Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-25T02:58:42.121Z Has data issue: false hasContentIssue false
  • English
  • Français

A comprehensive analysis of the factor structure of the Beck Depression Inventory-II in a sample of outpatients with adjustment disorder and depressive episode

Published online by Cambridge University Press:  24 October 2017

E. McElroy ,
P. Casey ,
G. Adamson ,
P. Filippopoulos  and
M. Shevlin Open the ORCID record for M. Shevlin [Opens in a new window]
E. McElroy*
Affiliation:
School of Psychology, Ulster University, Derry-Londonerry, Northern Ireland
P. Casey
Affiliation:
Department of Adult Psychiatry, School of Medicine and Medical Science, University College Dublin, Dublin, Ireland
G. Adamson
Affiliation:
School of Psychology, Ulster University, Derry-Londonerry, Northern Ireland
P. Filippopoulos
Affiliation:
Department of Psychology, City, University of London, London, UK
M. Shevlin
Affiliation:
School of Psychology, Ulster University, Derry-Londonerry, Northern Ireland
*
*Address for correspondence: E. McElroy, School of Psychology, Ulster University, Northern Ireland. (Email: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives

Despite being commonly used in research and clinical practice, the evidence regarding the factor structure of the Beck Depression Inventory-II (BDI-II) remains equivocal and this has implications on how the scale scores should be aggregated. Researchers continue to debate whether the BDI-II is best viewed as a unidimensional scale, or whether specific subscales have utility. The present study sought to test a comprehensive range of competing factor analytic models of the BDI-II, including traditional non-hierarchical multidimensional models and confirmatory bifactor models.

Method

Participants (n=370) were clinical outpatients diagnosed with either depressive episode or adjustment disorder. Confirmatory factor analysis and confirmatory bifactor modelling were used to test 15 competing models. The unidimensionality of the best fitting model was assessed using three strength indices (explained common variance, percentage of uncontaminated correlations and ω hierarchical).

Results

Overall, bifactor solutions provided superior fit than both unidimensional and non-hierarchical multidimensional models. The best fitting model consisted of a general depression factor and three specific factors: cognitive, somatic and affective. High factor loadings and strength indices for the general depression factor supported the view that the BDI-II measures a single latent construct.

Conclusions

The BDI-II should primarily be viewed as a unidimensional scale, and should be scored as such. Although it is not recommended that scores on individual subscales are used in isolation, they may prove useful in clinical assessment and/or treatment planning if used in conjunction with total scores.

Keywords

Adjustment disorderBeck Depression Inventory-IIconfirmatory bifactor analysisdepressive episodeexplained common varianceomega hierarchical
Type
Original Research
Information
Irish Journal of Psychological Medicine , Volume 35 , Issue 1 , March 2018 , pp. 53 - 61
Copyright
© College of Psychiatrists of Ireland 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

