A Framework for Estimating Causal Effects in Latent Class Analysis: Is There a Causal Link Between Early Sex and Subsequent Profiles of Delinquency?
- 927 Downloads
Prevention scientists use latent class analysis (LCA) with increasing frequency to characterize complex behavior patterns and profiles of risk. Often, the most important research questions in these studies involve establishing characteristics that predict membership in the latent classes, thus describing the composition of the subgroups and suggesting possible points of intervention. More recently, prevention scientists have begun to adopt modern methods for drawing causal inference from observational data because of the bias that can be introduced by confounders. This same issue of confounding exists in any analysis of observational data, including prediction of latent class membership. This study demonstrates a straightforward approach to causal inference in LCA that builds on propensity score methods. We demonstrate this approach by examining the causal effect of early sex on subsequent delinquency latent classes using data from 1,890 adolescents in 11th and 12th grade from wave I of the National Longitudinal Study of Adolescent Health. Prior to the statistical adjustment for potential confounders, early sex was significantly associated with delinquency latent class membership for both genders (p = 0.02). However, the propensity score adjusted analysis indicated no evidence for a causal effect of early sex on delinquency class membership (p = 0.76) for either gender. Sample R and SAS code is included in an Appendix in the ESM so that prevention scientists may adopt this approach to causal inference in LCA in their own work.
KeywordsLatent class analysis Causal inference Propensity scores Delinquency Adolescent sexual initiation
Preparation of this manuscript was supported by National Institute on Drug Abuse (NIDA) Center grant P50 DA10075-16. The authors thank Mildred Maldonado-Molina, Bethany Bray, and Amanda Applegate for feedback on an early draft of this manuscript. This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design.
Conflict of Interest
The content is solely the responsibility of the authors and does not necessarily represent the official views of NIDA or the National Institutes of Health (NIH). No direct support was received from grant P01-HD31921 for this analysis.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Lawrence Erlbaum Associates.Google Scholar
- Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. Hoboken, NJ: Wiley.Google Scholar
- Harris, K. M. (2009). The National Longitudinal Study of Adolescent Health (Add Health), Waves I & II, 1994–1996; Wave III, 2001–2002; Wave IV, 2007–2009 [Machine-readable data file and documentation]. Chapel Hill, NC: Carolina Population Center, University of North Carolina at Chapel Hill.Google Scholar
- Harris, K. M., Halpern, C. T., Whitsel, E., Hussey, J., Tabor, J., Entzel, P., & Udry, J. R. (2009). The National Longitudinal Study of Adolescent Health: Research design [www document]. Retrieved from http://www.cpc.unc.edu/projects/addhealth/design. Accessed 7 June 2012.
- Lanza, S. T., Dziak, J. J., Huang, L., Xu, S., & Collins, L. M. (2011). PROC LCA & PROC LTA users’ guide (Version 1.2.7). University Park: The Methodology Center, Penn State. Retrieved from http://methodology.psu.edu. Accessed 1 Nov 2011.
- Lanza, S. T., Coffman, D. L., & Xu, S. (2013). Causal inference in latent class analysis. Structural Equation Modeling (in press).Google Scholar
- McDonald, R. P. (1985). Factor analysis and related methods. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
- PROC LCA (Version 1.2.7) [Software]. (2011). University Park: The Methodology Center, Penn State. Retrieved from http://methodology.psu.edu. Accessed 15 May 2011.
- Ridgeway, G., McCaffrey, D., Morral, A., Griffin, B. A., & Burgette, L. (2012). twang: Toolkit for Weighting and Analysis of Nonequivalent Groups (Version 1.2-5) [Software]. Retrieved from http://CRAN.R-project.org/package=twang. Accessed 25 Apr 2012.
- Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33–38.Google Scholar
- Rubin, D. B. (1980). Peer commentary on the paper “Randomization analysis of experimental data: The Fisher randomization test” by D. Basu. Journal of the American Statistical Association, 75, 591–593.Google Scholar