Using Multivariate Statistics [平装]

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		Using Multivariate Statistics [平装]
		Using Multivariate Statistics [平装]

目录

1. Introduction. 1.1 Multivariate Statistics: Why? 1.1.1 The Domain of Multivariate Statistics: Numbers of IVs and DVs. 1.1.2 Experimental and Nonexperimental Research. 1.1.3 Computers and Multivariate Statistics. 1.1.4 Words of Caution. 1.2 Some Useful Definitions. 1.2.1 Continuous, Discrete, and Dichotomous Data. 1.2.2 Samples and Populations. 1.2.3 Descriptive and Inferential Statistics. 1.2.4 Orthogonality: Standard and Sequential Analyses. 1.3 Linear Combinations of Variables. 1.4 Number and Nature of Variables to Include. 1.5 Statistical Power. 1.6 Data Appropriate for Multivariate Statistics. 1.6.1 The Data Matrix. 1.6.2 The Correlation Matrix. 1.6.3 The Variance-Covariance Matrix. 1.6.4 The Sum-Of-Squares and Cross-Products Matrix. 1.6.5 Residuals. 1.7 Organization of the Book. 2. A Guide to Statistical Techniques: Using the Book. 2.1 Research Questions and Associated Techniques. 2.1.1 Degree of Relationship among Variables. 2.1.1.1 Bivariate r. 2.1.1.2 Multiple R. 2.1.1.3 Sequential R. 2.1.1.4 Canonical R. 2.1.1.5 Multiway Frequency Analysis. 2.1.1.6 Multilevel Modeling. 2.1.2 Significance of Group Differences. 2.1.2.1 One-Way ANOVA and t Test. 2.1.2.2 One-Way ANCOVA. 2.1.2.3 Factorial ANOVA. 2.1.2.4 Factorial ANCOVA. 2.1.2.5 Hotelling=s T2. 2.1.2.6 One-Way MANOVA. 2.1.2.7 One-Way MANCOVA. 2.1.2.8 Factorial MANOVA. 2.1.2.9 Factorial MANCOVA. 2.1.2.10 Profile Analysis of Repeated Measures. 2.1.3 Prediction of Group Membership. 2.1.3.1 One-Way Discriminant. 2.1.3.2 Sequential One-Way Discriminant. 2.1.3.3 Multiway Frequency Analysis (Logit). 2.1.3.4 Logistic Regression. 2.1.3.5 Sequential Logistic Regression. 2.1.3.6 Factorial Discriminantanalysis. 2.1.3.7 Sequential Factorial Discriminant Analysis. 2.1.4 Structure. 2.1.4.1 Principal Components. 2.1.4.2 Factor Analysis. 2.1.4.3 Structural Equation Modeling. 2.1.5 Time Course of Events. 2.1.5.1 Survival/Failure Analysis. 2.1.5.2 Time-Series Analysis. 2.2 Some Further Comparisons. 2.3 A Decision Tree. 2.4 Technique Chapters. 2.5 Preliminary Check of the Data. 3. Review of Univariate and Bivariate Statistics. 3.1 Hypothesis Testing. 3.1.1 One-Sample z Test as Prototype. 3.1.2 Power. 3.1.3 Extensions of the Model. 3.1.4 Controversy Surrounding Significance Testing. 3.2 Analysis of Variance. 3.2.1 One-Way Between-Subjects ANOVA. 3.2.2 Factorial Between-Subjects ANOVA. 3.2.3 Within-Subjects ANOVA. 3.2.4 Mixed Between-Within-Subjects ANOVA. 3.2.5 Design Complexity. 3.2.5.1 Nesting. 3.2.5.2 Latin-Square Designs. 3.2.5.3 Unequal n and Nonorthogonality. 3.2.5.4 Fixed and Random Effects. 3.2.6 Specific Comparisons. 3.2.6.1 Weighting Coefficients for Comparisons. 3.2.6.2 Orthogonality of Weighting Coefficients. 3.2.6.3 Obtained F for Comparisons. 3.2.6.4 Critical F for Planned Comparisons. 3.2.6.5 Critical F for Post Hoc Comparisons. 3.3 Parameter Estimation. 3.4 Effect Size. 3.5 Bivariate Statistics: Correlation and Regression. 3.5.1 Correlation. 3.5.2 Regression. 3.6 Chi-Square Analysis. 4. Cleaning Up Your Act: Screening Data Prior to Analysis. 4.1 Important Issues in Data Screening. 4.1.1 Accuracy of Data File. 4.1.2 Honest Correlations. 4.1.2.1 Inflated Correlation. 4.1.2.2 Deflated Correlation. 