960s and was widely and rapidly diffused.

Cole then answered the questions of “why” and “how” the change occurred in some countries but not others. Regarding why Japanese and Swedish managers were motivated to introduce workplace change due to perceived managerial problems and the changing national labor market. Differences in the political processes also influenced change. Management, labor, and government interest in workplace change was evident in Japan and Sweden but not in the United States where widespread resistance occurred. As to how, the change occurred through macropolitical processes (Cole, 1985 , p. 120), specifically, the commitment of the national business leadership to the change and whether or not the change was contested or uncontested by labor impacted the adoption of change. Organizational change usually occurs through broad macropolitical processes, hence “the importance of macro-political variables in explaining these outcomes” (p. 122).

Interpretive Induction

Two streams of qualitative research claim the label of “interpretive research” in management and organization studies. The first stream, interpretive induction, emphasizes induction as its primary logical strategy (e.g., Locke, 2001 , 2002 ; Pratt, 2009 ). It assumes a “real world” that is inherently objective but interpreted through subjective lenses, hence different people can perceive or report different things. This research is interpretive because it addresses the meanings and interpretations people give to organizational phenomena, and how this meaning is provided and used. Interpretive induction contributes to scientific knowledge by providing empirical descriptions, generalizations, and low-level theories about specific contexts based on thick descriptions of members’ settings and interactions (first-order understandings) as data.

The interpretive induction paradigm addresses “what” questions that describe and explain the existence and features of phenomena. It seeks to uncover the subjective, personal knowledge that subjects have of the objective world and does so by creating descriptive accounts of the activities of organizational members. Interpretive induction creates inductive theories based on limited samples that provide low-scope, abstract theory. Limitations (Table 5 ) include the fact that inductive generalizations are limited to the sample used for induction and need to be subjected to additional tests and comparisons for substantiation. Second, research reports often fail to provide details to allow replication of the research. Third, formal methods for assessing the accuracy and validity of results and findings are limited. Fourth, while many features of scientific research are evident in interpretive induction research, the research moves closer to humanistic knowledge than to science when the basic assumptions of inductive analysis are relaxed—a common occurrence.

An illustrative example of interpretive induction research . Adler and Adler ( 1988 , 1998 ) undertook a five-year participant-observation study of a college basketball program (Adler, 1998 , p. 32). They sought to “examine the development of intense loyalty in one organization.” Intense loyalty evokes “devotional commitment of . . . (organizational) members through a subordination that sometime borders on subservience” (p. 32). The goal was to “describe and analyze the structural factors that emerged as most related” to intense loyalty (p. 32).

The researchers divided their roles. Peter Adler was the active observer and “expert” who undertook direct observations while providing counsel to players (p. 33). Patricia Adler took the peripheral role of “wife” and debriefed the observer. Two research questions were posed: (a) “what” kinds of organizational characteristics foster intense loyalty? (b) “how” do organizations with intense loyalty differ structurally from those that lack intense loyalty?

The first design stage (Table 5 ) recorded unbiased observations in extensive field notes. Detailed “life history” accounts were obtained from 38 team members interviewed (Adler & Adler, 1998 , p. 33). Then analytical induction and the constant comparative method (Glaser & Strauss, 1967 ) were used to classify and compare observations (p. 33). Once patterns emerged, informants were questioned about variations in patterns (p. 34) to develop “total patterns” (p. 34) reflecting the collective belief system of the group. This process required a “careful and rigorous means of data collection and analysis” that was “designed to maximize both the reliability and validity of our findings” (p. 34). The study found five conceptual elements were essential to the development of intense loyalty: domination, identification, commitment, integration, and goal alignment (p. 35).

The “what” question was answered by inducing a generalization (stage 3): paternalistic organizations with charismatic leadership seek people who “fit” the organization’s style and these people require extensive socialization to foster intense loyalty. This description contrasts with rational bureaucratic organizations that seek people who fit specific, generally known job descriptions and require limited socialization (p. 46). The “how” question is answered by inductive creation of another generalization: organizations that control the extra-organizational activities of members are more likely to evoke intense loyalty by forcing members to subordinate all other interests to those of the organization (p. 46).

The Interpretive Abduction Approach

The second stream of interpretive research—interpretive abduction—produces scientific knowledge using qualitative methods (Gephart, 2018 ). The approach assumes that commonsense knowledge is foundational to how actors know the world. Abductive theory is scientifically built from, and refers to, everyday life meanings, in contrast to positivist and interpretive induction research that omits concern with the worldview of members. Further, interpretive abduction produces second-order or scientific theory and concepts from members’ first-order commonsense concepts and meanings (Gephart, 2018 , p. 34; Schutz, 1973a , 1973b ).

The research process, detailed in Table 5 (process and stages), focuses on collecting thick descriptive data on organizations, identifying and interpreting first-order lay concepts, and creating abstract second-order technical constructs of science. The second-order concepts describe the first-order principles and terms social actors use to organize their experience. They compose scientific concepts that form a theoretical system to objectively describe, predict, and explain social organization (Gephart, 2018 , p. 35). This requires researchers to understand the subjective view of the social actors they study, and to develop second-order theory based on actors’ subjective meanings. Subjective meaning can be shared with others through language use and communication and is not private knowledge.

A central analytical task for interpretive abduction is creating second-order, ideal-type models of social roles, motives, and interactions that describe the behavioral trajectories of typical actors. Ideal-type models can be objectively compared to one another and are the special devices that social science requires to address differences between social phenomena and natural phenomena (Schutz, 1973a , 1973b ). The models, once built, are refined to preserve actors’ subjective meanings, to be logically consistent, and to present human action from the actor’s point of view. Researchers can then vary and compare the models to observe the different outcomes that emerge. Scientific descriptions can then be produced, and theories can be created. Interpretive abduction (Gephart, 2018 , p. 35) allows one to addresses what, why, and how questions in a holistic manner, to describe relationships among scientific constructs, and to produce “empirically ascertainable” and verifiable relations among concepts (Schutz, 1973b , p. 65) that are logical, hold practical meaning to lay actors, and provide abstract, objective meaning to interpretive scientists (Gephart, 2018 , p. 35). Abduction produces knowledge about socially shared realities by observing interactions, uncovering members’ first-order meanings, and then developing technical second-order or scientific accounts from lay accounts.

Interpretive abduction (Gephart, 2018 ) uses well-developed methods to create, refine, test, and verify second-order models, and it provides well-developed tools to support technical, second-level analyses. Research using the interpretive abduction approach includes a study of how technology change impacts sales automobile practices (Barley, 2015 ) and an investigation study of how abduction was used to develop new prescription drugs (Dunne & Dougherty, 2016 ).

An illustrative example of the interpretive abduction approach . Perlow ( 1997 ) studied time management among software engineers facing a product launch deadline. Past research verified the widespread belief that long working hours for staff are necessary for organizational success. This belief has adversely impacted work life and led to the concept of a “time bind” faced by professionals (Hochschild, 1997 ). One research question that subsequently emerged was, “what underlies ‘the time bind’ experienced by engineers who face constant deadlines and work interruptions?” (Perlow, 1997 , p. xvii). This is an inductive question about the causes and consequences of long working hours not answered in prior research that is hard to address using induction or deduction. Perlow then explored assumption underlying the hypothesis, supported by lay knowledge and management literature, that even if long working hours cause professionals to destroy their life style, long work hours “further the goals of our organizations” and “maximize the corporation’s bottom line” (Perlow, 1997 , p. 2).

