### Contents at a glance:

**Models for categorical dependent variables:**

Multinomial regression models

Ordinal regression models (PLUM)

Logit and probit models

Hierarchical loglinear models

**Models for time-to-event (survival) data:**

Actuarial life tables

Kaplan-Meier estimates

Cox regression, with and without time-dependent covariates

**Regression models:**

Nonlinear regression

Two-stage least squares

Weighted least squares

**Additional procedures:**

Linear mixed models

Generalized linear mixed models

ALSCAL

Variance-component estimation

### Features:

- Clear and straightforward explanations of the statistical procedures and IBM SPSS Statistics output
- Detailed, integrated instructions for obtaining all the results shown
- Several examples for most procedures
- Datasets from various disciplines are analyzed and included on the accompanying CD
- Book reviewed by IBM staff

### Examples include:

- Multinomial regression to predict degree of support for spending money on space exploration
- Ordinal regression models to examine ratings of the justice system
- Linear mixed models for testing hypotheses about achievement when students are clustered within schools
- Multidimensional scaling for examining perceived body-part structure
- Cox regression models for evaluating predictors of survival for patients with Hodgkin's disease

### New to this edition:

Updated for version 19 of the IBM SPSS Statistics software. This edition includes a new chapter describing the Generalized Linear Mixed Models procedure.