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Mixed Rasch Models for Analyzing the Stability of Response Styles Across Time: An Illustration with the Beck Depression Inventory (BDI-II)

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Dependent Data in Social Sciences Research

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 145))

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Abstract

Questionnaires for clinical studies are often evaluated in cross-sectional settings and on the basis of classical test theory. Some of them, like the BDI-II which is one of the most widely used self-report instruments for assessing depression severity, are considered to have very good psychometric properties. However, these properties are rarely evaluated in longitudinal designs, and even less with models of item response theory (IRT). In addition, analyses of self-report questionnaires with IRT models provided evidence of two major response styles: the tendency to prefer extreme response categories, and the tendency to prefer the middle categories. Rasch models, in particular their extension to the so-called mixed Rasch model, are well suited to address these questions. They allow one to determine latent classes with different response styles and to analyze qualitative aspects of change such as the consistency of response styles across time. In this chapter first, an introduction to response styles and an overview of the mixed Rasch model, especially in the context of measuring change, are given and second, a practical example is elaborated using a sample of in-patients from a psychosomatic clinic that were assessed with the BDI-II at the beginning and at the end of in-patient treatment. The presence of two response styles is confirmed for the admission data, whereas for the discharge data the Rasch model seems sufficient. A combined analysis of both time points reveals three classes, one of which is a low symptom class and the other two reflect, again, the two response styles; these two classes remain quite stable over time.

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References

  • Adams, R. J., Wilson, M., & Wang, W.-C. (1997). The multidimensional random coefficient multinomial logit model. Applied Psychological Measurement, 21(1), 1–23.

    Article  Google Scholar 

  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.

    Article  MathSciNet  MATH  Google Scholar 

  • American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.

    Google Scholar 

  • Baghaei, P., & Carstensen, C. H. (2013). Fitting the mixed Rasch model to a reading comprehension test: Identifying reader types. Practical Assessment, Research & Evaluation, 18(5). Retrieved from http://pareonline.net/getvn.asp?v=18&n=5.

  • Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Beck depression inventory—Second edition. Manual. San Antonio, TX: The Psychological Corporation.

    Google Scholar 

  • Bozdogan, H. (1987). Model selection and Akaike’s information criterion (AIC): The general theory and its analytic extensions. Psychometrika, 52(3), 345–370.

    Article  MathSciNet  MATH  Google Scholar 

  • Brouwer, D., Meijer, R. R., & Zevalkink, J. (2013a). On the factor structure of the Beck Depression Inventory-II: G is the key. Psychological Assessment, 25(1), 136–145.

    Article  Google Scholar 

  • Brouwer, D., Meijer, R. R., & Zevalkink, J. (2013b). Measuring individual significant change on the Beck Depression Inventory-II through IRT-based statistics. Psychotherapy Research, 23(5), 489–501.

    Article  Google Scholar 

  • Bühler, J., Keller, F., & Läge, D. (2014). Activation as an overlooked factor in the BDI-II: A factor model based on core symptoms and qualitative aspects of depression. Psychological Assessment, 26(3), 970–979.

    Article  Google Scholar 

  • Eid, M., & Zickar, M. J. (2010). Detecting response styles and faking in personality and organizational assessments by mixed Rasch models. In M. von Davier & C. H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 255–270). New York, NY: Springer.

    Google Scholar 

  • Embretson, S. E. (2010). Mixed Rasch models for measurement in cognitive psychology. In M. von Davier & C. H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 235–254). New York, NY: Springer.

    Google Scholar 

  • Glück, J., & Spiel, C. (1997). Item response models for repeated measures designs: Application and limitation of four different approaches. Methods of Psychological Research, 2(1). Retrieved from http://www.dgps.de/fachgruppen/methoden/mpr-online/issue2/art6/article.html.

  • Glück, J., & Spiel, C. (2010). Studying development via item response models: A wide range of potential uses. In M. von Davier & C. H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 281–292). New York, NY: Springer.

    Google Scholar 

  • Gollwitzer, M., Eid, M., & Jürgensen, R. (2005). Response styles in the assessment of anger expression. Psychological Assessment, 17(1), 56–69.

    Article  Google Scholar 

  • Hautzinger, M., Keller, F., & Kühner, C. (2006). BDI-II. Beck depressions inventar revision—Manual [BDI-II. Revision of the Beck Depression Inventory—Manual]. Frankfurt, Germany: Harcourt Test Services.

    Google Scholar 

  • Jacobson, N. S., & Truax, P. (1991). Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. Journal of Consulting and Clinical Psychology, 59(1), 12–19.

    Article  Google Scholar 

  • Keller, F. (2012). Das Beck-Depressions-Inventar (BDI-II): Psychometrische Analysen mit probabilistischen Testmodellen [The Beck-Depression-Inventory (BDI-II): Psychometric analyses with probabilistic test models]. In W. Baros & J. Rost (Eds.), Natur- und kulturwissenschaftliche Perspektiven in der Psychologie [Natural science and cultural studies perspectives in psychology] (pp. 120–132). Berlin, Germany: Verlag irena regener.

    Google Scholar 

  • Keller, F., & Kempf, W. (1997). Some latent trait and latent class analyses of the Beck-Depression-Inventory (BDI). In J. Rost & R. Langeheine (Eds.), Applications of latent trait and latent class models in the social sciences (pp. 314–323). Münster, Germany: Waxmann.