The research commenced (Table 5 , step 1) when Perlow gained access to “Ditto,” a leader in implementing flexible work policies (Perlow, 1997 , p. 141) and spent nine months doing participant observation four days a week. Perlow collected descriptive data by walking around to observe and converse with people, attended meetings and social events, interviewed engineers at work and home and spouses at home, asked participants to record activities they undertook on selected working days (Perlow, 1997 , p. 143), and made “thousands of pages of field notes” (p. 146) to uncover trade-offs between work and home life.

Perlow ( 1997 , pp. 146–147) analyzed first-order concepts uncovered through his observations and interviews from 17 stories he wrote for each individual he had studied. The stories described workstyles, family lives, and traits of individuals; provided objective accounts of subjective meanings each held for work and home; offered background information; and highlighted first-order concepts. Similarities and differences in informant accounts were explored with an empirically grounded scheme for coding observations into categories using grounded theory processes (Gioia, Corley, & Hamilton, 2012 ). The process allowed Perlow to find key themes in stories that show work patterns and perceptions of the requirements of work success, and to create ideal-type models of workers (step 3). Five stories were selected for detailed analysis because they reveal important themes Perlow ( 1997 , p. 147). For example, second-order, ideal-type models of different “roles” were constructed in step 3 including the “organizational superstar” (pp. 15–21) and “ideal female employee” (pp. 22–32) based on first-order accounts of members. The second-order ideal-type scientific models were refined to include typical motives. The models were compared to one another (step 4) to describe and understand how the actions of these employee types differed from other employee types and how these variations produced different outcomes for each trajectory of action (steps 4 and 5).

Perlow ( 1997 ) found that constant help-seeking led engineers to interrupt other engineers to get solutions to problems. This observation led to the abductively developed hypothesis that interruptions create a time crisis atmosphere for engineers. Perlow ( 1997 ) then created a testable, second-order ideal-type (scientific) model of “the vicious working cycle” (p. 96), developed from first-order data, that explains the productivity problems that the firm (and other research and development firms)—commonly face. Specifically, time pressure → crisis mentality → individual heroics → constant interruptions of others’ work to get help → negative consequences for individual → negative consequences for the organization.

Perlow ( 1997 ) then tested the abductive hypothesis that the vicious work cycle caused productivity problems (stage 5). To do so, the vicious work cycle was transformed into a virtuous cycle using scheduling quiet times to prevent work interruptions: relaxed work atmosphere → individuals focus on own work completion → few interruptions → positive consequences for individual and organization. To test the hypothesis, an experiment was conducted (research process 2 in Table 5 ) with engineers given scheduled quiet times each morning with no interruptions. The experiment was successful: the project deadline was met. The hypothesis about work interruptions and the false belief that long hours are needed for success were supported (design stage 6). Unfortunately, the change was not sustained and engineers reverted to work interruptions when the experiment ended.

There are three additional qualitative approaches used in management research that pursue objectives other than producing empirical findings and developing or testing theories. These include critical theory and research, postmodernism, and change intervention research (see Table 6 ).

The Critical Theory and Research Approach

The term “critical” has many meanings including (a) critiques oriented to uncovering ideological manifestations in social relations (Gephart, 2013 , p. 284); (b) critiques of underlying assumptions of theories; and (c) critique as self-reflection that reflexively encapsulates the investigator (Morrow, 1994 , p. 9). Critical theory and critical management studies bring these conceptions of critical to bear on organizations and employees.

Critical theory and research extend the theories Karl Marx, and the Frankfurt School in Germany (Gephart & Kulicki, 2008 ; Gephart & Pitter, 1995 ; Habermas, 1973 , 1979 ; Morrow, 1994 ; Offe, 1984 , 1985 ). Critical theory and research assume that social science research differs from natural science research because social facts are human creations and social phenomena cannot be controlled as readily as natural phenomena (Gephart, 2013 , p. 284; Morrow, 1994 , p. 9). As a result, critical theory often uses a historical approach to explore issues that arise from the fundamental contradictions of capitalism. Critical research explores ongoing changes within capitalist societies and organizations, and analyzes the objective structures that constrain human imagination and action (Morrow, 1994 ). It seeks to uncover the contradictions of advanced capitalism that emerge from the fundamental contradiction of capitalism: owners of capital have the right to appropriate the surplus value created by workers. This basic contradiction produces further contradictions that become sources of workplace oppression and resistance that create labor issues. Thus contradictions reveal how power creates consciousness (Poutanen & Kovalainen, 2010 ). Critical reflection is used to de-reify taken-for-granted structures that create power inequities and to motivate resistance and critique and escape from dominant structures (see Table 6 ).

Critical management studies build on critical theory in sociology. It seeks to transform management and provide alternatives to mainstream theory (Adler, Forbes, & Willmott, 2007 ). The focus is “the social injustice and environmental destruction of the broader social and economic systems” served by conventional, capitalist managers (Adler et al., 2007 , p. 118). Critical management research examines “the systemic corrosion of moral responsibility when any concern for people or for the environment . . . requires justification in terms of its contribution to profitable growth” (p. 4). Critical management studies goes beyond scientific skepticism to undertake a radical critique of socially divisive and environmentally destructive patterns and structures (Adler et al., 2007 , p. 119). These studies use critical reflexivity to uncover reified capitalist structures that allow certain groups to dominate others. Critical reflection is used to de-reify and challenge the facts of social life that are seen as immutable and inevitable (Gephart & Richardson, 2008 , p. 34). The combination of dialogical inquiry, critical reflection, and a combination of qualitative and quantitative methods and data are common in this research (Gephart, 2013 , p. 285). Some researchers use deductive logics to build falsifiable theories while other researchers do grounded theory building (Blaikie, 2010 ). Validity of critical research is assessed as the capability the research has to produce critical reflexivity that comprehends dominant ideologies and transforms repressive structures into democratic processes and institutions (Gephart & Richardson, 2008 ).

An illustrative example of critical research . Barker ( 1998 , p. 130) studied “concertive control” in self-managed work teams in a small manufacturing firm. Concertive control refers to how workers collaborate to engage in self-control. Barker sought to understand how control practices in the self-managed team setting, established to allow workers greater control over their work, differed from previous bureaucratic processes. Interviews, observations, and documents were used as data sources. The resultant description of work activities and control shows that rather than allowing workers greater control, the control process enacted by workers themselves became stronger: “The iron cage becomes stronger” and almost invisible “to the workers it incarcerates” (Barker, 1998 , p. 155). This study shows how traditional participant observation methods can be used to uncover and contest reified structures and taken-for-granted truths, and to reveal the hidden managerial interests served.

Postmodern Perspectives

The postmodern perspective (Boje, Gephart, & Thatchenkery, 1996 ) is based in philosophy, the humanities, and literary criticism. Postmodernism, as an era, refers to the historical stage following modernity that evidences a new cultural worldview and style of intellectual production (Boje et al., 1996 ; Jameson, 1991 ; Rosenau, 1992 ). Postmodernism offers a humanistic approach to reconceptualize our experience of the social world in an era where it is impossible to establish any foundational underpinnings for knowledge. The postmodern perspective assumes that realities are contradictory in nature and value-laden (Gephart & Richardson, 2008 ; Rosenau, 1992 , p. 6). It addresses the values and contradictions of contemporary settings, how hidden power operates, and how people are categorized (Gephart, 2013 ). Postmodernism also challenges the idea that scientific research is value free, and asks “whose values are served by research?”

