Roelofs Et Al BDI 2013

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Norms for the Beck Depression Inventory (BDI-II)

in a Large Dutch Community Sample

Jeffrey Roelofs & Gerard van Breukelen &

L. Esther de Graaf & Aaron T. Beck & Arnoud Arntz &

Marcus J. H. Huibers

Published online: 31 July 2012# Springer Science+Business Media, LLC 2012

Abstract The Beck Depression Inventory (BDI-II) is a

widely used instrument that provides information about the

presence and severity of depressive symptoms. Although

the BDI-II is a psychometrically sound instrument, relative-ly little is known about norm scores. This study aimed to

develop reliable norms for the BDI-II in a Dutch community

sample. Gender, age, and education were hypothesized to

predict BDI-II scores. A total of 7,500 respondents from a

community sample in The Netherlands completed the BDI-

II. It was investigated by means of multiple regression

analysis whether distinct norms for genders, education lev-

els, and age group are appropriate. BDI-II scores depended

on gender and education level, but not on age. BDI-II norms

were computed based on the final regression model. These

BDI-II norms can be used for diagnostic purposes, clinical

decision making, or the evaluation of treatment effects.

Keywords BDI-II .

Beck DepressionInventory .

Depression .

Norming

Depression is one of the most common psychiatric disor-

ders. The epidemiological data suggest that prevalence rates

of depression range from 5 % to 17 % (Rihmer and Angst

2005), which underscores the importance of using reliable

and well-validated screening instruments. The measurement

of depressive symptoms was advanced by the development

of the Beck Depression Inventory (BDI: Beck et al. 1961).

The BDI was originally developed to detect, assess, and

monitor changes in depressive symptoms among individuals

in mental health care settings as well as primary care setting.

A second version of the inventory (BDI-II) was developed

to reflect revisions in the DSM-IV (APA 2000). The BDI-II

contains 21 items assessing symptoms and level of depres-

sion. Respondents are asked to choose one of four descrip-

tions that best fit how they have been feeling over the past

2 weeks. Each response is assigned a score ranging from

zero to three, indicating the severity of the symptom, with

total BDI-II scores ranging from 0 to 63. Although the BDI-

II is designed to provide information about the presence and

severity of depressive symptoms, it cannot by itself yield a

psychiatric diagnosis.

Despite the widespread research attention to examining

psychometric properties of the BDI-II, relatively few studies

have documented test norms. The revised manual describes

norm scores obtained by means of percentiles in 500 adult

psychiatric outpatients and a student sample of 120 college

students in Canada served as a control group (Beck et al.

1996b). This has led to interpretation guidelines of the BDI-

II in clinical samples with minimal depression (BDI-II range

0 – 13), mild depression (BDI-II range 14 – 19), moderate (BDI-

II range 20 – 28), and severe depression (BDI-II range 29 – 63).

J. Roelofs (*) : L. E. de Graaf : A. Arntz : M. J. H. Huibers

Department of Clinical Psychological Science,

Maastricht University,

P.O. Box 616, 6200 MD Maastricht, The Netherlands

e-mail: [email protected]

G. van Breukelen

Department of Methodology and Statistics, Maastricht University,

Maastricht, The Netherlands

L. E. de Graaf

Department of Medical Psychology and Psychotherapy,

Erasmus Medical Center,

Rotterdam, The Netherlands

A. T. Beck

Department of Psychiatry, University of Pennsylvania,

Philadelphia, PA, USA

A. Arntz

Netherlands Institute for Advanced Study in the Humanities

and Social Sciences,

Wassenaar, The Netherlands

M. J. H. Huibers

Department of Clinical Psychology, VU University Amsterdam,

Amsterdam, The Netherlands

J Psychopathol Behav Assess (2013) 35:93 – 98

DOI 10.1007/s10862-012-9309-2



Although these guidelines can assist the health care profes-

sional in determining the severity of depressive symptomatol-

ogy, it is recommended to determine norm scores in a variety

of non-clinical samples as well. The current study aimed to

establish norm scores in a large adult community sample.

