Spanish Journal of Psychology (2013), 16, e72, 1–8. © Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid doi:10.1017/sjp.2013.62
Zung Scale Factor Invariance in Male Coronary Patients and Healthy People Antonio del Pino Pérez1, Ignacio Ibáñez Fernández1, Francisco Bosa Ojeda2, Ruth Dorta González1 and María Teresa Gaos Meizoso1 1 2
Universidad de La Laguna (Spain) Hospital Universitario de Canarias (Spain)
Abstract. The objective of this study was, firstly, to determine the factor structure and factor invariance of the Zung Self-Rating Depression Scale (ZSDS) and, secondly, to justify its use in coronary patients (CPs) and healthy people (HP). Two comparable samples of males were studied: 217 CPs and 191 HP. Exploratory and confirmatory factor analyses (EFA and CFA) for ordinal data were carried out with Mplus. Two models obtained from all participants in this study and another two, the model of Shafer (2006) from a meta-analysis and that of Barefoot et al. (2000) with CPs, were analyzed in CFA. A two-factor structure was supported by EFA in both samples, but none of the models showed adequate goodness-of-fit for the CPs and the HP in CFA. Only the two and three-factor models obtained from the combined sample of CPs and HP showed adequate goodness-of-fit for HP. The ZSDS showed good reliability, replicated the prevalence of depressive symptoms found in other studies and was able to distinguish between CPs and HP. We conclude that the best fit is obtained from the two-factor solution in HP, that the factor structure of the ZSDS is not invariant and is linked to positively and negatively worded items. Received 8 January 2012; Revised 29 April 2012; Accepted 24 July 2012 Keywords: Zung scale, coronary patients, depression, factor invariance.
The importance of depression following a cardiac event in the evolution of cardiac disease has been demonstrated in different meta-analyses (Melle et al., 2004; Nicholson, Kuper, & Hemingway, 2006). However, assessment of depression in heart patients faces serious problems, inter alia the difficulty of distinguishing symptoms of cardiac disease from depressive symptoms. These problems are reflected in prevalence rates of depression in cardiac patients (CPs). The metaanalysis by Melle et al. (2004) showed that prevalence rates of major depression range from 8% to 47% and from 5% to 47% for major depressive disorder in CPs. These differences in prevalence rates may also be attributable to methodological differences in evaluating depression (e.g., questionnaires or diagnostic interviews) and the variety of instruments used (Rutledge, Reis, Linke, Greenberg, & Mills, 2006). Frasure-Smith and Lespérance (2005), for example, report 23 different instruments used in the studies they reviewed. This lack of consensus motivated the National Heart, Lung, and Blood Institute (USA) to convene an interdisciplinary working group to address the issues relating to reliable assessment of depression in patients with cardiovascular Correspondence concerning this article should be addressed to Antonio del Pino Pérez. Departamento de Personalidad, Evaluación y Tratamientos Psicológicos. Facultad de Psicología. Universidad de La Laguna. (Spain). La Laguna. 38071. Santa Cruz de Tenerife. Phone:+34-922317479. Fax: +34-922317474. E-mail: [email protected]
disease, resulting in a recommendation to investigate whether depression-measuring instruments used in the general population are applicable to these patients (Davidson et al., 2006). The present study is a response to this recommendation and focuses on the study of depression measured with the Zung Self-rating Depression Scale (ZSDS) (Zung, 1965) in adult male CPs and healthy people (HP). The ZSDS was used because: (a) it is a widely used instrument (Koivula, Halme, & Astedt-Kurki, 2010); (b) it is one of the least complicated self-administered measures of depression and, probably, the easiest to understand (Shumway, Sentell, Unick, & Bamberg, 2004); (c) it independently predicts clinical outcome, especially: 1) death or new hospitalization in hospitalized chronic heart failure patients (Parissis et al., 2008); 2) the relationship between depression after a cardiac event and survival in the elderly (Kamphuis et al., 2009); 3) the risk of death from coronary heart disease or myocardial infarction in subjects free of coronary heart disease at baseline (Ahto, Raimo, Puolijokis, Vahlberg, & Kivela, 2007). We only included men in the study to eliminate a source of variation in the results, because: (a) men experience depression less often than women (Gottlieb et al., 2004); (b) the factor structure of depression is different in the two genders (Steer, Beck, & Brown, 1989). Depression consists of various groups of symptoms, but the most widely used instruments in clinical settings
2 A. del Pino Pérez et al. have generally failed to justify the clusters of symptoms experienced by individuals and instead have typically offered only global indices of depression. However, excessive dependence on total scores is undesirable, because various profiles of heterogeneous symptoms can be contained in the single dimension of severity. If only total scores are used, it is not possible to identify the different types of depressed people, with prognostic and therapeutic implications. It is not easy, however, to identify common clusters across miscellaneous populations evaluated with different instruments, and especially in CPs, because: (a) the profiles of depressive symptoms differ in various populations (Steer at al., 1989); (b) the different instruments may be measuring different components of the construct depression; (c) none of the traditional instruments of assessment of depression was specifically designed as an assessment tool for depression in heart patients. All these problems are evident when reviewing the results obtained with the ZSDS. Most studies involve healthy or non-sick people, with students and the elderly predominating. The structures range from two to four factors. Kitamura, Hirano, Chen, and Hirata (2004) gathered different factorial solutions published until that date. Later, Shafer (2006) published a meta-analysis of instruments for measuring depression and in the case of the ZSDS opted for a three-factor solution. Construct validity of the ZSDS in CPs using factor analysis has, to our knowledge, only been presented in the study by Barefoot et al. (2000). They analyzed the results of a sample of CPs (1031 men and 219 women) between the ages of 46 and 58 years and for them four factors were the best solution. The internal consistency of the ZSDS with CPs is not reported in any studies. Zung (1973), using a split-half correlation for the even-odd ZSDS items, obtained a correlation coefficient of .73 and Koivula et al. (2010) a Cronbach’s alpha value of .82 with the Finnish version of the ZSDS. This study aims, as a first objective, to contribute to the process of ZSDS validation, analyzing the structure of depressive symptoms measured by the ZSDS in a sample of CPs and another of HP and whether dimensional structure is invariant across health status and, as a second objective, to justify its use for screening purposes with CPs. Method Participants Two groups of men, 217 CPs and 191 HP. The CPs correspond to codes I20–I25, ischemic heart disease, according to the International Classification of Diseases-10. Healthy participants were men in whom company