Postmodern essays depart from concerns with systematic, replicable research methods and designs (Calas, 1987 ). They seek instead to explore the values and contradictions of contemporary organizational life (Gephart, 2013 , p. 289). Research reports have the character of essays that seek to reconceptualize how people experience the world (Martin, 1990 ; Rosenau, 1992 ) and to disrupt this experience by producing “reading effects” that unsettle a community (Calas & Smircich, 1991 ).

Postmodernism examines intertextual relations—how texts become embedded in other texts—rather than causal relations. It assumes there are no singular realities or truths, only multiple realities and multiple truths, none of which are superior to other truths (Gephart, 2013 ). Truth is conceived as the outcome of language use in a context where power relations and multiple realities exist.

From a methodological view, postmodern research tends to focus on discourse: texts and talk. Data collection (in so far as it occurs) focuses on records of discourse—texts of spoken and written verbal communication (Fairclough, 1992 ). Use of formal or official records including recordings, texts and transcripts is common. Analytically, scholars tend to use critical discourse analysis (Fairclough, 1992 ), narrative analysis (Czarniawska, 1998 ; Ganzin, Gephart, & Suddaby, 2014 ), rhetorical analysis (Culler, 1982 ; Gephart, 1988 ; McCloskey, 1984 ) and deconstruction (Calais & Smircich, 1991 ; Gephart, 1988 ; Kilduff, 1993 ; Martin, 1990 ) to understand how categories are shaped through language use and come to privilege or subordinate individuals.

Postmodernism challenges models of knowledge production by showing how political discourses produce totalizing categories, showing how categorization is a tool for social control, and attempting to create opportunities for alternative representations of the world. It thus provides a means to uncover and expose discursive features of domination, subordination, and resistance in society (Locke & Golden-Biddle, 2004 ).

An illustrative example of postmodern research . Martin ( 1990 ) deconstructed a conference speech by a company president. The president was so “deeply concerned” about employee well-being and involvement at work that he encouraged a woman manager “to have her Caesarian yesterday” so she could participate in an upcoming product launch. Martin deconstructs the story to reveal the suppression of gender conflict in the dialogue and how this allows gender conflict and subjugation to continue. This research established the existence of important domains of organizational life, such as tacit gender conflict, that have not been adequately addressed and explored the power dynamics therein.

The Organization Development Approach

OD involves a planned and systematic diagnosis and intervention into an organizational system, supported by top management, with the intent of improving the organization’s effectiveness (Beckhard, 1969 ; Palmer, Dunford, & Buchanan, 2017 , p. 282). OD research (termed “clinical research” by Schein, 1987 ) is concerned with changing attitudes and behaviors to instantiate fundamental values in organizations. OD research often follows the general process of action research (Lalonde, 2019 ) that involves working with actors in an organization to help improve the organization. OD research involves a set of stages the OD practitioner (the leader of the intervention) uses: (a) problem identification; (b) consultation between OD practitioner and client; (c) data collection and problem diagnosis; (d) feedback; (e) joint problem diagnosis; (f) joint action planning; (g) change actions; and (h) further data gathering to move recursively to a refined step 1.

An illustrative example of the organization development approach . Numerous OD techniques exist to help organizations change (Palmer et al., 2017 ). The OD approach is illustrated here by the socioeconomic approach to management (SEAM) (Buono & Savall, 2007 ; Savall, 2007 ). SEAM provides a scientific approach to organizational intervention consulting that integrates qualitative information on work practices and employee and customer needs (socio) with quantitative and financial performance measures (economics). The socioeconomic intervention process commences by uncovering dysfunctions that require attention in an organization. SEAM assumes that organizations produce both (a) explicit benefits and costs and (b) hidden benefits and costs. Hidden costs refer to economic implications of organizational dysfunctions (Worley, Zardet, Bonnet, & Savall, 2015 , pp. 28–29). These include problems in working conditions; work organization; communication, co-ordination, and co-operation; time management; integrated training; and strategy implementation (Savall, Zardet, & Bonnet, 2008 , p. 33). Explicit costs are emphasized in management decision-making but hidden costs are ignored. Yet hidden costs from dysfunctions often greatly outstrip explicit costs.

For example, a fishing company sought to protect its market share by reducing the price and quality of products, leading to the purchase of poor-quality fish (Savall et al., 2008 , pp. 31–32). This reduced visible costs by €500,000. However, some customers stopped purchasing because of the lower-quality product, producing a loss of sales of €4,000,000 in revenue or an overall drop in economic performance of €3,500,000. The managers then changed their strategy to focus on health and quality. They implemented the SEAM approach, assessed the negative impact of the hidden costs on value added and revenue received, and purchased higher-quality fish. Visible costs (expenses) increased by €1,000,000 due to the higher cost for a better-quality product, but the improved quality (performance) cut the hidden costs by increasing loyalty and increased sales by €5,000,000 leaving an increased profit of €4,000,000.

SEAM allows organizations to uncover hidden costs in their operations and to convert these costs into value-added human potential through a process termed “qualimetrics.” Qualimetrics assesses the nature of hidden costs and organizational dysfunctions, develops estimates of the frequencies and amounts of hidden costs in specific organizational domains, and develops actions to reduce the hidden costs and thereby release additional value added for the organization (Savall & Zardet, 2011 ). The qualimetric process is participative and involves researchers who use observations, interviews and focus groups of employees to (a) describe, qualitatively, the dysfunctions experienced at work (qualitative data); (b) estimate the frequencies with which dysfunctions occur (quantitative data); and (c) estimate the costs of each dysfunction (financial data). Then, strategic change actions are developed to (a) identify ways to reduce or overcome the dysfunction, (b) estimate how frequently the dysfunction can be remedied, and (c) estimate the overall net costs of removing the hidden costs to enhance value added. The economic balance is then assessed for changes to transform the hidden costs into value added.

OD research creates actionable knowledge from practice (Lalonde, 2019 ). OD intervention consultants use multistep processes to change organizations that are flexible practices not fixed research designs. OD plays an important role in developing evidence-based practices to improve organizational functioning and performance. Worley et al. ( 2015 ) provide a detailed example of the large-scale implementation of the SEAM OD approach in a large, international firm.

Here we discuss implication of qualitative research designs for covert research, reporting qualitative work and novel integrations of qualitative and quantitative work.

Covert Research

University ethics boards require researchers who undertake research with human participants to obtain informed consent from the participants. Consent requires that all participants must be informed of details of the research procedure in which they will be involved and any risks of participation. Researchers must protect subjects’ identities, offer safeguards to limit risks, and insure informant anonymity. This consent must be obtained in the form of a signed agreement from the participant, obtained prior to the commencement of research observations (McCurdy et al., 2005 , pp. 29–32).

Covert research that fails to fully disclose research purposes or practices to participants, or that is otherwise deceptive by design or tacit practice, has long been considered “suspect” in the field (Graham, 1995 ; Roulet, Gill, Stenger, & Gill, 2017 ). This is changing. Research methodologists have shown that the over/covert dimension is a continuum, not a dichotomy, and that unintended covert elements occur in many situations (Roulet et al., 2017 ). Thus all qualitative observation involves some degree of deception due practical constraints on doing observations since it is difficult to do fully overt research, particularly in observational contexts with many people, and to gain advance consent from everyone in the organization one might encounter.

