Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-24T18:59:49.902Z Has data issue: false hasContentIssue false

Steps substantive researchers can take to build a scientifically strong case for the existence of trajectory groups

Published online by Cambridge University Press:  28 April 2010

Nicholas Ialongo*
Affiliation:
Johns Hopkins University
*
Address correspondence and reprint requests to: Nicholas Ialongo, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, 8th Floor, Baltimore, MD 21205; E-mail: [email protected].

Abstract

Sterba and Bauer's Keynote Article does a superb job of reviewing the “… assumptions, strengths, and limitations of model-based person-oriented methods—clarifying which theoretical principles [researchers] can test and the compromises and trade-offs required to do so.” Their writing is exceptionally clear, and the examples given highly instructive. At the same time, their arguments may be so convincing that the reader may be reluctant to pursue person-oriented analyses in a longitudinal context. The purpose of this Commentary is not to contradict Sterba and Bauer's arguments but to briefly review the steps that substantive researchers can take in building a scientifically strong case for either assuming continuously varied growth “… or that [trajectory groups] actually exist” according to Raudenbush. These steps have been elaborated in a series of papers by Muthén and colleagues, but it is useful to briefly review them here.

Type
Special Section Commentary
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Cook, R. D. (1986). Assessment of local influence (with discussion). Journal of the Royal Statistical Society, B, 48, 133169.Google Scholar
Farrington, D. P., & West, D. J. (1990). The Cambridge Study in delinquent development: A long-term follow-up of 411 London males. In Kerner, H. J. & Kaiser, G. (Eds.), Kriminalitaet. Berlin: Springer.Google Scholar
Haviland, A., & Nagin, D. S. (2005). Causal inference with group-based trajectory models. Psychometrika, 70, 122.Google Scholar
Haviland, A., & Nagin, D. S. (2007). Using group-based trajectory modeling in conjunction with propensity scores to improve balance. Journal of Experimental Criminology, 3, 6582.Google Scholar
Haviland, A., Nagin, D., & Rosenbaum, P. (2007). Combining propensity score matching and group-based trajectory analysis in an observational study. Psychological Methods, 12, 247267.Google Scholar
Hunter, A. M., Muthén, B. O., Cook, I. A., & Leuchter, A. F. (2010). Antidepressant response trajectories and quantitative electroencephalography (QEEG) biomarkers in major depressive disorder. Journal of Psychiatric Research, 44, 9098.CrossRefGoogle ScholarPubMed
Kreuter, F., & Muthén, B. (2008). Analyzing criminal trajectory profiles: Bridging multilevel and group-based approaches using growth mixture modeling. Journal of Quantitative Criminology, 24, 131.CrossRefGoogle Scholar
Liski, E. P. (1991). Detecting influential measurements in a growth curve model. Biometrics, 47, 659668.CrossRefGoogle Scholar
McLachlan, G., & Peel, D. (2000). Finite mixture nodels. New York: Wiley.CrossRefGoogle Scholar
Muthén, B. (2001a). Latent variable mixture modeling. In Marcoulides, G. A. & Schumacker, R. E. (Eds.), New developments and techniques in structural equation modeling (pp. 133). Mahwah, NJ: Erlbaum.Google Scholar
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 Collins, L. M. & Sayer, A. (Eds.), New methods for the analysis of change (pp. 291322). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
Muthén, B. (2003). Statistical and substantive checking in growth mixture modeling. Psychological Methods, 8, 369377.CrossRefGoogle ScholarPubMed
Muthén, B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In Kaplan, D. (Ed.), Handbook of quantitative methodology for the social sciences (pp. 345368). Newbury Park, CA: Sage.Google Scholar
Nagin, D. S. (1999). Analyzing developmental trajectories: A semi-parametric, group-based approach. Psychological Methods, 4, 139177.CrossRefGoogle Scholar
Nagin, D. S., & Land, K. C. (1993). Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric, mixed Poisson model. Criminology, 31, 327362.Google Scholar
Patterson, G. R., Reid, J., & Dishion, T. (1992). A social learning approach: IV. Antisocial boys. Eugene, OR: Castalia.Google Scholar
Raudenbush, S. W. (2005). How do we study “what happens next”? Annals of the American Academy of Political and Social Science, 602, 131144.Google Scholar
Rosenbaum, P., & Rubin, D. (1983). Assessing sensitivity to an unobserved binary covariate in an observational study with a binary outcome. Journal of the Royal Statistical Society, 45, 212218.Google Scholar
Schaeffer, C. M., Petras, H, Ialongo, N., Poduska, J., & Kellam, S. (2003). Modeling growth in boys' aggressive behavior across elementary school: Links to later criminal involvement, conduct disorder, and antisocial personality disorder. Developmental Psychology, 39, 10201035.CrossRefGoogle ScholarPubMed
Schwartz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461464.Google Scholar
Sterba, S. K., & Bauer, D. J. (2010). Matching method with theory in person-oriented developmental psychopathology research. Development and Psychopathology, 22, 239254.CrossRefGoogle ScholarPubMed