Bayesian versus Frequentist Estimation for SEM: A Systematic Review
The Performance of Maximum Likelihood and Bayesian Estimation With Small and Unbalanced Samples in a Latent Growth Model is compared in a simulation study
Predicting a Distal Outcome Variable From a Latent Growth Model
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.
Small Sample Size Conference
Researchers often have difficulties collecting enough data to obtain statistical power: when target groups are small, hard to access, or measuring the participants requires prohibitive costs. Such obstacles to collecting data usually lead to a limited data set. Researchers can overcome this through simplifying their…
Typical for developmental psychology are models that capture change over time, such as latent growth (mixture) models and to a lesser extent cross-lagged panel models too. Such models have typically been applied aiming to capture change over time in individuals.
Researchers often have difficulties collecting enough data to obtain statistical power: when target groups are small (e.g., children with severe burn injuries), hard to access (e.g., infants of drug-dependent mothers), or measuring the participants requires prohibitive costs (e.g., measuring phonological difficulties of babies). Such obstacles to collecting data usually leads to a limited data set. Researchers can overcome this through simplifying their hypotheses and statistical models. However, this strategy is undesirable since the intended research question cannot be answered in this way.