| Owner: |
Craig K. Enders
|
| Owner Email: |
cenders@psych.ucla.edu
|
| Paper Title: |
A Simple Monte Carlo Method for Estimating Power in Multilevel Designs
|
| Session Title: |
Statistical Power Considerations in Multilevel Designs
|
| Paper Type: |
Paper
|
| Presentation Date: |
4/13/2023
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| Presentation Location: |
Chicago, IL
|
| Descriptors: |
Effect Size, Hierarchical Modeling, Power
|
| Methodology: |
Quantitative
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| Author(s): |
Craig K. Enders, University of California - Los Angeles; Michael Woller, University of California - Los Angeles
|
| Unit: |
SIG-Multilevel Modeling
|
| Abstract: |
This paper describes a Monte Carlo approach to estimating power for multilevel models. Our method allows any number of within-cluster, between-cluster, and covariate effects at either level, cross-level interactions, and random coefficients. Moreover, we do not assume orthogonal predictors, and regressors can correlate at either level. Additionally, our approach accommodates multiple cross-level interaction effects, deriving exact expressions for the variances and covariances of product random variables. Finally, our strategy allows researchers to specify multilevel population parameters using R^2 effect size expressions, and we use these equations to derive solutions for a model’s fixed effect coefficients and variance components. We provide an R function that computes these solutions and automates the process of generating artificial data sets and summarizing the simulation results.
|
| DOI: |
https://doi.org/10.3102/2014417
|