IBM SPSS Statistics 19 Step by Step A Simple Guide and Referenceby George, Darren; Mallery, Paul
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Darren George is currently a professor at Canadian University College where he teaches personality psychology, social psychology, and research methods. He completed his M.A. in Experimental Psychology at California State University, Fullerton (1982); taught high school mathematics for nine years (1980-1989) at Mark Keppel High School (Alhambra, CA) and Mountain View High School (El Monte, CA); then completed a Psychology Ph.D. at UCLA with emphases in personality psychology, social psychology, and measurement & psychometrics.
Paul Mallery is currently a professor at La Sierra University where he teaches social psychology and experimental methodology (including the application of SPSS). He received his Ph.D. in Social Psychology from UCLA (1994) with emphases in statistics and political psychology. Paul formerly worked as a computer specialist, both programming and teaching computer usage.
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