Journal of Affective Disorders, 26 ( 1992) 251-260 0 1992 Elsevier Science Publishers B.V. All rights reserved 0165-0327/92/$05.00
251
JAD 00952
Mike Startup a, Anne Rees b and Michael Barkham b a Department of Psychology, University College of North Wales, Bangor, UK and b MRC/ESRC
Social and Appked Psychology Unit,
University of Sheffield, Sheffield, UK
(Received 6 April 1992) (Reuisicn received 25 August 1992) (Accepted 1 September 1992)
Summary Although more than 20 factor analytic studies have been published on the Beck Depression Inventory (BDI), only Steer and co-workers (1987) have used a sample composed exclusively of patients diagnosed with depression. The component structure found in their study of depressed patients differs in important respects from the structure summarised in several reviews. The main aim of the present study was to investigate whether this structure could be confirmed with the BDI responses of an independent sample of 139 patients diagnosed with DSM-III Major Depressive Episode. Three principal components were extracted and rotated to maximum congruence with a target based on the results of Steer et al. (1987). The significance of the fit to this target was then evaluated by rotating the same matrix of loadings to 5000 random permutations of the target. The fit was found to be highly significant, though some possible improvements could be identified ad hoc. An alternative factor structure for the BDI, derived from covariance structure analysis by ‘Tanaka and Huba (19841, was also tested but could not be confirmed.
Key words: Beck Depression Inventory; Depression;
Introduction The Beck Depression Inventory (BDI; Beck et al., 1961) is one of the most widely used self-report instruments for assessing the severity of de-
Correspondence to: M. Startup,
Department of Psychology, University College of North Wales, Bangor, Gwynedd, LL57 2DC, UK.
Confirmatory
factor analysis
pression in diagnosed patients (Piotrowski et al., 1985) and for detecting depression in non-psychiatric populations (Steer et al., 1986). Altogether, it has been used in well over one thousand research studies (Beck et al., 1988). Despite this popularity, little has been done to discover what the BDI reveals about symptom clustering in depression. In a review of its psychometric properties, Beck et al. (1988) identified 20 factor analytic studies but only one of these used a sample
of diagnosed depressed patients (Clark et al., 1983, and that sample was mixed with an equal number of normal subjects showing no current psychopathology. Five of the studies employed samples of alcoholics, two employed samples of heroin addicts, and most of the remaining studies were based on non-psychiatric samples, chiefly students. If the BDI is to be used to investigate depression as a nosologic category, care must be taken to screen out from the sample those subjects whose depressive symptoms are, or might be, secondaT to some other disorder. Samples of patients with substance use disorders, for example, are unsatisfactory for the investigation of components of depression since it is unclear to what degree their depressive symptoms should be explained by the state of chronic intoxication, the physiological effects of withdrawal or some aspect of their personalities or lifestyles (Schuckit, 1983). When the sample consists of patients with a variety of psychiatric diagnoses there is uncertainty how much the results are affected by individuals with psychotic disturbances and other non-affective disorders. Although the BDI appears to be a sensitive measure of depression as a syndrome, it is not adequate as a nosological screening device (Kendall et al., 1987). Several studies have shown that significant proportions of individuals in community samples (e.g., Oliver and Simmons, 1984) and student samples (e.g., Deardorff and Funabiki, 198.5) who score highly on the BDI do not have diagnosable disorders. Factor analytic studies based on non-psychiatric samples also suffer from highly skewed distributions of item scores and tend to produce large general factors which merely discriminate the distressed from the nondistressed (Weckowicz et al., 1967). In their review, Beck et al. (1988) concluded that the balance of evidence suggests that the BDI measures three intercorrelated components reflecting Negative Attitudes Toward Self, Performance Impairment, and Somatic Disturbance. However, this conclusion does not appear to take into account the only published study exclusively based on diagnosed depressed patients. Using a sample of 300 patients diagnosed with DSM-III Major Depressive Disorder, Steer et al. (1987)
found evidence for three clear components. The first component had salient loadings from items reflecting affective and performance difficulties while all of the items reflecting self-denigration loaded separately on the second component. The third component had salient loadings from four items dealing with physiological disturbances. Thus this solution differs in important respects from the structure supported by some previous reviews (Beck and Lester, 1973; Steer et al., 1986) which have suggested that the self-denigratory items load together with the affective ones while items reflecting performance difficulties load on a separate factor. A common failing of factor-analytic research is that reported structures are not replicated independently. Therefore, the main aim of the present study was to investigate whether the factor structure reported by Steer et al. (1987) could be replicated, using a confirmatory approach, with an independent sample of patients meeting criteria for a diagnosis of major depression. Since there appear to have been no previous factor analytic studies of the BDI with a British sample: this study also tests the cross-cultural stability of Steer et al.‘s structure. A confirmatory analysis of the BDI has previously been reported by Tanaka and Huba (1984). They found evidence tending to confirm the three-factor structure reported by Beck and Lester (1973). Since the two samples they used consisted of 606 psychiatric patients with mixed diagnoses and 103 alcoholics, it is unclear what their results reveal about the components of major depression. However their study, which used covariance structure analysis, is the most methodologically sophisticated to date. Therefore, a second aim of the present study was to test whether the structure they obtained could be replicated with a sample of depressed patients. ethod Subjects
The sample initially consisted of 309 consecutive individuals who had applied, or who had been referred, for treatment in the Second Sheffield Psychotherapy Project (SPP2; Shapiro et al., 1990), which is currently investigating the
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comparative effectiveness of two contrasting forms of individual psychotherapy for depression. They completed the BDI soon after their initial contact with the clinic. Subjects were assessed within three weeks by means of the Diagnostic Interview Schedule, version III (Robins et al., 1981; Nelzer et al., 19851, provided they met the following criteria: they scored 16 or more on the BDI at screening, they were employed in professional, managerial or white-collar occupations and, if they were receiving medication, they had been on a stable regimen for at least the previous six weeks. Subjects were then excluded when the interview revealed a bereavement during tke previous six months or any symptoms in adult life of a manic episode, substance use disorders, schizophrenic, parancid, or obsessive-compulsive disorders. Sixteen subjects were excluded according to these criteria. Of those who remained, 63 men and 76 women met DSM-III (American Psychiatric Association, 1980) criteria for a diagnosis of Major Depressive
Episode (MDE). This is the sample used in the following analyses. Their mean BDI score was 24.5 (SD = 6.731, with a range from 16 to 49, and their mean age was 38 years (SD = 9.4 years), with a range from 19 to 60. Of these 139 subjects, 73 also met DSM-III criteria for a diagnosis of Generalised Anxiety Disorder, 3 met criteria for Panic Disorder and 36 met criteria for both Generalised Anxiety Disorder and Panic Disorder. Instrument The original version of the Beck Depression Inventory was used (Beck et al., 1961). It assesses each of 21 symptoms by asking subjects to choose between statements reflecting increasing levels of severity of the symptom. There are between four and six of these statements for each symptom and subjects are asked to choose the one which most applies to them, considering how they have felt over the past two weeks. The titles of these items, taken from Beck et al. (1988), are shown in Table 1.
