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Development and validation of health beliefs model scale for prostate cancer screenings (HBM-PCS): Evidence from exploratory and confirmatory factor analyses
European Journal of Oncology Nursing 15 (2011) 478e485
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European Journal of Oncology Nursing journal homepage: www.elsevier.com/locate/ejon
Development and validation of health beliefs model scale for prostate cancer screenings (HBM-PCS): Evidence from exploratory and confirmatory factor analysesq Cantürk Çapık a, Sebahat Gözüm b, * a b
Kafkas University, Kars School of Health, 36100 Kars, Turkey Akdeniz University, Antalya School of Health, 07058 Antalya, Turkey
a b s t r a c t Keywords: Prostate cancer Screening Beliefs Scale Nursing
Purpose: Primary diagnostic tools for prostate cancer are prostate examination and detection of prostate specific antigen. It is important to know what factors affect individuals in their use of these tools. The aim of this study was to create a scale that combined the basic components of the health belief model with a focus on prostate cancer screening. Method: A total of 240 healthy men (mean age and standard deviation ¼ 53.8 10.8) was selected by convenient sampling. In this methodological study, exploratory and confirmatory factor analyses were used for psychometric evaluation. Cronbach’s alpha coefficient was used to evaluate the reliability of the scale. Results: The scale was composed of 41 items and five subscales. The initial analysis extracted five factors. Confirmatory factor analysis showed that the data obtained were compatible with Health Beliefs Model (HBM) (c2 ¼ 769 (n ¼ 240) ¼ 324.25, p > 0.05, GFI ¼ 0.93, AGFI ¼ 0.93, RMSA ¼ 0.00, CFI ¼ 1.00. Cronbach’s a coefficient of the subscales ranged from 0.83 to 0.94. Conclusions: The scale was found to be appropriate for the measurement of health beliefs about prostate cancer screening. The scale may be used in prostate cancer screenings for males who are 40 years and older to measure perceived susceptibility, perceived seriousness, health motivation, perceived barriers and perceived benefits with regard to prostate cancer screenings. Ó 2010 Elsevier Ltd. All rights reserved.
Introduction Prostate cancer is one of the most commonly observed cancer types in old age (Prostate Cancer Foundation, 2010). While it is generally experienced in advanced age, awareness should be created in younger men to encourage screening and early diagnosis. The American Cancer Society (2010) recommends that men over 50 years of age should be informed about prostate cancer screenings. Advice should be provided at an earlier age for those at higher risk for prostate cancer.
q This study was presented at the 1st International Congress on Nursing Education, Research & Practice, Grand Hotel Palace, Thessaloniki, Greece, 15e17 October 2009. * Corresponding author. Tel.: þ90 242 3106103; fax: þ90 242 226 14 69. E-mail addresses: [email protected] (C. Çapık), [email protected], [email protected] (S. Gözüm). 1462-3889/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ejon.2010.12.003
Among many studies in a qualitative study Rai et al. (2007) investigated the factors that affect men’s participation in prostate cancer screenings and determined that the belief in early diagnosis, existence of a friend with prostate cancer, media sources, family history and ongoing urinary complaints were significant factors in participation. Weller et al. (1998) reported that experiencing lower urinary tract symptoms was a powerful factor affecting participation in screenings, while occupation and educational status were not significant factors. Alternatively, some researchers have explored the issue from various dimensions within the framework of a certain model, instead of directly determining the factors that affect participation in prostate cancer screenings (Ford et al., 2006; Oliver, 2007; Tingen et al., 1997). One such approach, the health belief model, provides a useful conceptual framework for understanding and estimating health-related behaviour (Pierce et al., 2003). The health belief model identifies the specific attitudes and beliefs that influence people in choosing preventive health care and engaging in recommended medical regimens (Dossey and Keegan,
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2009). In the last 50 years, researchers have used the health belief model with regard to matters like condom use, breast self-examination, mammography screening behaviour and dental hygiene (Lu, 2007). Investigating the behaviour of participation in prostate cancer screenings is among these examples Oliver (2007), Plowden (2006), Tingen et al. (1998) and Whaley (2006). Among the components of the health belief model, benefit perception and high motivation are known to increase participation in prostate cancer screenings and decrease barrier perception (Oliver, 2007; Tingen et al., 1998; Whaley, 2006). There are several important factors that restrict men’s participation in screenings, such as lack of knowledge about cancer, fear of cancer, different between screening results and cancer diagnosis, having no health insurance, not knowing which specialist to see, not knowing where to consult, and the problem cause by the working hours (Ford et al., 2006; Weinrich et al., 2000). The health belief model was used in explaining several health behaviours since it was first developed as an applicable model by Rosenstock in 1966 (Ogden, 2004). However, a review of related literature has revealed the absence of a standard measurement tool that incorporates all essential components of a model for investigating participation in prostate cancer screenings. In one of their studies, Price et al. (1993) used a measurement tool that integrated essential components of the health belief model; although this was a tool developed specifically for their own study and used only in performing validation measurements. The absence of the analysis of factor structure was perceived as a deficiency in this tool. Price et al. (1993) and Tingen et al. (1998) have also investigated the perceived benefits of the model; Weinrich et al. (2000) explored the perceived barriers of the model and Oliver (2007) examined the level of participants’ motivationdhowever, only certain subscales of the model were identified in each of these publications. In literature, despite many studies related to health beliefs about prostate cancer screenings (Ford et al., 2006; Held-Warmkessel, 2002; Lu, 2007; Oliver, 2007; Plowden, 2006; Rai et al., 2007; Tingen et al., 1997, 1998; Weinrich et al., 2000), in each study different questionnaires were used to measure health beliefs. There is a need to develop an instrument for men to describe their perception about prostate cancer screenings so that the factors exploring screening behaviours of healthy men can be studied systematically. The use of a measurement tool for measuring the health beliefs in related to prostate cancer screenings, explaining all the features above, will make the results possible to compare. An instrument that describes health behaviours screenings health beliefs can be used in each culture by necessary linguistic adaptation. Relevant items of the scale developed by Champion for breast cancer were used in the study, by combining the basic components of the health belief model with a focus on prostate cancer screening.
Methods Item selection Items associated with health beliefs were selected from previously developed instruments and from the available literature for inclusion in the new instrument (Champion, 1999; Gozum and Aydin, 2004; Oliver, 2007; Tingen et al., 1998). While the previous studies were analyzed, papers were also studied measuring various components belonging to the health beliefs in prostate cancer, as well as those related to studies of the health belief model within areas such as breast cancer and osteoporosis (Champion, 1999; Gozum and Aydin, 2004; Kılıç and Erci, 2004). The literature survey revealed several concepts representing important features
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of health beliefs, including susceptibility, seriousness, benefits, barriers, motivation, self-efficiency, and cues to activation (Conner and Norman, 2005; Ogden, 2004). Five concepts (susceptibility, seriousness, benefits, barriers, motivation) were selected to be used in developing this questionnaire. The self-efficiency subscale was not included in the study scale due to the absence of any application that individuals can perform by themselves to diagnose prostate cancer. A pool of 82 items was generated from the concept dimensions. After examination by the researchers, some items were altered or excluded from the scale because they either duplicated other items, they did not represent the relevant dimension, or they failed to convey a clear expression of the intended point. Following this elimination process, 48 items were selected from the item pool and grouped under the five domains identified by the literature survey. In the selection of these items, five academics were consulted. Two of these academics serve as lecturers in the Department of Obstetrics and Gynaecology Nursing, while three of them serve as lecturers in the Department of Public Health Nursing. All these academics are researchers with experience in the health belief model. Sample The sample consisted of 240 men (mean age and standard deviation ¼ 53.8 10.8). Gable and Wolf (1993) suggested a sample size of 5e10 subjects per item to ensure a conceptually clear factor structure for factor analysis. The desired minimum sample size required was determined to be 205 participants based on 41 items. Table 1 introduces the demographic characteristics of the respondents. Procedures This is a methodological study and the population consisted of male participants who were aged 40 years or more and living in Erzurum, Turkey. The participants were individuals who were selected with convenience sampling from the general community and who had not previously received a diagnosis of prostate cancer. Potential participants were informed verbally about the aim of the study by the first author, and then asked if they agreed to answer the questionnaire; they were assured of their right to refuse to participate or to withdraw at any time. The data were collected between November 2008 and March 2009 and was approved by the
Table 1 Demographic Characteristics of the Respondents (n ¼ 240). N
%
Age (mean) 53.8(10.8) Education Primary education and lower High school and upper
140 100
58.3 41.7
Marital status Not married Married
26 214
10.8 89.2
80 94 66
33.3 39.2 27.5
Income Low Moderate High Health Insurance No Yes
60 180
25 75
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local ethical committee. Participants were gathered from coffee houses and hospital waiting rooms. The reason why coffee houses were preferred in selecting the sample group was because men in Turkey frequently meet in these places to spend time together. Men over middle age gather in these places to get involved in social interaction, thus these venues were appropriate for selecting this sample group. In addition, in hospital waiting rooms it was easy to find men who were visiting their relatives or who came to the hospital to receive treatment.
