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Escaping the snare of chronological growth and launching a free curve alternative: General deviance as latent growth model

Published online by Cambridge University Press:  23 July 2013

Phillip Karl Wood*
Affiliation:
University of Missouri
Kristina M. Jackson
Affiliation:
Brown University
*
Address correspondence and reprint requests to: Phillip Karl Wood, Department of Psychological Sciences, 210 McAlester Hall, University of Missouri, Columbia, MO 65211; E-mail: [email protected].

Abstract

Researchers studying longitudinal relationships among multiple problem behaviors sometimes characterize autoregressive relationships across constructs as indicating “protective” or “launch” factors or as “developmental snares.” These terms are used to indicate that initial or intermediary states of one problem behavior subsequently inhibit or promote some other problem behavior. Such models are contrasted with models of “general deviance” over time in which all problem behaviors are viewed as indicators of a common linear trajectory. When fit of the “general deviance” model is poor and fit of one or more autoregressive models is good, this is taken as support for the inhibitory or enhancing effect of one construct on another. In this paper, we argue that researchers consider competing models of growth before comparing deviance and time-bound models. Specifically, we propose use of the free curve slope intercept (FCSI) growth model (Meredith & Tisak, 1990) as a general model to typify change in a construct over time. The FCSI model includes, as nested special cases, several statistical models often used for prospective data, such as linear slope intercept models, repeated measures multivariate analysis of variance, various one-factor models, and hierarchical linear models. When considering models involving multiple constructs, we argue the construct of “general deviance” can be expressed as a single-trait multimethod model, permitting a characterization of the deviance construct over time without requiring restrictive assumptions about the form of growth over time. As an example, prospective assessments of problem behaviors from the Dunedin Multidisciplinary Health and Development Study (Silva & Stanton, 1996) are considered and contrasted with earlier analyses of Hussong, Curran, Moffitt, and Caspi (2008), which supported launch and snare hypotheses. For antisocial behavior, the FCSI model fit better than other models, including the linear chronometric growth curve model used by Hussong et al. For models including multiple constructs, a general deviance model involving a single trait and multimethod factors (or a corresponding hierarchical factor model) fit the data better than either the “snares” alternatives or the general deviance model previously considered by Hussong et al. Taken together, the analyses support the view that linkages and turning points cannot be contrasted with general deviance models absent additional experimental intervention or control.

