Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-31T17:54:22.486Z Has data issue: false hasContentIssue false
  • English
  • Français

Matching method with theory in person-oriented developmental psychopathology research

Published online by Cambridge University Press:  28 April 2010

Sonya K. Sterba  and
Daniel J. Bauer
Sonya K. Sterba*
Affiliation:
The University of North Carolina at Chapel Hill
Daniel J. Bauer
Affiliation:
The University of North Carolina at Chapel Hill
*
Address correspondence and reprint requests to: Sonya Sterba, L. L. Thurstone Psychometric Laboratory, Department of Psychology, The University of North Carolina at Chapel Hill, Campus Box 3270, Chapel Hill, NC 27599-3270; E-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

The person-oriented approach seeks to match theories and methods that portray development as a holistic, highly interactional, and individualized process. Over the past decade, this approach has gained popularity in developmental psychopathology research, particularly as model-based varieties of person-oriented methods have emerged. Although these methods allow some principles of person-oriented theory to be tested, little attention has been paid to the fact that these methods cannot test other principles, and may actually be inconsistent with certain principles. Lacking clarification regarding which aspects of person-oriented theory are testable under which person-oriented methods, assumptions of the methods have sometimes been presented as testable hypotheses or interpreted as affirming the theory. This general blurring of the line between person-oriented theory and method has even led to the occasional perception that the method is the theory and vice versa. We review assumptions, strengths, and limitations of model-based person-oriented methods, clarifying which theoretical principles they can test and the compromises and trade-offs required to do so.

Type
Special Section Keynote Article
Information
Development and Psychopathology , Volume 22 , Issue 2 , May 2010 , pp. 239 - 254
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

Bartholomew, D. J., & Knott, M. (1999). Latent variable models and factor analysis (2nd ed.). London: Arnold.Google Scholar
Bauer, D. J. (2007). Observations on the use of growth mixture models in psychological research. Multivariate Behavioral Research, 24, 757–786.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Bauer, D. J., & Curran, P. J. (2004). The integration of continuous and discrete latent variable models: Potential problems and promising opportunities. Psychological Methods, 9, 3–29.CrossRefGoogle ScholarPubMed
Bauer, D. J., & Shanahan, M. J. (2007). Modeling complex interactions: Person-centered and variable-centered approaches. In Little, T. D., Bovaird, J. A., & Card, N. A. (Eds.), Modeling contextual effects in longitudinal studies (pp. 255–284). Mahwah, NJ: Erlbaum.Google Scholar
Bergman, L. R. (1998). A person-oriented approach to studying individual development: Snapshots and processes. In Cairns, R. B., Bergman, L. R., & Kagan, J. (Eds.), Methods and models for studying the individual (pp. 83–121). London: Sage.Google Scholar
Bergman, L. R. (2001). A person approach in research on adolescence: Some methodological challenges. Journal of Research on Adolescence, 16, 28–53.CrossRefGoogle Scholar
Bergman, L. R., & Magnusson, D. (1991). Stability and change in patterns of extrinsic adjustment problems. In Magnusson, D., Bergman, L. R., & Torestad, B. (Eds.), Problems and methods in longitudinal research: Stability and change (pp. 323–346). New York: Cambridge University Press.CrossRefGoogle Scholar
Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291–319.CrossRefGoogle ScholarPubMed
Bergman, L. R., & Magnusson, D., & El-Khouri, B. (2003). Studying individual development in an interindividual context: A person-oriented approach. Mahwah, NJ: Erlbaum.CrossRefGoogle Scholar
Bergman, L. R., & Trost, K. (2006). The person-oriented versus variable-oriented approach: Are they complementary, opposites, or exploring different worlds? Merrill–Palmer Quarterly, 52, 601–632.CrossRefGoogle Scholar