American Psychiatric Association (1994). The Diagnostic and Statistical Manual of Mental Disorders, 4th edn., APA: Washington, DC.Google Scholar
Arnau, RC, Meagher, MW, Norris, MP, Bramson, R (2001). Psychometric evaluation of the Beck Depression Inventory-II with primary care medical patients. Health Psychology 20, 112–119.Google Scholar
Barrett, P (2007). Structural equation modelling: adjudging model fit. Personality and Individual Differences 42, 815–824.Google Scholar
Beck, AT, Steer, RA, Brown, GK (1996). Beck Depression Inventory-II. Psychological Corporation: San Antonio, TX.Google Scholar
Beck, AT, Steer, RA, Brown, GK, Van der Does, AJ (2002). BDI-II-NL Handleiding [BDI-II-Dutch Manual]. Psychological Corporation: Lisse, The Netherlands.Google Scholar
Bentler, PM (1990). Comparative fit indexes in structural models. Psychological Bulletin 107, 238–246.Google Scholar
Bonifay, WE, Reise, SP, Scheines, R, Meijer, RR (2015). When are multidimensional data unidimensional enough for structural equation modelling? An evaluation of the DETECT multidimensionality index. Structural Equation Modelling: A Multidisciplinary Journal 22, 504–516.Google Scholar
Brouwer, D, Meijer, RR, Zevalkink, J (2013). On the factor structure of the Beck Depression Inventory–II: G is the key. Psychological Assessment 25, 136–145.CrossRefGoogle ScholarPubMed
Buckley, TC, Parker, JD, Heggie, J (2001). A psychometric evaluation of the BDI-II in treatment-seeking substance abusers. Journal of Substance Abuse Treatment 20, 197–204.CrossRefGoogle ScholarPubMed
Corbière, M, Bonneville-Roussy, A, Franche, RL, Coutu, MF, Choiniere, M, Durand, MJ, Boulanger, A (2011). Further validation of the BDI-II among people with chronic pain originating from musculoskeletal disorders. The Clinical Journal of Pain 27, 62–69.Google Scholar
de Miranda Azevedo, R, Roest, A, Carney, R, Denollet, J, Freedland, K, Grace, S, Hoseini, SH, Lane, AD, Parakh, K, Pilote, L, De Jonge, P (2016). A bifactor model of the Beck Depression Inventory and its association with medical prognosis after myocardial infarction. Health Psychology 35, 614–624.Google Scholar
Dozois, DJ, Dobson, KS, Ahnberg, JL (1998). A psychometric evaluation of the Beck Depression Inventory–II. Psychological Assessment 10, 83–89.CrossRefGoogle Scholar
Ghassemzadeh, H, Mojtabai, R, Karamghadiri, N, Ebrahimkhani, N (2005). Psychometric properties of a Persian‐language version of the Beck Depression Inventory‐Second edition: BDI‐II‐PERSIAN. Depression and Anxiety 21, 185–192.Google Scholar
Hu, LT, Bentler, PM (1998). Fit indices in covariance structure modeling: sensitivity to underparameterized model misspecification. Psychological Methods 3, 424–453.Google Scholar
Hu, LT, Bentler, PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6, 1–55.Google Scholar
Huang, C, Chen, JH (2015). Meta-analysis of the factor structures of the Beck Depression Inventory–II. Assessment 22, 459–472.Google Scholar
Mallinckrodt, CH, Goldstein, DJ, Detke, MJ, Lu, Y, Watkin, JG, Tran, PV (2003). Duloxetine: a new treatment for the emotional and physical symptoms of depression. The Primary Care Companion to the Journal of Clinical Psychiatry 5, 19–28.Google Scholar
Mallinckrodt, CH, Prakash, A, Houston, JP, Swindle, R, Detke, MJ, Fava, M (2007). Differential antidepressant symptom efficacy: placebo-controlled comparisons of duloxetine and SSRIs (fluoxetine, paroxetine, escitalopram). Neuropsychobiology 56, 73–85.Google Scholar
McDonald, RP (1999). Test Theory: A Unified Approach. Erlbaum: Mahwah, NJ.Google Scholar
Morgan, GB, Hodge, KJ, Wells, KE, Watkins, MW (2015). Are fit indices biased in favor of bi-factor models in cognitive ability research?: a comparison of fit in correlated factors, higher-order, and bi-factor models via Monte Carlo simulations. Journal of Intelligence 3, 2–20.Google Scholar
Murray, AL, Johnson, W (2013). The limitations of model fit in comparing the bi-factor versus higher-order models of human cognitive ability structure. Intelligence 41, 407–422.Google Scholar
Muthén, L, Muthén, B (2010). Mplus 6.0. Muthén & Muthén: Los Angeles, CA.Google Scholar
Osman, A, Barrios, FX, Gutierrez, PM, Williams, JE, Bailey, J (2008). Psychometric properties of the Beck Depression Inventory‐II in nonclinical adolescent samples. Journal of Clinical Psychology 64, 83–102.Google Scholar
Osman, A, Downs, WR, Barrios, FX, Kopper, BA, Gutierrez, PM, Chiros, CE (1997). Factor structure and psychometric characteristics of the Beck Depression Inventory-II. Journal of Psychopathology and Behavioral Assessment 19, 359–376.Google Scholar
