research type facet

What is facet methodology?

Facet methodology is a creative approach to researching that aims to create insights into our social world. It is an approach developed by a team of colleagues at the Morgan Centre for Research into Everyday Lives at The University of Manchester and launched by Jennifer Mason in her 2011 paper Facet Methodology: The Case for an Inventive Research Orientation .

There are two key things to understand about facet methodology. First, it is not a set of procedures but an approach or an orientation. It is in other words a methodology rather than a method. The second thing to keep in mind is that facet methodology does not necessarily entail mixing methods – in other words, facet methodology is not just another term for mixed methods research.

The term facet methodology emerges from the central metaphor used to describe this orientation, namely that of the facets of a gemstone. In facet methodology, the gemstone is the phenomenon under study, the thing that we are interested in. And like the facets of the gemstone that reflect and refract light in different ways, we can think of facet methodology as adopting different theoretical and methodological lenses or facets to shed light on the phenomenon that we are studying. Facets are chosen in a strategic manner such that they can shed light on telling aspects of this phenomenon.

The defining characteristic of facet methodology is that it is an inventive research orientation that shapes the whole research process. In other words, facet methodology cannot be reduced to the methods we use in research. Instead, it is an orientation that guides how we think of the phenomenon that we are interested in, the concepts we use to make sense of it, how we empirically study it and how we make sense of our data. Throughout this process, Mason urges researchers to remain open and inquisitive so as not to close off any avenues of investigation from the outset. Adopting such a playful research orientation can mean that we make use of a range of theoretical approaches and techniques of data collection and analysis – and indeed, Mason encourages researchers to experiment with epistemologies. This same playfulness and inquisitiveness can also shape how we write about our research, for example by trying to find alternative, more creative ways of conveying our findings.

NCRM’s one-day course A Practical Introduction to Facet Methodology is aimed at researchers at various career stages, from PhD students onwards. The morning sessions in this workshop will introduce some of the key elements of facet methodology and offer examples of how the approach has been used in research in the Morgan Centre. In the afternoon, participants will get a chance to work in small groups to consider whether and how a facet methodology approach might be useful in their own projects.

Register for A Practical Introduction to Facet Methodology

Introducing the Facet Methodology

by admin · 26th November 2014

(An alternative to mixed methods especially within the sociology of digital technology)

Mixed methods in practice usually involves using quantitative and qualitative methods to allow researchers to cross-reference corroborating sources of data as they add layers of credibility to their studies (Creswell 2003). Mason’s facet methodology (Mason 2011) is an alternative to this “methods-driven integration or triangulation” of data that can characterise mixed methods “where methods and their products are fitted together in a predetermined or hierarchical way” (p84). The facet methodology “requires a blend of scientific and artistic or artful thinking, involving not only deductive but also imaginative, inventive, creative and intuitive reasoning” (p80). The facet is a metaphor for a mixed, yet more sophisticated and multi-dimensional methodology.

The facets in the gemstone:

“Are conceived as different methodological-substantive planes and surfaces, which are designed to be capable of casting and refracting light in a variety of ways that help to define the overall object of concern” (p77).

The facet methodology assumes;

“That the world and what we seek to understand about it is not only lived and experienced, but is multi-dimensional, contingent, relationally implicated and entwined”. (P78)

This is especially applicable to the study of digital technology. Digital technology is not an external to our social world; it is a multi-faceted “contingent, relationally implicated and entwined” presence in or lives. We use digital technology for different motivations, at different times of the day, on different devices, and in a complex interrelationship between our personal circumstances and developments in technology, our patterns of usage continuously evolve. The relationship between us and digital technology can be what Mason calls, an“entwined problematic”(p83) that lends itself to the facet methodology.

The facet methodology is particularly useful in helping resolve the tension between micro and macro phenomenon inherent in many studies involving large data sets such as online social networks. The facet methodology assumes the “different registers of scale that social scientists sometimes like to apply analytically” (p79) are not always self-evidently revealed.

As corrective to this mode of discussion, facets are:

“Mini investigations that involve clusters of methods focussed on strategically and artfully selected sets of related questions and problematics. Each facet represents a way or ways of looking at and investigating something that is theoretically interesting in relation to the overall enquiry and each seeks out particular instances or versions of the kinds of entwinements and contingencies that are thought to be characteristic of the object of concern in some way.” (P79)

The facet methodology is also “ontologically and epistemologically orientated” (p83). This means that it is “implicated in all stages of research, including how we theorise from or with data” (p83). The resultant processes of writing, representation and argumentation are “driven by ideas about how different facets, and facets in combination, can tell us about the entwined problematic” (p83). It encourages the researcher to examine think laterally and critically about the data. The facet methodology can reveal how macro phenomena are not immediately self-evident but subtlety entwined and mobilised in different contexts in sometimes counter-intuitive ways.

Creswell, J.W., 2003. Research Design 2nd ed., Thousand Oaks, CA: Sage Publications, Inc.

Mason, J., 2011. Facet Methodology : the case for an inventive research orientation. Methodological Innovations Online , 6(3), pp.75–92.

Tags: Digital Sociology Methodology sociological lens sociology

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Morgan Centre for Research into Everyday Lives

Facet methodology

Facet Methodology is a new model for mixed-method approaches to research.

About facet methodology

• creative interview encounters around questions about the living of resemblances in family life;
• experimental methods exploring how resemblances are perceived in a variety of contexts;
• a small set of 'expert' interviews;
• a photoshoot combined with 'vox pops' to observe the performance of resemblances in public.

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Advances in Facet Theory Research: Developments in Theory and Application and Competing Approaches

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Facet theory is an approach to research in the social sciences. Since its inception in the work of Louis Guttman in the mid twentieth century, facet theory has become an established approach within social science research. In addition, over the past 70 years a wide range of research publications have appeared ...

Keywords : facet theory, mds, research methods, social psychology, psychometrics, environmental psychology, psychology, sociology

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The Efficient Measurement of Job Satisfaction: Facet-Items versus Facet Scales

The measurement of job satisfaction as a central dimension for workplace health and well-being is crucial to set suitable health- and performance-enhancing management decisions. Measuring different facets of job satisfaction leads to a more precise understanding about job satisfaction in research as well as to more specific interventions in companies. This study examines the measurement of job satisfaction with facet scales (multiple-items for one facet) and facet-items (one item for one facet). Facet-items are a cost-effective and fast way to measure job satisfaction in facets, whereas facet scales are more detailed and provide further information. Results from 788 bank employees showed that facet-items of job satisfaction were significantly correlated with the corresponding facet scales and had high factor loadings within the appropriate factor. Furthermore, the same correlational pattern between facet scales and external criteria was found for facet-items and external criteria (identification with the company, work engagement, stress, resources). The findings support the usage of facet-items in companies and in research where cost- and time-effectiveness is imperative and the usage of facet scales where an even deeper understanding of job satisfaction is needed. In practice, the usage of efficient measurements is evident, especially in the upcoming field of eHealth tools.

1. Introduction

Is your job making you feel satisfied? Job satisfaction (JS) is one of the most studied fields of work design research in psychology [ 1 ]. The wide interest of JS is valid for research and for organizations [ 2 ]. JS can be seen in two different ways: On one hand, JS is used as a measurement for well-being of employees [ 3 ]. In this respect, JS displays the emotional state of the employees that is also frequently used as an indicator in workplace health promotion projects to develop specific interventions [ 4 ]. On the other hand, JS is seen in a dynamic process as a predictor and outcome variable for other job-related factors in the direction of performance [ 5 , 6 ], e.g., work engagement. Since engaged employees are more productive [ 7 ], organizations want to explore the level of JS of their employees. High JS can be a goal for organizations to reduce their fluctuations and to enhance their performance, but for the employees by themselves, it is of deep interest to enhance their own quality of life. JS is defined as a pleasurable or positive emotional state [ 8 ] and is developed through evaluative judgments, affective experiences at work, and beliefs about jobs [ 9 ]. It is associated with important work-related and general outcomes [ 10 , 11 ] as e.g., JS shows a high variance proportion for the prediction of general life satisfaction [ 12 ]. Furthermore, JS and work engagement show a high positive relationship [ 13 ]. Therefore, the measurement of JS plays a fundamental role as organizations want highly involved and satisfied employees to reach their goals (e.g., [ 14 ]). The measurement of JS gives insight into the attitudes of the employees in the company and can be used to support the design of corporate strategies [ 15 ].