physicians or family physicians found no cardiac problems or other physical or mental illness. Design and Procedure This study is descriptive and cross-sectional. Study protocols conformed to the ethical guidelines of the 1975 Declaration of Helsinki and were approved by the Ethics Committee of the University Hospital of the Canary Islands, Spain, where the study was carried out. All participants gave informed, written consent. Data were collected by direct interview. All patients admitted to the Cardiology Department during two calendar years were invited to participate in a study about psychological risks factors for heart disease. Patients were eligible for the study unless they were judged by the clinician to be too debilitated to complete the research protocol (n = 17), or could not understand the informed consent form (n = 36). Twenty four patients refused participation. Although the interval between hospital admission and the interview ranged from 3 to 24 days, more than 85% of the CPs was interviewed within 4 days of admission. The sample of 191 healthy men completed the protocol on attending their annual medical examination in their respective companies (89%) or during an outpatient medical consultation (11%). Forty one HP refused participation. We selected HP aged between 31–66 years with socio-demographic characteristics as similar as possible to those of the CPs. However, CPs had a significantly lower level of education (25.8% secondary school or higher education), and a higher percentage of CPs were retired (24.9%), compared to the HP (61.3% and 5.2%, respectively, in these socio-demographic categories). Occupational status (manual workers 52.5% of CPS versus 51.8% of HP) was similar between samples, as was marital status (living with partner 92.2% versus 93.7%). The age range of the two samples was the same, 31–66 years, but mean age was significantly higher in CPs, 53.41 (SD = 7.7) versus 48.63 years (SD = 8.5) in HP, t(406) = 5.96, p < .001. Data Collection Instruments We used a socio-demographic questionnaire, medical history and the ZSDS among other instruments to assess psychological risk factors for coronary disease. The ZSDS contains 20 items covering affective, cognitive, behavioral, psychological and physiological symptoms of depression. Item responses are ranked from 1 to 4, with high scores corresponding to more frequent or more lasting symptoms. Half of the items are worded negatively and half positively. Total scores on the ZSDS do not correspond with a clinical diagnosis of depression but rather indicate the level of depressive symptoms that may be of clinical relevance. As originally described
Zung Scale Factor Invariance 3 by Zung, Richards, and Short (1965), raw scores are summed and then transformed into a ZSDS Index, by dividing the raw score by the maximum score and multiplying by 100 (ZSDS Index = rs/80 x 100). ZSDS Index scores < 50 is normal, i.e. no depression. The mean of the ZSDS Index in CPs is around 48 (Koivula et al., 2010) and the prevalence of depressive symptoms is close to 37% (Barefoot et al., 2000; Welin, Lappas, & Wilhelmsen, 2000). In this study we used the second Spanish version of the ZSDS by Conde and Esteban (1974). The coefficient of reliability for this version found by Conde and Esteban (1975) was .80 using a split-half correlation for the even-odd ZSDS items. Aragonés, Masdéu, Cando, and Coll (2001) with this same version of ZSDS obtained a Cronbach’s alpha of .78 in primary health-care patients. Statistical analysis Exploratory and confirmatory factor analyses (EFA, CFA) were conducted with Mplus 3 using weighted least-squares with mean and variance (WLSMV) adjustment which is optimal choice for categorical outcomes. Mixed linear model (MLM) for non normal data was used when results with WLSMV do not converge. The sample size was adjusted to the recommendations for factor analysis of Guilford (1954), at least 200 cases, and to those of Thorndike (1982), at least 10 subjects per item. The promax rotation algorithm and various criteria for the number of factors, such as Kaiser’s Criterion (eigen values >1.0), the scree test, the Minimum Average Partial (MAP) test and the interpretability of resulting factor structures were used. The primary criterion for item inclusion was set at least at .30 (absolute value). The confirmatory factor model fit was evaluated with the Tucker-Lewis Index (TLI), the Comparative Fit Index (CFI), and the Root Mean Square Error of Approximation (RMSEA). The TLI and CFI are measures that compare a baseline model in which no relationships are estimated between the variables with a theoretical model in which hypothesized paths are estimated. This index ranges from 0 to 1, where .9 indicates adequate fit, and .8 is considered marginal fit. The RMSEA with fit values less than .05 indicate close fit, and values less than .08 reasonable fit. The RMSEA is sensitive to over-fit, that is, it begins to increase when too many paths have been included (Rigdon, 1996). These cut-offs apply to models with continuous outcomes, although Yu and Muthén (2001) report that they are reasonable for models with categorical outcomes as well. Results The factor structure of the ZSDS was analyzed in two ways: separately and jointly in CPs and HP. The criteria
used to choose a factorial solution suggested two or three factors. In the two-factor solution, the same structure of the items appeared in the three groups except item 19 which changed factor in CPs, as depicted in Table 1. In the sample of CPs, items 11, 12 and 18 loaded ≥ .30 on both factors and items 2, 7 and 8 did not reach this factor loading on either of them. In the HP sample, no item loaded on two factors ≥ .30 and only item 2 did not load ≥ .30 on a factor. In the total sample, all the items loaded ≥ .30 on a factor and only item 12, Psychomotor retardation, loaded on two factors. Items 1, Depressed affect, 3, Crying spells, 4, Sleep Disturbances, and 13, Psychomotor agitation, loaded ≥ .50 on factor 1 in all samples. Based on items 1 and 3, which Zung (1965) considered to correspond to pervasive depressive affect, this factor can be interpreted as reflecting Negative affect. The items highest loadings on factor 2 show a predominantly cognitive content of depression and the factor can be called Cognitive symptoms. The congruence between factors of the CPs and HP was .86 for Negative affect and .95 for Cognitive symptoms. These values, on surpassing the critical point of .75, allow us to affirm that the factors are similar (Cliff, 1966). MacCallum, Widaman, Zhang, and Hong (1999) consider these indices respectively borderline and good. Internal consistency of the factors was low (α between .68 and .79) or acceptable, Cronbach’s alpha for ordinal data between .73 and .88 and omega between 75 and 88. The internal consistency of the total scale was acceptable (between .77 and .80 for traditional α) or high (between .83 and .88 for ordinal data and between .85 and .89 for ω). The rotated factor solution with three factors is shown in Table 2. Factors 1, Negative affect, and 2, Cognitive symptoms, remained and a third factor, Somatic symptoms, appeared with items 12 and 11 showing the highest load. This order of factors did not prevail in the factor structure of HP, in which Somatic symptoms relegated Cognitive symptoms to a third factor. In the total sample factor 1 contained practically all the items of factor 1 in HP. Factors 2 and 3 of the total sample share, nevertheless, more items with the same factors of the sample of CPs. The coefficient of congruence between the factor Negative affect in CPs and that of the HP was .76; for factor Cognitive symptoms of the same samples the coefficient of congruence was .72, and for factor Somatic symptoms it was .87. The value .72, on not surpassing the critical point of .75, did not allow us to affirm that the factors are similar (Cliff, 1966), but, as it did exceed .30, congruence between them cannot be ruled out according to MacCallum et al. (1999). Internal consistency of these factors is slightly lower than that of the two-factors solution except factor 1, Negative Affect, in the sample
4 A. del Pino Pérez et al. Table 1. Two-factor structure of the ZSDS in all samples CPs Samples Symptoms 01.Depressed affect 02. Diurnal variation 03. Crying spells 04. Sleep disturbance 05. Decreased appetite 06. Decreased libido 07. Weight loss 08. Constipation 09. Tachycardia 10. Fatigue 11. Confusion 12. Psychomotor retardation 13. Psychomotor agitation 14. Hopelessness 15. Irritability 16. Indecisiveness 17. Personal Devaluation 18. Emptiness 19. Suicidal Rumination 20. Dissatisfaction Eigenvalue rotated Explained variance ZSDS Cronbach’s Alpha Total ZSDS Cronbach’s ordinal Alpha Total ZSDS Omega Omega Total ZSDS
F1
HP F2
F1
.71
.70
.61 .51
.66 .63 −.45 −.51
.31 .33 −.31 −.30 .56
−.48 −.54
−.75 −.63
.72 .80 .81
−.48 −.49
−.30 .64
−.60 .66
2.99
−.62 −.57 −.64
.58 −.54 −.58 −.68
.74
−.51 −.60 −.70 .30
−.65 4.5 43% .79 .80 .88 .88 .88 .89
F2
.38 .38 .54 .53
.67
−.52 −.63 −.61 .35 −.57
F1
.72 .34 .69 .59
.45 .41 .64 .50
.46
2.38 34% .68 .77 .73 .83 .75 .84
F2
−.55 −.53
−.58
−.30
All Participants
3.5 .77 .83 .85
−.63 3.4 39% .76 .79 .83 .85 .83 .85
3.23 .72 .79 .81
Note: CPs: coronary patients; HP: Healthy people; All samples: F1 = Negative affect; F2 = Cognitive symptoms.
