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Analytical Strategies for Structural Equation Models with Missing Data
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Dissertation
Analytical Strategies for Structural Equation Models with Missing Data
2010
Overview
Structural Equation Model (SEM) with missing data is common in empirical studies. This thesis examined the use of auxiliary variables in inclusive strategies as well as the assessment of fit in SEM when data are Missing at Random (MAR). Study-1 showed that the high-order items (e.g., interaction or quadratic terms) could be essential for an effective inclusive strategy, even in instances when we are certain that the inclusive strategy only with the linear items has already achieved MAR. The non-parsimony by these inclusive items does not impair the parsimony in the eventual interpretation of the model. Study-2 provided empirical estimates of a safe sample size to include fixed covariates in regression analyses. Each increment of 55 in the sample size would allow the entry of one more covariate to the model with the assurance that the confidence radius of the original parameters will increase less than 1% at the probability level of 99.9%. Furthermore, Study-2 supported that suppression of sampling error throu
Publisher
ProQuest Dissertations & Theses
Subject
ISBN
9781124360393, 1124360395