A typical way of using JS is by including it as organizational diagnostic variable in employee surveys [ 16 ] to derive actions according to the goals and strategies of the organization. These goals can be derived with the Balanced Scorecard [ 17 ]. The Balanced Scorecard is an instrument to translate the strategies and goals of a company into precise measurable indicators. The learning and growth perspective of the Balanced Scorecard, which is one of four perspectives, is the foundation for all other perspectives. Investments in further education, information technologies, and systems are part of the learning and growth perspective where the base to reach other defined goals is set. The learning and growth perspective includes, amongst other indicators, the satisfaction of the employees to increase productivity and quality [ 18 ], where JS can be measured with an employee survey [ 19 ]. Recent research shows that economic measurement is important to get information and to decrease the reaction time to set actions in a company [ 20 ].

As part of most employee surveys, JS can be on one hand considered as a global construct or on the other hand can be seen as consisting of various facets [ 21 ], where, according to Judge and Kammeyer-Mueller [ 22 ], JS is a hierarchical construct. Global JS combines all feelings and cognitions toward the job whereas the facet approach considers various aspects of the job like work task, payment, promotion, supervision, or coworkers [ 15 , 21 ]. These different approaches lead to various measurements of JS [ 23 ]. We differentiate the approaches into the goal of assessing global JS or facets of JS as presented in Table 1 .

Different forms of measurement of assessing job satisfaction (JS).

The contrasts between these approaches can be seen at different levels. JS is often measured with multiple-items aggregated to global JS [ 24 ]. A new way of assessing JS is with one single-item asking for global JS (e.g., [ 25 ]). Global JS is an efficient way of measuring well-being either in a single-item version or with multiple-items, where the advantage of multiple-items lies in higher reliability [ 26 ]. Measuring dimensions (or facets) of JS can be done with multiple-items aggregated to different facets of JS [ 27 , 28 ]. Assessing facets of JS is useful to get a deeper insight into the different organizational processes [ 14 ] as also the multidimensional complexity of JS has to be taken into account [ 29 , 30 ]. The facets that are considered, e.g., by the Job Descriptive Index [ 27 ], are the work by itself, compensation and benefits, attitudes toward supervisors, relations with co-workers, and opportunities for promotion. But there can be other facets like working conditions [ 8 ]. Given the current measurements of JS, there exist different facets of JS as well as different compositions of facets [ 15 ]. Regarding the idea of an efficient way of measuring JS, which at the same time covers all important facets, can be seen best presented with the use of single-items where every item describes one facet of JS, e.g., the facet “satisfaction with working conditions” is assessed with one item (e.g., [ 20 , 31 ]). We define single-items, which are used as assessment for a single facet of JS, as facet-items.

The aim of this study is to introduce and test a set of JS instruments: an instrument of JS consisting of different facet scales (Profile Analysis of Job Satisfaction (PAJS); [ 28 ]) and a specially developed screening version are compared. This screening version of the measurement of JS consists of a battery of facet-items, where every item represents one facet of the facet scales. Instruments with facet-items have been developed before (e.g., [ 20 , 31 ]) but have included only a small range of possible facets, focusing strongly on facets measuring JS with social aspects (supervisor, coworkers) or with task-related aspects (work tasks, payment, career possibilities). To get a deeper insight into the workplace, a more detailed assessment is needed where a wide range of possible facets must be considered (e.g., the information processes, the working and vacation times, or working conditions). The PAJS contains 11 facets with multiple-items for every facet and, therefore, allows a very specific assessment of the workplace. A screening version with one item for each of the 11 facets could provide more information than single- or multiple-items of global JS and be at the same time more cost- and time-efficient than the PAJS. Nonetheless the usage of multiple-item measurements for JS is necessary to get a deeper insight into the facets and to derive interventions more precisely.

1.1. Measuring Job Satisfaction with Facet-Items versus Facet Scales

Regarding single-items of JS, they need less time and space, are more cost-effective, and may contain more face validity than scales with multiple-items [ 32 ]. Cost- and time-effectiveness is an important issue for companies. Shorter surveys are more likely to be approved by companies and are more likely to be completed by the employees or participants in a study [ 33 , 34 ]. Therefore, an economic measurement will lead to higher participation of employees in an employee survey or in research studies [ 20 ]. These advantages can also be assumed for facet-items of JS [ 31 ].

Criticism against single-item measurements refer to the assessment of reliability: the test-retest-reliability for single-item measurements can be estimated, but the more psychometrically important estimation of internal consistency cannot be generated [ 26 ]. Furthermore, Wanous et al. [ 32 ] report in their meta-analysis test-retest-reliabilities for single-items between 0.45 and 0.69, which is a large bandwidth. But as Gardner et al. [ 35 ] state, it is possible that one “good” item shows better reliability and validity than many “bad” items. Wanous et al. [ 32 ] report correlations at 0.63 between single-items and scale measures showing that single-items of JS have an acceptable psychometric quality. Besides that, not only reliability but also validity for the practical usage of scales is at least of high importance [ 36 ].

For other constructs, like work engagement, it was already shown that a short and efficient measurement can be used [ 13 ]: the Utrecht Work Engagement Scale (UWES) measures the three dimensions of vigor, dedication, and absorption with one item for each dimension and shows acceptable reliability and validity. For narcissism, a single-item measurement was also developed, showing high test-retest-reliability and acceptable validity [ 37 ]. Even assessing the health status with global single-items as a valid, reliable, and sensitive measurement can be done [ 38 ].

In the present study, facet-items of JS are direct statements. Facet-items in form of questions in a discrepancy approach [ 31 ] can lead to psychometric problems [ 39 ]. Furthermore, the used measurement, in fact the PAJS, contains 11 facets of JS, and therefore, it is even more comprehensive and easier to derive practical interventions than with less facets. As the measurement of 11 facets with multiple-items for every scale is costly, a facet-item approach is more efficiently and less cost-intensive. Therefore, a screening version was developed to measure the facets of JS with facet-items: 11 items were defined to measure 11 facets. The items included the name of the facet scale and in parentheses further descriptions of the scale (this is presented in Table 2 , see third column). To be efficient, the circumstance of having items measuring more than one aspect was deliberately accepted, but for that more comprehensible items were possible.

Comparison of the Profile Analysis of Job Satisfaction (PAJS) and the PAJS-Facet-Item (PAJS-FI) 1 .

1 The three points “…” refer to the rating-scale from “1, very satisfied” to “5, unsatisfied”. An example for the rating of a single-item of the facet “organization and management” with a rating of “very satisfied” is “I am very satisfied with the image of the company”.

1.2. Job Satisfaction and External Criteria

On one hand, JS is used as an indicator for well-being, e.g., as an output of changes within a company inducing organizational health performance interventions [ 40 ]. On the other hand, JS is supposed to be a cause for other outcomes like absenteeism, performance, productivity, work engagement, organizational inefficiency (like counterproductive behaviour), intention to quit, or commitment [ 11 , 21 , 41 ]. In the applied field of organizational psychology, JS is used as a broad indicator of these outcomes or as an indicator of well-being, concluding that any measurement of JS either as facet-items or facet scales has to be tested with the respective outcomes as external criteria. The facets of JS lead to different predictions of behaviour and therefore, different interventions can be derived [ 20 ]. In an international study for example the relational aspect of JS has been identified as the most important facet for performance [ 42 ] and in a study among senior managers in the forestry and wood-processing sector, base salary was the most important factor for motivation [ 43 ]. By using organizational-specific results like the previous one then subsequent steps for changes in an organization can be developed in a tailored manner.

Organizational identification, declared as a strong affective and cognitive bond between employee and organization, shows a positive relationship with JS [ 44 ], whereas intention to quit is related negatively to JS [ 41 ]. The positive relationship between organizational identification and JS also occurs when JS is summarized from different facets [ 45 ]. Jiménez [ 46 ] states that different facets of JS have influence on intention to quit and organizational identification. Satisfaction with making career and satisfaction with how demanding the job is are the most important influence factors for organizational identification.

Furthermore, JS and work engagement can be viewed as two different constructs in organizational psychology with a positive relationship [ 13 , 47 ] but higher levels of arousal for work engagement than for JS [ 13 ]. The importance of work engagement for todays’ work environment has been proved in many studies [ 13 ]. Different facets of JS are meant to make different predictions of work engagement, e.g., satisfaction with the work by itself is the key driver of work engagement, whereas satisfaction with payment is not linked to work engagement [ 48 ].

