Psychometric properties of the Beck Depression Inventory-II: a comprehensive review

Yuan-Pang Wang Clarice Gorenstein About the authors

Abstract

Objective:

To review the psychometric properties of the Beck Depression Inventory-II (BDI-II) as a self-report measure of depression in a variety of settings and populations.

Methods:

Relevant studies of the BDI-II were retrieved through a search of electronic databases, a hand search, and contact with authors. Retained studies (k = 118) were allocated into three groups: non-clinical, psychiatric/institutionalized, and medical samples.

Results:

The internal consistency was described as around 0.9 and the retest reliability ranged from 0.73 to 0.96. The correlation between BDI-II and the Beck Depression Inventory (BDI-I) was high and substantial overlap with measures of depression and anxiety was reported. The criterion-based validity showed good sensitivity and specificity for detecting depression in comparison to the adopted gold standard. However, the cutoff score to screen for depression varied according to the type of sample. Factor analysis showed a robust dimension of general depression composed by two constructs: cognitive-affective and somatic-vegetative.

Conclusions:

The BDI-II is a relevant psychometric instrument, showing high reliability, capacity to discriminate between depressed and non-depressed subjects, and improved concurrent, content, and structural validity. Based on available psychometric evidence, the BDI-II can be viewed as a cost-effective questionnaire for measuring the severity of depression, with broad applicability for research and clinical practice worldwide.

Psychometric scale; depression; reliability; validity; classical testing theory; item response theory


Introduction

Depression is projected to become a globally prevalent disorder11. Ferrari AJ, Somerville AJ, Baxter AJ, Norman R, Patten SB, Vos T, et al. Global variation in the prevalence and incidence of major depressive disorder: a systematic review of the epidemiological literature. Psychol Med. 2013;43:471-81.,22. Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B. Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet. 2007;370:851-8. with a huge burden to the population.33. World Health Organization (WHO). The Global burden of disease. 2004 Update. Geneva: WHO; 2008. Among the available self-assessment instruments, the 21-item Beck Depression Inventory (BDI) is one of the most popular measures of depressive symptoms worldwide.44. McDowell I. Measuring health: a guide to rating scales and questionnaires. 3rd ed. New York: Oxford University; 2006. First proposed by Beck et al.,55. Beck AT, Ward CH, Mendelson M, Mock JE, Erbaugh JK. An inventory for measuring depression. Arch Gen Psychiatry. 1961;4:561-71. this instrument has been used in more than 7,000 studies so far. The theoretical assumption of the original BDI relied upon the belief that negativistic distorted cognitions would be the core characteristic of depression.66. Beck AT, Steer RA, Garbin MG. Psychometric properties of the Beck Depression Inventory: twenty-five years of evaluation. Clin Psychol Rev. 1988;8:77-100.

The BDI has undergone two major revisions: in 1978 as the BDI-IA77. Beck AT, Rush AJ, Shaw BF, Emery G. Cognitive therapy of depression. New York: Guilford; 1979. and in 1996 as the Beck Depression Inventory-II (BDI-II).88. Beck AT, Steer RA, Brown GK. BDI-II: Beck Depression Inventory Manual. 2nd ed. San Antonio: Psychological Corporation; 1996. The updated BDI-II taps psychological and somatic manifestations of 2-week major depressive episodes, as operationalized in the DSM-IV.99. American Psychiatric Association. Diagnostic and statistical manual of mental disorders - DSM-IV-TR¯. 4th ed. Washington: American Psychiatric Publishing; 1994. This version was modified to reword and replace some items. Four items of the BDI-IA that proved less sensitive for identification of typical symptoms of severe depression - weight loss, distorted body image, somatic preoccupation, and inability to work - were dropped and replaced by agitation, worthlessness, difficulty concentrating, and energy loss to assess a distinctive degree of intensity of depression. In addition, the items on appetite and sleep change were amended to evaluate the increase and decrease of these depression-related behaviors. Unlike the original version, the BDI-II does not reflect any particular theory of depression.

Despite widespread use in both non-clinical and clinical studies for more than 15 years after its publication, to the best of our knowledge, no relevant summary of the performance of this version has been conducted. In addition, the last decade has seen major progress in psychometric theories that were not fully developed at the time the BDI was reformulated. Within this context, we carried out a search of articles dealing with the psychometric properties of the BDI-II. This review is not intended to be a systematic review or meta-analysis, but a synopsis of the subject matter addressing the feasibility of using BDI-II in different population samples. Whenever possible, psychometric advantages and criticisms are underscored, discussing recommendations for use in a variety of settings.

Methods

Both investigators, with previous experience in psychometric instruments, searched MEDLINE and PsycINFO databases. The following MeSH terms were used to filter relevant studies: psychometrics and depression. We restricted the search to articles containing the BDI and published between the time periods of January 1st, 1996 and October 10th, 2012. The following non-psychometric article types were left out: clinical trials, editorials, letters, meta-analyses, practice guidelines, randomized controlled trials, and case reports. There was no language or age range restriction.

All retained articles were read for exclusion of additional criteria: non-psychometric studies; other versions of the BDI; small samples (fewer than 30 participants1010. Nunnally JC, Bernstein IH. Psychometric theory. New York: McGraw; 1994.), unless the study addressed a very important problem, such as between-version comparison or content analysis. Secondary analyses of previously reported datasets were excluded. Summary analysis of the complete sample was preferable when multiple analyses were available (such as separate reports by gender, ethnicity, or depressed vs. non-depressed groups).

The reference sections of review articles1111. Furukawa TA. Assessment of mood: guides for clinicians. J Psychosom Res. 2010;68:581-9.

12. McPherson A, Martin CR. A narrative review of the Beck Depression Inventory (BDI) and implications for its use in an alcohol-dependent population. J Psychiatr Ment Health Nurs. 2010;17:19-30.
-1313. Shafer AB. Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. J Clin Psychol. 2006;62:123-46. and book chapters44. McDowell I. Measuring health: a guide to rating scales and questionnaires. 3rd ed. New York: Oxford University; 2006.,1414. Dozois DJA. Beck Depression Inventory-II. In: Weiner IB, Craighead WE, editors. The Corsini Encyclopedia of psychology. 4th ed. New York: John Wiley & Sons; 2010. p. 210-1.,1515. Kazdin AE. Encyclopedia of Psychology. Oxford: American Psychological Association; 2000. that were not retrieved in the computer search were examined to identify potential studies for inclusion. Additional efforts to locate relevant studies included contacting authors in the field and a hand search of the reference lists of retained articles.

Results and discussion

Overview

The MeSH search strategy detailed above yielded 2,611 articles. Filtering these studies using BDI resulted in 253 articles, 198 of which matched the time period of interest. The exclusion of non-psychometric study types narrowed the sample to 178 articles. Among those retained from the electronic database plus hand search, 60 did not meet the inclusion criteria: 33 articles did not present relevant psychometric data; 18 used the BDI-I; five used the BDI-Fast Screen; and four presented a small sample. The final list resulted in 118 articles dedicated to investigate psychometric performance of the BDI-II.

For the sake of comparison between similar investigations, the studies were grouped by sample recruitment source as: non-clinical (k = 47); psychiatric/institutionalized (k = 37); or medical samples (k = 34). Typically, non-clinical studies were conducted in student analogue depression samples (average age, 18-23 years), which are referred to in this study as student studies to describe university-recruited samples (k = 29) and adolescent studies to describe school-based underage respondents (k = 8). Psychiatric samples were stratified as inpatient, outpatient, or institutionalized. Medical samples were grouped according to the disease and recruitment setting. The instrument was applied to over 60,000 respondents.

The English version of the BDI-II has been translated into 17 languages, and is used in Europe, the Middle East, Asia, and Latin America (Table 1). Although the English version prevailed among the studies (65%), the increasing number of language versions suggests international acceptance of the instrument.

Table 1
Studies using the BDI-II by language version, sample size, target sample, gender distribution, mean (SD) score, and reliability (alpha and Pearson's r)

Table 1 shows that the mean score ranged from 5.1 to 38.4. In general, psychiatric samples presented the highest mean scores, medical samples intermediate, and non-clinical samples the lowest means. Since sample standardization is not demographically representative of the population and little evidence has been provided regarding the gender and culture fairness of the items and total score, the original authors recommended development of local norms.

Reliability

Twenty-nine of the 118 retrieved articles (25%) did not report reliability coefficients, indicating that the assumption of test score reliability generally has not prevailed in clinical practice regarding application of the BDI. In comparison to the internal consistency of the previous versions of the BDI (average Cronbach's alpha coefficient around 0.85),88. Beck AT, Steer RA, Brown GK. BDI-II: Beck Depression Inventory Manual. 2nd ed. San Antonio: Psychological Corporation; 1996. most studies on BDI-II reported an average alpha coefficient around 0.9, ranging from 0.83 to 0.96 (Table 1). Probably, the replacement of particular items has improved the homogeneity of the scale. Its ability to assess different types of depression, e.g., atypical depression, is superior to that of the BDI-IA, as symptoms of increased and decreased appetite and sleep were included in the BDI-II items. However, superior reliability does not necessarily indicate improvement of the clinical validity of the scale.

Retest reliability (Pearson's r) showed relative stability through re-application of the BDI-II, with good to excellent coefficients (range, 0.73 to 0.96),1717. Al-Musawi NM. Psychometric properties of the beck depression inventory-II with university students in Bahrain. J Pers Assess. 2001;77:568-79.,2929. Ghassemzadeh H, Mojtabai R, Karamghadiri N, Ebrahimkhani N. Psychometric properties of a Persian-language version of the Beck Depression Inventory-Second edition: BDI-II-PERSIAN. Depress Anxiety. 2005;21:185-92.,3333. Kapci EG, Uslu R, Turkcapar H, Karaoglan A. Beck Depression Inventory-II: evaluation of the psychometric properties and cut-off points in a Turkish adult population. Depress Anxiety. 2008;25:E104-10.,5959. Wiebe JS, Penley JA. A psychometric comparison of the Beck Depression Inventory-II in English and Spanish. Psychol Assess. 2005;17:481-5.,127127. Huprich SK, Roberts CR. The two-week and five-week dependability and stability of the depressive personality disorder inventory and its association with current depressive symptoms. J Pers Assess. 2012;94:205-9. with a mean re-application interval of 2 weeks (range, 1 week to 6 months) for the majority of studies (82%). However, two remarks should be taken into account when interpreting these coefficients: 1) as true changes in depressive symptoms can occur without any intervention, while a high correlation is more likely after a short time, a longer interval could explain a smaller correlation; 2) there is no available retest information for patient samples, whether psychiatric or medical. The observed retest coefficients were similar to the values found by the authors of the BDI-II with clinical and non-clinical populations,88. Beck AT, Steer RA, Brown GK. BDI-II: Beck Depression Inventory Manual. 2nd ed. San Antonio: Psychological Corporation; 1996. 0.92 and 0.93 respectively for an average time interval of 7 days between application and the re-application of the scale. A reliability generalization analysis showed an average coefficient around 0.65 for the previous version of the BDI.128128. Yin P, Fan X. Assessing the reliability of Beck Depression Inventory scores: reliability generalization. Educ Psychol Meas. 2000;60:201-23. Comparison of the retest coefficients of the BDI-I and BDI-II could only be considered definitive if the time intervals of the studies were similar.

To address the potential source of this retest effect, Longwell & Truax129129. Longwell BT, Truax P. The differential effects of weekly, monthly, and bimonthly administrations of the Beck Depression Inventory-II: psychometric properties and clinical implications. Behav Ther. 2005;36:265-75. randomly assigned non-clinical participants (n=237) without intervention to complete the BDI-II at weekly, monthly, or bimonthly intervals. Scores were found to significantly decrease for the weekly administration group only, indicating that lower retest scores could be the result of a measurement effect and the frequency of administration. Re-application of the BDI-II in healthcare settings might be problematic, since lower scores, or true change in severity of depression, can be obtained even without intervention and might be attributable to the measurement process. The measurement error due to time length as captured by the retest estimate is probably larger than the error due to item heterogeneity and content as captured by cross-sectional internal consistency.128128. Yin P, Fan X. Assessing the reliability of Beck Depression Inventory scores: reliability generalization. Educ Psychol Meas. 2000;60:201-23.

On the other hand, Hiroe et al.7272. Hiroe T, Kojima M, Yamamoto I, Nojima S, Kinoshita Y, Hashimoto N, et al. Gradations of clinical severity and sensitivity to change assessed with the Beck Depression Inventory-II in Japanese patients with depression. Psychiatry Res. 2005;135:229-35. investigated sensitivity to change by anchoring the BDI-II against the Clinical Global Impression-Change (CGI-I) subscale 2 weeks after first consultation of 40 patients with major depression. The instrument was able to distinguish between all grades of depression severity. Since changes in score could also be the result of a measurement effect, clinicians should be careful when making important treatment decisions based solely on information from the BDI-II.

Item characteristics

The true score of a given scale, as well as its reliability, is the result of a set of scores that are susceptible to the influence of individual item errors.130130. Cronbach LJ. Essentials of psychological testing. 3nd ed. New York: Harper and Row; 1990. Further analysis of item characteristics might overcome this measurement effect.

