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: