IBM SPSS COMPLEX SAMPLES 19 manual 192

192

Chapter 20

Each term in the model, plus the model as a whole, is tested for whether its effect equals 0. Terms with significance values less than 0.05 have some discernible effect. Thus, age, employ, debtinc, and creddebt contribute to the model, while the other main effects do not. In a further analysis of the data, you would probably remove ed, address, income, and othdebt from model consideration.

Parameter Estimates

Figure 20-9

Parameter estimates

The parameter estimates table summarizes the effect of each predictor. Note that parameter values affect the likelihood of the “did default” category relative to the “did not default” category. Thus, parameters with positive coefficients increase the likelihood of default, while parameters with negative coefficients decrease the likelihood of default.

The meaning of a logistic regression coefficient is not as straightforward as that of a linear regression coefficient. While B is convenient for testing the model effects, Exp(B) is easier to interpret. Exp(B) represents the ratio change in the odds of the event of interest attributable to a one-unit increase in the predictor for predictors that are not part of interaction terms. For example, Exp(B) for employ is equal to 0.798, which means that the odds of default for people who have been with their current employer for two years are 0.798 times the odds of default for those who have been with their current employer for one year, all other things being equal.

The design effects indicate that some of the standard errors computed for these parameter estimates are larger than those you would obtain if you assumed that these observations came from a simple random sample, while others are smaller. It is vitally important to incorporate the sampling design information in your analysis because you might otherwise infer, for example, that the age coefficient is no different from 0!