In comparison with its previous version, the item characteristics of the BDI-II have been changed in terms of item endorsement rate, content coverage, and homogeneity. Most investigations of non-clinical samples reported item scores in the low end of the possible range (0-3), resulting in a skewed distribution of item scores. Typically, non-clinical participants tended to report an average item score below 1.3131. Gorenstein C, Wang YP, Argimon IL, Werlang BSG. Manual do Inventário de Depressão de Beck - BDI-II. São Paulo: Casa do Psicólogo; 2011.,131131. Alansari BM. Beck depression inventory (BDI-II) items characteristics among undergraduate students of nineteen islamic countries. Soc Behav Pers. 2005;33:675-84. Furthermore, the mean item score does not exceed 2 in most clinical samples. In the case of extreme scores, endorsement bias might push the distribution of the results upward. Some researchers have criticized the possibility of malingered or deceitful ratings by the respondents due to the self-report nature of the scale.7575. Krefetz DG, Steer RA, Gulab NA, Beck AT. Convergent validity of the Beck depression inventory-II with the reynolds adolescent depression scale in psychiatric inpatients. J Pers Assess. 2002;78:451-60.,8383. Perry AE, Gilbody S. Detecting and predicting self-harm behaviour in prisoners: a prospective psychometric analysis of three instruments. Soc Psychiatry Psychiatr Epidemiol. 2009;44:853-61.,8686. Seignourel PJ, Green C, Schmitz JM. Factor structure and diagnostic efficiency of the BDI-II in treatment-seeking substance users. Drug Alcohol Depend. 2008;93:271-8. The potential fakability of the inventory should be kept in mind during the interpretation of the test.

The item suicidal thoughts had the lowest endorsement rate; however, the substantial correlation still provides evidence of its contribution to the measured construct. Similarly, loss of sexual interest displayed the worst item-total correlation, although it remained significantly related to the whole construct under consideration.88. Beck AT, Steer RA, Brown GK. BDI-II: Beck Depression Inventory Manual. 2nd ed. San Antonio: Psychological Corporation; 1996.,3131. Gorenstein C, Wang YP, Argimon IL, Werlang BSG. Manual do Inventário de Depressão de Beck - BDI-II. São Paulo: Casa do Psicólogo; 2011. Conversely, somatic items such as change in sleeping pattern and in appetite also presented low scores for non-clinical samples. The hypothesis of gender differences in somatic symptoms132132. Silverstein B, Edwards T, Gamma A, Ajdacic-Gross V, Rossler W, Angst J. The role played by depression associated with somatic symptomatology in accounting for the gender difference in the prevalence of depression. Soc Psychiatry Psychiatr Epidemiol. 2013;48:257-63. was not supported by Delisle et al.,6868. Delisle VC, Beck AT, Dobson KS, Dozois DJ, Thombs BD. Revisiting gender differences in somatic symptoms of depression: much ado about nothing? PLoS One. 2012;7:e32490. who showed that the experience and reporting of somatic symptoms could explain merely a small portion of discrepancy in depressed patients. Testing the hypothesis of whether individual baseline depressive symptoms in the interest-activity domain would predict outcome, the items pessimism and loss of energy were found to be independent predictors of both remission and response in the treatment setting.9090. van Noorden MS, van Fenema EM, van der Wee NJ, Zitman FG, Giltay EJ. Predicting outcome of depression using the depressive symptom profile: the Leiden Routine Outcome Monitoring Study. Depress Anxiety. 2012;29:523-30. The effects of new items and wording revisions on the psychometric performance of the scale have not been fully assessed, and sample type should be taken into account when interpreting scores.

Because the selected items and content of the BDI-II were modified in accordance with symptoms defined in the DSM-IV as specific to a subtype of depression, it is reasonable to expect a more stringent degree of homogeneity. Beck88. Beck AT, Steer RA, Brown GK. BDI-II: Beck Depression Inventory Manual. 2nd ed. San Antonio: Psychological Corporation; 1996. reported a median item-total scale correlation of 0.59 for the BDI-II in a sample of college students (n=120). Acceptable item-total scale correlations (rit ≥ 0.5)1010. Nunnally JC, Bernstein IH. Psychometric theory. New York: McGraw; 1994. were described for 17 out of 21 items. Nonetheless, this correlation can vary across studies. For the Arabic version, substantial item-total correlation was described for 10 items among Islamic students,131131. Alansari BM. Beck depression inventory (BDI-II) items characteristics among undergraduate students of nineteen islamic countries. Soc Behav Pers. 2005;33:675-84. whereas adequate item-total correlation of the Portuguese version in Brazilian samples was reported for 15 items.3131. Gorenstein C, Wang YP, Argimon IL, Werlang BSG. Manual do Inventário de Depressão de Beck - BDI-II. São Paulo: Casa do Psicólogo; 2011. Factors such as language version, type of sample, age range, educational level, and severity of depression might affect the difficulty of item endorsement.133133. Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16:297-334. Insight into which items should be assigned to a scale can improve its performance through item-level analysis.

Item response theory and Rasch analysis

Most validation studies of the BDI-II were analyzed on the grounds of classic test theory (CTT), assuming a true score for each respondent and disregarding the measurement error. In other words, two individuals with the same total score may differ in terms of the relative severity and frequency of symptoms. In CTT, most test performances are computed as a whole rather than at the item level. Error is often assumed to be normally distributed and uncorrelated with the true score. Although the statistics produced are usually generalized to similar respondents taking a similar test, the results should only apply to those individuals taking that test. As a psychometric breakthrough to these limitations, latent trait models based on item response theory (IRT) aim to look beyond the CTT: at the underlying traits that are producing the test performance. The results of an IRT-based test can provide sample-free measurement and are measured at the item level in terms of difficulty and discrimination. This method is being increasingly used in the empirical construction and evaluation of modern psychometric instruments.

A sound rating scale should measure a single psychopathological construct (i.e., an illness or syndrome) and be composed of items that adequately cover a constellation of symptoms that are associated with the syndrome. According to IRT, a given scale and its constituent items may have good reliability estimates but still fail to meet IRT criteria of unidimensionality.134134. Hambleton RK, Swaminathan H, Rogers HJ. Fundamentals of item response theory. Newbury Park: Sage; 1991. Efforts to analyze individual items and to identify a single dimension of depression severity can benefit from several IRT models, e.g., Rasch analysis. This method assesses the extent to which empirical data correspond to an ideal dimension, by identifying a unidimensional set of items from a rating scale, and evaluates how adequately these items measure the full range of clinical severity.

Use of the IRT is particularly pressing in studies investigating clinical change in depressive syndromes. Items that are insensitive to change will underestimate the strength of actual treatment effects. In contrast, a true treatment effect can be weakened if patients are falsely identified as not having changed, thus leading to spurious claims of ineffectiveness of the therapeutic intervention. If only items measuring mild depression were used to compose a depression scale, it would be very difficult to discriminate between moderate and severe cases of depression with this instrument, since high scores on all items would characterize both states.

The magnitude to which a severity score actually measures depression is related to a unidimensional syndrome. When depression is heterogeneous, the interpretation of a single summed score is unclear. For example, if items assessing psychological and physical symptoms were only loosely related, a single score would not distinguish between two potentially different groups of depressed patients - with primarily psychological or with primarily vegetative symptoms. Any effects of an intervention targeting only one of these aspects would be harder to detect.