Table 2 Subscales, n of items, minimum and maximum scores of HBM-PCS. Itemsa
Subscale
n of Items
Min. Point
Max. Point
Items Items Items Items Items
Susceptibility Seriousness Motivation Barriers Benefits
5 4 10 15 7
5 4 10 15 7
25 20 50 75 35
a
Susceptibility.
Administration and scoring of the instrument The final version of the health beliefs model scale for prostate cancer screenings (HBM-PCS) is composed of 41 items and five subscales (Susceptibility, Seriousness, Motivation, Barriers, and Benefits). A five-point Likert format was used to measure each statement. Accordingly, the range of possible responses to each item was determined as follows: 1 ¼ strongly disagree, 2 ¼ disagree, 3 ¼ neither agree nor disagree, 4 ¼ agree and 5 ¼ strongly agree. High scores obtained from susceptibility, seriousness, motivation and benefits subscales were associated with a positive situation; where as high scores obtained from the barriers subscale were associated with a negative situation due to the high level of perceived barriers to participate in screening. Table 2 presents a summary of subscales, number of items, and minimum and maximum scores. The scale does not introduce a total score, and every subscale is scored individually. The scale can be applied in 10 min.
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I have a high probability of having prostate cancer. I have a high probability of having prostate cancer in the next few years. I have a feeling that I will have prostate cancer at some time in my life. I fear that I may die because of prostate cancer. I have a high probability of having prostate cancer when compared to other men of my age.
Seriousness 6- It frightens me to think of prostate cancer. 7- I will experience several problems for a long time if I have prostate cancer. 8- Prostate cancer will have a negative effect on my relationship with my wife or partner. 9- My whole life will change in a negative way if I have prostate cancer.
Motivation 101112131415-
Content validity and pilot study Prior to the administration of the questionnaire, the content validity of 48 items selected from the original 82 items was evaluated by five nurse academicians. The nurse researchers then reviewed the preliminary questionnaire for selection, construction, and wording of the statements. Changes were made based on these evaluations. Four items were removed because of repetition within the scale. The preliminary questionnaire (comprising 44 items) was pilot tested on a convenience sample of 15 men. Male teachers and workers at a high school were invited to participate in the pilot study and all of them consented to participate and were eligible. Inclusion criteria for the sample were age older than 40 and not having a diagnosis of prostate cancer. Data were collected by selfreport method in the teachers’ room. Respondents were asked to complete the questionnaire and make comments on the back page related to the intelligibility of items and the clarity of instructions. No changes were needed as a result of this pilot application. Thus, 44 items were selected for further analysis. Data analysis
1e5 6e9 10e19 20e34 35e41
16171819-
I follow new information and developments in order to improve my health. I believe that it is important to perform activities to improve my health. I keep a balanced diet. I do sports at least 3 times a week. I have my medical check-ups regularly even if I am not sick. It is easy for me to plan participating in prostate cancer screenings (rectal examination and blood test performed by taking blood sample, PSA measurement). Participating in prostate cancer screenings will contribute to my health. I want to have blood test [PSA] for prostate cancer in the next 6 months. I want to have prostate examination in the next 6 months. If I have prostate cancer; I want to know it as soon as possible.
Barriers 202122232425262728293031323334-
I fear prostate cancer screenings because I do not know how it is performed. I do not know where and how to go for prostate cancer screenings. It takes a lot of time to participate in prostate cancer screenings. I forget to participate in prostate cancer screenings. I have more important problems than participating in prostate cancer screenings. I do not know whether the health insurance covers prostate cancer screenings. I do not know which specialist to see for prostate cancer screenings. I fear participating in prostate cancer screenings because I feel that something is wrong. If I am diagnosed with prostate cancer after prostate cancer screenings; there will be nothing to do for its treatment. I do not need to participate in prostate cancer screenings, since I am not experiencing any problems. I fear that the results of prostate cancer screening will be bad. Prostate examination is very unsettling. Prostate examination is very painful. Doctors who perform the prostate examination treat patients impolite. Sexual ability declines after prostate cancer treatment.
Benefits All data analyses were performed using the Statistical Package for the Social Sciences (version 11.5, SPSS Inc and Lisrel [Linear Structural Relationships] version 8. 5). There were no missing values in the data. Descriptive statistics were calculated to describe the characteristics of the sample, and exploratory and confirmatory factor analyses were used to determine the psychometric properties of the new instrument. Principal component analysis and varimax rotation were used for exploratory factor analysis. All factors with values greater than or equal to 1.0 (unity root criterion) were retained (Akgül, 2005). Structural equation modelling (SEM) was the statistical technique used to analyze the proposed model structure of HBM-PCS. A first-order confirmatory factor analysis of data from HBM-PCS was
35- I will be doing something good for myself if I participate in prostate cancer screenings. 36- If I participate in prostate cancer screenings and if I do not receive any diagnosis, I won’t have to worry about prostate cancer. 37- Participating in prostate cancer screenings will help an early diagnosis of cancer. 38- If prostate cancer is diagnosed early and if it is treated successfully, I will have a chance to live a long life. 39- If prostate cancer screenings do not reveal any negative results; I will know that I am healthy. 40- If prostate cancer is diagnosed early; the growth of cancer may be prevented by treatment. 41- If I participate in prostate cancer screenings; I will know the truth about my health condition.
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conducted. The asymptomatic covariance matrix was formed for the estimation. The Diagonally Weighted Least Squares method (DWLS) was employed for estimation (Scientific-Software-International). Various fit indices were used to determine if the proposed model’s covariance structure differed from the observed relationships. Goodness of the calculated fit indices included the Pearson chisquare (c2) statistic with degrees of freedom, the goodness-of-fit index (GFI), the comparative fit index (CFI), and the root mean error of approximation (RMSEA). Cronbach’s alpha (reliability coefficient) was used to test the internal consistency for the instrument and for each of the factors resulting from the factor analysis. For total item correlation, a total correlation of 0.40 of all items was accepted as the inclusion criterion (Gözüm and Aksayan, 2002).
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Each factor was then labelled to describe the items loading on it. Factor 1 was labelled ‘Susceptibility’, Factor 2 was labelled ‘Seriousness’, Factor 3 was labelled ‘Benefits’, Factor 4 was labelled ‘Barriers’ and Factor 5 was labelled ‘Motivation’. Table 3 presents a summary of the 41 items, factors and factor loadings. The findings from the exploratory factor analysis resulted in the omission of three items.
