Knowledge and attitudes of incoming pharmacy students toward pharmacogenomics and survey reliability - Supplemental Material
Supplementary Table 1. Checklist for Reporting Results of Internet E-Surveys (CHERRIES
Supplementary Table 2. Comparison of models with different number of factors in the exploratory factor analysis
Supplementary Table 3. Goodness of fit statistics comparison among the exploratory factor analysis?based and theory-based survey instruments
Supplementary Table 4. Internal reliability of the final 38-item pharmacogenomics survey instrument with 3 factors measured by item-to-total correlations, inter-item correlations, and Cronbach’s α
Supplementary Figure 1. Diagnostic plots of multivariate assumptions for the exploratory factor analysis. (A) Residual plot showed the standardized residuals mostly centered around a horizontal line. There was a slight bias suggesting non-linear relationship. (B) Normal quantile-quantile plot shows most of the data points are aligned with the identity line which appears to satisfy the normality assumption. (C) Scale-location plot shows the residuals mostly spread equally along the ranges of predictors indicating homogeneity and there is no obvious bias suggesting heteroscedasticity.
Supplementary Figure 2. Scatter plot to examine linearity between items in each section. (A) Knowledge in genetic concepts (B) Knowledge in clinical pharmacogenomics (first 11 items) (C) Knowledge in clinical pharmacogenomics (second 11 items) (D) Benefit of genetic/ pharmacogenomics testing (E) Role of pharmacist
Supplementary Figure 3. Parallel analysis scree plot suggested that 4 factors can best explain the observed data.
Supplementary Figure 4. Association between education level and knowledge in genetics and clinical pharmacogenomics. Each education level was compared with Bachelor’s degree using Wilcoxon rank sum test without multiple comparison adjustment.