Subsequently, a subset of BDI-II items that would measure a single dimension of depression across a wide range of severity can be sensitive at mild, moderate, or severe levels. IRT analysis can improve the scale items in a psychometrically stronger fashion. When disturbed thresholds are identified, item rescoring may be necessary. One expects diverse item ratings at different levels of severity, with zeroes more frequent at mild levels of overall depression and higher item scores more common with more severe presentations of depression. Moreover, whereas most items on the BDI-II are sensitive to the level of depression severity, many items may present response options that can be considered awkward, at the very least.

Seigert et al.119119. Siegert RJ, Tennant A, Turner-Stokes L. Rasch analysis of the Beck Depression Inventory-II in a neurological rehabilitation sample. Disabil Rehabil. 2009;32:8-17. examined each BDI-II item for differential item functioning in a neurological sample (n=315). Three items (changes in sleeping pattern, changes in appetite, and loss of interest in sex) were removed in an iterative fashion after identification of misfit to model expectations. Possibly, these items measure different dimensions. In the real world, the likelihood of receiving a rating of 1 on the insomnia item was essentially the same regardless of the overall severity of depression, but the likelihood of receiving a rating of 3 on sad mood was very low even when overall depression was severe. These findings suggest that the rating scheme was not ideal for many BDI-II items, decreasing its capacity to detect change. Additional applications of this type of technique include detection of translation or equivalence problems between language versions at the item level.2323. Canel-Çinarbas D, Cui Y, Lauridsen E. Cross-cultural validation of the Beck Depression Inventory-II across US and Turkish samples. Meas Eval Couns Develop. 2011;44:77-91.

Measurement invariance is a prerequisite for considering the equivalence of the scale across versions, as well as for using it to make valid and interpretable comparisons of the severity of depression among different groups. Applying the IRT-related item functioning analysis, Hambrick et al.135135. Hambrick JP, Rodebaugh TL, Balsis S, Woods CM, Mendez JL, Heimberg RG. Cross-ethnic measurement equivalence of measures of depression, social anxiety, and worry. Assessment. 2010;17:155-71. compared response patterns of African American and Asian American undergraduates to those of white counterparts on measures of depression, social anxiety, and worry. While the response patterns of African American participants were roughly equivalent to those of their white counterparts, there were substantial differences in measures of worry and social anxiety. Using a mixed item response model incorporating both latent class and Rasch analysis, Wu & Huang136136. Wu PC, Huang TW. Person heterogeneity of the BDI-II-C and its effects on dimensionality and construct validity: using mixture item response models. Meas Eval Couns Develop. 2010;43:155-67. showed that person heterogeneity (e.g., different response usage and styles) of a student sample could reflect two latent classes without compromising scale construct validity. These investigations are examples of how the family of IRT techniques can address several psychometric questions at the item level, beyond the summed score of CTT.

Concurrent and discriminant validity

Table 2 displays studies that report a comparison of the BDI-II with scales measuring depression, anxiety, and miscellaneous constructs as criterion, determined at essentially the same time to check for concurrent validity. The convergent validity between the BDI-I and the BDI-II was high, with Pearson's product-moment correlation coefficients (r) ranging from 0.82 to 0.94.2727. Dozois DJA, Dobson K, Ahnberg J. A psychometric evaluation of the Beck Depression Inventory-II. Psychol Assess. 1998;10:83-9.,3333. Kapci EG, Uslu R, Turkcapar H, Karaoglan A. Beck Depression Inventory-II: evaluation of the psychometric properties and cut-off points in a Turkish adult population. Depress Anxiety. 2008;25:E104-10.,137137. Beck AT, Steer RA, Ball R, Ranieri WF. Comparison of Beck Depression Inventories -IA and II in psychiatric outpatients. J Pers Assess. 1996;67:588-97. The overlap of the construct measured by BDI-II with that of other widely used scales to assess depression, e.g., the Center for Epidemiologic Studies of Depression (CES-D), the Hamilton Depression Rating Scale (HAM-D), the Zung Self-Rating Depression Scale (SDS), the Montgomery-Åsberg Depression Rating Scale (MADRS), and the Geriatric Depression Scale (GDS), was also quite high, ranging from 0.66 to 0.86 (Table 2). Researchers and clinicians need to be aware of the different constructs covered by depression instruments, which, while supposedly measuring the same attribute, might be focused on different components of this mood condition. Although BDI-II was designed to be a non-theoretically driven instrument, its coverage seems to be broader than the intended DSM-IV description of major depression.

Table 2
Concurrent and discriminant validity of the Beck Depression Inventory-II with measures of depression, anxiety, and other miscellaneous constructs*

The convergent validity between the BDI-II and scales that assess anxiety - such as the Beck Anxiety Inventory (BAI), the Hamilton Anxiety Rating Scale (HAM-A), and the State-Trait Anxiety Inventory (STAI) - was also significant, with a wide range of correlation coefficients (0.37 to 0.83; rough estimate of 0.50). On the other hand, overlap between the BDI-II and scales that assess general psychopathology (e.g., K10 and Self-Report Questionnaire [SEQ]) was good to excellent.3131. Gorenstein C, Wang YP, Argimon IL, Werlang BSG. Manual do Inventário de Depressão de Beck - BDI-II. São Paulo: Casa do Psicólogo; 2011.,123123. Turner A, Hambridge J, White J, Carter G, Clover K, Nelson L, et al. Depression screening in stroke: a comparison of alternative measures with the structured diagnostic interview for the diagnostic and statistical manual of mental disorders, fourth edition (major depressive episode) as criterion standard. Stroke. 2012;43:1000-5. These significant concurrent correlations are expected and might be linked to the underlying constructs and the characteristics of the instruments. This overlap between anxiety and depressive symptoms is indicative of symptomatic co-occurrence as well as of the high rate of comorbidity of these clinical syndromes. As depression is one of the broadest indicators of mental health, a high score on the BDI scale could be explained by many other disorders, physical illness, or social problems. In this respect, BDI should not be viewed as a specific indicator of depression. In practice, BDI-II scores can be misinterpreted, leading the clinician to assume depression as a primary issue, when used without a thorough assessment.

Concerning discriminant validity, studies have indicated low correlation (r < 0.4) with instruments assessing alcohol and drug use7070. Dum M, Pickren J, Sobell LC, Sobell MB. Comparing the BDI-II and the PHQ-9 with outpatient substance abusers. Addict Behav. 2008;33:381-7.,7171. Hepner KA, Hunter SB, Edelen MO, Zhou AJ, Watkins K. A comparison of two depressive symptomatology measures in residential substance abuse treatment clients. J Subst Abuse Treat. 2009;37:318-25. and chronic pain.105105. Harris CA, D'Eon JL. Psychometric properties of the Beck Depression Inventory--second edition (BDI-II) in individuals with chronic pain. Pain. 2008;137:609-22. It is noteworthy that suicidal ideation, which is one of the core features of depression and an item on the BDI-II, correlated only poorly to moderately with the instrument.88. Beck AT, Steer RA, Brown GK. BDI-II: Beck Depression Inventory Manual. 2nd ed. San Antonio: Psychological Corporation; 1996.,8181. Osman A, Barrios FX, Gutierrez PM, Williams JE, Bailey J. Psychometric properties of the Beck Depression Inventory-II in nonclinical adolescent samples. J Clin Psychol. 2008;64:83-102. More investigations should be conducted to document concurrent validity in comparison with well-known constructs.

