Related Experiment Videos
Using multilevel models to analyze couple and family treatment data: basic and advanced issues
1Travis Research Institute, Fuller Graduate School of Psychology, Pasadena, CA 91101, USA. datkins@fuller.edu
Summary
Multilevel models offer a flexible statistical approach for analyzing couple and family data, addressing challenges like data similarity and missing values. This method enhances statistical power and provides alternative data representations for longitudinal studies.
Area of Science:
- Statistics
- Psychology
- Family Studies
Background:
- Couple and family treatment data present unique statistical analysis challenges due to inherent similarities among individuals within units.
- Classical statistical methods struggle with the non-independence of observations common in family and couple datasets.
- Longitudinal family data requires advanced analytical techniques to accurately model within- and between-unit variations.
Purpose of the Study:
- To introduce multilevel models (hierarchical linear models, mixed-effects models) as a robust analytic strategy for couple and family longitudinal data.
- To explore key extensions of multilevel models relevant to family research, including handling missing data and power analysis.
- To provide practical guidance and reproducible examples for applying these statistical methods to family and couple datasets.
Main Methods:
- Review of fundamental properties of multilevel modeling.
- Tutorial-style presentation of advanced topics: managing missing data, power and sample size calculations, and alternative couple data representations.
- Utilizes SPSS and R code, with accompanying datasets in a web appendix for practical application.
Main Results:
- Multilevel models effectively address the statistical complexities arising from correlated data in couple and family studies.
- Extensions discussed provide solutions for common issues like missing data and optimizing sample size for family research.
- Alternative representations offer new ways to analyze and interpret couple dynamics within a multilevel framework.
Conclusions:
- Multilevel models are a powerful and flexible tool for analyzing complex couple and family longitudinal data.
- The presented extensions equip researchers with methods to overcome common analytical hurdles in family studies.
- This approach facilitates more accurate and nuanced understanding of family and couple treatment outcomes.