## 4 Data Analyses and Results
### Multivariate Analyses  
In order to determine if gender and nationality were still significant predictors of socially interactive violence and attitudes toward its seriousness, two separate explorations were conducted. First, logistic regression analysis was implemented using nested models to examine the influence of various  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Nationality**} \\ \cline{2-3}  & The United States & Poland \\ \hline Punishment \% Yes & 75.3 & 81.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cross-tabulations of nationality and attitudes toward punishment (N = 526).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Gender**} \\ \cline{2-3}  & Male & Female \\ \hline Punishment \% Yes & 61.5 & 82.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cross-tabulations of gender and attitudes toward punishment (N = 526).  
variables socially interactive intimate violence victimization. Age was entered first in the first model, followed by the number of text messages sent/received per day and the amount of time spent per day on social networking in the second model, followed by gender and nationality in the final model. This permitted for an investigation of their independent effects.  
The results indicated that the overall model was statistically significant (\(\chi^{2}=60.53\), df = 5, \(p<0.001\)), explaining 15.2% of variance in socially interactive intimate victimization. The difference in Chi-Square statistic suggested that the addition of _Gender_ and _Nationality_ in the final model significantly improved the model fit. See Table 5.  
Results of the analyses signaled that Age, Gender, and Nationality were all significant in the final model, but number of text messages sent per day and number of hours spent on social networks per day were not. As age increased, the odds of victimization decreased. Gender and Nationality were more robust predictors of socially interactive intimate violence than Age. In comparison to males, females had 2.1 times the odds of experiencing socially interactive relationship violence. In comparison to those in the United States, those in Poland had.22\(\times\) the odds of experiencing it. Stated in another way, those in the United States had nearly 5\(\times\) times the odds of experiencing socially interactive intimate violence when compared to their Polish counterparts.  
In the second investigation, the results suggested that the overall model was statistically significant (\(\chi^{2}=21.95,\ \mathrm{df}=5,\ p<0.001\)), explaining 8.0% of variance in socially interactive intimate victimization. The change in Chi-Square statistic suggested that the addition of the final block significantly improved the model fit. See Table 6. _Gender_ was the only significant variable in the final model. In comparison to males, females had over 3\(\times\) the odds of believing punishment was warranted for socially interactive intimate partner violence (\(p<0.001\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **1** & **2** & **3** \\ \hline Age & \(-0.043\)** & \(-0.031\) & \(-0.045\)* \\ \# Texts Sent Per Day & - & \(0.002\)* & \(0.001\) \\ \# Hours Spent on Social Networking Per Day & - & \(0.046\) & \(0.043\) \\ Gender & - & - & \(0.745\)*** \\ Nationality & - & - & \(-1.53\)*** \\ Nagelkerke R Square & \(0.019\) & \(0.038\) & \(0.152\) \\ Model \(\chi^{2}\) & \(7.041\)** & \(14.391\)* & \(60.534\)*** \\ Change in \(\chi^{2}\) & - & \(7.351\) & \(46.143\) \\ \hline \multicolumn{4}{l}{*\(p<0.05\); **\(p<0.01\); ***\(p<0.001\) (two-tailed test of statistical significance).} \\ \end{tabular}
\end{table}
Table 5: Logit estimates of characteristics on socially interactive intimate violence (N = 562).