TABLE 1 Target matrix from Steer et al. (1987) and Procrustes Rotated Principal Components Loadings Item No.
Title
13. 1. 17. 4. 2. 15. 11. 12. 14. 20. 21. 10.
Indecisiveness Mood Fatigability Lack of Satisfaction Pessimism Work Inhibition Irritability Social Withdrawal Distorted Body Image Somatic Preoccupation Loss of Libido Crying
(1) (1) (1) (1) (1) (1) (1) (1) (0) (0) (1) (0)
Self-accusation Guilt Feelings Self-dislike Sense of Failure Sense of Punishment Weight Loss Loss of Appetite Sleep Disturbance Suicidal Wishes
8. 5. 7. 3. 6. 19. 18. 16. 9.
Component 1
2
3
(0) 0.13 (0) 0.15 (0) -0.07 (0) 0.13 (0) 0.24 (0) -0.11 (0) -0.09 (0) 0.03 (1) 0.31 (0) 0.06 (0) 0.06 (0) 0.23
(0) (0) (0) (0) (0) (0) (0) (0) (0) (1) (0) (0)
(0) 0.02 (0) 0.03 (0) 0.19 (0) 0.15 (0) -0.02
(1) (1) (1) (1) (1)
(0) q.10 (01 -0Jx (0) 0.03 (0) -0.08 (0) 0.16
(0) (0) (0) (1)
(0) - 0.09 (0) 0.14 (0) 0.11 (0) 0.24
0.60 0.60 0.59 0.59 0.5s 0.55 0.53 0.51 0.35 0.28 0.27 0.29
0.10 0.25 0.07 0.30
Note: The elements of the target matrix are shown in parentheses.
0.67 0.64 0.64 0.62 0.57
(1) (1) (1) (0)
-0.09 0.20 -0.10 -0.13 0.20 0.16 -0.13 0.12 -0.26 0.27 -0.03 -0.03
0.68 0.68 0.58 0.47
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to evaluate the probability of obtaining the indices for the true target. In the analyses to be reported below the elements of the targets consisted of OS and 1s. They were constructed from the item loadings reported by Steer et al. (1987) and by Tanaka and Huba (1984) by representing each salient loading by 1 and each non-salient loading by 0. This seems the most realistic kind of target matrix since we would not expect to repeat the exact values these authors obtained and, in any case, they did not report the loadings for items regarded as nonsalient. Since both of thzse previous studies reported three factors, PROCRUS was set to extract and rotate just three components. It was also set to perform rotations to five thousand random permutations of the target.
Analyses
The data were analysed by a computer program called PROCRUS (Version 1.7; Reddon, 1991). This program takes a matrix of item correlations as input together with a target matrix. It computes a principal components analysis on the correlation matrix followed by an orthogonal rotation to the least-squares best fit with a target matrix (procrustes rotation; cf. Schonemann, 1966). The goodness-of-fit between the rotated loadings and the target is then measured by the sum of squared errors and the congruences between rotated and target components. Since little is known about the sampling distributions of these indices in cases where data are forced to a target, permutations of the rows of the target matrix are generated at random and a procrustes rotation of the component loadings is performed to each one (following a suggestion from Jackson and Morf, 1974). The distributions of the fit indices for the randomly generated permutations are then used
esults Table 1 shows the target matrix which was derived from the principal components analysis
TABLE 2 Target matrix from Tanaka and Huba’s (1984) Study 2 and Procrustes Rotated Principal Components Loadings Item No.
Title
7. 3. 8. 5. 2. 6. 14. 10.
Self-dislike Sense of Failure Self-accusation Guilt Feelings Pessimism Sense of Punishment Distorted Body Image Crying
(1) (1) (1) iI) (1) (1) (0) (1)
0.64 0.61 0.59 0.56 0.50 0.48 0.44 0.34
(0) (0) (0) (0) (0) (0) (0) (0)
-0.17 -0.19 -0.32 -0.31 0.37 -0.29 0.11 0.13
15. 17. Il. I. 12. 13. 4. 20. 21.
Work Inhibition Fatigability Irritability Mood Social Withdrawal Indecisiveness Lack of Satisfaction Somatic Preoccupation Loss of Libido
(0) 0.19 ((1) 0.25 (1) 0.20 (1) 0.44 (1) 0.29 (0) 0.42 (1) 0.41 (0) -0.02 (0) 0.19
(1) (11 (01 (0) (0) (1) (0) (I) (1)
0.54 0.52 0.48 0.45 0.44 0.43 0.42 0.37 0.19
(0) (0) (0) (0) (0) (0) (0) (0) (0)
0.10 -0.17 -0.19 0.14 0.07 -0.15 -0.19 0.23 -0.06
19. 18. 16. 9.
Weight Loss Loss of Appetite Sleep Disturbance Suicidal Wishes
(0) -0.01 (0) 0.27 (0) 0.15 (1) 0.37
(0) (0) (1) (0)
0.21 0.22 0.06 0.19
(1) (1) (0) (0)
0.66 0.66 0.57 0.46
Note: The
Component 1
2
elements of the target matrix are shown in parentheses.