Boker, S. M., & Graham, J. (1998). Dynamical systems analysis of adolescent substance abuse. Multivariate Behavioral Research, 33, 479–507.CrossRefGoogle ScholarPubMed
Bollen, K. A., & Curran, P. J. (2004). Autoregressive latent trajectory (ALT) models: A synthesis of two traditions. Sociological Methods and Research, 32, 336.CrossRefGoogle Scholar
Block, J. (1971). Lives through time. Berkeley, CA: Bancroft.Google Scholar
Block, J. (2000). Three tasks for personality psychology. In Bergman, L. R., Cairns, R. B., Nilsson, L. G., & Nystedt, L. (Eds.), Developmental science and the holistic approach (pp. 155–164). Mahwah, NJ: Erlbaum.Google Scholar
Borsboom, D., & Dolan, C. V. (2007). Theoretical equivalence, measurement invariance, and the idiographic filter. Measurement, 5, 236–242.Google Scholar
Browne, M. W., & Zhang, G. (2005). DyFA: Dynamic factor analysis of lagged correlation matrices: Version 2.03 [Computer software and manual]. Retrieved from http://faculty.psy.ohio-state.edu/browne/Google Scholar
Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation approach. Hoboken, NJ: Wiley.Google Scholar
Cairns, R. B. (1979). Social development: The origins and plasticity of interchanges. San Francisco, CA: Freeman.Google Scholar
Cairns, R. B., Bergman, L. R., & Kagan, J. (1998). Methods and models for studying the individual. London: Sage.Google Scholar
Cattell, R. B., & Cattell, A. K., & Rhymer, R. M. (1947). P-technique demonstrated in determining psychophysical source traits in a normal individual. Psychometrika, 12, 267–288.CrossRefGoogle Scholar
Chow, S.-M., Nesselroade, J. R., Shifren, K., & McArdle, J. J. (2004). Dynamic structure of emotions among individuals with Parkinson's disease. Structural Equation Modeling, 11, 560–582.CrossRefGoogle Scholar
Cicchetti, D., & Cannon, T. D. (1999). Neurodevelopmental processes in the ontogenesis and epigenesist of psychopathology. Development and Psychopathology, 11, 375–393.CrossRefGoogle Scholar
Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8, 597–600.CrossRefGoogle Scholar
Cicchetti, D., & Schneider-Rosen, K. (1986). An organization approach to childhood depression. In Rutter, M., Izard, C., & Read, P. (Eds.), Depression in young people, clinical and developmental perspectives (pp. 71–134). New York: Guilford Press.Google Scholar
Coffman, D., & Millsap, R. (2006). Evaluating latent growth curve models using individual fit statistics. Structural Equation Modeling, 13, 1–27.CrossRefGoogle Scholar
Collins, L. M., Hyatt, S. L., & Graham, J. W. (2000). Latent transition analysis as a way of testing models of stage sequential change in longitudinal data. In Little, T. D., Schnabel, K. U., & Baumert, J. (Eds), Modeling longitudinal and multilevel data: Practical issues, applied approaches and specific examples (pp. 147–162). Mahwah, NJ: Erlbaum.Google Scholar
Collins, L. M. & Wugatler, S. E. (1992). Latent class models for stage-sequential dynamic latent variables. Multivariate Behavioral Research, 27, 131–157.CrossRefGoogle Scholar
Cudeck, R., & Klebe, K. (2002). Multiphase mixed-effects models for repeated measures data. Psychological Methods, 7, 41–63.CrossRefGoogle ScholarPubMed
Curran, P. J., Bauer, D. J., & Willoughby, M. T. (2004). Testing and probing main effects and interactions in latent curve analysis. Psychological Methods, 9, 220–237.CrossRefGoogle ScholarPubMed
Curran, P. J., & Willoughby, M. T. (2003). Implications of latent trajectory models for the study of developmental psychopathology. Development and Psychopathology, 15, 581–612.CrossRefGoogle Scholar
Curran, P. J., & Wirth, R. J. (2004). Interindividual differences in intraindividual variation: Balancing internal and external validity. Measurement, 2, 219–247.Google ScholarPubMed
Eggleston, E. P., Laub, J. H., & Sampson, R. J. (2004). Methodological sensitivities to latent class analysis of long-term criminal trajectories. Journal of Quantitative Criminology, 20, 1–26.CrossRefGoogle Scholar
Emde, R. N., & Spicer, P. (2000). Experience in the midst of variation: New horizons for development and psychopathology. Development and Psychopathology, 12, 313–331.CrossRefGoogle ScholarPubMed
