Portal de Periódicos da CAPES

States and Traits

2017; Hogrefe Verlag; Volume: 33; Issue: 4 Linguagem: Inglês

10.1027/1015-5759/a000413

2151-2426

Christian Geiser, Thomas Götz, Franzis Preckel, Philipp Alexander Freund,

Cognitive Abilities and Testing

Free AccessStates and TraitsTheories, Models, and AssessmentChristian Geiser, Thomas Götz, Franzis Preckel, and Philipp Alexander FreundChristian Geiser Department of Psychology, Utah State University, Logan, UT, USA , Thomas Götz Educational Science, University of Konstanz, Germany Thurgau University of Teacher Education, Franzis Preckel Department of Psychology, University of Trier, Germany , and Philipp Alexander Freund Department of Psychology, Leuphana University Lüneburg, Germany Published Online:September 22, 2017https://doi.org/10.1027/1015-5759/a000413PDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit SectionsMoreThe question of whether characteristics of persons (stable personality dispositions that are often referred to as traits), characteristics of situations, or Person × Situation interactions are more relevant to psychological behavior has been debated for decades (e.g., Donnellan, Lucas, & Fleeson, 2009; Epstein, 1983; Fleeson, 2001, 2004; Funder, 1991, 2008; Funder, Guillaume, Kumagai, Kawamoto, & Sato, 2012; Kenrick & Funder, 1988; Mischel, 1968). In psychological measurement, the distinction between trait (enduring or stable) components and state residual (variable or fluctuating) components has been of ever-increasing interest to researchers since the 1980s and 1990s when more sophisticated theoretical and statistical approaches became available (Steyer, Ferring, & Schmitt, 1992; Steyer, Majcen, Schwenkmezger, & Buchner, 1989). Furthermore, new methodological approaches have recently been developed to better conceptualize and understand psychological situations (e.g., Rauthmann, Sherman, & Funder, 2015).To illustrate the increasing interest in person-situation research, a search in the international database PsychINFO using the term "state-trait" (carried out in November 2016) returned 491 hits for the period between 1970 and 1979, 1,538 hits between 1980 and 1989, 1,989 hits between 1990 and 1999, 6,277 hits between 2000 and 2009, and 9,044 hits between 2010 and 2016 (19,339 results total). Moreover, the increase in publication rates related to "states and traits" was stronger relative to the overall increase in scientific publications in psychology as shown in Figure 1. Publication rates are presented as the number of "state-trait" papers per 100,000 publications in the PsychINFO database. Figure 1 illustrates that the scientific interest in the "state-trait" topic has strongly increased since 1970. In this special issue, our contributors present new methodological developments and substantive findings in the context of state and trait analysis.Figure 1 Number of papers on "state-trait" per 100,000 publications in PsycINFO databases.States and Traits in PsychologyPast studies have shown that most psychological variables contain both state and trait components to varying degrees (e.g., Deinzer et al., 1995). On the one extreme, measurements of hormone levels (e.g., Kirschbaum et al., 1990) and mood (Eid, Schneider, & Schwenkmezger, 1999) show rather low amounts of person-specific trait variance and can thus be considered more "state-like" constructs. On the other extreme, individual differences in intelligence and other ability scores have been found to be highly stable ("trait-like") across time, reflecting only negligible amounts of situation-specific variance (Danner, Hagemann, Schankin, Hager, & Funke, 2011). However, most psychological variables appear to be somewhere "in between," containing both substantial trait and substantial state residual components. Therefore, it seems important to consider both aspects as well as potential interactions between traits and situations (e.g., Schmitt & Baumert, 2010).Latent State-Trait TheoryThe high interest in psychological states and traits has resulted not only in many substantive contributions to the field, but also in the development of more and more sophisticated methods for measuring states and traits. The development of latent state-trait (LST) theory and models (Steyer et al., 1989, 1992) represented a significant methodological advancement in the area of person-situation research in the late 1980s and early 1990s. Formulated on the basis of stochastic measurement theory (Zimmerman, 1975), LST theory demonstrated how state, trait, and state residual latent variables can be constructively defined based on conditional expectations of observed (measured) variables and separated from random measurement error. "Constructively" means that latent variables in LST theory are not simply "assumed to exist," but are given explicit definitions in terms of (conditional expectations of) observed variables. This is helpful to clarify