SPSS 14.0 statistical procedures companion
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書誌事項
SPSS 14.0 statistical procedures companion
Prentice Hall, c2005
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注記
Includes bibliographical references (p.587-590) and index
内容説明・目次
内容説明
The SPSS 14.0 Statistical Procedures Companion covers many of the more advanced statistical procedures in SPSS, which are not discussed in theSPSS 14.0 Guide to Data Analysis. This book is intended as the continuation of the GDA. The audience is no longer the beginning student but is instead the data analyst, either an advanced student or a professional. The Companion offers practical suggestions and emphasizes topics that arise when analyzing real data for presentations, reports, and dissertations, instead of the pristine data of homework assignments. A data CD is included with this book.
For additional information, go to This site offers a detailed Table of Contents, features, examples included in the book, and a sample chapter for download.
目次
- SPSS 14.0 Statistical Procedures Companion: Chapters 1. Introduction. An overview of the statistical procedures described in the book. 2. Getting to Know SPSS. Tutorials
- windows
- dialog boxes
- the Help system
- Pivot Table Editor
- Chart Editor
- using command syntax. 3. Introducing Data. Planning the data file
- getting data into SPSS
- the Text Wizard
- creating new variables
- selecting cases. 4. Preparing Your Data. Checking variable definitions
- case counts
- data values. 5. Transforming Your Data. Computing new variables
- changing coding schemes
- ranking. 6. Describing Your Data. Taking a first look at your data with tables, charts, and descriptive statistics. 7. Testing Hypotheses. Samples and populations
- missing values
- steps in testing a hypothesis
- calculating confidence intervals
- reporting your results correctly
- commonly used tests for popular hypotheses. 8. T-tests. One-sample, paired-samples, and independent-samples t-tests
- data setups for the different t-tests
- interpreting the output. 9. One-way Analysis of Variance. Preparing the data file
- examining the data
- checking assumptions
- interpreting the output
- atoning for multiple comparisons
- setting up contrasts. 10. Crosstabulation. Chi-square tests
- McNemar's test
- measures of association and agreement
- measures of risk
- testing hypotheses about odds ratios. 11. Correlation. Plotting the data
- scatterplot matrices
- correlation coefficients based on ranks
- partial correlation coefficients
- identifying points in a scatterplot. 12. Bivariate Linear Regression. Least squares regression line
- measures of fit
- assumptions and transformations
- looking for unusual points. 13. Multiple Linear Regression. Formulating the problems
- interpreting the coefficients
- including categorical variables
- comparing models
- automated model building
- checking for violations of assumptions
- residuals
- unusual observations. 14. Discriminant Analysis. Calculating the functions
- testing hypotheses
- classifying cases into groups
- automated model building
- analyzing more than two groups
- classification function coefficients. 15. Logistic Regression Analysis. Basics of the model
- predicted probabilities
- coefficients
- testing hypotheses
- categorical variables
- interaction terms
- evaluating linearity
- automated model building
- diagnostics
- model calibration
- model discrimination
- diagnostics for individual cases. 16. Cluster Analysis. Hierarchical clustering
- k-means clustering
- two-step clustering
- distance and similarity measures
- interpreting the results. 17. Factor Analysis. Basics of the model
- determining the number of factors
- goodness-of-fit tests
- methods for factor extraction and rotation
- computing factor scores. 18. Reliability Analysis. Reliability coefficients: Cronbach's alpha, split-half reliability, Guttman's lower bounds
- testing hypotheses about scales: parallel and strictly parallel models
- Cochran's Q
- intraclass correlation coefficients. 19. Nonparametric Tests. One-sample tests: chi-square, binomial, runs
- two related groups: sign test, Wilcoxon test
- two independent groups: Wilcoxon, Wald-Wolfowitz runs test
- three or more groups: Kruskal-Wallis, median, Friedman, Kendall's W, Cochran's Q. 20. General Loglinear Analysis. Basic model
- fitting a saturated model
- fitting an unsaturated model
- goodness-of-fit tests
- models for ordinal data
- incomplete tables
- tests for square tables
- Poisson regression
- standardizing tables. 21. Univariate General Linear Model. Regression
- two-way ANOVA
- randomized complete block design
- randomized complete block design with empty cells
- analysis of covariance
- mixed effects nested designs
- split-plot designs. 22. Multivariate General Linear Model. Multivariate two-way fixed-effects model with interaction
- profile analysis
- setting up custom linear hypotheses. 23. Repeated Measures Designs. Checking assumptions
- testing hypotheses
- doubly multivariate repeated measures analysis of variance.
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