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
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
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
- 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
- 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.