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Testing Differential Item Functioning by Regularized Moderated Nonlinear Factor Analysis
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Wang, Weimeng – ProQuest LLC, 2022
Recent advancements in testing differential item functioning (DIF) have greatly relaxed restrictions made by the conventional multiple group item response theory (IRT) model with respect to the number of grouping variables and the assumption of predefined DIF-free anchor items. The application of the L[subscript 1] penalty in DIF detection has…
Descriptors: Factor Analysis, Item Response Theory, Statistical Inference, Item Analysis
Investigating Performance of Model Fit Indices in Multiple-Group Confirmatory Factor Analysis: Complications with Ordinal Data
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Ning Jiang – ProQuest LLC, 2022
The purpose of this study is to evaluate the performance of three commonly used model fit indices when measurement invariance is tested in the context of multiple-group CFA analysis with categorical-ordered data. As applied researchers are increasingly aware of the importance of testing measurement invariance, as well as Likert-type scales are…
Descriptors: Goodness of Fit, Factor Analysis, Data, Monte Carlo Methods
A Monte Carlo Study of Parallel Analysis, Minimum Average Partial, Indicator Function, and Modified Average Roots for Determining the Number of Dimensions with Binary Variables in Test Data: Impact of Sample Size and Factor Structure
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Pornchanok Ruengvirayudh – ProQuest LLC, 2018
Determining the number of dimensions underlying many variables in the data or many items in the test is a crucial process prior to performing exploratory factor analysis. Failure to do so leads to serious consequences concerning construct validity. Parallel analysis (PA) has been found to be useful to determine the number of dimensions (i.e.,…
Descriptors: Monte Carlo Methods, Tests, Data, Sample Size
Properties of Partially Convergent Models and Effect of Re-Imputation on These Properties
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Dogucu, Mine – ProQuest LLC, 2017
When researchers fit statistical models to multiply imputed datasets, they have to fit the model separately for each imputed dataset. Since there are multiple datasets, there are always multiple sets of model results. It is possible for some of these sets of results not to converge while some do converge. This study examined occurrence of such a…
Descriptors: Statistical Analysis, Error of Measurement, Goodness of Fit, Monte Carlo Methods
An Analysis of Factor Extraction Strategies: A Comparison of the Relative Strengths of Principal Axis, Ordinary Least Squares, and Maximum Likelihood in Research Contexts That Include Both Categorical and Continuous Variables
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Coughlin, Kevin B. – ProQuest LLC, 2013
This study is intended to provide researchers with empirically derived guidelines for conducting factor analytic studies in research contexts that include dichotomous and continuous levels of measurement. This study is based on the hypotheses that ordinary least squares (OLS) factor analysis will yield more accurate parameter estimates than…
Descriptors: Comparative Analysis, Least Squares Statistics, Maximum Likelihood Statistics, Factor Analysis
Determining the Number of Factors in Data Containing a Single Outlier: A Study of Factor Analysis of Simulated Data
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Victor Snipes Swaim – ProQuest LLC, 2009
Numerous procedures have been suggested for determining the number of factors to retain in factor analysis. However, previous studies have focused on comparing methods using normal data sets. This study had two phases. The first phase explored the Kaiser method, Scree test, Bartlett's chi-square test, Minimum Average Partial (1976&2000),…
Descriptors: Factor Analysis, Factor Structure, Maximum Likelihood Statistics, Evaluation Methods