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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