Although the construction of the BDI-II adopted a non-theoretical strategy, the high concurrent validity between scales assessing depressive and anxiety states (and, to a lesser extent, the poor discriminant validity between BDI-II and other constructs) suggest the need for a theoretical model to elucidate the relationship, whether similarity or dissimilarity, between these disorders. In light of empirical structural evidence, Watson & Clark's contributions on a psychopathological construct named negative affect138138. Clark LA, Watson D. Tripartite model of anxiety and depression: psychometric evidence and taxonomic implications. J Abnorm Psychol. 1991;100:316-36.

139. Watson D, Clark LA, Carey G. Positive and negative affectivity and their relation to anxiety and depressive disorders. J Abnorm Psychol. 1988;97:346-53.
-140140. Watson D. Differentiating the mood and anxiety disorders: a quadripartite model. Annu Rev Clin Psychol. 2009;5:221-47. advocated that the boundaries of mood and anxiety disorders might be collapsed together into an overarching class of emotional disorders and further decomposed into some meaningful subclasses of disorders.

Criterion-oriented validity

Based on the scores of 500 outpatients recruited from four clinics, the original authors of the instrument88. Beck AT, Steer RA, Brown GK. BDI-II: Beck Depression Inventory Manual. 2nd ed. San Antonio: Psychological Corporation; 1996. proposed the following rules of thumb for score interpretation with different specifiers of severity: 0-13 to indicate minimal or no depression; 14-19, mild depression; 20-28, moderate depression; and 29-63, severe depression. For instance, the average BDI-II score in this patient sample with mood disorders was M=26.6. Mean scores for major depressive episode, recurrent depression, and dysthymia were, respectively, 28.1, 29.4, and 24.0.

Although the instrument was originally designed to measure the severity of depression, existing evidence shows that the BDI-II can be recommended to screen for probable cases of major depression (Table 3). In general, studies reported a sensitivity of ≥ 0.70. Sensitivity should be viewed as the most important indicator to minimize the chance of false-negative diagnosis of depressive disorders. Significant diagnostic accuracy, as expressed by the area under the receiver operating characteristics (ROC) curve, was around 75% and higher. Sources of variation may depend on the type of the sample (non-clinical or clinical), percentage of depressive subjects, and external gold-standard criterion for DSM-IV depression. As shown in Table 3, non-clinical samples displayed the lowest range of cutoff points (from 10 to 16) to detect major depression, medical samples had an intermediate cutoff (from 7 to 20), and psychiatric samples had the highest cutoff (from 19 to 31). However, caution is warranted when using the cutoff guidelines presented for criterion-referenced interpretation and regarding misuse of the BDI-II as a diagnostic instrument. While the reported thresholds are helpful indicators for detecting suspected cases that should be referred for additional clinical assessment, the validity of these findings is essentially limited by the arbitrary external criterion adopted for comparison. Regardless of sound criterion validity, most investigators were unanimous in recommending the BDI-II as a screening tool as the first phase of two-stage studies to prevent excessive cases of false-positive detection if the scale is used as a single tool.5050. Shean G, Baldwin G. Sensitivity and specificity of depression questionnaires in a college-age sample. J Genet Psychol. 2008;169:281-8.

Table 3
Criterion validity and cutoff point of the Beck Depression Inventory-II to detect major depressive episode

Some BDI-II items were associated with treatment response in a treatment setting.9090. van Noorden MS, van Fenema EM, van der Wee NJ, Zitman FG, Giltay EJ. Predicting outcome of depression using the depressive symptom profile: the Leiden Routine Outcome Monitoring Study. Depress Anxiety. 2012;29:523-30. In the regression model, the items pessimism and loss of energy emerged as predictors of response after 2 years. When both symptoms were endorsed at baseline, these items could predict a 61.1% chance of response, and absence of both symptoms predicted a 49.4% chance of response. Routine clinical assessment of these depressive symptoms can provide information about treatment progress as early as the initial assessment of the intake phase.

Content and construct validity

Besides test performance and criteria scores, the underlying trait or quality of a given test is a matter of the utmost importance for its validity.141141. Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess. 1994;6:284-90. Two relevant topics are the description of content validity and the latent construct assessed by the instrument.142142. Byrne BM. Factor analytic models: viewing the structure of an assessment instrument from three perspectives. J Pers Assess. 2005;85:17-32. While content coverage was established by ordinary deduction of the universe of items accepted to define the construct, structural or construct validity can be demonstrated by statistical methods, such as factor analyses. The development of a sound measurement instrument for large-scale use requires demonstration of the latent trait being measured, and of the types, categories, and behaviors that constitute an adequate representation of depression.

The content validity of the BDI-II appears to be adequate but narrower than that of its former version.1010. Nunnally JC, Bernstein IH. Psychometric theory. New York: McGraw; 1994.,4242. Osman A, Kopper BA, Barrios F, Gutierrez PM, Bagge CL. Reliability and validity of the Beck Depression Inventory-II with adolescent psychiatric inpatients. Psychol Assess. 2004;16:120-32. The BDI-I reflected six of the nine criteria for DSM-based depression,143143. Moran PW, Lambert MJ. A review of current assessment tools for monitoring changes in depression. In: Lambert MJ, Christensen ER, DeJulio SS, editors. The assessment of psychotherapy outcome. New York: Wiley; 1983. p. 263-303.,144144. Richter P, Werner J, Heerlein A, Kraus A, Sauer H. On the validity of the Beck Depression Inventory. A review. Psychopathology. 1998;31:160-8. while the BDI-II presented an improved performance on specificity to indicate DSM-based depression. Consequently, the sensitivity of the test to detect a broader concept of depression may have been affected.2727. Dozois DJA, Dobson K, Ahnberg J. A psychometric evaluation of the Beck Depression Inventory-II. Psychol Assess. 1998;10:83-9.,5050. Shean G, Baldwin G. Sensitivity and specificity of depression questionnaires in a college-age sample. J Genet Psychol. 2008;169:281-8. The acceptance of the content universe as a qualitative representation of the trait to be measured is critical in this type of validity.130130. Cronbach LJ. Essentials of psychological testing. 3nd ed. New York: Harper and Row; 1990. Although this DSM-based instrument for assessment of depression can allow reliable comparisons in an array of settings and facilitates tailoring of therapeutic interventions, this trend should not be viewed as the true representation of the construct of depression.145145. Maj M. Development and validation of the current concept of major depression. Psychopathology. 2012;45:135-46.

