Factor structure and internal reliability of breast cancer screening Champion’s Health Belief Model Scale in Yemeni women in Malaysia: a cross-sectional study

Abstract

Background The reliability and validity of the Champion’s Health Belief Model Scale (CHBMS) used in assessing the belief of women regarding breast cancer (BC) and breast cancer screening (BCS) have been examined on various populations. However, the use of this tool has not been adequately assessed for its validity in ethnic minorities. This study assessed the validity and reliability of CHBMS by analyzing the factor structure and internal reliability of the factors among Yemeni women in Malaysia. Methods A survey was conducted among 103 female teachers from 10 schools. SPSS version 22.0 was utilized in analyzing the data. Descriptive statistics were computed for the socio-demographic characteristics. The Cronbach’s alpha coefficients were used in assessing the internal reliability. The Exploratory Factor Analysis (EFA) was used to analyze the factor structure of the translated items. Parallel analysis was performed to determine the number of factors accurately. Results The alpha coefficients of the factors had acceptable values ranging between 0.76 and 0.87. The factor analysis yielded six and five factors for breast self-examination (BSE) and mammography (MMG), with a total explained variance of 47.69% and 52.63%, respectively. The Kaiser–Meyer–Olkin (KMO) index values of 0.64 and 0.72, and the Bartlett’s Test of Sphericity (P = 0.0001) for BSE and MMG, respectively, verified the normality distribution and the adequacy of the sample size for EFA. All the items on each factor were from the same construct that were consistent with the number of factors obtained in the scale development study. The items achieved adequate factor loadings that ranged between 0.47 and 0.88. Conclusions The translated version of the CHBMS is a validated scale used in assessing the beliefs related to BC and BCS among Yemeni women living in Malaysia. Healthcare workers could use the scales to assess women’s beliefs on BC and BCS. This instrument could be used to test the effectiveness of the intervention programs.

Rasch Modeling and Multilevel Confirmatory Factor Analysis for the Usability of the Impact of Event Scale-Revised (IES-R) during the COVID-19 Pandemic

“…The objective of this step is to obtain goodness of fit statistics, which include the p -value of the Chi-Square statistic (non-significant), the Bentler–Bonett normed fit index (NFI) (≥0.90), the Bentler–Bonett non-normed fit index (NNFI) (≥0.90), the comparative fit index (CFI) (≥0.90), Bollen’s incremental fit index (IFI) (≥0.90), the Joreskog–Sorbom goodness of fit index (GFI) (≥0.90), the root mean square residual (RMR) (≤0.08), the standardized root mean square residual (SRMR) (≤0.08), the root mean square error of approximation (RMSEA) (≤0.08), and the 90% confidence interval of RMSEA (90% CI RMSEA) (≤0.08) [ 66 , 68 , 72 , 82 , 83 , 90 , 91 , 93 , 94 , 95 ]. The researcher can check for the re-specified model if the original model is a misfit until the researcher holds the fit model.…”

Group-based trajectory modeling to identify health beliefs of COVID-19 vaccination and its predictors: A cohort study in China

“…Previous studies have confirmed that some aspects of beliefs can be statistically analyzed in the form of numbers. 20–25 – 28 Many scholars developed questionnaires with numerical values based on the health belief model to measure an individual’s health beliefs about various diseases and health behaviors, such as oral health beliefs in adolescence, 25 popular beliefs about COVID-19 infection risks, 27 beliefs about colorectal cancer screening prevention, 20 and health beliefs about breast screening behaviors. 28 …”

The Associations between Sibling Victimization, Sibling Bullying, Parental Acceptance–Rejection, and School Bullying

“…Moreover, SEM allows for simultaneously estimating a series, but independent, multiple regression equations cannot. It can also incorporate latent variables into the analysis and accounts for measurement errors in the estimation process [ 60 , 61 , 62 , 63 , 64 ]. In other words, SEM is a statistical technique that establishes measurement models and structural models to address complicated behavioral relationships [ 61 , 62 , 65 , 66 ].…”