Confirmatory factor analysis Confirmatory factor analysis was subsequently applied to the data in order to examine the construct validity of the five factor models extracted from the EFA. Initially, 41 predictors (items) were used to test the model. Factor loadings of items were found to be between 0.45 and 0.86 after the application of confirmatory factor analysis (see Fig. 1). All factor loadings were found to have a critical ratio (CR) that was greater than 1.96, indicating statistical significance. The fit indices for the 41 itemsemodel (final model) were c2 ¼ 324.25, df ¼ 769, p < 0.05, GFI ¼ 0.93, CFI ¼ 1.00, AGFI ¼ 0.93, RMSEA ¼ 0.00 and SRMR ¼ 0.09. Modification indices for the regression weights were examined next to identify the parameters that were indicative of cross-loadings and misspecifications. However, modifications were not performed because a remarkable improvement on fit indexes was not observed. The model was accepted in its current form considering its complexity.
Results Construct validity Exploratory factor analysis The Kaiser-Meyer-Olkin and Barlett tests were applied before evaluating the results of exploratory factor analysis (EFA). The Kaiser-Meyer-Olkin measurement of sampling adequacy was 0.85 and Barlett test results were quite significant (c2 ¼ 6278. 254, SD 820, p ¼ 0.000). Later, we used exploratory factor analysis to guide the factor structure of the developed model. Principal components factoring was performed on all 44 items by using a varimax rotation. A minimal factoreitem correlation of 0.40 was set for inclusion of an item in a factor (Gözüm and Aksayan, 2002). The initial analysis extracted five factors, but three items were omitted because these took a higher factor loading than other factors and were illusive, which was unsuitable for these items in terms of theoretical structure (S¸encan, 2005). All items on each factor were from the same construct except the three items. For example, an item related to barrier was unusually loaded on the benefit subscale, and two items about benefit loaded in health motivation. Therefore, the three items were deleted. The analysis was subsequently repeated. The variance reported before the exclusion of three items was 58.1%, and it was determined as 58.6% after the exclusion of the three items. The loadings ranged from 0.43 to 0.87 in the rotated pattern matrix in the present study.
Instrument reliability After the explanatory and confirmatory factor analyses, Cronbach’s alpha coefficient was evaluated. Cronbach’s alpha is an index of the degree to which a measuring instrument is internally reliable (Powers and Knapp, 2005). A reliability coefficient of 0.70 or above is accepted as evidence of internal consistency for new instruments (Bring and Wood, 1998). Alpha coefficient applied separately for all five factors ranged between 0.83 and 0.94, which is an acceptable value. No item was predicted to significantly increase the scale reliability if omitted. No omission was made at this time. Table 4 introduces a summary of Cronbach’s alpha coefficient values for each factor and other results. In this study, item 33 had the lowest total item correlation value of 0.428, and the values of all items were above 0.40.
Table 3 Factor Loadings (EFA) for HBMS-PCS Subscales. Susceptibility
Seriousness
Item
Factor loadings
Item
Factor loadings
Item
Factor loadings
Item
1 2 3 4 5
0.71 0.83 0.81 0.70 0.80
6 7 8 9
0.60 0.75 0.73 0.76
10 11 12 13 14 15 16 17 18 19
0.76 0.71 0.68 0.65 0.67 0.65 0.64 0.71 0.71 0.66
20 0.68 21 0.73 22 0.72 23 0.74 24 0.64 25 0.70 26 0.77 27 0.60 28 0.59 29 0.57 30 0.52 31 0.60 32 0.64 33 0.47 34 0.43 Exp. Variance 8.4%
Exp. Variance 14.7% Total Explained Variance 58.1%
Motivation
Exp. Variance 13.8%
Barriers
Exp. Variance 13.2%
Benefits Factor loadings
Item
Factor loadings
35 36 37 38 39 40 41
0.78 0.80 0.87 0.81 0.86 0.79 0.87
Exp. Variance 8.1%
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Fig. 1. Factor loading for HBMS-PCS subscales.
C. Çapık, S. Gözüm / European Journal of Oncology Nursing 15 (2011) 478e485 Table 4 Reliability and validity testing for HBM-PCS. Items
Item Mean
SD
Corrected Item/Total correlation
Susceptibility (Mean ¼ 2.33, SD ¼ 0.92, Alpha ¼ 0.86) 1 2.45 1.11 2 2.2 1.07 3 2.38 1.13 4 2.44 1.26 5 2.3 1.14
0.64 0.76 0.72 0.6 0.72
Seriousness (Mean ¼ 3.36, SD ¼ 1.03, Alpha ¼ 0.83) 6 2.93 1.31 7 3.49 1.23 8 3.4 1.29 9 3.63 1.18
0.56 0.71 0.71 0.68
Motivation (Mean ¼ 3.12, SD ¼ 0.94, Alpha ¼ 0.90) 10 3.15 1.47 11 3.7 1.19 12 2.98 1.4 13 2.51 1.36 14 2.74 1.36 15 2.9 1.17 16 3.54 1.16 17 3.0 1.28 18 3.02 1.24 19 3.69 1.28
0.72 0.64 0.64 0.59 0.69 0.64 0.6 0.71 0.69 0.6
Barriers (Mean ¼ 3.02, SD ¼ 0.80, Alpha ¼ 0.90) 20 3.0 1.29 21 3.2 1.36 22 3.02 1.14 23 2.97 1.27 24 2.96 1.25 25 3.04 1.33 26 3.01 1.35 27 2.84 1.24 28 2.61 1.21 29 3.0 1.24 30 3.06 1.2 31 3.27 1.11 32 3.22 1.1 33 2.81 1.18 34 3.29 1.19
0.62 0.63 0.69 0.67 0.52 0.57 0.65 0.52 0.57 0.53 0.53 0.63 0.68 0.43 0.47
Benefits (Mean ¼ 3.82, SD ¼ 0.99, Alpha ¼ 0.94) 35 3.85 1.14 36 3.82 1.1 37 3.94 1.13 38 3.65 1.25 39 3.8 1.18 40 3.7 1.21 41 3.95 1.1
0.77 0.78 0.84 0.8 0.82 0.75 0.84
Discussion This study evaluates and develops the health belief model scale for use in prostate cancer screenings. Subscales of susceptibility, seriousness, motivation, barriers, and benefits were included in the new scale as the main components of the health belief model (Ogden, 2004). The final version of the scale consists of 41 items. Previous publications and available literature were consulted while preparing the scale items (Champion, 1999; Gozum and Aydin, 2004; Oliver, 2007; Tingen et al., 1998). Due to its structural similarity, Champion’s health belief scale in breast cancer provided major guidance in preparing the scale developed in this study (Champion, 1999). Validity and reliability studies should be performed to standardize a scale and confirm its ability to produce
483
appropriate information (Ercan and Kan, 2004). During the development process of this scale, validity of structure was tested after the item pool was formed and appropriate items were selected. Both exploratory and confirmatory factor analyses were subsequently used to measure reliability and validity tests were applied. We used the Kaiser-Meyer-Olkin (KMO) measurement of sampling adequacy and Bartlett’s test of sphericity to determine whether the data were appropriate for factor analysis. The value of KMO measurement of sampling adequacy obtained from the analysis was 0.85, indicating that the sample size is adequate for principal component analysis. Similarly, the results obtained from Bartlett’s test of sphericity (c2 ¼ 6278.254, SD ¼ 820, p ¼ 0.000) indicated that the variables are correlated and therefore suitable for factor analysis (Pett et al., 2003). In principal component analysis, the model was examined on a structure with five factors, and after three items were removed from the model due to their high loadings on different factors, it was determined that the structure with five factors consisting of 41 questions explained 58.6% of total variance and factor loading value was above 0.40 in all items of the factors included in the scale. All these results indicate that structure validity of the scale is at an acceptable level (Gözüm and Aksayan, 2002). Confirmatory factor analysis is a method based on the evaluation of fit indexes demonstrating the coherence between the data and structure. In exploratory factor analysis, an item has the tolerance of being in more than one factor, while this is not possible in confirmatory factor analysis; therefore, there is still no consensus about which of the two methods is to be selected. In this study, confirmatory factor analysis was applied after exploratory factor analysis in order to determine the coherence between the scale and theoretical structure, and it was concluded at the end