The factor structure was determined by several steps. First of all, using confirmatory factor analysis (CFA), we tested the 4-factor and 2-factor structures of AMI from previous studies [9, 15]. However, the model fit indices suggested that these two models didn’t fit into our data. Therefore, following previous studies [13, 25], we randomly split the weighted dataset into 2 separate parts with equal number of observations for each (n = 1,503). Exploratory factor analysis (EFA) was conducted for the first half-dataset. Eigenvalues > 1.0, scree plot, pattern of loadings on each item (e.g. cross-loading), and the interpretability were used to determine the appropriate number of factors that should be extracted. To allow the correlation between factors, oblique promax rotation was used, and the factor loading cut-off was set as 0.4. This was followed by a CFA for the second half-dataset to confirm this factor structure derived from EFA. A good model was defined as 1) the comparative fit index (CFI) > 0.95; 2), the Tucker-Lewis index (TLI) >0.95, and 3) the root mean square error of approximation (RMSEA) <0.06 . All structural equation modelling analyses were performed on polychoric correlation matrix using Mplus version 7.0 with the weighted least squares with mean and variance adjusted estimator for categorical variables . The internal reliability (Cronbach’s alpha) for each factor was calculated as well.