Abstract
The purpose of this study was to determine whether qualitatively distinct trajectories of antisocial behavior could be identified in 1,708 children (843 boys, 865 girls) from the 1979 National Longitudinal Survey of Youth–Child Data (NLSY-C). Repeated ratings were made on the Behavior Problems Index (BPI: Peterson and Zill Journal of Marriage and the Family, 48, 295–307, 1986) antisocial scale by the mothers of these children when the children were 6, 8, 10, 12, and 14 years of age. Scores on three indicators constructed from the six BPI Antisocial items (callousness, aggression, noncompliance) were then analyzed longitudinally (by summing across the rating periods) and cross-sectionally (by testing each individual rating period) in the full sample as well as in subsamples of boys and girls. Results obtained with the mean above minus below a cut (MAMBAC), maximum covariance (MAXCOV), and latent mode factor analysis (L-Mode) taxometric procedures revealed consistent evidence of continuous latent structure despite the fact Growth Mixture Modeling (GMM) and Latent Class Growth Analysis (LCGA) identified between two and eight trajectories, depending on the stopping rule, in the three antisocial indicators. From these results, it is concluded that the structural model underlying these data is better represented as continuous rather than as categorical. The implications of these results for future research on developmental trajectories of antisocial behavior are discussed.
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References
Baker, P., Keck, C. K., Mott, F. L., & Quinlan, S. V. (1993). NLSY child handbook: A guide to the 1986–1990 NLSY child data (rev ed.). Columbus: The Ohio State University, Center for Human Resource Research.
Bartlett, M. S. (1937). The statistical conception of mental factors. British Journal of Psychology, 28, 97–104.
Bauer, D. J. (2007). Observations on the use of growth mixture models in psychological research. Multivariate Behavioral Research, 42, 757–786.
Bauer, D. J., & Curran, P. J. (2003). Distributional assumptions of growth mixture models: implications for overextraction of latent trajectory classes. Psychological Methods, 8, 338–363.
Broidy, L. M., Nagin, D. S., Tremblay, R. E., Bates, J. E., Brame, R., Dodge, K., et al. (2003). Developmental trajectories of childhood disruptive behaviors and adolescent delinquency: a six-site, cross-national study. Developmental Psychology, 39, 222–245.
Burt, S. A., Donnellan, M. B., Iacono, W. G., & McGue, M. (2011). Age-of-onset or behavioral sub-types? A prospective comparison of two approaches to characterizing the heterogeneity with antisocial behavior. Journal of Abnormal Psychology, 39, 633–644.
Bushway, S. D., Thornberry, T. P., & Krohn, M. D. (2003). Desistance as a developmental process: a comparison of static and dynamic approaches. Journal of Quantitative Criminology, 19, 129–153.
Center for Human Resource Research (2009). NLSY79 user’s guide. The Ohio State University, CHRR NLS User Services.
Cleland, C. M., Rothschild, L., & Haslam, N. (2000). Detecting latent taxa: Monte Carlo comparison of taxometric, mixture model, and clustering procedures. Psychological Reports, 87, 37–47.
Cronbach, L., & Furby, L. (1970). How we should measure ‘change’: or should we? Psychological Bulletin, 74, 68–80.
De Los Reyes, A., & Kazdin, A. E. (2005). Informant discrepancies in the assessment of childhood psychopathology: a critical review, theoretical framework, and recommendations for further study. Psychological Bulletin, 131, 483–509.
Frazier, T. W., Algorta, G. P., Youngstrom, E. A., & Ruscio, J. (2011). Comparing taxometric analysis and latent variable models in the identification of categorical and dimensional data. Manuscript submitted for publication.
Gresham, F. M., Elliott, S. N., Cook, C. R., Vance, M. J., & Kettler, R. (2010). Cross-informant agreement for ratings for social skill and problem behavior ratings: an investigation of the Social Skills Improvement System-Rating Scales. Psychological Assessment, 22, 157–166.
