# The Mechanism of Tendentious Information Dissemination in Cyberspace
## 4 Results
### Results of the Structural Model  
#### 4.2.1 Test Results of TIDM Model Hypotheses  
This study used the ML method to conduct structural equation modeling and test the hypotheses regarding the mechanism of tendentious information dissemination. Table 3 shows the path analysis results. In Table 3, Unstd. represents the unstandardized path coefficient between variables; _S.E._ is the standard error, which reflects the accuracy and stability of the path coefficient; and _C.R._ denotes the critical ratio, that is, the ratio of the estimated value of the path coefficient to its standard error, which is used to test the significance of the path coefficient. Additionally, \(p\) is used to evaluate the significance of each path coefficient in the model, that is, whether the influence path contributes significantly to the explanatory power of the model. The path coefficient is significant when the absolute value of _C.R._ is greater than 1.96 or the \(p\)-value is less than 0.05, at a 95% confidence level. Analyzed independently, results show that the partial test of the hypothesis paths with SND, PBC, and DTR as independent variables is not valid, refuting H3(a), H3(b), H3(c), H4(b), H4(c), H4(d), H5(b), and H5(c).  
However, paths deemed invalid in the model analysis were not necessarily insignificant. This may be due to the interaction between the latent variables resulting in the diversion of their effect. Nonetheless, the invalid path hypotheses were deleted according to their \(p\)-value in descending order, with the final model gradually determined by observing the changes in the model test results. As Table 4 shows, after deleting H5(b), H4(c), H5(c), H4(b), H3(b), and H4(d), all remaining hypothesized paths are significantly present.  
Exploring the mechanism of tendentious information dissemination involves testing the significance of the influence of hypotheses in the path model. Figure 5 illustrates the final TIDM model, which was refined according to the results of the model testing.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **AVE** & **SND** & **ATD** & **DTR** & **ISO** & **PBC** & **IDI** & **IDB** \\ \hline SND & 0.541 & **0.736** & & & & & & \\ ATD & 0.761 & \(-0.119\) & **0.872** & & & & & \\ DTR & 0.484 & \(-0.049\) & 0.391 & **0.696** & & & & \\ ISO & 0.585 & 0.133 & 0.475 & 0.298 & **0.765** & & & \\ PBC & 0.564 & 0.424 & \(-0.104\) & \(-0.017\) & 0.042 & **0.751** & & \\ IDI & 0.586 & 0.097 & 0.525 & 0.278 & 0.562 & 0.054 & **0.766** & \\ IDB & 0.655 & \(-0.029\) & 0.514 & 0.288 & 0.649 & \(-0.066\) & 0.667 & **0.809** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Discriminant validity of the tendentious information dissemination measurement scale.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Adjusted Model Hypothesis** & **Unstd.** & **S.E.** & **C.R.** & \(p\) & **Label** & **Hypothesis Verification Result** \\ \hline ATD\(\rightarrow\)IDI & 0.276 & 0.061 & 4.532 & *** & H1 & Accepted \\ IDI\(\rightarrow\)IDB & 0.510 & 0.088 & 5.788 & *** & H2 & Accepted \\ SND\(\rightarrow\)ATD & \(-\)0.350 & 0.136 & \(-\)2.576 & ** & H3(a) & Accepted \\ SND\(\rightarrow\)IDB & \(-\)0.243 & 0.110 & \(-\)2.212 & * & H3(c) & Accepted \\ PBC\(\rightarrow\)SND & 0.423 & 0.086 & 4.915 & *** & H4(a) & Accepted \\ DTR\(\rightarrow\)ATD & 0.332 & 0.098 & 3.395 & *** & H5(a) & Accepted \\ ISO\(\rightarrow\)DTR & 0.277 & 0.074 & 3.719 & *** & H6(a) & Accepted \\ ISO\(\rightarrow\)ATD & 0.501 & 0.087 & 5.790 & *** & H6(b) & Accepted \\ ISO\(\rightarrow\)IDI & 0.391 & 0.078 & 5.006 & *** & H6(c) & Accepted \\ ISO\(\rightarrow\)IDB & 0.465 & 0.083 & 5.580 & *** & H6(d) & Accepted \\ \hline \end{tabular} Note: \({}^{*}p<0.05\), \({}^{**}p<0.01\), \({}^{***}p<0.001\).  
\end{table}
Table 4: Test results of the adjusted TIDM model hypotheses.  
Figure 5: Final TIDM model.