Portal de Periódicos da CAPES

“F*** You, I Won’t Do What You Told Me!” – Response Biases as Threats to Psychological Assessment

2015; Hogrefe Verlag; Volume: 31; Issue: 3 Linguagem: Inglês

10.1027/1015-5759/a000292

2151-2426

Matthias Ziegler,

Psychological Testing and Assessment

Free Access"F*** You, I Won't Do What You Told Me!" – Response Biases as Threats to Psychological AssessmentMatthias ZieglerMatthias Ziegler Humboldt-Universität zu Berlin, Germany Published Online:July 30, 2015https://doi.org/10.1027/1015-5759/a000292PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit SectionsMoreAn overwhelming number of papers in the European Journal of Psychological Assessment focus on what Cattell called Q-data (Cattell, 1958). More specifically, Cattell described three methods to assess personality: L-, T-, and Q-data. Regarding Q-data he wrote: "Observations of personality which come to the psychologist in terms of introspective, verbal, self-record and self-evaluation…" (p. 286). Ever since questionnaire data are used, there is a seemingly never-ending discussion regarding their shortcomings. McClelland (1987) coined the famous quote: "A scientist cannot believe what people say about their motives." (p. 11). Of course, McClelland was only referring to questionnaires capturing motives and not personality per se. Moreover, there is ample empirical evidence that Q-data are valid predictors of human behavior in such diverse areas as scholastic and academic performance (Poropat, 2009, 2014), income and employment (Lindqvist & Vestman, 2011), job success in general (Mount & Barrick, 1995), and health (Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007). Thus, the current editorial is not a call to end the use of questionnaires.Nevertheless, despite all the encouraging empirical evidence, there are still many issues related to questionnaire use that potentially endanger the psychometric quality of the test scores derived. In a recent editorial (Ziegler, Kemper, & Lenzner, 2015), we focused on issues relevant during the process of test construction. Within this editorial, the focus will be on the test taker, more specifically, on response biases of test takers. This is the reason for the admittedly very striking title of this editorial, which uses a line from a song protesting racism and police brutality by the band Rage Against the Machine. Aims of the current editorial are far less political in nature. Still, the issues addressed are worth a stronger research focus.Defining Response BiasThe term response bias is usually used as an umbrella term underneath which a large number of different phenomena are placed. A useful framework to better structure all the different terms was provided by Jackson and Messick (1958) who differentiated between response styles (consistent across time and questionnaires) and response sets (specific to a specific assessment situation). Following this differentiation, this editorial tries to steer attention to specific response styles and response sets. The response styles discussed are acquiescence and midpoint- and extreme point responding. Social desirable responding (SDR) and faking will be two response sets discussed here. There are of course other potential biases such as the frame of reference (Smith, Hanges, & Dickson, 2001) or cross-cultural biases connected with answering personality questionnaires (Mõttus et al., 2012; Van de Vijver & Poortinga, 1997). Thus, the selection of biases here should not be understood as rendering other biases as uninteresting.Aim of these short discussions of some response sets and styles is not a comprehensive review of the existing literature. Rather than that the general issue is introduced and potential research gaps will be pointed out. By doing this I hope to attract more submissions covering these issues which I deem to be very important for psychological assessment. Therefore, the editorial will conclude with a list of some key research questions. However, before delving into the topic of response sets and response biases it is important to talk about the actual answering process.Models of Answering a Personality Questionnaire ItemThere are a few models which theoretically describe how test takers determine the answer to a specific statement in a personality questionnaire. Krosnick (1999) summarized prior models and suggested that the answering process can be divided into the four stages comprehension, retrieval, judgment, and response selection. Within the first stage test takers form a mental representation of the item content. This is followed by an information retrieval phase. The information retrieved is meant to be compared with the item content and contains prior experiences, self-views, and other similar things. During the judgment phase the retrieved information is compared to the item