Grove, W. M., & Meehl, P. E. (1993). Simple regression based procedures for taxometric investigation. Psychological Reports, 73, 707–737.
Harzing, A. W. (2007). Publish or perish. (http://www.harzing.com/pop.htm). Accessed 28 July 2012.
Haslam, N., & Cleland, C. (2002). Taxometric analysis of fuzzy categories: a Monte Carlo study. Psychological Reports, 90, 401–404.
Hoeve, M., Blokland, A., Dubas, J. S., Loeber, R., Gerris, J. R. M., & Van der Laan, P. H. (2008). Trajectories of delinquency and parenting styles. Journal of Abnormal Child Psychology, 36, 223–235.
Lacourse, E., Cote, S., Nagin, D. S., Vitaro, F., Brendgen, M., & Tremblay, R. E. (2002). A longitudinal–experimental approach to testing theories of antisocial behavior. Development and Psychopathology, 14, 909–924.
Lahey, B. B., Van Hulle, C. A., Waldman, I. D., Rodgers, J. L., D’Onofrio, B. M., & Pedlow, S. (2006). Testing descriptive hypotheses regarding sex differences in the development of conduct problems and delinquency. Journal of Abnormal Child Psychology, 34, 737–755.
Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767–778.
McGrath, R. E., & Walters, G. D. (2012). Taxometric analysis as a general strategy for distinguishing categorical from dimensional latent structure. Psychological Methods, 17, 284–293.
McLachlan, G., & Peel, D. (2000). Finite mixture models. New York: Wiley.
Meehl, P. E. (1992). Factors and taxa, traits and types, differences of degree and differences of kind. Journal of Personality, 60, 117–174.
Meehl, P. E. (1995). Bootstraps taxometrics: solving the classification problem in psychopathology. American Psychologist, 50, 266–275.
Meehl, P. E. (2004). What’s in a taxon? Journal of Abnormal Psychology, 113, 39–43.
Meehl, P. E., & Yonce, L. J. (1994). Taxometric analysis: I. Detecting taxonicity with two quantitative indicators using means above and below a sliding cut (MAMBAC procedure). Psychological Reports, 74, 1059–1274.
Meehl, P. E., & Yonce, L. J. (1996). Taxometric analysis: II. Detecting taxonicity using covariance of two quantitative indicators in successive intervals of a third indicator (MAXCOV procedure). Psychological Reports, 78, 1091–1227.
Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy. Psychological Review, 100, 674–701.
Muthén, B. (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: Sage Publications.
Muthén, B., & Muthén, L. (1998–2007). Mplus user’s guide (5th ed.). Los Angeles: Muthén & Muthén.
Nagin, D. S. (1999). Analyzing developmental trajectories: a semiparametric group-based approach. Psychological Methods, 4, 139–157.
Nagin, D. S., & Tremblay, R. E. (1999). Trajectories of boys’ physical aggression, opposition, and hyperactivity on the path to physically violent and nonviolent juvenile delinquency. Child Development, 70, 1181–1196.
Nagin, D. S., & Tremblay, R. E. (2005). Developmental trajectory groups: fact or a useful statistical fiction? Criminology, 43, 873–904.
Nagin, D. S., Farrington, D. P., & Moffitt, T. E. (1995). Life-course trajectories of different types of offenders. Criminology, 33, 111–139.
Peterson, J. L., & Zill, N. (1986). Marital disruption, parent–child relationships, and behavioral problems in children. Journal of Marriage and Family, 48, 295–307.
R Development Core Team (2005). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org.
Ruscio, J. (2007). Taxometric analysis: an empirically-grounded approach to implementing the method. Criminal Justice and Behavior, 34, 1588–1622.
Ruscio, J. (2009). Assigning cases to groups using taxometric results: an empirical comparison of classification techniques. Assessment, 16, 55–70.
