The Structure of the Beck Hopelessness Scale a Con

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The structure of the Beck Hopelessness Ho pelessness Scale: A confirmatory factor analysis in UK students Article in

Personality and and Individual Differences · July 2011

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Personality and Individual Differences 51 (2011) 17–22

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Personality and Individual Differences j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / p a i d

The structure of the Beck Hopelessness Scale: A confirmatory factor analysis in UK students ⇑

Donncha Hanna, Ross White, Kristopher Lyons, Michael J. McParland , Ciaran Shannon, Ciaran Mulholland Department of Mental Health, The Queen’s University of Belfast, Whitla Medical Building, 97 Lisburn Road, Belfast BT9 7BL, United Kingdom

a r t i c l e

i n f o

Article history: Received 2 July 2010 Received in revised form 27 February 2011 Accepted 1 March 2011 Available online 2 April 2011 Keywords: Hopelessness Confirmatory factor analysis Normal population Beck Hopelessness Scale Depressive disorder

a b s t r a c t

The aim of this paper was to confirm the factor structure of the 20-item Beck Hopelessness Scale in a nonclinical population. Previous research has highlighted a lack of clarity in its construct validity with regards to this population. Based on previous factor analytic findings from both clinical and non-clinical studies, 13 separate confirmatory factor models were specified and estimated using LISREL 8.72 to test the one, two and threefactor models. Psychology and medical students at Queen’s University, Belfast (n = 581) completed both the BHS and the Beck Depression Inventory (BDI). All models showed reasonable fit, but only one, a four-item single-factor model demonstrated a nonsignificant chi-squared statistic. These four items can be used to derive a Short-Form BHS (SBHS) in which increasing scores (0–4) corresponded with increasing scores in the BDI. The four items were also drawn from all three of Beck’s proposed triad, and included both positively and negatively scored items. This study in a UK undergraduate non-clinical population suggests that the BHS best measures a onefactor model of hopelessness. It appears that a shorter four-item scale can also measure this one-factor model.  2011 Elsevier Ltd. All rights reserved.

1. Introduction

In 1974 Beck and colleagues constructed the Hopelessness Scale (BHS) as an instrument designed to quantify hopelessness (Beck, Weissman, Lester, & Trexler, 1974). Up until that point, it had been thought that hopelessness was a concept so difficult to define and measure objectively, that no meaningful scale could ever be developed. The BHS now is the most widely used measure of hopelessness (Velting, 1999). The importance of the BHS has been consistently demonstrated as a predictor of suicide ideation, suicide attempts and suicide completion (Beck, Steer, Kovacs, & Garrison, 1985; Beck et al., 1974; Chochinov, Wilson, Enns, & Lander, 1998; Dyer & Kreitman, 1984; Ellis & Ratliff, 1986). Although most research has examined the relationship between hopelessness and suicidality, the relationship between hopelessness and depression is also well established (Meites, Deveney, Steele, Holmes, & Pizzagalli, 2008). According to Beck’s (1967) cognitive model, depressed individuals view themselves as ineffective, readily internalize blame for

⇑ Corresponding author. Address: ST6 Psychiatry, Department of Psychiatry, Queen’s University of Belfast, The Whitla Medical Building, 97 Lisburn Road, Belfast BT9 7BL, United Kingdom. Tel.: +44 2890975790; fax: +44 2890975870. E-mail address: [email protected] (M.J. McParland).

0191-8869/$ - see front matter  2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2011.03.001