of CPs, Cronbach’s alpha for ordinal data 74 versus 73, and in the HP sample, omega 90 versus 88. The fit of the results of EFA to four models was analyzed by CFA with all participants. We analyzed a model of two factors obtained from our total sample; two models of three factors, one obtained from our total sample and the other from the meta-analysis by Shafer (2006); and a model of four factors obtained from the study of Barefoot et al. (2000) in a sample of CPs. The results of these analyses are shown in Table 3. Only the HP sample showed adequate or reasonable goodness-of-fit indices with the models of two and three factors specified from our total sample. The CPs showed marginal goodness-of-fit indices for these models. The models of Shafer (2006) and Barefoot et al. (2000) had convergence problems, a high number of non-significant parameters and inadmissible solutions, e.g., matrices of negative covariance between the factors.
These problems, specially presented in table 3, persisted even though we applied the maximum likelihood method (MLM) (associated to the statistical SatorraBentler χ2 used frequently for ordinal data) instead of WLSMV. Given the problems of fit or of models, the process of testing for factorial invariance did not seem justified. The descriptive statistics of reporting symptoms and comparison of mean values between the two samples are shown in Table 4. The two groups showed significant differences in eleven of the twenty items that compose the ZSDS. In all of them, except in item 2, Diurnal variation, the CPs scored higher than the HP. Mean ZSDS score in CPs was significantly higher, as was the prevalence of CPs above the cut-off point (ZSDS Index ≥ 50) suggested by Zung (1965) to indicate clinically significant depression, 37.85% versus 20.95% in HP.
Zung Scale Factor Invariance 5 Table 2. Three-factor structure of the ZSDS in all samples CPs Samples Symptoms 01.Depressed affect 02. Diurnal variation 03. Crying spells 04. Sleep disturbance 05. Decreased appetite 06. Decreased libido 07. Weight loss 08. Constipation 09. Tachycardia 10. Fatigue 11. Confusion 12. Psychomotor retardation 13. Psychomotor agitation 14. Hopelessness 15. Irritability 16. Indecisiveness 17. Personal Devaluation 18. Emptiness 19. Suicidal Rumination 20. Dissatisfaction Eigenvalue rotated Explained variance ZSDS Cronbach’s Alpha Cronbach’s ordinal Alpha Omega
F1
HP F2
F3
F1
.74
.70
.62 .46
.60 .64 −.37 −.38 −.37
All Participants F2
.47 .42 .59 .42
.34 .40 −.63 −.80 .57
.68
−.65 .58
−.45 −.57 −.69
−.47 −.65 −.82 .78
.32
−.62 2.15 51% .68 .74 .75
−.50
2.38
1.79
.70 .75 .75
.56 .66 .68
.36 −.70 −.90
.64 −.66
.44
F3
−.36 −.42 .36 .38 .52 .49
.61
.39
F2
−.32
.48 −.71 −.77
−.67
−.38
F1
.72 .33 .68 .58 −.35 −.40
.35
F3
3.90 42% .79 .88 .90
1.96
2.48
.66 .72 .75
.72 .78 .79
−.40 −.65 −.92 .32 −.40
−.30
2.30
1.93
.68 .78 .80
.64 .73 .74
3.25 46% .75 .82 .83
Note: CPs: coronary patients; HP: Healthy people; CPs: F1 = Negative affect; F2 = Cognitive symptoms; F3 = Somatic symptoms. HP: F1 = Negative affect ; F2 = Somatic symptoms; F3 = Cognitive symptoms. All Participants: F1 = Negative affect; F2 = Cognitive symptoms; F3 = Somatic symptoms.
Table 3. Confirmatory factor analyses of four factor models of the ZSDS Model
Sample
CFI
TLI
RMSEA
OBSERVATIONS
TWO FACTORS (All participants)
CPs HP ALL CPs HP ALL CPs HP ALL CPs HP ALL
.792 .887 .846 .835 .911 .882
.826 .906 .880 .860 .925 .908
.080 .076 .067 .079 .068 .086
Items 7, 9 and 12 ns.
THREE FACTORS (All participants)
THREE FACTORS (Shafer, 2006)
FOUR FACTORS (Barefoot et al, 2000)
Cut-off point criteria for fit indexes
.760 .719
≥.90
.727 .675
.063 .066
MLM, F1 and F3 ns MLM phi negative. F1 and F3 ns MLM, F1 and F3 ns. MLM, F1 and F4 ns No converging MLM phi negative. F1 ns
between .08–.05
Note: ns: not significant. MLM: mixed linear model. MLM for non normal data is used when results with weighted least-squares with mean and variance (WLSMV) do not converge. The indicative values of a reasonable goodness-of-fit are bolded.
