Throughout this section, we’ve been interested in determining what influences enrolment in full time education after secondary school. We’ve already explored the significance of the effects of independent variables like truancy history and parental degree on s2q10. Now, we are curious what the odds are that a respondent will not be enrolled in full time education. Odds are similar to a probability, but have slightly different properties. You might have heard of them if you’ve watched horse racing! They are related to the chances of something happening – so a horse with odds of 4 to 1 is expected to win once and lose four times if it ran five times. In regard to full time education, the odds are the chances that they are enrolled compared to the chances of that they are not.
Remember that s2q10 is a variable that catalogues answers to the following question: “At present, are you enrolled on a full-time education course at school or college?” Respondents could have only answered “yes” or “no” to this question, making s2q10 a binary variable, with just two possible categories.
In the other sections of this site, we analyze continuous dependent variables. However, s2q10 is binary. If the dependent variable is categorical and binary, meaning that the outcome can be either 0 or 1, we can use the binary logistic regression method to illustrate trends in our data. Logistic regression is a statistical analysis that is very similar to linear regression. You may recall from other sections that linear regression allows us to model the relationship between two (or more) variables and predict certain values of the dependent variable. Because binary logistic regression is used when dependent variables are binary, this test allows us to predict the odds of certain values of the dependent variable.
First, we may want to investigate if total GCSE score in Year 11 has a relationship with our outcome variable, s2q10. To do so, we are going to use logistic regression to look at the relationship between the s2q10 (our dependent or outcome variable Y) and s1gcseptsnew (our independent variable X). This analysis can be found under the "Simple Logistic Regression: Continuous" tab.