How does beck depression inventory work

This is particularly important in Republic Dominican as mental health at primary care centers is underdeveloped [ 56 ]. Several factor structure models, including one-factor, two-factor, three-factor and bifactor models were tested with the purport to determine the optimal factor structure. Results showed that a bifactor model with a general depression factor and three specific factors consisting of cognitive, affective, and somatic factors provided the best fit to data.

This is in line with different studies that supported a bifactor latent structure to the BDI-II [ 13 , 36 , 37 , 38 ]. In practice, this finding implies that BDI-II items can be summed to form an overall score, with higher total scores indicating greater level of depression severity [ 32 ]. Moreover, despite most of the items variances were accounted by the general depression factor, the three specific factors cognitive, affective, and somatic explained a non-redundant amount of variance.

Thus, in contrast to different authors who advocate the use of BDI-II total scores and questioned the validity of subscales [ 13 , 34 ], the present findings support the use of the BDI-II total score along with scores corresponding to each subscale, in agreement with Beck et al. Furthermore, since research indicates that depression symptoms response differentially to treatment [ 57 ] the use of BDI-II global score alone as a measure to detect changes in response to treatment may obscure the impact of interventions.

In conclusion, for both statistical and clinical reasons it seems more appropriate to use BDI-II total and factor scores.

Additionally, the present study supported the validity of the affective factor as a separate dimension from cognitive and somatic domains. This finding differs from common findings indicating that the affective factor should be subsumed by the cognitive [ 17 , 18 , 58 ] or the somatic factor [ 5 , 10 , 59 , 60 ]. According to Steer et al. As such, it would be valuable to test the invariance measurement of the BDI-II factor structure found in this study across different samples in order to examine the robustness of the affective component as a single and differentiated domain of depression.

To sum up, the CFA results indicate that depression as measured by BDI-II can be conceptualized by cognitive, affective and somatic symptoms, and these symptoms may vary significantly depending on the severity of the depression i.

Subsequent reliability analysis of the BDI-II total score and subscale scores showed acceptable to high internal consistency, with alpha coefficients ranging from. As expected, t-test analysis revealed that BDI-II scores discriminated between individuals from hospital and general population. Notwithstanding the implications aforementioned, the current study has a number of limitations that should be mentioned.

First, the sample study was selected by convenience being primarily compounded by individuals stem from general population. Therefore, findings cannot be generalized and further replication in both representative samples from general population and clinical samples are needed. Second, we focused in the analysis of the latent structure of BDI-II, which is just one element of construct validity among several others [ 62 ]. Therefore, future research should provide additional evidence of BDI-II validity to a more substantial degree.

In particular, it would be worthwhile to further examine the capacity of BDI-II scores to discriminate between depressed and non-depressed subjects. Indeed, Hunt et al [ 63 ] demonstrated that subjects who administered a manipulated version of BDI-II in which the purpose was disguised and the content was padded with items that not tap depression symptoms, scored significantly higher than subjects who completed the original scale.

Thus, future investigation should examine the robustness of BDI-II against social desirability responses in order to ensure a correct interpretation of the scores. Fourth, it has been suggested that bifactor models are more robust to model misspecification e. Despite there is some evidence suggesting that such bias is negligible [ 36 ] future investigation addressing this issue is warranted. Despite all limitations, we note that this is the first study to demonstrate the construct validity and reliability of the BDI-II in Dominican Republic.

The lack of psychometrically well-established measures for assessing depression in community hinder the early detection of symptoms, the evaluation of the effectiveness of interventions and the development of research programs aimed to identify risk factors associated to depression in Dominican population.

Hopefully, this study will help to change this situation. The funder had no role in the design of the study, data collection and analysis, decision to publish or preparation of the manuscript. National Center for Biotechnology Information , U. PLoS One. Published online Jun Chung-Ying Lin, Editor. Author information Article notes Copyright and License information Disclaimer. Competing Interests: The authors have declared that no competing interests exist. Received Jun 22; Accepted Jun This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

This article has been cited by other articles in PMC. Current study In summary, although factorial data suggests that bifactor models outperform multidimensional models—regardless of the number of specific factors—findings are not conclusive [ 36 , 37 , 38 ]. Results Model comparisons Based on previous BDI-II research findings, several competing models were tested including one, two, three-factor models and bifactor models.

Open in a separate window. Fig 1. Bifactor model with a general depression factor and three specific factors consisting of cognitive, affective, and somatic factors. Discussion Depression represents the fourth leading cause of disability worldwide [ 3 ] with the higher prevalence in low and middle-income countries [ 54 ].

Supporting information S1 File Dataset. SAV Click here for additional data file. Data Availability All relevant data are within the paper and its Supporting Information files. References 1. World Health Organization. Informe sobre la salud en el mundo Salud mental: Nuevos conocimientos, nuevas esperanzas.

Ginebra, Suiza: Sanz J. Beck Depression Inventory. American Psychiatric Association. Diagnostic and statistical manual of mental disorders.

Wang Y, Gorenstein C. Psychometric properties of the Beck depression inventory-II: a comprehensive review. Revista Brasileira de Psiquiatria.

Psychiatry Research. Behavior Modification. Psychological Assessment. Factor structure and diagnostic validity of the Beck Depression Inventory-II with adult clinical inpatients: Comparison to a gold-standard diagnostic interview. Journal Clinical Psychology. Journal of Personality Assessment. Campos R, Goncalves B. European Journal of Psychological Assesment. Measurement and Evaluation in Counseling and Development. Depression and Anxiety.