Projects in workplace health promotion aim to enhance resources and to reduce stress. In the end, these projects should improve well-being, often measured in the form of JS [ 49 ]. As job resources are a positive feature of work environment related to motivational outcomes, resources are of an outstanding interest for companies [ 50 , 51 ] and are further related to well-being [ 52 ]. Stress, seen as a process caused by a load beyond the level of normal functioning [ 53 ], is an indispensable part in workplace health promotion projects. It has been shown that stress and JS are negatively related, whereas the different facets of JS are differently important [ 54 ].

1.3. Research Questions

1.3.1. facet-items in comparison with facet scales of job satisfaction.

One aim of this study is to compare a facet-item measurement with a facet scale measurement of JS. The used facet scale measurement, the PAJS, is a standardized approach to measure a wide range of facets of JS and it is possible to compare the results of an organization with a representative sample. It is hypothesized that facet-items are significantly correlated to the appropriate facet scale of the PAJS. Furthermore, intercorrelations between the facets of JS should be moderate [ 55 ]. In addition, the facet-items should show high factor loadings within the appropriate factor in a confirmatory factor analysis with all items of the PAJS. The findings should provide evidence to use a facet-item approach to measure JS where cost- and time-effectiveness is imperative.

1.3.2. Facets of Job Satisfaction and External Criteria

In addition, as the validity of a measurement must be checked, this is another aim of the present study. To test validity, the facet-item measurement as well as the facet scale measurement is related to other external criteria. External criteria in this study are identification with the company, work engagement, stress, and resources. Correlations between external criteria and the two measurements of JS should show similar patterns. To interpret the correlation coefficients, the classification from Cohen [ 56 ] was used, pointing out correlations higher than 0.30 are moderate and correlations higher than 0.50 are high. The level of correlations between JS and organizational identification should be moderate [ 44 ]. Furthermore, relationships between JS and work engagement should be moderate to high [ 13 , 47 ] as well as relationships between JS and resources and stress [ 54 ].

1.3.3. Efficiency of a Facet-Items Approach

To prove the efficiency of the newly developed facet-items approach compared to a facet scales approach, the comparison between the average answer times of the two measurements is another aim of the present study. It is hypothesized, that the average answer time for the facet-items measurement is shorter than for the facet scales approach.

2. Materials and Methods

2.1. participants and procedure.

The participants in this online-study were 788 employees working for an Austrian bank. The overall response rate was 53% (total of 1495 employees). The study is a first result in a longitudinal project about workplace health promotion (“Employee Survey 2015—Main Focus on Psychosocial Risk Management according to the Austrian Employee Protection Act”) and was conducted in the year 2015. The participation in the workplace health promotion project was voluntary. Nearly half of the employees were female (49.4%), and the others were male (50.6%). The largest portion had no leading position (74.6%). In this study, 13.8% of the bank employees had no contact to clients (employees in the back office), and 74.2% worked full-time, the others part-time. Age was measured in four categories: 13.1% were between 21 and 30 years old, 22.7% between 31 and 40 years, 33% between 41 and 50 years, and 31.2% were older than 50 years.

The survey was advertised on the intranet of the bank and through e-mail. The bank employees were asked to participate in the workplace health promotion project (including an employee survey) that takes place every two years and is conducted by an external, independent research institute. The first part of the survey included the questionnaires of the typical part of the employee survey, and in the second part, employees were asked to participate in a research project from the University of Graz to get some information about the effects of JS. At first, participants had to rate their JS measured with a screening version (measurement of facet-items). The second part of the survey included the PAJS (measurement of facet scales). The participation in the study was completely voluntary, anonymous, and confidential. Participants were promised total data protection. The study was carried out in accordance with the recommendations of the guidelines of the Ethics Commission of the University of Graz and approved by the Ethics Commission of the University of Graz from 27 March 2015.

2.2. Measures

2.2.1. job satisfaction (facet scales and facet-items).

The PAJS by Jiménez [ 28 ] with 38 items belonging to 11 facets was used to measure the facets of JS with several items for every facet (facet scales). Three to five items belong to every facet of the PAJS. This scale for job satisfaction (published at a test publisher [ 28 ]) has been used in organizational diagnostic studies in research and in practice. Studies showed Cronbach’s alpha for the facets of JS measured with the PAJS from 0.82 to 0.91 [ 28 , 57 ]. Criterion validity for the PAJS showed in different studies a negative relationship of JS with burnout, intention to quit, or absenteeism [ 28 , 58 ]. Furthermore, construct validity was proven with the Job Diagnostic Survey by Hackman and Oldham [ 59 , 60 ]. The practical requirements often requested for a shorter version with high psychometric quality.

Based on the ideas of being efficient (cost and time), a screening version was developed with the items of the PAJS to measure the facets of JS with facet-items. Therefore, it was accepted to have items measuring more than one aspect, but for that getting more comprehensible items. With the idea of being efficient, for the PAJS-Facet-Item (PAJS-FI) eleven items were developed to measure eleven facets. The development of the facet-items included the name of the facet scale and in parentheses the single-items of the facet scales or other descriptions of the scale. As an example, the facet information and communication with three single-items of the PAJS was measured in the PAJS-FI with the facet-item “I am … (rated from “1, very satisfied” to “5, unsatisfied”) with information and communication (activities in company, treatment of my suggestions, information from the management, information about innovations)”. Another example for the facet organization and management is the facet-item “I am … (rated from “1, very satisfied” to “5, unsatisfied”) with the organization and management (effort regarding employees, participation possibilities, image)”. It seems on one hand to be trivial to compare these two measurements, on the other hand it is important for practice and economic science to get valuable results for the practical use of efficient measurements in employee surveys with scientific accuracy [ 61 ]. In Table 2 , the different facets as well as the facet-items from the PAJS-FI and sample items from the facet scales of the PAJS are shown. The facet to which the items belong is also represented in Table 2 .

Employees were asked to indicate their agreement on a five-point scale (1 = very satisfied, 5 = unsatisfied). For an easier interpretation of the results, the values were inverted to get high values referring to high JS. In the present study, the Cronbach’s alpha for the global JS score in PAJS-FI was α = 0.89 and for the PAJS also α = 0.89.

2.2.2. Identification with the Company

Identification with the company was measured with four different items [ 28 , 47 ] which were adapted to the employee survey in the company and contained the aspects of recommendation and reapplying to the company, identification with the company, and proudness of working in this company. The items had to be rated on a five-point scale (1 = does apply perfectly, 5 = does not apply at all). For an easier interpretation, the values of identification with the company were inverted to get high values referring to high identification. A sample item is “I identify myself strongly with the company”. Cronbach’s alpha for identification with the company was α = 0.90.

2.2.3. Work Engagement

Work engagement was measured with the short version of the UWES [ 62 ]. The UWES-9 includes nine items pertaining to three dimensions: vigor , dedication , and absorption (three items for each dimension). Participants were asked to rank their answers on a seven-point scale from 0 (never) to 6 (always/every day). Sample items are “At my work, I feel bursting with energy” (vigor), “I am proud on the work that I do” (dedication), and “I feel happy when I am working intensely” (absorption). Cronbach’s alpha for work engagement was α = 0.96.

2.2.4. Resources and Stress

Resources and stress were measured with the Recovery-Stress-Questionnaire for Work [ 63 ]. With this measurement, the two dimensions stress and resources can be displayed. In the present study, a short version of the RESTQ-Work (RESTQ-Work-27) was used. For the dimension stress, the sub-dimensions social emotional stress and loss of meaning can be generated. The dimension resources contains the sub-dimensions overall recovery, leisure/breaks, psychosocial resources, and work-related resources. Employees were asked to rate their answers from 0 (never) to 6 (always). A sample item for the dimension resources is “In the last 7 days and nights I felt physically relaxed” and a sample item for the dimension stress is “In the last 7 days and nights I felt down”. Cronbach’s alpha for resources was α = 0.93 and for stress α = 0.94.

Data were analysed using Version 24 of Statistical Package for Social Sciences (SPSS (IBM SPSS software, Armonk, NY, USA) and using the program Mplus (Version 7.3 (Muthén & Muthén, Los Angeles, CA, USA)).

3.1. Relationship between Facets of PAJS (Facet Scales) and PAJS-FI (Facet-Items)

To test if facet-item measures belong to the appropriate facet scale, Pearson correlations between the PAJS-FI facet-items and the PAJS facet scales (scales obtained by aggregation of single-items) were calculated in SPSS. In Table 3 , the correlations between the facets of the PAJS and the PAJS-FI are displayed.