content and a general decision is made. This decision is than mapped onto the rating scale (response selection). Tourangeau and Rasinski (1988) describe how response distortion can potentially affect each of the stages. An interesting notion Krosnick introduced is the one of a satisficing versus optimizing test taker. Whereas the latter tries to come up with the best possible answer by engaging in each of the four stages, the former is less intent on an optimal answer. This satisficing behavior can manifest in a less thorough engagement during the four phases but also in leaving out some stages.Ziegler (2011) investigated the answer process under faking instructions and found some important deviations from the general answer process (also see Robie, Brown, & Beaty, 2007). For example, in his model Ziegler suggested that there is an additional stage between comprehension and retrieval influencing the following stages. In this additional stage test takers decide whether the item in question reveals information potentially important for the employer conducting the assessment. Depending on this overall evaluation, faking occurs or does not occur.Thus, research into response sets and styles should be aware of these different stages and try to locate the specific processes within such a general framework.Response SetsIn line with Jackson and Messick (1958) response sets are regarded as temporary response distortions being due to specific situational or intrapersonal circumstances.Social Desirable RespondingThe most popular and theoretically as well as empirically sound model of social desirable responding (SDR) was suggested by Paulhus (2011) in a seminal book chapter. According to this model, SDR represents a deviation from reality. Moreover, Paulhus suggested that this misrepresentation can either be directed at oneself or at others. Moreover, Paulhus also proposed that the misrepresentation has either an agentic (egoistic bias) or a communal (moralistic bias) theme. Thus, four different branches of SDR are differentiated: self-deceptive enhancement and agency management representing egoistic biases and self-deceptive denial and communion management representing moralistic biases.It has been shown that SDR potentially affects construct validity of questionnaire-based scores (e.g., Bäckström, 2007; Schmit & Ryan, 1993; Ziegler & Bühner, 2009; Ziegler, MacCann, & Roberts, 2011). This, however, is not the only threat to valid psychological assessment. If questionnaire scores contain variance due to SDR, comparing the scores between different people or even within the same person across time is highly problematic because it is unclear where the differences originate: the construct measured or SDR. In order to facilitate such interpretations a number of so-called lie scales have been published. Unfortunately, there is evidence that such scales do not capture SDR in some settings (Griffith & Peterson, 2008) or are often highly saturated with true interindividual differences (Paulhus, 2011). Thus, the use of such scales to extract or control SDR variance is at least severely limited.Despite the relatively large knowledge base with regard to SDR, research dealing with psychological assessment very rarely addresses the issue. This might be due to a numbing sense of helplessness: "What can we do, it's just there!" However, there are some promising research tracks potentially leading to better solutions than burying one's head in the sand. Bäckström, Björklund, and Larsson (2009) could show that neutralizing the item content takes out SDR variance in Big Five questionnaires. This approach seems viable in low stakes settings. For high stakes settings the method suggested by Brown and Maydeu-Olivares (2013) which is based on forced choice items but yields normative data is also promising. Likewise, the anchoring technique seems to have great potential (Bolt, Lu, & Kim, 2014). Finally, approaches to capturing SDR variance through other means such as overclaiming tests (Paulhus, 2011; Paulhus, Harms, Bruce, & Lysy, 2003; Ziegler, Kemper, & Rammstedt, 2013) are supported by recent empirical results (Bing, Kluemper, Kristl Davison, Taylor, & Novicevic, 2011; Kemper & Menold, 2014). Nevertheless, future research should try to take up the research on this issue. Based on the theoretical framework by Paulhus the new approaches mentioned as well as other possible remedies to capture or prevent SDR should be investigated and systematically be integrated into psychological assessment processes.FakingWithin Paulhus' SDR model (2002) other-directed misrepresentations can be regarded as faking. Different definitions of faking all stress the intentional and goal-directed nature of this response set originating in an interaction between the person and situational demands (MacCann, Ziegler, & Roberts, 2011; Ziegler