Core Symptom Index (CSI): Testing for bifactor model and differential item functioning

© 2019 International Psychogeriatric Association. Objectives:The Core Symptom Index (CSI) is designed to measure anxiety, depression and somatization symptoms. This study examined the construct validity of CSI using confirmatory factor analysis (CFA) including a bifactor model and explored different...

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Main Authors: Nahathai Wongpakaran, Tinakon Wongpakaran, Surang Lertkachatarn, Thanitha Sirirak, Pimolpun Kuntawong
Format: Journal
Published: 2019
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/65828
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-658282019-08-05T04:43:45Z Core Symptom Index (CSI): Testing for bifactor model and differential item functioning Nahathai Wongpakaran Tinakon Wongpakaran Surang Lertkachatarn Thanitha Sirirak Pimolpun Kuntawong Medicine Nursing Psychology © 2019 International Psychogeriatric Association. Objectives:The Core Symptom Index (CSI) is designed to measure anxiety, depression and somatization symptoms. This study examined the construct validity of CSI using confirmatory factor analysis (CFA) including a bifactor model and explored differential item functioning (DIF) of the CSI. The criterion and concurrent validity were evaluated.Methods:In all, 803 elderly patients, average age 69.24 years, 70% female, were assessed for depressive disorders and completed the CSI and the geriatric depression scale (GDS). A series involving CFA for ordinal scale was applied. Factor loadings and explained common variance were analyzed for general and specific factors; and Omega was calculated for model-based reliability. DIF was analyzed using the Multiple-Indicator Multiple-Cause model. Pearson's correlation, ANOVA, and ROC analysis were used for associations and to compare CSI and GDS in predicting major depressive disorders (MDD).Results:The bifactor model provided the best fit to the data. Most items loaded on general rather than specific factors. The explained common variance was acceptable, while Omega hierarchical for the subscale and explained common variance for the subscales were low. Two DIF items were identified; 'crying' for sex items and 'self-blaming' for education items. Correlation among CSI and clinical disorders and the GDS were found. AUC for the GDS was 0.83, and for the CSI was 0.81.Conclusion:CSI appears sufficiently unidimensional. Its total score reflected a single general factor, permitting users to interpret the total score as a sufficient reliable measure of the general factors. CSI could serve as a screening tool for MDD. 2019-08-05T04:41:55Z 2019-08-05T04:41:55Z 2019-01-01 Journal 1741203X 10416102 2-s2.0-85063603498 10.1017/S1041610219000140 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85063603498&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/65828
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Medicine
Nursing
Psychology
spellingShingle Medicine
Nursing
Psychology
Nahathai Wongpakaran
Tinakon Wongpakaran
Surang Lertkachatarn
Thanitha Sirirak
Pimolpun Kuntawong
Core Symptom Index (CSI): Testing for bifactor model and differential item functioning
description © 2019 International Psychogeriatric Association. Objectives:The Core Symptom Index (CSI) is designed to measure anxiety, depression and somatization symptoms. This study examined the construct validity of CSI using confirmatory factor analysis (CFA) including a bifactor model and explored differential item functioning (DIF) of the CSI. The criterion and concurrent validity were evaluated.Methods:In all, 803 elderly patients, average age 69.24 years, 70% female, were assessed for depressive disorders and completed the CSI and the geriatric depression scale (GDS). A series involving CFA for ordinal scale was applied. Factor loadings and explained common variance were analyzed for general and specific factors; and Omega was calculated for model-based reliability. DIF was analyzed using the Multiple-Indicator Multiple-Cause model. Pearson's correlation, ANOVA, and ROC analysis were used for associations and to compare CSI and GDS in predicting major depressive disorders (MDD).Results:The bifactor model provided the best fit to the data. Most items loaded on general rather than specific factors. The explained common variance was acceptable, while Omega hierarchical for the subscale and explained common variance for the subscales were low. Two DIF items were identified; 'crying' for sex items and 'self-blaming' for education items. Correlation among CSI and clinical disorders and the GDS were found. AUC for the GDS was 0.83, and for the CSI was 0.81.Conclusion:CSI appears sufficiently unidimensional. Its total score reflected a single general factor, permitting users to interpret the total score as a sufficient reliable measure of the general factors. CSI could serve as a screening tool for MDD.
format Journal
author Nahathai Wongpakaran
Tinakon Wongpakaran
Surang Lertkachatarn
Thanitha Sirirak
Pimolpun Kuntawong
author_facet Nahathai Wongpakaran
Tinakon Wongpakaran
Surang Lertkachatarn
Thanitha Sirirak
Pimolpun Kuntawong
author_sort Nahathai Wongpakaran
title Core Symptom Index (CSI): Testing for bifactor model and differential item functioning
title_short Core Symptom Index (CSI): Testing for bifactor model and differential item functioning
title_full Core Symptom Index (CSI): Testing for bifactor model and differential item functioning
title_fullStr Core Symptom Index (CSI): Testing for bifactor model and differential item functioning
title_full_unstemmed Core Symptom Index (CSI): Testing for bifactor model and differential item functioning
title_sort core symptom index (csi): testing for bifactor model and differential item functioning
publishDate 2019
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85063603498&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/65828
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