Correlations between Profile Analysis of Job Satisfaction (PAJS, facet scales) and PAJS-Facet-Item (PAJS-FI, facet-items, N = 788) 1 .

1 Correlations between the facet-items of the PAJS-FI and their corresponding facet scales of the PAJS are printed in boldface. Communication = information and communication, Demanding = demanding work, Colleagues = relationship to direct colleagues, Supervisor = relationship to direct supervisor, Organization = organization and management, Career = chances of making career, Conditions = working conditions, Decision range = decision range, Time aspects = working and vacation times, Compensations = compensations of the employer, Framework = general framework conditions. FI = facet-items, FS = facet scales. All correlations are significant at p < 0.01.

Every facet-item showed a moderate to high correlation with the appropriate facet scale, ranging from 0.50 to 0.82 ( p < 0.01). The lowest correlation between facet-items and facet scales was shown for the facet general framework conditions (0.50, p < 0.01) and the highest correlation for the facet relationship to direct supervisor (0.82, p < 0.01). By further examination of the correlations, it can be seen that the facet-item organization and management correlates higher with the facet scale relationship to direct supervisor (0.67, p < 0.01) than with the facet scale organization and management (0.54, p < 0.01).

The intercorrelations between the facets of the PAJS as well as between the PAJS-FI were small to high and ranged from 0.20 to 0.70 ( p < 0.01) for PAJS-FI and from 0.24 to 0.64 ( p < 0.01) for PAJS.

3.2. Confirmatory Factor Analysis for PAJS (Facet Scales) and PAJS-FI (Facet-Items)

To test if the facet-items of the PAJS-FI belong to the same factor as the single-items of the facet scales of the PAJS and show high factor loadings, a confirmatory factor analysis was conducted. For every facet of JS, the single-items of the PAJS and the facet-item of the PAJS-FI belonging to the same facet, were modelled as one factor. All 11 facets were put into one higher-order factor. The confirmatory factor analysis was conducted in the program Mplus.

In Table 4 , the standardized factor loadings of PAJS-FI facet-items with the appropriate facet are shown. The factor loadings of the single-items of the facet scales (PAJS) are not shown in Table 4 . Standardized loadings for the items of the PAJS-FI reached from 0.64 for the facets general framework conditions and organization and management to 0.85 for the facets relationship to direct supervisor and relationship to direct colleagues. Model fit also showed appropriate results: χ 2 (1116) = 4300.86, CFI = 0.90, SRMR = 0.07, RMSEA = 0.06.

Factor loadings in a confirmatory factor analysis for Profile Analysis of Job Satisfaction Facet-Item (PAJS-FI, facet-items, N = 788) 1 .

1 Standardized factor loadings are only shown for the facet-items of the PAJS-FI. Items of PAJS are not displayed in the table due to lack of space but are all higher than 0.60. Communication = information and communication, Demanding = demanding work, Colleagues = relationship to direct colleagues, Supervisor = relationship to direct supervisor, Organization = organization and management, Career = chances of making career, Conditions = working conditions, Decision range = decision range, Time aspects = working and vacation times, Compensations = compensations of the employer, Framework = general framework conditions. All standardized factor loadings are significant at p < 0.01.

3.3. The Facets of Job Satisfaction and External Criteria

To test validity, the facet-items of the PAJS-FI were correlated with external criteria. The correlations of the external criteria with the facet-items of PAJS-FI were compared to the same correlations between the facet scales of the PAJS and the same external criteria. As external criteria identification with the company, work engagement, stress, and resources were used. In Table 5 , the Pearson correlations between the facet-items of the PAJS-FI and the facet scales of the PAJS with the external criteria identification with the company, work engagement, stress, and resources, calculated in SPSS, can be found. To test if differences between the correlations from PAJS and PAJS-FI with the respective external criteria exist, a test of the difference between two dependent correlations with one variable in common was calculated [ 61 , 64 , 65 ]. For that reason, Bonferroni correction was applied [ 66 , 67 ], and the results were compared with α = 0.0004 (0.05 divided by 143 possibilities). Results showed significant differences for several facets especially for organization and management (see Table 5 ).

Correlations between facet-items (Profile Analysis of Job Satisfaction Facet-Item, PAJS-FI; on the left) and facet scales (Profile Analysis of Job Satisfaction, PAJS; on the right) with the external criteria Identification with Company, Work Engagement, Stress, and Resources (N = 788).

1 Communication = information and communication, Demanding = demanding work, Colleagues = relationship to direct colleagues, Supervisor = relationship to direct supervisor, Organization = organization and management, Career = chances of making career, Conditions = working conditions, Decision range = decision range, Time aspects = working and vacation times, Compensations = compensations of the employer, Framework = general framework conditions. 2 Identification with Company: Single-item measures, Recommend = I would recommend the company as employer, Identify = I identify myself strongly with the company, Reapply = I would reapply at the company, Proud = I am proud to work at the company. 3 Soc. em. stress = social emotional stress. 4 Loss mean = loss of meaning. 5 Ov. recovery = overall recovery. 6 Psychosoc. res. = psychosocial resources. 7 Work-rel. res. = work-related resources. A star (*) denotes a significant differences between correlations of PAJS-FI and PAJS with external criteria.

The correlations between the different items of identification with the company and the facet-items of the PAJS-FI ranged from 0.23 (relationship to direct colleagues and identify with the company) to 0.44 (demanding work and reapply at the company, chances of making career and proud to work at the company). Correlations between the facet scales of the PAJS and the items of identification with the company reached from 0.27 (relationship to direct colleagues and identify with the company) to 0.65 (organization and management and recommendation of the company). The correlations between identification with the company and the facet scales (PAJS) were always slightly higher than with the facet-items of the PAJS-FI except for a few single correlations (mainly for the facet compensations of the employer). The pattern of the correlational structure remained the same for PAJS and PAJS-FI. Significant results between PAJS and PAJS-FI were found for the facet organization and management. These differences were analysed additionally and are described below.

For work engagement, the correlations for the facet-items of the PAJS-FI ranged from 0.23 (relationship to direct colleagues and absorption) to 0.61 (demanding work and dedication). The facet scales of the PAJS and work engagement showed correlations from 0.26 (compensations of the employer and absorption) to 0.66 (demanding work and dedication). The aforementioned correlations were slightly higher than with the facet-items of the PAJS-FI except for the facet compensations of the employer, but the correlational structure was the same for both measurements. Significant results were found for the facets information and communication, organization and management, chances of making career, working and vacation times, and general framework conditions.

Concerning stress (sub-dimensions social emotional stress and loss of meaning) the correlations between the facet-items of the PAJS-FI and stress reached from −0.30 (compensations of the employer and social emotional stress) to −0.50 (organization and management and loss of meaning). For the facet scales of the PAJS and stress the correlations reached from −0.21 (compensations of the employer and social emotional stress) to −0.60 (decision range and loss of meaning), higher than the correlations with the facet-items of the PAJS-FI except for the facets organization and management, chances of making career, working conditions, compensations of the employer, and general framework conditions. Significant differences via Steiger’s Equations were found for the facets decision range as well as compensations of the employer.

Resources (sub-dimensions overall recovery, leisure/breaks, psychosocial resources, work-related resources) showed correlations from 0.20 (demanding work and psychosocial resources) to 0.68 (relationship to direct colleagues and psychosocial resources) for the facet-items of the PAJS-FI. For the facet scales of the PAJS, the correlations ranged from 0.18 (compensations of the employer and psychosocial resources) to 0.72 (relationship to direct colleagues and psychosocial resources). Most of the facets showed higher correlations for the facet scales of the PAJS and resources than for the facet-items of the PAJS-FI except for a few single correlations. The structural pattern for the facet scales (PAJS) and the facet-items (PAJS-FI) with stress and resources was the same. Significant differences were found for the facets relationship to direct colleagues as well as compensations of the employer.

In sum, the correlational structures between the facet scales (PAJS) and external criteria were the same like between facet-items (PAJS-FI) and external criteria. Most correlations showed a moderate to high level, except for the facets compensations of the employer, working and vacation times, and relationship to direct colleagues, and a few single correlations.