et al., 2011). Whereas prior research often claimed that faking might not affect test-criterion validities, more recent research is less optimistic (Birkeland, Manson, Kisamore, Brannick, & Smith, 2006; Ziegler, Danay, Schölmerich, & Bühner, 2010). One of the main reasons for these differing views lies within the interaction between person and situation leading to faking (Ellingson, 2011; Ellingson & McFarland, 2011). Depending on how the person views the situation and depending on some personality characteristics, a person might decide not to fake at all. Moreover, the amount of faking within one situation could differ substantially between different test takers due to this mechanisms. As interesting as this suggested interaction between person(ality) and situation(al perception) (also see Ziegler & Horstmann, 2015) is, investigating it requires to model faking variance within faked test items or scores. Several attempts have been made to achieve this goal. Mueller-Hanson, Heggestad, and Thornton (2006) used difference scores between honest and faked measurement points. In a similar vein, Paulhus and John (1998) used residuals partialling out other-ratings from self-ratings, claiming that the residual represent deviation from reality. Zickar and colleagues (Zickar, Gibby, & Robie, 2004; Zickar & Robie, 1999; Zickar & Sliter, 2011) used mixed Rasch models to uncover so-called slight and extreme fakers. Those terms stand for latent classes representing specific faking styles (also see Eid & Rauber, 2000; Eid & Zickar, 2007). In a recent paper, the ideas behind both approaches were combined (Ziegler, Maaß, Griffith, & Gammon, 2015), that is, the difference between an honest score and a faked one as well as the idea of qualitatively different faking patterns, by combining a latent change score model (McArdle, 2001) with a factor mixture model (Lubke & Muthén, 2007; Lubke & Spies, 2008). First results using this model support the idea of faking as an interindividual difference variable. However, before psychological assessment can advance with regard to dealing with faking such models need to be used to empirically test the nature of the interaction between person and situation leading to faking. Based on the findings from such research it might be better to correct scores for faking or even to prevent it.Response StylesFollowing Jackson and Messick (1958) response styles are seen as stable response distortions occurring independent of the scale used, the situation or time. However, importantly there are interindividual differences with regard to the extent these response styles affect a test takers answering behavior.AcquiescenceThe idea behind the phenomenon of acquiescence is that test takers tend to agree with items simply because they have a tendency to agree in general. Interestingly, the most commonly used approach to prevent acquiescence is to balance positively and negatively keyed items. It is assumed that test takers who agree with a positively keyed item but do not disagree with a negatively keyed item with the same content, answer in an acquiescent way. What makes this assumption so noteworthy is that there is empirical evidence suggesting that answering a negatively keyed item requires verbal ability (Marsh, 1996). Moreover, using confirmatory factor analyses many authors showed that negatively keyed items form a method factor unsettling factorial validity of the scales comprising both, positively and negatively keyed items (Roth, Decker, Herzberg, & Brahler, 2008; Sliter & Zickar, 2014). Twenty years ago, Schmitt and Stuits (1985) already suggested that such method factors might be due to careless responding by some test takers. In other words, the negatively keyed items might not capture acquiescent responding but differences in verbal ability or the amount of care invested in answering the items. The latter could be seen as an example of satisficing affecting the item comprehension stage (see above). Sliter and Zickar (2014) added to the already extensive critique and showed the limited utility of negatively keyed items using IRT models. More importantly though, they could show positively and negatively keyed items are not psychometrically interchangeable. Thus, it might be about time to realize that simply using negatively keyed items introduces more problems than it helps to solve. Before proposing new ways of controlling for or preventing acquiescence, further research into the phenomenon itself and into its effects is necessary (Rammstedt & Farmer, 2013). This, however requires a new thinking about how to measure and model acquiescence (e.g., Wetzel, Lüdtke, Zettler, & Böhnke, 2015).Midpoint- and Extreme Point RespondingUsing a Likert-type rating scale seems to have many advantages and is therefore very popular. However, it also comes with a few problems. I am