Type
Regular Articles
Copyright
Copyright © Cambridge University Press 2013 

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References

Bauer, D., & Curran, P. (2003). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods, 8, 338363.Google Scholar
Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291319Google Scholar
Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.Google Scholar
Bollen, K. A., & Curran, P. J. (2006). Latent curve models. Hoboken, NJ: Wiley.Google Scholar
Browne, M. W. (1993). Structured latent curve models. In Cuadras, C. M. & Rao, C. R. (Eds.), Multivariate analysis: Future directions (Vol. 2, pp. 171197). New York: Elsevier Science.Google Scholar
Cattell, R. B. (1978). The scientific use of factor analysis. New York: Plenum Press.Google Scholar
Cattell, R. B. (1988). Multivariate method and theory construction. In Nesselroade, J. R. & Cattell, R. B. (Eds.), Handbook of multivariate experimental psychology (2nd ed., pp. 320). New York: Plenum Press.Google Scholar
Cicchetti, D., & Rogosch, F. A. (1996). Adaptive coping under conditions of extreme stress: Multilevel influences on the determinants of resilience in maltreated children. New Directions for Child and Adolescent Development, 124, 4759.Google Scholar
Demetriou, A., & Raftopoulos, A. (2004). The shape and direction of development: Teleologically but erratically lifted up or timely harmonious? Journal of Cognition and Development, 5, 8995.Google Scholar
Granger, C. W. J., & Morris, M. J. (1976). Time series modelling and interpretation. Journal of the Royal Statistical Society, A, 139, 246257.Google Scholar
Hussong, A. M., Curran, P. J., Moffitt, T. E., & Caspi, A. (2008). Testing turning points using latent growth curve models: Competing models of substance abuse and desistance in young adulthood. In Cohen, P. (Ed.), Applied data analytic techniques for turning points research (pp. 81104). New York: Routledge.Google Scholar
Hussong, A. M., Curran, P. J., Moffit, T. E., Caspi, A., & Carrig, M. (2005). Substance abuse hinders desistance in young adults' antisocial behavior. Development and Psychopathology, 16, 10291046.Google Scholar
Jackson, K. M., Sher, K. J., & Wood, P. K. (2000). Prospective analysis of comorbidity: Tobacco and alcohol use disorders. Journal of Abnormal Psychology, 109, 679694.Google Scholar
Jessor, R., & Jessor, S. (1977). Problem behavior and psychosocial development. New York: Academic Press.Google Scholar
Johnston, L. D., O'Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2011). Monitoring the future national survey results on drug use, 1975–2010: Vol. 2. College students and adults ages 19–50. Ann Arbor, MI: University of Michigan, Institute for Social Research.Google Scholar
Johnstone, B. M., Leino, E., Ager, C. R., Ferrer, H., & Fillmore, K. M. (1996). Determinants of life-course variation in the frequency of alcohol consumption: Meta-analysis of studies from the collaborative alcohol-related longitudinal project. Journal of Studies on Alcohol, 57, 494506.Google Scholar
Jöreskog, K. G. (1971). Simultaneous factor analysis in several populations. Psychometrika, 36, 409426.Google Scholar
Kan, K.-J., Boomsma, D. I., Dolan, C. V., & van der Maas, H. L. J. (2012). Commentary: The presence of bifurcations as a “third component of individual differences”: Implications for quantitative (behaviour) genetics. International Journal of Epidemiology. Advance online publication. doi:10.1093/ije/dyr222Google Scholar
Krueger, R. F., Markon, K. E., Patrick, C. J., & Iacono, W. G. (2005). Externalizing psychopathology in adulthood: A dimensional-spectrum conceptualization and its implications for DSM-V. Journal of Abnormal Psychology, 114, 537550.CrossRefGoogle ScholarPubMed
Kuljanin, G., Braun, M. R., & DeShon, R. P. (2011). A cautionary note on modeling growth trends in longitudinal data. Psychological Methods, 16, 249264.CrossRefGoogle ScholarPubMed
Kwok, O., Luo, W., & West, S. G. (2010). Using modification indices to detect turning points in longitudinal data: A Monte Carlo study. Structural Equation Modeling, 17, 216240.Google Scholar
Liu, S., Rovine, M. J., & Molenaar, P. C. M. (2012). Selecting a linear mixed model for longitudinal data: Repeated measures analysis of variance, covariance pattern model, and growth curve approaches. Psychological Methods, 17, 1530.Google Scholar
Maxwell, S. E., & Delaney, H. D. (2004). Designing experiments and analyzing data: A model comparison perspective (2nd ed.). Mahwah, NJ: Erlbaum.Google Scholar
McArdle, J. J., & Anderson, E. (1990). Latent variable growth models for research on aging. In Birren, J. E. & Schaie, K. W. (Eds.), Handbook of the psychology of aging (3rd ed., pp. 2244). New York: Academic Press.Google Scholar