personal problems, and see investment in the long-term future as unlikely to pay off. Hopelessness is seen at the third ingredient of this cognitive triad and, as such, is a cornerstone of his model of depression. Individuals envisage a futurein which nothing will turn out right, in which failure is an inevitable consequence of any attempt to achieve goals and in which all their major problems are unsolvable (Beck, Epstein, Brown, & Steer, 1988). The result of these beliefs is a reduction in goal-striving behaviour which further perpetuates attitudinal and emotional dimensions of hopelessness. Although hopelessness is often observed in depressive illness, and depression and hopelessness correlate very highly, it is not a necessary component of the depressive syndrome (Rooke & Birchwood, 1998). Beck et al. (1988) have shown that there is correlation between the Beck Depression Inventory (BDI) and the BHS when measuring depression (Hill, Gallagher, Thompson, & Ishida, 1988; Nekanda-Trepka, Bishop, & Blackburn, 1983; Nissim et al., 2009; Steer, Iguchi, & Platt, 1994). The BHS is a uni-polar scale and does not conceptualize hopelessness–hopefulness on a single bi-polar scale. In addition, the BHS is a cognitive framework, representing negative state and trait expectations of the future (Glanz, Haas, & Sweeney, 1995). Low scores do not represent hope; they represent the absence of hopelessness. This lack of delineation between state or trait construct has been a point of stricture (Glanz et al., 1995). However,

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D. Hanna et al. / Personality and Individual Differences 51 (2011) 17–22

relatively high test-reliability reported in undergraduate university students (r = .85; Holden & Fekken, 1988) and in advanced cancer patients (r = .78; Mystakidou et al., 2008) suggest that it is best conceptualized as a trait based variable. Furthermore, the items tend to relate to the future, as opposed to tapping instantaneous states, strengthening the argument that it is a trait based construct. Steed (2001) has reported a strong correlation between the BHS and measures of optimism–pessimism in a normal population. The internal consistency of the scale has been reported as acceptably high (a = .93–.83) (Beck et al., 1974; Durham, 1982; Dyce, 1996; Young, Halper, Clark, Scheftner, & Fawcett, 1992) within clinical populations and slightly lower ( a = .65–.88) in non-clinical samples (Chang, D’Zurilla, & Maydeu-Olivares, 1994; Durham, 1982; Steed, 2001). Despite the predictive validity that the BHS has demonstrated there has been criticism of the measure, for example, its relationship with social desirability which may be especially prevalent in non-clinical samples (Glanz et al., 1995). However, perhaps a more fundamental question remains over the conceptual status of Beck’s triad hypothesis, as there exists considerable overlap between the hypothesized mutually exclusive categories (Haaga, Dyck, & Ernst, 1991). This has prompted a number of researchers over the last 20 years to examine the factor structure of the BHS. Within clinical populations, it has been proposed that a threefactor structure is the most appropriate (Rosenfeld, Gibson, Kramer, & Breitbart, 2004), but there has been suggestion this may be largely based on item phrasing and even within these studies, the large proportion of variance accounted for by the first factor could be seen as an argument for an uni-dimensional construct (Steed, 2001). In several studies multi-factor models provided marginally better statistical fit but additional factors explained little variance (Mystakidou et al., 2008; Young et al., 1992) and had high correlations between the factors (Chang et al., 1994; Nissim et al., 2009) or only reported acceptable fit indices (Rosenfeld, Gibson, Kramer, and Breitbart, 2004; Steed, 2001). A consensus on factor structure may have been further hampered by the variety of analysis and extraction methods employed, the use of different response formats, translations of the scale, differences between populations and, in some studies, insufficient sample size. Aish and Wasserman (2001) studied one, two and three-factor models in 324 Swedish patients who had attempted suicide. They found that a one-factor model fitted the best, and examined a number of models using various items from the BHS. Their study showed that the number of items could be considerably reduced, and that a four-item scale showed an excellent fit. It hasbeen suggested that thestructure of the BHSmay be different for clinical and non-clinical sample (Dyce, 1996; Pompili & Tatarelli, 2007) and a simpler structure may exist in non-clinical populations, where hopelessness is not as well established (Tanaka, Sakamoto, Ono, Fujihara, & Kitamura, 1998). There have been several studies examining the factor structure of the BHS in a non-clinical population. Tanaka et al. (1998) reported two-factor solutions after conducting exploratory analyses on 508 community residents in a Japanese city. Their two factors were labelled ‘doubt about a hopeful future’ and ‘belief about a hopeless future’. Marshall, Wortman,Kusulas,Hervig,and Vickers (1992) conducted exploratory factor analysis and confirmatory factor analysis in two samples (n = 346, n = 543) of male navy recruits. They reported that a twofactor structure, measuring optimism and pessimism, had good fit. Chang et al. (1994) conducted both exploratory and confirmatory factor analysis on the data from 389 US undergraduate students. They reported both a one and two-factor structure fitted the data well but concluded that the one-factor was more appropriate due to the large correlation (r = .93) between the two latent factors. Steed (2001) initially conducted an exploratory factor analysis of the results of 544 undergraduate students reporting a four-factor