Construct validity tests how well a given psychological measure relates to measures of theory-driven constructs. Therefore, construct validation refers to the simultaneous procedure of measurement and theory validation.146146. Smith GT. On construct validity: issues of method and measurement. Psychol Assess. 2005;17:396-408.,147147. Strauss ME, Smith GT. Construct validity: advances in theory and methodology. Annu Rev Clin Psychol. 2009;5:1-25. However, since the BDI-II was built on non-theoretical assumptions, investigators often choose factor analysis to account for variance in test performance and determine which psychological events make up test performance. Besides reducing the items to explain the structure of data covariance, factor analysis depicts the latent structure of a given test. This family of techniques can determine how and to what extent selected items cluster on one or more factors.148148. Byrne BM. Structural equation modeling with LISREL, PRELIS and SIMPLIS: basic concepts, applications and programming. Mahwah: Lawrence Erlbaum Associates; 1998. Table 4 lists 74 investigations reporting the factor structure of BDI-II, which represented around two-thirds of the retained studies, grouped by type of sample and specified strategy for factor extraction. Some investigators have adopted both exploratory and confirmatory strategies with different purposes, e.g., to identify problems with items reported to have non-significant factor loadings, or for cross-validation of data. The use of the state-of-art confirmatory approach is a trend in studies investigating the latent structure of BDI-II.

Table 4
Construct validity of latent structure of the Beck Depression Inventory-II

Using the means of exploratory factor analysis, Beck88. Beck AT, Steer RA, Brown GK. BDI-II: Beck Depression Inventory Manual. 2nd ed. San Antonio: Psychological Corporation; 1996. reported a structure of two oblique factors, represented by the cognitive-affective and somatic-vegetative dimensions (between-factor correlation, r = 0.62 and 0.66 for student and outpatient samples respectively). A similar two-dimensional structure was obtained in non-clinical samples using a different language version of the BDI-II,2727. Dozois DJA, Dobson K, Ahnberg J. A psychometric evaluation of the Beck Depression Inventory-II. Psychol Assess. 1998;10:83-9.,3131. Gorenstein C, Wang YP, Argimon IL, Werlang BSG. Manual do Inventário de Depressão de Beck - BDI-II. São Paulo: Casa do Psicólogo; 2011.,5555. Uslu RI, Kapci EG, Oncu B, Ugurlu M, Turkcapar H. Psychometric properties and cut-off scores of the Beck Depression Inventory-II in Turkish adolescents. J Clin Psychol Med Settings. 2008;15:225-33. in psychiatric samples,88. Beck AT, Steer RA, Brown GK. BDI-II: Beck Depression Inventory Manual. 2nd ed. San Antonio: Psychological Corporation; 1996.,3333. Kapci EG, Uslu R, Turkcapar H, Karaoglan A. Beck Depression Inventory-II: evaluation of the psychometric properties and cut-off points in a Turkish adult population. Depress Anxiety. 2008;25:E104-10.,5555. Uslu RI, Kapci EG, Oncu B, Ugurlu M, Turkcapar H. Psychometric properties and cut-off scores of the Beck Depression Inventory-II in Turkish adolescents. J Clin Psychol Med Settings. 2008;15:225-33.,6262. Bedi RP, Koopman RF, Thompson JM. The dimensionality of the Beck Depression Inventory-II and its relevance for tailoring the psychological treatment of women with depression. Psychother. 2001;38:306-18.,6565. Brown M, Kaplan C, Jason L. Factor analysis of the Beck Depression Inventory-II with patients with chronic fatigue syndrome. J Health Psychol. 2012;17:799-808.,8282. Palmer EJ, Binks C. Psychometric properties of the Beck Depression Inventory-II with incarcerated male offenders aged 18-21 years. Crim Behav Ment Health. 2008;18:232-42.,8888. Steer RA, Ball R, Ranieri WF, Beck AT. Dimensions of the Beck Depression Inventory-II in clinically depressed outpatients. J Clin Psychol. 1999;55:117-28.,9191. VanVoorhis CRW, Blumentritt TL. Psychometric properties of the Beck Depression Inventory-II in a clinically-identified sample of Mexican American adolescents. J Child Fam Stud. 2007;16:789-98. and in medical patients.9696. Chilcot J, Norton S, Wellsted D, Almond M, Davenport A, Farrington K. A confirmatory factor analysis of the Beck Depression Inventory-II in end-stage renal disease patients. J Psychosom Res. 2011;71:148-53.,114114. Patterson AL, Morasco BJ, Fuller BE, Indest DW, Loftis JM, Hauser P. Screening for depression in patients with hepatitis C using the Beck Depression Inventory-II: do somatic symptoms compromise validity? Gen Hosp Psychiatry. 2011;33:345-62.,116116. Poole H, Bramwell R, Murphy P. Factor Structure of the Beck Depression Inventory-II in patients With chronic pain. Clin J Pain. 2006;22:790-8.,124124. Viljoen JL, Iverson GL, Griffiths S, Woodward TS. Factor structure of the Beck Depression Inventory - II in a medical outpatient sample. J Clin Psychol Med Settings. 2003;10:289-91. The between-factor correlation coefficients in the two-dimensional structure of the BDI-II were generally high (> 0.50, range 0.49-0.87) and could account for a large amount of common data variance. Meta-analysis of selected empirical studies on the factor structure of the BDI concluded that much of the data variability can be attributed to the common dimension of severity of depression and the other part to somatic symptoms.1212. McPherson A, Martin CR. A narrative review of the Beck Depression Inventory (BDI) and implications for its use in an alcohol-dependent population. J Psychiatr Ment Health Nurs. 2010;17:19-30. However, some investigators also reached different results, with more than two dimensions and different item loadings.2121. Byrne BM, Stewart SM, Lee PWH. Validating the Beck Depression Inventory-II for Hong Kong community adolescents. Int J Testing. 2004;4:199-216.,4545. Rodriguez-Gomez JR, Davila-Martinez MG, Collazo-Rodriguez LC. Factor structure of the Beck Depression Inventory-Second Edition (BDI-II) with Puerto Rican elderly. P R Health Sci J. 2006;25:127-32.,7070. Dum M, Pickren J, Sobell LC, Sobell MB. Comparing the BDI-II and the PHQ-9 with outpatient substance abusers. Addict Behav. 2008;33:381-7. These conflicting findings posited the existence of alternative structural models.

