Multivariate – Predicting Event Success Sample Clauses

Multivariate – Predicting Event Success. The bivariate analyses above establish that there is a significant inverse correlation between the two key variables – engaging in an online network and learning/planning online – and the success of the event. Furthermore, there was no significant correlation between a specific online behaviour – the use of end-to-end encryption and event success. To analyse this further, a binary logistic regression is conducted to incorporate these three variables to assess whether one of these behaviours could significantly predict success. 63 USA v. Xxxxxxx Xxxxxxx, Statement of Facts, Case 3:15-cr-00016-JAG, United States District Court for the Eastern District of Virginia, 2015. However, given the significant correlation between the three independent variables, it is important to test for multicollinearity. That is to say, if two or more potential predictors of a phenomenon are highly related themselves, then it can produce unstable and biased standard errors (Vatcheva and Xxx 2016). The independent variables are tested for collinearity diagnostics against each other, and each iteration returns a variance inflation factor (VIF) of less than 1.386. Although there is no firm consensus of what constitutes an acceptable VIF threshold, Xxxx and Xxxx (2012) note that the commonly recommended maximum values are 10, 5, and 3.3. As such, the independent variables in this study all fall well beneath this threshold and are considered acceptable. Given that there are more instances of failure than success, the former is chosen as the baseline – therefore a positive Exp(B) ratio is indicative of failure. The first step includes just the two key variables, which can be seen in Table 14, which finds that interacting in an online network is a significant predictor; those that fail are 3.41 times more likely to engage with co-ideologues online. In the second step, the use of end-to-end encryption is introduced (Table 15) and the model still finds that the only significant predictor is interacting in an online network – those that failed were 3.69 times more likely to engage in a virtual network. Although the use of end-to-end encryption makes success more likely than other online behaviours, introducing the variable of end-to-end encryption in this model does not mitigate this effect significantly. Behaviour B(SE) df Sig. Exp(B) Online Network 1.226 (.416) 1 .003 3.406 Online Learn/Plan .349 (.550) 1 .526 1.417 Constant -.712 (.170) 1 .000 .491 Table 14 - Event Success Logistic Regre...
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