Publication: Addressing missing data in specification search in measurement invariance testing with Likert-type scale variables: A comparison of two approaches
Addressing missing data in specification search in measurement invariance testing with Likert-type scale variables: A comparison of two approaches
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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
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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 var
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- Chen, Po-Yi
- Wu, Wei
- Jia, Fan
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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