not going to talk about the many issues which make using a middle category problematic (Hernández, Drasgow, & González-Romá, 2004; Kulas & Stachowski, 2009, 2013). The focus here will be on the phenomenon of midpoint- and extreme point responding (MPR and EPR). Rost and von Davier used mixed Rasch models to identify test takers using MPR or EPR (Rost, Carstensen, & von Davier, 1997, 1999). Their findings have been replicated by many other researchers (Austin, Deary, & Egan, 2006; Meiser & Machunsky, 2008; Ziegler & Kemper, 2013). The important implication for psychological assessment is that as long as such person heterogeneity exists, test scores cannot be compared interindividually without knowing which group, that is, MPR or EPR, a test taker belongs to. The reason for this is that two test takers with the same standing on the latent trait but different response styles will not have the same test score. This implies a false difference. It has been shown that such response styles are very time persistent (Wetzel et al., 2015), stable across different traits (Wetzel, Carstensen, & Böhnke, 2013), and affect differential item functioning (Wetzel, Böhnke, Carstensen, Ziegler, & Ostendorf, 2013). Thus, they are a viable threat to psychological assessment using questionnaires with Likert-type scales. Unfortunately, so far little is known with regard to the roots of these response styles (e.g., Meisenberg & Williams, 2008; Naemi, Beal, & Payne, 2009). This should consequently be one of the future research foci. Based on the knowledge derived from such research, it should then be possible to start trying to develop new and more resistant ways to measure personality.Editorial RecommendationsWithin this editorial a few common response biases have been discussed. The focus as on how they potentially affect psychological assessment and what future research questions might be drawn from these issues. One of the conclusions which can be drawn from this editorial is that all research dealing with such phenomena should take the answer process into account. It is important to understand that answers to questionnaire items are the product of a thought process consisting of several stages. Response biases can affect each of those stages. Thus, explaining response biases or even preventing them requires anchoring them into the answer process.In order to discuss different response biases, response sets and styles were differentiated. Social desirable responding and faking were discussed as representatives for response sets. After reviewing some of the literature within this field, a few promising new approaches to measuring, modeling, or preventing SDR were shortly introduced. It was then requested to investigate and systematically integrate these methods into the psychological assessment practice. With regard to faking a clear call was made to use new methods of modeling faking in order to empirically test the nature of the interaction between person and situation leading to faking (also see Helmes, Holden, & Ziegler, 2014). This is seen as a prerequisite before faking can be corrected form test scores or even prevented.When discussing response styles, it was concluded that using negatively and positively keyed items to prevent acquiescence should no longer be seen as a feasible method. Instead new approaches were called for which requires further research into the phenomenon itself and into its effects. Finally, midpoint- and extreme point responding were discussed. Again, the actual interindividual differences underlying these phenomena are widely unknown and should be aim of future research.All of these ideas can be summed up in the following research agenda which is emphatically welcomed in the European Journal of Psychological Assessment:1.Anchor bias in answer process.2.Differentiate between response set and response style.3.Integrate new methods to control or prevent SDR into the psychological assessment practice.4.Investigate the interaction between person and situation leading to faking in order to explore the nature of faking.5.Find new ways to measure and model acquiescence and use them to investigate its nature.6.Investigate the roots of midpoint- and extreme point responding and similar phenomena.Once this agenda has been successfully worked off, test scores derived from questionnaires might be regarded as more trustworthy because we can be surer that the test takers answer the items as they were told and as was intended. Or to conclude with another line from Rage Against the Machine's famous song: "And now you [test taker] do what they [instructions and test administrator] told ya!".References Austin, E. J., Deary, I. J. & Egan, V. (2006). Individual differences in response scale use: Mixed Rasch modelling of responses to NEO-FFI items. Personality and Individual Differences, 40, 