On the Power and Performance of a Doubly Latent Residual Approach to Explain Latent Specific Factors in Multilevel-Bifactor-(S-1) Models
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In: Structural Equation Modeling: A Multidisciplinary Journal, Vol. 26, No. 2, 04.03.2019, p. 212-235.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - On the Power and Performance of a Doubly Latent Residual Approach to Explain Latent Specific Factors in Multilevel-Bifactor-(S-1) Models
AU - Koch, Tobias
AU - Semmler-Busch, Ulrike
N1 - This work was supported by the Deutsche Forschungsgemeinschaft [grant number KO4770/1-1]. Publisher Copyright: © 2019, © 2019 The Author(s). Published with license by Taylor & Francis Group, LLC.
PY - 2019/3/4
Y1 - 2019/3/4
N2 - A doubly latent residual approach (DLRA) is presented to explain latent specific factors in multilevel bifactor-(S-1) models. The new approach overcomes some important limitations of the multiple indicators multiple causes (MIMIC) approach and allows researchers to predict latent specific factors at different levels. The DLRA is illustrated using real data from a large-scale assessment study. Furthermore, the statistical performance and power of the DLRA is examined in a Monte Carlo simulation study. The results show that the new DLRA performs well if more than 50 clusters and more than 10 observations per cluster are sampled. The power to test structural parameters at level 2 was lower than at level 1. To test a medium effect at level 2, we recommend to sample at least 100 clusters with a minimum cluster size of 10. The advantages and limitations of the new approach are discussed and guidelines for applied researchers are provided.
AB - A doubly latent residual approach (DLRA) is presented to explain latent specific factors in multilevel bifactor-(S-1) models. The new approach overcomes some important limitations of the multiple indicators multiple causes (MIMIC) approach and allows researchers to predict latent specific factors at different levels. The DLRA is illustrated using real data from a large-scale assessment study. Furthermore, the statistical performance and power of the DLRA is examined in a Monte Carlo simulation study. The results show that the new DLRA performs well if more than 50 clusters and more than 10 observations per cluster are sampled. The power to test structural parameters at level 2 was lower than at level 1. To test a medium effect at level 2, we recommend to sample at least 100 clusters with a minimum cluster size of 10. The advantages and limitations of the new approach are discussed and guidelines for applied researchers are provided.
KW - Transdisciplinary studies
KW - MIMIC approach
KW - multilevel bifactor models
KW - multilevel structural equation models
KW - simulation study
UR - http://www.scopus.com/inward/record.url?scp=85054050501&partnerID=8YFLogxK
U2 - 10.1080/10705511.2018.1516506
DO - 10.1080/10705511.2018.1516506
M3 - Journal articles
VL - 26
SP - 212
EP - 235
JO - Structural Equation Modeling: A Multidisciplinary Journal
JF - Structural Equation Modeling: A Multidisciplinary Journal
SN - 1070-5511
IS - 2
ER -