# The Impact of Job Insecurity on Miner Safety Behavior--A Study Based on SEM and fsQCA
## 4 Data Analysis and Results  
### Data Analysis Based on Structural Equation Modeling  
This research method is based on the views of [PERSON] et al. [45]. PLS-SEM (partial least squares structural equation modeling) is more suitable for complex models than covariance SEM, as it can estimate the explanatory power within samples and predictive ability outside samples. It is also suitable for exploring or extending theoretical models; therefore, this study uses SmartPLS4 to construct PLS-SEM to validate hypotheses. Common Method Bias  
Considering that it may not be feasible to completely eliminate common method bias, for the data analysis stage, two methods, namely the [PERSON] single factor test ([PERSON], 2003) and the full collinearity assessment approach by [PERSON] (2017), were used to assess whether there was a common method problem in the questionnaire survey data. [PERSON]'s single-factor method was used for testing. All items involved in the scale were included in the factor analysis. Through principal component analysis, a total of eight common factors with eigenvalues greater than 1 were obtained, and the cumulative variance explained was 65.713%. Among them, the contribution rate of the first unrotated factor variance was 28.664%, which was less than 50%, indicating that this study did not have a serious common method bias. Full Collinearity Assessment Approach  
Referring to the method of [PERSON] (2017), a method based on collinearity tests was used to identify common method biases, namely when the variance inflation factor (VIF) of endogenous variables was less than 3.3, there was no serious common method bias in the study. The maximum VIF value of the endogenous variables (Inner Model) in this study was 1.564, indicating that there was no serious common method bias in this study. Reliability and validity analysis  
The questionnaire data were input into SPSS 24.0 for reliability analysis, obtaining [PERSON]'s \(\alpha\) coefficients of each variable, as shown in Table 2. [PERSON]'s alpha coefficients for each first-order variable were all above 0.7 (minimum value = 0.751), indicating good internal consistency and stability of the designed scale, as well as high reliability. The composite reliability (CR) indicators were all above 0.7 (minimum value = 0.857), indicating good reliability. The average variance extracted (AVE) for the construct was above 0.5 for all variables (minimum value = 0.586), indicating good convergent validity. In addition, [PERSON]'s alpha coefficient for the second-order variable of miner safety behavior was 0.859, the CR was 0.840, and the AVE was 0.637, all meeting the requirements, indicating good reliability and validity of the second-order variable in this study. In addition, the Heterotrait-Monotrait Ratio (HTMT) was employed to test discriminant validity, with results shown in Table 3: the HTMT ratios between each construct were less than 0.85, and the confidence intervals of the HTMT statistics, tested by the Bootstrap method, did not include 1, indicating that the variables in this study can be well distinguished. The Cornell-Larker criterion was also used to test the discriminant validity between variables. By comparing the square root of the average variance extracted (AVE) of each variable in this study, it was found that they were all greater than the correlation \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Cr** & **AVE** & **Cronbach’s Alpha Coefficient** \\ \hline JLI & 0.897 & 0.684 & 0.846 \\ JPI & 0.865 & 0.617 & 0.792 \\ II & 0.901 & 0.695 & 0.854 \\ PR & 0.895 & 0.586 & 0.858 \\ TSC & 0.953 & 0.668 & 0.945 \\ PSQ & 0.880 & 0.647 & 0.818 \\ JC & 0.857 & 0.667 & 0.751 \\ SM & 0.878 & 0.644 & 0.815 \\ MSB & 0.859 & 0.637 & 0.859 \\ \hline \hline \end{tabular} Note: Job loss insecurity (JLI), job performance insecurity (JPI), interpersonal insecurity (II), personal security quality (PSQ), job characteristics (IC), safe management (SM), psychological resilience (PR), miner safety behavior (MSB), team safety climate (TSC).  
\end{table}