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Modeling Latent Trait-Change

  • Rolf Steyer
  • Sindy Krambeer
  • Wolfgang Hannöver
Chapter
Part of the Mathematical Modelling: Theory and Applications book series (MMTA, volume 19)

Abstract

Psychological interventions such as, for example, therapeutic treatments or educational training programs do not aim at ephemeral but primarily at permanent changes in behavior, feelings, attitudes etc., that is, at changes which are not situation-dependent, but rather consistent across situations. Similarly, changes of interest for developmental psychologists are not ephemeral but permanent changes in behavior, abilities, feelings, attitudes etc. Hence, in terms of latent state-trait theory (LST theory; see, e.g., Steyer, Ferring & Schmitt, 1992; Deinzer et al., 1995; or Steyer, Schmitt & Eid, 1999), psychological interventions aim at changing traits, not at changing states, and trait change, not mere State change, is also the primary interest in develop­mental psychology.

Keywords

Group Therapy Method Factor Latent Trait Interindividual Difference Manifest Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Anastasi, A. (1983). Traits, states and situations: A comprehensive view. In H. Weiner & S. Messik (Eds.), Principals of modern psychological measurement(pp. 345–356). Hillsdale, N. J.: Erlbaum.Google Scholar
  2. Arbuckle, J. L. (1996). Füll Information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling(pp. 243–277). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.Google Scholar
  3. Baltes, P. B., Reese, H. W. & Nesselroade, J. R. (1977). Life-span development psychology: Introduction to research methods. Monterey: Brooks-Cole.Google Scholar
  4. Bollen, K. A. & Praxton, P. (1998a). Interactions of latent variables in structural equation modeis. Structural Equation Modeling, 5, 267–293.CrossRefGoogle Scholar
  5. Bollen, K. A. & Praxton, P. (1998b). Two-stage least Squares estimation of interaction effects. In R. E. Schumacker & G. A. Marcoulides (Eds.), Interaction and nonlinear effects in structural equation modeling(pp. 125–151). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  6. Brähler, E. & Scheer, J. W. (1995). Der Giessener Beschwerdebogen GBB. 2. Auflage [The Gießen Checklist of physical complaints, 2nd edition].Bern: Huber.Google Scholar
  7. Deinzer, R., Steyer, R., Eid, M., Notz, P., Schwenkmezger, P., Ostendorf, F. et al. (1995). Situational effects in trait assessment: the FPI, NEOFFI, and EPI questionnaires. European Journal of Personality, 9, 1–23.CrossRefGoogle Scholar
  8. Derogatis, L. R. (1977). SCL-90-R, administration, scoring and procedures manual -1 for the R(evised) version. Baltimore, MD: John Hopkins University School of Medicine.Google Scholar
  9. Eid, M. (2000). A multitrait-multimethod model with minimal assumptions. Psychometrika, 65, 241–261.CrossRefGoogle Scholar
  10. Eid, M. & Diener, E. (1999). Intraindividual variability in affect: Reliability, validity, and personality correlates. Journal of Personality and Social Psychology,76, 662–676.CrossRefGoogle Scholar
  11. 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.Google Scholar
  12. Eid, M., Lischetzke, T., Trierweiler, L. I. & Nußbeck, F. W. (2003). Sepa-rating trait effects from trait-specific method effects in multitrait-multimethod modeis: A multiple indicator CTC(M-l) model. Psychological Methods, 8, 38–60.PubMedCrossRefGoogle Scholar
  13. Enders, C. K. & Bandalos, D. L. (2001). The relative Performance of füll Information maximum likelihood estimation for missing data in structural equation modeis. Structural Equation Modeling, 8, 430–457.CrossRefGoogle Scholar
  14. Franke, G. (1995). Die Symptom-Checkliste von Derogatis, SCL-90-R deut­sche Version. [The Symptom-Checklist of Derogatis: SCL-90-R. German version]. Göttingen: Beltz-Test.Google Scholar
  15. Graham, J. W., Hofer, S. M. & MacKinnon, D. P. (1996). Maximizing the usefulness of data obtained with planned missing value patterns: An application of maximum likelihood procedures. Multivariate Behavioral Research, 31,197–218.CrossRefGoogle Scholar
  16. Hamerle, A., Singer, H. & Nagl, W. (1993). Identification and estimation of continuous time dynamic Systems with exogenous variables using panel data. Econometric Theory, 9, 283–295.CrossRefGoogle Scholar
  17. Hertzog, C. & Nesselroade, J. R. (1987). Beyond autoregressive modeis: Some implications of the trait- State distinction for the structural modeling of developmental change. Child Development, 58, 93–109.PubMedCrossRefGoogle Scholar
  18. Horowitz, L. M., Strauß, B., Kordy, H. & Alden, L. E. (2000). Inventar zur Erfassung interpersonaler Probleme: deutsche Version; IIP-D (2). Göttingen: Beltz-Test.Google Scholar
  19. Hsiao, C. (1986). Analysis of panel data. Cambridge: Cambridge University Press.Google Scholar
