Journal article
The Power to Detect and Predict Individual Differences in Intra-Individual Variability Using the Mixed-Effects Location-Scale Model
Multivariate behavioral research, Vol.53(3), pp.360-374
05/2018
DOI: 10.1080/00273171.2018.1449628
PMID: 29565691
Abstract
Our goal is to provide empirical scientists with practical tools and advice with which to test hypotheses related to individual differences in intra-individual variability using the mixed-effects location-scale model. To that end, we evaluate Type I error rates and power to detect and predict individual differences in intra-individual variability using this model and provide empirically-based guidelines for building scale models that include random and/or systematically-varying fixed effects. We also provide two power simulation programs that allow researchers to conduct a priori empirical power analyses. Our results aligned with statistical power theory, in that, greater power was observed for designs with more individuals, more repeated occasions, greater proportions of variance available to be explained, and larger effect sizes. In addition, our results indicated that Type I error rates were acceptable in situations when individual differences in intra-individual variability were not initially detectable as well as when the scale-model individual-level predictor explained all initially detectable individual differences in intra-individual variability. We conclude our paper by providing study design and model building advice for those interested in using the mixed-effects location-scale model in practice.
Details
- Title: Subtitle
- The Power to Detect and Predict Individual Differences in Intra-Individual Variability Using the Mixed-Effects Location-Scale Model
- Creators
- Ryan W Walters - a Creighton UniversityLesa Hoffman - b University of KansasJonathan Templin - b University of Kansas
- Resource Type
- Journal article
- Publication Details
- Multivariate behavioral research, Vol.53(3), pp.360-374
- Publisher
- United States
- DOI
- 10.1080/00273171.2018.1449628
- PMID
- 29565691
- ISSN
- 1532-7906
- eISSN
- 1532-7906
- Language
- English
- Date published
- 05/2018
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9983993329102771
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