how states and traits can be operationalized mathematically, to avoid ambiguities in the labeling and interpretation of latent variables that are common in latent variable modeling, and to derive the properties of latent variables.According to LST theory, latent state scores reflect individuals' true scores within a specific situation, that is, averages of (hypothetical) intra-individual distributions of measured (e.g., item, test, or questionnaire) scores within a given situation. Trait scores reflect averages of (hypothetical) intra-individual distributions of measured scores across situations. State scores thus reflect persons in situations, whereas trait scores reflect the persons only. State residual latent variables are defined as the difference between state and trait latent variables and reflect both situation effects and potential person-situation interactions (Steyer et al., 1992).On the applied side, LST theory showed how state- and trait-related variance components can be estimated in statistical measurement models based on repeated measurements of observed variables. The LST methodology uses latent variables, allowing researchers to separate systematic effects of persons and situations from unsystematic effects due to random measurement error. LST models make it possible to determine what proportion of the observed score variance is due to trait (person-specific), occasion-specific (situation or person-situation interaction), and error variance and thus help clarify the question of whether person or situation/interaction influences determine individual differences in psychological measurements to a larger extent.In a recently published updated version of LST theory (so-called LST-R theory) the static person concept in the original theory has been revised to take into account the fact that persons constantly make experiences so that psychologically, the person u at time t is not necessarily exactly the same as the person u at time t + 1 (Steyer, Mayer, Geiser, & Cole, 2015). Related to this idea of a more "dynamic" person concept, recent methodological studies have clarified connections between pure "variability" (classical LST) models and models for measuring trait changes (Geiser, Keller, et al., 2015) and have discussed hybrid approaches that allow researchers to study state, trait, and trait change processes simultaneously (Bishop, Geiser, & Cole, 2015; Eid & Hoffmann, 1998; Tisak & Tisak, 2000). Furthermore, extended LST designs including both random and fixed situations allow for an analysis of the potential situation specificity of traits and make it possible to study main effects of situations as well as person-situation interactions in more detail (Geiser, Litson, et al., 2015).Other recent extensions of LST models have enabled researchers to take into account and analyze systematic method-specific effects. Method effects are of interest to psychologists in general (e.g., Podsakoff, MacKenzie, & Podsakoff, 2012), because their presence can indicate a lack of validity of psychological measures (Campbell & Fiske, 1959) or because different methods' unique perspectives can provide special insights (Eid & Diener, 2006). Multi-method extensions of LST approaches allow researchers to examine the consistency of states and traits across different methods (Courvoisier, Nussbeck, Eid, Geiser, & Cole, 2008; Eid, 1996; Eid et al., 1999; Geiser & Lockhart, 2012; Koch, Schultze, Holtmann, Geiser, & Eid, in press; Steyer et al., 1992) and our special issue features a novel contribution in this area as discussed below.Contributions in This Special IssueThe present special issue features both methodological and substantive advancements related to the analysis of states and traits. On the methodological side, two areas of state-trait modeling that appear to be receiving more and more attention in recent years concern (1) the use of item-level data (i.e., categorical observed variables such as binary or ordinal response variables) for measuring categorical or continuous latent state and trait variables and (2) the question of how method effects can be appropriately modeled in longitudinal studies. As part of this special issue, Crayen, Eid, Lischetzke, and Vermunt (2017) present a new continuous-time latent Markov modeling approach that uses categorical observed variables as indicators for measuring latent classes (i.e., categorical latent variables). In this approach, several different state and trait latent class variables can be examined that represent stability and variability at different levels. Crayen et al.'s new approach represents an extension of Eid and Langeheine's (1999) latent class LST approach to intensive longitudinal designs with many measurements for each person. The new approach should be of particular interest to substantive researchers who want to model stability and change based on many repeated measurements of the same individuals, for