1235–1245. First citation in articleCrossref, Google Scholar Bäckström, M. (2007). Higher-order factors in a Five-Factor Personality Inventory and its relation to social desirability. European Journal of Psychological Assessment, 23, 63–70. First citation in articleLink, Google Scholar Bäckström, M., Björklund, F. & Larsson, M. R. (2009). Five-factor inventories have a major general factor related to social desirability which can be reduced by framing items neutrally. Journal of Research in Personality, 43, 335–344. First citation in articleCrossref, Google Scholar Bing, M. N., Kluemper, D., Kristl Davison, H., Taylor, S. & Novicevic, M. (2011). Overclaiming as a measure of faking. Organizational Behavior and Human Decision Processes, 116, 148–162. First citation in articleCrossref, Google Scholar Birkeland, S. A., Manson, T. M., Kisamore, J. L., Brannick, M. T. & Smith, M. A. (2006). A meta-analytic investigation of job applicant faking on personality measures. International Journal of Selection and Assessment, 14, 317–335. First citation in articleCrossref, Google Scholar Bolt, D. M., Lu, Y. & Kim, J.-S. (2014). Measurement and control of response styles using anchoring vignettes: A model-based approach. Psychological Methods, 19, 528–541. doi: 10.1037/met0000016 First citation in articleCrossref, Google Scholar Brown, A. & Maydeu-Olivares, A. (2013). How IRT can solve problems of ipsative data in forced-choice questionnaires. Psychological Methods, 18, 36–52. doi: 10.1037/a0030641 First citation in articleCrossref, Google Scholar Cattell, R. B. (1958). What is" objective" in" objective personality tests?". Journal of Counseling Psychology, 5, 285–289. First citation in articleCrossref, Google Scholar Eid, M. & Rauber, M. (2000). Detecting measurement invariance in organizational surveys. European Journal of Psychological Assessment, 16, 20–30. First citation in articleLink, Google Scholar Eid, M. & Zickar, M. (2007). Detecting response styles and faking in personality and organizational assessments by mixed Rasch models. In M. von DavierC. H. CarstensenEds.. Multivariate and mixture distribution Rasch models, (pp. 255–270). New York, NY: Springer. First citation in articleGoogle Scholar Ellingson, J. E. (2011). People fake only when they need to fake. In M. ZieglerC. MacCannR. RobertsEds.. New perspectives on faking in personality assessment, (pp. 19–33). New York, NY: Oxford University Press. First citation in articleGoogle Scholar Ellingson, J. E. & McFarland, L. A. (2011). Understanding faking behavior through the lens of motivation: An application of VIE theory. Human Performance, 24, 322–337. doi: 10.1080/08959285.2011.597477 First citation in articleCrossref, Google Scholar Griffith, R. L. & Peterson, M. H. (2008). The failure of social desirability measures to capture applicant faking behavior. Industrial and Organizational Psychology, 1, 308–311. First citation in articleCrossref, Google Scholar Helmes, E., Holden, R. R. & Ziegler, M. (2014). Response bias, malingering, and impression management. In G. J. BoyleD. H. SaklofskeG. MatthewsEds.. Measures of personality and social psychological constructs (pp. 16–46). London, UK: Elsevier. First citation in articleGoogle Scholar Hernández, A., Drasgow, F. & González-Romá, V. (2004). Investigating the functioning of a middle category by means of a mixed-measurement model. The Journal of Applied Psychology, 89, 687. First citation in articleCrossref, Google Scholar Jackson, D. N. & Messick, S. (1958). Content and style in personality-assessment. Psychological Bulletin, 55, 243–252. First citation in articleCrossref, Google Scholar Kemper, C. J. & Menold, N. (2014). Nuisance or remedy? The utility of stylistic responding as an indicator of data fabrication in surveys. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 3, 92–99. doi: 10.1027/1614-2241/a000078 First citation in articleLink, Google Scholar Krosnick, J. A. (1999). Survey research. Annual Review of Psychology, 50, 537–567. First citation in articleCrossref, Google Scholar Kulas, J. T. & Stachowski, A. A. (2009). Middle category endorsement in odd-numbered Likert response scales: Associated item characteristics, cognitive demands, and preferred meanings. Journal of Research in Personality, 43, 489–493. First citation in articleCrossref, Google Scholar Kulas, J. T. & Stachowski, A. A. (2013). Respondent rationale for neither agreeing nor disagreeing: Person and item contributors to middle category endorsement intent on Likert personality indicators. Journal of Research in Personality, 47, 254–262. doi: 10.1016/j.jrp.2013.01.014 First citation in articleCrossref, Google Scholar Lindqvist, E. & Vestman, R. (2011). The labor market returns to cognitive and noncognitive ability: Evidence from the Swedish enlistment. American Economic Journal: Applied Economics, 3, 101–128. doi: 10.1257/app.3.1.101 First citation in articleCrossref, Google Scholar Lubke, G. H. & Muthén, B. O. (2007). Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Structural Equation Modeling, 14, 26–47. First citation in articleCrossref, Google Scholar Lubke, G. H. & Spies, J. R. (2008). Choosing a "correct" factor mixture model: Power, limitations, and graphical data exploration. In G. R. HancockK. M. SamuelsenEds.. Advances in latent variable mixture models (pp. 343–361). Charlotte, NC: Information Age. First citation in articleGoogle Scholar MacCann, C., Ziegler, M. & Roberts, R. D. (2011). Faking in personality assessment: Reflections and recommendations. In M. ZieglerC. MacCannR. RobertsEds.. New perspectives on faking in personality assessment (pp. 309–329). New York, NY: Oxford University Press. First citation in articleGoogle Scholar Marsh, H. W. (1996). Positive and negative global self-esteem: A substantively meaningful distinction or artifactors? Journal of Personality and Social Psychology, 70, 810–819. First citation in articleCrossref, Google Scholar McArdle, J. J. (2001). A latent difference score approach to longitudinal dynamic structural analysis. In R. CudeckS. du ToitD. SorbomEds.. Structural equation modeling: Present and future (pp. 342–380). Lincolnwood, IL: Scientific Software International. First citation in articleGoogle Scholar McClelland, D. C. (1987). Human Motivation. New York, NY: Cambridge University Press First citation in articleGoogle Scholar Meisenberg, G. & Williams, A. (2008). Are acquiescent and extreme response styles related to low intelligence and education? Personality and Individual Differences, 44, 1539–1550. First citation in articleCrossref, Google Scholar Meiser, T. & Machunsky, M. (2008). The personal structure of personal need for structure – a mixture-distribution Rasch analysis. European Journal of Psychological Assessment, 24, 27–34. doi: 10.1027/1015-5759.24.1.27 First citation in articleLink, Google Scholar Mõttus, R., Allik, J., Realo, A., Rossier, J., Zecca, G., Ah-Kion, J., … Barry, O. (2012). The effect of response style on self-reported conscientiousness across 20 countries. Personality and Social Psychology Bulletin, 38, 1423–1436. First citation in articleCrossref, Google Scholar Mount, M. K. & Barrick, M. R. (1995). The Big Five personality dimensions: Implications for research and practice in human resources management. Research in personnel and human resources management, 13, 153–200. First citation in articleGoogle Scholar Mueller-Hanson, R., Heggestad, E. D. & Thornton, G. C. (2006). Individual differences in impression management: An exploration of the psychological processes underlying faking. Psychology Science, 3, 288–312. First citation in articleGoogle Scholar Naemi, B. D., Beal, D. J. & Payne, S. C. (2009). Personality predictors of extreme response style. Journal of Personality, 77, 261–286. doi: 10.1111/j.1467-6494.2008.00545.x First citation in articleCrossref, Google Scholar Paulhus, D. L. (2011). Overclaiming on Personality Questionnaires. In M. ZieglerC. MacCannR. D. RobertsEds.. New perspectives on faking in personality assessments (pp. 151–164). New York, NY: Oxford University Press. First citation in articleGoogle Scholar Paulhus, D. L., Harms, P. D., Bruce, M. N. & Lysy, D. C. (2003). The over-claiming technique: Measuring self-enhancement independent of ability. Journal of Personality and Social Psychology, 84, 890–904. First citation in articleCrossref, Google Scholar Paulhus, D. L. & John, O. P. (1998). Egoistic and moralistic biases in self‐perception: The interplay of self‐deceptive styles with basic traits and motives. Journal of Personality, 66, 1025–1060. First citation in articleCrossref, Google Scholar Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135, 322–338. First citation in articleCrossref, Google Scholar Poropat, A. E. (2014). Other-rated personality and academic performance: Evidence and implications. Learning and Individual Differences, 34, 24–32. doi: 10.1016/j.lindif.2014.05.013 First citation in articleCrossref, Google Scholar Rammstedt, B. & Farmer, R. F. (2013). The impact of acquiescence on the evaluation of personality structure. Psychological Assessment, 25, 1137–1145. doi: 10.1037/a0033323 First citation in articleCrossref, Google Scholar Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A. & Goldberg, L. R. (2007). The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science, 2, 313–345. doi: 10.1111/j.1745-6916.2007.00047.x First citation in articleCrossref, Google Scholar Robie, C., Brown, D. J. & Beaty, J. C. (2007). Do people fake on personality inventories? A verbal protocol analysis. Journal of Business and Psychology, 21, 489–509. doi: 10.1007/s10869-007-9038-9 First citation in articleCrossref, Google Scholar Rost, J., Carstensen, C. H. & von