The psychometric properties of the Persian menopause rating scale

Background: To measure the severity of menopausal complaints and determine the pattern of menopausal symptoms, a valid and reliable instrument is needed in women’s healthcare. The Menopause Rating Scale (MRS) is one of the best-known tools in response to the lack of standardized scales. The purpose of this study was to examine the psychometric properties of the MRS in an Iranian example. Methods: Participants were randomly selected from women referred to healthcare centers in Miandoab, West Azerbaijan, Iran. A total of 330 questionnaires were completed (response rate of 96.9%). Two samples were considered for analysis in the validation process. An exploratory factor analysis (EFA) was conducted on the first sample (n1 = 165), and a confirmatory factor analysis (CFA) was done using a second study sample (n2 = 165). The psychometric properties process was concluded with assessment of internal consistency and test-retest reliability. Results: The EFA with Principal Component Analysis extracted three factors explaining 75.47% cumulative variance. The CFA confirmed a three-factor structure of the 11-items MRS. All fit indices proved to be satisfactory. The relative chi-square (χ2/df) was 3.686 (p < .001). The Root Mean Square Error of Approximation (RMSEA) of the model was .04 (90% CI = .105–.150). All comparative indices of the model, including the Comparative Fit Index, Normed Fit Index, and Relative Fit Index, were more than .80 (.90, .87, and .80, respectively). For the overall scale, Cronbach’s alpha was .931, whereas the alpha for the subscales ranged from 0.705–0.950. The intraclass correlation was .91 (95% CI = .89–.93), p < 0.001. Conclusion: The results of the study indicate that the Persian model of the MRS is a valid and reliable scale. As a screening tool, the Persian MRS could be used to identify the pattern of symptoms among menopausal, premenopausal, and postmenopausal women to care for and educate them on how to identify and treat the symptoms.

Jaar van publicatie:2020

Toegankelijkheid:Open