There are compelling benefits to covert research. It can provide insights not possible if subjects are fully informed of the nature or existence of the research. For example, the year-long, covert observational study of an asylum as a “total institution” (Goffman, 1961 ) showed how ineffective the treatment of mental illness was at the time. This opened the field of mental health to social science research (Roulet et al., 2017 , p. 493). Covert research can also provide access to institutions that researchers would otherwise be excluded from, including secretive and secret organizations (p. 492). This could allow researchers to collect data as an insider and to better see and experience the world from members’ perspective. It could also reduce “researcher demand effects” that occur when informants obscure their normal behavior to conform to research expectations. Thus, the inclusion of covert research data collection in research designs and proposals is an emerging trend and realistic possibility. Ethics applications can be developed that allow for aspects of covert research, and observations in many public settings do not require informed consent.

The Appropriate Style for Reporting Qualitative Work

The appropriate style for reporting qualitative research has become an issue of concern. For example, editors of the influential Academy of Management Journal have noted the emergence of an “AMJ style” for qualitative work (Bansal & Corley, 2011 , p. 234). They suggest that all qualitative work should use this style so that qualitative research can “benefit” from: “decades of refinement in the style of quantitative work.” The argument is that most scholars can assess the empirical and theoretical contributions of quantitative work but find it difficult to do so for qualitative research. It is easier for quantitatively trained editors and scholars “to spot the contribution of qualitative work that mimics the style of quantitative research.” Further, “the majority of papers submitted to . . . AMJ tend to subscribe to the paradigm of normal science that aims to find relationships among valid constructs that can be replicated by anyone” (Bansal, Smith, & Vaara, 2018 , p. 1193). These recommendations appear to explicitly encourage the reporting of qualitative results as if they were quantitatively produced and interpreted and highlights the advantage of conformity to the prevailing positivist perspective to gain publication in AMJ.

Yet AMJ editors have also called for researchers to “ensure that the research questions, data, and analysis are internally consistent ” (Bansal et al., 2018 , p. 1193) and to “Be authentic , detailed and clear in argumentation” (emphasis added) (Bansal et al., 2018 , p. 1193). These calls for consistency appear to be inconsistent with suggestions to present all qualitative research using a style that mimics quantitative, positivist research. Adopting the quantitative or positivist style for all qualitative reports may also confuse scholars, limit research quality, and hamper efforts to produce innovative, non-positivist research. This article provides six qualitative research designs to ensure a range of qualitative research publications are internally consistent in methods, logics, paradigmatic commitments, and writing styles. These designs provide alternatives to positivist mimicry in non-positivist scholarly texts.

Integrating Qualitative and Quantitative Research in New Ways

Qualitative research often omits consideration of the naturally occurring uses of numbers and statistics in everyday discourse. And quantitative researchers tend to ignore qualitative evidence such as stories and discourse. Yet knowledge production processes in society “rely on experts and laypeople and, in so doing, make use of both statistics and stories in their attempt to represent and understand social reality” (Ainsworth & Hardy, 2012 , p. 1649). Numbers and statistics are often used in stories to create legitimacy, and stories provide meaning to numbers (Gephart, 1988 ). Hence stories and statistics cannot be separated in processes of knowledge production (Ainsworth & Hardy, 2012 , p. 1697). The lack of attention to the role of quantification in everyday life means a huge domain of organizational discourse—all talk that uses numbers, quantities, and statistics—is largely unexplored in organizational research.

Qualitative research has, however, begun to study how words and numbers are mutually used for organizational storytelling (Ainsworth & Hardy, 2012 ; Gephart, 2016 ). This focus offers the opportunity to develop research designs to explore qualitative features and processes involved in quantitative phenomena such as financial crises (Gephart, 2016 ), to address how stories and numbers need to work together to create legitimate knowledge (Ainsworth & Hardy, 2012 ), and to show how statistics are used rhetorically to convince others of truths in organizational research (Gephart, 1988 ).

Ethnostatistics (Gephart, 1988 ; Gephart & Saylors, 2019 ) provides one example of how to integrate qualitative and quantitative research. Ethnostatistics examines how statistics are constructed and used by professionals. It explores how statistics are constructed in real settings, how violations of technical assumptions impact statistical outcomes, and how statistics are used rhetorically to convince others of the truth of research outcomes. Ethnostatistics has been used to reinterpret data from four celebrated network studies that themselves were reanalyzed (Kilduff & Oh, 2006 ). The ethnostatistical reanalyses revealed how ad hoc practices, including judgment calls and the imputation of new data into old data set for reanalysis, transformed the focus of network research from diffusion models to structural equivalence models.

Another innovative study uses a Bayesian ethnostatistical approach to understand how the pressure to produce sophisticated and increasingly complex theoretical narratives for causal models has impacted the quantitative knowledge generated in top journals (Saylors & Trafimow, 2020 ). The use of complex causal models has increased substantially over time due to a qualitative and untested belief that complex models are true. Yet statistically speaking, as the number of variables in a model increase, the likelihood the model is true rapidly decreases (Saylors & Trafimow, 2020 , p. 3).

The authors test the previously untested (qualitative) belief that complex causal models can be true. They found that “the joint probability of a six variable model is about 3.5%” (Saylors & Trafimow, 2020 , p. 1). They conclude that “much of the knowledge generated in top journals is likely false” hence “not reporting a (prior) belief in a complex model” should be relegated to the set of questionable research practices. This study shows how qualitative research that explores the lay theories and beliefs of statisticians and quantitative researchers can challenge and disrupt conventions in quantitative research, improve quantitative practices, and contribute qualitative foundations to quantitative research. Ethnostatistics thus opens the qualitative foundations of quantitative research to critical qualitative analyses.

The six qualitative research design processes discussed in this article are evident in scholarly research on organizations and management and provide distinct qualitative research designs and approaches to use. Qualitative research can provide research insights from several theoretical perspectives, using well-developed methods to produce scientific and scholarly insights into management and organizations. These approaches and designs can also inform management practice by creating actionable knowledge. The intended contribution of this article is to describe these well-developed methods, articulate key practices, and display core research designs. The hope is both to better equip researchers to do qualitative research, and to inspire them to do so.

Acknowledgments

The authors wish to acknowledge the assistance of Karen Lund at The University of Alberta for carefully preparing Figure 1 . Thanks also to Beverly Zubot for close reading of the manuscript and helpful suggestions.

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1. The fourth logic is retroduction. This refers to the process of building hypothetical models of structures and mechanisms that are assumed to produce empirical phenomena. It is the primary logic used in the critical realist approach to scientific research (Avenier & Thomas, 2015 ; Bhaskar, 1978 ). Retroduction requires the use of inductive or abductive strategies to discover the mechanisms that explain regularities (Blaikie, 2010 , p. 87). There is no evident logic for discovering mechanisms and this requires disciplined scientific thinking aided by creative imagination, intuition, and guesswork (Blaikie, 2010 ). Retroduction is likr deduction in asking “what” questions and differs from abduction because it produces explanations rather than understanding, causes rather than reasons, and hypothetical conceptual mechanisms rather than descriptions of behavioral processes as outcomes. Retroduction is becoming important in the field but has not as yet been extensively used in management and organization studies (for examples of uses, see Avenier & Thomas, 2015 ); hence, we do not address it at length in this article.

