Training Opportunities: Exploring what happens two months later

Summary of effects

Table 1 shows the summary of the model. The variables are ranked in the order of the amount of variation in the model each accounts for. The closer the variable is to the top of the list, the more important it is in terms of explaining differences in outcomes. In this case, the most important predictor is the variable that records the number of credits attained per week. This explained 25 percent of the variance accounted for by the variables in the model. The second largest effect was Region, which explained 17 percent. The employment history of the learner accounted for 12 percent of the observed variance, while the course major field of study was the next most powerful predictor.

The model suggests that the number of credits attained in each placement, regional conditions and differences, the employment history of learner and the course content have the most influence on learners’ destinations two months after leaving a placement. The education history of the learner before entering Training Opportunities is also important. In fact, all of the variables tabulated show statistical significance; that is they are shown to have an influence on outcomes. However, some (those closer to the top of the table) are more influential than others.

The tables in the remainder of this paper compare the likelihood of different outcomes from Training Opportunities. They do this by comparing the probability of an outcome category with the probability of the outcome being unemployment/out of the labour force (which is referred to as an other outcome). This gives an ‘odds ratio’. If the odds ratio of an outcome category is greater than 1, this means that the outcome is more likely than unemployment/out of the labour force. If the odds ratio of an outcome is less than 1, then the outcome is less likely than unemployment/out of the labour force.