6 A. del Pino Pérez et al. Table 4. Descriptive statistics of reporting symptoms and comparison of mean values between coronary patients (CPs) and healthy people (HP) CPs
HP
Samples
Mean
SD
Mean
SD
F(1 ,406)
Symptoms 01.Depressed affect 02. Diurnal variation 03. Crying spells 04. Sleep disturbance 05. Decreased appetite 06. Decreased libido 07. Weight loss 08. Constipation 09. Tachycardia 10. Fatigue 11. Confusion 12. Psychomotor retardation 13. Psychomotor agitation 14. Hopelessness 15. Irritability 16. Indecisiveness 17. Personal Devaluation 18. Emptiness 19. Suicidal Rumination 20. Dissatisfaction Total ZSDS(1)
2.00 2.52 1.76 2.08 1.94 1.66 1.83 1.62 1.56 2.03 2.06 2.44 2.35 1.80 1.84 2.50 1.73 1.42 1.13 1.44 47.15
.91 1.22 .87 1.18 1.16 .95 1.08 .96 .82 1.15 1.18 1.18 1.10 1.00 1.01 1.20 .92 .75 .50 .81 9.95
1.48 3.08 1.25 1.58 2.10 1.66 1.33 1.28 1.30 1.40 2.05 2.19 1.75 1.93 1.43 2.30 1.86 1.57 1.13 1.49 42.71
.70 1.09 .59 .91 1.16 .91 .74 .66 .62 .75 1.10 1.06 .94 1.04 .72 .99 .90 .85 .47 .80 9.11
41.09+ 24.12+ 48.61+ 22.35+ 1.80 .01 29.01+ 16.51+ 13.20+ 43.33+ .01 4.93* 33.86+ 1.65 22.13+ 3.14 1.95 3.39 .01 .39 21.92+
Note: Mean values in total ZSDS according to ZSDS Index. * p < .05; + p < .001
Discussion The EFAs supported a two-factor solution in both samples and the congruence between the factors Negative affect and Cognitive symptoms suggested possible invariance. In the three-factor solution the factor Somatic symptoms showed little consistency; strikingly, however, it obtained a higher coefficient in factorial congruence analysis than the other two. The generalized problems of goodness-of-fit to the models proposed in CFAs indicate that the models of Barefoot et al. (2000) and Shafer (2006) did not seem suitable for our data, possibly due to cultural and linguistic attributes of the samples. The models elaborated from the data of our participants, only showed reasonable goodness-of-fit in the HP sample, which ruled out evaluation of factorial invariance. The different goodness-of-fit of the factor structure in the two samples highlights the fact that depressive symptoms tend to be structured according to the samples (Costello, 1992). Only the HP sample presented adequate goodness-of-fit, suggesting that the ZSDS assesses slightly different aspects of depressive symptoms in CPs and HP. Total ZSDS showed good reliability (ordinal α, and ω) in the three samples analyzed (CPs, HP and CPs+HP). This result is higher and consistent with the reliability
obtained with the same Spanish version of the ZSDS (Aragonés et al., 2001; Conde & Esteban, 1975) and with the internal consistency obtained in other countries and languages (Koivula et al., 2010; Zung, 1973). We found that in the immediate period after a coronary event, CPs presented significantly more depressive symptoms than HP, and that the prevalence of CPs with depressive symptoms was significantly higher than in HP. These results are consistent with the data available and the prevalence of depressive symptoms is practically identical to that found by Barefoot et al. (2000) and Welin et al. (2000) in CPs. These results also support the validity and usefulness of the Spanish version of ZSDS for CPs. Our conclusions would, however, have been reinforced if the socio-demographic characteristics of the HP had better matched those of the CPS. The significant differences in depressive symptoms occurred in nine positively-worded items that load on the factor Negative affect and only on one negativelyworded item, which raises serious doubts about whether the items with reversed scoring are comprehensible. In this way the differences between groups would not only appear in positively-worded items, and the items would not be grouped in factor analyses according to direct or reverse evaluation. It also raises the question
Zung Scale Factor Invariance 7 about whether the items need reformulation to avoid bias related to direct or reverse evaluation. The ZSDS item content corresponds to the construct depression, although to update it, this content would need an adjustment to the American Psychiatric Association’s new criteria of depression. Our study supports the existence of two factors in both CPs and HP, although only the latter group showed adequate goodness-of-fit with a two-factor model. The three-factor structure highlighted important differences between the two groups, and only showed acceptable goodness-of-fit in HP. Our results thus favor a bi-factorial solution and do not allow us to affirm the existence of structure invariance. This implies the need for specific information and research on the depressive dimensions of CPs, because depression in CPs can be different from that suffered by HP. The total score provides reliable and consistent results regarding degree and prevalence of depressive symptoms in CPs and HP in different countries; its use is therefore justified as a measure of degree of depression for screening purposes in CPs and HP, although the diagnosis of depression should be validated with data obtained from an external criterion as it is a structured clinical interview administered by well-trained individuals. The results of this study suggest that we should continue to use ZSDS total score as opposed to factor scores, and that CPs may have depressive symptoms associated or not associated with coronary disease, which require further investigation. References Ahto M., Raimo I., Puolijokis H., Vahlberg T., & Kivela S. L. (2007). Stronger symptoms of depression predict high coronary heart disease mortality in older men and women. International Journal of Geriatric Psychiatry, 22, 757–763. http://dx.doi.org/10.1002/gps.1735 Aragonés E., Masdéu R., Cando G., & Coll G. (2001). Validez diagnóstica de la Self-Rating Depression Scale de Zung en pacientes de atención primaria [Diagnostic validity of the Zung Self-Rating Depression Scale in primary care patients]. Actas Españolas de Psiquiatría, 29, 310–316. Barefoot J., Brummett B., Helms M., Mark D., Siegler I., & Williams R. (2000). Depressive symptoms and survival of patients with coronary artery disease. Psychosomatic Medicine, 62, 790–795. Cliff N. (1966). Orthogonal rotation to congruence. Psychometrika, 31, 33–42. http://dx.doi.org/10.1007/ BF02289455 Conde V., & Esteban T. (1974). Contribución al estudio de la S.D.S. (Self-Rating Depression Scale) de Zung, en una muestra estratificada de población normal [Contribution to the study of the Zung Self-Rating Depression Scale in a
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8 A. del Pino Pérez et al. Equation Modeling, 3, 369–379. http://dx.doi. org/10.1080/10705519609540052 Romera I., Delgado-Cohen H., Pérez T., Caballero L., & Gilaberte I. (2008). Factor analysis of the Zung self-rating depression scale in a large sample of patients with major depressive disorder in primary care. Biomedicalcentral Psychiatry, 8, 4. http://dx.doi.org/10.1186/1471-244X-8-4 Rutledge Th., Reis V., Linke S., Greenberg B., & Mills P. (2006). Depression in heart failure. A meta-analytic review of prevalence, intervention effects, and associations with clinical outcomes. Journal of the American College of Cardiology, 48, 1527–1537. http://dx.doi.org/10.1016/j. jacc.2006.06.055 Shafer A. (2006). Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. Journal of Clinical Psychology, 62, 123–146. http://dx.doi.org/10.1002/jclp.20213 Shumway M., Sentell T., Unick G., & Bamberg W. (2004). Cognitive complexity of self-administered depression measures. Journal of Affective Disorders, 83, 191–198. http:// dx.doi.org/10.1016/j.jad.2004.08.007 Steer R. A., Beck A. T., & Brown G. (1989). Sex differences on the revised beck depression inventory for outpatients with