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Instant insights, infinite possibilities

A comprehensive guide to qualitative market research

Last updated

3 April 2024

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Grow your business

Understand the needs of your customers

Launch a new product

Expand into new markets

Meet any lofty goals you set for your business

The two research methods you can use to glean these insights are quantitative and qualitative. 

Quantitative research provides you with hard data you can use to find the size and scale of customer sentiment, discover causal relationships between variables, and support generalizations about macro-level populations.

Qualitative market research is an open-ended research method that looks at the reasons and motivations behind customer behavior, at the micro level. Qualitative market research gives you actionable insights you can use to improve everything from your customer service strategies to your products and services.

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• What is qualitative market research?

Qualitative market research is an open-ended research method that studies people's behavior and motivations within a specific market. While quantitative research is about hard numbers and analytics, qualitative market research takes a more generalized approach. It focuses on small sample sizes to encourage in-depth analysis of individual customers’ experiences.

The conversational nature of qualitative research is designed to encourage in-depth discussion. For businesses, qualitative market research is a powerful way to understand customers' points of view, as well as their pain points and desires.

• Why is it important to do qualitative market research?

Whether you are a CEO or a project manager, the thoughts and feelings of your customers should matter deeply to you. Through qualitative market research, you can identify the needs of your customers in a more nuanced, in-depth way than is possible with quantitative research. 

Depending on the questions you pose, you can also get a feel for how customers perceive your marketing messages and communications, as well as more broad perceptions of your company as a whole.

If you're planning on launching a new product or service, qualitative market research can help you refine the launch and even make improvements. By using the feedback and insights from your research to make changes leading up to the launch, you are more likely to increase your revenue and receive glowing reviews.

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• Advantages of qualitative market research

There are many advantages to qualitative market research. It's flexible, so you can adapt to the quality of information you receive. For example, if the available information isn't providing what you hoped, it's easy to change direction and collect more data using new questions.

Qualitative market research also helps you gather more detailed information than most quantitative data. While quantitative market research gives you metrics, qualitative market research allows you to better understand the subtleties within the data.

Long-term, qualitative market research can reduce customer churn. By conducting regular qualitative market research, businesses can better understand what consumers want (and what they don’t) and learn whether they are fulfilling their needs. This reduces customer churn and helps build a stronger relationship between a business and the people it serves.

• Disadvantages of qualitative market research

The most notable disadvantage of market research is that it’s time-consuming. Depending on the scope of the research and the amount of people dedicated to the project, it can take weeks or even months to complete. If you're working on a tight timeline, or if you have limited resources to dedicate to research, it might not be feasible.

Qualitative market research can also be expensive. While much of the cost will depend on the size and scope of the project, you might also need to hire additional people to help you complete the research.

If you compensate participants for their time (and experts advise some sort of compensation), that's another expense to consider.

Finally, qualitative market research is highly subjective, as the conclusions are drawn by individual researchers and their interpretation and analysis.

• Eight qualitative market research methods

The most common methods for qualitative market research include focus groups, individual interviews, and observations. However, many other methods should be considered as viable options for your market research.

Social media analysis

Social media has become an important part of many people's lives, with millions of people around the world interacting with their favorite platforms on a daily, even hourly, basis. Social media analysis can, therefore, be a powerful way to gather and analyze information.

If your brand is active on social media, take the opportunity to solicit responses from customers who follow you. This can be via a survey feedback form or some sort of direct response from customers.

You can also perform content analysis on social media, scanning comments left by consumers on your posts and checking for frequently used words.

For the most in-depth responses, consider gathering insights directly from the people who follow your pages and regularly interact with you.

Lifestyle immersion

If customer comfort is one of your top priorities as you conduct market research, lifestyle immersion might be the best option.

Lifestyle immersion is a research method that allows the researcher to observe the customer in their natural environment. By observing the participant in a natural setting, you can see their unguarded behavior and learn more about their needs and motives.

Focus groups

Focus groups are a popular method for conducting qualitative market research. Focus groups are typically comprised of 6–10 people, along with a market researcher who functions as a moderator.

During the focus group, participants are encouraged to share their unguarded thoughts and opinions on a product, service, or marketing campaign.

Traditionally, focus groups were held in person, since verbal and non-verbal reactions are an important part of measuring responses. However, web-based focus groups have been gaining popularity in recent years, especially in light of the COVID-19 pandemic. Online focus groups tend to be more cost-effective and convenient for most participants.

Observations

Observations, also known as shop-alongs, involve researchers following participants as they walk through a store. The goal of observations in qualitative market research is to gauge customers’ interactions and reactions to things they encounter, including products, displays, and advertising.

Observations don't require the market researcher to physically accompany participants. Typically, the researcher will observe from a distance or watch a camera feed.

Individual interviews

Individual interviews are a highly personalized method of conducting market research. These interviews are in person, over the phone, or through video-conferencing software.

They tend to be most successful when held as part of a free-flowing conversation that puts the participant at ease and makes them feel comfortable sharing their unfiltered thoughts and opinions. The interviews can be structured or unstructured, depending on the nature of the questions and your overall goals for the project.

Include plenty of open-ended questions in your interview outline to keep the conversation moving. Pay attention throughout the interview to see how the participant responds to the questions and if they seem uncomfortable or ill at ease. If they do, switch gears to make the conversation more relaxed again.

Diary or journal-logging

A diary study, also known as journal-logging, is a research method that aims to collect data about user behaviors, activities, and experiences over a set period.

During the designated reporting period, participants are asked to keep a diary and record specific information about the activities you want to analyze. The data is self-reported by participants when the reporting period is up.

Diary studies can be useful for gathering information about users’ habits and thought patterns. They can also effectively capture attitudes and motivations. However, it can be challenging to recruit dedicated users, since diary studies require greater involvement over a longer period than more traditional market research methods.

Surveys are a popular method of conducting market research. A powerful form of primary research, surveys are endlessly customizable. They can be done:

Over the phone

Via email or other online delivery method

If you opt for an online survey, test the software ahead of time, so you can be sure everything works properly and displays well on mobile devices.

It's also a good idea to run a test survey with a smaller group. This allows you to refine your questions and eliminate any confusing wording.

Ethnographic research

Ethnographic research involves observing participants in their natural environment, primarily how they go about mundane tasks such as cleaning their house or preparing a meal. Unlike observations, ethnography can involve a variety of approaches, including diary studies and video recordings.

The goal of ethnographic research is to understand the social dynamics, beliefs, and behaviors of participants through direct observation and participation in their daily activities. Ethnographic research can take place over an extended period, from a few weeks to a year or more. It's versatile and is best done with the assistance of an experienced ethnographic researcher.

• An example of qualitative market research

One of the main benefits of qualitative market research is its flexibility. No matter what your goal is or what outcome you're hoping for, you can design an effective study.

One example of qualitative market research using a focus group is a cereal company wishing to update the packaging of one of its most popular products. After producing several design concepts, the company opts to commission a series of focus groups to gauge responses to each concept.

During the focus groups, with the help of a moderator, participants discuss each design, evaluating the pros and cons. Based on the feedback received in the focus groups, the cereal company can move forward with the design most appealing to their customers.

