Measuring diabetes-related attitudes: the use of a USA-developed instrument with Australian nurses

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Patient Education and Counseling 29 (1996) 41-47 ELSEVIER

Measuring diabetes-related attitudes: the use of a USA-developed instrument with Australian nurses

Paul Williamson* a, Rosalyn Shute”, Meri Kingb, Jayne L.ehmannc aSchool of Psychology, The Flinders Universiry of S. Australia, GPO Box 2100, Adelaide 5001, S. Australia

bSchool of Nursing, The Flinders Universiry of S. Australia, GPO Box 2100, Adelaide 5001, S. Australia

‘Diabetes Education Unit, Flinders Medical Centre, The Flinders University of S. Australia, GPO Box 2100, Adelaide 5001, S. Australia

Received 21 October 1994; revised 22 March 1995; accepted 18 April 1995

Abstract

The revised Diabetes Attitudes Scale (DA!3 was completed by nearly 1000 nurses and nursing students in South Australia. Confirmatory factor analysis was used to compare the factor structure with that previously obtained in the USA with patients with diabetes. The instrument was found to be robust. It was concluded that only one item of the 50-item scale is unsuitable for inclusion in studies which aim to compare patient and health professional attitudes.

Keywords: Diabetes; Attitudes; Patients; Nurses; Health care professionals

1. Introduction

Patient attitudes to diabetes have for some time been recognized as important for self-care, but it is being increasingly asserted that the attitudes of health care professionals also need to be considered in the promotion of self-management [l]. Indeed, one writer has gone so far as to suggest that most diabetes patient-provider inter- actions are more heavily influenced by the per- spectives of the practitioner than the patient [2].

Research in this area has been considerably strengthened by the work of Anderson and col- leagues in Michigan in developing a Diabetes Attitude Scale (DAS). The scale was developed

* Corresponding author.

within the framework of the theory of reasoned action [3], whereby the best predictor of a person’s behaviour is seen as the intention to behave in a certain way. Intention is determined in part by the individual’s attitude towards the behaviour (how positively or negatively he or she feels about it). Attitude is in turn determined by specific beliefs. Thus, an attitude scale such as the DAS consists of numbers of belief statements to which respondents indicate their degree of agreement or disagreement, and responses may be factor analyzed to determine the structure of the under- lying attitudes.

Originally, the DAS was devised to measure the attitudes of health care professionals [41, but it was later revised to be suitable for use with patients. In a study comparing the responses of health professionals to the two versions of the

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42 P. Williamson et al. /Patient Education and Counseling 29 (1996) 41-47

scale, it was clear that the revision (which ren- dered the items less technical to make them un- derstandable by patients) had changed its psycho- metric properties [5], and that the revised DAS should be regarded as a new instrument requiring validation with a patient population.

To this end, it was administered to over 1000 patients with diabetes in Michigan [6], and princi- pal axes factor analysis yielded seven attitude factors (with reliabilities ranging from 0.61 to 0.71) as follows:

(1) the need for special training in order to provide diabetes care;

(2) patient compliance; (3) the seriousness of non-insulin-dependent di-

abetes (NIDDM); (4) the relationship between blood glucose lev-

els and complications; (5) the impact of diabetes on the patient’s life; (6) patient autonomy; (7) team care.

The authors of the revised DAS regard it as an instrument suitable for measuring and comparing the attitudes of health care professionals and patients towards diabetes (at least, for research purposes), while noting the need for further studies to gather additional evidence regarding its construct validity.

The present paper adds to the body of information on the revised DAS already collected in the United States. The aim of the study was to ex- amine whether the factor structure of the revised DAS as obtained with patients in the USA would be maintained when the instrument was used with a different sample in a different culture, i.e. with health professionals in Australia. The revised DAS was administered to pre-registration nursing students and to hospital-based nurses in South Aus- tralia, and exploratory and confirmatory factor analyses were used to compare results with the model derived from US patients.

2. Methods

2.1. Subjects

After relevant ethical permission for the study

had been obtained, the revised Diabetes Attitude Scale was sent to 522 preregistration nursing students enrolled in the Diploma of Nursing and to 1034 hospital nurses in South Australia. Respon- ses were received from 363 students and 629 nurses, representing response rates of 70% and 61%, respectively. Demographic information was collected and is to be reported elsewhere.

