FACTOR STRUCTURE OF THE NATIONAL CENTER TEST 2005 BY THE FULL-INFORMATION PSEUDO-ML METHOD

概要

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We analyzed data from the National Center Test for University Admissions (NCT) administered in January 2005 by applying three different factor analysis (FA) models: an exploratory FA model, a confirmatory FA model, and a hierarchical FA model. The data was collected from 385,494 students and included 17 variables of the 15 principal subjects.Two difficulties were experienced in applying FA models to the NCT data: structural and nonstructural missing data patterns. The structural difficulty is derived from the administration schedule, and the non-structural difficulty is caused by the a la carte system of the NCT. Consequently, very complicated missing data patterns exist in the NCT data. We solved the problems of the missing data patterns by using the pseudo-maximum likelihood method and the full-information maximum likelihood method.We extracted two factors by using the exploratory FA model. One factor was for linguistic and social studies, and the other was for mathematics and sciences. These factors were then examined by using the confirmatory FA model. We then confirmed the strong influence of the general factor by using the hierarchical FA model. Furthermore, we performed a multi-group analysis on the confirmatory and hierarchical FA models, focusing on the distinction of sex.
日本行動計量学会の論文