The traditional approach to deriving norm scores is split-

ting a group into subgroups based on relevant background

variables. A disadvantage of this approach is that the samplesize is reduced resulting in less reliable norms. A multiple

regression analysis approach to norming questionnaire data

overcomes this problem. This approach allows for exami-

nation of whether background variables (e.g., gender, age)

are important for calculating norm scores. Importantly, it is

possible to test for interactions between predictors. In the

case these interactions are significant, norms on the basis of

subgroups should be created, which boils down to the tra-

ditional approach to norming questionnaire data. The

strength of a multiple regression approach is thus that one

can examine whether it is necessary to provide norm data

separately for various background variables. For example,Van Breukelen and Vlaeyen (2005) found that pain coping

and cognitions were not predicted by gender, but by level of

education instead, suggesting that norm data do not have to

be given for males and females separately. Similarly, Van

der Elst et al. (2006) found that performance on the Concept

Shifting Test was influenced by age, gender, and level of

education, but not by any of their interactions. Consequent-

ly, the most stable and simple norming was obtained by

applying a regression model with age, gender and education

as predictors of test score, and applying that model to the

complete sample.

In the current study, we calculated norm scores of the

BDI-II in a large community sample of adults. Gender was

hypothesized to be a significant predictor of BDI-II scores

as prevalence rates of depression in females is generally

higher compared to males (Picinelli and Wilkinson 2000).

Evidence for a relation between age and symptoms of de-

pression is equivocal with studies finding positive associa-

tions (e.g., Glenn et al. 2001) a curvilinear relationship

(Steer et al. 1999), or no relationship (Beck et al. 1996b).

Age was therefore examined as both a linear and curvilinear

(quadratic) effect. There is also some evidence that educa-

tion level is (negatively) related to depressive symptoms

(Arnau et al. 2001) and education level was therefore hy-

pothesized to predict BDI-II scores as well. The aim of the

current study was to determine norm scores for the BDI-II

following a state-of-the-art methodology in a large commu-

nity sample. The effects of gender, age, education level and

the interaction effects of these predictors were examined to

determine whether norming should take place in the total

group or in subgroups. Norming of the BDI-II was done on

the total scale scores as factor analytic studies have yielded

variability in, and instability of, obtained factor solutions of

the BDI-II (Osman et al. 2008; Ward 2006). Moreover, the

BDI-II has been advanced as a general screening instrument

for depressive symptomatology in general, more than for

subfactors.

Method

Participants and Procedure

Data were collected as part of a large-scale community

screening program for the recruitment of individuals who

could participate in a randomized clinical trial on the effec-

tiveness of computerized cognitive behavioral therapy for

depression. A random selection of individuals in the general

population (age range: 18 – 65 years) received an invitation

letter to complete a screening questionnaire via the internet.

Six municipalities in the Southern part of the Netherlands

cooperated by providing names and addresses of their resi-

dents. More specifically, the municipalities provided a totalof 217.816 names and addresses and letters were sent to

these addresses with the request to participate in this study.

In the letter, no reference was made to research on the

treatment of depression but this information was provided

on the website where participants completed the BDI-II. In

the letter, it was emphasized that everyone could participate

even if one had no symptoms of depression. A total of 8,960

individuals responded but the first 1,460 individuals com-

pleted the BDI-primary care version and not the BDI-II.

After these 1,460 respondents, the BDI-II was used instead

of the BDI-primary care version. A total number of 7,500

individuals completed the BDI-II. Participants were not

reimbursed for their efforts (see de Graaf et al. 2009). A

comparison of the demographic variables of the current

sample and the population in the Southern part of the Neth-

erlands (Statistics Netherlands, www.cbs.nl) did not reveal

any major discrepancies. The Ethical Committee of the

academic hospital Maastricht and Maastricht University ap-

proved the study protocol. In the description of the commu-

nity sample, 57.3 % was female ( N 04,300) and 42.7 % male

( N 03,200). Mean age was 43.3 years (SD 013.3 years,

range 18 – 65 years). Mean duration of depressive complaints

was 2.2 months (SD03.2). A total of 70.7 % had a paid job,

10 % were students, 8.2 % received a disablement insurance

benefit, 6.5 % were retired early, and 4.2 % had no work. A

total of 97 % of the total sample were Caucasian.