3 (01 0.03 (0) 0.00 (0) 0.12 (@I -0.03 (0) 0.15 (0) 0.18 (II -0.29 (0) -0.05
2.55
of the BDI (Steer et al., 1987), together with the item loadings for the procrustes rotation of three principal components. In general, the structure of the previous analysis was well replicated; 19 of the 21 items had loadings equal to or greater than 0.30 on their target component and 17 of these had their highest loading on their target component. The sum of squared errors for the fit was 5.92, which is significant at P < 0.0002 (i.e., it is a smaller sum than was obtained with any of the 5000 random permutations of the target). The congruence coefficients comparing the target with the rotated components in the same sequential order were 0.91, 0.91 and 0.81, respectively (P < 0.001 in each case), whereas the average of the six off-diagonal congruences was only 0.11. Although the structure of the analysis by Steer et al. (1987) was well replicated, there were discrepancies in some of the details: two items that had salient loadings in the previous analysis (Somatic Preoccupation and Loss of Libido) had non-salient loadings here and the items Distorted Body Image and Suicidal Wishes had larger salient loadings on non-target components than they had on their target components. These findings suggest that the structure reported by Steer et al. may not be the optimum; it may have capitalised on chance to some extent. Therefore, the target matrix was modified as follows: Distorted Body Image was given a 1 on the first component instead of the second, Suicidal Wishes was given a 1 on the third component instead of the first, and both Somatic Preoccupation and Loss of Libido were given OSon all three components. The same analysis as before was then repeated with this modified target. The results showed all items to have their highest loadings on their target component (if any) and the sum of squared errors to have been reduced to 4.19. The target congruences were also higher than any found for the 5000 random permutations of the target. A third analysis used Tanaka and Huba’s (1984) Study 2 to construct a target. Their Study 1 used covariance structure analysis to confirm a postulated three-factor solution. This solution was then used in their Study 2 as a model for cross-validation with a second sample. In the event, they could confirm only a simplified model but never-
theless felt that the substantive interpretation of the factors remained largely unchanged. It is their Fig. 2 (P. 626), showing parameter estimates for their restricted model, which was used here to construct a target matrix. Table 2 shows the target matrix which was derived from Tanaka and Huba’s (1984) Study 2, together with the item loadings following the rotation of three principal components to the least-squares best fit. When PROCRUS was run with this target, but otherwise under the same conditions as before, the sum of squared errors was found to be 11.25 (P = 0.02, i.e., this sum was smaller than only 49 out of every 50 of the random permutations). The congruence coefficient for the first component, 0.87, was significant (P< O.Ol), but those for the second and third components, 0.56 (P < 0.25) and 0.45 (P = 0.24) respectively, were non-significant. Thus the fit of the rotated data to this model, though adequate for the first component, was clearly very much poorer overall than it was to the first model analysed above. Three subscales were constructed from the results of the first analysis above (shown in Table 1) by taking the means of all items with loadings greater than 0.4 on each of the rotated principal components. For convenience, they are called Mood/motivation, Self-denigration and Vegetative, following the order of the components. The intercorrelations of these subscales are shown in Table 3 together with their internal consistencies (standardised item alphas). Considering the subscale lengths, the internal consistencies are all appreciable, though the alphas for the Self-denigration and Vegetative subscales are rather too low for use with individuals. The low intercorrelaTABLE 3 Internal consistencies Subscales %i
Subscales
1, Mood/motivation 2. Self-denigration 3. Vegetative
and intercorrelations
for three BDI
Number of items
Subscale 1
2
3
8 5 4
0.72 0.20 0.28
0.67 0.18
0.61
Note: Standardised item alphas are shown on the diagonal.
tions show the subscales to be largely independent of each other. Mean subscale scores were calculated for the following diagnostic groups: (1) Major Lepressive Episode (MDE) alone, (2) MDE with Generalised Anxiety Disorder, (3) MDE with Panic Disorder, and (4) MDE with both Generalised Anxiety Disorder and Panic Disorder. However, none of the differences between these groups on the three subscales were significant when tested in three one-way ANOVAs (Fc3,135)< 1 in each case). For the entire sample, the means of the mean item scores for subscales (with SDS in parentheses) were: Mood/motivation, 1.38 (0.42); 1.16 (0.56); Vegetative, 0.84 Self-denigration, (0.53). Discussion In general, the first analysis reported above gave strong support for the structure reported by Steer et al, (1987). This consists of three components which might be interpreted as representing (1) Mood-motivation, (2) Self-denigration, and (3) Vegetative Disturbances. Subscales constructed from these components showed low intercorrelations but high internal consistencies (considering the limited subscale lengths). This structure is somewhat different to the structures that have been reported for patients with mixed diagnoses, which have generally found the self-denigratory items to load together with affective items while symptoms of performance difficulties, such as Fatigability and Work Inhibition, form a separate factor (Beck and Lester, 1973; Steer et al., 1986). Such differences underline the importance of selecting patients who are homogeneous with respect to diagnosed depression. Dimensions found to underlie variations in depressive symptoms in other diagnostic groups cannot be relied upon to generalise to depression itself. Although Steer et al.‘s structure was well supported in this study, there were signs that it could be improved. When the results of the procrustes rotation to their structure were used in the second analysis to construct a modified target, the fit of the data was even better. It was then found that the items Somatic Preoccupation and Loss of Libido, like Crying in Steer et al.‘s analysis, had
no salient loadings on any of the components. More importantly, it was also found that Suicidal Wishes had a substantial loading (0.51) on the Vegetative component. This finding is in direct contrast with three earlier studies of attempted suicides and suicidal ideators (Beck et al., 1973; Beck and Lester, 1973; Lester and Beck, 1977) which found that suicidal wishes correlated most highly with cognitive and mood factors and least highly with somatic symptoms. However, caution is required in interpreting this result since the modified target was constructed ad hoc and is itself in need of confirmation. The third analysis above found very little support for a factor structure reported by Tanaka and Huba (1984). This failure of confirmation needs to be interpreted with care, however, since Tanaka and Huba used covariance structure analysis, rather than principal components, and they tested a hierarchical model in which a large second-order factor explained most of the covariation among the primary factors. However, it seems likely that the differences between their reqults and those of Steer et al. (1987) can he explained at least as much by the differences in the patient samples as by the differences in analytical methods. The sample for Tanaka and Huba’s Stur’y 2 consisted of 103 alcoholics whereas Step: et al.‘s sample consisted of 300 patients diagnosed with major depressive disorder. Confidence in Steer et al.‘s (1987) three components would be increased if they were found to correspond to dimensions that had been identified in research using different methods of discovery. Quite strong support has been obtained for clusters resembling Mood-mo::ivation and Vegetative Disturbances. Young et al. (1986) found evidence for two sub-types of depression, which they called the anhedonic and the vegetative, by applying latent class analysis to evaluate two sets of diagnostic criteria for endogenous depression. The anhedonic sub-type included pervasive loss of pleasure, lack of reactivity and distinct quality of mood. The BDI does not assess lack of reactivity and its mood item assesses severity rather than distinct quality. However, the emphasis on the motivational and emotional aspects of depression seems similar to the Moodmotivation factor reported above. Young et al’s
