# The Mechanism of Tendentious Information Dissemination in Cyberspace
## 4 Results
### Comparative Experiments
#### 4.3.3 Comparison of Different Data Samples  
In order to further illustrate the stability of the TIDM model constructed in this study, we conducted cross-validation of the TIDM model using different data samples. According to the rule of thumb, a reasonable sample size is approximately 200-500. With reference to the upper limit of the rule, this study performed repeated sampling on the target group and randomly selected 516 samples from the valid data samples to form a new sample subset for comparative analysis with the 227 sample subsets used in the previous modeling. Figure 8 presents the cross-validation based on the different data samples.  
We substituted two effective sample subsets with sample sizes of 227 and 516 into the TIDM model for comparative analysis. As Table 10 shows, for different data samples,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Fit Index** & **Reference Value** & **SEM Value (SEM)** & **TSM Value** \\ \hline \multicolumn{4}{c}{Absolute fit index} \\ \(\chi^{2}/df\) & 1\(\sim\)3 & 1.474 (Yes) & 1.750 (Yes) \\ _GFI_ & \(>\)0.80 & 0.861 (Yes) & 0.979 (Yes) \\ _AGFI_ & \(>\)0.80 & 0.836 (Yes) & 0.940 (Yes) \\ _RMSEA_ & \(<\)0.08 & 0.046 (Yes) & 0.058 (Yes) \\ \multicolumn{4}{c}{Relative fit index} \\ _CFI_ & \(>\)0.90 & 0.951 (Yes) & 0.979 (Yes) \\ _NNFI_ & \(>\)0.90 & 0.946 (Yes) & 0.956 (Yes) \\ \multicolumn{4}{c}{Parsimony fit index} \\ _PGFI_ & \(>\)0.50 & 0.729 (Yes) & 0.350 (No) \\ _PNFI_ & \(>\)0.50 & 0.782 (Yes) & 0.454 (No) \\ \hline \hline \end{tabular} Note: SEM = structural equation model, TSM = traditional statistical model.  
\end{table}
Table 8: Model fitting comparison based on different modeling methods.  
Figure 8: Cross-validation based on different data samples: (**a**) \(N\) = 227 and (**b**) \(N\) = 516.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Modeling Approach**} & \multicolumn{2}{c}{**ATD**} & \multicolumn{2}{c}{**IDI**} & \multicolumn{2}{c}{**IDB**} \\ \cline{2-7}  & \(\mathbf{R}^{2}\) & \(\Delta\mathbf{R}^{2}\) & \(\mathbf{R}^{2}\) & \(\Delta\mathbf{R}^{2}\) & \(\mathbf{R}^{2}\) & \(\Delta\mathbf{R}^{2}\) \\ \hline Structural equation modeling & 0.333 & 0.064\(\uparrow\) & 0.407 & 0.046\(\uparrow\) & 0.588 & 0.106\(\uparrow\) \\ Traditional statistical modeling & 0.269 & 0.361 & 0.046\(\uparrow\) & 0.482 & 0.106\(\uparrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Variables’ explainability comparison based on different modeling methods.  
the TIDM model performed well in fitting the samples, confirming the model's stability in fitting the effective data samples.  
We performed further path analysis on the fitting results of the TIDM model with the larger sample (\(N=516\)). As Table 11 shows, compared with the TIDM model with the sample size of 227 responses, all of the path coefficients of the model (\(N=516\)) are significant. ISO had the greatest direct influence on the attitude, intention, and behavior of tendentious information dissemination, while SND had a relatively minor negative influence on the attitude and behavior of tendentious information dissemination. Therefore, the TIDM model demonstrated good stability in the hypothesized relationship, mode of action, and force of action among the variables, indicating that it can reliably reflect the general law of tendentious information dissemination.