IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0197908.html
   My bibliography  Save this article

Assessing the dimensionality of the CES-D using multi-dimensional multi-level Rasch models

Author

Listed:
  • Rainer W Alexandrowicz
  • Rebecca Jahn
  • Johannes Wancata

Abstract

Objectives: The CES-D is a widely used depression screening instrument. While numerous studies have analysed its psychometric properties using exploratory and various kinds of confirmatory factor analyses, only few studies used Rasch models and none a multidimensional one. Methods: The present study applies a multidimensional Rasch model using a sample of 518 respondents representative for the Austrian general population aged 18 to 65. A one-dimensional model, a four-dimensional model reflecting the subscale structure suggested by [1], and a four-dimensional model with the background variables gender and age were applied. Results: While the one-dimensional model showed relatively good fit, the four-dimensional model fitted much better. EAP reliability indices were generally satisfying and the latent correlations varied between 0.31 and 0.88. In the analysis involving background variables, we found a limited effect of the participants’ gender. DIF effects were found unveiling some peculiarities. The two-items subscale Interpersonal Difficulties showed severe weaknesses and the Positive Affect subscale with the reversed item wordings also showed unexpected results. Conclusions: While a one-dimensional over-all score might still contain helpful information, the differentiation according to the latent dimension is strongly preferable. Altogether, the CES-D can be recommended as a screening instrument, however, some modifications seem indicated.

Suggested Citation

  • Rainer W Alexandrowicz & Rebecca Jahn & Johannes Wancata, 2018. "Assessing the dimensionality of the CES-D using multi-dimensional multi-level Rasch models," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-19, May.
  • Handle: RePEc:plo:pone00:0197908
    DOI: 10.1371/journal.pone.0197908
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197908
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0197908&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0197908?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Karl Holzinger & Frances Swineford, 1937. "The Bi-factor method," Psychometrika, Springer;The Psychometric Society, vol. 2(1), pages 41-54, March.
    2. Geoff Masters, 1982. "A rasch model for partial credit scoring," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 149-174, June.
    3. D. A. Grayson & A. Mackinnon & A. F. Jorm & H. Creasey & G. A. Broe, 2000. "Item Bias in the Center for Epidemiologic Studies Depression Scale," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 55(5), pages 273-282.
    4. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    5. Stanley Sclove, 1987. "Application of model-selection criteria to some problems in multivariate analysis," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 333-343, September.
    6. Sen-Chi Yu & Yuan-Horng Lin & Wei-Hsin Hsu, 2013. "Applying structural equation modeling to report psychometric properties of Chinese version 10-item CES-D depression scale," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(3), pages 1511-1518, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tsukasa Kato, 2021. "Measurement Invariance in the Center for Epidemiologic Studies-Depression (CES-D) Scale among English-Speaking Whites and Asians," IJERPH, MDPI, vol. 18(10), pages 1-10, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aline Riboli Marasca & Maurício Scopel Hoffmann & Anelise Reis Gaya & Denise Ruschel Bandeira, 2021. "Subjective Well-Being and Psychopathology Symptoms: Mental Health Profiles and their Relations with Academic Achievement in Brazilian Children," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 14(3), pages 1121-1137, June.
    2. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. dos Santos, Fabio Luis Marques & Duboz, Amandine & Grosso, Monica & Raposo, María Alonso & Krause, Jette & Mourtzouchou, Andromachi & Balahur, Alexandra & Ciuffo, Biagio, 2022. "An acceptance divergence? Media, citizens and policy perspectives on autonomous cars in the European Union," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 224-238.
    4. Kan, Kees-Jan & van der Maas, Han L.J. & Levine, Stephen Z., 2019. "Extending psychometric network analysis: Empirical evidence against g in favor of mutualism?," Intelligence, Elsevier, vol. 73(C), pages 52-62.
    5. Nicolas Depraetere & Martina Vandebroek, 2014. "Order selection in finite mixtures of linear regressions," Statistical Papers, Springer, vol. 55(3), pages 871-911, August.
    6. repec:jss:jstsof:06:i02 is not listed on IDEAS
    7. Yang, Chih-Chien, 2006. "Evaluating latent class analysis models in qualitative phenotype identification," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1090-1104, February.
    8. Minjeong Jeon & Frank Rijmen & Sophia Rabe-Hesketh, 2013. "Modeling Differential Item Functioning Using a Generalization of the Multiple-Group Bifactor Model," Journal of Educational and Behavioral Statistics, , vol. 38(1), pages 32-60, February.
    9. Md. Matiar Rahman & Mahbubul Muttakin & Animesh Pal & Abu Zar Shafiullah & Bidyut Baran Saha, 2019. "A Statistical Approach to Determine Optimal Models for IUPAC-Classified Adsorption Isotherms," Energies, MDPI, vol. 12(23), pages 1-34, November.
    10. Tian, Amy Wei & Meyer, John P. & Ilic-Balas, Tatjana & Espinoza, Jose A. & Pepper, Susan, 2023. "In search of the pseudo-transformational leader: A person-centered approach," Journal of Business Research, Elsevier, vol. 158(C).
    11. D. R. Anderson & K. P. Burnham & G. C. White, 1998. "Comparison of Akaike information criterion and consistent Akaike information criterion for model selection and statistical inference from capture-recapture studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(2), pages 263-282.
    12. Marianna Virtanen & Jussi Vahtera & Jenny Head & Rosemary Dray-Spira & Annaleena Okuloff & Adam G Tabak & Marcel Goldberg & Jenni Ervasti & Markus Jokela & Archana Singh-Manoux & Jaana Pentti & Marie , 2015. "Work Disability among Employees with Diabetes: Latent Class Analysis of Risk Factors in Three Prospective Cohort Studies," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
    13. Danks, Nicholas P. & Sharma, Pratyush N. & Sarstedt, Marko, 2020. "Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM)," Journal of Business Research, Elsevier, vol. 113(C), pages 13-24.
    14. Martin Lukac & Nadja Doerflinger & Valeria Pulignano, 2019. "Developing a Cross-National Comparative Framework for Studying Labour Market Segmentation: Measurement Equivalence with Latent Class Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 145(1), pages 233-255, August.
    15. Po-Hsien Huang, 2017. "Asymptotics of AIC, BIC, and RMSEA for Model Selection in Structural Equation Modeling," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 407-426, June.
    16. Morgan, Grant B. & Hodge, Kari J. & Baggett, Aaron R., 2016. "Latent profile analysis with nonnormal mixtures: A Monte Carlo examination of model selection using fit indices," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 146-161.
    17. Qi Chen & Wen Luo & Gregory J. Palardy & Ryan Glaman & Amber McEnturff, 2017. "The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored," SAGE Open, , vol. 7(1), pages 21582440177, March.
    18. Lu, Zhenqiu (Laura) & Zhang, Zhiyong, 2014. "Robust growth mixture models with non-ignorable missingness: Models, estimation, selection, and application," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 220-240.
    19. Roy Levy & Gregory R. Hancock, 2011. "An Extended Model Comparison Framework for Covariance and Mean Structure Models, Accommodating Multiple Groups and Latent Mixtures," Sociological Methods & Research, , vol. 40(2), pages 256-278, May.
    20. Peida Zhan & Xin Qiao, 2022. "DIAGNOSTIC Classification Analysis of Problem-Solving Competence using Process Data: An Item Expansion Method," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1529-1547, December.
    21. Wang, Wan-Lun & Castro, Luis M. & Lin, Tsung-I, 2017. "Automated learning of t factor analysis models with complete and incomplete data," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 157-171.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0197908. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.