期刊名称：International Online Journal of Education and Teaching
电子版ISSN：2148-225X
出版年度：2018
卷号：5
期号：3
页码：609-634
语种：English
出版社：Informascope
摘要：An empirical evidence for independent samples of a population regarding measurement invariance implies that factor structure of a measurement tool is equal across these samples;in other words,it measures the intended psychological trait within the same structure.In this case,the evidence of construct validity would be strengthened within the frame of the scores obtained from the tool.When measurement invariance is not supported,the researchers should consider the possibility of the different factor designs for each group.Ignoring such a situation brings forward the probability about differentiation of the trait(s) measured by measurement tool for that/those group(s),so it causes to suspect the validity of the scores obtained from the tool.The aim of this study is to examine measurement invariance in the context of the conceptual foundations of multi-group confirmatory factor analysis,and discuss the subject through the results from two hypothetical data set that one supports measurement invariance,but the other does not.As a result of analysis performed in this direction,it is determined that the five-factor design derived from the first data set is equal across the groups in the majors of science,health,and social science.It is also concluded that the three-factor design obtained from the secondary data set is not equal for female and male groups.Besides,the exploratory factor analysis performed for female and male groups separately shows that the three-factor design of the tool is valid for females,but the number of factors was four in males.When the factor design for male group is examined,it is determined that the three items in the second factor separate significantly.That leads to the conclusion that it is crucial to test measurement invariance in studies regarding the determination of the psychometric properties of the tool.
关键词：measurement invariance;equality of factor structures;multi-group confirmatory factor analysis;structural equation modeling