As the most significant comparisons between PAJS and PAJS-FI resulted for the facet organization and management, further reliability analyses were explored. The internal consistency for the three single-items for the facet organization and management from PAJS was α = 0.85, whereas reliability analysis including the facet-item organization and management showed α = 0.83 (analysed with Cronbach’s alpha). A more stricter analysis with McDonald’s coefficient omega (omega hierarchical; [ 68 ]) showed ωh = 0.85 vs. ωh = 0.78. Furthermore, item selectivity was lowest for the facet-item. These coefficients present that the reliability is stronger in the multiple-item version compared to the reliability including the facet-item.

3.4. Efficiency of the Short Version PAJS-FI

A t -test for dependent samples was calculated to see if the answer time for the measurement PAJS is different from the answer time for the PAJS-FI. People who interrupted the survey were excluded from the analysis. The test showed that for answering the items of the PAJS ( M = 195.92 s, SD = 188.24), the answer time lasted significantly longer than for the facet-items of the PAJS-FI ( M = 43.74 s, SD = 83.52, t (577) = −18.34, p < 0.01). The answer time was measured in seconds.

4. Discussion

This paper aimed to test a comprehensive facet-item measurement of JS that includes the assessment of eleven facets of JS. Furthermore, the facet-item measurement was compared to a facet scale measurement of JS to analyse construct and criterion validity. The results showed that the facet-items of JS were significantly and highly related to the facet scales. Additionally, results of a confirmatory factor analysis showed that the facet-items loaded highly within the appropriate factor for each facet scale. Furthermore, correlations with external criteria were acceptable showing valid measurements.

4.1. Comparison between Facet-Items and Facet Scales of Job Satisfaction

JS has been introduced as a global construct measured with one single-item or with multiple items or as a construct representing different facets measured with facet-items or with multiple items aggregated to facets (facet scales). The usage of facets is useful for a deeper understanding of organizational processes [ 14 ], and therefore, efficiency plays a major role.

Regarding the first research question, we conclude with the high correlations of the facet scales with the facet-items (diagonal in bold in Table 3 ) that both versions can be used for their different usages. This seems at first sight trivial, but the goal of the study was to develop an efficient measurement for practical use without forgetting precise, scientific standards. Therefore, the simple correlations were analysed, and additionally, a confirmatory factor analysis has been conducted. As expected, the correlations between the facet scales of PAJS and the facet-items of the PAJS-FI were high, and it is summarized that the PAJS-FI showed appropriate correlations for the eleven facets. Moreover, confirmatory factor analysis showed for all eleven facets that the facet-items of the PAJS-FI had high loadings within the appropriate factor. Single-item approaches, like the PAJS-FI for facets of JS, are useful where cost- and time-effectiveness plays an important role [ 20 ], whereas multiple-item measurements, like the PAJS, are needed in research and organizations to measure relatively complex constructs reliably [ 26 ].

Looking into the details, it can be seen, that the facet-item organization and management showed a high correlation with the facet scale relationship to direct supervisor (0.67). This is not surprising as the organization and the management of a company has much in common with the supervisors of a company, because the management depends on supervisors. Moreover, employees can assess their direct formal leaders better than management [ 69 ]. On the contrary, the facet scale organization and management did correlate at a moderate level (0.37) with the facet-item relationship to direct supervisor. Closer examined, the facet-item organization and management includes the nearer description “efforts regarding employees” (see Table 2 ). Such efforts depend on people who manage a company. The employees possibly have their supervisor in mind when thinking about management and do not include the satisfaction with the organization in their assessment. We suggest to strengthen the aspect of satisfaction with the organization in a next version of the PAJS-FI.

4.2. Job Satisfaction and External Criteria

By looking at the correlations with external criteria (identification with company, work engagement, stress and resources) to answer the second research question, it was shown that the correlations visible looked higher for the facet scale approach, but not in a statistical comparison of the different correlations. The level of correlations was mostly moderate to high, except for the facets compensations of the employer, working and vacation times, and relationship to direct colleagues, and a few single correlations. The correlations showed that the level of correlations for different facets of JS with external criteria is different, as hypothesized.

The correlational structure remained the same for the facet scales of the PAJS and the facet-items of the PAJS-FI. The higher identification with the company was, the higher was JS, no matter which measurement (PAJS or PAJS-FI) was used. For identification with the company, the facet organization and management showed significantly higher correlations with PAJS than with PAJS-FI. As already suggested, the facet organization and management in PAJS-FI should be further investigated as Cronbach’s alpha dropped when including the facet-item. Work engagement seems to have much in common with JS [ 48 ]. Here, also the correlational structure for PAJS and PAJS-FI remained the same: the higher work engagement was, the higher was JS. Stress (sub-dimensions social emotional stress and loss of meaning) and JS shared a negative relationship for the facet scales of the PAJS as well as for the facet-items of the PAJS-FI, whereas the relationship between resources (sub-dimensions overall recovery, leisure/breaks, psychosocial resources, work-related resources) and JS was positive. Summarized, the PAJS-FI and PAJS showed similar internal consistencies whereas the PAJS-FI has a shorter test-length.

The correlations with external criteria were the same for the facet-items of the PAJS-FI and for the facet scales of the PAJS, except for a few correlations and especially for the facet organization and management. Schaufeli et al. [ 13 ] also concluded in their approach of getting a shorter version of the UWES scale, that an expectable consequence and drawback of this shortening is that the coefficient alpha is reduced. We therefore made a deeper analysis for the facet organization and management and found a lower internal consistency in this facet which may explain the differences between the two versions of the PAJS. A facet scales approach is more accurate and therefore, different correlations may remain between the two compared measurements. On the other hand, the correlational structure is in the same direction as described before. The differences have to be kept in mind when interpreting the results with the PAJS-FI.

4.3. Efficiency of the PAJS-FI

As it was shown, the relative answer time for PAJS was much longer than for PAJS-FI. To answer the PAJS-FI, employees needed 44 s whereas the answer time for the items of the PAJS took more than three minutes. An efficient measurement should not be to simply have a shorter version replacing a longer version with the drawback of possibly losing information. Instead, the shorter version should lead to approximately the same information and show first results, e.g., in employee surveys. Shorter measurements meet the demands of survey participants as otherwise research has to assess fewer constructs or assess constructs with fewer items [ 13 ]. In employee surveys it is useful to have short measurements to gain as much information as possible [ 32 , 61 ] as there are often also time constraints [ 61 ]. On the other side, the more precise the facets of JS are measured, the more detailed interventions in a company can be derived. Therefore, the usage of multiple-item measurements of JS is as legitimate as the usage of facet-items for efficient measurement.

4.4. Limitations

One limitation in this study is the specific sample of employees working in the bank sector. In agreement with the participating bank, there is no permission to show descriptive data (means and standard deviations), only the relative relationships. As the interest of this study was to compare the two different versions PAJS and PAJS-FI, and not to show specific results from bank employees, the relative relationships suffice. For this study, it was important to test a sample that can participate in a study where the PAJS and the PAJS-FI are presented at one measure point. This leads to another limitation in the study: the completion of PAJS and PAJS-FI was not randomized. Bank employees had to first fill out the PAJS-FI, and in a second step, they had to complete the PAJS.

External criteria like job performance or turnover-rates and their relationship with facet-items of JS might be interesting and should be further investigated. Moreover, test-retest reliabilities would be of further interest.

4.5. Practical Implications: Advantages and Disadvantages—The Right Placement for the Right Instrument

JS is seen as a predictor for various outcomes [ 11 ] and is therefore a useful variable for organizational development and hence supports making management decisions [ 2 ]. But also as an indicator for well-being, JS is important [ 57 ]. This can be seen in dynamic views of JS e.g., from Büssing and Bissels [ 5 ] or Jiménez [ 6 ]. These system theoretical oriented views consider especially the facets of JS and draw the attention to the qualitative forms of JS. A result of JS can be seen in a “satisfied” rating but this judgment possibly could be evaluated as a “resigned work satisfaction” [ 5 ]. Knowing more about different aspects of the working life with facets of job satisfaction supports to understand possible coping strategies of employees which in turn influence the organization.

Employee surveys, especially when measuring JS, are an important instrument for organizational diagnosis by using the subjective assessment of a company [ 11 ]. Such employee surveys should be adopted to companies and therefore, the results should lead to topics discussed in the management area of the company and to implementations in the strategy of a company, e.g., in the Balanced Scorecard [ 16 ].