Quilty, LC, Zhang, KA, Bagby, RM (2010). The latent symptom structure of the Beck Depression Inventory–II in outpatients with major depression. Psychological Assessment 22, 603–608.Google Scholar
Raftery, AE (1995). Bayesian model selection in social research. Sociological Methodology 25, 111–163, https://doi.org/10.2307/271063 Google Scholar
Reise, SP (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research 47, 667–696.CrossRefGoogle ScholarPubMed
Reise, SP, Bonifay, WE, Haviland, MG (2013). Scoring and modelling psychological measures in the presence of multidimensionality. Journal of Personality Assessment 95, 129–140.Google Scholar
Reise, SP, Moore, TM, Haviland, MG (2010). Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores. Journal of Personality Assessment 92, 544–559.Google Scholar
Reise, SP, Morizot, J, Hays, RD (2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research 16, 19–31.Google Scholar
Reise, SP, Scheines, R, Widaman, KF, Haviland, MG (2013). Multidimensionality and structural coefficient bias in structural equation modeling: a bifactor perspective. Educational and Psychological Measurement 73, 5–26.Google Scholar
Rodriguez, A, Reise, SP, Haviland, MG (2016). Applying bifactor statistical indices in the evaluation of psychological measures. Journal of Personality Assessment 98, 223–237.Google Scholar
Ruhé, HG, Dekker, JJ, Peen, J, Holman, R, De Jonghe, F (2005). Clinical use of the Hamilton depression rating scale: is increased efficiency possible? A post hoc comparison of Hamilton depression rating scale, Maier and Bech subscales, clinical global impression, and symptom checklist-90 scores. Comprehensive Psychiatry 46, 417–427.Google Scholar
Schwarz, G (1978). Estimating the dimension of a model. The Annals of Statistics 6, 461–464.Google Scholar
Shafer, AB (2006). Meta‐analysis of the factor structures of four depression questionnaires: Beck, CES‐D, Hamilton, and Zung. Journal of Clinical Psychology 62, 123–146.Google Scholar
Steer, RA, Ball, R, Ranieri, WF (1999). Dimensions of the Beck Depression Inventory-II in clinically depressed outpatients. Journal of Clinical Psychology 55, 117–128.Google Scholar
Steiger, JH (1990). Structural model evaluation and modification: an interval estimation approach. Multivariate Behavioral Research 25, 173–180.Google Scholar
Storch, EA, Roberti, JW, Roth, DA (2004). Factor structure, concurrent validity, and internal consistency of the beck depression inventory—second edition in a sample of college students. Depression and Anxiety 19, 187–189.CrossRefGoogle Scholar
Stucky, BD, Edelen, MO (2014). Using hierarchical IRT models to create unidimensional measures from multidimensional data. In Handbook of Item Response Theory Modeling: Applications to Typical Performance Assessment (ed. S. P. Reise, D. A. Revicki), pp. 183–206. Routledge: New YorkGoogle Scholar
Subica, AM, Fowler, JC, Elhai, JD, Frueh, BC, Sharp, C, Kelly, EL, et al. (2014). Factor structure and diagnostic validity of the Beck Depression Inventory–II with adult clinical inpatients: comparison to a gold-standard diagnostic interview. Psychological Assessment 26, 1106–1115.Google Scholar
Ten Berge, JM, Sočan, G (2004). The greatest lower bound to the reliability of a test and the hypothesis of unidimensionality. Psychometrika 69, 613–625.Google Scholar
Tucker, LR, Lewis, C (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika 38, 1–10.Google Scholar
Vanheule, S, Desmet, M, Groenvynck, H, Rosseel, Y, Fontaine, J (2008). The factor structure of the Beck Depression Inventory–II: an evaluation. Assessment 67, 588–597.Google Scholar
Wang, YP, Gorenstein, C (2013). Psychometric properties of the Beck Depression Inventory-II: a comprehensive review. Revista Brasileira de Psiquiatria 35, 416–431.CrossRefGoogle ScholarPubMed
Ward, CL (2006). Comparison of factor structure models for the Beck Depression Inventory–II. Psychological Assessment 18, 81–88.Google Scholar
Whisman, MA, Perez, JE, Ramel, W (2000). Factor structure of the Beck Depression Inventory—Second Edition (BDI-ii) in a student sample. Journal of Clinical Psychology 56, 545–551.Google Scholar
World Health Organisation (1992). International Classification of Diseases , 10th edn. WHO: Geneva.Google Scholar
World Medical Association (2008). WMA Declaration of Helsinki – Ethical Principles for Medical Research Involving Human Subjects. Bulletin of the World Health Organization 79, 373.Google Scholar