• Best practices for qualitative market research

While qualitative research is flexible, there are still best practices to follow. Regardless of which research method you choose, consider these tips when crafting your approach and designing the questions.

Accurately identify research goals

Before embarking on any market research, you should know your end goal. Think about the specific questions you want answered, including the nature of the product or service you wish to refine or develop. Outline your goals and share them with every project stakeholder, including managers and the CEO, if necessary.

Understand your customers

Knowing your customers is vital for accurately targeting survey participants. Your business should have a customer profile that includes basic demographics such as:

Shopping habits

Use this profile to create questions that are useful for your study. When crafted thoughtfully, your questions will identify needs that aren't being met and meet study participants where they are.

Choose the most appropriate research method

There are many ways to conduct qualitative market research, but not all of them might be right for your unique needs. Think about what method will give you the optimal results and work best for the study participants you wish to recruit.

Focus groups are an ever-popular research method, but it isn't always possible to dedicate time and energy to moderating one. A survey or series of observations might be more effective, depending on your available resources and goals.

Use open-ended questions

The goal of qualitative market research is to gain thoughtful responses from participants. Use open-ended questions that require more than a simple yes or no response. The idea is to maintain an open dialogue, even through vehicles such as surveys or focus groups.

Test out questions on yourself and your team members before launching them to participants, so you can be sure they make sense and give people the chance to truly share their thoughts.

• Tips for qualitative data analysis

Qualitative data analysis is rarely a linear process. Since qualitative market research often doesn't result in hard numbers, be flexible in your approach to analysis.

After you finish your research, organize and collate your responses into one location for further analysis. If you have audio or video files, allocate time to transcribe the data, whether that means bringing in a transcriptionist or guiding your team members through the process.

As you go through the responses, become familiar with the data. This will help you better understand your customers and identify any potential gaps in the research. Always involve other stakeholders in the process, not only along the way but also once the final results have been collated. This promotes transparency in the project and improves communication across the board.

Are customer surveys qualitative?

Customer surveys are one method of market research. They can be made qualitative or quantitative, depending on the nature of the questions. They are one of the most popular forms of qualitative market research because they are versatile and highly customizable. Surveys can be done in person or through web software, such as email.

What are qualitative marketing objectives examples?

While quantitative objectives are usually specific and measurable, qualitative marketing objectives are more subjective. They tend to be conceptually broad, such as "we want to learn more about how our customers rank our service compared to our competitors,” "we want to increase brand awareness," and "we want to improve customer satisfaction." It can be helpful to have qualitative and quantitative objectives for your market research, depending on the nature of the project and whether it's related to a specific product or service.

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Qualitative Research in Business and Management

• Michael D Myers - University of Auckland, New Zealand
• Description

This accessible and expansive, yet remarkably concise textbook is designed to help readers with their research project. As well as guiding them through the key methods of collecting and analysing qualitative data, this book provides invaluable information on writing up their research and how to get published.

Now in its third edition,  Qualitative Research in Business and Management  has been fully updated to include a range of recent examples of aspects of qualitative research in action, and a new look at the methods and ethics of using social media data.

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The online resources featured are further reading, weblinks and powerpoint slides.

Qualitative Research in Business and Management is an accessible and well-structured introduction in qualitative research. Myers clearly explains the main concepts and principles of qualitative research, illustrating them with recent examples from top journals. Being concise and comprehensive, I consider this the best textbook in the field.

Perfect for Dissertation support

Great book, well written

This is one of few books on qualitative research method in business. The coverage is extensive. It includes both the design and data analysis of qualitative research. Although the author is a scholar in information systems, he managed to include all the business and management sub-areas in the book. The explanation is also very easy to follow.

A good book to invite students to prepare for theses using qualitative methods. The book covers the essential in an abordable way.

a useful text aimed at Business and management students which will provide a useful source of advice and guidance as well as introducing the basics.

Well explained qualitative research concepts and the exercise sections are useful.

Currently this book is still in the university (building). Due to Covid 19 I was not able to collect it.

It is a clear and concise volume that summarises many of the issues my students face in conducting their qualitative research.

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Criteria for Good Qualitative Research: A Comprehensive Review

• Regular Article
• Open access
• Published: 18 September 2021
• Volume 31 , pages 679–689, ( 2022 )

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This review aims to synthesize a published set of evaluative criteria for good qualitative research. The aim is to shed light on existing standards for assessing the rigor of qualitative research encompassing a range of epistemological and ontological standpoints. Using a systematic search strategy, published journal articles that deliberate criteria for rigorous research were identified. Then, references of relevant articles were surveyed to find noteworthy, distinct, and well-defined pointers to good qualitative research. This review presents an investigative assessment of the pivotal features in qualitative research that can permit the readers to pass judgment on its quality and to condemn it as good research when objectively and adequately utilized. Overall, this review underlines the crux of qualitative research and accentuates the necessity to evaluate such research by the very tenets of its being. It also offers some prospects and recommendations to improve the quality of qualitative research. Based on the findings of this review, it is concluded that quality criteria are the aftereffect of socio-institutional procedures and existing paradigmatic conducts. Owing to the paradigmatic diversity of qualitative research, a single and specific set of quality criteria is neither feasible nor anticipated. Since qualitative research is not a cohesive discipline, researchers need to educate and familiarize themselves with applicable norms and decisive factors to evaluate qualitative research from within its theoretical and methodological framework of origin.

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Good Qualitative Research: Opening up the Debate

Beyond qualitative/quantitative structuralism: the positivist qualitative research and the paradigmatic disclaimer.

What is Qualitative in Research

Avoid common mistakes on your manuscript.

Introduction

“… It is important to regularly dialogue about what makes for good qualitative research” (Tracy, 2010 , p. 837)

To decide what represents good qualitative research is highly debatable. There are numerous methods that are contained within qualitative research and that are established on diverse philosophical perspectives. Bryman et al., ( 2008 , p. 262) suggest that “It is widely assumed that whereas quality criteria for quantitative research are well‐known and widely agreed, this is not the case for qualitative research.” Hence, the question “how to evaluate the quality of qualitative research” has been continuously debated. There are many areas of science and technology wherein these debates on the assessment of qualitative research have taken place. Examples include various areas of psychology: general psychology (Madill et al., 2000 ); counseling psychology (Morrow, 2005 ); and clinical psychology (Barker & Pistrang, 2005 ), and other disciplines of social sciences: social policy (Bryman et al., 2008 ); health research (Sparkes, 2001 ); business and management research (Johnson et al., 2006 ); information systems (Klein & Myers, 1999 ); and environmental studies (Reid & Gough, 2000 ). In the literature, these debates are enthused by the impression that the blanket application of criteria for good qualitative research developed around the positivist paradigm is improper. Such debates are based on the wide range of philosophical backgrounds within which qualitative research is conducted (e.g., Sandberg, 2000 ; Schwandt, 1996 ). The existence of methodological diversity led to the formulation of different sets of criteria applicable to qualitative research.