affective disorders. Journal of Personality Assessment, 53, 693–702. http://dx.doi.org/10.1207/s15327752jpa5304_6 Thorndike R. L. (1982). Applied Psychometrics. Boston, MA: Houghton-Mifflin Welin C., Lappas G., & Wilhelmsen L. (2000). Independent importance of psychosocial factors for prognosis after myocardial infarction. Journal of Internal Medicine, 247, 629–639. http://dx.doi.org/10.1046/j.1365-2796.2000.00694.x Yu C. Y., & Muthén B. (2001). Evaluation of model fit indices for latent variable models with categorical and continuous outcomes. (Unpublished technical report). Los Angeles, CA: University of California, Zung W. W. K. (1965). A Self-Rating Depression Scale. Archives of General Psychiatry, 12, 63–70. http://dx.doi. org/10.1001/archpsyc.1965.01720310065008 Zung W. W. K. (1973). From art to science: The diagnosis and treatment of depression. Archives of General Psychiatry, 29, 328–337. http://dx.doi.org/10.1001/archpsyc. 1973.04200030026004 Zung W. W. K., Richards C. B., & Short M. J. (1965). Self-rating depression scale in an outpatient clinic. Further validation of the SDS. Archives of General Psychiatry, 13, 508–515. http:// dx.doi.org/10.1001/archpsyc.1965.01730060026004
Zung Scale Factor Invariance in Male Coronary Patients and Healthy People Antonio del Pino Pérez1, Ignacio Ibáñez Fernández1, Francisco Bosa Ojeda2, Ruth Dorta González1 and María Teresa Gaos Meizoso1 1 2
Universidad de La Laguna (Spain) Hospital Universitario de Canarias (Spain)
Abstract. The objective of this study was, firstly, to determine the factor structure and factor invariance of the Zung Self-Rating Depression Scale (ZSDS) and, secondly, to justify its use in coronary patients (CPs) and healthy people (HP). Two comparable samples of males were studied: 217 CPs and 191 HP. Exploratory and confirmatory factor analyses (EFA and CFA) for ordinal data were carried out with Mplus. Two models obtained from all participants in this study and another two, the model of Shafer (2006) from a meta-analysis and that of Barefoot et al. (2000) with CPs, were analyzed in CFA. A two-factor structure was supported by EFA in both samples, but none of the models showed adequate goodness-of-fit for the CPs and the HP in CFA. Only the two and three-factor models obtained from the combined sample of CPs and HP showed adequate goodness-of-fit for HP. The ZSDS showed good reliability, replicated the prevalence of depressive symptoms found in other studies and was able to distinguish between CPs and HP. We conclude that the best fit is obtained from the two-factor solution in HP, that the factor structure of the ZSDS is not invariant and is linked to positively and negatively worded items. Received 8 January 2012; Revised 29 April 2012; Accepted 24 July 2012 Keywords: Zung scale, coronary patients, depression, factor invariance.
The importance of depression following a cardiac event in the evolution of cardiac disease has been demonstrated in different meta-analyses (Melle et al., 2004; Nicholson, Kuper, & Hemingway, 2006). However, assessment of depression in heart patients faces serious problems, inter alia the difficulty of distinguishing symptoms of cardiac disease from depressive symptoms. These problems are reflected in prevalence rates of depression in cardiac patients (CPs). The metaanalysis by Melle et al. (2004) showed that prevalence rates of major depression range from 8% to 47% and from 5% to 47% for major depressive disorder in CPs. These differences in prevalence rates may also be attributable to methodological differences in evaluating depression (e.g., questionnaires or diagnostic interviews) and the variety of instruments used (Rutledge, Reis, Linke, Greenberg, & Mills, 2006). Frasure-Smith and Lespérance (2005), for example, report 23 different instruments used in the studies they reviewed. This lack of consensus motivated the National Heart, Lung, and Blood Institute (USA) to convene an interdisciplinary working group to address the issues relating to reliable assessment of depression in patients with cardiovascular Correspondence concerning this article should be addressed to Antonio del Pino Pérez. Departamento de Personalidad, Evaluación y Tratamientos Psicológicos. Facultad de Psicología. Universidad de La Laguna. (Spain). La Laguna. 38071. Santa Cruz de Tenerife. Phone:+34-922317479. Fax: +34-922317474. E-mail: [email protected]
disease, resulting in a recommendation to investigate whether depression-measuring instruments used in the general population are applicable to these patients (Davidson et al., 2006). The present study is a response to this recommendation and focuses on the study of depression measured with the Zung Self-rating Depression Scale (ZSDS) (Zung, 1965) in adult male CPs and healthy people (HP). The ZSDS was used because: (a) it is a widely used instrument (Koivula, Halme, & Astedt-Kurki, 2010); (b) it is one of the least complicated self-administered measures of depression and, probably, the easiest to understand (Shumway, Sentell, Unick, & Bamberg, 2004); (c) it independently predicts clinical outcome, especially: 1) death or new hospitalization in hospitalized chronic heart failure patients (Parissis et al., 2008); 2) the relationship between depression after a cardiac event and survival in the elderly (Kamphuis et al., 2009); 3) the risk of death from coronary heart disease or myocardial infarction in subjects free of coronary heart disease at baseline (Ahto, Raimo, Puolijokis, Vahlberg, & Kivela, 2007). We only included men in the study to eliminate a source of variation in the results, because: (a) men experience depression less often than women (Gottlieb et al., 2004); (b) the factor structure of depression is different in the two genders (Steer, Beck, & Brown, 1989). Depression consists of various groups of symptoms, but the most widely used instruments in clinical settings
