Title: Building a model - Controlling variables continued
Course: UGQuants (SSPS) > Section: Logistic Regression

Building a model - Controlling variables continued

The relationship to having a permanent work contract, controlling for being in paid work, age, religion, number of household members, and education.

We previously tried to model the influence of job insecurity with a measure of how secure the respondent perceived their job in the last three years, but we found this indicator unhelpful. Let us instead look at a perhaps more ‘objective’ measure: whether the respondent holds a permanent employment contract, a temporary contract, or none at all.

We can examine this by first running a model for this variable only, and then including all the other variables that we have found to be significant and looking at what happens to the effect of having a permanent contract.

Model for mnrgtjb_bin, only including wrkctra.

Model for mnrgtjb_bin by wrkctra_dummy, controlling for age, work status, years of education, household members, and religion.

By comparing our two models we can see that when we enter wrkctra on its own (telling SPSS that it is a categorical variable), it does quite well. The odds for people without a permanent work contract (dummy 1) to disagree with our statement are about 1.8 times bigger than the odds for a permanently contracted worker. The second dummy’s odds are not statistically significant, yet we can easily see why: this dummy contains those workers without a contract at all, a fairly small number, and small samples often lead to statistically insignificant results.

In a model with our control variables, (where the 3 category variable wrkctra was replaced by a dummy variable asking whether the respondent has a permanent contract or not), the odds of wrkctra_dummy decrease slightly (from 1.839 to 1.529 ). Education also loses some ground while remaining a useful variable. On the other hand, the new control variable pdwrkp clearly helps our model: the odds for people in paid work to disagree with the claim that men should have precedence over women on the jobmarket in economically hard times are nearly one and a half times as big as for all other respondents. One might suspect that years in education are associated being employed and the likeliness of having a permanent contract, thus explaining the reduction in the odds of the education control variable. Also, the odds of disagreeing with our statement decrease by about 14% for every additional person the respondent lives with.

The model we arrive at is reasonably good, although of modest explanatory power. We can correctly predict 72% of cases, an increase of 2.7% from the null model (this correctly predicted 69.3% of all cases, due to the reduced sample size – we have 19.7% missing cases). Notice how the Exp(B) for religion has slightly increased in comparison to the model in which it was only combined with age.