There are many advantages for a facet-item approach to measure the facets of JS: Cost-effective and short questionnaires need less time and space [ 32 ], and the response rate may be higher for shorter questionnaires [ 33 ]. According to Schaufeli et al. [ 13 ], shorter forms of questionnaires help to fulfil the requirements of employee surveys in companies: Due to time constraints, researchers either need to assess fewer constructs or have to assess the constructs with fewer items, which is especially the case in employee surveys. In this case, the usage of PAJS-FI is supported. Of course, multiple-item measures of the facets of JS (facet scales) have their use and advantages. They are because of the single-items even more differentiated for the employees as well as for the companies to generate interventions. One fundamental advantage of multiple-item measures is the estimation of internal consistency reliability, where high reliability is essential for statistical analysis to minimize effects of error [ 26 ]. In cases where small differentiations and the estimation of reliability is needed, the usage of PAJS is advised.

Another application area for efficient and short measurements is in the upcoming field of eHealth tools. In this area shorter “scales” and even visual analogue scales are also used as single-item measures [ 70 ] and can be part of workplace health promotion projects. With eHealth tools using visual analogue scales or the approach of facet-items, it is possible for supervisors to get short and efficient feedback [ 71 ]. This feedback has to be valid and accurate despite usage of short versions [ 72 ].

5. Conclusions

Facet-item and facet scale measurements of JS have both their areas of application. The aim of this study was to show that a facet-item measurement can be a replacement for a facet scale measurement of JS with multiple-items. In an online study, bank employees filled out both versions of assessments of JS, and by comparing the structural resemblance of the two measurements, it was demonstrated that facet-items are an appropriate approach. Facet-items can be an approach to measure the facets of JS in employee surveys efficiently, but facet scales are also appropriate when the facets should be measured more accurately. Which approach is used depends on the goals that should be reached. In projects where efficiency plays a major role, facet-item measures are preferred. This is especially the case in larger studies [ 32 ] or in practice when using eHealth tools [ 70 ]. In research or organizations where an even deeper understanding of JS with the aim of deriving interventions is needed, facet scale measures have a lot of advantages. This study showed that both approaches are appropriate measures.

As we could see that both forms for measuring JS can be seen as equivalent, they both help in deriving steps for interventions in organizations. In a next step of analyses, the sociological factors have to be regarded too. For example, if there are differences between groups like in gender [ 73 ] or in age [ 74 ], then the organization has to consider special actions and has to look up for the reasons. Here, the more detailed version of JS has advantages over the PAJS-FI. Typically, it can be advised in practice to think about the usage of the short versus long version of any scale in advance, if possible. If there are already some hypotheses regarding special facets, then the longer version could be helpful. In the other case when the facet-items had been used, then the next step in practice could be to investigate the results in small groups [ 75 ] (e.g., with so called “health circles”) where the effects are discussed to see which special aspects of the facets are important.

Furthermore, the usage of approaches to measure JS can also be examined in other areas of work, which has been shown in other studies [ 76 , 77 ]. In practice, it is recommended to define at first the goals of measuring JS. In workplace health promotion projects or employee surveys, where efficiency is a main aim and a lot of other constructs are explored, a facet-item measurement is preferred. In cases where the facets of JS should be explored and differentiated, the usage of a facet scale measurement is advised. This study showed for practice that short and efficient measurements are appropriate to measure the facets of JS.

Acknowledgments

This publication was printed with the financial support of the University of Graz.

Author Contributions

A.L. and P.J. formulated the objectives, designed the method, supervised the data assessment and worked together on the last draft of the paper. A.L. wrote the first draft of the paper, prepared the current state of the literature and carried out the data assessment. A.L. analysed the data with the support of N.T. A.B. and N.T. supported the administration and quality assurance. P.J. developed the questionnaire Profile Analysis of Job Satisfaction Facet-Item (PAJS-FI). All authors reflected the results and discussion.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Methodology

• The 4 Types of Validity in Research | Definitions & Examples

The 4 Types of Validity in Research | Definitions & Examples

Published on September 6, 2019 by Fiona Middleton . Revised on June 22, 2023.

Validity tells you how accurately a method measures something. If a method measures what it claims to measure, and the results closely correspond to real-world values, then it can be considered valid. There are four main types of validity:

• Construct validity : Does the test measure the concept that it’s intended to measure?
• Content validity : Is the test fully representative of what it aims to measure?
• Face validity : Does the content of the test appear to be suitable to its aims?
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

In quantitative research , you have to consider the reliability and validity of your methods and measurements.

Note that this article deals with types of test validity, which determine the accuracy of the actual components of a measure. If you are doing experimental research, you also need to consider internal and external validity , which deal with the experimental design and the generalizability of results.

Table of contents

Construct validity, content validity, face validity, criterion validity, other interesting articles, frequently asked questions about types of validity.

Construct validity evaluates whether a measurement tool really represents the thing we are interested in measuring. It’s central to establishing the overall validity of a method.

What is a construct?

A construct refers to a concept or characteristic that can’t be directly observed, but can be measured by observing other indicators that are associated with it.

Constructs can be characteristics of individuals, such as intelligence, obesity, job satisfaction, or depression; they can also be broader concepts applied to organizations or social groups, such as gender equality, corporate social responsibility, or freedom of speech.

There is no objective, observable entity called “depression” that we can measure directly. But based on existing psychological research and theory, we can measure depression based on a collection of symptoms and indicators, such as low self-confidence and low energy levels.

What is construct validity?

Construct validity is about ensuring that the method of measurement matches the construct you want to measure. If you develop a questionnaire to diagnose depression, you need to know: does the questionnaire really measure the construct of depression? Or is it actually measuring the respondent’s mood, self-esteem, or some other construct?

To achieve construct validity, you have to ensure that your indicators and measurements are carefully developed based on relevant existing knowledge. The questionnaire must include only relevant questions that measure known indicators of depression.

The other types of validity described below can all be considered as forms of evidence for construct validity.

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Content validity assesses whether a test is representative of all aspects of the construct.

To produce valid results, the content of a test, survey or measurement method must cover all relevant parts of the subject it aims to measure. If some aspects are missing from the measurement (or if irrelevant aspects are included), the validity is threatened and the research is likely suffering from omitted variable bias .

A mathematics teacher develops an end-of-semester algebra test for her class. The test should cover every form of algebra that was taught in the class. If some types of algebra are left out, then the results may not be an accurate indication of students’ understanding of the subject. Similarly, if she includes questions that are not related to algebra, the results are no longer a valid measure of algebra knowledge.

Face validity considers how suitable the content of a test seems to be on the surface. It’s similar to content validity, but face validity is a more informal and subjective assessment.

You create a survey to measure the regularity of people’s dietary habits. You review the survey items, which ask questions about every meal of the day and snacks eaten in between for every day of the week. On its surface, the survey seems like a good representation of what you want to test, so you consider it to have high face validity.

As face validity is a subjective measure, it’s often considered the weakest form of validity. However, it can be useful in the initial stages of developing a method.

Criterion validity evaluates how well a test can predict a concrete outcome, or how well the results of your test approximate the results of another test.

What is a criterion variable?

A criterion variable is an established and effective measurement that is widely considered valid, sometimes referred to as a “gold standard” measurement. Criterion variables can be very difficult to find.

What is criterion validity?

To evaluate criterion validity, you calculate the correlation between the results of your measurement and the results of the criterion measurement. If there is a high correlation, this gives a good indication that your test is measuring what it intends to measure.

A university professor creates a new test to measure applicants’ English writing ability. To assess how well the test really does measure students’ writing ability, she finds an existing test that is considered a valid measurement of English writing ability, and compares the results when the same group of students take both tests. If the outcomes are very similar, the new test has high criterion validity.

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

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

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time .
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalizability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritize internal validity over external validity , including ecological validity .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

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• Personality

What Everyone Should Understand About the Big Five Personality Traits

You need to understand the traits, but also their underlying facets..

Posted May 27, 2021 | Reviewed by Jessica Schrader

• What Is Personality?
• Take our Agreeableness Test
• Find a therapist near me
• Each personality trait has underlying facets.
• These facets are valuable to understand to have a more nuanced view of people's motivation.
• A great example comes from recent work on personality change in the college years.

Personality characteristics reflect fairly stable differences between people in their motivations. While situations exert a strong influence on people’s behavior, these default motivations affect both what people do in the absence of a powerful situation, and they also influence the kinds of situations people gravitate toward when they have an option about what to do.

The most prominent scientifically validated set of global personality traits is the Big Five , which consists of the dimensions of Openness, Conscientiousness , Extraversion , Agreeableness , and Neuroticism . These broad traits reflect the biggest differences between people in their motivations.

From the early days of personality research, though, the field has recognized that there are several aspects of motivation underlying each of the dimensions. For example, conscientiousness labels the overall tendency for people to complete the tasks they start. But it reflects this dependability, but also a tendency to take on new goals as well as a desire to have an orderly environment. While these subcomponents hang together, they are not identical. For example, there are people who strive for achievement without desiring an orderly environment (and vice versa).

The broader personality characteristics named by the Big Five are normally called traits . These narrower elements making up the traits can be called facets .

Researchers are still working through which facets ought to be included in each of the Big Five characteristics, but there is reasonable overlap between the various systems that have been developed. To give you a feel for these facets, I’ll describe the characteristics that are part of the NEO-FFI scale developed by Paul Costa and Robert McCrae.

Openness to Experience : The openness to experience trait reflects people’s orientation toward new ideas and experiences. The facets of this trait are interest in aesthetic experiences (like art and poetry), interest in intellectual pursuits (curiosity), and interest in new ideas and ways of doing things.

Conscientiousness : The conscientiousness trait reflects the degree to which people complete the things they start. The core facts involve an interest in being orderly, a tendency to strive toward goals, and the desire to complete tasks (which makes people dependable).

Extraversion : The trait of extraversion reflects people’s engagement with social life . The core facets involve a person’s degree of sociability, their overall level of positive feeling, and their desire for an energetic and fast-paced existence.

Agreeableness . The trait of agreeableness reflects people’s desire to get along with others. The core facets involve both an avoidance of conflict, and a desire to do things to help others.

Neuroticism . Neuroticism reflects people’s reactions to negative events in the environment . The key facets involve anxiety (the strength of the reaction to threat), depression (the strength of the reaction to loss), and self-judgment (the strength of people’s negative feelings about themselves).

As an example of the way these traits and facets have been used in research, a paper by Theo Klimstra, Erik Noftle, Koen Luyckx Luc Goossens, and Richard Robins in the August 2018 issue of the Journal of Personality and Social Psychology looked at change in traits and facets over the college years. This study looked at samples of college students who rated their personality characteristics and also responded to measures of social and academic measures of adjustment to college life both early and late in their college careers.

Consistent with many studies of personality change , the personality measures for this sample were fairly stable overall. Although personality does change over the lifespan, it tends to change slowly. At the trait level, there was a consistent tendency for people’s conscientiousness to increase over the college years and for their neuroticism to decrease.

At the facet level, the pattern was a little different. Within conscientiousness, the facets of being dependable and being orderly were the ones that tended to increase. In addition, while there wasn’t a consistent increase in agreeableness overall, the facets of avoiding conflict and helping others did tend to increase. There was also a tendency for the facet of Openness to Experience, the facet reflecting interest in new ways of doing things tended to increase. Finally, the facet of neuroticism related to depression tended to decrease across the college years.

An interesting aspect of looking at change over time is the possibility of exploring how changes in one variable at one time affect changes in another variable later. This kind of analysis permits an exploration of how people’s adjustment to college life affects their later personality characteristics. For example, high levels of academic adjustment early in college predict later increases in conscientiousness (and all of its facets), increases in agreeableness and the facet of avoiding confrontation, and decreases in neuroticism and decreases in the facets of self-judgment and depression.

The main thing to take away from this discussion is that personality characteristics often label quite broad traits, but the particular level of a trait that a person exhibits might reflect different combinations of these underlying facets. As a result, it is worthwhile to understand both the traits and the facets.

Facebook image: Mariia Korneeva/Shutterstock

Klimstra, T.A., Noftle, E.E., Luycks, K., Goosens, L., & Robins, R.W. (2018). Personality development and adjustment in college: A multi-faceted and cross-national view. Journal of Personality and Social Psychology, 115( 2), 338-361.

Art Markman, Ph.D. , is a cognitive scientist at the University of Texas whose research spans a range of topics in the way people think.

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Home » Face Validity – Methods, Types, Examples

Face Validity – Methods, Types, Examples

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Face Validity

Face validity refers to the extent to which a measurement or assessment appears, on the surface, to measure what it is intended to measure. It is a subjective assessment of whether a test or measurement appears to be valid based on its “face” or outward appearance.

In other words, face validity is a preliminary evaluation of whether a test or measurement looks like it is measuring the construct or concept it claims to be measuring.

Face Validity Methods

Face validity is typically assessed through various methods, including the following:

Expert Judgment

Experts in the field review the measurement instrument, such as a questionnaire or test, and evaluate whether the items appear to measure the intended construct. These experts can provide valuable insights and opinions based on their knowledge and experience.

Pilot Testing

The measurement instrument is administered to a small sample of individuals who represent the target population. These individuals are then asked for their feedback regarding the clarity, relevance, and appropriateness of the items. Their responses can help identify any confusing or irrelevant items that may need modification.

Self-Report

The individuals who will be taking the measurement are asked directly about their perception of the instrument’s face validity. They are questioned whether they believe the items are relevant to the construct being measured and whether the instrument appears to capture what it claims to measure.

Comparison to Existing Measures

If there are already established measures for the construct, the new measurement instrument can be compared to them. Experts or individuals familiar with the existing measures can evaluate whether the new instrument includes similar items or covers the same range of content, thus demonstrating face validity.

Review of Literature

Reviewing relevant literature and research studies can provide insights into the items or tasks commonly used to measure the construct of interest. By aligning the measurement instrument with established practices in the field, face validity can be enhanced.

Types of Face Validity

In the context of face validity, there are two primary types that can be distinguished:

Logical Face Validity

Logical face validity is based on the logical judgment and reasoning of experts or individuals familiar with the construct being measured. They assess whether the items or tasks included in the measurement instrument appear to be logically related to the construct. For example, if a measurement claims to assess anxiety, logically valid items might include questions about worry, restlessness, or physical symptoms associated with anxiety.

Pragmatic Face Validity

Pragmatic face validity focuses on the practical and functional aspects of the measurement instrument. It involves considering whether the items or tasks make sense to the individuals who will be taking the measurement and whether they perceive them as relevant and meaningful. This type of face validity considers the practical application and usability of the instrument rather than relying solely on expert judgment.

Threats to Face Validity

There are several threats or challenges to face validity that can affect the accuracy and credibility of a measurement instrument. These threats should be considered when evaluating the face validity of a measurement:

Biased Item Wording:

The wording of items or questions in the measurement instrument may introduce bias or lead to misinterpretation. If the items are not phrased clearly or if they contain ambiguous language, respondents may have difficulty understanding or providing accurate responses. Biased wording can also influence how respondents perceive the construct being measured, potentially undermining face validity.

Social Desirability Bias:

Respondents may feel pressure to provide socially desirable responses rather than answering truthfully. This bias can be particularly influential when measuring sensitive or stigmatized constructs. In such cases, individuals may alter their responses to align with societal norms or avoid disclosing information that could be perceived negatively. This bias can compromise the face validity of the instrument.

Lack of Representativeness:

The sample used to assess face validity may not adequately represent the target population or the construct being measured. If the individuals providing feedback or participating in pilot testing do not reflect the characteristics or experiences of the intended population, the face validity assessment may be compromised. It is important to ensure diverse representation to enhance the generalizability of face validity findings.

Response Bias:

Respondents may exhibit response biases that can affect the perceived face validity of the instrument. For example, acquiescence bias, where individuals tend to agree with statements regardless of their content, or extreme response bias, where respondents consistently choose extreme response options, can distort the interpretation of face validity results.

Limited Scope or Coverage:

If the measurement instrument fails to cover the full range of the construct being measured, it may lack face validity. Incomplete or inadequate representation of the construct can lead to an inaccurate perception of the instrument’s ability to measure what it claims to measure. It is essential to ensure comprehensive coverage of the construct to enhance face validity.

Cultural and Contextual Differences:

Face validity can be influenced by cultural or contextual factors. Items or tasks that are considered relevant and meaningful in one cultural context may not be perceived the same way in another. It is crucial to consider the cultural and contextual appropriateness of the measurement instrument to ensure face validity across different populations or settings.

Face Validity Examples

Here are a few examples to illustrate face validity in different contexts:

Employee satisfaction survey: A company wants to assess employee satisfaction levels, so they design a survey with items such as “Do you feel valued by your supervisor?” and “Are you satisfied with your work-life balance?” Employees reviewing the survey might perceive it as having face validity because the items directly address aspects related to their satisfaction at work.

