Twelve frequently asked questions about growth curve modeling

Journal of Cognition and Development

Volumn 11, Issue 2, 2010, Pages 121-136

  Curran, Patrick J ^a   Obeidat, Khawla ^a   Losardo, Diane ^a  

^a UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DEVELOPMENTAL PSYCHOLOGY; GROWTH CURVE; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MAXIMUM LIKELIHOOD METHOD; PREDICTION; STRUCTURAL EQUATION MODELING;

EID: 77951747413     PISSN: 15248372     EISSN: None     Source Type: Journal    
DOI: 10.1080/15248371003699969     Document Type: Article

References (68)

1. Allison, P. D. (2001). Missing data. Newbury Park, CA: Sage Publications, Inc.
2. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumaker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 243-278). Mahwah, NJ: Lawrence Erlbaum Associates.
3. Bauer, D. J. (2003). Estimating multilevel linear models as structural equation models. Journal of Educational and Behavioral Statistics, 28, 134-167.
4. Bauer, D. J. (2007). Observations on the use of growth mixture models in psychological research. Multivariate Behavioral Research, 42, 757-786.
5. Bauer, D. J., & Curran, P. J. (2003). Distributional assumptions of growth mixture models: Implications for over-extraction of latent trajectory classes. Psychological Methods, 8, 338-363.
6. 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.
7. Bollen, K. A. (2007). On the origins of latent curve models. In R. Cudeck & R. MacCallum (Eds.), Factor analysis at 100 (pp. 79-98). Mahwah, NJ: Lawrence Erlbaum Associates.
8. Bollen, K. A., & Curran, P. J. (2004). Autoregressive latent trajectory (ALT) models: A synthesis of two traditions. Sociological Methods and Research, 32, 336-383.
9. Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Hoboken, NJ: Wiley.
10. Bollen, K. A., & Long, J. S. (1993). Testing structural equation models. Newbury Park, CA: Sage.
11. Boscardin, C., Muthen, B., Francis, D., & Baker, E. (2008). Early identification of reading difficulties using heterogeneous developmental trajectories. Journal of Educational Psychology, 100, 192-208.
12. Brown, J. S., Meadows, S. O., & Elder, G. H. (2007). Race-ethnic inequality and psychological distress: Depressive symptoms from adolescence to young adulthood. Developmental Psychology, 43, 1295-1311.
13. Browne, M., & du Toit, S. H. C. (1991). Models for learning data. In L. Collins & J. L. Horn (Eds.), Best methods for the analysis of change (pp. 47-68). Washington, DC: American Psychological Association Press.
14. Bryk, A. S., & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101, 147-158.
15. Coffman, D. L., & Millsap, R. E. (2006). Evaluating latent growth curve models using individual fit statistics. Structural Equation Modeling, 13, 1-27.
16. Cronbach, L. J., & Furby, L. (1970). How should we measure 'change' - or should we? Psychological Bulletin, 74, 68-80.
17. Cudeck, R., & Harring, J. R. (2007). Analysis of nonlinear patterns of change with random coefficient models. Annual Review of Psychology, 58, 615-637.
18. Curran, P. J. (2000). A latent curve framework for studying developmental trajectories of adolescent substance use. In J. Rose, L. Chassin, C. Presson & J. Sherman (Eds.), Multivari ate applications in substance use research (pp. 1-42). Hillsdale, NJ: Erlbaum.
19. Curran, P. J. (2003). Have multilevel models been structural equation models all along? Multivariate Behavioral Research, 38, 529-569.
20. 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.
21. Curran, P. J., & Bollen, K. A. (2001). The best of both worlds: Combining autoregressive and latent curve models. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 105-136). Washington, DC: American Psychological Association Press.
22. Curran, P. J., & Hussong, A. M. (2002). Structural equation modeling of repeated measures data. In D. Moskowitz & S. Hershberger (Eds.), Modeling intra-individual variability with repeated measures data: Methods and applications (pp. 59-86). New York: Erlbaum Associates.
23. Curran, P. J., & Hussong, A. M. (2003). The use of latent trajectory models in psychopathology research. Journal of Abnormal Psychology, 112, 526-544.
24. Curran, P. J., & Willoughby, M. T. (2003). Implications of latent trajectory models for the study of developmental psychopathology. Development and Psychopathology, 15, 581-612.
25. Duncan, T. E., & Duncan, S. C. (2004). An introduction to latent growth curve modeling. Behavior Therapy, 35, 333-363.
26. Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2006). An introduction to latent variable growth curve modeling: Concepts, issues, and applications (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
27. Heckman, J. J. (1976). The common structure of statistical models of truncation, sample selection, and limited dependent variables and a simple estimator for such models. Annals Of Economic and Social Measurement, 5, 475-492.
28. Hedeker, D., & Gibbons, R. (2006). Longitudinal data analysis. New York: Wiley Inc.
29. Huttenlocher, J., Haight, W., Bryk, A., Seltzer, M., & Lyons, T. (1991). Early vocabulary growth: Relation to language input and gender. Developmental Psychology, 27, 236-248.
30. Little, R. J., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley Inc.