Ruscio, J. (2011). Taxometric programs for the R computing environment: User’s manual. (http://www.tcnj.edu/~ruscio/taxometrics.html). Accessed 3 November 2011.
Ruscio, J., & Kaczetow, W. (2008). Simulating multivariate nonnormal data using an iterative algorithm. Multivariate Behavioral Research, 43, 355–381.
Ruscio, J., & Marcus, D. K. (2007). Detecting small taxa using simulated comparison data: a reanalysis of Beach, Amir, and Bau’s (2005) data. Psychological Assessment, 19, 241–246.
Ruscio, J., & Ruscio, A. M. (2004). A conceptual and methodological checklist for conducting a taxometric investigation. Behavior Therapy, 35, 403–447.
Ruscio, J., & Walters, G. D. (2009). Using comparison data to differentiate categorical and dimensional data by examining factor score distributions: resolving the mode problem. Psychological Assessment, 21, 578–594.
Ruscio, J., & Walters, G. D. (2011). Differentiating categorical and dimensional data with taxometric analysis: are two variables better than none? Psychological Assessment, 23, 287–299.
Ruscio, J., Haslam, N., & Ruscio, A. M. (2006). Introduction to the taxometric method: A practical guide. Mahwah: Lawrence Erlbaum.
Ruscio, J., Ruscio, A. M., & Meron, M. (2007). Applying the bootstrap to taxometric analysis: generating empirical sampling distributions to help interpret results. Multivariate Behavioral Research, 42, 349–386.
Ruscio, J., Walters, G. D., Marcus, D. K., & Kaczetow, W. (2010). Comparing the relative fit of categorical and dimensional latent variable models using consistency tests. Psychological Assessment, 22, 5–21.
Sampson, R. J., & Laub, J. H. (2005). Seductions of method: rejoinder to Nagin and Tremblay’s “developmental trajectory groups: fact or fiction?”. Criminology, 43, 905–913.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Mifflin.
Van Dulmen, M., Goncy, E., Vest, A., & Flannery, D. (2009). Group-based trajectory modeling of externalizing behavior problems from childhood through adulthood: Exploring discrepancies in the empirical findings. In J. Savage (Ed.), The development of persistent criminality (pp. 288–314). New York: Oxford University Press.
Waller, N. G., & Meehl, P. E. (1998). Multivariate taxometric procedures: Distinguishing types from continua. Thousand Oaks: Sage.
Walters, G. D. (2011). The latent structure of life-course-persistent antisocial behavior: is Moffitt’s developmental taxonomy a true taxonomy? Journal of Consulting and Clinical Psychology, 79, 96–105.
Walters, G. D. (2012). Taxometrics and criminal justice: assessing the latent structure of crime-related constructs. Journal of Criminal Justice, 40, 10–20.
Walters, G. D., & Ruscio, J. (2009). To sum or not to sum: taxometric analysis with ordered categorical assessment items. Psychological Assessment, 21, 99–111.
Walters, G. D., & Ruscio, J. (2010). Where do we draw the line? Assigning cases to subsamples for MAMBAC, MAXCOV, and MAXEIG taxometric analyses. Assessment, 17, 321–333.
Walters, G. D., McGrath, R. E., & Knight, R. A. (2010). Taxometrics, polytomous constructs, and the Comparison Curve Fit Index: a Monte Carlo analysis. Psychological Assessment, 22, 149–156.
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The authors would like to thank anonymous reviewers of an earlier version of this paper for their helpful comments. Program code and a user’s manual for the taxometric programs used in this study can be accessed at http://www.tcnj.edu/~ruscio/taxometrics.html.
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Walters, G.D., Ruscio, J. Trajectories of Youthful Antisocial Behavior: Categories or Continua?. J Abnorm Child Psychol 41, 653–666 (2013). https://doi.org/10.1007/s10802-012-9700-1
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DOI: https://doi.org/10.1007/s10802-012-9700-1