Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Department

Psychology

First Advisor's Name

Stefany Coxe

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Chockalingam Viswesvaran

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Stacy Frazier

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Wensong Wu

Fourth Advisor's Committee Title

Committee Member

Keywords

Multilevel Models, Monte Carlo simulation, cluster size, intraclass correlation, hierarchical linear modeling

Date of Defense

9-14-2016

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.

Identifier

FIDC001246

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