Correcting Correlation and Covariance Matrices for Measurement Errors Before Further Analysis. Issue 4 (4th July 2021)

• Record Type: Journal Article
• Authors: DeCastellarnau, Anna; Saris, Willem E.
• Is Part Of: Structural equation modeling. Volume 28:Issue 4(2021)
• Page Start: 572
• Page End: 581
• Publication Date: 2021-07-04
• Subjects: SEM -- MTMM -- SQP -- correction for measurement error -- composite scores; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2020.1870229
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: Although the approach to correct for measurement errors in research has been known since the early 1970s, most researchers in the social sciences appear to ignore these recommendations. One possible reason is that the models with latent variables that were originally suggested may be too difficult to apply in practice. We suggest an alternative but simpler procedure to correct for measurement errors. If one knows the sizes of the random errors and method effects for the different measures, the correlation and covariance matrices for the observed variables can be corrected. This approach has already been shown for simple concepts measured by a single measure. In this paper, we show that this approach can be generalized to studies that use a mixture of simple and complex concepts measured by several questions. Using these corrected matrices as the basis for the estimation, the parameters of the models are automatically corrected for measurement errors. The conditions for the quality of these procedures will also be discussed.