What to do when scalar invariance fails: The extended alignment method for multi-group factor analysis comparison of latent means across many groups
Journal article
Marsh, Herbert W., Guo, Jiesi, Parker, Philip D., Nagengast, Benjamin, Asparouhov, T., Muthen, Bengt and Dicke, Theresa. (2018). What to do when scalar invariance fails: The extended alignment method for multi-group factor analysis comparison of latent means across many groups. Psychological Methods. 23(3), pp. 524 - 545. https://doi.org/10.1037/met0000113
Authors | Marsh, Herbert W., Guo, Jiesi, Parker, Philip D., Nagengast, Benjamin, Asparouhov, T., Muthen, Bengt and Dicke, Theresa |
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Abstract | Scalar invariance is an unachievable ideal that in practice can only be approximated; often using potentially questionable approaches such as partial invariance based on a stepwise selection of parameter estimates with large modification indices. Study 1 demonstrates an extension of the power and flexibility of the alignment approach for comparing latent factor means in large-scale studies (30 OECD countries, 8 factors, 44 items, N = 249,840), for which scalar invariance is typically not supported in the traditional confirmatory factor analysis approach to measurement invariance(CFA-MI). Importantly, we introduce an alignment-within-CFA (AwC) approach, transforming alignment from a largely exploratory tool into a confirmatory tool, and enabling analyses that previously have not been possible with alignment (testing the invariance of uniquenesses and factor variances/covariances; multiple-group MIMIC models; contrasts on latent means) and structural equation models more generally. Specifically, it also allowed a comparison of gender differences in a 30-country MIMIC AwC (i.e., a SEM with gender as a covariate) and a 60-group AwC CFA (i.e., 30 countries × 2 genders) analysis. Study 2, a simulation study following up issues raised in Study 1, showed that latent means were more accurately estimated with alignment than with the scalar CFA-MI, and particularly with partial invariance scalar models based on the heavily criticized stepwise selection strategy. In summary, alignment augmented by AwC provides applied researchers from diverse disciplines considerable flexibility to address substantively important issues when the traditional CFA-MI scalar model does not fit the data. |
Keywords | Alignment method; measurement invariance; MIMIC models; modification indices; stepwise selection strategies |
Year | 2018 |
Journal | Psychological Methods |
Journal citation | 23 (3), pp. 524 - 545 |
Publisher | American Psychological Association |
ISSN | 1082-989X |
Digital Object Identifier (DOI) | https://doi.org/10.1037/met0000113 |
Scopus EID | 2-s2.0-85009812375 |
Open access | Open access |
Page range | 524 - 545 |
Research Group | Institute for Positive Psychology and Education |
Author's accepted manuscript | |
Publisher's version | |
ARC Funded Research | This output has been funded, wholly or partially, under the Australian Research Council Act 2001 |
Grant ID | ARC/LP160101056 |
ARC/DP130102713 | |
Additional information | ©American Psychological Association, 2018. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available, upon publication, at: https://doi.org/10.1037/met0000113 |
https://acuresearchbank.acu.edu.au/item/88w68/what-to-do-when-scalar-invariance-fails-the-extended-alignment-method-for-multi-group-factor-analysis-comparison-of-latent-means-across-many-groups
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