Testing Measurement Invariance over Time with Intensive Longitudinal Data and Identifying a Source of Non-invariance

被引:3
|
作者
Kim, Eunsook [1 ,4 ]
Cao, Chunhua [2 ]
Liu, Siyu [1 ]
Wang, Yan [3 ]
Dedrick, Robert [1 ]
机构
[1] Univ S Florida, Tampa 33620, FL USA
[2] Univ Alabama, Tuscaloosa, AL USA
[3] Univ Massachusetts Lowell, Lowell, MA USA
[4] Univ S Florida, Dept Educ & Psychol Studies, 4202 E Fowler Ave,EDU 105, Tampa, FL 33620 USA
关键词
Alignment; autoregression; cross-classified; longitudinal; measurement invariance; FACTORIAL INVARIANCE; MODEL; DYNAMICS; GROWTH;
D O I
10.1080/10705511.2022.2130331
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Longitudinal measurement invariance (LMI) is a critical prerequisite to assessing change over time with intensive longitudinal data (ILD). For LMI testing with ILD, we propose cross-classified factor analysis (CCFA) to detect non-invariant item parameters and alignment optimization (AO) to detect non-invariant time points as a supplement to CCFA. In addition, we use a covariate in CCFA to identify a source of non-invariance. To evaluate the proposed models under unique features of ILD, such as autoregression (AR), we conducted a Monte Carlo simulation study. The results showed CCFA can be an excellent tool for ILD LMI testing regardless of simulation factors even when AR was misspecified and can identify a source of non-invariance using a covariate. AO can supplement CCFA to find non-invariant time points although AO requires a large number of persons. We provide detailed discussions and practical suggestions.
引用
收藏
页码:393 / 411
页数:19
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