## 3 An Improved Effectiveness Evaluation Method Based on Factor Analysis  
### Traditional Factor Analysis  
By using this process, we have obtained the evaluation data for each application of the Tactical Mobile Communications System. We collected 31 groups of real data from participating units in several war-games performed in Northern China. In analyzing the eight evaluation indexes, data standardization is required prior to the evaluation because the dimensions and numerical ranges expressed are different. Data standardization includes assimilation processing and dimensionless processing [50; 51]. The assimilation processing of indexes is also called forward processing [52]. In the evaluation, the larger the value of some indexes, the better the effectiveness is. In some situations, the larger the value of some indexes, the worse the effectiveness is [53]. Moreover, the closer some indexes are to the middle range, the better the effectiveness is and the more they are above or below the middle range, the worse the effectiveness is [54]. We first process the collected evaluation data into the assimilation matrix. Then, the Z-core method is adopted to conduct dimensionless processing on the data set, and the data set shown in Table 2 is obtained. All calculation and analysis diagrams in this paper are produced by R of version 3.6.3. The KMO value is 0.788 \(\geq\) 0.5 and Bartlett value is 0.000 \(\leq\) 0.05, which meets the criteria, indicating that the data set is suitable for factor analysis. From the data set in Table 2, the eigenvalues of the correlation coefficient matrix are 5.1925, 1.2606, 0.6471, 0.3883, 0.2346, 0.1462, 0.0879 and 0.0428. By calculating the variance contribution rate of the factor, we observe that the cumulative variance contribution rate of the first three factors is more than 85%. Therefore, the first three components were selected as the main factors for factor analysis. According to the eigenvectors corresponding to the selected factors, the non-rotated factor loading matrix on the left side of Table 3 is generated. Factor loading is one of the important indexes in factor analysis, and it is the data source of factor naming. It can be observed from the Table 3 that the loading of factor 1 on most indexes is large, which indicates that factor 1 is an important comprehensive factor. However, some indexes, such as \(X7\), have larger loads on factor 1 and factor 2, which cannot directly explain the factors. It is necessary to rotate the factor load matrix so that indexes can only show a larger load on one factor and a smaller load on other factors. The orthogonal rotation method of maximum variance is used to rotate the data to obtain the factor loading on the right side of Table 3.  
It can be observed from the factor load matrix after rotation in Table 3 that factor 1 has larger loads on indexes \(X2\), \(X3\), \(X7\) and \(X8\), which indicates that factor 1 mainly reflects the characteristics of four indexes: network throughput, end to end average delay, voice interruption rate and voice link building time. According to the specific meaning and characteristics of these indexes, factor 1 is named as the information transmission factor. Factor 2 has a larger load on indexes \(X4\), \(X5\) and \(X6\), which indicates that factor 2 mainly reflects the characteristics of three indexes: 'network state perception', 'timeliness of network adjustment' and'success rate of temporary network access'. According to the specific meaning and characteristics of these indexes, factor 2 is named as network control factor. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Unit** & **X1** & **X2** & **X3** & **X4** & **X5** & **X6** & **X7** & **X8** & **Factor 1** & **Factor 2** & **Factor 3** & \begin{tabular}{c} **Total** \\ **Points** \\ \end{tabular}