Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error

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dc.contributor.authorHwang, Heungsun
dc.contributor.authorTakane, Yoshio
dc.contributor.authorJung, Kwanghee
dc.date.accessioned2019-03-02T16:41:01Z
dc.date.available2019-03-02T16:41:01Z
dc.date.copyright2017en_US
dc.date.issued2017
dc.description.abstractGeneralized structured component analysis (GSCA) is a component-based approach to structural equation modeling (SEM), where latent variables are approximated by weighted composites of indicators. It has no formal mechanism to incorporate errors in indicators, which in turn renders components prone to the errors as well. We propose to extend GSCA to account for errors in indicators explicitly. This extension, called GSCAM, considers both common and unique parts of indicators, as postulated in common factor analysis, and estimates a weighted composite of indicators with their unique parts removed. Adding such unique parts or uniqueness terms serves to account for measurement errors in indicators in a manner similar to common factor analysis. Simulation studies are conducted to compare parameter recovery of GSCAM and existing methods. These methods are also applied to fit a substantively well-established model to real data.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.identifier.citationHwang, H.; Takane, Y.; & Jung, K. (2017). Generalized structured component analysis with uniqueness terms for accommodating measurement error. Frontiers in Psychology, 8, article 2137. DOI: 10.3389/fpsyg.2017.02137en_US
dc.identifier.urihttps://doi.org/10.3389/fpsyg.2017.02137
dc.identifier.urihttp://hdl.handle.net/1828/10629
dc.language.isoenen_US
dc.publisherFrontiers in Psychologyen_US
dc.subjectgeneralized structured component analysis
dc.subjectuniqueness
dc.subjectmeasurement error
dc.subjectbias correction
dc.subjectstructural equation modeling
dc.subject.departmentDepartment of Psychology
dc.titleGeneralized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Erroren_US
dc.typeArticleen_US

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