Using multilevel models to analyze couple and family treatment data: Basic and advanced issues

David C. Atkins

Travis Re

Research Institute, Fuller Graduate School of Psychology

Couple and family treatment data present particular challenges to statistical analyses. Partners and family members tend to be more similar to one another than to other individuals, which raises interesting possibilities in the data analysis but also causes significant problems with classical, statistical methods. This article presents multilevel models (also called hierarchical linear models, mixed-effects models, or random coefficient models) as a flexible analytic approach to couple and family longitudinal data. The paper reviews basic properties of multilevel models but primarily focuses on three important extensions: missing data, power and sample size, and alternative representations of couple data. Information is presented as a tutorial with a web appendix providing datasets with SPSS and R code to reproduce the examples.