2.2. The Revised Diabetes Attitude Scale

The revised DAS consists of 50 Likert scale items rated from 5 - strongly agree to 1 - strongly disagree. As described earlier, seven factors have been found previously, representing seven attitudes towards diabetes care. The num- ber of items loading substantially on each factor (0.30 or above), and examples of items, are given below (each statement being prefaced with ‘In general I believe that...‘).

Factor 1 - Need for Special Training: seven items, e.g. ‘... health care professionals should be required to continue to learn about diabetes because diabetes care is changing fast’.

Factor 2 - Attitude Towards Patient Compli- ance: six items, e.g. ‘ . ..decisions about caring for diabetes should be made by the doctor’.

Factor 3 - Seriousness of Non-insulin-Depen- dent Diabetes: three reverse-coded items, e.g. ‘...diabetes that can be controlled by just being on a diet is a pretty mild disease’.

Factor 4 - Blood Glucose Control and Com- plications: four items e.g. ‘...having high blood sugar over a long period of time is linked to getting long-term diabetic complications’.

Factor 5 - Impact of Diabetes on Patients’ Lives: five items, e.g. ‘ . ..having diabetes changes a person’s outlook on life’.

Factor 6 - Attitude Towards Patient Au- tonomy: five items, e.g. ‘...people with diabetes have the right to decide how hard they will work to control their blood sugar’.

Factor 7 - Attitudes Towards Team Care: four items, e.g. ‘ . ..doctors should send people with diabetes to a dietitian to help them with their diet.’

A score for each subscale is calculated by taking the mean score of the items making up that

P. Williamson et al. /Patient Education and Counseling 29 (1996141-47 43

factor, taking account of reverse coding where necessary (n.b. an alternative would be to take factor loadings into account, but as Anderson et al. [6] used the simpler method, this was adopted in the present study also).

2.3. Statistical analyses

As a preliminary analysis, exploratory factor analysis was carried out, forcing seven factors. A confirmatory factor analysis was then used with the present data to test the factor structure of the DAS proposed by Anderson et al. [6]. Using LISREL 6, two models were tested. Both models preserved the factor structure indicated by An- derson et al. [6]; however, Model I assumed un- correlated factors whereas Model II allowed for the factors to be correlated.

Data from the nurses and nursing students were analyzed separately. Because the sample sixes for the two groups were relatively large, 600 + and 350 + , respectively, the two groups could be analyzed separately and congruent results would indicate reliability in the findings.

The viability of the two models can be evalu- ated using various goodness-of-fit indices. LIS- REL 6 provides three such indices: the chi square statistic, Joreskog and Sorbom’s 171 goodness-of-fit index (GFI), and the root mean squared of the residuals (RMS). With the chi square statistic, a good fit is indicated by a non-significant chi-square statistic; however, the statistic has been criticized due to its sensitivity to violations of normality and because it will nearly always be significant when the sample size is large. Because the difference between two chi square statistics is also dis- tributed as a chi square, then the difference between two chi square statistics can be tested as a chi square with degrees of freedom equal to the difference between the two degrees of freedom for the initial chi square statistics. In this paper, the latter approach will be used to test between Model I and Model II. Cole [81 has suggested benchmark goodness-of-fit criteria for evaluating the fit of a model when using the GFI, the ad- justed (for degrees of freedom) GFI (AGFI), and the RMS. He proposes that values of the GFI greater than 0.90, values of the AGFI greater

than 0.80, and values of the RMS less than 0.10 are usually associated with well-fitting models.

3. Results

3.1. Exploratory factor analysis

Initially, the results of the exploratory factor analysis were examined for similarities and dif- ferences with the structure obtained by Anderson et al. [6]. The strongest factor, as in the USA study was Need for Special Training, with the same seven items loading strongly on this, as well as two additional items. The second factor, again as in the US study, related to Attitudes Towards Compliance, with five of the six items loading on this being the same as five of the six items loading in the US study. Seriousness of NIDDM, the 3rd factor in the US study, was the 6th factor in the present study, with the same three items loading on it. Blood Glucose Control and Complications was 4th in each study, with the four US items plus one other loading in the present study. The remaining factors were also very similar, with the Team Care subscale items being identical in the two studies. Overall, then, the exploratory analysis was suggestive of a factor structure very similar to that obtained with patients in the USA [6]. However, some of the items in the present study loaded relatively highly on more than one factor and the exploratory procedure did not allow a measure of goodness-of-fit to be obtained. Nor does the procedure allow a test of a pre-defined factor structure.

