Power for dyadic models – Jonathan Helm
Many psychological researchers examine the degree to which members of a dyad relate to one another. However, researchers often face challenges while attempting to identify the minimal sample size required to detect a specific dyadic effect (i.e., perform a power analysis). More specifically, researchers often have considerable challenge identifying effect sizes (e.g., exact values for fixed effects and variances for random effects) required to perform power analysis. This talk describes those challenges, provides new methodology for identifying effect sizes (i.e., via pseudo R-square values), demonstrates how to perform power analysis using those new effect sizes (i.e., via Monte Carlo simulation), and presents general results for minimal sample sizes to detect dyadic effect under a range of conditions.
San Diego State University