Among qualitative researchers, the dilemma of governing the measures to assess the quality of research is not a new phenomenon, especially when the virtuous triad of objectivity, reliability, and validity (Spencer et al., 2004 ) are not adequate. Occasionally, the criteria of quantitative research are used to evaluate qualitative research (Cohen & Crabtree, 2008 ; Lather, 2004 ). Indeed, Howe ( 2004 ) claims that the prevailing paradigm in educational research is scientifically based experimental research. Hypotheses and conjectures about the preeminence of quantitative research can weaken the worth and usefulness of qualitative research by neglecting the prominence of harmonizing match for purpose on research paradigm, the epistemological stance of the researcher, and the choice of methodology. Researchers have been reprimanded concerning this in “paradigmatic controversies, contradictions, and emerging confluences” (Lincoln & Guba, 2000 ).

In general, qualitative research tends to come from a very different paradigmatic stance and intrinsically demands distinctive and out-of-the-ordinary criteria for evaluating good research and varieties of research contributions that can be made. This review attempts to present a series of evaluative criteria for qualitative researchers, arguing that their choice of criteria needs to be compatible with the unique nature of the research in question (its methodology, aims, and assumptions). This review aims to assist researchers in identifying some of the indispensable features or markers of high-quality qualitative research. In a nutshell, the purpose of this systematic literature review is to analyze the existing knowledge on high-quality qualitative research and to verify the existence of research studies dealing with the critical assessment of qualitative research based on the concept of diverse paradigmatic stances. Contrary to the existing reviews, this review also suggests some critical directions to follow to improve the quality of qualitative research in different epistemological and ontological perspectives. This review is also intended to provide guidelines for the acceleration of future developments and dialogues among qualitative researchers in the context of assessing the qualitative research.

The rest of this review article is structured in the following fashion: Sect.  Methods describes the method followed for performing this review. Section Criteria for Evaluating Qualitative Studies provides a comprehensive description of the criteria for evaluating qualitative studies. This section is followed by a summary of the strategies to improve the quality of qualitative research in Sect.  Improving Quality: Strategies . Section  How to Assess the Quality of the Research Findings? provides details on how to assess the quality of the research findings. After that, some of the quality checklists (as tools to evaluate quality) are discussed in Sect.  Quality Checklists: Tools for Assessing the Quality . At last, the review ends with the concluding remarks presented in Sect.  Conclusions, Future Directions and Outlook . Some prospects in qualitative research for enhancing its quality and usefulness in the social and techno-scientific research community are also presented in Sect.  Conclusions, Future Directions and Outlook .

For this review, a comprehensive literature search was performed from many databases using generic search terms such as Qualitative Research , Criteria , etc . The following databases were chosen for the literature search based on the high number of results: IEEE Explore, ScienceDirect, PubMed, Google Scholar, and Web of Science. The following keywords (and their combinations using Boolean connectives OR/AND) were adopted for the literature search: qualitative research, criteria, quality, assessment, and validity. The synonyms for these keywords were collected and arranged in a logical structure (see Table 1 ). All publications in journals and conference proceedings later than 1950 till 2021 were considered for the search. Other articles extracted from the references of the papers identified in the electronic search were also included. A large number of publications on qualitative research were retrieved during the initial screening. Hence, to include the searches with the main focus on criteria for good qualitative research, an inclusion criterion was utilized in the search string.

From the selected databases, the search retrieved a total of 765 publications. Then, the duplicate records were removed. After that, based on the title and abstract, the remaining 426 publications were screened for their relevance by using the following inclusion and exclusion criteria (see Table 2 ). Publications focusing on evaluation criteria for good qualitative research were included, whereas those works which delivered theoretical concepts on qualitative research were excluded. Based on the screening and eligibility, 45 research articles were identified that offered explicit criteria for evaluating the quality of qualitative research and were found to be relevant to this review.

Figure  1 illustrates the complete review process in the form of PRISMA flow diagram. PRISMA, i.e., “preferred reporting items for systematic reviews and meta-analyses” is employed in systematic reviews to refine the quality of reporting.

PRISMA flow diagram illustrating the search and inclusion process. N represents the number of records

Criteria for Evaluating Qualitative Studies

Fundamental criteria: general research quality.

Various researchers have put forward criteria for evaluating qualitative research, which have been summarized in Table 3 . Also, the criteria outlined in Table 4 effectively deliver the various approaches to evaluate and assess the quality of qualitative work. The entries in Table 4 are based on Tracy’s “Eight big‐tent criteria for excellent qualitative research” (Tracy, 2010 ). Tracy argues that high-quality qualitative work should formulate criteria focusing on the worthiness, relevance, timeliness, significance, morality, and practicality of the research topic, and the ethical stance of the research itself. Researchers have also suggested a series of questions as guiding principles to assess the quality of a qualitative study (Mays & Pope, 2020 ). Nassaji ( 2020 ) argues that good qualitative research should be robust, well informed, and thoroughly documented.

Qualitative Research: Interpretive Paradigms

All qualitative researchers follow highly abstract principles which bring together beliefs about ontology, epistemology, and methodology. These beliefs govern how the researcher perceives and acts. The net, which encompasses the researcher’s epistemological, ontological, and methodological premises, is referred to as a paradigm, or an interpretive structure, a “Basic set of beliefs that guides action” (Guba, 1990 ). Four major interpretive paradigms structure the qualitative research: positivist and postpositivist, constructivist interpretive, critical (Marxist, emancipatory), and feminist poststructural. The complexity of these four abstract paradigms increases at the level of concrete, specific interpretive communities. Table 5 presents these paradigms and their assumptions, including their criteria for evaluating research, and the typical form that an interpretive or theoretical statement assumes in each paradigm. Moreover, for evaluating qualitative research, quantitative conceptualizations of reliability and validity are proven to be incompatible (Horsburgh, 2003 ). In addition, a series of questions have been put forward in the literature to assist a reviewer (who is proficient in qualitative methods) for meticulous assessment and endorsement of qualitative research (Morse, 2003 ). Hammersley ( 2007 ) also suggests that guiding principles for qualitative research are advantageous, but methodological pluralism should not be simply acknowledged for all qualitative approaches. Seale ( 1999 ) also points out the significance of methodological cognizance in research studies.

Table 5 reflects that criteria for assessing the quality of qualitative research are the aftermath of socio-institutional practices and existing paradigmatic standpoints. Owing to the paradigmatic diversity of qualitative research, a single set of quality criteria is neither possible nor desirable. Hence, the researchers must be reflexive about the criteria they use in the various roles they play within their research community.

Improving Quality: Strategies

Another critical question is “How can the qualitative researchers ensure that the abovementioned quality criteria can be met?” Lincoln and Guba ( 1986 ) delineated several strategies to intensify each criteria of trustworthiness. Other researchers (Merriam & Tisdell, 2016 ; Shenton, 2004 ) also presented such strategies. A brief description of these strategies is shown in Table 6 .

It is worth mentioning that generalizability is also an integral part of qualitative research (Hays & McKibben, 2021 ). In general, the guiding principle pertaining to generalizability speaks about inducing and comprehending knowledge to synthesize interpretive components of an underlying context. Table 7 summarizes the main metasynthesis steps required to ascertain generalizability in qualitative research.

Figure  2 reflects the crucial components of a conceptual framework and their contribution to decisions regarding research design, implementation, and applications of results to future thinking, study, and practice (Johnson et al., 2020 ). The synergy and interrelationship of these components signifies their role to different stances of a qualitative research study.