3.2. Conjhmatoly factor analysis

To overcome these limitations, a confirmatory factor analysis was carried out. Models I (uncor- related factors) and II (correlated factors) were analyzed using confirmatory factor analysis in LISREL 6 for the two subgroups, nurses and students. Using the nurses data, Model I revealed an unsatisfactory fit with the data (GFI = 0.827, AGFI = 0.805, and RMS = 0.116), whereas Model II yielded a satisfactory fit (GFI = 0.895, AGFI = 0.876, and RMS = 0.062). Moreover, relaxing the assumptions of Model I by allowing the factors to be correlated (Model II) gave a significantly im- proved fit with the data compared to Model I ( x2

44 P. Williamson et al. /Patient Education and Counseling 29 (I 996) 41-47

diff = 609.35, df= 21, P < 0.001). Similarly, using the students’ data, Model I provided an unsatisfactory fit (GFI = 0.748, AGFI = 0.716, and RMS = 0.143), whereas Model II revealed a relatively good fit (GFI = 0.837, AGFI = 0.809, and RMS = 0.077). Furthermore, the improvement in fit obtained with Model II over Model I was significant ( x2 diff = 543.03, u!f= 21, P < 0.001). These analyses indicate that, for the two samples, a factor structure that assumes that the factors may be correlated provides the best fit with the data.

Assuming a correlated factor structure, the fit of Model II was assessed further. For the nurses’ data, in addition to the satisfactory levels of the goodness of fit statistics reported above, an inspection of the standardized residuals for the fitted model revealed that only 76 (13.55%) of the 561 reproduced correlations had standardized residuals of greater than 2.5 in magnitude. In- spection of the estimated factor loadings revealed similar estimates to those obtained by Anderson et al. [6], with all factor loadings in the predicted direction. Moreover, the t-values associated with each estimated factor loading showed that all factor loadings were significantly different from zero. However, item 15 had a relatively low factor loading estimate onto factor 5 (A = 0.123, SE. = 0.051) and this estimate was only just significantly different from zero (t(600) = 2.44, P < 0.05). This factor loading was also substantially lower (more than seven standard errors> than the factor loading obtained by Anderson et al. [6] in the patients’ data set (h = 0.460).

Similar results were obtained from the students’ data. Of the 561 reproduced correlations, 63 (11.23%) had standardized residuals of greater than 2.5. There was a general correspondence between the estimated factor loadings and those obtained by Anderson et al. [6], with nearly all t-values revealing that the estimated factor loadings differed significantly from zero. The one exception was the estimated factor loading for item 15 (A = - 0.122, S.E. = 0.063), which, although significantly different from zero (t(350) = - 1.95, P < 0.051, was in the opposite direction to that predicted.

These findings suggested that an alternative

model needs to be considered. The new model should reflect the structure of the DAS as proposed by Anderson et al. [6], but also account for the discrepant findings in relation to item 15. Inspection of the modification indices and ex- pected change parameters for item 15 indicated that allowing item 15 to load onto another factor would provide little improvement in the fit of the model. As a consequence, a new model was tested on the two samples which used the same factor structure proposed by Anderson et al. [6] except that item 15 was removed. Hence factor 5 only comprised items 22,27, 2, and 30. This model was termed Model III.

This new model was tested using confirmatory factor analysis within the LISREL 6 program. Using the nurses’ data, this model produced a better fit to the data when the factors were allowed to be correlated (x2 diff = 619.51, df = 21, P < 0.001). Model III also yielded a satisfactory fit to the data (GFI = 0.898, AGFI = 0.879, and RMS = 0.061). The factor loadings were estimated using maximum likelihood estimation, and the factor loading coefficients and their standard errors are shown below in Table 1. In addition, further inspection of the results revealed that only 73 (13.90%) of the reproduced correlations had standardized residuals greater than 2.5. Fur- thermore, all lambda coefficients were significantly different from zero and were very compa- rable with the estimates from patients obtained by Anderson et al. [6] (Table 1).