Instruments

Beck Depression Inventory

The Beck Depression Inventory-II (BDI-II; Beck et al.

1996b; Dutch version: Van der Does 2002) is a 21-item

94 J Psychopathol Behav Assess (2013) 35:93 – 98

http://www.cbs.nl/

http://www.cbs.nl/



self-report depression inventory designed to assess symp-

toms and level of depression. The questionnaire consists of

21 items comprising a list of four statements each about a

particular symptom of depression. Scores on the individual

items range from zero to three. The respondent has to

choose the statement that best represented his or her mood

during the last 2 weeks. Total scores can range between 0

and 63 with higher scores reflecting higher levels of depres-sion. Reliability and validity of the BDI-II have been sup-

ported (e.g., Osman et al. 2008; Van der Does 2002).

Demographic Variables

Demographic variables included gender, age, and education.

Regarding education, the highest completed level of training

was rated on an 8-point scale with 10no education,

20elementary school, 30lower technical and vocational

training, 40medium technical and vocational training,

50higher general secondary education, 60 pre-university ed-

ucation, 70 bachelor ’s degree, 80master ’s degree. Educationlevel was further categorized as follows: low education

(1,2,3: 14.7 %]), medium education (4,5,6: 50.4 %), and

high education (7,8: 35 %). Duration of depressive symp-

toms was rated on an 8-point scale with 10less than 1 month,

201 mo n th , 302 mo n th s , 403 mo n th s , 504 months,

605 months, 706 months, and 80more than 6 months.

Statistical Analyses

The Statistical Package for the Social Sciences (SPSS, ver-

sion 15.0) was used to perform regression analyses in order

to determine a parsimonious model for obtaining BDI-II

norms (see Van Breukelen and Vlaeyen 2005, for a detailed

description). Total BDI-II score was the dependent variable

in the regression analyses, and gender, age, education level,

and their interactions were the predictor variables. Dummy

coding was used for the categorical predictors gender

(females 0 0, males 0 1) and education level (low, medium,

high), with low education as reference group. Dummy cod-

ing involves the inclusion of a regression weight in the

model to represent the mean scale difference between the

reference category and each other category, adjusted for all

other predictors in the model. Linear and quadratic terms

were included for the quantitative predictor age, which was

centered to prevent collinearity between linear and quadratic

age terms.

The regression model was reduced in a stepwise fashion

by eliminating the least significant predictor, starting with

the interaction terms between the various predictors. Varia-

bles with a two-tailed p >.001 were excluded to prevent type

I error due to multiple testing. Note that with α0.001 two-

tailed, the present sample size of N 07,500 still gives a

power of 90 % to detect a correlation as small as .05, so

that the risk for type II error is negligible. For the final

model, residuals were plotted and analyzed to check the

assumptions of normality and homogeneity of residual var-

iance across the entire range of predicted scale scores and

the absence of outliers. Within the final model, a raw scale

score of an individual can be converted into a standardized

z-score by computing the predicted score Y (by means of

filling in the regression equation), computing the residualerror (subtracting predicted Y from observed Y), and finally,

dividing the residual error by the SD(e), which is the square

root of the MS(residual). If the residuals are normally dis-

tributed with the same variance, then z is normally distrib-

uted and the standard normal distribution can be used to

interpret z-values (e.g., Van Breukelen and Vlaeyen 2005).

If normality or homogeneity is seriously violated, percen-

tiles of the residuals can be used instead of z-values for

norming.

