MixWILD
Lead PI
- Donald Hedeker, University of Chicago
Co PI
- Genevieve Dunton, University of South California
- Aditya Ponnada
- Jixin Li
Abstract
Data gathered using intensive longitudinal methods such as ecological momentary assessment are often messy with a lot missing values. We designed Mix-WILD – “Mixed model analysis With Intensive Longitudinal Data” – a desktop application (for Windows and MacOS) for multilevel modeling of behavior using EMA data. Mix-WILD provides a graphical user interface to add or remove regressors from the model, manipulate missing value codes, and configure other model parameters such as quadrature, convergence criteria, ridge, and the number of resamples. MixWILD allows testing of random intercepts and slopes as predictors, mediators, and moderators of outcome variables in intensive longitudinal data.
Mix-WILD allows behavioral researchers to:
1. Extend the stage 1 regressor model with the possibilities of random slope
2. Feed random slope and other effects into stage 2 model
3. Save output files in the users’ desired format and location
4. Run the analysis on both Windows and Mac operating systems
For more information, visit the detailed project page.
(Credit: Aditya Ponnada)
Funding
Related Publications
- Yang, Chih-Hsiang, Jaclyn P. Maher, Aditya Ponnada, Eldin Dzubur, Rachel Nordgren, Stephen Intille, Donald Hedeker, and Genevieve F. Dunton. “An empirical example of analysis using a two-stage modeling approach: within-subject association of outdoor context and physical activity predicts future daily physical activity levels.” Translational Behavioral Medicine (2020). DOI: 10.1093/tbm/ibaa107
- Dzubur, Eldin, Aditya Ponnada, Rachel Nordgren, Chih-Hsiang Yang, Stephen Intille, Genevieve Dunton, and Donald Hedeker. “MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data.” Behavior Research Methods (2020): 1-25. DOI: 10.3758/s13428-019-01322-1