of this analysis that all fit indexes were almost perfect. When fit index values in the literature were reviewed, it was observed that coherence was considered perfect when c2/SD ratio was below 2, CFI, GFI and AGFI values were higher than 0.95, and RMSA and SRMR values were lower than 0.05 (Schermelleh-Engel et al., 2003). In this study, c2/SD ratio, CFI value, GFI value, AGFI value, RMSA value and SRMR value were determined as 0.042, 1, 0.93, 0.93, 0.00 and 0.09, respectively. These results indicate that the data comply with the theoretical structure perfectly for the c2/ SD, CFI, RMSA, SRMR results, and reasonably for the GFI, SRMR results. The results obtained from both exploratory and confirmatory factor analyses indicate that the factor structure of HBM-PCS may provide an applicable scale. The most widely used method for evaluating internal consistency is coefficient alpha (or Cronbach’s alpha). The normal range of values is between 0.00 and þ1.00, and higher values reflect a higher internal consistency (Polit and Beck, 2003). Cronbach’s alpha coefficient value in all five factors of HBM-PCS ranged between 0.83 and 0.94 and introduced a high level of reliability (Bring and Wood, 1998; Pett et al., 2003). Item analysis is another method that shows the internal consistency of a scale. In this study, total item score correlations of HBM-PCS and alpha coefficients in the absence of items were calculated. Items with 0.40 and higher values of total item score correlation are considered as highly distinguishing items (Özgüven, 1999). Items with coefficient values lower than 0.30 are recommended to be excluded from the scale, disregarding their statistical significance (Polit and Beck, 2003). In this study, total item correlation values for all items are statistically significant; item 33 has the lowest total item correlation value of 0.428 and the values of all items are above 0.40. These values indicate that the scale items have distinguishing properties and show consistency with one another (Gözüm and Aksayan, 2002; Özgüven, 1999; Polit and Beck, 2003).
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It was determined in this study that the new scale developed is a valid and reliable measurement tool in all statistical operations. This newly developed scale has several qualities that may be especially useful and provide first-line service and assistance to health professionals. Health professionals will be able to measure the health beliefs of individuals and determine their perceived barriers and perceived benefits for participating in screenings, motivation levels, and level of risk perception. This information will provide health professionals with significant information to design strategies to encourage individuals to participate in screenings. The internal consistency coefficient should be re-evaluated when the scale is applied to different sample groups. Cues to action and some components were not included in the scale but subsequent researchers may add these components and further develop the scale. The sampling group for the scale consists of relatively young/adult individuals, thus the validity and reliability of the study should be repeated with a group of individuals who are 55 years old. This scale was tested in a sampling group in the east of Turkey and therefore should be evaluated in different cultures. It will be beneficial to test its validity in groups with different demographic characteristics. Conclusion The Health Belief Model Scale may be used in prostate cancer screenings in males who are 40 years and older to measure perceived susceptibility, perceived seriousness, health motivation, perceived barriers and perceived benefits with regard to prostate cancer screenings. In conclusion, the HBM-PCS shows adequate psychometric properties for application within prostate cancer screenings in healthy men and it deserves to be widely used in at-risk populations. Prospective studies may contribute to the validation of the tool. As construct validity is an ongoing process, the validity and reliability of HBM-PCS should be reassessed with each new and older population. Funding No financial support was received for this study. Conflict of interest None of the authors have any financial or personal relationship with other persons or organizations that could inappropriately influence the work reported here. Appendix. Turkish health beliefs model scale for prostate cancer screenings ıda Prostat Kanseri konusundaki inançlarınızla ilgili birkaç As¸ag ru veya yanlıs¸ cevap yoktur. Bu soruları dog al inansoru vardır. Dog çlarınıza göre cevaplamanız önemlidir. Her bir ifadeyi okuduktan sonra “Kesinlikle Katılmıyorum”, “Katılmıyorum”, “Kararsızım”, “Katılıyorum”, ‘Tamamen Katılıyorum’ size en uygun olan bir tek ifadeye (x) is¸aretini yerles¸tiriniz. Sorular içinde geçen bazı ifadelerin anlamı s¸öyledir: i alınarak PSA ¼ Prostat Spesifik Antijeni ¼ Kan testi (kan örneg yapılan prostat kanseri testi) Prostat Muayenesi ¼ Uzman bir doktor tarafından rektal yolla (makattan) yapılan muayene. i alınarak yapılan Prostat Kanseri Taramaları ¼ Hem kan örneg prostat kanseri testi (PSA) hem de uzman bir doktor tarafından rektal yolla (makattan) yapılan muayene.
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C. Çapık, S. Gözüm / European Journal of Oncology Nursing 15 (2011) 478e485 Bring, P.J., Wood, M.J., 1998. Advanced Design in Nursing Research, second ed. Sage Publications, Thousand Oaks. Champion, V.L., 1999. Revised susceptibility, benefits, and barriers scale for mammography screening. Research in Nursing & Health 22, 341e348. Conner, M., Norman, P. (Eds.), 2005. Predicting Health Behaviour. McGraw-Hill Education, Berkshire, England. Dossey, B.M., Keegan, L., 2009. Holistic Nursing: a Handbook for Practice. Jones & Bartlett Publishers, Sudbury, MA. _ Kan, I, _ 2004. Ölçeklerde Güvenirlik ve Geçerlik. Uludag Üniversitesi Tıp Ercan, I, Fakültesi Dergisi 30, 211e216. Ford, M.E., Vernon, S.W., Havstad, S.L., Thomas, S.A., Davis, S.D., 2006. Factors influencing behavioral intention regarding prostate cancer screening among older African-American men. Journal of the National Medical Association 98, 505e514. Gable, R.K., Wolf, M.B., 1993. Instrument Development in the Affective Domain, second ed. Kluwer Academic Publishers, Boston. Gözüm, S., Aksayan, S., 2002. Kültürlerarası ölçek uyarlaması için rehber II: psikometrik özellikler ve kültürlerarası kars¸ilas¸tırma. Hems¸irelikte Aras¸tırma Dergisi 4, 9e20. Gozum, S., Aydin, I., 2004. Validation evidence for Turkish adaptation of champion’s health belief model scales. Cancer Nursing 27, 491e498. Held-Warmkessel, J., 2002. What your patient needs to know about prostate cancer. Nursing 32, 36e42. lık inanç ölçegi, osteoporoz öz-etkililik/ Kılıç, D., Erci, B., 2004. Osteoporoz sag i. Atatürk yeterlik ölçegi ve osteoporoz bilgi testi’nin geçerlilik ve güvenirlig Üniversitesi Hems¸irelik Yüksekokulu Dergisi 7, 89e102. Lu, H.-Y., 2007. Understanding prostate cancer screenings among African American men: combining the behavioral science and anthropological perspectives. Journal of African American Studies 11, 126e139. Ogden, J., 2004. Health Psychology: a Text Book. McGrawHill Education, Berkshire, England. Oliver, J.S., 2007. Prostate Cancer Screening Patterns Among African American Men in the Rural South. Unpublished doctoral dissertation, Byrdine F. Lewis School of Nursing, Georgia. _ Özgüven, I.E., 1999. Psikolojik Testler. PDREM Yayınları, Ankara. Pett, M.A., Lackey, N.K., Sullivan, J.J., 2003. Making Sense of Factor Analysis the Use of Factor Analysis for Instrument Development in Health Care Research. Sage, California. Pierce, R., Chadiha, L.A., Vargas, A., Mosley, M., 2003. Prostate cancer and psychosocial concerns in African American men: literature synthesis and recommendations. Health & Social Work 28, 302e311.