McArdle, J. J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Developmental Psychology, 58, 110133.Google Scholar
McDonald, R. P. (1967). Nonlinear factor analysis (Psychometric Monograph 15). Richmond, VA: Byrd Press.Google Scholar
McGue, M., Iacono, W. G., & Krueger, R. F. (2006). The association of early adolescent problem behavior and adult psychopathology: A multivariate behavioral genetic perspective. Behavior Genetics, 36, 591602.Google Scholar
Meredith, W., & Horn, J. (2001). The role of factorial invariance in modeling growth and change. In Collins, L. M. & Sayer, A. G.(Eds.), New methods for the analysis of change. (pp. 203240). Washington, DC: American Psychological Association.Google Scholar
Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107122.Google Scholar
Molenaar, P. C. M. (2003). State space techniques in structural equation modeling. Retrieved from http://www.hhdev.psu.edu/hdfs/faculty/docs/StateSpaceTechniques.pdfGoogle Scholar
Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement, 2, 201218.Google Scholar
Molenaar, P. C. M., Huizenga, H. M., & Nesselroade, J. R. (2003). The relationship between the structure of interindividual and intraindividual variability: A theoretical and empirical vindication of developmental systems theory. In Staudinger, U. M. & Lindenberger, U. (Eds.), Understanding human development: Dialogues with lifespan psychology (pp. 339360). New York: Kluwer.Google Scholar
Muthén, B. (2003). Statistical and substantive checking in growth mixture modeling: Comment on Bauer and Curran (2003). Psychological Methods, 8, 369377.Google Scholar
Partridge, T., & Lerner, J. V. (2007). A latent growth-curve approach to difficult temperament. Infant and Child Development, 16, 255265.Google Scholar
Ram, N., & Grimm, K. (2007). Using simple and complex growth models to articulate developmental change: Matching theory to method. International Journal of Behavioral Development, 31, 303316.Google Scholar
Robins, L., Helzer, J., Cottler, L., & Goldring, E. (1989). NIMH Diagnostic Interview Schedule—Version III—Revised (DIS-III-R). Bethesda, MD: National Institute of Mental Health.Google Scholar
Rodgers, J. L. (2010). The epistemology of mathematical and statistical modeling: A quiet methodological revolution. American Psychologist, 65, 112.Google Scholar
Rovine, M. J., & Molenaar, P. C. M. (2000). A structural modeling approach to a multilevel random coefficients model. Multivariate Behavioral Research, 35, 5188.Google Scholar
Rovine, M. J., & Molenaar, P. C. M. (2005). Relating factor models for longitudinal data to quasi-simplex and NARMA models. Multivariate Behavioral Research, 40, 83114.Google Scholar
Rutter, M. (1996). Transitions and turning points in developmental psychopathology: As applied to the age span between childhood and mid-adulthood. International Journal of Behavioral Development, 19, 603626.Google Scholar
Schmitz, B. (2000). Auf der Suche nach dem verlorenen Individuum: Vier Theoreme zur Aggregation von Prozessen. Psychologische Rundschau, 51, 8392.Google Scholar
Silva, P. A., & Stanton, W. R. (1996). From child to adult: The Dunedin Multidisciplinary Health and Development Study. Auckland: Oxford University Press.Google Scholar
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, 229239.Google Scholar
Sterba, S. K., & Bauer, D. J. (2010). Matching method with theory in person-oriented developmental psychopathology research. Development and Psychopathology, 22, 239254.Google Scholar
Vazsonyi, A. T., & Huang, L. (2010). Where self-control comes from: On the development of self-control and its relationship to deviance over time. Developmental Psychology, 46, 245257.Google Scholar
von Eye, A., & Bergman, L. R. (2003). Research strategies in developmental psychopathology: Dimensional identity and the person-oriented approach. Development and Psychopathology, 15, 553580.Google Scholar
Welte, J., Barnes, G., Hoffman, J., Wieczorek, W., & Zhang, L. (2005). Substance involvement and the trajectory of criminal offending in young males. American Journal of Drug and Alcohol Abuse, 31, 267284.Google Scholar
Wohlwill, J. (1973). The study of behavioral development. New York: Academic Press.Google Scholar
Wood, P. K. (2011). Developmental models for children's temperament: Alternatives to chronometric polynomial curves. Infant and Child Development, 20, 194212.Google Scholar
Wood, P. K., Jackson, K., & Steinley, D. (2013). Have repeated measures MANOVA, growth curve, and multilevel models been poor factor models all along? Unpublished manuscript.Google Scholar