example, through ecological momentary assessments.Thielemann, Sengewald, Kappler, and Steyer (2017) also present a methodological innovation for the modeling of state-trait data with categorical observed variables. Their new latent state item response model for longitudinal data enables researchers to study method effects (such as response styles) at the level of binary items relative to a "gold-standard" item using a latent difference score approach. Thielemann et al.'s approach should be useful for researchers interested in using binary items to measure continuous latent state and trait variables and to investigators who want to study method effects at the item level. Item-level method effects can occur, for example, when an item set includes both positively and negatively keyed items or when multi-method measurement designs are used (e.g., self- and other reports). The approach is also useful when different items are not perfectly homogenous in measuring a single state or trait (i.e., when items capture different facets of a construct such as sadness versus sleeping problems as indicators of depression).Researchers dealing with measurement designs in which measurement occasions are closely spaced in time (e.g., multiple measurement occasions per week) often find that adjacent measurements are more strongly related to one another than measurements that are spaced out farther in time. LST modeling extensions that take into account such additional sources of stability beyond pure trait effects have therefore been of great interest to researchers, especially since Cole, Martin, and Steiger's (2005) seminal discussion of autoregressive effects in LST models. However, it has previously been unclear whether models with autoregressive effects are in line with the concepts of LST-R theory (Steyer et al., 2015). In this special issue, Eid, Holtmann, Santangelo, and Ebner-Priemer (2017) propose a new model with autoregressive effects and show that this model is fully in line with LST-R theory. Eid et al.'s (2017) approach is innovative in that it represents the first LST model that explicitly links situational influences to subsequent trait scores. This constitutes a radically different approach to the modeling of the interplay of traits and states compared to previous LST approaches. It offers exciting new possibilities for the modeling of intensive longitudinal data from a more dynamic trait perspective: In Eid et al.'s (2017) modeling framework, traits can change due to previous situational influences such as momentary hassles or uplifts or critical life events.In an applied paper, Gnambs and Butins (2017) examined longitudinal measurement properties of single life satisfaction items versus multi-item scales using sophisticated hybrid LST/trait change models. Their contribution is relevant from both a methodological and a substantive point of view. Methodologically, Gnambs and Butins' paper addresses an important assessment-related question: (When) should researchers use single items versus multi-item scales to measure variability and change in life satisfaction across time? Substantively, their paper examines state variability (short-term fluctuations) and trait changes (long-term growth or decline) in life satisfaction.The assessment of family-level and dyadic data is of increasing interest to psychologists. Consequently, many novel methodological contributions related to the analysis of such data in both cross-sectional and longitudinal studies have been presented (e.g., Ledermann & Macho, 2014). Loncke et al. (2017) fill an important gap in the dyadic data modeling literature by presenting an LST modeling approach for dyadic data. Their new approach allows researchers to model states and traits with dyadic data. From a substantive perspective, Loncke et al. (2017) show that variability in perceived family support can mostly be attributed to individual perceiver effects. However, Loncke et al. (2017) found that the degree to which perceiver effects of support can be attributed to traits rather than states was smaller for adolescents and siblings as compared to parents. Furthermore, the relative importance of trait and state components in adolescents' perceptions changed over time whereas supportive family climate remained rather stable. These findings offer new insights into developmental aspects in relational processes within families and underline the usefulness of state-trait models.Nett, Bieg, and Keller (2017) present an application of multiconstruct LST models to academic emotions (enjoyment, pride, anger, anxiety, and boredom) in the context of mathematics classes. Nett et al. found comparable proportions of trait variance and state residual components for all five emotions. Interestingly, on the one hand latent trait components of emotions of different valence (i.e., positive vs. negative connotation) were mostly unrelated to each other. On