Results

Before addressing the main results, two remarks need to be

made. First, total BDI-II scores were not normally distrib-

uted with skewness and kurtosis outside the acceptable

range of −1 to +1. A square root-transformation of the

BDI-II total score was successful in ‘normalizing’ the total

scores to a reasonable extent (note that the residual of the

regression requires a normal distribution, not the dependent

variable itself). These square root-transformed BDI-II scores

were back-transformed into normal BDI-II scores after the

regression analyses in order to obtain norm data. Second,

mean BDI-II score was 10.6 (SD010.9; range 0 – 62) and the

BDI-II was reliable in terms of internal consistency with an

alpha of .95. Mean of the square root transformed BDI-II

scores was 2.77 (SD01.71; range 0 – 7.87).

Predictors of the BDI-II Score

The final model containing significant predictors of the

BDI-II score consisted of gender and education level. None

of the interactions was significant, and after their deletion

from the model, age (linear and quadratic terms) did not

predict BDI-II total scores either. So norming can be per-

formed on the total sample, using gender and education

level as the only predictors. The final model is presented

in Table 1.

Model Checks

To apply the model for norming purposes, the model assump-

tions need careful checking as prediction of individual scores

depends even more on such assumptions than the regression

analysis does. More specifically, the use of (standardized)

J Psychopathol Behav Assess (2013) 35:93 – 98 95



residuals requires a normal distribution with homogeneous

variances of the residual. Normality was checked by means

of skewness and kurtosis and the Kolmogorov-Smirnov test.

Skewness and kurtosis were within the acceptable range of −1

to +1, but the Kolmogorov-Smirnov test revealed violation of

normality ( z 03.51, p <.001). However, note that theKolmogorov-Smirnov test gives significance even with minor

violation of normality in large samples like the present one. In

fact, the residual distribution looked quite normal in both

samples. As a further check, actual percentiles (5, 10, 25, 50,

75, 90, 95) of the standardized residuals were compared to the

corresponding percentiles of the standard normal distribution,

which revealed no deviation larger than .10 on the z -scale for

the standardized residuals. The homogeneity of variances was

tested by grouping patients into quartiles of the predicted scale

score and applying Levene’s test to the residuals. The homo-

geneity assumption was not violated ( p>.05) and the residual

standard deviation within each quartile did not deviate morethan 10 % from the overall residual standard deviation of the

scale. Thus, the overall residual standard deviation can be

used to compute z-scores.

Computing z-Scores

The model in Table 1 can be used to convert raw BDI-II

scores of any individual into a standardized residual or z-

score. The results are shown in the upper part of Table 2. To

illustrate how Tables 1 and 2 can be applied, consider a

woman, with medium education level and a BDI-II score of

25. Table 1 gives a predicted square root-transformed BDI-II

score of 3.51 (constant) −.25 (gender 00) −.55 (education

medium01) −1.03 (education high 0 0) 02.96. The residual

standard deviation is √ 2.7701.66. Thus, the z-score is equal

to (√ 25 – 2.96)/1.6601.23 according to Table 1.

Finally, to further enhance user-friendliness of the present

norming, a norm table was derived (see upper part of Table 2).

Note that the norm scores depicted in Table 2 are estimated on

the basis of the full sample using the model in Table 1. The

following z-scores were chosen: −1.64, −.84, −.25, .25, .84,

and 1.64. As the residuals from which the z-scores were

computed had a normal distribution and the actual per-

centiles agreed very well with percentiles according to

the normal distribution, these z-scores correspond to the

traditional norming boundaries of 5th, 20th, 40th, 60th,

80th, and 95th percentile respectively. Raw BDI-II scores

corresponding to the boundaries were computed. Table 2 also

provides the formula for each subgroup by which one caneasily convert raw BDI-II scores into standardized scores.

BDI-II scores that, in terms of their standardized residual, lie

below the 80th ( z 0.84) percentile are considered normal

depression scores, whereas scores between the 80th and

95th ( z 01.64) percentile are elevated depression scores.

