### Contents at a glance:

**Preparing data for analysis:**

Introduction to IBM SPSS Statistics; the data file; defining the data; creating new variables; transforming existing variables; checking data definitions; cleaning data
**Describing data:**

Tables; graphs; OLAP cubes; measures of central tendency and dispersion; standard scores; the normal distribution; measures of association

**Testing simple hypotheses:**

Basics of hypothesis testing; t-tests; oneway analysis of variance; multiple comparisons; nonparametric tests; chi square tests; correlation; partial correlation

**Building models:**

Bivariate and multiple linear regression; loglinear models; discriminant analysis; binary logistic regression; factor analysis; cluster analysis

**Using the General Linear Model:**

Univariate models; multivariate models; repeated measures

**Analyzing scales:**

Reliability analysis

### Features:

- Chapters start with concise overviews and examples of the use of the procedure
- Tips and warnings help you to avoid common mistakes and work efficiently
- Practical discussions explain the statistical background for each procedure
- Instructions make it easy to obtain the output in the book
- Examples are from diverse disciplines, including psychology, sociology, education, archaeology, medicine, library science, nursing and journalism
- Reviewed by IBM staff

### Examples include:

- Is a truancy reduction program effective?
- What variables are associated with newspaper readership?
- Can you predict percent body fat from easily obtainable measurements?
- What factors are associated with "getting ahead"?
- How can you predict Internet use from demographic characteristics?

### New to this edition:

Updated for version 19 of the IBM SPSS Statistics software. A new chapter on the Automatic Linear Modeling procedure has been added.