the other hand, latent state residual components of emotions of different valence were negatively correlated. According to Nett et al. (2017, p. 239), this shows that "an anxious student can also be a happy student" with regard to psychological traits, but in a given situation (during typical mathematics classes), a student will feel either anxious or happy, but not experience emotions of different valence simultaneously.OutlookThe analysis of state and trait components is of increasing interest to psychological research. We hope that this special issue will stimulate further substantive and methodological research in this area. As we have alluded to in this editorial, LST theory provides suitable statistical models for studying the nature of psychological constructs with regard to the question if they should be conceptualized and defined as states, traits, or both. From a developmental perspective, the interaction between traits and situations and their relative influence at different developmental stages deserves further investigation.In addition, LST theory can help researchers justify their operationalization of a given construct. For example, LST theory can be useful in exploring one central aspect of construct validity – construct representation (Embretson, 1983). Construct representation refers to a cognitive theory that explains response behavior for that measure. LST models can be used to investigate the impact of situations on response behavior. Therefore, we like to suggest that LST studies could routinely be implemented in the development of new psychometric measures and help test developers gain a better understanding of the relationship between the constructs they target and the tests they devise to measure them. If the measurement of a construct does not work in the intended way, it is theoretically useless in the given situation, be it a research study or a practical context. Working toward a better understanding of the nature of constructs should actually be an important research goal in itself, especially in the light of the ever-growing number of constructs encountered in published studies. We argue that such endeavors will ultimately improve the quality of psychological research.References Bishop, J., Geiser, C. & Cole, D. A. (2015). Modeling latent growth with multiple indicators: A comparison of three approaches. Psychological Methods, 20, 43–62. First citation in articleCrossref, Google Scholar Courvoisier, D. S., Eid, M. & Nussbeck, F. W. (2007). Mixture distribution latent state-trait analysis: Basic ideas and applications. Psychological Methods, 12, 80–104. First citation in articleCrossref, Google Scholar Crayen, C., Eid, M., Lischetzke, T. & Vermunt, J. K. (2017). A continuous-time mixture latent state-trait Markov model for experience sampling data: Application and evaluation. European Journal of Psychological Assessment, 33, 296–311. doi: 10.1027/1015-5759/a000418 First citation in articleLink, Google Scholar Danner, D., Hagemann, D., Schankin, A., Hager, M. & Funke, J. (2011). Beyond IQ: A latent state-trait analysis of general intelligence, dynamic decision making, and implicit learning. Intelligence, 39, 323–334. doi: 10.1016/j.intell.2011.06.004 First citation in articleCrossref, Google Scholar Deinzer, R., Steyer, R., Eid, M., Notz, P., Schwenkmezger, P., Ostendorf, F. & Neubauer, A. (1995). Situational effects in trait assessment: The FPI, NEOFFI and EPI questionnaires. European Journal of Personality, 9, 1–23. First citation in articleCrossref, Google Scholar Donnellan, M. B., Lucas, R. E. & Fleeson, W. (2009). Editorial: Introduction to personality and assessment at age 40: Reflections on the legacy of the person-situation debate and the future of person-situation integration. Journal of Research in Personality, 43, 117–119. First citation in articleCrossref, Google Scholar Eid, M. (1996). Longitudinal confirmatory factor analysis for polytomous item responses: Model definition and model selection on the basis of stochastic measurement theory. Methods of Psychological Research – Online, 1, 65–85. First citation in articleGoogle Scholar Eid, M. & Hoffmann, L. (1998). Measuring variability and change with an item response model for polytomous variables. Journal of Educational and Behavioral Statistics, 23, 193–215. First citation in articleCrossref, Google Scholar Eid, M., Holtmann, J., Santangelo, P. & Ebner-Priemer, U. (2017). On the definition of latent state-trait models with autoregressive effects: Insights from LST-R theory. European Journal of Psychological Assessment, 33, 285–295. First citation in articleLink, Google Scholar Eid, M. & Langeheine, R. (1999). Measuring consistency and occasion specificity with latent class models: A new model and its application to the measurement of affect. Psychological Methods, 4, 100–116. First citation in articleCrossref, Google Scholar Eid, M., Schneider, C. & Schwenkmezger, P. (1999). Do