BDI-II scores above the 95th percentile are high depres-

sion scores. Back to the example, the woman with a BDI-II

score of 25 has a z-score of 1.23, which is indicative of an

elevated depression score. 1

Discussion

The aim of the present study was to develop reliable and

representative norms for the BDI-II in a large-scale Dutch

community sample. Using multiple regression, predictors

for BDI-II total scores were identified (i.e., gender, educa-

tion level) and, after some model checks, norm scores were

calculated.

Predictors for BDI-II Norm Scores

As hypothesized, gender was a significant predictor of BDI-

II total scores, with females having somewhat higher BDI-IIscores than males. These findings are in line with prevalence

rates in depression being higher in females than in males

(Picinelli and Wilkinson 2000) and with research findings

showing that females have higher scores than males (Arnau

et al. 2001; Beck et al. 1996b; Coelho et al. 2002; Kojima et

al. 2002; Kumar et al. 2002; Steer et al. 1997, 1998, 1999),

but contradict other studies reporting no significant gender

differences on the BDI-II (Beck et al. 1996a ; Dozois et al.

1998; O’Hara et al. 1998; Penley et al. 2003; Schulenberg

and Yutrzenka 2001; Steer and Clark 1997; Steer et al.

2000). Age did not correlate with the BDI-II total scores

(i.e., no linear or quadratic age effects). These findings addto research showing little relationship between the BDI-II

scores and age in samples of outpatients or college students

(Beck et al. 1996b; Kojima et al. 2002; Penley et al. 2003;

Steer et al. 1997), but are not in line with studies reporting

1 As an ancillary analysis, we determined the BDI-II norm scores for a

model with no predictors to allow for a comparison of an individual

BDI-II score with the total sample. Table 2 presents the BDI-II norm

scores for that model.

Table 1 Regression model for predictors of the (square root) BDI-II

scores

N 07,500, R20.05, MSresidual02.77

Predictor B SE of B p (two-tailed)

Constant 3.51 .050 <.001

Gender −.25 .039 <.001

Medium education −.55 .055 <.001

High education −1.03 .058 <.001

Gender was coded 0 for females and 1 for males. Low education was

the reference groups. Square root depression scores (BDI-II) range

roughly between 0 and 8




that depression is more likely with increasing age (Glenn et

al. 2001) or that severity of self-reported depression

scores gradually increases from 18 to 38 years of age

and then decrease to age 82 years (Steer et al. 1999),

suggesting a quadratic age effect. Note, however, that

the current study included individuals aged between 18and 65 so that a quadratic age effect should be visible.

It might be that a quadratic age effect disappears when

controlling for the effects of gender and education level

as was done in the current study. For education level,

higher BDI-II scores were found to relate to lower

education levels, which is in line with our hypothesis

and with previous research (Arnau et al. 2001).

Implications

With respect to the norm data, the interpretation of raw

BDI scores depends on gender and education level. Z -

scores computed from the residuals of the present re-

gression of (square root) scores of BDI-II on these

variables, can provide a more objective picture of the

meaningfulness of depressive symptoms in adults. That

is, z -scores can be helpful to identify the severity of the

problems and also to evaluate treatment success. How-

ever, the results should be cautiously interpreted in

terms of what BDI-II scores may have (preventive)

treatment implications. In this respect, a number of

cutoff scores for the BDI-II have been identified ranging

from 14 to 18 in different samples (Sprinkle et al. 2002;

Arnau et al. 2001; Dutton et al. 2004). The choice of a

particular cutoff point depends in part on the purpose

for using the test. If the purpose is to detect the max-

imum number of persons with depression, then the cut-

score threshold must be lowered to minimize false neg-

atives. If it is important to obtain as pure a group of

pers ons wi th de pres sion as poss ib le , th e cut- score

should be raised to reduce the number of false positives.