Similarly, for the students’ data, allowing correlated factors produced a better fit to the data ( x2 diff = 544.48, df= 21, P < O.OOl>, and the overall fit to the data for this model was relatively good (GFI = 0.848, AGFI = 0.820, and RMS = 0.075). The maximum likelihood estimates of the factor loadings and their standard errors are shown in Table 1. Of the possible 525 reproduced correlations, only 56 (10.67%) had standardized residuals which were greater than 2.5. In addition, all estimated factor loadings were significantly different from zero.

The statistics in Table 1 show that the estimated factor loadings obtained in this study for the sample of nurses and for the sample of students are very similar to those obtained by An-

P. Williamson et al. /Patient Education and Counseling 29 (1996) 41-47 45

Table 1 Estimated lambda coefficients for the modified DAS

Item Nurses

A estimate S.E. (A)

Students

h estimate S.E. (A)

Patients factor loading

Factor 1: Need for special training

DAS21 DAS12

DAS13

DASl DAS38

DAS45 DAS42

0.683 0.040 0.632 0.054 0.550 0.572 0.042 0.574 0.055 0.590

0.527 0.042 0.453 0.057 0.500

0.376 0.044 0.434 0.057 0.370 0.547 0.042 0.470 0.056 0.380

0.494 0.043 0.569 0.055 0.450 0.462 0.043 0.351 0.058 0.410

Factor 2: Attitude towards patient compliance DAS28 0.640 DAS4 0.312

DAS41 0.452

DAS43 0.424 DAS32 0.503

DAS24 0.511

0.046 0.622 0.056 0.610 0.048 0.179 0.061 0.490 0.047 0.496 0.058 0.520

0.047 0.591 0.057 0.470 0.047 0.387 0.059 0.410

0.047 0.507 0.058 0.430

Factor 3: Seriousness of non-insulin-dependent diabetes DASlO 0.499

DAS18 0.576 DAS6 0.538

0.053 0.591 0.059 0.650

0.055 0.535 0.059 0.500 0.054 0.509 0.059 0.540

Factor 4: Blood glucose control and complications

DAS26 0.743 DAS14 0.619 DAS49 0.596

DAS35 - 0.439

0.043 0.693 0.054 0.650 0.043 0.719 0.054 0.610 0.044 0.585 0.056 0.490

0.045 - 0.528 0.057 - 0.350

Factor 5: Impact of diabetes on patients’ lives DAS22 0.460

DAS27 - 0.458 DAS2 0.362

DAS30 0.581

0.049 0.444 0.062 0.660 0.049 - 0.576 0.061 - 0.550 0.050 0.370 0.062 0.610 0.050 0.489 0.061 0.330

Factor 6: Attitude towards patient autonomy

DAS46 0.589 DASll 0.437

DAS48 0.463 DAS3 0.365 DAS37 0.468

0.047 0.550 0.060 0.670 0.048 0.335 0.062 0.550 0.048 0.603 0.060 0.510 0.048 0.322 0.062 0.410 0.048 0.467 0.061 0.340

Factor 7: Attitudes towards team care

DAS34 DAS25 DAS29

DAS47

0.557 0.045 0.548 0.058 0.630 - 0.653 0.044 - 0.690 0.057 - 0.550 - 0.404 0.046 - 0.476 0.059 - 0.520

- 0.483 0.045 - 0.336 0.060 -0.390

46 P. WWarnson et al. /Patient Education and Counseling 29 (1996) 41-47

derson et al. [6], using a sample of patients. Therefore, there is good evidence to support the idea that the factor structure proposed by Ander- son et al. is robust and generalizable to other samples. The one exception is a minor modification to factor 5, the evidence from this study suggesting that item 15 should be removed from factor 5.

The factor structure of the modified DAS iden- tified in Table 1 was then used to construct variables to represent each factor. As in Anderson et al.‘s work [6], this was done by taking the mean score for the items making up each subscale. The correlation matrix for each sample (nurses and

students) with the correlation matrix obtained by Anderson et al. (patients’ data) is shown in Table 2.