4.1.3 Missing Data. 4.1.3.1 Deleting Cases or Variables. 4.1.3.2 Estimating Missing Data. 4.1.3.3 Using a Missing Data Correlation Matrix. 4.1.3.4 Treating Missing Data as Data. 4.1.3.5 Repeating Analyses with and Without Missing Data. 4.1.3.6 Choosing among Methods for Dealing with Missing Data. 4.1.4 Outliers. 4.1.4.1 Detecting Univariate and Multivariate Outliers. 4.1.4.2 Describing Outliers. 4.1.4.3 Reducing the Influence of Outliers. 4.1.4.4 Outliers in a Solution. 4.1.5 Normality, Linearity, and Homoscedasticity. 4.1.5.1 Normality. 4.1.5.2 Linearity. 4.1.5.3 Homoscedasticity, Homogeneity of Variance, and Homogeneity of Variance-Covariance Matrices. 4.1.6 Common Data Transformations. 4.1.7 Multicollinearity and Singularity. 4.1.8 A Checklist and Some Practical Recommendation. 4.2 Complete Examples of Data Screening. 4.2.1 Screening Ungrouped Data. 4.2.1.1 Accuracy of Input, Missing Data, Distributions, and Univariate Outliers. 4.2.1.2 Linearity and Homoscedasticity. 4.2.1.3 Transformation. 4.2.1.4 Detecting Multivariate Outliers. 4.2.1.5 Variables Causing Cases to Be Outliers. 4.2.1.6 Multicollinearity. 4.2.2 Screening Grouped Data. 4.2.2.1 Accuracy of Input, Missing Data, Distributions, Homogeneity of Variance, and Univariate Outliers. 4.2.2.2 Linearity. 4.2.2.3 Multivariate Outliers. 4.2.2.4 Variables Causing Cases to Be Outliers. 4.2.2.5 Multicollinearity. 5. Multiple Regression. 5.1 General Purpose and Description. 5.2 Kinds of Research Questions. 5.2.1 Degree of Relationship. 5.2.2 Importance of IVs. 5.2.3 Adding IVs. 5.2.4 Changing IVs. 5.2.5 Contingencies among IVs. 5.2.6 Comparing Sets of IVs. 5.2.7 Predicting DV Scores for Members of a New Sample. 5.2.8 Parameter Estimates. 5.3 Limitations to Regression Analyses. 5.3.1 Theoretical Issues. 5.3.2 Practical Issues. 5.3.2.1 Ratio of Cases to IVs. 5.3.2.2 Absence of Outliers among the IVs and on the DV. 5.3.2.3 Absence of Multicollinearity and Singularity. 5.3.2.4 Normality, Linearity, Homoscedasticity of Residuals. 5.3.2.5 Independence of Errors. 5.3.2.6 Outliers in the Solution. 5.4 Fundamental Equations for Multiple Regression. 5.4.1 General Linear Equations. 5.4.2 Matrix Equations. 5.4.3 Computer Analyses of Small-Sample Example. 5.5 Major Types of Multiple Regression. 5.5.1 Standard Multiple Regression. 5.5.2 Sequential Multiple Regression. 5.5.3 Statistical (Stepwise) Regression. 5.5.4 Choosing among Regression Strategies. 5.6 Some Important Issues. 5.6.1 Importance of IVs. 5.6.1.1 Standard Multiple Regression. 5.6.1.2 Sequential or Statistical Regression. 5.6.2 Statistical Inference. 5.6.2.1 Test for Multiple R. 5.6.2.2 Test of Regression Components. 5.6.2.3 Test of Added Subset of IVs. 5.6.2.4 Confidence Limits around B. 5.6.2.5 Comparing Two Sets of Predictors. 5.6.3 Adjustment of R2. 5.6.4 Suppressor Variables. 5.6.5 Regression Approach to ANOVA. 5.6.6 Centering When Interactions and Powers of IVs Are Included. 5.6.7 Mediation in Causal Sequences. 5.7 Complete Examples of Regression Analysis. 5.7.1 Evaluation of Assumptions. 5.7.1.1 Ratio of Cases to IVs. 5.7.1.2 Normality, Linearity, Homoscedasticity, and Independence of Residuals. 5.7.1.3 Outliers. 5.7.1.4 Multicollinearity and Singularity. 5.7.2 Standard Multiple Regression. 5.7.3 Sequential Regression. 5.8 Comparison of Programs. 5.8.1 SPSS Package. 5.8.2 SAS System. 5.8.3 SYSTAT System. 6. Analysis of Covariance. 6.1 General Purpose and Description. 