Take the following route through SPSS: Analyse> Regression > Binary Logistic You can also follow the process using our video demonstration. We will create a logistic regression model with three explanatory variables (ethnic, SEC and gender) and one outcome ( fiveem) – this should help us get used to things! You can open up the LSYPE 15,000 Dataset to work through this example with us. Let’s get started by setting up the logistic regression analysis. To do this we will need to run a logistic regression which will attempt to predict the outcome fiveem based on a student’s ethnic group, SEC and gender. To gauge the extent and significance of any interactions between the explanatory variables in their effects on the outcome.Specifically we are interested here in what the OR for ethnicity looks like after we have controlled for differences in exam achievement associated with SEC and gender. ethnicity) on the outcome variable when controlling for other variables also associated with the outcome (e.g. To gauge the effect of one explanatory variable (e.g.Are the effects in the sample sufficiently large relative to their standard errors that they are likely to be true in the population? Remember that this data represents only a sample (although a very large sample) from the population of all students in England (approximately 600,000 students in any one year group). To evaluate the statistical significance of the above associations.Why would we want to get involved in logistic regression modelling? There are three rather good reasons: So we can see the associations between ethnic group, social class (SEC), gender and achievement quite clearly without the need for any fancy statistical analysis.