The correlations between factors in all three samples were relatively similar. This even applied to the correlations involving factor 5 where the factors created from the students’ and nurses’ samples were different (only four as compared to five items) from the factor scores created from the patients’ sample.

Finally, reliability analyses were carried out for each of the created factor variables. The Cron- bath’s alpha coefficients for the factors are shown in Table 3. The reliability coefficients for the

Table 2 Correlations between subscales of the modified DAS

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

Factor 2

Factor 3

Factor 4

Factor 5

Factor 6

Factor 7

Patients 0.11 NUlX% 0.04 Students - 0.02 Patients 0.07 Nurses 0.01 Students - 0.15 Patients 0.35 Nurses 0.38 Students 0.37 Patients 0.35 Nurses 0.33 Students 0.34 Patients 0.21 Nurses 0.34 Students 0.46 Patients 0.50 Nurses 0.52 Students 0.51

-0.19 0.29 0.51 0.10

- 0.02 -0.14 -0.11

0.04 - 0.01 - 0.24 - 0.20 -0.15

0.12 0.01

- 0.06

0.09 - 0.01 -0.19

0.10 - 0.02 -0.16 - 0.05 - 0.02 -0.13

0.12 - 0.02 -0.16

0.16 0.21 0.24 0.09 0.19 0.30 0.19 0.39 0.22 0.38 0.24 0.09 0.33 0.31 0.29 0.34 0.25 0.34

For the nurses’ sample, N varies between 623 and 625; for the students’ sample, N = 358; the patients’ sample data refers to that obtained by Anderson et al. [6].

Table 3 Cronbach’s alpha coefficients for the subscales of the modified DAS

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7

Patients 0.71 0.67 0.61 0.64 0.68 0.63 0.67 Nurses 0.71 0.62 0.54 0.68 0.51 0.57 0.58 Students 0.68 0.61 0.56 0.72 0.52 0.55 0.59

For the nurses’ sample, N varies between 602 and 620; for the students’ sample, N varies between 344 and 354; the patients’ sample data refers to that obtained by Anderson et al. [6].

P. Williamson et al. /Patient Education and Counseling 29 (1996) 41-47 47

nurses’ and students’ samples are all similar to those obtained in the patients’ sample, although they are marginally lower.

4. Discussion

The results as a whole provide good support for the notion that the modified DAS [6] is a robust instrument that can be generalized from the orig- inal patient group to other groups, such as nurses and nursing students. Furthermore, while the instrument was developed within the USA, the present study has shown that it is applicable to Australian samples.

Only one factor was found to require some modification, and that was Factor 5, ‘Impact of diabetes on patients’ lives’. Three items loading on this factor are clear statements about the impact of diabetes on patients (e.g. Item 22 is ‘In general I believe that diabetes affects almost every part of a diabetic person’s life’), while one item relates to the seriousness of diabetes. The remaining item, Item 15, did not fit the factor structure with the present samples. This item is ‘In general, I believe that it is frustrating to treat diabetes’. A likely explanation for the difference between the present study and the previous work with patients is that this item would be inter- preted differently depending on whether one is a patient or a health care worker. Patients (as in the US study) would presumably interpret the question as referring to their own frustration in self-managing their condition, and thus it would be associated with the other items relating to impact on the individual. Health workers, on the other hand, would presumably interpret this not from the patient’s viewpoint, but from their own professional perspective, that is, with regard to their own frustrations in managing patients’ diabetes. If this is so it would not be surprising if this

item did not load on the factor concerning impact on patients’ lives. With this one exception, the factor structure has been maintained across the samples and, therefore, with the omission of Item 15, the instrument fulfils the intentions of its authors to develop an instrument enabling direct comparisons of health worker and patient attitudes to be made.

Acknowledgement

This research was supported by Novo Nordisk Pharmaceutical Company, Flinders University of S. Australia and Centre for Nursing Research. Thanks are due to these bodies, to the study participants and to Dr Robert Anderson, Univer- sity of Michigan Medical School.

References

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Measuring diabetes-related attitudes: the use of a USA-developed instrument with Australian nurses