Contents at a glance:

Models for categorical dependent variables:
Multinomial regression models
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
ALSCAL
Variance-component estimation
Generalized Linear Models
Generalized Estimating Equations

Features:

• Clear and straightforward explanations of the statistical procedures and SPSS output
• Detailed, integrated instructions for obtaining all the results shown
• Several examples for most procedures
• Data sets from various disciplines are analyzed and included on the accompanying CD
• Book reviewed by SPSS Inc. staff

Examples include:

• Multinomial regression to predict degree of support for spending money on space exploration
• 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
• Generalized Linear Models to model lung cancer deaths rates in smokers and nonsmokers
• Generalized Estimating Equations to model the success of arthritis treatment with a new drug

New to this edition: