The effect of extreme response and non-extreme response styles on testing measurement invariance
Files
Published version
Date
2017-05-23
Authors
Liu, Min
Harbaugh, Allen G.
Harring, Jeffrey R.
Hancock, Gregory R.
Version
OA Version
Citation
Min Liu, Allen G Harbaugh, Jeffrey R Harring, Gregory R Hancock. 2017. "The Effect of Extreme Response and Non-extreme Response Styles on Testing Measurement Invariance." FRONTIERS IN PSYCHOLOGY, v. 8
Abstract
Extreme and non-extreme response styles (RSs) are prevalent in survey research using
Likert-type scales. Their effects on measurement invariance (MI) in the context of
confirmatory factor analysis are systematically investigated here via a Monte Carlo
simulation study. Using the parameter estimates obtained from analyzing a 2007 Trends
in International Mathematics and Science Study data set, a population model was
constructed. Original and contaminated data with one of two RSs were generated and
analyzed via multi-group confirmatory factor analysis with different constraints of MI.
The results indicated that the detrimental effects of response style on MI have been
underestimated. More specifically, these two RSs had a substantially negative impact on
both model fit and parameter recovery, suggesting that the lack of MI between groups
may have been caused by the RSs, not the measured factors of focal interest. Practical
implications are provided to help practitioners to detect RSs and determine whether RSs
are a serious threat to MI.
Description
License
Attribution 4.0 International