  20. Jöreskog, K. G. & Yang, F. (1996). Nonlinear structural equation modeis: The Kenny-Judd model with interaction effects. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques(pp. 57–88). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  21. Kenny, D. & Judd, C. M. (1984). Estimating the nonlinear and interaction effects of latent variables. Psychological Bulletin, 96, 201–210.CrossRefGoogle Scholar
  22. Klein, A. & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65, 457–474.CrossRefGoogle Scholar
  23. Little, R. J. A. & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.Google Scholar
  24. McArdle, J. J. (2001). A latent difference score approach to longitudinal dynamic structural analysis. In R. Cudeck, S. du Toit, & D. Sörbom (Eds.), Structural equation modeling: Present and future(pp. 341–380). Lincolnwood: Scientific Software International.Google Scholar
  25. McClelland, G. H. & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological Bulletin, 114, 376–390.PubMedCrossRefGoogle Scholar
  26. Nachtigall, C, Kraus, K. & Steyer, R. (2000). The analysis of change: True change modeis and growth curves. In J. Blasius, J. Hox, E. de Leeuw, & P. Schmidt (Eds.), Social science methodology in the new millennium: Proceedings of the fifth international Conference on logic and methodology, Cologne, October 3–6, 2000(pp. 1–12). Cologne: TT-Publikaties.Google Scholar
  27. Neale, M. C. (1998). Modeling interaction and nonlinear effects with Mx: A general approach. In R. E. Schumacker & G. A. Marcoulides (Eds.), Interaction and nonlinear effects in structural equation modeling(pp. 43–61). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  28. Raykov, T. (1999). Are simple change scores obsolete? An approach to studying correlates and predictors of change. Applied Psychological Measurement, 23, 120–126.CrossRefGoogle Scholar
  29. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581–592.CrossRefGoogle Scholar
  30. Schafer, J. L. (1997). Analysis of incomplete multivariate data. New York: Chapman & Hall.CrossRefGoogle Scholar
  31. Schermelleh-Engel, K., Klein, A. & Moosbrugger, H. (1998). Estimating nonlinear effects using a latent moderated structural equation approach. In G. A. Marcoulides & R. E. Schumacker (Eds.), Interactions and nonlinear effects in structural equation modeling(pp. 203–238). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  32. Stelzl, I. (1986). Changing a causal hypothesis without changing fit: Some rules for generating equivalent path modeis. Multivariate Behavioral Research, 21, 309–331.CrossRefGoogle Scholar
  33. Steyer, R., Eid, M. & Schwenkmezger, P. (1997). Modeling true intraindi-vidual change: True change as a latent variable. Methods of Psychological Research Online,2, 21–34.Google Scholar
  34. Steyer, R., Ferring, D. & Schmitt, M. J. (1992). States and traits in psycho­logical assessment. European Journal of Psychological Assessment, 8, 79–98.Google Scholar
  35. Steyer, R., Nachtigall, C, Wüthrich-Martone, O. & Kraus, K. (2002). Causal regression modeis HI: Covariates, conditional, and unconditional average causal effects. Methods of Psychological Research Online, 7, 41–68.CrossRefGoogle Scholar
  36. Steyer, R., Partchev, I. & Shanahan, M. J. (2000). Modeling true intra-individual change in structural equation modeis: The case of poverty and children’s psychosocial adjustment. In T. D. Little & K. U. Schnabel (Eds.), Modeling longitudinal and multilevel data: Practical issues, applied approaches, and specific examples(pp. 109–126). Mahwah, NJ, US: Lawrence Erlbaum Associates.Google Scholar
  37. Steyer, R., Schmitt, M. & Eid, M. (1999). Latent state-trait theory and re-search in personality and individual differences. European Journal of Personality, 13, 389–408.CrossRefGoogle Scholar
  38. Steyer, R., Schwenkmezger, P. & Auer, A. (1990). The emotional and cogni-tive components of trait anxiety: A latent state-trait model. Personality and Individual Differences,11, 125–134.CrossRefGoogle Scholar
  39. Tisak, J. & Tisak, M. S. (2000). Permanency and ephemerality of psycholo­gical measures with application to organizational commitment. Psychological Methods, 5, 175–198.PubMedCrossRefGoogle Scholar
  40. Vautier, S. & Raufaste, E. (2002). Measuring dynamic bipolarity in positive and negative activation (unpublished work). Google Scholar
  41. Vautier, S., Rauf aste, E. & Steyer, R. (submitted). A latent change model for testing perfect linear dynamic bipolarity. Manuscript submitted for publication.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2004

Authors and Affiliations

  • Rolf Steyer
    • 1
  • Sindy Krambeer
    • 1
  • Wolfgang Hannöver
    • 1
  1. 1.Friedrich Schiller UniversityJenaGermany

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