Essential elements of a conceptual framework

In a nutshell, to assess the rationale of a study, its conceptual framework and research question(s), quality criteria must take account of the following: lucid context for the problem statement in the introduction; well-articulated research problems and questions; precise conceptual framework; distinct research purpose; and clear presentation and investigation of the paradigms. These criteria would expedite the quality of qualitative research.

How to Assess the Quality of the Research Findings?

The inclusion of quotes or similar research data enhances the confirmability in the write-up of the findings. The use of expressions (for instance, “80% of all respondents agreed that” or “only one of the interviewees mentioned that”) may also quantify qualitative findings (Stenfors et al., 2020 ). On the other hand, the persuasive reason for “why this may not help in intensifying the research” has also been provided (Monrouxe & Rees, 2020 ). Further, the Discussion and Conclusion sections of an article also prove robust markers of high-quality qualitative research, as elucidated in Table 8 .

Quality Checklists: Tools for Assessing the Quality

Numerous checklists are available to speed up the assessment of the quality of qualitative research. However, if used uncritically and recklessly concerning the research context, these checklists may be counterproductive. I recommend that such lists and guiding principles may assist in pinpointing the markers of high-quality qualitative research. However, considering enormous variations in the authors’ theoretical and philosophical contexts, I would emphasize that high dependability on such checklists may say little about whether the findings can be applied in your setting. A combination of such checklists might be appropriate for novice researchers. Some of these checklists are listed below:

The most commonly used framework is Consolidated Criteria for Reporting Qualitative Research (COREQ) (Tong et al., 2007 ). This framework is recommended by some journals to be followed by the authors during article submission.

Standards for Reporting Qualitative Research (SRQR) is another checklist that has been created particularly for medical education (O’Brien et al., 2014 ).

Also, Tracy ( 2010 ) and Critical Appraisal Skills Programme (CASP, 2021 ) offer criteria for qualitative research relevant across methods and approaches.

Further, researchers have also outlined different criteria as hallmarks of high-quality qualitative research. For instance, the “Road Trip Checklist” (Epp & Otnes, 2021 ) provides a quick reference to specific questions to address different elements of high-quality qualitative research.

Conclusions, Future Directions, and Outlook

This work presents a broad review of the criteria for good qualitative research. In addition, this article presents an exploratory analysis of the essential elements in qualitative research that can enable the readers of qualitative work to judge it as good research when objectively and adequately utilized. In this review, some of the essential markers that indicate high-quality qualitative research have been highlighted. I scope them narrowly to achieve rigor in qualitative research and note that they do not completely cover the broader considerations necessary for high-quality research. This review points out that a universal and versatile one-size-fits-all guideline for evaluating the quality of qualitative research does not exist. In other words, this review also emphasizes the non-existence of a set of common guidelines among qualitative researchers. In unison, this review reinforces that each qualitative approach should be treated uniquely on account of its own distinctive features for different epistemological and disciplinary positions. Owing to the sensitivity of the worth of qualitative research towards the specific context and the type of paradigmatic stance, researchers should themselves analyze what approaches can be and must be tailored to ensemble the distinct characteristics of the phenomenon under investigation. Although this article does not assert to put forward a magic bullet and to provide a one-stop solution for dealing with dilemmas about how, why, or whether to evaluate the “goodness” of qualitative research, it offers a platform to assist the researchers in improving their qualitative studies. This work provides an assembly of concerns to reflect on, a series of questions to ask, and multiple sets of criteria to look at, when attempting to determine the quality of qualitative research. Overall, this review underlines the crux of qualitative research and accentuates the need to evaluate such research by the very tenets of its being. Bringing together the vital arguments and delineating the requirements that good qualitative research should satisfy, this review strives to equip the researchers as well as reviewers to make well-versed judgment about the worth and significance of the qualitative research under scrutiny. In a nutshell, a comprehensive portrayal of the research process (from the context of research to the research objectives, research questions and design, speculative foundations, and from approaches of collecting data to analyzing the results, to deriving inferences) frequently proliferates the quality of a qualitative research.

Prospects : A Road Ahead for Qualitative Research

Irrefutably, qualitative research is a vivacious and evolving discipline wherein different epistemological and disciplinary positions have their own characteristics and importance. In addition, not surprisingly, owing to the sprouting and varied features of qualitative research, no consensus has been pulled off till date. Researchers have reflected various concerns and proposed several recommendations for editors and reviewers on conducting reviews of critical qualitative research (Levitt et al., 2021 ; McGinley et al., 2021 ). Following are some prospects and a few recommendations put forward towards the maturation of qualitative research and its quality evaluation:

In general, most of the manuscript and grant reviewers are not qualitative experts. Hence, it is more likely that they would prefer to adopt a broad set of criteria. However, researchers and reviewers need to keep in mind that it is inappropriate to utilize the same approaches and conducts among all qualitative research. Therefore, future work needs to focus on educating researchers and reviewers about the criteria to evaluate qualitative research from within the suitable theoretical and methodological context.

There is an urgent need to refurbish and augment critical assessment of some well-known and widely accepted tools (including checklists such as COREQ, SRQR) to interrogate their applicability on different aspects (along with their epistemological ramifications).

Efforts should be made towards creating more space for creativity, experimentation, and a dialogue between the diverse traditions of qualitative research. This would potentially help to avoid the enforcement of one's own set of quality criteria on the work carried out by others.

Moreover, journal reviewers need to be aware of various methodological practices and philosophical debates.

It is pivotal to highlight the expressions and considerations of qualitative researchers and bring them into a more open and transparent dialogue about assessing qualitative research in techno-scientific, academic, sociocultural, and political rooms.

Frequent debates on the use of evaluative criteria are required to solve some potentially resolved issues (including the applicability of a single set of criteria in multi-disciplinary aspects). Such debates would not only benefit the group of qualitative researchers themselves, but primarily assist in augmenting the well-being and vivacity of the entire discipline.

To conclude, I speculate that the criteria, and my perspective, may transfer to other methods, approaches, and contexts. I hope that they spark dialog and debate – about criteria for excellent qualitative research and the underpinnings of the discipline more broadly – and, therefore, help improve the quality of a qualitative study. Further, I anticipate that this review will assist the researchers to contemplate on the quality of their own research, to substantiate research design and help the reviewers to review qualitative research for journals. On a final note, I pinpoint the need to formulate a framework (encompassing the prerequisites of a qualitative study) by the cohesive efforts of qualitative researchers of different disciplines with different theoretic-paradigmatic origins. I believe that tailoring such a framework (of guiding principles) paves the way for qualitative researchers to consolidate the status of qualitative research in the wide-ranging open science debate. Dialogue on this issue across different approaches is crucial for the impending prospects of socio-techno-educational research.

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Yadav, D. Criteria for Good Qualitative Research: A Comprehensive Review. Asia-Pacific Edu Res 31 , 679–689 (2022). https://doi.org/10.1007/s40299-021-00619-0

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What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

Qualitative research involves collecting and analysing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analysing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, and history.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organisation?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography, action research, phenomenological research, and narrative research. They share some similarities, but emphasise different aims and perspectives.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves ‘instruments’ in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analysing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organise your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorise your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analysing qualitative data. Although these methods share similar processes, they emphasise different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analysing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analysing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalisability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalisable conclusions because the data may be biased and unrepresentative of the wider population .

• Labour-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Qualitative vs Quantitative Research Methods & Data Analysis

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The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

On This Page:

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography .

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

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 goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo 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 .

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.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.