Addressing missing data in specification search in measurement invariance testing with Likert-type scale variables: A comparison of two approaches

Date
2020-12
Language
English
Embargo Lift Date
Committee Members
Degree
Degree Year
Department
Grantor
Journal Title
Journal ISSN
Volume Title
Found At
Springer
Abstract

In measurement invariance testing, when a certain level of full invariance is not achieved, the sequential backward specification search method with the largest modification index (SBSS_LMFI) is often used to identify the source of non-invariance. SBSS_LMFI has been studied under complete data but not missing data. Focusing on Likert-type scale variables, this study examined two methods for dealing with missing data in SBSS_LMFI using Monte Carlo simulation: robust full information maximum likelihood estimator (rFIML) and mean and variance adjusted weighted least squared estimator coupled with pairwise deletion (WLSMV_PD). The result suggests that WLSMV_PD could result in not only over-rejections of invariance models but also reductions of power to identify non-invariant items. In contrast, rFIML provided good control of type I error rates, although it required a larger sample size to yield sufficient power to identify non-invariant items. Recommendations based on the result were provided.

Description
item.page.description.tableofcontents
item.page.relation.haspart
Cite As
Chen, P.-Y., Wu, W., Brandt, H., & Jia, F. (2020). Addressing missing data in specification search in measurement invariance testing with Likert-type scale variables: A comparison of two approaches. Behavior Research Methods, 52(6), 2567–2587. https://doi.org/10.3758/s13428-020-01415-2
ISSN
Publisher
Series/Report
Sponsorship
Major
Extent
Identifier
Relation
Journal
Behavior Research Methods
Rights
Publisher Policy
Source
Author
Alternative Title
Type
Article
Number
Volume
Conference Dates
Conference Host
Conference Location
Conference Name
Conference Panel
Conference Secretariat Location
Version
Author's manuscript
Full Text Available at
This item is under embargo {{howLong}}