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vegetative sub-type included terminal insomnia and weight loss. This therefore is closely related to the Vegetative Disturbances component of the present study. Additional support for the independence of these two clusters comes from Hibbert et al., (1984) who found that reduced sleep and appetite did not covary over time with reduced interest, reduced pleasure and low mood. Cooke (1980) also obtained a dimension which included early waking, weight loss and diurnal variation and which was independent of dissatisfaction, depressed mood and personal devaluation. It is also of interest that Bouman and Kok (19871 obtained three subsets of items that resembled Steer et al.‘s (1987) three components when they applied Rasch analysis to the BDI. Their ‘Guilt and failure’ subset was almost identical to the Self-denigration component and was independent of their ‘Mood and inhibition’ and ‘Somatic’ subsets. The main difference between these two structures is that Bouman and Kok’s ‘Somatic’ subset included two items from Mood/motivation component (Fatigability and Irritability), and several items that had no salient loadings, in Steer et al.? analysis. However, Bouman and Kok’s sample consisted of general inpatient and outpatient psychiatric patients with mixed diagnoses and their analysis required that item scores be dichotomised, so the meaning of comparisons with Steer et al.‘s results is unclear. Apart from previous investigations of the BDI, few studies have provided evidence that self-denigration tends to vary independently of other aspects of depression. Partly this may be because most assessment instruments pay scant attention to this aspect. For example, the 17-item Hamilton Rating Scale for Depression (Hamilton, 1960) and the Newcastle Diagnosis Scale (Carney et al., 1965) include only guilt, and DSM-IT1 criteria (American Psychiatric Association, 1980) inclurle only feeling worthless or sinful or guilty. The relative independence of such a component may also have been overlooked because no prominent theory expects it. Indeed, Beck (19871, the author of the most influential of the cognitive theories, views ‘ . . . deviant cognitive processes as intrinsic to the depressive disorder.. . ’ (p. 10). This appears to mean that all depressed people necessarily think negatively about themselves, the world
and the future (Haaga et al., 1991). However, evidence has gradually been accumulating that suggests this may not be so; using a variety of measures of negative thinking, several studies have found that between one quarter and one half of individuals who meet acceptable criteria for a diagnosis of major depression exhibit cognitive styles that are within the normal range (e.g. Hamilton and Abramson, 1983; Miller and Norman, 1986; Greene, 1989). As Hamilton and Abramson (1983) observed, ‘simply being in a depressive state, by itself, does not appear to be sufficient to cause an individual to exhibit a negative cognitive style’ (p. 183). Clearly the components that have been supported in the present study do not correspond with the most widely accepted classifications into sub-types, such as Major Depression with and without Melancholia (American Psychiatric Association, 1980) or the sub-types diagnosed by Research Diagnostic Criteria (Endicott and Spitzer, 19791. Indeed, since they suggest that there is continuous and relatively independent variation in at least three clusters of symptoms, the components do not correspond to sub-types at all. What they point to, t-Ether, is separable processes within individuals, each of which can become disturbed to a varying degree. What these processes might be is not entirely clear. One of them appears to be distinctly biological. Healy and Williams (1988), for example, have argued that it is difficult to imagine psychological processes that could account for diurnal variation of mood and vegetative symptoms such as early waking and anorexia. They suggest the most plausible account for such symptoms is that they arise from disruptions of circadian rhythms. The Self-denigration component, however, appears to be more clearly psychological and may be related to misinterpretation of the symptoms of depression (Teasdale, 1985) and a tendency to make social comparisons which are unfavourable to the qlf (Swallow and Griper, 1988). The Mood-motivational component seems to rePresent the basic syndrome of depression, a view which is reflected in the DSM-III criteria which require there to be either dysphoric mood or loss of pleasure before a diagnosis of depression may be made, regardless of other symptoms that are
258
present. Thus, this component tends to be the focus of many theories of depression and might be accounted for by any one, or any combination, of them. In conclusion, this study has provided confirmatory evidence for three dimensions underlying covariation in those symptoms of depression that are measured by the BDI. These dimensions differ in important respects from those that have been reported for patients with diagnoses other than major depression, and for subjects with no diagnoses at all. Many previous investigations have been conducted with the aim of finding the dimensions of the BDI itself. Such an aim is probably misconceived since it appears that symptoms do not covary in identical ways in different diagnostic groups. Our ucderstanding of the processes, both physiological and psychological, underlying major depression is likely to progress faster if attention is focussed on samples composed exclusively of patients diagnosed with depression. Acknowledgements We are grateful to John Reddon for providing a copy of his program PROCRUS and for discussions about its interpretation. We would also like to express our appreciation to Leslie Morrison and Heather Harper for interviewing clients.
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Carney, M.W.P., Roth, M. and Garside, R.F. (1965) The diagnosis of depressive syndromes and the prediction of ECT response. Br. J. Psychiatry 111, 659-674. Clark, D.C., Cavanaugh, S.V. and Gibbons, R.D. (1983) The core symptoms of depression in medical and psychiatric patients. J. New. Ment. Dis. 171, 705-713. Cooke, D.J. (1980) The structure of depression found in the general population. Psychol. Med. 10, 455-463. Deardorff, W.W. and Funabiki, D. (1985) A diagnostic caution in screening for depressed college students. Cog. Ther. Res. 9, 277-284. Greene, S.M. (1989) The relationship between depression and hopelessness: Implications for current theories of depression. Br. J. Psychiatry 154, 650-659. Haaga, D.A.F., Dyck. M.J. and Ernst, D. (1991) Empirical status of cognitive theory of depression. Psychol. Bull. 110, 215-236. Hamilton, E.W. and Abramson, L.Y. (1983) Cognitive patterns and Major Depressive Disorder: A longitudinal study in a hospital setting. J. Abnorm. Psychol. 92, 173-184. Hamilton, M. (1960) A rating scale for depression. J. Neurol. Neurosurp. Psychiatry 23, 56-62. Healy, D. and Williams, J.M.G. (1988) Dysrhythmia, dysphoria, and depression: the interaction of learned helplessness and circadian dysrhythmia in the pathogenesis of depression. Psychol. Bull. 103, 163-178. Helzer, J.E., Robins, L.N., McEvoy, L.T., Spitznagel, E.L., Stoltzman, R.K., Farmer, A. and Brockington, I.F. (1985) A comparison of clinical and Diagnostic Interview Schedule diagnoses: physician reexamination of lay-interviewed cases in the general population. Arch. Gen. Psychiatry 42, 657-666. Hibbert, G.A., Teasdale. J.D. and Spencer, P. (1984) Covariation of depressive symptoms over time. Psychol. Med. 14, 451-455. Jackson, D.N. and Morf, M.E. (1974) Testing the null hypothesis for rotation to a target. Multivar. Behav. Res. 9, 303-310. Kendall, P.C., Hollon, S.D., Beck, A.T., Hammen, C, L. and Ingram, R.E. (1987) Issues and recommendations regarding use of the Beck Depression Inventory. Cog. Ther. Res. 11, 289-299. Lester, D. and Beck, A.T. (1977) Suicidal wishes and depression in suicidal ideators: a comparison with attempted suicides. J. Clin. Psycho]. 33, 92-94. Miller, 1.W. and Norman, W.H. (1986) Persistence of depressive cognitions within a subgroup of depressed inpatients. Cog. Ther. Res. 10, 21 l-224. Oliver, J.M. and Simmons, M.E. (1984) Depression as measured by the DSM-III and the Beck Depression Inventory in an unselected adult population. J. Consult. Clin. Psychol. 52, 892-898. Piotrowski, C., Sherry, D. and Keller, J.W. (1985) Psychodiagnostic test usage: a survey of the Society for Personality Assessment. J. Pers. Assess. 49, 15-29. Reddon, J.R. (1991) PROCRUS: Procrustes analysis package (Software: version 1.7). Department of Educational Psychology, University of Alberta, Edmonton, Canada.
