The integration of modern methods for causal inference with latent class analysis (LCA) allows social, behavioral, and health researchers to address important questions about the determinants of latent class membership. In the present article, two propensity score techniques, matching and inverse propensity weighting, are demonstrated for conducting causal inference in LCA. The different causal questions that can be addressed with these techniques are carefully delineated. An empirical analysis based on data from the National Longitudinal Survey of Youth 1979 is presented, where college enrollment is examined as the exposure (i.e., treatment) variable and its causal effect on adult substance use latent class membership is estimated. A step-by-step procedure for conducting causal inference in LCA, including multiple imputation of missing data on the confounders, exposure variable, and multivariate outcome, is included. Sample syntax for carrying out the analysis using SAS and R is given in an appendix.

The results encourage the use of the G statistic for its consistent performance across the simulation conditions. We recommend separately reporting the rates of agreement on the presence and absence of a behavior or diagnosis alongside G as an index of chance corrected overall agreement.

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The psychometrics of the Parenting Scale's Overreactivity and Laxness subscales were evaluated using item response theory (IRT) techniques. The IRT analyses were based on two community samples of cohabiting parents of 3- to 8-year-old children, combined to yield an N of 852 families. The results supported the utility of the Overreactivity and Laxness subscales, particularly in discriminating among parents in the mid to upper reaches of each construct. The original versions of the Overreactivity and Laxness subscales were more reliable than alternative, shorter versions identified in replicated factor analyses from previously published research and in IRT analyses in the present research. Moreover, in several cases, the original versions of these subscales, in comparison with the shortened versions, exhibited greater six-month stabilities and correlations with child externalizing behavior and couple relationship satisfaction. Reliability was greater for the Laxness than for the Overreactivity subscale. Item performance on each subscale was highly variable. Together, the present findings are generally supportive of the psychometrics of the Parenting Scale, particularly for clinical research and practice. They also suggest areas for further development.

Mixed models are used for the analysis of data measured over time to study population-level change and individual differences in change characteristics. Linear and nonlinear functions may be used to describe a longitudinal response, individuals need not be observed at the same time points, and missing data, assumed to be missing at random (MAR), may be handled. While the mechanism giving rise to the missing data cannot be determined by the observations, the sensitivity of parameter estimates to missing data assumptions can be studied, for example, by fitting multiple models that make different assumptions about the missing data process. Sensitivity analysis of a mixed model that may include nonlinear parameters when some data are missing is discussed. An example is provided.