The confirmatory strategy has been employed to compare the structure and model fit of previous studies in relation to the construct validity of the BDI-II. In general, a two-dimensional structure composed of a cognitive-affective and a somatic-vegetative factor can be replicated empirically across studies.2727. Dozois DJA, Dobson K, Ahnberg J. A psychometric evaluation of the Beck Depression Inventory-II. Psychol Assess. 1998;10:83-9.,2929. Ghassemzadeh H, Mojtabai R, Karamghadiri N, Ebrahimkhani N. Psychometric properties of a Persian-language version of the Beck Depression Inventory-Second edition: BDI-II-PERSIAN. Depress Anxiety. 2005;21:185-92.,3838. Lipps GE, Lowe GA, Young R. Validation of the beck depression inventory-II in a Jamaican university student cohort. West Indian Med J. 2007;56:404-8.,5353. Storch EA, Roberti JW, Roth DA. Factor structure, concurrent validity, and internal consistency of the Beck Depression Inventory-Second Edition in a sample of college students. Depress Anxiety. 2004;19:187-9.,5757. Whisman MA, Perez JE, Ramel W. Factor structure of the Beck Depression Inventory-Second Edition (BDI-II) in a student sample. J Clin Psychol. 2000;56:545-51.,5959. Wiebe JS, Penley JA. A psychometric comparison of the Beck Depression Inventory-II in English and Spanish. Psychol Assess. 2005;17:481-5. The stability of the obtained solutions seems to substantiate the proposal of the DSM-IV, where the cognitive-affective symptoms are central to making the diagnosis, supplemented by the vegetative-somatic symptoms in the assessment of depressive syndrome. Nevertheless, some studies have suggested that the structure of BDI-II can be best described as three-dimensional, distributing the cognitive-affective dimension into two distinct factors.1717. Al-Musawi NM. Psychometric properties of the beck depression inventory-II with university students in Bahrain. J Pers Assess. 2001;77:568-79.,2020. Arnarson TO, Olason DT, Smari J, Sigurethsson JF. The Beck Depression Inventory Second Edition (BDI-II): psychometric properties in Icelandic student and patient populations. Nord J Psychiatry. 2008;62:360-5.,4141. Osman A, Downs WR, Barrios FX, Kopper BA, Gutierrez PM, Chiros CE. Factor structure and psychometric characteristics of the Beck Depression Inventory-II. J Psychopathol Behav Assess. 1997;19:359-76.,5656. Vanheule S, Desmet M, Groenvynck H, Rosseel Y, Fontaine J. The factor structure of the Beck Depression Inventory-II: an evaluation. Assessment. 2008;15:177-87.,6666. Buckley TC, Parker JD, Heggie J. A psychometric evaluation of the BDI-II in treatment-seeking substance abusers. J Subst Abuse Treat. 2001;20:197-204.,9898. Corbiàre M, Bonneville-Roussy A, Franche RL, Coutu MF, Choiniere M, Durand MJ, et al. Further validation of the BDI-II among people with chronic pain originating from musculoskeletal disorders. Clin J Pain. 2011;27:62-9.,122122. Tully PJ, Winefield HR, Baker RA, Turnbull DA, de Jonge P. Confirmatory factor analysis of the Beck Depression Inventory-II and the association with cardiac morbidity and mortality after coronary revascularization. J Health Psychol. 2011;16:584-95.,136136. Wu PC, Huang TW. Person heterogeneity of the BDI-II-C and its effects on dimensionality and construct validity: using mixture item response models. Meas Eval Couns Develop. 2010;43:155-67. Further analyses revealed that the BDI-II presents reasonable factorial invariance when assessing the severity of depressive symptoms; this covariance structure is equivalent across gender and ethnicity in American college students5858. Whisman MA, Judd CM, Whiteford NT, Gelhorn HL. Measurement Invariance of the Beck Depression Inventory-Second Edition (BDI-II) across gender, race, and ethnicity in college students. Assessment. 2013;20:419-28. and across gender in Taiwanese college students and adolescents.6060. Wu PC. Measurement invariance and latent mean differences of the Beck Depression Inventory II across gender groups. J Psychoeduc Assess. 2010;28:551-63.,6161. Wu PC, Huang TW. Gender-Related Invariance of the Beck Depression Inventory II for Taiwanese adolescent samples. Assessment. 2012 Apr 18. [Epub ahead of print]

Sophisticated alternative structural analysis of the BDI-II was strengthened by two investigative breakthroughs: the hierarchical model and the bifactor model. The first group of strategies depicted a general depression dimension as a higher-order structure to explain the variance of lower-order dimensions.2121. Byrne BM, Stewart SM, Lee PWH. Validating the Beck Depression Inventory-II for Hong Kong community adolescents. Int J Testing. 2004;4:199-216.,5858. Whisman MA, Judd CM, Whiteford NT, Gelhorn HL. Measurement Invariance of the Beck Depression Inventory-Second Edition (BDI-II) across gender, race, and ethnicity in college students. Assessment. 2013;20:419-28.,6767. Cole JC, Grossman I, Prilliman C, Hunsaker E. Multimethod validation of the Beck Depression Inventory-II and Grossman-Cole Depression Inventory with an inpatient sample. Psychol Rep. 2003;93:1115-29.,7373. Joe S, Woolley ME, Brown GK, Ghahramanlou-Holloway M, Beck AT. Psychometric properties of the Beck Depression Inventory-II in low-income, African American suicide attempters. J Pers Assess. 2008;90:521-3.,8787. Steer RA, Kumar G, Ranieri WF, Beck AT. Use of the Beck Depression Inventory-II with adolescent psychiatric outpatients. J Psychopathol Behav Assess. 1998;20:127-37.,101101. Grothe KB, Dutton GR, Jones GN, Bodenlos J, Ancona M, Brantley PJ. Validation of the Beck Depression Inventory-II in a low-income African American sample of medical outpatients. Psychol Assess. 2005;17:110-4.,105105. Harris CA, D'Eon JL. Psychometric properties of the Beck Depression Inventory--second edition (BDI-II) in individuals with chronic pain. Pain. 2008;137:609-22. Although still scant, the bifactor model (G) was able to identify a non-hierarchical general depression in addition to the traditional two-dimensional structure.1818. Al-Turkait FA, Ohaeri JU. Dimensional and hierarchical models of depression using the Beck Depression Inventory-II in an Arab college student sample. BMC Psychiatry. 2010;10:60.,3434. Kneipp SM, Kairalla JA, Stacciarini J, Pereira D. The Beck Depression Inventory II factor structure among low-income women. Nurs Res. 2009;58:400-9.,6464. Brouwer D, Meijer RR, Zevalkink J. On the Factor Structure of the Beck Depression Inventory-II: G Is the Key. Psychol Assess. 2013;25:136-45.,8181. Osman A, Barrios FX, Gutierrez PM, Williams JE, Bailey J. Psychometric properties of the Beck Depression Inventory-II in nonclinical adolescent samples. J Clin Psychol. 2008;64:83-102.,8484. Quilty LC, Zhang KA, Bagby RM. The latent symptom structure of the Beck Depression Inventory-II in outpatients with major depression. Psychol Assess. 2010;22:603-8.,9696. Chilcot J, Norton S, Wellsted D, Almond M, Davenport A, Farrington K. A confirmatory factor analysis of the Beck Depression Inventory-II in end-stage renal disease patients. J Psychosom Res. 2011;71:148-53.,121121. Thombs BD, Ziegelstein RC, Beck CA, Pilote L. A general factor model for the Beck Depression Inventory-II: validation in a sample of patients hospitalized with acute myocardial infarction. J Psychosom Res. 2008;65:115-21. These investigations shared the view that much of the variance of the BDI-II items can be accounted for by a hierarchical higher order or a parallel dimension of depression, where much of the common variance can be explained by a general construct. Practitioners should be careful when interpreting subscale scores, which might be greatly related to the heterogeneous characteristics of depressive conditions.

