Developmental and prevention researchers aim to determine how unhealthy behaviors emerge. Mediation analysis offers a statistical tool that allows researchers to describe the processes underlying early risk and later health outcomes. Among existing longitudinal mediation models, latent difference score mediation stands out due to its unique ability to capture variations in changes both within and across individuals, as well as its ability to examine non-linear change over time. However, the literature currently lacks sample size guidelines for latent difference mediation models, which has proven to make the use of these models difficult. The current project addresses this limitation by offering an empirical set of sample guidelines for a variety of latent difference mediation score models through a Monte Carlo simulation study. By offering empirical sample size guidelines for latent difference score mediation models, future developmental and prevention researchers can make informed sampling decisions prior to data collection. Moreover, women who misuse alcohol have been found to experience more severe medical consequences than men. However, minimal research has evaluated how gender specific risk factors influence its onset. The current project addresses this limitation by applying latent difference score mediation to evaluate how disordered eating behaviors among adolescent girls influence alcohol misuse among adult women.

abstract: Time metric is an important consideration for all longitudinal models because it can influence the interpretation of estimates, parameter estimate accuracy, and model convergence in longitudinal models with latent variables. Currently, the literature on latent difference score (LDS) models does not discuss the importance of time metric. Furthermore, there is little research using simulations to investigate LDS models. This study examined the influence of time metric on model estimation, interpretation, parameter estimate accuracy, and convergence in LDS models using empirical simulations. Results indicated that for a time structure with a true time metric where participants had different starting points and unequally spaced intervals, LDS models fit with a restructured and less informative time metric resulted in biased parameter estimates. However, models examined using the true time metric were less likely to converge than models using the restructured time metric, likely due to missing data. Where participants had different starting points but equally spaced intervals, LDS models fit with a restructured time metric resulted in biased estimates of intercept means, but all other parameter estimates were unbiased, and models examined using the true time metric had less convergence than the restructured time metric as well due to missing data. The findings of this study support prior research on time metric in longitudinal models, and further research should examine these findings under alternative conditions. The importance of these findings for substantive researchers is discussed. / Dissertation/Thesis / Doctoral Dissertation Psychology 2016