MixWILD - Mixed models With Intensive Longitudinal Data

Mix-WILD is a statistical software designed to perform multilevel modeling on intensive longitudinal experience sampling data.

View the Project on GitHub reach-lab/MixWildGUI

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MixWild

What is this project about?

MixWILD (Also Mixed model analysis with Intensive Longitudinal Data) is a desktop GUI-based application for examining the effects of variance and slope of time-varying variables in intensive longitudinal data, especially the ones collected using ecological momentary assessments.

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Download MixWild for Mac or PC

macOS (current version: 1.3)

Windows (current version: 1.3)

In the current version

  1. Ability to save and reload model configurations
  2. Error and performance logging
  3. Bugfixes

Manuscript Supplement

MixWild User Guide

Example Dataset MixWILD

Research paper and citation request

MixWILD is a free and open-source tool by and for the behavioral researchers. If you are using MixWILD in your research or data analysis, please read ans cite the following paper for more details:

Dzubur, E., Ponnada, A., Nordgren, R., Yang, C. H., Intille, S., Dunton, G., & Hedeker, D. (2020). MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data. Behavior Research Methods, 1-25.

Open access manuscript available by clicking here

Video tutorial

You can access the video tutorial to using MixWILD here

Reference reading materials

Download introductary slides here: Details and Application of EMA
Download user guide here: User Guide for MixWILD
Download example data here: Example Dataset MixWILD

Q and A

Project Information

Agency: National Institutes of Health (NIH)

Institute: National Heart, Lung, and Blood Institute (NHLBI)

Project: 5R01HL121330-05 and 1R01CA240713-01

Title: Novel Statistical Models for Intensive Longitudinal Analysis of Cancer Control Behaviors

Principal Investigator(s): Hedeker, Donald (University of Chicago); Dunton, Genevieve Fridlund (University of Southern California); Intille, Stephen (Northeastern University)

Team: Aditya Ponnada (Northeastern University), Jixin Li (Northeastern University), Eldin Dzubur (University of Southern Califrnia), Rachel Nordgren (University of Illinois-Chicago)

Research Groups: REACH Lab, University of Southern California and mHealth Research Group, Northeastern University

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Logotype credits

Gentium Book Basic by Victor Gaultney and Roboto by Christian Robertson