Robins, L.N., Helzer, J.E., Croughan, J. and Ratcliff, K.S. (19811 National Institute of Mental Health Diagnostic Interview Schedule: its history. characteristics and validity. Arch. Gen. Psychiatry 38, 381-389. Schonemann, P.H. (1966). A generalized solution of the orthogonal procrustes problem. Psychometrika, 31, l-10. Schuckit, M. (1983) Alcoholic patients with secondary depression. Am. J. Psychiatry 140, 711-714. Shapiro, D.A., Barkham, M., Hardy, G.E. and Morrison, L.A. (1990) The Second Sheffield Psychotherapy Project: rationale, design and preliminary outcome data. Br. J. Med Psychol. 63, 97-108. Spitzer, R.L., Endicott, J. and Robins, E. (19781 Research diagnostic criteria: rationale and reliability. Arch. Gen. Psychiatry 35, 773-782. Steer, R.A., Beck, A.T. and Garrison, B. (1986) Applications of the Beck Depression Inventory. In: N. Sartorious and T.A. Ban, (Eds.), Assessment of Depression. World Health Orgenisation, Geneva, Switzerland.
Steer, R.A., Beck, A.T., Riskind, J.H. and Brown, G. (1987) Relationships between the Beck Depression Inventory and the Hamilton Rating Scale for Depression in depressed outpatients. J. Psychopath. Behav. Assess. 9, 327-339. Swallow, S.S. and Kuiper, N.A. (1988) Social comparison and negative self-evaluations: an application to depression. Clin. Psychol. Rev. 8, 55-76. Tanaka, J.S. and Huba, G.J. (1984) Confirmatory hierarchical factor analysis of psychological distress measures. J. Pets. Sot. Psychol. 46,621-635. Teasdale, J.D. (1985) Psychological treatments for depression: how do they work? Behav. Res. Ther. 23, lS7-165. Weckowicz, T., Muir, W. and Cropley, J. (19671 A factor analysis of the Beck Depression Inventory. J. Consult. Psychol. 31, 23-28. Young, M.A., Scheftner, W.A., Klerman, G.L., Andreason, N.C. and Hirschfeld, R.M.A. (1986) The endogenous subtype of depression: a study of its internal construct validity. Br. J. Psychiatry 148, 257-267.
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JAD 00952
Mike Startup a, Anne Rees b and Michael Barkham b a Department of Psychology, University College of North Wales, Bangor, UK and b MRC/ESRC
Social and Appked Psychology Unit,
University of Sheffield, Sheffield, UK
(Received 6 April 1992) (Reuisicn received 25 August 1992) (Accepted 1 September 1992)
Summary Although more than 20 factor analytic studies have been published on the Beck Depression Inventory (BDI), only Steer and co-workers (1987) have used a sample composed exclusively of patients diagnosed with depression. The component structure found in their study of depressed patients differs in important respects from the structure summarised in several reviews. The main aim of the present study was to investigate whether this structure could be confirmed with the BDI responses of an independent sample of 139 patients diagnosed with DSM-III Major Depressive Episode. Three principal components were extracted and rotated to maximum congruence with a target based on the results of Steer et al. (1987). The significance of the fit to this target was then evaluated by rotating the same matrix of loadings to 5000 random permutations of the target. The fit was found to be highly significant, though some possible improvements could be identified ad hoc. An alternative factor structure for the BDI, derived from covariance structure analysis by ‘Tanaka and Huba (19841, was also tested but could not be confirmed.
Key words: Beck Depression Inventory; Depression;
Introduction The Beck Depression Inventory (BDI; Beck et al., 1961) is one of the most widely used self-report instruments for assessing the severity of de-
Correspondence to: M. Startup,
Department of Psychology, University College of North Wales, Bangor, Gwynedd, LL57 2DC, UK.
Confirmatory
factor analysis
pression in diagnosed patients (Piotrowski et al., 1985) and for detecting depression in non-psychiatric populations (Steer et al., 1986). Altogether, it has been used in well over one thousand research studies (Beck et al., 1988). Despite this popularity, little has been done to discover what the BDI reveals about symptom clustering in depression. In a review of its psychometric properties, Beck et al. (1988) identified 20 factor analytic studies but only one of these used a sample
of diagnosed depressed patients (Clark et al., 1983, and that sample was mixed with an equal number of normal subjects showing no current psychopathology. Five of the studies employed samples of alcoholics, two employed samples of heroin addicts, and most of the remaining studies were based on non-psychiatric samples, chiefly students. If the BDI is to be used to investigate depression as a nosologic category, care must be taken to screen out from the sample those subjects whose depressive symptoms are, or might be, secondaT to some other disorder. Samples of patients with substance use disorders, for example, are unsatisfactory for the investigation of components of depression since it is unclear to what degree their depressive symptoms should be explained by the state of chronic intoxication, the physiological effects of withdrawal or some aspect of their personalities or lifestyles (Schuckit, 1983). When the sample consists of patients with a variety of psychiatric diagnoses there is uncertainty how much the results are affected by individuals with psychotic disturbances and other non-affective disorders. Although the BDI appears to be a sensitive measure of depression as a syndrome, it is not adequate as a nosological screening device (Kendall et al., 1987). Several studies have shown that significant proportions of individuals in community samples (e.g., Oliver and Simmons, 1984) and student samples (e.g., Deardorff and Funabiki, 198.5) who score highly on the BDI do not have diagnosable disorders. Factor analytic studies based on non-psychiatric samples also suffer from highly skewed distributions of item scores and tend to produce large general factors which merely discriminate the distressed from the nondistressed (Weckowicz et al., 1967). In their review, Beck et al. (1988) concluded that the balance of evidence suggests that the BDI measures three intercorrelated components reflecting Negative Attitudes Toward Self, Performance Impairment, and Somatic Disturbance. However, this conclusion does not appear to take into account the only published study exclusively based on diagnosed depressed patients. Using a sample of 300 patients diagnosed with DSM-III Major Depressive Disorder, Steer et al. (1987)