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Plowden, K.O., 2006. To screen or not to screen: factors influencing the decision to participate in prostate cancer screening among urban African-American men. Urology Nursing 26, 477e482. Polit, D.F., Beck, C.T., 2003. Nursing Research Principles and Methods, seventh ed. Lippincott Williams &Wilkins, Philadelphia. Powers, B.A., Knapp, T.R., 2005. Dictionary of Nursing Theory and Research. Springer, New York. Price, J.H., Colvin, T.L., Smith, D., 1993. Prostate cancer: perceptions of African-American males. Journal of the National Medical Association 85, 941e947. Prostate Cancer Foundation., 2010 Available at: http://www.pcf.org/site/c.leJRIROrEpH/ b.5802027/k.D271/Prostate_Cancer_Risk_Factors.htm (accessed October 2010). Rai, T., Clements, A., Bukach, C., Shine, B., Austoker, J., Watson, E., 2007. What influences men’s decision to have a prostate-specific antigen test? A qualitative study. Family Practice 24, 365e371. Schermelleh-Engel, K., Moosbrugger, H., Müller, H., 2003. Evaluating the fit of structural equation models:tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online 8, 23e74. Scientific-Software-International (nd) Asymptotic covariance matrix from file command. Available at: http://www.ssicentral.com/lisrel/resources.html (accessed 11 November 2009). S¸encan, H., 2005. Sosyal Ve Davranıs¸sal Ölçümlerde Güvenilirlik Ve Geçerlilik. Seçkin. Yayincılık, Ankara. Tingen, M.S., Weinrich, S.P., Boyd, M.D., Weinrich, M.C., 1997. Prostate cancer screening: predictors of participation. Journal of the American Academy of Nurse Practitioners 9, 557e567. Tingen, M.S., Weinrich, S.P., Heydt, D.D., Boyd, M.D., Weinrich, M.C., 1998. Perceived benefits: a predictor of participation in prostate cancer screening. Cancer Nursing 21, 349e357. Weinrich, S.P., Reynolds Jr., W.A., Tingen, M.S., Starr, C.R., 2000. Barriers to prostate cancer screening. Cancer Nursing 23, 117e121. Weller, D., Pinnock, C., Silagy, C., Hiller, J.E., Marshall, V.R., 1998. Prostate cancer testing in SA men: influence of sociodemographic factors, health beliefs and LUTS. Australian and New Zealand Journal of Public Health 22, 400e402. Whaley, Q.M., 2006. The Relationship Between Perceived Barriers and Prostate Cancer Screening Practices Among African-American Men. Unpublished master’s degree, The Florida State University College Of Nursing, Florida.
Contents lists available at ScienceDirect
European Journal of Oncology Nursing journal homepage: www.elsevier.com/locate/ejon
Development and validation of health beliefs model scale for prostate cancer screenings (HBM-PCS): Evidence from exploratory and confirmatory factor analysesq Cantürk Çapık a, Sebahat Gözüm b, * a b
Kafkas University, Kars School of Health, 36100 Kars, Turkey Akdeniz University, Antalya School of Health, 07058 Antalya, Turkey
a b s t r a c t Keywords: Prostate cancer Screening Beliefs Scale Nursing
Purpose: Primary diagnostic tools for prostate cancer are prostate examination and detection of prostate specific antigen. It is important to know what factors affect individuals in their use of these tools. The aim of this study was to create a scale that combined the basic components of the health belief model with a focus on prostate cancer screening. Method: A total of 240 healthy men (mean age and standard deviation ¼ 53.8 10.8) was selected by convenient sampling. In this methodological study, exploratory and confirmatory factor analyses were used for psychometric evaluation. Cronbach’s alpha coefficient was used to evaluate the reliability of the scale. Results: The scale was composed of 41 items and five subscales. The initial analysis extracted five factors. Confirmatory factor analysis showed that the data obtained were compatible with Health Beliefs Model (HBM) (c2 ¼ 769 (n ¼ 240) ¼ 324.25, p > 0.05, GFI ¼ 0.93, AGFI ¼ 0.93, RMSA ¼ 0.00, CFI ¼ 1.00. Cronbach’s a coefficient of the subscales ranged from 0.83 to 0.94. Conclusions: The scale was found to be appropriate for the measurement of health beliefs about prostate cancer screening. The scale may be used in prostate cancer screenings for males who are 40 years and older to measure perceived susceptibility, perceived seriousness, health motivation, perceived barriers and perceived benefits with regard to prostate cancer screenings. Ó 2010 Elsevier Ltd. All rights reserved.
Introduction Prostate cancer is one of the most commonly observed cancer types in old age (Prostate Cancer Foundation, 2010). While it is generally experienced in advanced age, awareness should be created in younger men to encourage screening and early diagnosis. The American Cancer Society (2010) recommends that men over 50 years of age should be informed about prostate cancer screenings. Advice should be provided at an earlier age for those at higher risk for prostate cancer.
q This study was presented at the 1st International Congress on Nursing Education, Research & Practice, Grand Hotel Palace, Thessaloniki, Greece, 15e17 October 2009. * Corresponding author. Tel.: þ90 242 3106103; fax: þ90 242 226 14 69. E-mail addresses: [email protected] (C. Çapık), [email protected], [email protected] (S. Gözüm). 1462-3889/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ejon.2010.12.003
Among many studies in a qualitative study Rai et al. (2007) investigated the factors that affect men’s participation in prostate cancer screenings and determined that the belief in early diagnosis, existence of a friend with prostate cancer, media sources, family history and ongoing urinary complaints were significant factors in participation. Weller et al. (1998) reported that experiencing lower urinary tract symptoms was a powerful factor affecting participation in screenings, while occupation and educational status were not significant factors. Alternatively, some researchers have explored the issue from various dimensions within the framework of a certain model, instead of directly determining the factors that affect participation in prostate cancer screenings (Ford et al., 2006; Oliver, 2007; Tingen et al., 1997). One such approach, the health belief model, provides a useful conceptual framework for understanding and estimating health-related behaviour (Pierce et al., 2003). The health belief model identifies the specific attitudes and beliefs that influence people in choosing preventive health care and engaging in recommended medical regimens (Dossey and Keegan,
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2009). In the last 50 years, researchers have used the health belief model with regard to matters like condom use, breast self-examination, mammography screening behaviour and dental hygiene (Lu, 2007). Investigating the behaviour of participation in prostate cancer screenings is among these examples Oliver (2007), Plowden (2006), Tingen et al. (1998) and Whaley (2006). Among the components of the health belief model, benefit perception and high motivation are known to increase participation in prostate cancer screenings and decrease barrier perception (Oliver, 2007; Tingen et al., 1998; Whaley, 2006). There are several important factors that restrict men’s participation in screenings, such as lack of knowledge about cancer, fear of cancer, different between screening results and cancer diagnosis, having no health insurance, not knowing which specialist to see, not knowing where to consult, and the problem cause by the working hours (Ford et al., 2006; Weinrich et al., 2000). The health belief model was used in explaining several health behaviours since it was first developed as an applicable model by Rosenstock in 1966 (Ogden, 2004). However, a review of related literature has revealed the absence of a standard measurement tool that incorporates all essential components of a model for investigating participation in prostate cancer screenings. In one of their studies, Price et al. (1993) used a measurement tool that integrated essential components of the health belief model; although this was a tool developed specifically for their own study and used only in performing validation measurements. The absence of the analysis of factor structure was perceived as a deficiency in this tool. Price et al. (1993) and Tingen et al. (1998) have also investigated the perceived benefits of the model; Weinrich et al. (2000) explored the perceived barriers of the model and Oliver (2007) examined the level of participants’ motivationdhowever, only certain subscales of the model were identified in each of these publications. In literature, despite many studies related to health beliefs about prostate cancer screenings (Ford et al., 2006; Held-Warmkessel, 2002; Lu, 2007; Oliver, 2007; Plowden, 2006; Rai et al., 2007; Tingen et al., 1997, 1998; Weinrich et al., 2000), in each study different questionnaires were used to measure health beliefs. There is a need to develop an instrument for men to describe their perception about prostate cancer screenings so that the factors exploring screening behaviours of healthy men can be studied systematically. The use of a measurement tool for measuring the health beliefs in related to prostate cancer screenings, explaining all the features above, will make the results possible to compare. An instrument that describes health behaviours screenings health beliefs can be used in each culture by necessary linguistic adaptation. Relevant items of the scale developed by Champion for breast cancer were used in the study, by combining the basic components of the health belief model with a focus on prostate cancer screening.
