Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error
Generalized 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 t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-12-01
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| Series: | Frontiers in Psychology |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fpsyg.2017.02137/full |
| _version_ | 1818146719872843776 |
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| author | Heungsun Hwang Yoshio Takane Kwanghee Jung |
| author_facet | Heungsun Hwang Yoshio Takane Kwanghee Jung |
| author_sort | Heungsun Hwang |
| collection | DOAJ |
| description | Generalized 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. |
| first_indexed | 2024-12-11T12:23:50Z |
| format | Article |
| id | doaj.art-e292288c601b4381ab4edad13753dc92 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-11T12:23:50Z |
| publishDate | 2017-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-e292288c601b4381ab4edad13753dc922022-12-22T01:07:28ZengFrontiers Media S.A.Frontiers in Psychology1664-10782017-12-01810.3389/fpsyg.2017.02137298190Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement ErrorHeungsun Hwang0Yoshio Takane1Kwanghee Jung2Department of Psychology, McGill University, Montreal, QC, CanadaDepartment of Psychology, University of Victoria, BC, CanadaDepartment of Educational Psychology and Leadership, Texas Tech University, Lubbock, TX, United StatesGeneralized 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.http://journal.frontiersin.org/article/10.3389/fpsyg.2017.02137/fullgeneralized structured component analysisuniquenessmeasurement errorbias correctionstructural equation modeling |
| spellingShingle | Heungsun Hwang Yoshio Takane Kwanghee Jung Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error Frontiers in Psychology generalized structured component analysis uniqueness measurement error bias correction structural equation modeling |
| title | Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error |
| title_full | Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error |
| title_fullStr | Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error |
| title_full_unstemmed | Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error |
| title_short | Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error |
| title_sort | generalized structured component analysis with uniqueness terms for accommodating measurement error |
| topic | generalized structured component analysis uniqueness measurement error bias correction structural equation modeling |
| url | http://journal.frontiersin.org/article/10.3389/fpsyg.2017.02137/full |
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