Davier, M. (1997). Applying the mixed Rasch model to personality questionnaires. In J. RostR. E. LangeheineEds.. Applications of latent trait and latent class models in the social sciences (pp. 324–332). New York, NY: Waxmann. Retrieved from http://www.ipn.uni-kiel.de/aktuell/buecher/rostbuch/ltlc.htm First citation in articleGoogle Scholar Rost, J., Carstensen, C. H. & von Davier, M. (1999). Are the Big Five Rasch scalable? A reanalysis of the NEO-FFI norm data. Diagnostica, 45, 119–127. First citation in articleLink, Google Scholar Roth, M., Decker, O., Herzberg, P. Y. & Brahler, E. (2008). Dimensionality and norms of the Rosenberg self-esteem scale in a German general population sample. European Journal of Psychological Assessment, 24, 190–197. doi: 10.1027/1015-5759.24.3.190 First citation in articleLink, Google Scholar Schmit, M. J. & Ryan, A. M. (1993). The Big Five in personnel selection: Factor structure in applicant and nonapplicant populations. The Journal of Applied Psychology, 78, 966–974. First citation in articleCrossref, Google Scholar Schmitt, N. & Stuits, D. M. (1985). Factors defined by negatively keyed items: The result of careless respondents? Applied Psychological Measurement, 9, 367–373. First citation in articleCrossref, Google Scholar Sliter, K. A. & Zickar, M. J. (2014). An IRT examination of the psychometric functioning of negatively worded personality items. Educational and Psychological Measurement, 74, 214–226. doi: 10.1177/0013164413504584 First citation in articleCrossref, Google Scholar Smith, D. B., Hanges, P. J. & Dickson, M. W. (2001). Personnel selection and the five-factor model: Reexamining the effects of applicant's frame of reference. The Journal of Applied Psychology, 86, 304–315. First citation in articleCrossref, Google Scholar Tourangeau, R. & Rasinski, K. A. (1988). Cognitive-processes underlying context effects in attitude measurement. Psychological Bulletin, 103, 299–314. First citation in articleCrossref, Google Scholar Van de Vijver, F. J. & Poortinga, Y. H. (1997). Towards an integrated analysis of bias in cross-cultural assessment. European Journal of Psychological Assessment, 13, 29. First citation in articleLink, Google Scholar Wetzel, E., Böhnke, J. R., Carstensen, C. H., Ziegler, M. & Ostendorf, F. (2013). Do individual response styles matter? Journal of Individual Differences, 34, 69–81. doi: 10.1027/1614-0001/a000102 First citation in articleLink, Google Scholar Wetzel, E., Carstensen, C. H. & Böhnke, J. R. (2013). Consistency of extreme response style and non-extreme response style across traits. Journal of Research in Personality, 47, 178–189. doi: 10.1016/j.jrp.2012.10.010 First citation in articleCrossref, Google Scholar Wetzel, E., Lüdtke, O., Zettler, I. & Böhnke, J. R. (2015). The stability of extreme response style and acquiescence over 8 years. Assessment, doi: 1073191115583714 First citation in articleGoogle Scholar Zickar, M. J., Gibby, R. E. & Robie, C. (2004). Uncovering faking samples in applicant, incumbent, and experimental data sets: An application of mixed-model item response theory. Organizational Research Methods, 7, 168–190. First citation in articleCrossref, Google Scholar Zickar, M. J. & Robie, C. (1999). Modeling faking good on personality items: An item-level analysis. The Journal of Applied Psychology, 84, 551–563. First citation in articleCrossref, Google Scholar Zickar, M. J. & Sliter, K. A. (2011). Searching for unicorns: Item response theory-based solutions to the faking problem. In M. ZieglerC. MacCannR. RobertsEds.. New perspectives on faking in personality assessment (pp. 113–130). New York, NY: Oxford University Press. First citation in articleGoogle Scholar Ziegler, M. (2011). Applicant faking: A look into the black box. The Industrial and Organizational Psychologist, 49, 29–36. First citation in articleGoogle Scholar Ziegler, M. & Bühner, M. (2009). Modeling socially desirable responding and its effects. Educational and Psychological Measurement, 69, 548–565. First citation in articleCrossref, Google Scholar Ziegler, M., Danay, E., Schölmerich, F. & Bühner, M. (2010). Predicting academic success with the Big 5 rated from different points of view: Self-rated, other rated and faked. European Journal of Personality, 24, 341–355. doi: 10.1002/per.753 First citation in articleCrossref, Google Scholar Ziegler, M. & Horstmann, K. (2015). Discovering the second side of the coin: Integrating situational perception into psychological assessment. European Journal of Psychological Assessment, 31, 69–74. doi: 10.1027/1015-5759/a000258 First citation in articleLink, Google Scholar Ziegler, M. & Kemper, C. (2013). Extreme response style and faking: Two sides of the same coin? In P. WinkerR. PorstN. MenoldEds.. Interviewers' Deviations in Surveys: Impact, Reasons, Detection and Prevention (Schriften Zur Empirischen