3.1.2. Test on Reliability, Validity and Multicollinearity

For the purpose of highly reasonable and effective model training, data pre-processing is crucial in machine learning. As each record is collected by questionnaires, data need to pass both reliability and Bartlett's tests. In particular, the reliability of input second-level indicators (Cronbach's Alpha) is 0.82 above the threshold value of 0.7 [44]. Bartlett's test of those input second-level indicators is 0.81, indicating that feature selection can be done.

Additionally, one common issue in machine learning is that the large regression coefficients cannot be estimated precisely when the features are multicollinear. In accordance to Hair et al., variance inflation factor (VIF) is calculated to determine whether there is multicollinearity among independent variables [44]. In general, when the VIF values are lower than the common cutoff threshold of 10, multicollinearity is not a significant issue. The results of the multicollinearity test for all input second-level indicators are shown in Table 1, and it can be concluded that there is no multicollinearity among them.



\* These codes refer to input second-level indicators, which are shown in Table A1 in Appendix A. For example, the code of 'GC1 refers to the first second-level indicator measuring the variable of goal congruency.