Methods Item selection Items associated with health beliefs were selected from previously developed instruments and from the available literature for inclusion in the new instrument (Champion, 1999; Gozum and Aydin, 2004; Oliver, 2007; Tingen et al., 1998). While the previous studies were analyzed, papers were also studied measuring various components belonging to the health beliefs in prostate cancer, as well as those related to studies of the health belief model within areas such as breast cancer and osteoporosis (Champion, 1999; Gozum and Aydin, 2004; Kılıç and Erci, 2004). The literature survey revealed several concepts representing important features
479
of health beliefs, including susceptibility, seriousness, benefits, barriers, motivation, self-efficiency, and cues to activation (Conner and Norman, 2005; Ogden, 2004). Five concepts (susceptibility, seriousness, benefits, barriers, motivation) were selected to be used in developing this questionnaire. The self-efficiency subscale was not included in the study scale due to the absence of any application that individuals can perform by themselves to diagnose prostate cancer. A pool of 82 items was generated from the concept dimensions. After examination by the researchers, some items were altered or excluded from the scale because they either duplicated other items, they did not represent the relevant dimension, or they failed to convey a clear expression of the intended point. Following this elimination process, 48 items were selected from the item pool and grouped under the five domains identified by the literature survey. In the selection of these items, five academics were consulted. Two of these academics serve as lecturers in the Department of Obstetrics and Gynaecology Nursing, while three of them serve as lecturers in the Department of Public Health Nursing. All these academics are researchers with experience in the health belief model. Sample The sample consisted of 240 men (mean age and standard deviation ¼ 53.8 10.8). Gable and Wolf (1993) suggested a sample size of 5e10 subjects per item to ensure a conceptually clear factor structure for factor analysis. The desired minimum sample size required was determined to be 205 participants based on 41 items. Table 1 introduces the demographic characteristics of the respondents. Procedures This is a methodological study and the population consisted of male participants who were aged 40 years or more and living in Erzurum, Turkey. The participants were individuals who were selected with convenience sampling from the general community and who had not previously received a diagnosis of prostate cancer. Potential participants were informed verbally about the aim of the study by the first author, and then asked if they agreed to answer the questionnaire; they were assured of their right to refuse to participate or to withdraw at any time. The data were collected between November 2008 and March 2009 and was approved by the
Table 1 Demographic Characteristics of the Respondents (n ¼ 240). N
%
Age (mean) 53.8(10.8) Education Primary education and lower High school and upper
140 100
58.3 41.7
Marital status Not married Married
26 214
10.8 89.2
80 94 66
33.3 39.2 27.5
Income Low Moderate High Health Insurance No Yes
60 180
25 75
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local ethical committee. Participants were gathered from coffee houses and hospital waiting rooms. The reason why coffee houses were preferred in selecting the sample group was because men in Turkey frequently meet in these places to spend time together. Men over middle age gather in these places to get involved in social interaction, thus these venues were appropriate for selecting this sample group. In addition, in hospital waiting rooms it was easy to find men who were visiting their relatives or who came to the hospital to receive treatment.
Table 2 Subscales, n of items, minimum and maximum scores of HBM-PCS. Itemsa
Subscale
n of Items
Min. Point
Max. Point
Items Items Items Items Items
Susceptibility Seriousness Motivation Barriers Benefits
5 4 10 15 7
5 4 10 15 7
25 20 50 75 35
a
Susceptibility.
Administration and scoring of the instrument The final version of the health beliefs model scale for prostate cancer screenings (HBM-PCS) is composed of 41 items and five subscales (Susceptibility, Seriousness, Motivation, Barriers, and Benefits). A five-point Likert format was used to measure each statement. Accordingly, the range of possible responses to each item was determined as follows: 1 ¼ strongly disagree, 2 ¼ disagree, 3 ¼ neither agree nor disagree, 4 ¼ agree and 5 ¼ strongly agree. High scores obtained from susceptibility, seriousness, motivation and benefits subscales were associated with a positive situation; where as high scores obtained from the barriers subscale were associated with a negative situation due to the high level of perceived barriers to participate in screening. Table 2 presents a summary of subscales, number of items, and minimum and maximum scores. The scale does not introduce a total score, and every subscale is scored individually. The scale can be applied in 10 min.
12345-
I have a high probability of having prostate cancer. I have a high probability of having prostate cancer in the next few years. I have a feeling that I will have prostate cancer at some time in my life. I fear that I may die because of prostate cancer. I have a high probability of having prostate cancer when compared to other men of my age.
Seriousness 6- It frightens me to think of prostate cancer. 7- I will experience several problems for a long time if I have prostate cancer. 8- Prostate cancer will have a negative effect on my relationship with my wife or partner. 9- My whole life will change in a negative way if I have prostate cancer.
Motivation 101112131415-
Content validity and pilot study Prior to the administration of the questionnaire, the content validity of 48 items selected from the original 82 items was evaluated by five nurse academicians. The nurse researchers then reviewed the preliminary questionnaire for selection, construction, and wording of the statements. Changes were made based on these evaluations. Four items were removed because of repetition within the scale. The preliminary questionnaire (comprising 44 items) was pilot tested on a convenience sample of 15 men. Male teachers and workers at a high school were invited to participate in the pilot study and all of them consented to participate and were eligible. Inclusion criteria for the sample were age older than 40 and not having a diagnosis of prostate cancer. Data were collected by selfreport method in the teachers’ room. Respondents were asked to complete the questionnaire and make comments on the back page related to the intelligibility of items and the clarity of instructions. No changes were needed as a result of this pilot application. Thus, 44 items were selected for further analysis. Data analysis
1e5 6e9 10e19 20e34 35e41
16171819-
I follow new information and developments in order to improve my health. I believe that it is important to perform activities to improve my health. I keep a balanced diet. I do sports at least 3 times a week. I have my medical check-ups regularly even if I am not sick. It is easy for me to plan participating in prostate cancer screenings (rectal examination and blood test performed by taking blood sample, PSA measurement). Participating in prostate cancer screenings will contribute to my health. I want to have blood test [PSA] for prostate cancer in the next 6 months. I want to have prostate examination in the next 6 months. If I have prostate cancer; I want to know it as soon as possible.
Barriers 202122232425262728293031323334-
I fear prostate cancer screenings because I do not know how it is performed. I do not know where and how to go for prostate cancer screenings. It takes a lot of time to participate in prostate cancer screenings. I forget to participate in prostate cancer screenings. I have more important problems than participating in prostate cancer screenings. I do not know whether the health insurance covers prostate cancer screenings. I do not know which specialist to see for prostate cancer screenings. I fear participating in prostate cancer screenings because I feel that something is wrong. If I am diagnosed with prostate cancer after prostate cancer screenings; there will be nothing to do for its treatment. I do not need to participate in prostate cancer screenings, since I am not experiencing any problems. I fear that the results of prostate cancer screening will be bad. Prostate examination is very unsettling. Prostate examination is very painful. Doctors who perform the prostate examination treat patients impolite. Sexual ability declines after prostate cancer treatment.