you feel better or worse? The validity of perceived deviations of mood states from mood traits. European Journal of Personality, 13, 283–306. First citation in articleCrossref, Google Scholar Embretson, S. E. (1983). Construct validity: Construct representation versus nomothetic span. Psychological Bulletin, 93, 179–197. First citation in articleCrossref, Google Scholar Epstein, S. (1983). The stability of confusion: Reply to Mischel and Peake. Psychological Review, 90, 179–184. First citation in articleCrossref, Google Scholar Fleeson, W. (2001). Toward a structure- and process-integrated view of personality: Traits as density distributions of states. Journal of Personality and Social Psychology, 80, 1011–1027. First citation in articleCrossref, Google Scholar Fleeson, W. (2004). Moving personality beyond the person-situation debate: The challenge and the opportunity of within-person variability. Current Directions in Psychological Science, 13, 83–87. First citation in articleCrossref, Google Scholar Funder, D. C. (1991). Global traits: A Neo-Allportian approach to personality. Psychological Science, 2, 31–39. First citation in articleCrossref, Google Scholar Funder, D. C. (2008). Persons, situations, and person-situation interactions. In O. P. JohnR. RobinsL. PervinEds., Handbook of personality (3rd ed, pp. 568–580). New York, NY: Guilford Press. First citation in articleGoogle Scholar Funder, D. C., Guillaume, E., Kumagai, S., Kawamoto, S. & Sato, T. (2012). The person-situation debate and the assessment of situations. Japanese Journal of Personality, 21, 1–11. First citation in articleCrossref, Google Scholar Geiser, C., Keller, B. T., Lockhart, G., Eid, M., Cole, D. A. & Koch, T. (2015). Distinguishing state variability from trait change in longitudinal data: The role of measurement (non)invariance in latent state-trait analyses. Behavior Research Methods, 47, 172–203. First citation in articleCrossref, Google Scholar Geiser, C., Litson, K., Bishop, J., Keller, B. T., Burns, G. L., Servera, M. & Shiffman, S. (2015). Analyzing person, situation, and person × situation interaction effects: Latent state-trait models for the combination of random and fixed situations. Psychological Methods, 20, 165–192. First citation in articleCrossref, Google Scholar Geiser, C. & Lockhart, G. (2012). A comparison of four approaches to account for method effects in latent state trait analysis. Psychological Methods, 17, 255–283. First citation in articleCrossref, Google Scholar Gnambs, T. & Butins, K. (2017). The measurement of variability and change in life satisfaction: A comparison of single-item and multi-item instruments. European Journal of Psychological Assessment, 33, 224–238. doi: 10.1027/1015-5759/a000414 First citation in articleLink, Google Scholar Hertzog, C. & Nesselroade, J. R. (1987). Beyond autoregressive models: Some implications of the trait-state distinction for the structural modeling of developmental change. Child Development, 58, 93–109. First citation in articleCrossref, Google Scholar Kenny, D. A. & Zautra, A. (1995). The trait-state-error model for multiwave data. Journal of Consulting and Clinical Psychology, 63, 52–59. First citation in articleCrossref, Google Scholar Kenrick, D. T. & Funder, D. C. (1988). Profiting from controversy: Lessons from the person-situation debate. The American Psychologist, 43, 23–34. First citation in articleCrossref, Google Scholar Kirschbaum, C., Steyer, R., Eid, M., Patalla, U., Schwenkmezger, P. & Hellhammer, D. H. (1990). Cortisol and behavior: 2 Application of a latent state-trait model to salivary cortisol. Psychoneuroendocrinology, 15, 297–307. doi: 10.1016/0306-4530(90)90080-S First citation in articleCrossref, Google Scholar Koch, T., Schultze, M., Holtmann, J., Geiser, C. & Eid, M. (in press). A multimethod latent state-trait model for structurally different and interchangeable methods. Psychometrika. First citation in articleGoogle Scholar Ledermann, T. & Macho, S. (2014). Analyzing change at the dyadic level: The common fate growth model. Journal of Family Studies, 28, 204–213. First citation in articleGoogle Scholar Loncke, J., Mayer, A., Eichelsheim, V., Branje, S., Meeus, W., Koot, H., … Loeys, T. (2017). Latent state-trait models for longitudinal family data: Investigating consistency in perceived support. European Journal of Psychological Assessment, 33, 256–270. doi: 10.1027/1015-5759/a000415 First citation in articleLink, Google Scholar Mischel, W. (1968). Personality and assessment. New York, NY: Wiley. First citation in articleGoogle Scholar Nett, U. E., Bieg, M. & Keller, M. M. (2017). How much trait variance is captured by measures of academic state emotions? A latent state-trait-analysis. European Journal of Psychological Assessment, 33, 239–255. doi: 10.1027/1015-5759/a000416 First citation in articleLink, Google Scholar Podsakoff, P. M., MacKenzie, S. B. & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 