2 A. del Pino Pérez et al. have generally failed to justify the clusters of symptoms experienced by individuals and instead have typically offered only global indices of depression. However, excessive dependence on total scores is undesirable, because various profiles of heterogeneous symptoms can be contained in the single dimension of severity. If only total scores are used, it is not possible to identify the different types of depressed people, with prognostic and therapeutic implications. It is not easy, however, to identify common clusters across miscellaneous populations evaluated with different instruments, and especially in CPs, because: (a) the profiles of depressive symptoms differ in various populations (Steer at al., 1989); (b) the different instruments may be measuring different components of the construct depression; (c) none of the traditional instruments of assessment of depression was specifically designed as an assessment tool for depression in heart patients. All these problems are evident when reviewing the results obtained with the ZSDS. Most studies involve healthy or non-sick people, with students and the elderly predominating. The structures range from two to four factors. Kitamura, Hirano, Chen, and Hirata (2004) gathered different factorial solutions published until that date. Later, Shafer (2006) published a meta-analysis of instruments for measuring depression and in the case of the ZSDS opted for a three-factor solution. Construct validity of the ZSDS in CPs using factor analysis has, to our knowledge, only been presented in the study by Barefoot et al. (2000). They analyzed the results of a sample of CPs (1031 men and 219 women) between the ages of 46 and 58 years and for them four factors were the best solution. The internal consistency of the ZSDS with CPs is not reported in any studies. Zung (1973), using a split-half correlation for the even-odd ZSDS items, obtained a correlation coefficient of .73 and Koivula et al. (2010) a Cronbach’s alpha value of .82 with the Finnish version of the ZSDS. This study aims, as a first objective, to contribute to the process of ZSDS validation, analyzing the structure of depressive symptoms measured by the ZSDS in a sample of CPs and another of HP and whether dimensional structure is invariant across health status and, as a second objective, to justify its use for screening purposes with CPs. Method Participants Two groups of men, 217 CPs and 191 HP. The CPs correspond to codes I20–I25, ischemic heart disease, according to the International Classification of Diseases-10. Healthy participants were men in whom company
physicians or family physicians found no cardiac problems or other physical or mental illness. Design and Procedure This study is descriptive and cross-sectional. Study protocols conformed to the ethical guidelines of the 1975 Declaration of Helsinki and were approved by the Ethics Committee of the University Hospital of the Canary Islands, Spain, where the study was carried out. All participants gave informed, written consent. Data were collected by direct interview. All patients admitted to the Cardiology Department during two calendar years were invited to participate in a study about psychological risks factors for heart disease. Patients were eligible for the study unless they were judged by the clinician to be too debilitated to complete the research protocol (n = 17), or could not understand the informed consent form (n = 36). Twenty four patients refused participation. Although the interval between hospital admission and the interview ranged from 3 to 24 days, more than 85% of the CPs was interviewed within 4 days of admission. The sample of 191 healthy men completed the protocol on attending their annual medical examination in their respective companies (89%) or during an outpatient medical consultation (11%). Forty one HP refused participation. We selected HP aged between 31–66 years with socio-demographic characteristics as similar as possible to those of the CPs. However, CPs had a significantly lower level of education (25.8% secondary school or higher education), and a higher percentage of CPs were retired (24.9%), compared to the HP (61.3% and 5.2%, respectively, in these socio-demographic categories). Occupational status (manual workers 52.5% of CPS versus 51.8% of HP) was similar between samples, as was marital status (living with partner 92.2% versus 93.7%). The age range of the two samples was the same, 31–66 years, but mean age was significantly higher in CPs, 53.41 (SD = 7.7) versus 48.63 years (SD = 8.5) in HP, t(406) = 5.96, p < .001. Data Collection Instruments We used a socio-demographic questionnaire, medical history and the ZSDS among other instruments to assess psychological risk factors for coronary disease. The ZSDS contains 20 items covering affective, cognitive, behavioral, psychological and physiological symptoms of depression. Item responses are ranked from 1 to 4, with high scores corresponding to more frequent or more lasting symptoms. Half of the items are worded negatively and half positively. Total scores on the ZSDS do not correspond with a clinical diagnosis of depression but rather indicate the level of depressive symptoms that may be of clinical relevance. As originally described
Zung Scale Factor Invariance 3 by Zung, Richards, and Short (1965), raw scores are summed and then transformed into a ZSDS Index, by dividing the raw score by the maximum score and multiplying by 100 (ZSDS Index = rs/80 x 100). ZSDS Index scores < 50 is normal, i.e. no depression. The mean of the ZSDS Index in CPs is around 48 (Koivula et al., 2010) and the prevalence of depressive symptoms is close to 37% (Barefoot et al., 2000; Welin, Lappas, & Wilhelmsen, 2000). In this study we used the second Spanish version of the ZSDS by Conde and Esteban (1974). The coefficient of reliability for this version found by Conde and Esteban (1975) was .80 using a split-half correlation for the even-odd ZSDS items. Aragonés, Masdéu, Cando, and Coll (2001) with this same version of ZSDS obtained a Cronbach’s alpha of .78 in primary health-care patients. Statistical analysis Exploratory and confirmatory factor analyses (EFA, CFA) were conducted with Mplus 3 using weighted least-squares with mean and variance (WLSMV) adjustment which is optimal choice for categorical outcomes. Mixed linear model (MLM) for non normal data was used when results with WLSMV do not converge. The sample size was adjusted to the recommendations for factor analysis of Guilford (1954), at least 200 cases, and to those of Thorndike (1982), at least 10 subjects per item. The promax rotation algorithm and various criteria for the number of factors, such as Kaiser’s Criterion (eigen values >1.0), the scree test, the Minimum Average Partial (MAP) test and the interpretability of resulting factor structures were used. The primary criterion for item inclusion was set at least at .30 (absolute value). The confirmatory factor model fit was evaluated with the Tucker-Lewis Index (TLI), the Comparative Fit Index (CFI), and the Root Mean Square Error of Approximation (RMSEA). The TLI and CFI are measures that compare a baseline model in which no relationships are estimated between the variables with a theoretical model in which hypothesized paths are estimated. This index ranges from 0 to 1, where .9 indicates adequate fit, and .8 is considered marginal fit. The RMSEA with fit values less than .05 indicate close fit, and values less than .08 reasonable fit. The RMSEA is sensitive to over-fit, that is, it begins to increase when too many paths have been included (Rigdon, 1996). These cut-offs apply to models with continuous outcomes, although Yu and Muthén (2001) report that they are reasonable for models with categorical outcomes as well. Results The factor structure of the ZSDS was analyzed in two ways: separately and jointly in CPs and HP. The criteria
