Factor analysis for fairness: Examining the impact of task type and examinee L1 background on scores of an ITA speaking test

Factor analysis for fairness: Examining the impact of task type and examinee L1 background on... This study investigated the construct validity of a local speaking test for international teaching assistants (ITAs) from a fairness perspective, by employing a multi-group confirmatory factor analysis (CFA) to examine the impact of task type and examinee first language (L1) background on the internal structure of the test. The test consists of three types of integrated speaking tasks (i.e., text-speaking, graph-speaking, and listening-speaking) and the three L1s that are most represented among the examinees are Mandarin, Hindi, and Korean. Using scores of 1804 examinees across three years, the CFA indicated a two-factor model with a general speaking factor and a listening task factor as the best-fitting internal structure for the test. The factor structure was invariant for examinees across academic disciplines and L1 backgrounds, although the three examinee L1 groups demonstrated different factor variances and factor means. Specifically, while Korean examinees showed a larger variance in oral English proficiency, Hindi examinees demonstrated a higher level of oral proficiency than did Mandarin and Korean examinees. Overall, the lack of significance for multiple task factors and the invariance of factor structure suggest that the test measures the same set of oral English skills for all examinees. Although the factor variances and factor means for oral proficiency differed across examinee L1 subgroups, they reflect the general oral proficiency profiles of English speakers from these selected L1 backgrounds in the university and therefore do not pose serious threats to the fairness of the test. Findings of this study have useful implications for fairness investigations on ITA speaking tests. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Language Testing SAGE

Factor analysis for fairness: Examining the impact of task type and examinee L1 background on scores of an ITA speaking test

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Publisher
SAGE Publications
Copyright
© The Author(s) 2018
ISSN
0265-5322
eISSN
1477-0946
D.O.I.
10.1177/0265532218775764
Publisher site
See Article on Publisher Site

Abstract

This study investigated the construct validity of a local speaking test for international teaching assistants (ITAs) from a fairness perspective, by employing a multi-group confirmatory factor analysis (CFA) to examine the impact of task type and examinee first language (L1) background on the internal structure of the test. The test consists of three types of integrated speaking tasks (i.e., text-speaking, graph-speaking, and listening-speaking) and the three L1s that are most represented among the examinees are Mandarin, Hindi, and Korean. Using scores of 1804 examinees across three years, the CFA indicated a two-factor model with a general speaking factor and a listening task factor as the best-fitting internal structure for the test. The factor structure was invariant for examinees across academic disciplines and L1 backgrounds, although the three examinee L1 groups demonstrated different factor variances and factor means. Specifically, while Korean examinees showed a larger variance in oral English proficiency, Hindi examinees demonstrated a higher level of oral proficiency than did Mandarin and Korean examinees. Overall, the lack of significance for multiple task factors and the invariance of factor structure suggest that the test measures the same set of oral English skills for all examinees. Although the factor variances and factor means for oral proficiency differed across examinee L1 subgroups, they reflect the general oral proficiency profiles of English speakers from these selected L1 backgrounds in the university and therefore do not pose serious threats to the fairness of the test. Findings of this study have useful implications for fairness investigations on ITA speaking tests.

Journal

Language TestingSAGE

Published: Jun 1, 2018

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