IBM SPSS Statistics Step by Step 12e makes data analysis and SPSS procedures clear and accessible.
Presents straightforward step-by-step instructions in each analysis chapter to clarify procedures. Hundreds of screen shots and step-by-step boxes guide the student through the program. All of the datasets used in the book are available for download online at www.pearsonhighered.com/IRC. Exercises at the end of each chapter give students an opportunity to practice using SPSS. Updated to reflect SPSS Version 19.0.
Table of Contents
1. An overview of IBM SPSS Statistics 19 Step by Step
2. IBM SPSS Statistics Processes for PC and Mac: Mouse and keyboard processing, frequently-used dialog boxes, editing output, printing results, the Options Option
3. Creating and Editing a Data File
4. Managing Data: Listing cases, replacing missing values, computing new variables, recoding variables, exploring data, selecting cases, sorting cases, merging files
5. GRAPHS: Creating anad editing graphs and charts
IBM SPSS STATISTICS BASE MODULE
6. FREQUENCIES: Frequencies, bar charts, histograms, percentiles
7. DESCRIPTIVE Statistics: Measures of central tendency, variability, deviation from normality, size, and stability
8. CROSSTABULATION and Chi-Square Analyses
9. The MEANS Procedure
10. Bivariate CORRELATION: Bivariate correlations, partial correlations, and the correlation matrix
11. The T TEST Procedure: Independent-samples, paired-samples, and one-sample tests
12. The One-Way ANOVA Procedure: One-Way Analysis of Variance
13. General Linear Models: Two-Way Analysis of Variance
14. General Linear Models: Three-Way Analysis of Variance and the influence of covariates
15. Simple Linear REGRESSION
16. MULTIPLE REGRESSION ANALYSIS
17. NONPARAMETRIC Procedures
18. RELIABILITY ANALYSIS: Coefficient alpha and split-half reliability
19. MULTIDIMENSIONAL SCALING
20. FACTOR ANALYSIS
21. CLUSTER ANALYSIS
22. DISCRIMINANT ANALYSIS
IBM SPSS REGRESSION AND ADVANCED STATISTICS MODULES
23. General Linear Models: MANOVA and MANCOVA Multivariate Analysis of Variance and Covariance
24. General Linear Models: Repeated-Measures MANOVA: Multivariate Analysis of Variance with repeated measures and within-subjects factors
25. LOGISTIC REGRESSION
26. Hierarchical LOGLINEAR MODELS
27. General LOGLINEAR MODELS
28. RESIDUALS: Analyzing left-over variance
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