Flaherty, B. P. (2008). Testing the degree of cross-sectional and longitudinal dependence between two discrete dynamic processes. Developmental Psychology, 44, 468–480.CrossRefGoogle ScholarPubMed
Gilliom, M., & Shaw, D. S. (2004). Codevelopment of externalizing and internalizing problems in early childhood. Development and Psychopathology, 16, 313–333.CrossRefGoogle ScholarPubMed
Gottleib, G., & Halpern, C. T. (2002). A relational view of causality in normal and abnormal development. Development and Psychopathology, 14, 421–435.CrossRefGoogle Scholar
Hershberger, S. L. (1998). Dynamic factor analysis. In Marcoulides, G. A. (Ed.), Modern methods for business research. Mahwah, NJ: Erlbaum.Google Scholar
Hirsh-Pasek, K., & Burchinal, M. (2006). Mother and caregiver sensitivity over time: Predicting language and academic outcomes with variable- and person-centered approaches. Merrill–Palmer Quarterly, 52, 449–485.CrossRefGoogle Scholar
Horn, J. L. (2000). Comments on integrating person-centered and variable-centered research on problems associated with the use of alcohol. Alcoholism: Clinical and Experimental Research, 24, 924–930.Google ScholarPubMed
Jackson, K. M., & Sher, K. J. (in press). Comparison of longitudinal phenotypes based on alternate heavy drinking cut scores: A systematic comparison of trajectory approaches III. Psychology of Addictive Behaviors.Google Scholar
Jones, K. (1991). The application of time series methods to moderate span longitudinal data. In Collins, L. & Horn, J. (Eds.), Best methods for the analysis of change (pp. 75–87). Washington, DC: American Psychological Association.Google Scholar
Kagan, J. (1994). Galen's prophecy: Temperament in human nature. New York: Basic Books.Google Scholar
Kaplan, D. (2008). An overview of Markov chain methods for the study of stage-sequential developmental processes. Developmental Psychology, 44, 457–467.CrossRefGoogle Scholar
Kelderman, H., & Molanaar, P. C. M. (2007). The effect of individual differences in factor loadings on the standard factor model. Multivariate Behavioral Research, 42, 435–456.CrossRefGoogle Scholar
Keller, T. E., Spieker, S. J., & Gilchrist, L. (2005). Patterns of risk and trajectories of preschool problem behaviors: A person-oriented analysis of attachment in context. Development and Psychopathology, 17, 349–384.CrossRefGoogle ScholarPubMed
Langeheine, R. (1994). Latent variable Markov models. In Eye, A. von & Clogg, C. C. (Eds.), Latent variable analysis: Applications for developmental research (pp. 373–395). Beverly Hills, CA: Sage.Google Scholar
Langeheine, R., & van de Pol, F. (1990) A unifying framework for Markov modeling in discrete space and discrete time. Sociological Methods and Research, 18, 416–441.CrossRefGoogle Scholar
Lubke, G., & Neale, M. (2006). Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood? Multivariate Behavioral Research, 41, 499–532.CrossRefGoogle ScholarPubMed
MacCallum, R. C., Zhang, S., Preacher, K. J., & Rucker, D. D. (2002). On the practice of dichotomization of quantitative variables. Psychological Methods, 7, 19–40.CrossRefGoogle ScholarPubMed
Magnusson, D. (1985). Implications of an interactional paradigm for research on human development. International Journal of Behavioral Development, 8, 115–137.CrossRefGoogle Scholar
Magnusson, D. (1998). The logic and implications of a person-oriented approach. In Cairns, R. B., Bergman, L. R., & Kagan, J. (Eds.), Methods and models for studying the individual (pp. 33–62). London: Sage.Google Scholar
Magnusson, D., & Torestad, B. (1993). A holistic view of personality: A model revisited. Annual Review of Psychology, 44, 427–452.CrossRefGoogle Scholar
Mannan, H. R., & Koval, J. J. (2003). Latent mixed Markov modelling of smoking transitions using Monte Carlo bootstrapping. Statistical Methods in Medical Research, 12, 125–146.CrossRefGoogle ScholarPubMed
Mehta, P. D., & West, S. G. (2000). Putting the individual back into individual growth curves. Psychological Methods, 5, 23–43.CrossRefGoogle ScholarPubMed
Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107–122.CrossRefGoogle Scholar
Molenaar, P. C. M. (1985). A dynamic factor model fofr the analysis of multivariate time series. Psychometrika, 50, 181–202.CrossRefGoogle Scholar