Benefits All data analyses were performed using the Statistical Package for the Social Sciences (version 11.5, SPSS Inc and Lisrel [Linear Structural Relationships] version 8. 5). There were no missing values in the data. Descriptive statistics were calculated to describe the characteristics of the sample, and exploratory and confirmatory factor analyses were used to determine the psychometric properties of the new instrument. Principal component analysis and varimax rotation were used for exploratory factor analysis. All factors with values greater than or equal to 1.0 (unity root criterion) were retained (Akgül, 2005). Structural equation modelling (SEM) was the statistical technique used to analyze the proposed model structure of HBM-PCS. A first-order confirmatory factor analysis of data from HBM-PCS was
35- I will be doing something good for myself if I participate in prostate cancer screenings. 36- If I participate in prostate cancer screenings and if I do not receive any diagnosis, I won’t have to worry about prostate cancer. 37- Participating in prostate cancer screenings will help an early diagnosis of cancer. 38- If prostate cancer is diagnosed early and if it is treated successfully, I will have a chance to live a long life. 39- If prostate cancer screenings do not reveal any negative results; I will know that I am healthy. 40- If prostate cancer is diagnosed early; the growth of cancer may be prevented by treatment. 41- If I participate in prostate cancer screenings; I will know the truth about my health condition.
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conducted. The asymptomatic covariance matrix was formed for the estimation. The Diagonally Weighted Least Squares method (DWLS) was employed for estimation (Scientific-Software-International). Various fit indices were used to determine if the proposed model’s covariance structure differed from the observed relationships. Goodness of the calculated fit indices included the Pearson chisquare (c2) statistic with degrees of freedom, the goodness-of-fit index (GFI), the comparative fit index (CFI), and the root mean error of approximation (RMSEA). Cronbach’s alpha (reliability coefficient) was used to test the internal consistency for the instrument and for each of the factors resulting from the factor analysis. For total item correlation, a total correlation of 0.40 of all items was accepted as the inclusion criterion (Gözüm and Aksayan, 2002).
481
Each factor was then labelled to describe the items loading on it. Factor 1 was labelled ‘Susceptibility’, Factor 2 was labelled ‘Seriousness’, Factor 3 was labelled ‘Benefits’, Factor 4 was labelled ‘Barriers’ and Factor 5 was labelled ‘Motivation’. Table 3 presents a summary of the 41 items, factors and factor loadings. The findings from the exploratory factor analysis resulted in the omission of three items.
Confirmatory factor analysis Confirmatory factor analysis was subsequently applied to the data in order to examine the construct validity of the five factor models extracted from the EFA. Initially, 41 predictors (items) were used to test the model. Factor loadings of items were found to be between 0.45 and 0.86 after the application of confirmatory factor analysis (see Fig. 1). All factor loadings were found to have a critical ratio (CR) that was greater than 1.96, indicating statistical significance. The fit indices for the 41 itemsemodel (final model) were c2 ¼ 324.25, df ¼ 769, p < 0.05, GFI ¼ 0.93, CFI ¼ 1.00, AGFI ¼ 0.93, RMSEA ¼ 0.00 and SRMR ¼ 0.09. Modification indices for the regression weights were examined next to identify the parameters that were indicative of cross-loadings and misspecifications. However, modifications were not performed because a remarkable improvement on fit indexes was not observed. The model was accepted in its current form considering its complexity.
Results Construct validity Exploratory factor analysis The Kaiser-Meyer-Olkin and Barlett tests were applied before evaluating the results of exploratory factor analysis (EFA). The Kaiser-Meyer-Olkin measurement of sampling adequacy was 0.85 and Barlett test results were quite significant (c2 ¼ 6278. 254, SD 820, p ¼ 0.000). Later, we used exploratory factor analysis to guide the factor structure of the developed model. Principal components factoring was performed on all 44 items by using a varimax rotation. A minimal factoreitem correlation of 0.40 was set for inclusion of an item in a factor (Gözüm and Aksayan, 2002). The initial analysis extracted five factors, but three items were omitted because these took a higher factor loading than other factors and were illusive, which was unsuitable for these items in terms of theoretical structure (S¸encan, 2005). All items on each factor were from the same construct except the three items. For example, an item related to barrier was unusually loaded on the benefit subscale, and two items about benefit loaded in health motivation. Therefore, the three items were deleted. The analysis was subsequently repeated. The variance reported before the exclusion of three items was 58.1%, and it was determined as 58.6% after the exclusion of the three items. The loadings ranged from 0.43 to 0.87 in the rotated pattern matrix in the present study.
Instrument reliability After the explanatory and confirmatory factor analyses, Cronbach’s alpha coefficient was evaluated. Cronbach’s alpha is an index of the degree to which a measuring instrument is internally reliable (Powers and Knapp, 2005). A reliability coefficient of 0.70 or above is accepted as evidence of internal consistency for new instruments (Bring and Wood, 1998). Alpha coefficient applied separately for all five factors ranged between 0.83 and 0.94, which is an acceptable value. No item was predicted to significantly increase the scale reliability if omitted. No omission was made at this time. Table 4 introduces a summary of Cronbach’s alpha coefficient values for each factor and other results. In this study, item 33 had the lowest total item correlation value of 0.428, and the values of all items were above 0.40.
Table 3 Factor Loadings (EFA) for HBMS-PCS Subscales. Susceptibility
Seriousness
Item
Factor loadings
Item
Factor loadings
Item
Factor loadings
Item
1 2 3 4 5
0.71 0.83 0.81 0.70 0.80
6 7 8 9
0.60 0.75 0.73 0.76
10 11 12 13 14 15 16 17 18 19
0.76 0.71 0.68 0.65 0.67 0.65 0.64 0.71 0.71 0.66
20 0.68 21 0.73 22 0.72 23 0.74 24 0.64 25 0.70 26 0.77 27 0.60 28 0.59 29 0.57 30 0.52 31 0.60 32 0.64 33 0.47 34 0.43 Exp. Variance 8.4%
Exp. Variance 14.7% Total Explained Variance 58.1%
Motivation
Exp. Variance 13.8%
Barriers
Exp. Variance 13.2%
Benefits Factor loadings
Item
Factor loadings
35 36 37 38 39 40 41
0.78 0.80 0.87 0.81 0.86 0.79 0.87
Exp. Variance 8.1%
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Fig. 1. Factor loading for HBMS-PCS subscales.
