SPSS for Windows Step by Step: A Simple Study Guide and Reference, 17.0 Update
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SPSS for Windows Step by Step: A Simple Guide and Reference, 17.0 Update makes data analysis and SPSS procedures clear and accessible by presenting straightforward “step by step” instructions in each analysis chapter to clarify procedures. Exercises at the end of each chapter and selected answers on the website give students an opportunity to practice using SPSS. Updated to reflect SPSS Version 17.0 .
1. An Overview of SPSS for Windows Step-by-Step2. SPSS Windows Processes: Mouse and keyboard processing, frequently-used dialog boxes, editing output, printing results, the Options Option3. Creating and Editing a Data File4. Managing Data: Listing cases, replacing missing values, computing new variables, recoding variables, exploring data, selecting cases, sorting cases, merging files5. GRAPHS: Creating and editing graphs and charts BASE SYSTEM MODULE6. FREQUENCIES: Frequencies, bar charts, histograms, percentiles7. DESCRIPTIVE Statistics: Measures of central tendency, variability, deviation from normality, size, and stability8. CROSSTABULATION and Chi-Square (2) Analyses9. The MEANS Procedure10. Bivariate CORRELATION: Bivariate correlations, partial correlations, and the correlation matrix11. The T TEST Procedure: Independent-samples, paired-samples, and one-sample tests12. The One-Way ANOVA Procedure: One-way Analysis of Variance13. General Linear Models: Two-Way Analysis of Variance14. General Linear Models: Three-Way Analysis of Variance and the influence of covariates15. Simple Linear REGRESSION16. MULTIPLE REGRESSION ANALYSIS17. NONPARAMETRIC Procedures18. RELIABILITY ANALYSIS: Coefficient alpha () and split-half reliability19. MULTIDIMENSIONAL SCALING20. FACTOR analysis21. CLUSTER ANALYSIS22. DISCRIMINANT ANALYSISADVANCED and REGRESSION MODULES23. General Linear Models: MANOVA and MANCOVA: Multivariate Analysis of Variance and Covariance24. General Linear Models: Repeated-Measures MANOVA: Multivariate Analysis of Variance with repeated measures and within-subjects factors25. LOGISTIC REGRESSION26. Hierarchical LOGLINEAR MODELS27. General LOGLINEAR MODELS28. RESIDUALS: Analyzing left-over varianceData FilesGlossaryReferencesIndex