fit but after specifying several models withconfirmatory factor analysis concluded a modified two-factor was the most appropriate model; although the fit statistics only indicated a ‘reasonable fit’ when four items wereremoved(4, 5,8 & 13) toimprovefit. Ina study of 340 Italian students, Pompili and Tatarelli (2007) reported that confirmatory factor analysis did not support Beck’s original threefactor structure but a subsequent exploratory factor analysis suggested a six-factor model which was subsequently reduced to a two-factor model due to insufficient items loading on factors. This model was not subjected to confirmatory analysis. The results from these studies are far from conclusive however. For example, Chang et al. (1994), Marshall et al. (1992), Tanaka et al. (1998) and Pompili and Tatarelli (2007) all reported a twofactor structure of the BHS and interpreted this as relating to pessimism and optimism in non-clinical samples. However, the items used in each factor used in these studies bear little resemblance to each other (three or four common items per factor). This may be due to the differences in analysis, the response scale used or simply translation issues. It should be noted that in both Marshall et al.’s (1992) study and Steed’s (2001) study, the analysis was based on replacing the normal dichotomous scoring of the BHS with a 5point Likert scale. The modification of the response format in these studies may mean the results are unsuitable to be compared to findings from studies utilizing the original BHS. Marshall et al. (1992) utilised orthogonal rotation in their exploratory analyses, implying that each of the subscales should not be related. It is difficult to justify the rationale of this technique, as each of those subscales contained items that were theoretically similar. Chang et al. (1994) and Pompili and Tatarelli (2007) employed an exploratory analysis which assessed the variance in the items in a scale; interpretations are post-hoc and subjective. Furthermore, unlike alternative methods, the Principal Components Analysis (PCA) utilised in the Chang et al. (1994) study, does not attempt to eliminate error variance from the factor matrix and may be less generalisable (Kline, 1998). Additionally, the total number of responses used for the PCA was less than the recommended minimum of 200 (Kline, 1986, 2000). There have been calls for more research to confirm the construct of hopelessness (Glanz et al., 1995) especially in non-clinical populations (Steed, 2001; Tanaka et al., 1998). In fact, the current lack of clarity prompted Rosenfeld and colleagues to state: ‘‘The factor structure and utility of the BHSin non-clinicalpopulationsis simply unknown’’ (Rosenfeld, Gibson, Kramer, and Breitbart, 2004 pp. 47). The aim of this study was to test the factor structure of the BHS for a non-clinical UK population. In addition to the commonly used onefactor and three-factor structures proposed by Beck and colleagues, a number of other published one-and multi-factor models were assessed. A secondary aim was to explore how depression (measured by Beck’s Depression Inventory) co-varied with best fitting factor hopeless model in a non-clinical sample. 2. Method

2.1. Participants

The sample consisted of 581 undergraduate students studying psychology or medicine at Queens University Belfast. The mean age of the participants was 19.21 (SD = 3.40) and most of the students were single (97.6%) and had no dependants (97.6%). 2.2. Measures 2.2.1. Bhs The BHS is a 20 item self-report inventory which reflects negative expectancies in the respondent (Beck & Steer, 1988).

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The response format is dichotomous, with rubric requiring the respondent to state whether each item is either true or false in describing their attitude over the past week, including on the day of assessment. To control for acquiescence, nine items are keyed false and 11 are keyed true. As mentioned in the introduction, the scale has demonstrated acceptable psychometric properties. 2.2.2. Bdi The BDI is a 21 item self-report questionnaire which measures depressive thoughts and physical symptoms of depression over the previous seven days. Each item is scored by severity (0, 1, 2, 3) giving a maximum score of 63 (Beck, Steer, Ball, & Ranieri, 1996). A score of 0–9 indicates the person is not depressed, 10– 18 indicates mild-moderate depression, 19–29 moderate-severe depression, and 30–63 severe depression. The Amended Beck Depression Inventory (BDI-IA) was used (Beck & Steer, 1988).