Cross-cultural issues

With the BDI-II being such a popular measure adapted for use in several countries, information on cross-cultural comparability is still remarkably scarce. The cross-cultural equivalence between the versions of the BDI-II stands out as a topic of fervent academic interest: the symptomatology of depression in different culture/races or languages can be compared by testing the measurement variance of the instrument.2323. Canel-Çinarbas D, Cui Y, Lauridsen E. Cross-cultural validation of the Beck Depression Inventory-II across US and Turkish samples. Meas Eval Couns Develop. 2011;44:77-91.,4848. Sashidharan T, Pawlow LA, Pettibone JC. An examination of racial bias in the Beck Depression Inventory-II. Cultur Divers Ethnic Minor Psychol. 2012;18:203-9.,5858. Whisman MA, Judd CM, Whiteford NT, Gelhorn HL. Measurement Invariance of the Beck Depression Inventory-Second Edition (BDI-II) across gender, race, and ethnicity in college students. Assessment. 2013;20:419-28.,5959. Wiebe JS, Penley JA. A psychometric comparison of the Beck Depression Inventory-II in English and Spanish. Psychol Assess. 2005;17:481-5.,150150. Byrne BM, Stewart SM, Kennard BD, Lee PWH. The Beck Depression Inventory-II: testing for measurement equivalence and factor mean differences across Hong Kong and American adolescents. Int J Testing. 2007;7:293-309. For example, large differential item functioning values were found for 12 BDI-II items between Turkish and U.S. students with same level of depression.2323. Canel-Çinarbas D, Cui Y, Lauridsen E. Cross-cultural validation of the Beck Depression Inventory-II across US and Turkish samples. Meas Eval Couns Develop. 2011;44:77-91. Besides suggesting an equivalence problem with the Turkish version, this study indicated that participants would respond in a different way to different language versions of the instrument. Likewise, the construct validity of the BDI-II (Table 4) also varies over existing language versions. Before a true cross-cultural difference can be acknowledged, more fine-grained analyses should be conducted to ascertain the sources of this dissimilarity.

Limitations

Before widespread adoption of the BDI-II as a standard measure of depression, the potential sources of its score variation should be examined. First, this review has attempted to minimize the file drawer bias by including psychometric articles published in journals, monographs, and book chapters. Explicit exclusion criteria were used to select high-quality investigations. Moreover, efforts were made to contact authors in the field to obtain primary psychometric data for the BDI-II. Unlike traditional experimental studies, psychometric analyses are more descriptive in nature, with both significant and non-significant studies being available. Therefore, the publication bias seems to affect the current review to a lesser degree than in experimental-type research.

The spectrum bias refers to the psychometric phenomenon of differential performance of a test in different settings, thus affecting the generalizability of the results. For example, the somatic factor can be the dominant dimension in patient samples8888. Steer RA, Ball R, Ranieri WF, Beck AT. Dimensions of the Beck Depression Inventory-II in clinically depressed outpatients. J Clin Psychol. 1999;55:117-28. vs. depressive cognition in non-clinical samples. On the other hand, the workup or verification bias arises when respondents with positive (or negative) diagnostic procedure results are preferentially referred to receive verification by the gold-standard procedure, producing considerable distortion in test accuracy. To the extent where these types of bias might occur, the investigators should consider the differential performance of the BDI-II when interpreting scores. Future revisions should include quantitative analysis to assess the sources of scale error.

The self-report nature of the BDI can affect its results according to social desirability, respondent educational attainment, and the gender effect of the condition.130130. Cronbach LJ. Essentials of psychological testing. 3nd ed. New York: Harper and Row; 1990. The BDI is sometimes criticized for being too transparent to respondents and thus easily faked by those wishing to present themselves in a favorable or unfavorable light. Fortunately, this does not seem to be a pervasive problem, as the BDI-II tended to provide an accurate index of depressive manifestations in voluntary and anonymous participants, with good correlation with measures of negative psychological states such as anxiety or psychological distress. Furthermore, the non-theoretical approach of the construction of the BDI-II might introduce more problems than solutions for understanding the scale in terms of psychometric and clinical parameters. In summary, despite its robust psychometric characteristics, as widely reported in available studies, the generalizability of the BDI-II is not free of limitations.

Comments

Depression is a common psychological state in both non-clinical and clinical conditions. The predicted high occurrence of depressive disorders worldwide justifies the use of self-assessment scales to detect a consensus definition of depression. These instruments must be inexpensive and easy to administer, with good acceptance by users in the public health domain. The pressure for rapid evidence-based decisions in clinical practice and the explosion of information in the scientific literature indicate the need for an updated review to summarize the growing body of psychometric literature on self-report measures of depression, such as the BDI-II.

A good measure must supply clinicians with evidence that they find useful and relevant to the needs of their patients. Advantages of this well-investigated inventory are its high internal consistency, capacity to discriminate between depressed and non-depressed subjects, and improved content and structural validity. Consequently, investigators can benefit from this simple, short, reliable, and validated tool to design research in a variety of settings. The fact that the BDI-II is copyrighted and must be obtained from the publisher is the major obstacle against the recommendation of its widespread use as a standard second-generation self-report tool worldwide.

After more than 15 years using the BDI-II in hundreds of investigations and thousands of respondents, evidence of the validity of this authoritative scale is growing, but its use is not free of caveats. Bearing in mind that the stated purpose of the BDI-II was not to establish a diagnosis of major depressive episode, continuous investigations must examine its appropriateness in monitoring treatment efficacy and its comparability with observer-rated scales, such as the HAM-D or the MADRS. Besides comparing the cross-cultural equivalence and conducting item-level analysis to uncover the factors affecting the interpretation of this scale for measurement of depressive symptoms, future studies of the BDI-II should be mindful of theory-based strategies of validation.

Fundação de Amparo è Pesquisa do Estado de São Paulo (FAPESP) sponsors this article and Yuan-Pang Wang is the recipient of the grant (protocol no. 2008/11415-9). Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) sponsors Clarice Gorenstein.

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Publication Dates

  • Publication in this collection
    Dec 2013

History

  • Received
    9 Nov 2012
  • Accepted
    13 Feb 2013
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