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Title page for ETD etd-03152016-150627


Type of Document Master's Thesis
Author Lee, Woo-yeol
Author's Email Address woo-yeol.lee@vanderbilt.edu
URN etd-03152016-150627
Title Detecting cluster bias in a multilevel item response model: A Monte Carlo evaluation of detection methods and consequences of ignoring cluster bias
Degree Master of Science
Department Psychology
Advisory Committee
Advisor Name Title
Sun-Joo Cho Committee Chair
Kristopher J. Preacher Committee Member
Sonya K. Sterba Committee Member
Keywords
  • multilevel item response model
  • cluster bias
  • model selection
Date of Defense 2016-01-25
Availability restricted
Abstract
Cluster bias in a multilevel item response model can be investigated by testing whether the within-level item discriminations are equal to the between-level item discriminations. However, in most multilevel item response model applications, the possibility of cluster bias is often ignored. Cluster bias detection methods using a multilevel item response model (the likelihood ratio test, Wald test, AIC, BIC, and saBIC) and the consequences of ignoring cluster bias are illustrated and discussed. Simulation results showed that all criteria performed well in detecting global cluster bias except the BIC with small sample sizes and high ICCs when some portion of the items exhibited cluster bias. For item cluster bias, the AIC outperformed the other criteria in the presence of partial cluster bias. When cluster bias was ignored, accuracy of item discrimination estimates and standard errors was mainly problematic. Implications of the findings and limitations are discussed.
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