Table 2

Response rates and loadings for each individual item.

No. 6 7 9 15 a

2.4. Analysis

As the variables were dichotomous and the data demonstrated multivariate non-normality the BHS factor models were specified and estimated using Diagonally Weighted Least Squares (DWLS) confirmatory factor analysis (CFA) using LISREL 8.72 ( Joreskog & Sorbom, 2005); the weight matrix was the asymptotic covariance matrix of the estimated polychoric correlations (Aish & Joreskog, 1990). The adequacy of the model was assessed by a range of fit indices as suggested by Hoyle and Panter (1995). These were the likelihood ratio chi-square test, the Root Mean Square Error of Approximation (RMSEA: Steiger, 1990) and the Expected Cross Validation Index (ECVI) with 90% confidence intervals was reported to allow the assessment of comparative models (Browne & Cudeck, 1993). A non-significant chi-square test and a RMSEA less than .06 were considered evidence of desirable model fit ( Hu & Bentler, 1999). Smaller values of ECVI indicated the better fitting models (Browne & Cudeck, 1993). It should be noted where alternative models were suggested (for example, non-significantly loaded items may have been removed) in previous literature, all models were tested and the best fitting model reported. The correlations between the latent factors are reported for the current sample as only 4 of the 10 multi-factor models reported this information in their original paper. The relationship between the best fitting model and the BDI was also explored. 3. Results

The fit indices for the models specified are reported in Table 1. On the basis of meeting the criteria associated with RMSEA all models were judged to exhibit reasonable fit however only one model (Aish & Wasserman, 2001) demonstrated a non-significant chi-square statistic. (It should be noted that significant chi-square

No. of hopelessness responses given

In the future, I expect to succeed in what concerns me most My future seems dark to me I just can not get the breaks, and there’s no reason I will in the future I have great faith in the future

Item loadings

55 (9.5%)

.79a

40 (6.9%) 77 (13.2%)

.83a .54a

132 (22.7%)

.27a

Indicates significance at the .05 level.

Table 3

Score frequency rates and related BDI mean scores.

2.3. Procedure

Undergraduate medical (n = 350) and psychology ( n = 400) students in their first and second years were approached in their lectures and asked to participate in the study. Students were assured participation was anonymous and voluntary. Questionnaire packs consisting of the BHS, the BDI and demographic questions (asking for gender, age, marital status, number of dependants and religion) were administered in situ. No information that could be used to identify individuals was sought. In total, 592 questionnaires were returned (response rate = 78.9%), although 11 questionnaires were not completed fully and were not used in any further analysis. Ethical approval for this study was granted by the university Research Ethics Committee.

Item

a

Hopelessness score

Frequency of SBHS occurring

Mean BDI score (SD)

0 1 2 3 4

381 (65.8%) 127 (22%) 46 (8.2%) 12 (2.1%) 11 (1.9%)

4.07 (3.67) 6.79 (4.90)a 10.26 (6.70)a 17.83 (6.67)a 27.45 (18.82)a

Indicates a significant difference (at the .01 level) from the score above.

statistic should not necessarily lead to the rejection of models as large sample sizes increase the power of the test increasing the likelihood of type II errors.) The one-factor four-item Aish and Wasserman model was judged to exhibit the best fit by both fit indices (v2 = 0.75; df = 2; p = .69; RMSEA < .001;). The ECVI for this model was also the lowest. Using these four items, a Short Form Beck Hopelessness Scale (SBHS) was then used for further analysis. The Kuder–Richardson reliability co-efficient for the four-item scale was 0.57. Table 2 summarises that all item loadings of the four items in the SBHS were significant. Furthermore it can be seen that the endorsements were relatively low ranging from 7% to 22.7%. Item 7 (my future seems dark to me) was the least likely to be endorsed by the sample while item 15 (I have great faith in the future) was the most commonly endorsed. The SBHS demonstrated a moderate positive significant relationship with the BDI (q = .43; n = 577; p < .001); where increased hopelessness scores was related to increased level of depression. Furthermore, an ANOVA with the BDI as the dependent variable revealed a significant main effect of Short-Form BHS score (F(4572) = 91.20; p < .001). Post-hoc test revealed that each increasing score (from 0 to 4) resulted in significantly higher mean BDI scores (see Table 3 for mean scores). 4. Discussion

