Performance of contextual multilevel models for comparing between-person and within-person effects
A Monte Carlo simulation was used to generate data based on the contextual multilevel model, where sample size, effect size, and intraclass correlation (ICC) of the predictor variable were varied. The effects of simulation factors on parameter bias, parameter variability, and standard error accuracy were assessed. Parameter estimates were in general unbiased. Power to detect the slope variance and contextual effect was over 80% for most conditions, except some of the smaller sample size conditions. Type I error rates for the contextual effect were also high for some of the smaller sample size conditions. Conclusions and future directions are discussed.
- Author (aut): Wurpts, Ingrid Carlson
- Thesis advisor (ths): Mackinnon, David P
- Committee member: West, Stephen G.
- Committee member: Grimm, Kevin J.
- Committee member: Suk, Hye Won
- Publisher (pbl): Arizona State University