65, 539–569. First citation in articleCrossref, Google Scholar Rauthmann, J. F., Sherman, R. A. & Funder, D. C. (2015). Principles of situation research: Towards a better understanding of psychological situations. European Journal of Personality, 29, 363–381. doi: 10.1002/per.1994 First citation in articleCrossref, Google Scholar Roberts, B. W. (2009). Back to the future: Personality and assessment and personality development. Journal of Research in Personality, 43, 137–145. First citation in articleCrossref, Google Scholar Schmitt, M. & Baumert, A. (2010). On the diversity of dynamic person × situation interactions. European Journal of Personality, 24, 497–500. First citation in articleGoogle Scholar Steyer, R., Ferring, D. & Schmitt, M. J. (1992). States and traits in psychological assessment. European Journal of Psychological Assessment, 8, 79–98. First citation in articleGoogle Scholar Steyer, R., Geiser, C. & Fiege, C. (2012). Latent state-trait models. In H. CooperEd., Handbook of research methods in psychology (pp. 291–308). Washington, DC: APA. First citation in articleGoogle Scholar Steyer, R., Majcen, A.-M., Schwenkmezger, P. & Buchner, A. (1989). A latent state-trait anxiety model and its application to determine consistency and specificity coefficients. Anxiety Research, 1, 281–299. First citation in articleCrossref, Google Scholar Steyer, R., Mayer, A., Geiser, C. & Cole, D. A. (2015). A theory of states and traits – revised. Annual Review of Clinical Psychology, 11, 71–98. doi: 10.1146/annurev-clinpsy-032813-153719 First citation in articleCrossref, Google Scholar Steyer, R., Schmitt, M. & Eid, M. (1999). Latent state-trait theory and research in personality and individual differences. European Journal of Personality, 13, 389–408. First citation in articleCrossref, Google Scholar Thielemann, D., Sengewald, M.-A., Kappler, G. & Steyer, R. (2017). A probit latent state IRT model with latent item-effect variables. European Journal of Psychological Assessment, 33, 271–284. doi: 10.1027/1015-5759/a000417 First citation in articleLink, Google Scholar Tisak, J. & Tisak, M. S. (2000). Permanency and ephemerality of psychological measures with application to organizational commitment. Psychological Methods, 5, 175–198. First citation in articleCrossref, Google Scholar Zimmerman, D. W. (1975). Probability spaces, Hilbert spaces, and the axioms of test theory. Psychometrika, 40, 395–412. doi: 10.1007/BF02291765 First citation in articleCrossref, Google ScholarChristian Geiser, Department of Psychology, Utah State University, 2810 Old Main Hill, Logan, UT 84322-2810, USA, E-mail christian.geiser@usu.eduFiguresReferencesRelatedDetailsCited byDoes team orientation matter? A state‐of‐the‐science review, meta‐analysis, and multilevel framework12 April 2022 | Journal of Organizational Behavior, Vol. 44, No. 2"It Depends on the Situation": How Hearing-Specific Contexts Affect the Way Adults With Hearing Loss Report Their Personal AttributesAmerican Journal of Audiology, Vol. 31, No. 4Stability and change in dispositional envy: Longitudinal evidence on envy as a stable trait6 October 2022 | European Journal of Personality, Vol. 2Development of adolescents' self-esteem and general academic self-concept: perceived classroom climate makes a difference16 September 2022 | European Journal of Personality, Vol. 4Understanding micro-level resilience enactment of everyday entrepreneurs under threat15 February 2022 | Journal of Small Business Management, Vol. 60, No. 5Conceptualizing and Studying Characteristics, Units, and Fits of Persons and Environments: A Coherent Synthesis29 September 2021 | European Journal of Personality, Vol. 36, No. 3Development of the Hedonic Overeating–Questionnaire (HEDO–Q)29 April 2022 | Nutrients, Vol. 14, No. 9Implementing Mindfulness in General Life and Organizations. Validation of the Time Flow Mindfulness Questionnaire for Effective Health Management7 April 2022 | Frontiers in Psychology, Vol. 13Changes in Entrepreneurs' Risk-Taking Propensity Across Venture Phases15 March 2022 | Journal of Enterprising Culture, Vol. 30, No. 01Gratitude and loneliness in daily life across the adult lifespan12 November 2021 | Current Psychology, Vol. 14Capturing interactions, correlations, fits, and transactions: A Person-Environment Relations ModelTrait personality in sport and exercise psychology: A mapping review and research agenda25 January 2019 | International Journal of Sport and Exercise Psychology, Vol. 36Assessing Behavior Difficulties in StudentsCarmen L. A. Zurbriggen, Susanne Schwab, Anke de Boer, and Ute Koglin28 March 2018 | European Journal of Psychological Assessment, Vol. 34, No. 2 Volume 33Issue 4July 2017ISSN: 1015-5759eISSN: 2151-2426 InformationEuropean Journal of Psychological Assessment (2017), 33, pp. 219-223 https://doi.org/10.1027/1015-5759/a000413.© 2017Hogrefe PublishingPDF download