Differential functioning of the Beck depression inventory in late-life patients: use of item response theory. Psychology and Aging. VanVoorhis R, Blumentritt L.

Journal of Child and FamilyStudies. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents A brief history. Key research. Availability and clinical use. Beck Depression Inventory. Oxford Academic. Select Format Select format. Permissions Icon Permissions. A brief history The Beck Depression Inventory BDI is a item self-reporting questionnaire for evaluating the severity of depression in normal and psychiatric populations [ 1 , 2 ].

Description The questionnaire was developed from clinical observations of attitudes and symptoms occurring frequently in depressed psychiatric patients and infrequently in non-depressed psychiatric patients [ 5 ].

Google Scholar Crossref. Search ADS. Google Scholar PubMed. All rights reserved. For Permissions, please email: journals. Issue Section:. Download all slides. View Metrics. Email alerts Article activity alert. Advance article alerts. New issue alert. Steer and his colleagues identified a three-factor solution for the BDI-II in adolescent psychiatric outpatients.

Similar to the studies in a clinical adolescent sample, the factor structure of the BDI-II has not been established in a nonclinical adolescent sample. A research proposed the two-factor model of the BDI-II for Turkish nonclinical adolescents, defined by cognitive and somatic-affective. The author claimed that adding the three error correlations might be explained by the features of the samples.

Adolescents in East Asia excessively value their academic performance and exams. When individuals have low academic performance, they devalue themselves, which may make them feel that there is no hope for the future.

As they spend most of their time studying, they do not have time to enjoy other leisure activities. In addition, they frequently do not have enough time to sleep, which leads to changes in their appetite. Therefore, it needs to be considered whether there is a cultural difference in adolescent depression. The present study aims to investigate the psychometric properties of the BDI-II in a large and stratified sample of Korean nonclinical adolescents.

It was designed to fulfill two main objectives: 1 to analyze the reliability and validity the BDI-II among Korean adolescents and 2 to evaluate the factorial structure in Korean nonclinical adolescents.

The participants included adolescent boys and girls who were recruited through education classes, recreational centers, advertisement, and via word-of-mouth.

The mean age of the total sample was Parents were asked to sign the consent form and give the survey to their child to complete in a private place. Completion of the survey was taken as a form of assent by the adolescents. All participants participated on a voluntarily and anonymous basis. The BDI-II is a item self-report inventory designed to assess the presence and severity in depressive symptoms. Each item is rated on a 4-point Likert-type scale ranging from 0 to 3, based on the severity in the last two weeks.

The total score ranges from 0 to 63, with higher scores indicating more severe depressive symptoms. With permission of the publisher, The Psychological Corporation, two independent licensed clinical psychologists J.

A proficient bilingual person who had a master's degree in clinical psychology re-translated it into English, and researchers reviewed and revised into its final version. The psychometric properties of the BDI-II among Korean adult population showed strong internal consistency, test-retest reliability and good concurrent and discriminant validity.

And the bi-factor model showed the best fit among Korean adults. The PHQ-9 is a 9-item depression-screening instrument. Each item was rated based on the frequency of a depressive symptom in the past two weeks. The Korean PHQ-9 showed good reliability and validity. State Anxiety is measured by 20 short descriptive statements, which the individual makes in reference to how he or she feels at the moment, whereas Trait Anxiety is measured by 20 statements that refer to one's general feelings.

We used Mplus 6. All analyses were conducted using the mean and variance- adjusted weighted least squares estimation WLSMV method for the total adolescent sample. All error covariances and item cross-loadings described in the original models were included in analyses. In order to investigate the item reliability of the Korean adolescents BDI-II items, we assessed their internal consistency using Cronbach alpha indices, corrected item-total correlations, and interitem correlations. To assess the convergent validity of the BDI-II, Pearson product-moment correlations with other self-report measures were calculated.

Item means, standard deviations, percentages symptomatic, and corrected item-total correlations are summarized in Table 1. Using Pearson correlation analysis, we investigated the relations between scores on the BDI-II and the other self-report measures. Several models were selected based on the previous findings using an adolescent sample.

We examined one-, two-, three-, and modified three-factor models, and additionally evaluated a bifactor solution for the BDI-II. Results of the CFA are summarized in Table 3. Detailed descriptions of the models tested in the present study are as follows. In this model, we constrained all 21 items to load onto a single factor.

We tested this solution as a baseline model. This model is based on the findings by Osman et al. This model is defined by two-correlated factors. The cognitive-affective factor was composed of items 1—10 and 12—14; and items 11 and 15—21 defined the somatic factor. Steer et al. This model consisted of three positively correlated first-order factors: cognitive factor items 2, 3, 7—9, 13, 14, and 19 , somatic-affective factor items 1, 4, 11, 12, 15—18, 20, and 21 , and guilty-punishment factor items 5, 6, and The Osman et al.

However, item 21 Loss of Interest in Sex failed to load onto the cognitive-affective factor. In addition, two items Loss of Pleasure, Loss of Interest showed negative relationships with the cognitive-affective factor.

Based on these findings, we concluded that this model could not explain the internal structure of the BDI-II in our sample. This model consisted of three oblique factors: negative attitude items 1—3, 5—10, and 14 , performance difficulty items 4, 11—13, 17, and 19 , and somatic elements items, 15, 16, 18, 20, and Wu et al. The factor loadings ranged from values of 0. The correlations among the factors ranged from 0. The purpose of the present study was twofold: 1 to evaluate the reliability and validity and 2 to establish the factor structure of the BDI-II in a nonclinical population of Korean adolescents.