## 3 Research Method
### Validity and Reliability Verification
#### 3.3.2 Reliability Test of Evaluation Index System  
In the process of constructing the evaluation index system, the subject of evaluation is affected by the heterogeneity of professionalism, cognitive framework, and knowledge reserve, and its scoring behavior is easily interfered with by subjective preferences, which leads to systematic bias in the assessment results. Such subjective cognitive differences will weaken the anti-interference and data reproduction ability of the indicator system, which in turn triggers controversy over the validity of the indicator screening. To solve this problem, this study proposes a reliability coefficient model (\(\rho\)) based on the strength of statistical association, which quantifies the intrinsic correlation between indicators to achieve validity verification, and the specific modeling process is as follows:  
1. Calculation of the mean value of a single indicator: For expert rating data processing of the jth indicator \(z_{i}\) in the evaluation system, its arithmetic mean can be determined using Equation (9). This calculation process integrates the results of the multidimensional assessment of the members of the expert group for a particular indicator. \[\overline{x_{j}}=\sum_{i=1}^{n}x_{ij}/n\] (9)
2. Calculation of the global mean for the data set: Equation (10) illustrates how the baseline scoring standards of the expert panel for each indicator are first established to create the comprehensive evaluation model. \(Y=\{y_{1}\), \(y_{2}\),..., \(y_{n}\}\) represents the expert scoring dataset corresponding to an evaluation system with n indicators. Based on this, the comprehensive mean score of this dataset must be determined using Equation (11), a formula system that integrates expert assessment data from multiple dimensions to achieve the quantitative characterization of the overall level of the evaluation system. \[\mathbf{y}_{i}=\frac{1}{M}\sum_{j=1}^{M}x_{ij}\] (10) \[\overline{\mathbf{y}}=\frac{1}{n}\sum_{i=1}^{n}\mathbf{y}_{i}\] (11)
3. Calculating the single-indicator reliability coefficient \(\rho_{j}\rho_{j}\)can be done using Equation (12): \[\rho_{j}=\frac{\sum_{i=1}^{n}(x_{ij}-\overline{x}_{j})(y_{j}-\overline{y})}{ \sqrt{\sum_{i=1}^{n}(x_{ij}-\overline{x}_{j})^{2}\sum_{i=1}^{n}(y_{j}-\overline {y})^{2}}}\] (12) Among these, when \(\rho_{j}>0\), it means that \(z_{j}\) has a positive correlation with the whole evaluation and that the score is consistent; the more closely \(\rho_{j}\) approaches one, the more reliable the indicator \(z_{j}\) is and the more powerful it is at explaining the total results.
4. The synthetic total reliability coefficient can be calculated using Equation (13). This value synthetically reflects the stability and internal consistency of the whole indicator system. When is high, it indicates that the indicators within the indicator system are highly correlated and the degree of consensus of expert scores is high; when \(\rho\) is low, it means that the logic between the indicators is loose or there is redundancy, and the structure needs to be optimized. \[\rho=\frac{1}{M}\sum_{j=1}^{M}\rho_{j}\] (13)
5. Reliability level determination: Generally speaking, when \(0.90\leq\rho<0.95\), it indicates that the reliability of the evaluation index system is high, when \(0.80\leq\rho<0.90\), its reliability is at a general level, and when \(0\leq\rho<0.80\), its reliability is relatively low.