C. Çapık, S. Gözüm / European Journal of Oncology Nursing 15 (2011) 478e485 Table 4 Reliability and validity testing for HBM-PCS. Items
Item Mean
SD
Corrected Item/Total correlation
Susceptibility (Mean ¼ 2.33, SD ¼ 0.92, Alpha ¼ 0.86) 1 2.45 1.11 2 2.2 1.07 3 2.38 1.13 4 2.44 1.26 5 2.3 1.14
0.64 0.76 0.72 0.6 0.72
Seriousness (Mean ¼ 3.36, SD ¼ 1.03, Alpha ¼ 0.83) 6 2.93 1.31 7 3.49 1.23 8 3.4 1.29 9 3.63 1.18
0.56 0.71 0.71 0.68
Motivation (Mean ¼ 3.12, SD ¼ 0.94, Alpha ¼ 0.90) 10 3.15 1.47 11 3.7 1.19 12 2.98 1.4 13 2.51 1.36 14 2.74 1.36 15 2.9 1.17 16 3.54 1.16 17 3.0 1.28 18 3.02 1.24 19 3.69 1.28
0.72 0.64 0.64 0.59 0.69 0.64 0.6 0.71 0.69 0.6
Barriers (Mean ¼ 3.02, SD ¼ 0.80, Alpha ¼ 0.90) 20 3.0 1.29 21 3.2 1.36 22 3.02 1.14 23 2.97 1.27 24 2.96 1.25 25 3.04 1.33 26 3.01 1.35 27 2.84 1.24 28 2.61 1.21 29 3.0 1.24 30 3.06 1.2 31 3.27 1.11 32 3.22 1.1 33 2.81 1.18 34 3.29 1.19
0.62 0.63 0.69 0.67 0.52 0.57 0.65 0.52 0.57 0.53 0.53 0.63 0.68 0.43 0.47
Benefits (Mean ¼ 3.82, SD ¼ 0.99, Alpha ¼ 0.94) 35 3.85 1.14 36 3.82 1.1 37 3.94 1.13 38 3.65 1.25 39 3.8 1.18 40 3.7 1.21 41 3.95 1.1
0.77 0.78 0.84 0.8 0.82 0.75 0.84
Discussion This study evaluates and develops the health belief model scale for use in prostate cancer screenings. Subscales of susceptibility, seriousness, motivation, barriers, and benefits were included in the new scale as the main components of the health belief model (Ogden, 2004). The final version of the scale consists of 41 items. Previous publications and available literature were consulted while preparing the scale items (Champion, 1999; Gozum and Aydin, 2004; Oliver, 2007; Tingen et al., 1998). Due to its structural similarity, Champion’s health belief scale in breast cancer provided major guidance in preparing the scale developed in this study (Champion, 1999). Validity and reliability studies should be performed to standardize a scale and confirm its ability to produce
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appropriate information (Ercan and Kan, 2004). During the development process of this scale, validity of structure was tested after the item pool was formed and appropriate items were selected. Both exploratory and confirmatory factor analyses were subsequently used to measure reliability and validity tests were applied. We used the Kaiser-Meyer-Olkin (KMO) measurement of sampling adequacy and Bartlett’s test of sphericity to determine whether the data were appropriate for factor analysis. The value of KMO measurement of sampling adequacy obtained from the analysis was 0.85, indicating that the sample size is adequate for principal component analysis. Similarly, the results obtained from Bartlett’s test of sphericity (c2 ¼ 6278.254, SD ¼ 820, p ¼ 0.000) indicated that the variables are correlated and therefore suitable for factor analysis (Pett et al., 2003). In principal component analysis, the model was examined on a structure with five factors, and after three items were removed from the model due to their high loadings on different factors, it was determined that the structure with five factors consisting of 41 questions explained 58.6% of total variance and factor loading value was above 0.40 in all items of the factors included in the scale. All these results indicate that structure validity of the scale is at an acceptable level (Gözüm and Aksayan, 2002). Confirmatory factor analysis is a method based on the evaluation of fit indexes demonstrating the coherence between the data and structure. In exploratory factor analysis, an item has the tolerance of being in more than one factor, while this is not possible in confirmatory factor analysis; therefore, there is still no consensus about which of the two methods is to be selected. In this study, confirmatory factor analysis was applied after exploratory factor analysis in order to determine the coherence between the scale and theoretical structure, and it was concluded at the end of this analysis that all fit indexes were almost perfect. When fit index values in the literature were reviewed, it was observed that coherence was considered perfect when c2/SD ratio was below 2, CFI, GFI and AGFI values were higher than 0.95, and RMSA and SRMR values were lower than 0.05 (Schermelleh-Engel et al., 2003). In this study, c2/SD ratio, CFI value, GFI value, AGFI value, RMSA value and SRMR value were determined as 0.042, 1, 0.93, 0.93, 0.00 and 0.09, respectively. These results indicate that the data comply with the theoretical structure perfectly for the c2/ SD, CFI, RMSA, SRMR results, and reasonably for the GFI, SRMR results. The results obtained from both exploratory and confirmatory factor analyses indicate that the factor structure of HBM-PCS may provide an applicable scale. The most widely used method for evaluating internal consistency is coefficient alpha (or Cronbach’s alpha). The normal range of values is between 0.00 and þ1.00, and higher values reflect a higher internal consistency (Polit and Beck, 2003). Cronbach’s alpha coefficient value in all five factors of HBM-PCS ranged between 0.83 and 0.94 and introduced a high level of reliability (Bring and Wood, 1998; Pett et al., 2003). Item analysis is another method that shows the internal consistency of a scale. In this study, total item score correlations of HBM-PCS and alpha coefficients in the absence of items were calculated. Items with 0.40 and higher values of total item score correlation are considered as highly distinguishing items (Özgüven, 1999). Items with coefficient values lower than 0.30 are recommended to be excluded from the scale, disregarding their statistical significance (Polit and Beck, 2003). In this study, total item correlation values for all items are statistically significant; item 33 has the lowest total item correlation value of 0.428 and the values of all items are above 0.40. These values indicate that the scale items have distinguishing properties and show consistency with one another (Gözüm and Aksayan, 2002; Özgüven, 1999; Polit and Beck, 2003).
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It was determined in this study that the new scale developed is a valid and reliable measurement tool in all statistical operations. This newly developed scale has several qualities that may be especially useful and provide first-line service and assistance to health professionals. Health professionals will be able to measure the health beliefs of individuals and determine their perceived barriers and perceived benefits for participating in screenings, motivation levels, and level of risk perception. This information will provide health professionals with significant information to design strategies to encourage individuals to participate in screenings. The internal consistency coefficient should be re-evaluated when the scale is applied to different sample groups. Cues to action and some components were not included in the scale but subsequent researchers may add these components and further develop the scale. The sampling group for the scale consists of relatively young/adult individuals, thus the validity and reliability of the study should be repeated with a group of individuals who are 55 years old. This scale was tested in a sampling group in the east of Turkey and therefore should be evaluated in different cultures. It will be beneficial to test its validity in groups with different demographic characteristics. Conclusion The Health Belief Model Scale may be used in prostate cancer screenings in males who are 40 years and older to measure perceived susceptibility, perceived seriousness, health motivation, perceived barriers and perceived benefits with regard to prostate cancer screenings. In conclusion, the HBM-PCS shows adequate psychometric properties for application within prostate cancer screenings in healthy men and it deserves to be widely used in at-risk populations. Prospective studies may contribute to the validation of the tool. As construct validity is an ongoing process, the validity and reliability of HBM-PCS should be reassessed with each new and older population. Funding No financial support was received for this study. Conflict of interest None of the authors have any financial or personal relationship with other persons or organizations that could inappropriately influence the work reported here. Appendix. Turkish health beliefs model scale for prostate cancer screenings ıda Prostat Kanseri konusundaki inançlarınızla ilgili birkaç As¸ag ru veya yanlıs¸ cevap yoktur. Bu soruları dog al inansoru vardır. Dog çlarınıza göre cevaplamanız önemlidir. Her bir ifadeyi okuduktan sonra “Kesinlikle Katılmıyorum”, “Katılmıyorum”, “Kararsızım”, “Katılıyorum”, ‘Tamamen Katılıyorum’ size en uygun olan bir tek ifadeye (x) is¸aretini yerles¸tiriniz. Sorular içinde geçen bazı ifadelerin anlamı s¸öyledir: i alınarak PSA ¼ Prostat Spesifik Antijeni ¼ Kan testi (kan örneg yapılan prostat kanseri testi) Prostat Muayenesi ¼ Uzman bir doktor tarafından rektal yolla (makattan) yapılan muayene. i alınarak yapılan Prostat Kanseri Taramaları ¼ Hem kan örneg prostat kanseri testi (PSA) hem de uzman bir doktor tarafından rektal yolla (makattan) yapılan muayene.
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