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

Published online by Cambridge University Press:  27 March 2019

Nahathai Wongpakaran Open the ORCID record for Nahathai Wongpakaran [Opens in a new window] ,
Tinakon Wongpakaran ,
Surang Lertkachatarn ,
Thanitha Sirirak  and
Pimolpun Kuntawong
Nahathai Wongpakaran*
Affiliation:
Chiang Mai University, Department of Psychiatry, Faculty of Medicine, Chiang Mai, Thailand
Tinakon Wongpakaran
Affiliation:
Chiang Mai University, Department of Psychiatry, Faculty of Medicine, Chiang Mai, Thailand
Surang Lertkachatarn
Affiliation:
Prasat Neurological Institute, Department of Psychiatry, Bangkok, Thailand
Thanitha Sirirak
Affiliation:
Prince of Songkla University, Department of Preventive Medicine, Faculty of Medicine, Songkhla, Thailand
Pimolpun Kuntawong
Affiliation:
Chiang Mai University, Department of Psychiatry, Faculty of Medicine, Chiang Mai, Thailand
*
Correspondence should be addressed to: Nahathai Wongpakaran, MD, FRCPsychT, Professor of Psychiatry, Geriatric Psychiatry Unit, Department of Psychiatry, Faculty of Medicine, Chiang Mai University 110 Intawaroros Rd., T. Sriphum, A. Muang, Chiang Mai, Kingdom of Thailand. 50200. Phone: +66 53 935422 ext 320; Fax: +66 53 935426. Email: [email protected].
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Abstract

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.

Keywords

Core Symptom Index (CSI)confirmatory factor analysisdifferential item functioningMIMIC modelbifactor model
Type
Original Research Article
Information
International Psychogeriatrics , Volume 31 , Issue 12: Issue Theme: Assessment of Cognitive Impairment in East Asia , December 2019 , pp. 1769 - 1779
Copyright
© International Psychogeriatric Association 2019 

