Latent growth models (LGMs) with a distal outcome allow researchers to assess longer-term patterns, and to detect the need to start a (preventive) treatment or intervention in an early stage. The aim of the current simulation study is to examine the performance of an LGM with a continuous distal outcome under maximum likelihood (ML) and Bayesian estimation with default and informative priors, under varying sample sizes, effect sizes and slope variance values. We conclude that caution is needed when predicting a distal outcome from an LGM when the: (1) sample size is small; and (2) amount of variation around the latent slope is small, even with a large sample size. We recommend against the use of ML and Bayesian estimation with Mplus default priors in these situations to avoid severely biased estimates. Recommendations for substantive researchers working with LGMs with distal outcomes are provided based on the simulation results.

Smid, S. C., Depaoli, S., & Van De Schoot, R. (2019 - online). Predicting a Distal Outcome Variable From a Latent Growth Model: ML versus Bayesian Estimation. Structural Equation Modeling: A Multidisciplinary Journal. DOI: 10.1080/10705511.2019.1604140

We put all the relevant information needed to replicate our findings, on the Open Science Framework (OSF; see


PhD Student

Sannes PhD focuses on the use of informative priors in latent growth models with small sample sizes. She is interested in how prior knowledge can be used to compensate for small sample sizes.

Sarah Depaoli
Assistant Professor at the University of California, Merced
Sarah’s research interests are largely focused on issues surrounding Bayesian estimation of latent variable models. She has a particular interest in estimation issues arising from nonlinear growth patterns over time. She is also interested in improving accuracy of uncovering unobserved (latent) groups of individuals. She is currently working with several students that are involved in research spanning a wide range of methodological topics .
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