Methodological & Technical Research Topics
LCA with a Distal Outcome
Latent class membership can be used to predict a distal outcome (an outcome at a later time). The Methodology Center has been at the forefront of research on LCA with a distal outcome for several years. In 2017, we developed a SAS macro and Stata function that estimate LCA with a distal outcome using the BCH approach. This is the approach that we currently recommend for LCA with a distal outcome.
References & recommended reading
Bakk, Z., & Vermunt, J. K. (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling, 23, 20-31. doi: 10.1080/10705511.2014.955104t
Bray, B. C., Lanza, S. T., & Tan, X. (2014). Eliminating bias in classify-analyze approaches for latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal. Advance online publication. doi: 10.1080/10705511.2014.935265.
. (2016). . , , 107-116. http://doi.org/10.1027/1614-2241/a000114
Lanza, S. T., Tan, X., & Bray, B. C. (2013). Latent class analysis with distal outcomes: A flexible model-based approach. Structural Equation Modeling: A Multidisciplinary Journal, 20, 1-26.
LCA with Causal Inference
Butera, N. M., Lanza, S. T., & Coffman, D. L. (2013). A framework for estimating causal effects in latent class analysis: Is there a causal link between early sex and subsequent profiles of delinquency? Prevention Science. doi: 10.1007/s11121-013-0417-3 PMCID: PMC3888479
Lanza, S.T., Schuler, M.S., & Bray, B.C. (2016). Latent class analysis with causal inference: The effect of adolescent depression on young adult substance abuse profiles. In A. von Eye, & W. Wiedermann (Eds.), Causality and Statistics. Hoboken, NJ: Wiley.
Lanza, S. T., Coffman, D. L., & Xu, S. (2013). Causal inference in latent class analysis. Structural Equation Modeling, 20(3), 361-383. PMCID: PMC4240500
Schuler, M. S., Leoutsakos, J. S., & Stuart, E. A. (2014). Addressing confounding when estimating the effects of latent classes on a distal outcome. Health Services Outcomes and Research Methodology, 14(4), 232-254.
Latent Class Moderation
Cooper, B. R., & Lanza, S. T. (2014). Who benefits most from Head Start? Using latent class moderation to examine differential treatment effects. Child Development. 85(6), 2317-2338.. doi: 10.1111/cdev.12278
Lanza, S. T. & Rhoades, B. L. (2013). Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment. Prevention Science, 14, 157-168. PMCID: PMC3173585
Cleveland, M. J., Lanza, S. T., Ray, A. E., Turrisi, R., & Mallett, K. M. (2012). Transitions in first-year college student drinking behaviors: Does drinking latent class membership moderate the effects of parent- and peer-based intervention components? Psychology of Addictive Behaviors, 26, 440-450. PMCID: PMC3413757