*2.4. Statistical Analysis*

The construct validity of the questionnaire was estimated with exploratory factor analysis, identifying the underlying factors. The varimax rotation method was used to identify correlations between items and construct the factors. Accordingly, the level for acceptable factor loading was set at >0.4 and for acceptable eigenvalues, set at >1. The Kaiser–Meyer–Olkin test was also used to measure the adequacy of the sample size for factor analysis, with values >0.7 considered as acceptable. Bartlett's test of sphericity was applied to estimate the covariance between the items and values <0.05 indicated that the correlation matrix was suitable for factor analysis. Internal consistency for the factors was measured with the use of the raw coefficient alpha and values >0.7 were considered as acceptable.

For each factor that emerged from factor analysis, a total score was calculated by adding the answers in the factor's items and dividing by the number of items. Thus, a total score from 1 to 5 was created, with higher values indicating greater agreement.

Continuous variables are presented as mean (standard deviation), while categorical variables are presented as numbers (percentages). The Kolmogorov–Smirnov test (*p* > 0.05) was used to test the normality assumption for the continuous variables. Bivariate analyses between demographic characteristics and total factor scores included a Student's *t*-test, Spearman's correlation coefficient, and Pearson's correlation coefficient. The Student's *t*-test was used to compare a continuous variable with a dichotomous one, while Spearman's correlation coefficient was used to correlate a continuous variable with an ordinal one. Furthermore, the correlation between two continuous variables that followed normal distribution was assessed with Pearson's correlation coefficient. Then, multivariable linear regression was performed with total factor scores as the dependent variables. Accordingly, the backward stepwise linear regression was applied and the coefficients' beta, 95% confidence intervals, and *p*-values were calculated. All tests of statistical significance were two-tailed, and *p*-values < 0.05 were considered as statistically significant. Statistical analysis was performed with the IBM SPSS 21.0 (IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0 Armonk, NY, USA).
