Model construction and sample size planning for mixed-effects location-scale models

Abstract

: With the advancement of research depth in psychology and the development of data collection technics, interest in Mixed-Effects Location-Scale Models (MELSM) has increased drastically. When residual variances are heterogeneous, these models are able to add predictors in different levels, then help explore the relationship among traits and simultaneously investigate the inter- and intra-individual variability, as well as their explanatory variables. This study includes both simulated studies and empirical studies. In detail, the main contents of this project are: 1) Comparing and selecting candidate models based on Bayesian fit indices to construct MELSM; 2) Planning sample size according to both power analysis and accuracy in parameter estimation analysis for MELSM; 3) Extending the sample size planning method for MELSM to better frame the considerations of uncertainty; 4) Developing an R package for MELSM and illustrating the application of MELSM in empirical psychological studies. Based on the study, we hope these statistical models can be widely implemented. Moreover, the reproducibility and replicability of psychological studies will be enhanced finally.