Multilevel Modeling Issues and Measurement of Stress in Multilevel Data
Abstract
Multilevel datasets are commonly used and increasingly popular in research in the organizational and other social sciences. These models are complex and have many elements beyond those found in more traditional linear models. However, research on how multilevel models perform is lacking. The current paper examined the impact of common factors (average cluster size, cluster size distribution, average number of clusters, strength of the intraclass correlation coefficient, and effect sizes of individual and cluster level variables, and their interaction) in multilevel datasets. Monte Carlo data simulation was used across 6,144 factor-combination conditions. The results of study factors on observed intraclass correlation coefficients, calculated design effect, and empirical design effect are discussed. The results of this study have implications for both researchers in both academic and applied fields. The scale of the simulation variables allow it to be germane to datasets from across the social sciences. However, the nature of data simulation and analysis is such that there are still many elements that can and should be accounted for in future research.
Subject Area
Quantitative psychology
Recommended Citation
Stout, Tyler James, "Multilevel Modeling Issues and Measurement of Stress in Multilevel Data" (2016). ProQuest ETD Collection for FIU. AAI10743992.
https://digitalcommons.fiu.edu/dissertations/AAI10743992