used to choose a factorial solution suggested two or three factors. In the two-factor solution, the same structure of the items appeared in the three groups except item 19 which changed factor in CPs, as depicted in Table 1. In the sample of CPs, items 11, 12 and 18 loaded ≥ .30 on both factors and items 2, 7 and 8 did not reach this factor loading on either of them. In the HP sample, no item loaded on two factors ≥ .30 and only item 2 did not load ≥ .30 on a factor. In the total sample, all the items loaded ≥ .30 on a factor and only item 12, Psychomotor retardation, loaded on two factors. Items 1, Depressed affect, 3, Crying spells, 4, Sleep Disturbances, and 13, Psychomotor agitation, loaded ≥ .50 on factor 1 in all samples. Based on items 1 and 3, which Zung (1965) considered to correspond to pervasive depressive affect, this factor can be interpreted as reflecting Negative affect. The items highest loadings on factor 2 show a predominantly cognitive content of depression and the factor can be called Cognitive symptoms. The congruence between factors of the CPs and HP was .86 for Negative affect and .95 for Cognitive symptoms. These values, on surpassing the critical point of .75, allow us to affirm that the factors are similar (Cliff, 1966). MacCallum, Widaman, Zhang, and Hong (1999) consider these indices respectively borderline and good. Internal consistency of the factors was low (α between .68 and .79) or acceptable, Cronbach’s alpha for ordinal data between .73 and .88 and omega between 75 and 88. The internal consistency of the total scale was acceptable (between .77 and .80 for traditional α) or high (between .83 and .88 for ordinal data and between .85 and .89 for ω). The rotated factor solution with three factors is shown in Table 2. Factors 1, Negative affect, and 2, Cognitive symptoms, remained and a third factor, Somatic symptoms, appeared with items 12 and 11 showing the highest load. This order of factors did not prevail in the factor structure of HP, in which Somatic symptoms relegated Cognitive symptoms to a third factor. In the total sample factor 1 contained practically all the items of factor 1 in HP. Factors 2 and 3 of the total sample share, nevertheless, more items with the same factors of the sample of CPs. The coefficient of congruence between the factor Negative affect in CPs and that of the HP was .76; for factor Cognitive symptoms of the same samples the coefficient of congruence was .72, and for factor Somatic symptoms it was .87. The value .72, on not surpassing the critical point of .75, did not allow us to affirm that the factors are similar (Cliff, 1966), but, as it did exceed .30, congruence between them cannot be ruled out according to MacCallum et al. (1999). Internal consistency of these factors is slightly lower than that of the two-factors solution except factor 1, Negative Affect, in the sample
4 A. del Pino Pérez et al. Table 1. Two-factor structure of the ZSDS in all samples CPs Samples Symptoms 01.Depressed affect 02. Diurnal variation 03. Crying spells 04. Sleep disturbance 05. Decreased appetite 06. Decreased libido 07. Weight loss 08. Constipation 09. Tachycardia 10. Fatigue 11. Confusion 12. Psychomotor retardation 13. Psychomotor agitation 14. Hopelessness 15. Irritability 16. Indecisiveness 17. Personal Devaluation 18. Emptiness 19. Suicidal Rumination 20. Dissatisfaction Eigenvalue rotated Explained variance ZSDS Cronbach’s Alpha Total ZSDS Cronbach’s ordinal Alpha Total ZSDS Omega Omega Total ZSDS
F1
HP F2
F1
.71
.70
.61 .51
.66 .63 −.45 −.51
.31 .33 −.31 −.30 .56
−.48 −.54
−.75 −.63
.72 .80 .81
−.48 −.49
−.30 .64
−.60 .66
2.99
−.62 −.57 −.64
.58 −.54 −.58 −.68
.74
−.51 −.60 −.70 .30
−.65 4.5 43% .79 .80 .88 .88 .88 .89
F2
.38 .38 .54 .53
.67
−.52 −.63 −.61 .35 −.57
F1
.72 .34 .69 .59
.45 .41 .64 .50
.46
2.38 34% .68 .77 .73 .83 .75 .84
F2
−.55 −.53
−.58
−.30
All Participants
3.5 .77 .83 .85
−.63 3.4 39% .76 .79 .83 .85 .83 .85
3.23 .72 .79 .81
Note: CPs: coronary patients; HP: Healthy people; All samples: F1 = Negative affect; F2 = Cognitive symptoms.
of CPs, Cronbach’s alpha for ordinal data 74 versus 73, and in the HP sample, omega 90 versus 88. The fit of the results of EFA to four models was analyzed by CFA with all participants. We analyzed a model of two factors obtained from our total sample; two models of three factors, one obtained from our total sample and the other from the meta-analysis by Shafer (2006); and a model of four factors obtained from the study of Barefoot et al. (2000) in a sample of CPs. The results of these analyses are shown in Table 3. Only the HP sample showed adequate or reasonable goodness-of-fit indices with the models of two and three factors specified from our total sample. The CPs showed marginal goodness-of-fit indices for these models. The models of Shafer (2006) and Barefoot et al. (2000) had convergence problems, a high number of non-significant parameters and inadmissible solutions, e.g., matrices of negative covariance between the factors.
These problems, specially presented in table 3, persisted even though we applied the maximum likelihood method (MLM) (associated to the statistical SatorraBentler χ2 used frequently for ordinal data) instead of WLSMV. Given the problems of fit or of models, the process of testing for factorial invariance did not seem justified. The descriptive statistics of reporting symptoms and comparison of mean values between the two samples are shown in Table 4. The two groups showed significant differences in eleven of the twenty items that compose the ZSDS. In all of them, except in item 2, Diurnal variation, the CPs scored higher than the HP. Mean ZSDS score in CPs was significantly higher, as was the prevalence of CPs above the cut-off point (ZSDS Index ≥ 50) suggested by Zung (1965) to indicate clinically significant depression, 37.85% versus 20.95% in HP.
Zung Scale Factor Invariance 5 Table 2. Three-factor structure of the ZSDS in all samples CPs Samples Symptoms 01.Depressed affect 02. Diurnal variation 03. Crying spells 04. Sleep disturbance 05. Decreased appetite 06. Decreased libido 07. Weight loss 08. Constipation 09. Tachycardia 10. Fatigue 11. Confusion 12. Psychomotor retardation 13. Psychomotor agitation 14. Hopelessness 15. Irritability 16. Indecisiveness 17. Personal Devaluation 18. Emptiness 19. Suicidal Rumination 20. Dissatisfaction Eigenvalue rotated Explained variance ZSDS Cronbach’s Alpha Cronbach’s ordinal Alpha Omega
F1
HP F2
F3
F1
.74
.70
.62 .46
.60 .64 −.37 −.38 −.37
All Participants F2
.47 .42 .59 .42
.34 .40 −.63 −.80 .57
.68
−.65 .58
−.45 −.57 −.69
−.47 −.65 −.82 .78
.32
−.62 2.15 51% .68 .74 .75
−.50
2.38
1.79
.70 .75 .75
.56 .66 .68
.36 −.70 −.90
.64 −.66
.44
F3
−.36 −.42 .36 .38 .52 .49
.61
.39
F2
−.32
.48 −.71 −.77
−.67
−.38
F1
.72 .33 .68 .58 −.35 −.40
.35
F3
3.90 42% .79 .88 .90
1.96
2.48
.66 .72 .75
.72 .78 .79
−.40 −.65 −.92 .32 −.40
−.30
2.30
1.93
.68 .78 .80
.64 .73 .74
3.25 46% .75 .82 .83
Note: CPs: coronary patients; HP: Healthy people; CPs: F1 = Negative affect; F2 = Cognitive symptoms; F3 = Somatic symptoms. HP: F1 = Negative affect ; F2 = Somatic symptoms; F3 = Cognitive symptoms. All Participants: F1 = Negative affect; F2 = Cognitive symptoms; F3 = Somatic symptoms.
Table 3. Confirmatory factor analyses of four factor models of the ZSDS Model
Sample
CFI
TLI
RMSEA
OBSERVATIONS
TWO FACTORS (All participants)
CPs HP ALL CPs HP ALL CPs HP ALL CPs HP ALL
.792 .887 .846 .835 .911 .882
.826 .906 .880 .860 .925 .908
.080 .076 .067 .079 .068 .086
Items 7, 9 and 12 ns.