Molenaar, P. C. M. (1994). Dynamic latent variable models in developmental psychology. In Eye, A. von & Clogg, C. C. (Eds.), Latent variables analysis: Applications for developmental research. London: Sage.Google 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, 201–218.Google Scholar
Molenaar, P. C. M. (2007). Psychological methodology will change profoundly due to the necessity to focus on intra-individual variation. Integrative Psychological & Behavioral Science, 41, 35–40.CrossRefGoogle Scholar
Molenaar, P. C. M., de Gooijer, J. G., & Schmitz, B. (1992). Dynamic factor analysis of nonstationary multivariate time series. Psychometrika, 57, 333–349.CrossRefGoogle Scholar
Molenaar, P. C. M., & von Eye, A. (1994). On the arbitrary nature of latent variables. In Eye, A. von & Clogg, C. C. (Eds.), Latent variable analysis: Applications for developmental research (pp. 226–242). Thousand Oaks, CA: Sage.Google Scholar
Mun, E. Y., Windle, M., & Schainker, L. M. (2008). A model-based cluster analysis approach to adolescent problem behaviors and young adult outcomes. Development and Psychopathology, 20, 291–318.CrossRefGoogle ScholarPubMed
Muthén, B. (2001). Latent variable mixture modeling. In Marcoulides, G. A. & Schumacker, R. E. (Eds.), New developments and techniques in structural equation modeling (pp. 1–33). Mahwah, NJ: Erlbaum.Google Scholar
Muthén, B., & Asparouhov, T. (2006). Growth mixture modeling: Analysis with non-Gaussian random effects. In Fitzmaurice, G., Davidian, M., Verbeke, G., & Molenberghs, G. (Eds.), Advances in longitudinal data analysis. Boca Raton, FL: Chapman & Hall/CRC Press.Google Scholar
Muthén, B. O. (2007). Latent variable hybrids: Overview of new and old models. In Handcock, G. R. & Samuelsen, K. M. (Eds.), Advances in latent variable mixture models (pp. 1–24.) Charlotte, NC: Information Age Publishing.Google Scholar
Muthén, B. O., & Muthén, L. K. (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882–891.CrossRefGoogle ScholarPubMed
Muthén, B. O., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463–469.CrossRefGoogle ScholarPubMed
Nagin, D. S. (1999). Analyzing developmental trajectories: A semiparametric group-based approach. Psychological Methods, 4, 139–157.CrossRefGoogle Scholar
Nagin, D. S. (2005). Group-based modeling of development (pp. 1–78). Cambridge, MA: Harvard University Press.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, 327–362.CrossRefGoogle Scholar
Nagin, D. S., Pagani, L., Tremblay, R. E., & Vitaro, F. (2003). Life course turning points: The effect of grade retention on physical aggression. Development and Psychopathology, 15, 343–361.CrossRefGoogle ScholarPubMed
Nagin, D. S., & Tremblay, R. E. (2001). Analyzing developmental trajectories of distinct but related behaviors: A group-based method. Psychological Methods 6, 18–34.CrossRefGoogle ScholarPubMed
Nesselroade, J. R., Gerstorf, D., Hardy, S. A., & Ram, N. (2007). Idiographic filters for psychological constructs. Measurement, 5, 217–235.Google Scholar
Nesselroade, J. R., McArdle, J. J., Aggen, S. H., & Meyers, J. M. (2002). Dynamic factor analysis models for representing process in multivariate time-series. In Moskowitz, D. S. & Hershberger, S. L. (Eds.), Modeling intraindividual variability with repeated measures data: Methods and applications (pp. 235–265). Mahwah, NJ: Erlbaum.Google Scholar
Nesselroade, J. R., & Molenaar, P. C. M. (1999). Pooling lagged covariance structures based on short, multivariate time series for dynamic factor analysis. In Hoyle, R. (Ed.), Statistical strategies for small sample research (pp. 223–250). London: Sage.Google Scholar
Nesselroade, J. R., & Molenaar, P. C. M. (2003). Quantitative models for developmental processes. In Valsiner, J. & Connolly, K. J. (Eds.), Handbook of developmental psychology (pp. 622–639). London: Sage.Google Scholar
Nurious, P. S., & Macy, R. J. (2008). Heterogeneity among violence-exposed women: Applying person-oriented research methods. Journal of Interpersonal Violence, 23, 389–415.CrossRefGoogle Scholar
Nylund, K. (2007). Latent transition analysis: Modeling extensions and an application to peer victimization. Doctoral dissertation, University of California, Los Angeles.Google Scholar
Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31, 437–448.CrossRefGoogle Scholar
Ratelle, C. F., Guay, F., Vallerand, R. J., Larose, S., & Senecal, C. (2007). Autonomous, controlled, and amotivated types of academic motivation: A person-oriented analysis. Journal of Educational Psychology, 99, 734–746.CrossRefGoogle Scholar
Reinecke, J. (2006). Longitudinal analysis of adolescents' deviant and delinquent behavior. Measurement, 2, 100–112.Google Scholar
Rutter, M. (1996). Developmental psychopathology as an organizing research construct. In Magnusson, D. (Ed.), The lifespan development of individuals: Behavioral, neurobiological, and psychosocial perspectives (pp. 394–413). New York: Cambridge University Press.Google Scholar
Sameroff, A. J. (1982). Development and the dialectic: The need for a systems approach. In Collins, W. A. (Ed.), The concept of development (pp. 83–103). Mahwah, NJ: Erlbaum.Google Scholar
Sameroff, A. J., & Mackenzie, M. J. (2003). Research strategies for capturing transactional models of development: The limits of the possible. Development and Psychopathology, 15, 613–640.CrossRefGoogle ScholarPubMed
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, 1020–1035.CrossRefGoogle ScholarPubMed
Schmittmann, V. D., Dolan, C. V., van der Maas, H. L. J., & Neale, M. (2005). Discrete latent Markov models for normally distributed response data. Multivariate Behavioral Research, 40, 461–488.CrossRefGoogle ScholarPubMed
Shifren, K., Hooker, K., Wood, P., & Nesselroade, J. R. (1997). Structure and variation of mood in individuals with Parkinson's disease: A dynamic factor analysis. Psychology and Aging, 12, 328–339.CrossRefGoogle ScholarPubMed
Sroufe, A., & Rutter, M. (1984). The domain of developmental psychopathology. Child Development, 55, 17–29.CrossRefGoogle ScholarPubMed
Sterba, S. K., Prinstein, M. J., & Cox, M. J. (2007). Trajectories of internalizing problems across childhood: Heterogeneity, external validity, and gender differences. Development and Psychopathology, 19, 345–366.CrossRefGoogle ScholarPubMed
Troop-Gordon, W., & Ladd, G. W. (2005). Trajectories of peer victimization and perceptions of the self and schoolmates: Precursors to internalizing and externalizing problems. Child Development, 76, 1072–1091.CrossRefGoogle ScholarPubMed
Verbeke, G., & Lesaffre, E. (1996). A linear mixed-effects model with heterogeneity in the random-effects population. Journal of the American Statistical Association, 91, 217–221.CrossRefGoogle Scholar
von Eye, A., & Bergman, L. R. (2003). Research strategies in developmental psychopathology: Dimensional identity and the person-oriented approach. Development and Psychopathology, 15, 553–580.CrossRefGoogle ScholarPubMed
von Eye, A., & Bogat, K. (2006). Person-oriented and variable-oriented research: Concepts, results, and development. Merrill–Palmer Quarterly, 52, 390–420.Google Scholar
Walls, T. A., & Schafer, J. L. (2006). Models for intensive longitudinal data. New York: Oxford University Press.CrossRefGoogle Scholar
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scale. Journal of Personality and Social Psychology, 54, 1063–1070.CrossRefGoogle Scholar
Wiggins, L. M. (1973). Panel analysis. Amsterdam: Elsevier.Google Scholar
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.CrossRefGoogle Scholar
Windle, M. (2000). A latent growth curve model of delinquent activity among adolescents. Applied Developmental Science, 4, 193–207.CrossRefGoogle Scholar
Wood, P., & Brown, D. (1994). The study of intraindividual differences by means of dynamic factor models: Rationale, implementation, and interpretation. Psychological Bulletin, 116, 166–186.CrossRefGoogle Scholar
Zahn-Waxler, C., Klimes-Dougan, B., & Slattery, M. J. (2000). Internalizing problems of childhood and adolescence: Prospects, pitfalls, and progress in understanding the development of anxiety and depression. Development and Psychopathology, 12, 443–466.CrossRefGoogle ScholarPubMed
Zhang, Z. (2006). Codes for Mplus using MLE based on block-Toeplitz to estimate dynamic factor models. Retrieved July 14, 2008, from http://www.psychstat.org/us/article.php/71.htmGoogle Scholar