Detecting specific patterns of change for two outcome scores in a mental health study by means of linear mixture models

Methodology

Volumn 2, Issue 3, 2006, Pages 113-123

  Matschinger, Herbert ^a   Kilian, Reinhold ^b   Angermeyer, Matthias C ^a  

^a UNIVERSITY OF LEIPZIG   (Germany)

^b UNIVERSITY OF ULM   (Germany)

Author keywords

Growth mixture models; Latent class analysis; Mental disorder; Quality of life

Indexed keywords

EID: 69949113598     PISSN: 16141881     EISSN: 16142241     Source Type: Journal    
DOI: 10.1027/1614-2241.2.3.113     Document Type: Article

References (41)

1. American Psychiatric Association (1987). Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R). (3rd ed.). Washington, DC: American Psychological Association
2. Anderson, J., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103, 411-423.
3. Anderson, J., & Gerbing, D. W. (1992). Assumptions and comparative strength of the two-step approach. Sociological Methods & Research, 20, 321-333.
4. Bauer, D. J., & Curran, P. J. (2003a). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods, 8, 338-363.
5. Bauer, D. J., & Curran, P. J. (2003b). Overextraction of latent trajectory classes: Much ado about nothing? Reply to Rindskopf (2003), Muthén (2003) and Cudeck & Henly (2003). Psychological Methods, 8, 384-393.
6. Cudeck, R., & Henly, S. J. (2003). A realistic perspective on pattern representation in growth data: Comment on Bauer and Curran (2003). Psychological Methods, 8, 378-383.
7. Curran, P. J., & Hussong, A. M. (2002). Structural equation modelling of repeated measures data: Latent curve analysis. In D. S. Moskowitz & S. L. Hershberger (Eds.), Modelling intraindividual variability with repeated measures data: methods and applications. (pp. 59-85). London: Lawrence Erlbaum Associates.
8. Fornell, C., & Yi, Y. (1992a). Assumption of the two-step approach: Reply to Anderson and Gerbing. Sociological Methods & Research, 20, 334-339.
9. Fornell, C., & Yi, Y. (1992b). Assumptions of the two-step approach to latent variable modeling. Sociological Methods & Research, 20, 291-320.
10. Jedidi, K., Jagpal, H. S., & DeSarbo, W. S. (1997a). Finite mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16, 39-59.
11. Jedidi, K., Jagpal, H. S., & DeSarbo, W. S. (1997b). STEMM: A general finite mixture structural equation model. Journal of Classification, 14, 23-50.
12. Jöreskog, K. G. (1973). A general method for estimating a linear structural equation system. In A. S. Goldberger & O. D. Duncan (Eds.), Structural equation models in the social sciences (pp. 85-112). New York: Seminar Press.
13. Kass, R. E., & Raftery, A. E. (1995). Bayes factor. Journal of the American Statistical Association, 90, 773-795.
14. Kass, R. E., & Wasserman, L. (1995). A reference Bayesian test for nested hypotheses and its relationship to the Schwartz criterion. Journal of the American Statistical Association, 90, 928-934.
15. Kilian, R., Matschinger, H., Becker, T., & Angermeyer, M. C. (2003). A longitudinal analysis of the impact of social and clinical characteristics on the costs of schizophrenia treatment. Acta Psychiatrica Scandinavica, 107, 351-360.
16. Kilian, R., Roick, C., Matschinger, H., Bernert, S., Mory, C., Becker, T., & Angermeyer, M. C. (2001). Instruments for the economic evaluation of psychiatric service systems: Methodological foundations of the European standardisation and the German adaption. Psychiatrische Praxis, 28, 74-78.
17. Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767-778.
18. McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: John Wiley.
19. Mehta, P. D., & West, S. G. (2000). Putting the individual back into individual growth curves. Psychological Methods, 5, 23-43.
20. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107-122.
21. Muthén, B. O. (1991). Analysis of longitudinal data using latent variable models with varying parameters. In L. M. Collins & J. Horn (Eds.), Best methods for the analysis of change. recent advances, unanswered questions, future directions (pp. 1-17). Washington, DC: American Psychological Association.
22. Muthén, B. O. (2001). Latent variable mixture modeling. In A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 1-33). London: Lawrence Erlbaum.
23. Muthén, B. O. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika, 29, 81-117.
24. Muthén, B. O. (2003). Statistical and substantive checking in growth mixture modeling: Comment on Bauer & Curran (2003). Psychological Methods, 8, 369-377.
25. Muthén, B. O. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 345-368). Newbury Park, CA: Sage Publications.
26. Muthén, B. O., & Curran, P. J. (1997). General longitudinal modelling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371-402.
27. Muthén, B. O., & Muthén, L. K. (2000). Integrating person-centered and variable-centered analysis: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882-891.
28. Muthén, L. K., & Muthén, B. O. (2004). Mplus User's Guide (3rd ed.). Los Angeles: Muthén & Muthén.
29. Nagin, D. S. (1999). Analyzing developmental trajectories: A semi-parametric, group-based approach. Psychological Methods, 4, 157.
30. Nagin, D. S., & Tremblay, R. E. (2001). Analyzing developmental trajectories of distinct but related behaviors: A group based method. Psychological Methods, 6, 18-34.
31. Reinecke, J. (2006). Longitudinal analysis of adolescents' deviant and delinquent behavior: Applications of latent class growth curves and growth mixture models. Methodology, 2, 100-112.
32. Rindskopf, D. (2003). Mixture or homogenous? Comment on Bauer & Curran. Psychological Methods, 8, 364-368.
33. Rogosa, D. R. (1988). Myths about longitudinal research. In K. W. Schaie, R. T. Campbell, W. Meredith, & S. C. Rawlings (Eds.), Methodological issues in aging research (pp. 171-209). New York: Springer.
34. Schwartz, G. (1978). Estimating the dimensions of a model. The Annals of Statistic, 6, 461-464.
35. StataCorp (2003). Stata Statistical Software: Release 8. College Station, TX: StataCorpLP.
36. Sörbom, D. (1974). A general method for studying differences in factor means and factor structure between groups. British Journal of Mathematical and Statistical Psychology, 27, 229-239.
37. Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57, 307-333.
38. Ware, J. E., & Gandek, B. The IQOLA Project Group (1994). The SF-36 Health Survey: Development and use on mental health research and the IQOLA project. International Journal of Mental Health, 23, 49-73.
39. White, H. R., Johnson, V., & Buyske, S. (2000). Parental modeling and parenting behavior effects on offspring alcohol and cigarette use: A growth curve analysis. Journal of Substance Abuse, 12, 287-310.
40. Willet, J. B. (1988). Questions and answers in the measurement of change. In E. Z. Rothkopf (Ed.), Review of research in education: Vol. 15 (pp. 345-422). Washington DC: American Educational Research Association.
41. Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116, 363-381.