THREE FACTORS (All participants)
THREE FACTORS (Shafer, 2006)
FOUR FACTORS (Barefoot et al, 2000)
Cut-off point criteria for fit indexes
.760 .719
≥.90
.727 .675
.063 .066
MLM, F1 and F3 ns MLM phi negative. F1 and F3 ns MLM, F1 and F3 ns. MLM, F1 and F4 ns No converging MLM phi negative. F1 ns
between .08–.05
Note: ns: not significant. MLM: mixed linear model. MLM for non normal data is used when results with weighted least-squares with mean and variance (WLSMV) do not converge. The indicative values of a reasonable goodness-of-fit are bolded.
6 A. del Pino Pérez et al. Table 4. Descriptive statistics of reporting symptoms and comparison of mean values between coronary patients (CPs) and healthy people (HP) CPs
HP
Samples
Mean
SD
Mean
SD
F(1 ,406)
Symptoms 01.Depressed affect 02. Diurnal variation 03. Crying spells 04. Sleep disturbance 05. Decreased appetite 06. Decreased libido 07. Weight loss 08. Constipation 09. Tachycardia 10. Fatigue 11. Confusion 12. Psychomotor retardation 13. Psychomotor agitation 14. Hopelessness 15. Irritability 16. Indecisiveness 17. Personal Devaluation 18. Emptiness 19. Suicidal Rumination 20. Dissatisfaction Total ZSDS(1)
2.00 2.52 1.76 2.08 1.94 1.66 1.83 1.62 1.56 2.03 2.06 2.44 2.35 1.80 1.84 2.50 1.73 1.42 1.13 1.44 47.15
.91 1.22 .87 1.18 1.16 .95 1.08 .96 .82 1.15 1.18 1.18 1.10 1.00 1.01 1.20 .92 .75 .50 .81 9.95
1.48 3.08 1.25 1.58 2.10 1.66 1.33 1.28 1.30 1.40 2.05 2.19 1.75 1.93 1.43 2.30 1.86 1.57 1.13 1.49 42.71
.70 1.09 .59 .91 1.16 .91 .74 .66 .62 .75 1.10 1.06 .94 1.04 .72 .99 .90 .85 .47 .80 9.11
41.09+ 24.12+ 48.61+ 22.35+ 1.80 .01 29.01+ 16.51+ 13.20+ 43.33+ .01 4.93* 33.86+ 1.65 22.13+ 3.14 1.95 3.39 .01 .39 21.92+
Note: Mean values in total ZSDS according to ZSDS Index. * p < .05; + p < .001
Discussion The EFAs supported a two-factor solution in both samples and the congruence between the factors Negative affect and Cognitive symptoms suggested possible invariance. In the three-factor solution the factor Somatic symptoms showed little consistency; strikingly, however, it obtained a higher coefficient in factorial congruence analysis than the other two. The generalized problems of goodness-of-fit to the models proposed in CFAs indicate that the models of Barefoot et al. (2000) and Shafer (2006) did not seem suitable for our data, possibly due to cultural and linguistic attributes of the samples. The models elaborated from the data of our participants, only showed reasonable goodness-of-fit in the HP sample, which ruled out evaluation of factorial invariance. The different goodness-of-fit of the factor structure in the two samples highlights the fact that depressive symptoms tend to be structured according to the samples (Costello, 1992). Only the HP sample presented adequate goodness-of-fit, suggesting that the ZSDS assesses slightly different aspects of depressive symptoms in CPs and HP. Total ZSDS showed good reliability (ordinal α, and ω) in the three samples analyzed (CPs, HP and CPs+HP). This result is higher and consistent with the reliability
obtained with the same Spanish version of the ZSDS (Aragonés et al., 2001; Conde & Esteban, 1975) and with the internal consistency obtained in other countries and languages (Koivula et al., 2010; Zung, 1973). We found that in the immediate period after a coronary event, CPs presented significantly more depressive symptoms than HP, and that the prevalence of CPs with depressive symptoms was significantly higher than in HP. These results are consistent with the data available and the prevalence of depressive symptoms is practically identical to that found by Barefoot et al. (2000) and Welin et al. (2000) in CPs. These results also support the validity and usefulness of the Spanish version of ZSDS for CPs. Our conclusions would, however, have been reinforced if the socio-demographic characteristics of the HP had better matched those of the CPS. The significant differences in depressive symptoms occurred in nine positively-worded items that load on the factor Negative affect and only on one negativelyworded item, which raises serious doubts about whether the items with reversed scoring are comprehensible. In this way the differences between groups would not only appear in positively-worded items, and the items would not be grouped in factor analyses according to direct or reverse evaluation. It also raises the question
Zung Scale Factor Invariance 7 about whether the items need reformulation to avoid bias related to direct or reverse evaluation. The ZSDS item content corresponds to the construct depression, although to update it, this content would need an adjustment to the American Psychiatric Association’s new criteria of depression. Our study supports the existence of two factors in both CPs and HP, although only the latter group showed adequate goodness-of-fit with a two-factor model. The three-factor structure highlighted important differences between the two groups, and only showed acceptable goodness-of-fit in HP. Our results thus favor a bi-factorial solution and do not allow us to affirm the existence of structure invariance. This implies the need for specific information and research on the depressive dimensions of CPs, because depression in CPs can be different from that suffered by HP. The total score provides reliable and consistent results regarding degree and prevalence of depressive symptoms in CPs and HP in different countries; its use is therefore justified as a measure of degree of depression for screening purposes in CPs and HP, although the diagnosis of depression should be validated with data obtained from an external criterion as it is a structured clinical interview administered by well-trained individuals. The results of this study suggest that we should continue to use ZSDS total score as opposed to factor scores, and that CPs may have depressive symptoms associated or not associated with coronary disease, which require further investigation. References Ahto M., Raimo I., Puolijokis H., Vahlberg T., & Kivela S. L. (2007). Stronger symptoms of depression predict high coronary heart disease mortality in older men and women. International Journal of Geriatric Psychiatry, 22, 757–763. http://dx.doi.org/10.1002/gps.1735 Aragonés E., Masdéu R., Cando G., & Coll G. (2001). Validez diagnóstica de la Self-Rating Depression Scale de Zung en pacientes de atención primaria [Diagnostic validity of the Zung Self-Rating Depression Scale in primary care patients]. Actas Españolas de Psiquiatría, 29, 310–316. Barefoot J., Brummett B., Helms M., Mark D., Siegler I., & Williams R. (2000). Depressive symptoms and survival of patients with coronary artery disease. Psychosomatic Medicine, 62, 790–795. Cliff N. (1966). Orthogonal rotation to congruence. Psychometrika, 31, 33–42. http://dx.doi.org/10.1007/ BF02289455 Conde V., & Esteban T. (1974). Contribución al estudio de la S.D.S. (Self-Rating Depression Scale) de Zung, en una muestra estratificada de población normal [Contribution to the study of the Zung Self-Rating Depression Scale in a
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