This study tested a series of alternative factor structures of the BHS, based on a sample of UK undergraduate students. The fouritem, one-factor model proposed by Aish and Wasserman (2001), was considered to be the best explanation of the data; this model demonstrated excellent fit and offered substantially better fit indices than the other models specified. In dealing with non-clinical samples with low levels of hopelessness it appears that the Short-Form BHS scale is appropriate. In fact, the brevity of the SBHS may be an advantage in decreasing completion time and increasing completion rates. The Short-Form BHS contains items from each of Beck’s hypothesized 3 factors; feelings about the future (items 6 & 15), loss of motivation (item 9) and future expectations (item 7) as well as both positively (items 7 & 9) and negatively scored (items 6 & 15). This enables the argument to be made that the four-item measure is not simply tapping into one of Beck’s triad constructs nor is the factor simply measuring positive or negative item phrasing. As

D. Hanna et al./ Personality and Individual Differences 51 (2011) 17–22

in the Aish & Wasserman study (2001) item 7, ‘my future seems dark to me’, has the highest factor loading. Unlike Aish & Wasserman however, the authors would not recommend that the use of this one item would be sufficient to measure hopelessness, as it may address a narrower bandwidth of the construct and may have higher associated measurement error compared to the four-item measure. The correlation between the SBHS and the BDI ( q = .43) was comparable to those correlations found between the BDI and the complete BHS (r = .41–.45) (Hill et al., 1988; Nekanda-Trepka et al., 1983), the BDI and a two-factor BHS model (r = .20–.33) (Nissim et al., 2009) and the BDI and a three factor BHS model ( r = .23–.30) (Steer et al., 1994). Increasing scores on the SBHS corresponded with higher scores on the BDI (Table 3). Using ANOVA and subsequent post-hoc tests, increasing scores on the SBHS were also related to significantly higher scores on the BDI. A SBHS score of 0– 1 likely indicates no depression, while a score of 2 may indicate a mild depressive illness. A score of 3–4 is likely to indicate a moderate-severe depressive illness. However, the small numbers of participants scoring 2 or more on the SBHS means that the relationship between the SBHS and BDI requires replication. The low internal consistency figure can mainly be attributed to the low number of items ( n = 4) present in the scale. Justification for this claim can be demonstrated by using the Spearman–Brown formula to estimate the reliability if the scale had more items. If this scale had the same number of items present as the original scale, that is 20, the reliability co-efficient would be approximately 0.87. Additional items were not included simply to inflate the alpha, as Burisch (1997) argues that short scales can be as valid as longer scales for non-clinical use. Having very high alpha values may even lead to decreased validity ( Kline, 1998). One of the limitations of this study is that although this was a non-clinical sample, it consisted entirely of young university students. This restriction of range prevents generalisation to the normal population as a whole; further work from a broader range of backgrounds is required to confirm the findings of this study. For example, Greene (1981) reported mean of BHS score of 4.45 from an Irish community sample but a mean of 3.36 in subjects under 24 years old, suggesting age may be an important covariate of hopelessness. 5. Conclusion

This study in a non-clinical UK population shows that the BHS best measures a one-factor model of hopelessness. However, two- and three-factor models also showed reasonable fit. This study has also shown that a shortened four-item inventory based on this one factor, using negatively and positively scored items from all three of Beck’s original three-factor model, showed significant correlation with BDI scores. Increasing scores on this SBHS correlated with increasing scores on the BDI. It is possible that these four items could be used to measure hopelessness in situations where time is limited. However, in this study, the numbers of people scoring highly on the SBHS and BDI were small, but the relationship is strong enough to warrant further study. References

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The Structure of the Beck Hopelessness Scale a Con

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