6.2 Kinds of Research Questions. 6.2.1 Main Effects of IVs. 6.2.2 Interactions among IVs. 6.2.3 Specific Comparisons and Trend Analysis. 6.2.4 Effects of Covariates. 6.2.5 Effect Size. 6.2.6 Parameter Estimates. 6.3 Limitations to Analysis of Covariance. 6.3.1 Theoretical Issues. 6.3.2 Practical Issues. 6.3.2.1 Unequal Sample Sizes, Missing Data, and Ratio of Cases to IVs. 8.3.2.2 Absence of Outliers. 6.3.2.3 Absence of Multicollinearity and Singularity. 6.3.2.4 Normality of Sampling Distributions. 6.3.2.5 Homogeneity of Variance. 6.3.2.6 Linearity. 6.3.2.7 Homogeneity of Regression. 6.3.2.8 Reliability of Covariates. 6.4 Fundamental Equations for Analysis of Covariance. 6.4.1 Sums of Squares and Cross Products. 6.4.2 Significance Test and Effect Size. 6.4.3 Computer Analyses of Small-Sample Example. 6.5 Some Important Issues. 6.5.1 Choosing Covariates. 6.5.2 Evaluation of Covariates. 6.5.3 Test for Homogeneity of Regression. 6.5.4 Design Complexity. 6.5.4.1 Within-Subjects and Mixed Within-Between Designs. 6.5.4.1.1 Same Covariate(S) for All Cells. 6.5.4.1.2 Varying Covariate(S) Over Cells. 6.5.4.2 Unequal Sample Sizes. 6.5.4.3 Specific Comparisons and Trend Analysis. 6.5.4.4 Effect Size. 6.5.5 Alternatives to ANCOVA. 6.6 Complete Example of Analysis of Covariance. 6.6.1 Evaluation of Assumptions. 6.6.1.1 Unequal n and Missing Data. 6.6.1.2 Normality. 6.6.1.3 Linearity. 6.6.1.4 Outliers. 6.6.1.5 Multicollinearity and Singularity. 6.6.1.6 Homogeneity of Variance. 6.6.1.7 Homogeneity of Regression. 6.6.1.8 Reliability of Covariates. 6.6.2 Analysis of Covariance. 6.6.2.1 Main Analysis. 6.6.2.2 Evaluation of Covariates. 6.6.2.3 Homogeneity of Regression Run. 6.7 Comparison of Programs. 6.7.1 SPSS Package. 6.7.2 SAS System. 6.7.3 SYSTAT System. 7. Multivariate Analysis of Variance and Covariance. 7.1 General Purpose and Description. 7.2 Kinds of Research Questions. 7.2.1 Main Effects of IVs. 7.2.2 Interactions among IVs. 7.2.3 Importance of DVs. 7.2.4 Parameter Estimates. 7.2.5 Specific Comparisons and Trend Analysis. 7.2.6 Effect Size. 7.2.7 Effects of Covariates. 7.3 Limitations to Multivariate Analysis of Variance and Covariance. 7.3.1 Theoretical Issues. 7.3.2 Practical Issues. 7.3.2.1 Unequal Sample Sizes, Missing Data, and Power. 7.3.2.2 Multivariate Normality. 7.3.2.3 Absence of Outliers. 7.3.2.4 Homogeneity of Variance-Covariance Matrices. 7.3.2.5 Linearity. 7.3.2.6 Homogeneity of Regression. 7.3.2.7 Reliability of Covariates. 7.3.2.8 Absence of Multicollinearity and Singularity. 7.4 Fundamental Equations for Multivariate Analysis of Variance and Covariance. 7.4.1 Multivariate Analysis of Variance. 7.4.2 Computer Analyses of Small-Sample Example. 7.4.3 Multivariate Analysis of Covariance. 7.5 Some Important Issues. 7.5.1 MANOVA vs. ANOVAs. 7.5.2 Criteria for Statistical Inference. 7.5.3 Assessing DVs. 7.5.3.1 Univariate F. 7.5.3.2 Roy-Bargmann Stepdown Analysis. 7.5.3.3 Using Discriminant Function Analysis. 7.5.3.4 Choosing among Strategies for Assessing DVs. 7.5.4 Specific Comparisons and Trend Analysis. 7.5.5 Design Complexity. 7.5.5.1 Within-Subjects and Between-Within Designs. 7.5.5.2 Unequal Sample Sizes. 7.6 Complete Examples of Multivariate Analysis of Variance and Covariance. 7.6.1 Evaluation of Assumptions. 7.6.1.1 Unequal Sample Sizes and Missing D...

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