found evidence for three clear components. The first component had salient loadings from items reflecting affective and performance difficulties while all of the items reflecting self-denigration loaded separately on the second component. The third component had salient loadings from four items dealing with physiological disturbances. Thus this solution differs in important respects from the structure supported by some previous reviews (Beck and Lester, 1973; Steer et al., 1986) which have suggested that the self-denigratory items load together with the affective ones while items reflecting performance difficulties load on a separate factor. A common failing of factor-analytic research is that reported structures are not replicated independently. Therefore, the main aim of the present study was to investigate whether the factor structure reported by Steer et al. (1987) could be replicated, using a confirmatory approach, with an independent sample of patients meeting criteria for a diagnosis of major depression. Since there appear to have been no previous factor analytic studies of the BDI with a British sample: this study also tests the cross-cultural stability of Steer et al.‘s structure. A confirmatory analysis of the BDI has previously been reported by Tanaka and Huba (1984). They found evidence tending to confirm the three-factor structure reported by Beck and Lester (1973). Since the two samples they used consisted of 606 psychiatric patients with mixed diagnoses and 103 alcoholics, it is unclear what their results reveal about the components of major depression. However their study, which used covariance structure analysis, is the most methodologically sophisticated to date. Therefore, a second aim of the present study was to test whether the structure they obtained could be replicated with a sample of depressed patients. ethod Subjects
The sample initially consisted of 309 consecutive individuals who had applied, or who had been referred, for treatment in the Second Sheffield Psychotherapy Project (SPP2; Shapiro et al., 1990), which is currently investigating the
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comparative effectiveness of two contrasting forms of individual psychotherapy for depression. They completed the BDI soon after their initial contact with the clinic. Subjects were assessed within three weeks by means of the Diagnostic Interview Schedule, version III (Robins et al., 1981; Nelzer et al., 19851, provided they met the following criteria: they scored 16 or more on the BDI at screening, they were employed in professional, managerial or white-collar occupations and, if they were receiving medication, they had been on a stable regimen for at least the previous six weeks. Subjects were then excluded when the interview revealed a bereavement during tke previous six months or any symptoms in adult life of a manic episode, substance use disorders, schizophrenic, parancid, or obsessive-compulsive disorders. Sixteen subjects were excluded according to these criteria. Of those who remained, 63 men and 76 women met DSM-III (American Psychiatric Association, 1980) criteria for a diagnosis of Major Depressive
Episode (MDE). This is the sample used in the following analyses. Their mean BDI score was 24.5 (SD = 6.731, with a range from 16 to 49, and their mean age was 38 years (SD = 9.4 years), with a range from 19 to 60. Of these 139 subjects, 73 also met DSM-III criteria for a diagnosis of Generalised Anxiety Disorder, 3 met criteria for Panic Disorder and 36 met criteria for both Generalised Anxiety Disorder and Panic Disorder. Instrument The original version of the Beck Depression Inventory was used (Beck et al., 1961). It assesses each of 21 symptoms by asking subjects to choose between statements reflecting increasing levels of severity of the symptom. There are between four and six of these statements for each symptom and subjects are asked to choose the one which most applies to them, considering how they have felt over the past two weeks. The titles of these items, taken from Beck et al. (1988), are shown in Table 1.
TABLE 1 Target matrix from Steer et al. (1987) and Procrustes Rotated Principal Components Loadings Item No.
Title
13. 1. 17. 4. 2. 15. 11. 12. 14. 20. 21. 10.
Indecisiveness Mood Fatigability Lack of Satisfaction Pessimism Work Inhibition Irritability Social Withdrawal Distorted Body Image Somatic Preoccupation Loss of Libido Crying
(1) (1) (1) (1) (1) (1) (1) (1) (0) (0) (1) (0)
Self-accusation Guilt Feelings Self-dislike Sense of Failure Sense of Punishment Weight Loss Loss of Appetite Sleep Disturbance Suicidal Wishes
8. 5. 7. 3. 6. 19. 18. 16. 9.
Component 1
2
3
(0) 0.13 (0) 0.15 (0) -0.07 (0) 0.13 (0) 0.24 (0) -0.11 (0) -0.09 (0) 0.03 (1) 0.31 (0) 0.06 (0) 0.06 (0) 0.23
(0) (0) (0) (0) (0) (0) (0) (0) (0) (1) (0) (0)
(0) 0.02 (0) 0.03 (0) 0.19 (0) 0.15 (0) -0.02
(1) (1) (1) (1) (1)
(0) q.10 (01 -0Jx (0) 0.03 (0) -0.08 (0) 0.16
(0) (0) (0) (1)
(0) - 0.09 (0) 0.14 (0) 0.11 (0) 0.24
0.60 0.60 0.59 0.59 0.5s 0.55 0.53 0.51 0.35 0.28 0.27 0.29
0.10 0.25 0.07 0.30
Note: The elements of the target matrix are shown in parentheses.
0.67 0.64 0.64 0.62 0.57
(1) (1) (1) (0)
-0.09 0.20 -0.10 -0.13 0.20 0.16 -0.13 0.12 -0.26 0.27 -0.03 -0.03
0.68 0.68 0.58 0.47
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to evaluate the probability of obtaining the indices for the true target. In the analyses to be reported below the elements of the targets consisted of OS and 1s. They were constructed from the item loadings reported by Steer et al. (1987) and by Tanaka and Huba (1984) by representing each salient loading by 1 and each non-salient loading by 0. This seems the most realistic kind of target matrix since we would not expect to repeat the exact values these authors obtained and, in any case, they did not report the loadings for items regarded as nonsalient. Since both of thzse previous studies reported three factors, PROCRUS was set to extract and rotate just three components. It was also set to perform rotations to five thousand random permutations of the target.
Analyses
The data were analysed by a computer program called PROCRUS (Version 1.7; Reddon, 1991). This program takes a matrix of item correlations as input together with a target matrix. It computes a principal components analysis on the correlation matrix followed by an orthogonal rotation to the least-squares best fit with a target matrix (procrustes rotation; cf. Schonemann, 1966). The goodness-of-fit between the rotated loadings and the target is then measured by the sum of squared errors and the congruences between rotated and target components. Since little is known about the sampling distributions of these indices in cases where data are forced to a target, permutations of the rows of the target matrix are generated at random and a procrustes rotation of the component loadings is performed to each one (following a suggestion from Jackson and Morf, 1974). The distributions of the fit indices for the randomly generated permutations are then used
esults Table 1 shows the target matrix which was derived from the principal components analysis
TABLE 2 Target matrix from Tanaka and Huba’s (1984) Study 2 and Procrustes Rotated Principal Components Loadings Item No.
Title
7. 3. 8. 5. 2. 6. 14. 10.