    Google Scholar 

  • Khorramdel, L., & von Davier, M. (2014). Measuring response styles across the big five: A multiscale extension of an approach using multinomial processing trees. Multivariate Behavioral Research, 49(2), 161–177.

    Article  Google Scholar 

  • Langeheine, R., van de Pol, F., & Pannekoek, J. (1996). Bootstrapping goodness-of-fit-measures in categorical data analysis. Sociological Methods & Research, 24(4), 492–516.

    Article  Google Scholar 

  • Meiser, T. (2010). Rasch models for longitudinal data. In M. von Davier & C. H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 191–200). New York, NY: Springer.

    Google Scholar 

  • Meiser, T., Hein-Eggers, M., Rompe, P., & Rudinger, G. (1995). Analyzing homogeneity and heterogeneity of change using Rasch and latent class models: A comparative and integrative approach. Applied Psychological Measurement, 19(4), 377–391.

    Article  Google Scholar 

  • Meiser, T., Stern, E., & Langeheine, R. (1998). Latent change in discrete data: Unidimensional, multidimensional, and mixture distribution Rasch models for the analysis of repeated observations. Methods of Psychological Research Online, 3(2), 75–93. Retrieved http://www.dgps.de/fachgruppen/methoden/mpr-online/issue5/art6/meiser.pdf.

    Google Scholar 

  • Plieninger, H., & Meiser, T. (2014). Validity of multiprocess IRT models for separating content and response styles. Educational and Psychological Measurement. doi:.

    Google Scholar 

  • Preinerstorfer, D., & Formann, A. K. (2012). Parameter recovery and model selection in mixed Rasch models. British Journal of Mathematical and Statistical Psychology, 65(2), 251–262.

    Article  MathSciNet  Google Scholar 

  • Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen, Denmark: Danish Institute for Educational Research.

    Google Scholar 

  • Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14(3), 271–282.

    Article  MathSciNet  Google Scholar 

  • Rost, J. (1991). A logistic mixture distribution model for polytomous item responses. The British Journal for Mathematical and Statistical Psychology, 44(1), 75–92.

    Article  Google Scholar 

  • Rost, J. (2004). Lehrbuch Testtheorie—Testkonstruktion [Testtheory—Testconstruction]. Bern, Germany: Verlag Hans Huber.

    Google Scholar 

  • Rost, J., Carstensen, C. H., & von Davier, M. (1997). Applying the mixed Rasch model to personality questionnaires. In J. Rost & R. Langeheine (Eds.), Applications of latent trait and latent class models in the social sciences (pp. 324–332). Münster, Germany: Waxmann.

    Google Scholar 

  • Rost, J., Carstensen, C. H., & von Davier, M. (1999). Sind die Big Five Rasch-skalierbar? Eine Reanalyse der NEO-FFI-Normierungsdaten [Are the Big Five Rasch scalable? A reanalysis of the NEO-FFI norm data]. Diagnostica, 45(3), 119–127.

    Article  Google Scholar 

  • Rost, J., & von Davier, M. (1995). Mixture distribution Rasch models. In G. H. Fischer & I. W. Molenaar (Eds.), Rasch models: Foundations, recent developments, and applications (pp. 257–268). New York, NY: Springer.

    Chapter  Google Scholar 

  • Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–462.

    Article  MathSciNet  MATH  Google Scholar 

  • Spiel, C., & Glück, J. (1998). Item response models for assessing change in dichotomous items. International Journal of Behavioral Development, 22(3), 517–536.

    Article  Google Scholar 

  • Spiel, C., & Glück, J. (2008). A model-based test of competence profile and competence level in deductive reasoning. In J. Hartig, E. Klieme, & D. Leutner (Eds.), Assessment of competencies in educational contexts (pp. 45–65). Cambridge, MA: Hogrefe.

    Google Scholar 

  • Von Davier, M. (2001). WINMIRA 2001 user’s guide. Kiel: IPN.

    Google Scholar 

  • Von Davier, M., & Rost, J. (1995). Polytomous mixed Rasch models. In G. H. Fischer & I. W. Molenaar (Eds.), Rasch models: Foundations, recent developments, and applications (pp. 371–379). New York, NY: Springer.

    Chapter  Google Scholar 

  • Ward, L. C. (2006). Comparison of factor structure models for the Beck Depression Inventory-II. Psychological Assessment, 18(1), 81–88.

    Article  Google Scholar 

  • Weijters, B., Geuens, M., & Schillewaert, N. (2010). The stability of individual response styles. Psychological Methods, 15(1), 96–110.

    Article  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(2), 178–189.

    Article  Google Scholar 

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Acknowledgement

We thank Dr. Robert Mestel, head of Research/Quality Assurance of HELIOS Klinik Bad Grönenbach, for providing us with the dataset.

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Correspondence to Ferdinand Keller .

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Keller, F., Koller, I. (2015). Mixed Rasch Models for Analyzing the Stability of Response Styles Across Time: An Illustration with the Beck Depression Inventory (BDI-II). In: Stemmler, M., von Eye, A., Wiedermann, W. (eds) Dependent Data in Social Sciences Research. Springer Proceedings in Mathematics & Statistics, vol 145. Springer, Cham. https://doi.org/10.1007/978-3-319-20585-4_13

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