MixWILD - Mixed models With Intensive Longitudinal Data
Mix-WILD is statistical software designed to perform multilevel modeling on intensive longitudinal experience sampling data.
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.
Citation
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 and cite the following paper for more details:
Recommended Steps to Get Started
1. Watch introductory video(s)
- Video1: conceptual overview to MixWILD and research applications (15 min) (new)
- Video2: simple tutorial about MixWILD (13 min)
- Video3: intro to stage 1 modeling, the Mixed Effects Location Scale (MELS) Model (47 min)
- Video4: intro to stage 2 modeling (23 min) (new)
2. Download MixWILD for Mac or PC
Please submit your email prior to downloading the application so we can notify you of major software updates
| #Version | Description | Download links |
|---|---|---|
| 3.0 | Possibility of three-level modeling; Latex equation display; Simplified result output |
Windows 3.0 macOS 3.0 (supports Mac computers with Apple Silicon) |
| 2.0 | Stable version of two-level modeling; The current user guide focuses on this version |
Windows 2.0 macOS 2.0 (supports Mac computers with Intel processors or Apple Silicon that have Rosetta installed) |
3. Read introductory material
4. Run some basic models on example dataset and ask questions
- Download example dataset
- MELS model: examine whether a person’s positive mood is related to their negative mood (see user guide)
- MEMLS model: examine whether positive affect (PA) differs on weekend days (vs. weekdays) (see user guide)
Contact Us with Questions or Suggestions
- For questions, bug report, or new feature ideas, please post an issue on our GitHub discussion group
- For future collaborations, contact Prof. Don Hedeker, Prof. Genevieve Dunton, and Prof. Stephen Intille
Additional Tools Developed by Others
Recent Presentations
- 2023 SBM Paper on “Modeling variability of daily sleep quality and duration”
- 2023 SBM MixWILD Workshop
Selective papers that use MixWILD
- Smith, D. L., Tharaud, J. B., Pridgen, S. A., & Held, P. (2023). Predicting suicidal ideation 3 months following intensive posttraumatic stress disorder treatment. Psychological Trauma: Theory, Research, Practice, and Policy.
- Smith, D. L., Kovacevic, M., Montes, M., Pridgen, S., & Held, P. (2022). Improving mental, physical, and social functioning through participation in a 3-week cognitive processing therapy-based intensive PTSD treatment. Journal of Anxiety Disorders, 88, 102560.
- Kaden, S. J., & Dalton, E. D. (2022). Momentary fluctuations in emotional intelligence and stress predict changes in disordered eating. Journal of American College Health, 1-8.
- Kracht, C. L., Beyl, R. A., Maher, J. P., Katzmarzyk, P. T., & Staiano, A. E. (2021). Adolescents’ sedentary time, affect, and contextual factors: An ecological momentary assessment study. International Journal of Behavioral Nutrition and Physical Activity, 18(1), 1-10.
- Brick, L., Nugent, N., & Armey, M. (2021). Affective variability and childhood abuse increase the risk for nonsuicidal self‐injury following psychiatric hospitalization. Journal of traumatic stress, 34(6), 1118-1131.
- Smith, D. L., Kovacevic, M., Montes, M., Pridgen, S., & Held, P. (2022). Improving mental, physical, and social functioning through participation in a 3-week cognitive processing therapy-based intensive PTSD treatment. Journal of Anxiety Disorders, 88, 102560.
- Gandelman, E. M., Miller, S. A., & Back, S. E. (2022). Imaginal exposure processing during Concurrent Treatment of PTSD and Substance Use Disorders using Prolonged Exposure (COPE) therapy: Examination of linguistic markers of cohesiveness. Journal of traumatic stress.
- Sherwood, S. N. (2022). Feasibility and Efficacy of Virtual Darkness in Reducing Intra-Individual Sleep Variability Among Young Adults with Insomnia (Doctoral dissertation, University of Nevada, Las Vegas).
- Dzubur, E., Semborski, S., Redline, B., Hedeker, D., Dunton, G.F., & Henwood, B.F. (in press). Food insecurity and the effects of hunger on variability of stress among young adults who have experienced homelessness. Health Psychology.
Funding and Team
Agency: National Institutes of Health (NIH)
Institutes: National Heart, Lung, and Blood Institute (NHLBI) and National Cancer Institute (NCI)
Grant Numbers: 5R01HL121330-05 and 1R01CA240713-01
Principal Investigator(s): Hedeker, Donald (University of Chicago); Dunton, Genevieve Fridlund (University of Southern California); Intille, Stephen (Northeastern University)
Team: Rachel Nordgren, Chih-Hsiang “Jason” Yang, Eldin Dzubur, Wei-Lin Wang, Aditya Ponnada, Jixin Li
Research Groups: REACH Lab, University of Southern California and mHealth Research Group, Northeastern University
Listing on NIH RePOERTER:
- Novel Statistical Models for EMA Studies of Physical Activity
- Novel Statistical Models for Intensive Longitudinal Analyses of Cancer Control Behaviors
Logotype credits
Gentium Book Basic by Victor Gaultney and Roboto by Christian Robertson