31. MacCallum, R. C., Kim, C., Malarkey, W., & Kiecolt-Glaser, J. (1997). Studying multivariate change using multilevel models and latent curve models. Multivariate Behavioral Research, 32, 215-253.
32. McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In J. R. Nesselroade & R. B. Cattell (Eds.), Handbook of multivariate experimental psychology (pp. 561-614). New York: Plenum.
33. McArdle, J. J. (2001). A latent difference score approach to longitudinal dynamic structural analysis. In R. Cudeck, S. H. C. du Toit, & K. G. Joreskog (Eds.), Structural equation modeling: Present and future (pp. 341-380). Lincolnwood, IL: Scientific Software.
34. McArdle, J. J. (2009). Latent variable modeling of differences in changes with longitudinal data. Annual Review of Psychology, 60, 577-605.
35. McArdle, J. J., & Epstein, D. (1987). Latent growth curves within developmental structural equation models. Child Development, 58, 110-133.
36. McArdle, J. J., Ferrer-Caja, E., Hamagami, F., & Woodcock, R. W. (2002). Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Developmental Psychology, 38, 115-142.
37. McCoach, D. B., O'Connell, A. A., Reis, S. M., & Levitt, H. A. (2006). Growing readers: A hierarchical linear model of children's reading growth during the first 2 years of school. Journal of Educational Psychology, 98, 14-28.
38. Mehta, P. D., Neale, M. C., & Flay, B. R. (2004). Squeezing interval change from ordinal panel data: Latent growth curves with ordinal outcomes. Psychological Methods, 9, 301-333.
39. Mehta, P. D., & West, S. G. (2000). Putting the individual back into individual growth curves. Psychological Methods, 5, 23-43.
40. Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107-122.
41. Moffitt, T. E. (1993). Adolescence-limited and life-course persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100, 674-701.
42. Muthen, B. O. (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, CA: Sage Publications.
43. Muthen, B. O., & Curran, P. (1997). General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371-102.
44. Muthen, B. O., & Muthen, 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.
45. Muthen, B. O., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463-469.
46. Muthen, L. K., & Muthen, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9, 599-620.
47. Nagin, D. S. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press.
48. Nagin, D., & Land, K. C. (1993). Age, criminal careers, and population heterogeneity: Specification and estimation of a nonparametric, mixed Poisson model. Criminology, 31, 327-362.
49. Nesselroade, J. R. (1991). The warp and the woof of the developmental fabric. In R. Downs, L. Liben & D. S. Palermo (Eds.), Visions of aesthetics, the environment, & development: The legacy of Joachim F. Wohlwill (pp. 213-240). Hillsdale, NJ: Lawrence Erlbaum Associates.
50. Owens, E. B., & Shaw, D. S. (2003). Predicting growth curves of externalizing behavior across the preschool years. Journal of Abnormal Child Psychology, 31, 575-590.
51. Preacher, K. J., Wichman, A. L., MacCallum, R., & Briggs, N. E. (2008). Latent growth curve modeling. Thousand Oaks, CA: Sage Publications.
52. Raudenbush, S. W. (2001). Toward a coherent framework for comparing trajectories of individ-ual change. In L. Collins & A. Sayer (Eds.), Best methods for studying change (pp. 33-64). Washington, DC: The American Psychological Association.
53. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage Publications.
54. Rogosa, D. (1980). A critique of cross-lagged correlation. Psychological Bulletin, 88, 245-258.
55. Rogosa, D., & Willett, J. B. (1985). Understanding correlates of change by modeling individual differences in growth. Psychometrika, 50, 203-228.
56. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley Inc.
57. Satorra, A. (1990). Robustness issues in structural equation modeling: A review of recent developments. Quality & Quantity, 24, 367-386.
58. Schafer, J. L. (1997). Analysis of incomplete multivariate data. London: Chapman & Hall.
59. Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8, 3-15.
60. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.
61. Seltzer, M., Choi, K., & Thum, Y. M. (2003). Examining relationships between where students start and how rapidly they progress: Using new developments in growth modeling to gain insight into the distribution of achievement within schools. Educational Evaluation and Policy Analysis, 25, 263-286.
62. Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, 23, 323-355.
63. Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.
64. Stoolmiller, M. (2001). Synergistic interaction of child manageability problems and parent discipline tactics in predicting future growth in externalizing behavior for boys. Developmental Psychology, 37, 814-825.
65. 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.
66. Willett, J. B., Singer, J. D., & Martin, N. C. (1998). The design and analysis of longitudinal studies of development and psychopathology in context: Statistical models and methodological recommendations. Development and Psychopathology, 10, 395-426.
67. Williams, S. T., Conger, K. J., & Blozis, S. A. (2007). The development of interpersonal aggression during adolescence: The importance of parents, siblings, and family economics. Child Development, 78, 1526-1542.
68. Wohlwill, J. F. (1991). Relations between method and partial-isomorphism view. Annals of Theoretical Psychology, 7, 91-138.