Using structural equation modelling to detect measurement bias and response shift in longitudinal data

DOI10.1007/s10182-010-0129-yzbMath1443.62413OpenAlexW1977873107MaRDI QIDQ1633178

F. J. Oort, B. L. King-Kallimanis, G. J. A. Garst

Publication date: 19 December 2018

Published in: AStA. Advances in Statistical Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10182-010-0129-y

zbMATH Keywords

longitudinal datafactor analysismeasurement biasstructural equation modellingresponse shift

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Social and behavioral sciences: general topics (91C99) Sequential statistical analysis (62L10) Applications of statistics to psychology (62P15)

Related Items (4)

Measurement bias and multidimensionality; an illustration of bias detection in multidimensional measurement models ⋮ Introduction of a new measure for detecting poor fit due to omitted nonlinear terms in SEM ⋮ Nonlinear structural equation modeling: is partial least squares an alternative? ⋮ Multitrait-multimethod change modelling

Uses Software

• LISREL

Cites Work

• On the multivariate asymptotic distribution of sequential chi-square statistics
• Using restricted factor analysis with latent moderated structures to detect uniform and nonuniform measurement bias; a simulation study
• Measurement invariance, factor analysis and factorial invariance
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