Wirtschaftsforschung) (pp. 221–237). Frankfurt a. M., Germany: Peter Lang Gmbh. First citation in articleGoogle Scholar Ziegler, M., Kemper, C. & Rammstedt, B. (2013). The Vocabulary and Overclaiming Test (VOC-T). Journal of Individual Differences, 34, 32–40. First citation in articleLink, Google Scholar Ziegler, M., Kemper, C. J. & Lenzner, T. (2015). The issue of fuzzy concepts in test construction and possible remedies. European Journal of Psychological Assessment, 31, 1–4. doi: 10.1027/1015-5759/a000255 First citation in articleLink, Google Scholar Ziegler, M., Maaß, U., Griffith, R. & Gammon, A. (2015). What is the nature of faking? Modeling distinct response patterns and quantitative differences in faking at the same time. Organizational Research Methods, doi: 1094428115574518 First citation in articleCrossref, Google Scholar Ziegler, M., MacCann, C. & Roberts, R. D. (2011). Faking: Knowns, unknowns, and points of contention. In M. ZieglerC. MacCannR. R. RobertsEds.. New perspectives on faking in personality assessment (pp. 3–16). New York, NY: Oxford University Press. First citation in articleGoogle ScholarMatthias Ziegler, Institut für Psychologie, Humboldt Universität zu Berlin, Rudower Chaussee 18, 12489 Berlin, Germany, Tel. +49 30 2093-9447, Fax +49 30 2093-9361, E-mail zieglema@hu-berlin.deFiguresReferencesRelatedDetailsCited byDisentangling stable and malleable components—A latent state-trait analysis of vocational interestsJournal of Research in Personality, Vol. 103Counting the Muses in German Speakers – Evaluation of the German-Language Translation of the Kaufman Domains of Creativity Scales (K-DOCS)Kay Brauer, Rebekka Sendatzki, James C. Kaufman, and René T. Proyer16 August 2022 | Psychological Test Adaptation and Development, Vol. 3, No. 1Symptom coaching and symptom validity tests: An analog study using the structured inventory of malingered symptomatology, Self-Report Symptom Inventory, and Inventory of Problems-2913 April 2022 | Applied Neuropsychology: Adult, Vol. 27Sources of measurement error in pediatric intelligence testing28 March 2022 | Methodological Innovations, Vol. 15, No. 1"All or nothing"14 June 2021 | Psico, Vol. 52, No. 1Modeling Wording Effects Does Not Help in Recovering Uncontaminated Person Scores: A Systematic Evaluation With Random Intercept Item Factor Analysis2 June 2021 | Frontiers in Psychology, Vol. 12Daily life and women' stressors through a structural topic modeling application of online messages10 August 2021 | Cogent Social Sciences, Vol. 7, No. 1Testing competing claims about overclaimingIntelligence, Vol. 81Acquiescent responding can distort the factor structure of the BIS/BAS scalesPersonality and Individual Differences, Vol. 152Faking on a situational judgment test in a medical school selection setting: Effect of different scoring methods?27 May 2019 | International Journal of Selection and Assessment, Vol. 27, No. 3Forced-Choice Versus Likert Responses on an Occupational Big Five QuestionnaireLuc Watrin, Mattis Geiger, Maik Spengler, and Oliver Wilhelm21 March 2019 | Journal of Individual Differences, Vol. 40, No. 3Controlling Acquiescence Bias with Multidimensional IRT Modeling18 May 2019The "g" in Faking: Doublethink the Validity of Personality Self-Report Measures for Applicant Selection13 November 2018 | Frontiers in Psychology, Vol. 9Alexithymia as a potential source of symptom over-reporting: An exploratory study in forensic patients and non-forensic participants19 January 2018 | Scandinavian Journal of Psychology, Vol. 59, No. 2The World Beyond Rating Scales Why We Should Think More Carefully About the Response Format in QuestionnairesEunike Wetzel and Samuel Greiff16 February 2018 | European Journal of Psychological Assessment, Vol. 34, No. 1Students' multiple state goals as a function of appraisals, trait goals, and their interactionsContemporary Educational Psychology, Vol. 51A Look Back and a Glimpse Forward A Personal Exchange Between the Current and the Incoming Editor-in-Chief of EJPAMatthias Ziegler and Samuel Greiff23 November 2016 | European Journal of Psychological Assessment, Vol. 32, No. 4Current Challenges, New Developments, and Future Directions in Scale ConstructionDaniel Danner, Jörg Blasius, Bianka Breyer, Stefanie Eifler, Natalja Menold, Delroy L. Paulhus, Beatrice Rammstedt, Richard D. Roberts, Manfred Schmitt, and Matthias Ziegler19 September 2016 | European Journal of Psychological Assessment, Vol. 32, No. 3Testing the Unidimensionality of Items Pitfalls and LoopholesMatthias Ziegler and Dirk Hagemann14 December 2015 | European Journal of Psychological Assessment, Vol. 31, No. 4 Volume 31Issue 3July 2015ISSN: 1015-5759eISSN: 2151-2426 InformationEuropean Journal of Psychological Assessment (2015), 31, pp. 153-158 https://doi.org/10.1027/1015-5759/a000292.© 2015Hogrefe PublishingPDF download