IBM SPSS COMPLEX SAMPLES 19 manual Pseudo R-Squares

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Chapter 20

Figure 20-5

Logistic Regression Odds Ratios dialog box

EChoose to create odds ratios for the factor ed and the covariates employ and debtinc.

EClick Continue.

EClick OK in the Logistic Regression dialog box.

Pseudo R-Squares

Figure 20-6

Pseudo R-square statistics

In the linear regression model, the coefficient of determination, R2, summarizes the proportion of variance in the dependent variable associated with the predictor (independent) variables, with larger R2 values indicating that more of the variation is explained by the model, to a maximum of 1. For regression models with a categorical dependent variable, it is not possible to compute a single R2 statistic that has all of the characteristics of R2 in the linear regression model, so these approximations are computed instead. The following methods are used to estimate the coefficient of determination.

„Cox and Snell’s R2 (Cox and Snell, 1989) is based on the log likelihood for the model compared to the log likelihood for a baseline model. However, with categorical outcomes, it has a theoretical maximum value of less than 1, even for a “perfect” model.

„Nagelkerke’s R2 (Nagelkerke, 1991) is an adjusted version of the Cox & Snell R-square that adjusts the scale of the statistic to cover the full range from 0 to 1.

„McFadden’s R2 (McFadden, 1974) is another version, based on the log-likelihood kernels for the intercept-only model and the full estimated model.