Self-dislike Sense of Failure Self-accusation Guilt Feelings Pessimism Sense of Punishment Distorted Body Image Crying
(1) (1) (1) iI) (1) (1) (0) (1)
0.64 0.61 0.59 0.56 0.50 0.48 0.44 0.34
(0) (0) (0) (0) (0) (0) (0) (0)
-0.17 -0.19 -0.32 -0.31 0.37 -0.29 0.11 0.13
15. 17. Il. I. 12. 13. 4. 20. 21.
Work Inhibition Fatigability Irritability Mood Social Withdrawal Indecisiveness Lack of Satisfaction Somatic Preoccupation Loss of Libido
(0) 0.19 ((1) 0.25 (1) 0.20 (1) 0.44 (1) 0.29 (0) 0.42 (1) 0.41 (0) -0.02 (0) 0.19
(1) (11 (01 (0) (0) (1) (0) (I) (1)
0.54 0.52 0.48 0.45 0.44 0.43 0.42 0.37 0.19
(0) (0) (0) (0) (0) (0) (0) (0) (0)
0.10 -0.17 -0.19 0.14 0.07 -0.15 -0.19 0.23 -0.06
19. 18. 16. 9.
Weight Loss Loss of Appetite Sleep Disturbance Suicidal Wishes
(0) -0.01 (0) 0.27 (0) 0.15 (1) 0.37
(0) (0) (1) (0)
0.21 0.22 0.06 0.19
(1) (1) (0) (0)
0.66 0.66 0.57 0.46
Note: The
Component 1
2
elements of the target matrix are shown in parentheses.
3 (01 0.03 (0) 0.00 (0) 0.12 (@I -0.03 (0) 0.15 (0) 0.18 (II -0.29 (0) -0.05
2.55
of the BDI (Steer et al., 1987), together with the item loadings for the procrustes rotation of three principal components. In general, the structure of the previous analysis was well replicated; 19 of the 21 items had loadings equal to or greater than 0.30 on their target component and 17 of these had their highest loading on their target component. The sum of squared errors for the fit was 5.92, which is significant at P < 0.0002 (i.e., it is a smaller sum than was obtained with any of the 5000 random permutations of the target). The congruence coefficients comparing the target with the rotated components in the same sequential order were 0.91, 0.91 and 0.81, respectively (P < 0.001 in each case), whereas the average of the six off-diagonal congruences was only 0.11. Although the structure of the analysis by Steer et al. (1987) was well replicated, there were discrepancies in some of the details: two items that had salient loadings in the previous analysis (Somatic Preoccupation and Loss of Libido) had non-salient loadings here and the items Distorted Body Image and Suicidal Wishes had larger salient loadings on non-target components than they had on their target components. These findings suggest that the structure reported by Steer et al. may not be the optimum; it may have capitalised on chance to some extent. Therefore, the target matrix was modified as follows: Distorted Body Image was given a 1 on the first component instead of the second, Suicidal Wishes was given a 1 on the third component instead of the first, and both Somatic Preoccupation and Loss of Libido were given OSon all three components. The same analysis as before was then repeated with this modified target. The results showed all items to have their highest loadings on their target component (if any) and the sum of squared errors to have been reduced to 4.19. The target congruences were also higher than any found for the 5000 random permutations of the target. A third analysis used Tanaka and Huba’s (1984) Study 2 to construct a target. Their Study 1 used covariance structure analysis to confirm a postulated three-factor solution. This solution was then used in their Study 2 as a model for cross-validation with a second sample. In the event, they could confirm only a simplified model but never-
theless felt that the substantive interpretation of the factors remained largely unchanged. It is their Fig. 2 (P. 626), showing parameter estimates for their restricted model, which was used here to construct a target matrix. Table 2 shows the target matrix which was derived from Tanaka and Huba’s (1984) Study 2, together with the item loadings following the rotation of three principal components to the least-squares best fit. When PROCRUS was run with this target, but otherwise under the same conditions as before, the sum of squared errors was found to be 11.25 (P = 0.02, i.e., this sum was smaller than only 49 out of every 50 of the random permutations). The congruence coefficient for the first component, 0.87, was significant (P< O.Ol), but those for the second and third components, 0.56 (P < 0.25) and 0.45 (P = 0.24) respectively, were non-significant. Thus the fit of the rotated data to this model, though adequate for the first component, was clearly very much poorer overall than it was to the first model analysed above. Three subscales were constructed from the results of the first analysis above (shown in Table 1) by taking the means of all items with loadings greater than 0.4 on each of the rotated principal components. For convenience, they are called Mood/motivation, Self-denigration and Vegetative, following the order of the components. The intercorrelations of these subscales are shown in Table 3 together with their internal consistencies (standardised item alphas). Considering the subscale lengths, the internal consistencies are all appreciable, though the alphas for the Self-denigration and Vegetative subscales are rather too low for use with individuals. The low intercorrelaTABLE 3 Internal consistencies Subscales %i
Subscales
1, Mood/motivation 2. Self-denigration 3. Vegetative
and intercorrelations
for three BDI
Number of items
Subscale 1
2
3
8 5 4
0.72 0.20 0.28
0.67 0.18
0.61
Note: Standardised item alphas are shown on the diagonal.