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References

Barsky, A. J., Peekna, H. M. and Borus, J. F. (2001). Somatic symptom reporting in women and men. Journal of General Internal Medicine, 16, 266–275. doi: 10.1046/j.1525-1497.2001.016004266.x.CrossRefGoogle ScholarPubMed
Bland, P. (2012). Tackling anxiety and depression in older people in primary care. Practitioner, 256, 17–20, 2–3. doi: 10.7748/nop2006.02.18.1.25.c2411.Google ScholarPubMed
Brunner, M., Nagy, G. and Wilhelm, O. (2012). A tutorial on hierarchically structured constructs. Journal of personality, 80, 796–846. doi: 10.1111/j.1467-6494.2011.00749.x.CrossRefGoogle ScholarPubMed
Castro, S. M., Cúri, M., Torman, V. B. and Riboldi, J. (2015). Differential item functioning in the beck depression inventory. Revista Brasileira de Epidemiologia, 18, 54–67. doi: 10.1002/9781118758991.ch7.CrossRefGoogle ScholarPubMed
Deng, L. and Chan, W. (2017). Testing the difference between reliability coefficients alpha and omega. Educational And Psychological Measurement, 77, 185–203. doi: 10.1177/0013164416658325.CrossRefGoogle ScholarPubMed
Dere, J. et al. (2013). Beyond “Somatization” and “Psychologization”: Symptom-level variation in depressed Han Chinese and Euro-Canadian outpatients. Frontiers in Psychology, 4, 377. doi: 10.3389/fpsyg.2013.00377.CrossRefGoogle ScholarPubMed
Gierk, B. et al. (2014). The Somatic Symptom Scale-8 (Sss-8): A Brief Measure of Somatic Symptom Burden. JAMA Internal Medicine, 174, 399–407. doi: 10.1001/jamainternmed.2013.12179.CrossRefGoogle ScholarPubMed
Hammer, J. H. (2016). Percent of uncontaminated correlations (Puc) calculator: a Microsoft Excel-based tool to calculate the Puc statistic [Online]. Available: http://Drjosephhammer.Com/. [Accessed 31 March 2018]. doi: 10.1145/2878378.CrossRefGoogle Scholar
Heisel, M. J., Links, P. S., Conn, D., Van Reekum, R. and Flett, G. L. (2007). Narcissistic personality and vulnerability to late-life suicidality. American Journal of Geriatric Psychiatry, 15, 734–741. doi: 10.1097/JGP.0b013e3180487caa.CrossRefGoogle ScholarPubMed
Hu, L. and Bentler, P. M. (1999). Cut off criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modelling, 6, 1–55. doi: 10.1080/10705519909540118.CrossRefGoogle Scholar
Hybels, C. F., Landerman, L. R. and Blazer, D. G. (2012). Age differences in symptom expression in patients with major depression. International Journal of Geriatric Psychiatry, 27, 601–611. doi: 10.1002/gps.2759.CrossRefGoogle ScholarPubMed
Johnco, C., Knight, A., Tadic, D. and Wuthrich, V. M. (2015). Psychometric properties of the geriatric anxiety inventory (Gai) and its short-form (Gai-Sf) in a clinical and non-clinical sample of older adults. International Psychogeriatrics/Ipa, 27, 1089–1097. doi: 10.1017/S1041610214001586.CrossRefGoogle Scholar
Jones, R. N. (2006). Identification of measurement differences between English and Spanish language versions of the Mini-Mental State Examination. Detecting differential item functioning using mimic modeling. Medical Care, 44, S124–133. doi: 10.1097/01.mlr.0000245250.50114.0f.CrossRefGoogle ScholarPubMed
Kim, G., Decoster, J., Huang, C. H. and Bryant, A. N. (2013). A meta-analysis of the factor structure of the geriatric depression scale (Gds): the effects of language. International Psychogeriatrics, 25, 71–81. doi: 10.1017/S1041610212001421.CrossRefGoogle ScholarPubMed
Long, J. et al. (2014). Hypertension and risk of depression in the elderly: a meta-analysis of prospective cohort studies. Journal Of Human Hypertension, 29, 478. doi: 10.1038/jhh.2014.112.CrossRefGoogle ScholarPubMed
Muthen, L. K. and Muthen, B. O. (1998–2015). Mplus User’s Guide, 7th Edition, Los Angeles, CA. Muthén and Muthén. doi: 10.1002/sim.6388.Google Scholar
Nakao, M. and Yano, E. (2003). Reporting of somatic symptoms as a screening marker for detecting major depression in a population of Japanese white-collar workers. Journal Of Clinical Epidemiology, 56, 1021–1026. doi: 10.1016/S0895-4356(03)00154-9.CrossRefGoogle Scholar
Nguyen, H. T. and Zonderman, A. B. (2006). Relationship between age and aspects of depression: consistency and reliability across two longitudinal studies. Psychology and Aging, 21, 119–126. doi: 10.1037/0882-7974.21.1.119.CrossRefGoogle ScholarPubMed
Novick, D. et al. (2013). Which somatic symptoms are associated with an unfavorable course in Asian patients with major depressive disorder? The Journal of Affective Disorders, 149, 182–188. doi: 10.1016/j.jad.2013.01.020.CrossRefGoogle ScholarPubMed
