Language:
English
Pages:
1 Online-Ressource (16 Seiten)
Publ. der Quelle:
New York, NY : Psychology Press, Taylor & Francis Group, 2020
Angaben zur Quelle:
27,2, Seiten 240-254
DDC:
300
Keywords:
Individual score/factor score methods
;
Kalman filter
;
longitudinal autoregressive models
;
model misspecification
;
Sozialwissenschaften
Abstract:
Different methods to obtain individual scores from multiple item latent variable models exist, but their performance under realistic conditions is currently underresearched. We investigate the performance of the regression method, the Bartlett method, the Kalman filter, and the mean score under misspecification in autoregressive panel models. Results from three simulations show different patterns of findings for the mean absolute error, for the correlations between individual scores and the true scores (correlation criterion), and for the coverage in our settings: a) all individual score methods are generally quite robust against the chosen misspecification in the loadings, b) all methods are similarly sensitive to positively skewed as well as leptokurtic response distributions with regard to the correlation criterion, c) only the mean score is not robust against an integrated trend component, and d) coverage for the mean score is consistently below the nominal value.
Abstract:
Peer Reviewed
Note:
This article was supported by the Open Access Publication Fund of Humboldt-Universität zu Berlin.
DOI:
10.1080/10705511.2019.1642755
URN:
urn:nbn:de:kobv:11-110-18452/22040-4
URL:
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