tions show the subscales to be largely independent of each other. Mean subscale scores were calculated for the following diagnostic groups: (1) Major Lepressive Episode (MDE) alone, (2) MDE with Generalised Anxiety Disorder, (3) MDE with Panic Disorder, and (4) MDE with both Generalised Anxiety Disorder and Panic Disorder. However, none of the differences between these groups on the three subscales were significant when tested in three one-way ANOVAs (Fc3,135)< 1 in each case). For the entire sample, the means of the mean item scores for subscales (with SDS in parentheses) were: Mood/motivation, 1.38 (0.42); 1.16 (0.56); Vegetative, 0.84 Self-denigration, (0.53). Discussion In general, the first analysis reported above gave strong support for the structure reported by Steer et al, (1987). This consists of three components which might be interpreted as representing (1) Mood-motivation, (2) Self-denigration, and (3) Vegetative Disturbances. Subscales constructed from these components showed low intercorrelations but high internal consistencies (considering the limited subscale lengths). This structure is somewhat different to the structures that have been reported for patients with mixed diagnoses, which have generally found the self-denigratory items to load together with affective items while symptoms of performance difficulties, such as Fatigability and Work Inhibition, form a separate factor (Beck and Lester, 1973; Steer et al., 1986). Such differences underline the importance of selecting patients who are homogeneous with respect to diagnosed depression. Dimensions found to underlie variations in depressive symptoms in other diagnostic groups cannot be relied upon to generalise to depression itself. Although Steer et al.‘s structure was well supported in this study, there were signs that it could be improved. When the results of the procrustes rotation to their structure were used in the second analysis to construct a modified target, the fit of the data was even better. It was then found that the items Somatic Preoccupation and Loss of Libido, like Crying in Steer et al.‘s analysis, had
no salient loadings on any of the components. More importantly, it was also found that Suicidal Wishes had a substantial loading (0.51) on the Vegetative component. This finding is in direct contrast with three earlier studies of attempted suicides and suicidal ideators (Beck et al., 1973; Beck and Lester, 1973; Lester and Beck, 1977) which found that suicidal wishes correlated most highly with cognitive and mood factors and least highly with somatic symptoms. However, caution is required in interpreting this result since the modified target was constructed ad hoc and is itself in need of confirmation. The third analysis above found very little support for a factor structure reported by Tanaka and Huba (1984). This failure of confirmation needs to be interpreted with care, however, since Tanaka and Huba used covariance structure analysis, rather than principal components, and they tested a hierarchical model in which a large second-order factor explained most of the covariation among the primary factors. However, it seems likely that the differences between their reqults and those of Steer et al. (1987) can he explained at least as much by the differences in the patient samples as by the differences in analytical methods. The sample for Tanaka and Huba’s Stur’y 2 consisted of 103 alcoholics whereas Step: et al.‘s sample consisted of 300 patients diagnosed with major depressive disorder. Confidence in Steer et al.‘s (1987) three components would be increased if they were found to correspond to dimensions that had been identified in research using different methods of discovery. Quite strong support has been obtained for clusters resembling Mood-mo::ivation and Vegetative Disturbances. Young et al. (1986) found evidence for two sub-types of depression, which they called the anhedonic and the vegetative, by applying latent class analysis to evaluate two sets of diagnostic criteria for endogenous depression. The anhedonic sub-type included pervasive loss of pleasure, lack of reactivity and distinct quality of mood. The BDI does not assess lack of reactivity and its mood item assesses severity rather than distinct quality. However, the emphasis on the motivational and emotional aspects of depression seems similar to the Moodmotivation factor reported above. Young et al’s
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vegetative sub-type included terminal insomnia and weight loss. This therefore is closely related to the Vegetative Disturbances component of the present study. Additional support for the independence of these two clusters comes from Hibbert et al., (1984) who found that reduced sleep and appetite did not covary over time with reduced interest, reduced pleasure and low mood. Cooke (1980) also obtained a dimension which included early waking, weight loss and diurnal variation and which was independent of dissatisfaction, depressed mood and personal devaluation. It is also of interest that Bouman and Kok (19871 obtained three subsets of items that resembled Steer et al.‘s (1987) three components when they applied Rasch analysis to the BDI. Their ‘Guilt and failure’ subset was almost identical to the Self-denigration component and was independent of their ‘Mood and inhibition’ and ‘Somatic’ subsets. The main difference between these two structures is that Bouman and Kok’s ‘Somatic’ subset included two items from Mood/motivation component (Fatigability and Irritability), and several items that had no salient loadings, in Steer et al.? analysis. However, Bouman and Kok’s sample consisted of general inpatient and outpatient psychiatric patients with mixed diagnoses and their analysis required that item scores be dichotomised, so the meaning of comparisons with Steer et al.‘s results is unclear. Apart from previous investigations of the BDI, few studies have provided evidence that self-denigration tends to vary independently of other aspects of depression. Partly this may be because most assessment instruments pay scant attention to this aspect. For example, the 17-item Hamilton Rating Scale for Depression (Hamilton, 1960) and the Newcastle Diagnosis Scale (Carney et al., 1965) include only guilt, and DSM-IT1 criteria (American Psychiatric Association, 1980) inclurle only feeling worthless or sinful or guilty. The relative independence of such a component may also have been overlooked because no prominent theory expects it. Indeed, Beck (19871, the author of the most influential of the cognitive theories, views ‘ . . . deviant cognitive processes as intrinsic to the depressive disorder.. . ’ (p. 10). This appears to mean that all depressed people necessarily think negatively about themselves, the world
and the future (Haaga et al., 1991). However, evidence has gradually been accumulating that suggests this may not be so; using a variety of measures of negative thinking, several studies have found that between one quarter and one half of individuals who meet acceptable criteria for a diagnosis of major depression exhibit cognitive styles that are within the normal range (e.g. Hamilton and Abramson, 1983; Miller and Norman, 1986; Greene, 1989). As Hamilton and Abramson (1983) observed, ‘simply being in a depressive state, by itself, does not appear to be sufficient to cause an individual to exhibit a negative cognitive style’ (p. 183). Clearly the components that have been supported in the present study do not correspond with the most widely accepted classifications into sub-types, such as Major Depression with and without Melancholia (American Psychiatric Association, 1980) or the sub-types diagnosed by Research Diagnostic Criteria (Endicott and Spitzer, 19791. Indeed, since they suggest that there is continuous and relatively independent variation in at least three clusters of symptoms, the components do not correspond to sub-types at all. What they point to, t-Ether, is separable processes within individuals, each of which can become disturbed to a varying degree. What these processes might be is not entirely clear. One of them appears to be distinctly biological. Healy and Williams (1988), for example, have argued that it is difficult to imagine psychological processes that could account for diurnal variation of mood and vegetative symptoms such as early waking and anorexia. They suggest the most plausible account for such symptoms is that they arise from disruptions of circadian rhythms. The Self-denigration component, however, appears to be more clearly psychological and may be related to misinterpretation of the symptoms of depression (Teasdale, 1985) and a tendency to make social comparisons which are unfavourable to the qlf (Swallow and Griper, 1988). The Mood-motivational component seems to rePresent the basic syndrome of depression, a view which is reflected in the DSM-III criteria which require there to be either dysphoric mood or loss of pleasure before a diagnosis of depression may be made, regardless of other symptoms that are
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present. Thus, this component tends to be the focus of many theories of depression and might be accounted for by any one, or any combination, of them. In conclusion, this study has provided confirmatory evidence for three dimensions underlying covariation in those symptoms of depression that are measured by the BDI. These dimensions differ in important respects from those that have been reported for patients with diagnoses other than major depression, and for subjects with no diagnoses at all. Many previous investigations have been conducted with the aim of finding the dimensions of the BDI itself. Such an aim is probably misconceived since it appears that symptoms do not covary in identical ways in different diagnostic groups. Our ucderstanding of the processes, both physiological and psychological, underlying major depression is likely to progress faster if attention is focussed on samples composed exclusively of patients diagnosed with depression. Acknowledgements We are grateful to John Reddon for providing a copy of his program PROCRUS and for discussions about its interpretation. We would also like to express our appreciation to Leslie Morrison and Heather Harper for interviewing clients.
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