Reise, S. P., Morizot, J. and Hays, R. D. (2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research, 16, 19–31.CrossRefGoogle ScholarPubMed
Reise, S. P., Scheines, R., Widaman, K. F. and Haviland, M. G. (2012). Multidimensionality and structural coefficient bias in structural equation modeling: a bifactor perspective. Educational and Psychological Measurement, 73, 5–26. doi: 10.1007/s11136-007-9183-7.CrossRefGoogle Scholar
Rodriguez, A., Reise, S. P. and Haviland, M. G. (2016). Evaluating bifactor models: calculating and interpreting statistical indices. Psychological Methods, 21, 137–150. doi: 10.1037/met0000045.CrossRefGoogle ScholarPubMed
Romans, S. E., Tyas, J., Cohen, M. M. and Silverstone, T. (2007). Gender differences in the symptoms of major depressive disorder. Journal of Nervous and Mental Disease, 195, 905–911. doi: 10.1097/NMD.0b013e3181594cb7.CrossRefGoogle ScholarPubMed
Schwarz, G. E. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464. doi: 10.1214/aos/1176344136.CrossRefGoogle Scholar
Shiekh, J. and Yesavage, J. (1986). Geriatric depression scale (Gds): recent evidence and development of a shorter version. In Brink, T. (ed.), Clinical Gerontology: A Guide To Assessment And Intervention. New York, NY: The Haworth Press. doi: 10.1300/J018v05n01_09.Google Scholar
Simms, L. J., Grös, D. F., Watson, D. and O’hara, M. W. (2008). Parsing the general and specific components of depression and anxiety with bifactor modeling. Depression and Anxiety, 25, E34–46. doi: 10.1002/da.20432.CrossRefGoogle ScholarPubMed
Stucky, B. D. and Edelen, M. O. (2015). Using hierarchical IRT models to create unidimensional measures from multidimensional data. In Reise, S. P. and Revicki, D. A. (eds.), Handbook of Item Response Theory Modeling: Applications To Typical Performance Assessment. New York, NY: Routledge. doi: 10.1177/0146621615590600.Google Scholar
Taycan, O., Ozdemir, A., Erdogan-Taycan, S. and Jurcik, T. (2015). Associations of somatic symptom attribution in Turkish patients with major depression. Nordic Journal of Psychiatry, 69, 167–173. doi: 10.3109/08039488.2014.950328.CrossRefGoogle ScholarPubMed
Teresi, J. A. et al. (2009). Analysis of differential item functioning in the depression item bank from the patient reported outcome measurement information system (Promis): an item response theory approach. Psychology Science Quarterly, 51, 148–180. doi: 10.1016/j.jpsychires.2014.05.010.Google ScholarPubMed
Terluin, B. et al. (2006). The four-dimensional symptom questionnaire (4dsq): A validation study of a multidimensional self-report questionnaire to assess distress, depression, anxiety and somatization. BMC Psychiatry, 6, 34. doi: 10.1186/1471-244X-6-34.CrossRefGoogle ScholarPubMed
Tsai, C. H., Wu, J. S., Chang, Y. F., Lu, F. H., Yang, Y. C. and Chang, C. J. (2012). The relationship between psychiatric symptoms and glycemic status in a Chinese population. Journal of Psychiatry Research, 46, 927–932. doi: 10.1016/j.jpsychires.2012.04.003.CrossRefGoogle ScholarPubMed
Urbán, R. et al. (2014). Bifactor structural model of symptom checklists: Scl-90-R and brief symptom inventory (Bsi) in a non-clinical community sample. Psychiatry Research, 216, 146–154. doi: 10.1016/j.psychres.2014.01.027.CrossRefGoogle Scholar
Wongpakaran, N. and Wongpakaran, T. (2012). Prevalence of major depressive disorders and suicide in long-term care facilities: a report from Northern Thailand. Psychogeriatrics, 12, 11–17. doi: 10.1111/j.1479-8301.2011.00383.x.CrossRefGoogle ScholarPubMed
Wongpakaran, T. and Wongpakaran, N. (2013). Detection of suicide among the elderly in a -erm care facility. Clinical Interventions in Aging, 8, 1553–1559. doi: 10.2147/CIA.S53355.CrossRefGoogle Scholar
Wongpakaran, T., Wongpakaran, N. and Boripuntakul, T. (2011). Symptom checklist-90 (Scl-90) in a Thai sample. Journal of Medical Association of Thailand, 94, 1141–1149. doi: 10.1186/1471-2296-12-65.Google Scholar
Wongpakaran, N., Wongpakaran, T. and Van Reekum, R. (2013). The use of Gds-15 in detecting Mdd: a comparison between residents in a Thai long-term care home and geriatric outpatients. Journal of Clinical Medicine Research, 5, 101–111. doi: 10.4021/jocmr1239w.Google Scholar
Zinbarg, R. E., Revelle, W., Yovel, I. and Li, W. (2005) Cronbach’s α, Revelle’s β, and Mcdonald’s ω H: their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70, 123–133. doi: 10.1007/s11336-003-0974-7.CrossRefGoogle Scholar
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