Results

The Model Summary table above provides information about the model after the variables risk_perception, safety and gender have been added. The Deviance has dropped to 105.77, which is a change of 30.89 (i.e., =30.89 ). Thus, the Deviance value tells us about the model as a whole, whereas the  tells us how the model has improved relative to the previous model. The change in the amount of information explained by the model is significant ((3) = 30.89, p < .001) and so using perceived risk, relationship safety and gender as predictors significantly improves our ability to predict condom use, compared to predictions based solely on the base rate of condom use.

The output for Model 2 shows what happens to the model when our new predictors are added (previous use, self-control and sexual experience). So, we begin with the model that we had in block 1 and we then add previous, self_control and experience to it. The effect of adding these predictors to the model is to reduce the Deviance to 87.97, which is a reduction of 17.80 compared to the previous model (i.e.,  = 17.8). This additional improvement of block 2 is significant ((4) = 17.80, p = .001), which tells us that including these three new predictors in the model has significantly improved our ability to predict condom use.

The Coefficients table above tells us the parameters of both models.

Model 1

The significance values of the Wald statistics for each predictor indicate that both perceived risk (Wald = 17.78, p < .001) and relationship safety (Wald = 4.54, p = .033) significantly predict condom use. Gender, however, does not (Wald = 0.41, p = .523).

1. The odds ratio for perceived risk (Odds Ratio = 2.56 [1.65, 3.96] indicates that if the value of perceived risk goes up by 1, then the odds of using a condom also increase (because the odds ratio is greater than 1). The confidence interval for this value ranges from 1.65 to 3.96, so if this is one of the 95% of samples for which the confidence interval contains the population value the value of the odds ratio in the population lies somewhere between these two values. In short, as perceived risk increase by 1, people are just over twice as likely to use a condom.
2. The odds ratio for relationship safety (Odds Ratio = 0.63 [0.41, 0.96] indicates that if the relationship safety increases by one point, then the odds of using a condom decrease (because the odds ratio is less than 1). The confidence interval for this value ranges from 0.41 to 0.96, so if this is one of the 95% of samples for which the confidence interval contains the population value the value of the odds ratio in the population lies somewhere between these two values. In short, as relationship safety increases by one unit, subjects are about 1.6 times less likely to use a condom.
3. The odds ratio for gender (Odds Ratio = 0.729 [0.28, 1.93] indicates that as gender changes from 1 (female) to 0 (male), then the odds of using a condom decrease (because the odds ratio is less than 1). The confidence interval for this value crosses 1. Assuming that this is one of the 95% of samples for which the confidence interval contains the population value this means that the direction of the effect in the population could indicate either a positive (Odds Ratio > 1) or negative (Odds Ratio < 1) relationship between gender and condom use.

Model 2

The significance values of the Wald statistics for each predictor indicate that both perceived risk (Wald = 16.04, p < .001) and relationship safety (Wald = 4.17, p = .041) still significantly predict condom use and, as in Model 1, gender does not (Wald = 0.00, p = .996). Previous use has been split into two components (according to whatever level is highest in the Variable Settings for previous). Looking at the output, we can see that previous(Condom used) and previous(First Time with partner) are featured, which means that both of these categories are being compared to No Condom (which is in indeed the top category in the Variable Settings). Based on the regression results, we can tell that (1) using a condom on the previous occasion does predict use on the current occasion (Wald = 3.88, p = .049); and (2) there is no significant difference between not using a condom on the previous occasion and this being the first time (Wald = 0.00, p = .991). Of the other new predictors we find that self-control predicts condom use (Wald = 7.51, p = .006) but sexual experience does not (Wald = 2.61, p = .106).

1. The odds ratio for perceived risk (Odds Ratio = 2.58[1.62, 4.11] indicates that if the value of perceived risk goes up by 1, then the odds of using a condom also increase (because the odds ratio is greater than 1). The confidence interval for this value ranges from 1.62 to 4.11, so if this is one of the 95% of samples for which the confidence interval contains the population value the value of the odds ratio in the population lies somewhere between these two values. In short, as perceived risk increase by 1, people are just over twice as likely to use a condom.
2. The odds ratio for relationship safety (Odds Ratio = 0.62 [0.39, 0.98] indicates that if the relationship safety increases by one point, then the odds of using a condom decrease (because the odds ratio is less than 1). The confidence interval for this value ranges from 0.39 to 0.98, so if this is one of the 95% of samples for which the confidence interval contains the population value the value of the odds ratio in the population lies somewhere between these two values. In short, as relationship safety increases by one unit, subjects are about 1.6 times less likely to use a condom.
3. The odds ratio for gender (Odds Ratio = 0.996 [0.33, 3.07] indicates that as gender changes from 1 (female) to 0 (male), then the odds of using a condom decrease (because the odds ratio is less than 1). The confidence interval for this value crosses 1. Assuming that this is one of the 95% of samples for which the confidence interval contains the population value this means that the direction of the effect in the population could indicate either a positive (Odds Ratio > 1) or negative (Odds Ratio < 1) relationship between gender and condom use.
4. The odds ratio for previous(Condom used) (Odds Ratio = 2.97[1.01, 8.75) indicates that if the value of previous usage goes up by 1 (i.e., changes from not having used one to having used one), then the odds of using a condom also increase. If this is one of the 95% of samples for which the confidence interval contains the population value then the value of the odds ratio in the population lies somewhere between 1.01 and 8.75. In other words it is a positive relationship: previous use predicts future use. For previous(First Time with partner) the odds ratio (Odds Ratio = 0.98 [0.06, 15.29) indicates that if the value of previous usage goes changes from not having used one to this being the first time with this partner), then the odds of using a condom do not change (because the value is very nearly equal to 1). If this is one of the 95% of samples for which the confidence interval contains the population value then the value of the odds ratio in the population lies somewhere between 0.06 and 15.29 and because this interval contains 1 it means that the population relationship could be either positive or negative (and very wide ranging).
5. The odds ratio for self-control (Odds Ratio = 1.42 [1.10, 1.82] indicates that if self-control increases by one point, then the odds of using a condom increase also. As self-control increases by one unit, people are about 1.4 times more likely to use a condom. If this is one of the 95% of samples for which the confidence interval contains the population value then the value of the odds ratio in the population lies somewhere between 1.10 and 1.82. In other words it is a positive relationship
6. Finally, the odds ratio for sexual experience (Odds Ratio = 1.20[0.95, 1.49] indicates that as sexual experience increases by one unit, people are about 1.2 times more likely to use a condom. If this is one of the 95% of samples for which the confidence interval contains the population value then the value of the odds ratio in the population lies somewhere between 0.06 and 15.29 and because this interval contains 1 it means that the population relationship could be either positive or negative.

The Multicollinearity Diagnostics table above shows that the tolerance values for all variables are close to 1 and VIF values are much less than 10, which suggests no collinearity issues.

The Influential Cases table lists cases with standardized residuals greater than 2. In a sample of 100, we would expect around 5–10% of cases to have standardized residuals with absolute values greater than this value. For these data we have only four cases (out of 100) and only one of these has an absolute value greater than 3. Therefore, we can be fairly sure that there are no outliers (the number of cases with large standardized residuals is consistent with what we would expect).