Thus, the norm data are indicative of where a person with a

certain BDI-II score lies in terms of z -scores compared to the

best possible reference group but the norm data cannot be used

for determining thresholds for a certain intervention. That is, a

score of 29 (cutoff for severe depression according to Beck et

al. 1996b) on the BDI-II corresponds to different z -scores

depending on gender and education level, but it does not

indicate that the personal burden is also different. Further-more, a decision to offer treatment should never be based on a

BDI-II score alone, but should be decided upon a diagnostic

evaluation. In clinical practice, much more is needed for an

informed decision: only the severity of the depressive symp-

tomatology will not do, and additional data regarding comor-

bidity, functioning, motivation for trea tment is neede d.

Normative data merely position a respondent in the normal

population.

Strengths and Limitations of the Current Study

The results of the current study advance the BDI-II as screen-

ing instrument for depression and provide reliable and repre-

sentative norms for an adult community sample. It is a strength

of the BDI-II that it takes only 5 – 10 min to fill in and its

reliability is good. Another strength of the study is that a large

sample of adults was involved, deriving from different educa-

tion levels, which enhances the generalization. A number of

limitations of the present study need to be addressed. First,

norm data obtained in the current study were found for the

Dutch version of the BDI-II. It should be borne in mind that

norming is culture and translation bound. As a consequence,

the present norms are not generalizable to other language

versions of the BDI or to populations stemming from other

countries. It is quite likely that respondents from other

countries or cultures respond differently to self-report ques-

tionnaires. Second, the current study used an in internet-based

approach to completing the BDI-II (see also Schulenberg and

Yutrzenka 2001). Paper-and-pencil and web-based administra-

tions appear to yield equivalent results at least for the Child

Depression Inventory (see Roelofs et al. 2010). Third, it is

possible that selection bias might have occurred. Although

there was no reference to research on the treatment of

Table 2 Norms of the BDI-II total score in a community sample breakdown by gender and education level

5th

Percentile

( z 0−1.64)

20th

Percentile

( z 0−.84)

40th

Percentile

( z 0−.25)

60th

Percentile

( z 0 .25)

80th

Percentile

( z 0.84)

95th

Percentile

( z 01.64)

Formula for computing

z scores in (sub)groups:

Males Low education 0 3 8 14 22 36 z ¼ p BDI scoreð Þ 3:26ð Þ 1:66=

Medium education 0 2 5 10 17 30 z ¼ p BDI scoreð Þ 2:71ð Þ 1:66=

High education 0 1 3 7 13 25 z

¼ p BDI scoreð Þ

2:23

ð Þ 1

:66

=Females Low education 0 4 10 15 24 39 z ¼ p BDI scoreð Þ 3:51ð Þ 1:66=

Medium education 0 2 6 11 19 32 z ¼ p BDI scoreð Þ 2:96ð Þ 1:66=

High education 0 1 4 7 15 27 z ¼ p BDI scoreð Þ 2:48ð Þ 1:66=

Total sample 0 2 6 11 18 32 z ¼ p BDI scoreð Þ 2:77ð Þ 1:71=

J Psychopathol Behav Assess (2013) 35:93 – 98 97



depression in the recruitment letters that were sent and all

individuals, even those without depressive symptoms, were

encouraged to participate, one cannot rule out that those with

higher levels of depressive complaints were more likely to

participate, as indicated by the mean duration of depressive

symptoms, which was about 2 months. Finally, practitioners

should keep in mind that self-report inventories are subject to

response bias (Beck et al. 1996b; Hunt et al. 2003). Someindividuals may magnify symptoms, and other may minimize

them. Therefore, determination of the presence and severity of

depression will require additional exploration. Despite these

limitations, the current study contributes to the usefulness of

the BDI-II by providing norm scores that can assist researchers

and clinicians in assessing depression severity with the BDI-II.

Acknowledgments We would like to express our gratitude to Annie

Hendriks, Greet Kellens, and Sylvia Gerhards, who assisted with data

collection, and to Rosanne Janssen, who developed the infrastructure

for online data collection. Municipalities Eijsden, Meerssen, Sittard-

Geleen, Valkenburg, and Maastricht sponsored the study. Competing

interests: the authors have no competing interests.

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Roelofs Et Al BDI 2013