The Development of Deviant and Delinquent Behavior of Adolescents: Applications of Latent Class Growth Curves and Growth Mixture Models

Longitudinal Models in the Behavioral and Related Sciences

Volumn , Issue , 2017, Pages 239-266

  Reinecke, Jost ^a  

^a BIELEFELD UNIVERSITY   (Germany)

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[No Author keywords available]

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EID: 85118906545     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9781315091655-10     Document Type: Chapter

References (42)

1. Bentler, P. M. (2001). EQS 6: Structural equations program manual. Encino, CA: Multivariate Software.
2. Curran, P. J., and Hussong, A. M. (2002). Modeling intraindividual variability with repeated measures data: Method and applications. In D. S. Moskowitz and S. L. Hershberger (Eds.), Structural equation modeling of repeated measures data: Latent curve analysis (pp. 59-85). Mahwah, NJ: Lawrence Erlbaum Associates.
3. D’Unger, A., Land, K. C., McCall, P. L., and Nagin, D. S. (1998). How many latent classes of delinquent/criminal careers? Results from mixed Poisson regression analyses of the London, Philadelphia, and Racine cohort studies. American Journal of Sociology, 103, 1593-1630.
4. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39, 1-38.
5. Duncan, T. E., Duncan, S. C., Strycker, L. A., Li, F., and Alpert, A. (1999). An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Mahwah, NJ: Lawrence Erlbaum Associates.
6. Farrington, D. P., and West, D. J. (1990). The Cambridge study in delinquent development: A long term follow-up of 411 London males. In H. J. Kerner and G. Kaiser (Eds.), Kriminalität: Persönlichkeit, Lebensgeschichte und Verhalten (pp. 115-138). Berlin: Springer.
7. Hox, J. J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Lawrence Erlbaum Associates.
8. Jeffries, N. O. (2003). A note on ‘testing the number of components in a normal mixture.' Biometrika, 90, 991-994.
9. Jones, B. L., Nagin, D. S., and Roeder, K. (2001). A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods and Research, 29, 374-393.
10. Jöreskog, K. G., and Sörbom, D. (2004). LISREL 8.7 for Windows. Lincolnwood, IL: Scientific Software International.
11. Kreuter, F., (2004). Longitudinal models with zero inflation. Unpublished manuscript, University of California at Los Angeles, Department of Statistics.
12. Lambert, D. (1992). Zero-inflated Poisson regression with an application to defects in manufacturing. Technometrics, 34, 1-13.
13. Land, K. C., McCall, P. L., and Nagin, D. S. (1996). A comparison of Poisson, negative binomial, and semiparametric mixed Poisson regression models with empirical applications to criminal careers data. Sociological Methods and Research, 24, 387-442.
14. Li, F., Duncan, T. E., Duncan, S. C., and Acock, A. (2001). Latent growth modeling of longitudinal data: A finite growth mixture modeling approach. Structural Equation Modeling, 8, 493-530.
15. Liang, K.-Y., and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13-22.
16. Lo, Y., Mendell, N. R., and Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767-778.
17. McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In J. R. Nesselroade and R. B. Cattell (Eds.), Handbook of multivariate experimental psychology (pp. 561-614). New York: Plenum.
18. McArdle, J. J., and Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58, 110-133.
19. McDermott, S., and Nagin, D. S. (2001). Same or different? Comparing offender groups and covariates over time. Sociological Methods and Research, 29, 282-318.
20. Meredith, M., and Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107-122.
21. Muthén, B. (1991). Analysis of longitudinal data using latent variable models with varying parameters. In L. Collins and J. Horn (Eds.), Best methods for the analysis of change (pp. 1-17). Washington, DC: American Psychological Association.
22. Muthén, B. (1997). Latent variable modeling with longitudinal and multilevel data. In A. Raftery (Ed.), Sociological Methodology (pp. 453-480). Boston: Blackwell.
23. Muthén, B. (2001a). Latent variable mixture modeling. In G. A. Marcoulides and R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 1-33). Lawrence Erlbaum Associates.
24. Muthén, B. (2001b). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. In L. M. Collins and A. Sayer (Eds.), New methods for the analysis of change (pp. 291-322). Washington, DC: APA.
25. Muthén, B. O. (2002). Beyond SEM: General latent variable modeling. Be-haviormetrika, 29, 81-117.
26. Muthén, B. O. (2003). Statistical and substantive checking in growth mixture modeling: Comment on Bauer and Curran (2003). Psychological Methods, 8, 369-377.
27. Muthén, B. O. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 345-368). Thousand Oaks: Sage.
28. Muthén, B. O., and Curran, P. J. (1997). General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371-402.
29. Muthén, L., and Muthén, B. O. (2001). Mplus: The comprehensive modeling program for applied researchers: User’s guide. (2nd ed.). Los Angeles: Muthén and Muthén.
30. Muthén, L., and Muthén, B. O. (2004). Mplus user’s guide. (3rd ed.). Los Angeles: Muthén and Muthén.
31. Muthén, B., and Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469.
32. Nagin, D. S. (1999). Analyzing developmental trajectories: A semi-parametric, group-based approach. Psychological Methods, 4, 139-157.
33. Nagin, D. S., and Land, K. C. (1993). Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric, mixed Poisson model. Criminology, 31, 327-362.
34. Poge, A. (2005). Personliche Codes bei Langsschnittstudien: Ein Erfahrungsbericht [Personal codes in longitudinal studies: An empirical report]. ZA-Information, 56, 50-69.
35. Rao, C. R. (1958). Some statistical methods for comparison of growth curves. Biometrics, 14, 1-17.
36. Roeder, K., Lynch, K. G., and Nagin, D. S. (1999). Modeling uncertainty in latent class membership: A case study in criminology. Journal of the American Statistical Association, 94, 766-776.
37. Ross, S. M. (1993). Introduction to probability models. (5th ed.). New York: Academic Press.
38. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
39. Tracy, P. E., Wolfgang, M. E., and Figlio, R. M. (1990). Delinquency in two birth cohorts. New York: Plenum.
40. Tremblay, R. E., Desmarais-Gervais, L., Gagnon, C. and Charlebois, P. (1987). The preschool behavior questionnaire: Stability of its factor structure between cultures, sexes, ages and socioeconomic classes. International Journal of Behavioral Development, 10, 467-484.
41. Tucker, L. R. (1958). Determination of parameters of a functional relation by factor analysis. Psychometrika, 23, 19-23.
42. Yang, C. C. (1998). Finite mixture model selection with psychometric applications. Unpublished doctoral dissertation, University of Groningen, The Netherlands.