Depression screening questionnaire: A mental health professional develops a questionnaire to screen for symptoms of depression. The items in the questionnaire include statements like “Do you often feel sad or hopeless?” and “Have you lost interest in activities you used to enjoy?” Individuals familiar with depression and mental health might perceive the questionnaire as having face validity, as the items seem to capture common symptoms associated with depression.

Physical fitness assessment: A fitness trainer creates a physical fitness assessment that includes tasks such as running a certain distance within a specified time, performing a certain number of push-ups, and measuring body composition. Participants undergoing the assessment might perceive it as having face validity because the tasks directly align with the concept of physical fitness.

Job interview questions: During a job interview for a customer service role, candidates are asked questions like “How do you handle difficult customers?” and “Describe a situation where you went above and beyond to assist a customer.” Candidates familiar with the role might perceive these questions as having face validity because they directly relate to the skills and behaviors required for effective customer service.

Personality assessment: A psychologist develops a personality questionnaire that includes items such as “Do you prefer spending time alone or with a large group of people?” and “Are you more organized or spontaneous in your daily life?” Individuals reviewing the questionnaire might perceive it as having face validity because the items appear to tap into different aspects of personality traits.

Applications of Face Validity

Face validity has several applications across various domains. Here are some common applications:

Questionnaire and survey development:

Face validity is often used in the initial stages of questionnaire and survey development. Researchers and practitioners assess the face validity of the items to ensure that they are relevant, clear, and understandable to the target population. This helps in designing measurement instruments that appear to measure the intended constructs and are acceptable to respondents.

Program evaluation:

In program evaluation, face validity can be used to assess the relevance and appropriateness of evaluation measures. It helps ensure that the evaluation instruments are aligned with the goals and objectives of the program being evaluated. Evaluators review the instruments to determine if they capture the relevant outcomes and indicators of the program’s success.

Psychological and educational assessments:

Face validity is considered in the development of psychological and educational assessments. It helps ensure that the items or tasks included in the assessments are perceived as relevant and meaningful by the individuals being assessed. This is important to maintain the engagement and motivation of the test-takers and to enhance the overall validity of the assessments.

Selection and recruitment:

Face validity is relevant in the context of selection and recruitment processes. When designing interview questions or assessment tasks, face validity is considered to ensure that the measures align with the competencies, skills, or attributes required for the job. This helps in evaluating candidates in a way that appears to be relevant and job-related.

Quality assurance in research:

Face validity plays a role in ensuring the quality of research studies. Researchers review their measurement instruments to ensure that they are appropriate and relevant to the research objectives and questions. This helps in enhancing the credibility and validity of the research findings.

Curriculum and instructional design:

In the field of education, face validity is considered when designing curricula and instructional materials. Educators evaluate the content, activities, and assessments to ensure that they align with the learning objectives and appear relevant to the students’ needs. This helps in creating educational materials that are perceived as meaningful and effective by learners.

Advantages of Face Validity

Face validity offers several advantages in the context of measurement and assessment:

• Quick and intuitive assessment: Face validity provides a quick and intuitive evaluation of a measurement instrument or assessment. It allows researchers or practitioners to make an initial judgment about whether the items or tasks appear to measure the intended construct. This can be particularly useful in the early stages of instrument development or selection.
• Stakeholder engagement: Face validity involves the perspectives of experts and individuals familiar with the construct being measured. By involving these stakeholders in the evaluation process, face validity can enhance their engagement and acceptance of the measurement instrument. This can lead to increased cooperation and cooperation from participants or respondents.
• Face validity as a starting point: Face validity serves as an initial indicator of the plausibility and relevance of a measurement instrument. It can help researchers or practitioners identify potential issues or areas for improvement before conducting more rigorous validity testing. It acts as a starting point to guide further refinement and validation efforts.
• Enhancing participant motivation and cooperation: When respondents or participants perceive that the measurement instrument is relevant and meaningful, they are more likely to engage in the assessment process actively. Face validity helps create a positive impression and can enhance motivation, cooperation, and the quality of responses or performance.
• Practical utility and acceptability: Measurement instruments with face validity are more likely to be accepted and embraced by users, whether it’s participants, practitioners, or decision-makers. When the instrument appears to measure what it claims to measure, it is more likely to be considered useful, applicable, and valuable in practical settings.
• Cost and time efficiency: Face validity assessment can be a relatively quick and straightforward process compared to other forms of validity assessment. It allows researchers or practitioners to gain initial insights into the validity of an instrument without investing significant resources in more extensive validation procedures. This can be particularly advantageous in situations where time and resources are limited.

Limitations of Face Validity

While face validity offers some benefits, it also has limitations that should be considered:

• Subjectivity: Face validity relies on subjective judgments from experts or individuals familiar with the construct being measured. Different individuals may have different perspectives on what appears valid or relevant. This subjectivity can introduce bias and inconsistencies in the assessment of face validity.
• Lack of empirical evidence: Face validity does not provide empirical evidence of the instrument’s accuracy or its ability to measure the intended construct. It is based on superficial appearances rather than empirical testing or statistical analysis. Therefore, face validity alone cannot confirm whether the instrument is truly valid in measuring the construct.
• Limited in detecting hidden or subtle aspects: Face validity may not capture underlying dimensions or subtle aspects of the construct that are not immediately apparent. While items or tasks may appear relevant on the surface, they may not adequately capture all facets of the construct. This limitation can affect the comprehensiveness and accuracy of the measurement.
• Potential for response bias: Participants or respondents may provide biased responses when evaluating face validity. They may answer in a socially desirable manner or may not fully understand the purpose of the measurement instrument. This response bias can compromise the accuracy of the face validity assessment.
• Lack of generalizability: Face validity judgments may be influenced by cultural, contextual, or individual factors. What appears valid in one context or population may not hold true in another. Therefore, face validity judgments may lack generalizability and may not adequately represent diverse populations or settings.
• Insufficient evidence of measurement properties: Face validity alone does not provide evidence of other important measurement properties, such as reliability, sensitivity, or responsiveness. These properties are crucial for establishing the overall quality and utility of a measurement instrument.

Also see validity

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A Probabilistic Approach to Personalize Type-Based Facet Ranking for POI Suggestion

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• Esraa Ali   ORCID: orcid.org/0000-0003-1600-3161 12 ,
• Annalina Caputo   ORCID: orcid.org/0000-0002-7144-8545 13 ,
• Séamus Lawless   ORCID: orcid.org/0000-0001-6302-258X 12 &
• Owen Conlan   ORCID: orcid.org/0000-0002-9054-9747 12  

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Faceted Search Systems (FSS) have become one of the main search interfaces used in vertical search systems, offering users meaningful facets to refine their search query and narrow down the results quickly to find the intended search target. This work focuses on the problem of ranking type-based facets. In a structured information space, type-based facets (t-facets) indicate the category to which each object belongs. When they belong to a large multi-level taxonomy, it is desirable to rank them separately before ranking other facet groups. This helps the searcher in filtering the results according to their type first. This also makes it easier to rank the rest of the facets once the type of the intended search target is selected. Existing research employs the same ranking methods for different facet groups. In this research, we propose a two-step approach to personalize t-facet ranking. The first step assigns a relevance score to each individual leaf-node t-facet. The score is generated using probabilistic models and it reflects t-facet relevance to the query and the user profile. In the second step, this score is used to re-order and select the sub-tree to present to the user. We investigate the usefulness of the proposed method to a Point Of Interest (POI) suggestion task. Our evaluation aims at capturing the user effort required to fulfil her search needs by using the ranked facets. The proposed approach achieved better results than other existing personalized baselines.

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Acknowledgements

This research was conducted with the financial support of Science Foundation Ireland (SFI) under Grant Agreement No. 13/RC/2106 at the ADAPT SFI Research Centre at Trinity College Dublin. The ADAPT Centre for Digital Media Technology is funded by SFI through the SFI Research Centres Programme and is co-funded under the European Regional Development Fund (ERDF) Grant No. 13/RC/2106_P2.

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Ali, E., Caputo, A., Lawless, S., Conlan, O. (2021). A Probabilistic Approach to Personalize Type-Based Facet Ranking for POI Suggestion. In: Brambilla, M., Chbeir, R., Frasincar, F., Manolescu, I. (eds) Web Engineering. ICWE 2021. Lecture Notes in Computer Science(), vol 12706. Springer, Cham. https://doi.org/10.1007/978-3-030-74296-6_14

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