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Using Multivariate Statistics provides practical guidelines forconducting numerous types of multivariate statistical analyses. It givessyntax and output for accomplishing many analyses through the mostrecent releases of SAS, SPSS, and SYSTAT, some not available insoftware manuals. The book maintains its practical approach, stillfocusing on the benefits and limitations of applications of a techniqueto a data set -- when, why, and how to do it. Overall, it providesadvanced students with a timely and comprehensive introduction totoday's most commonly encountered statistical and multivariatetechniques, while assuming only a limited knowledge of higher-levelmathematics.

Tabla de contenidos provista por Syndetics

• Preface(p. xxvii)
• 1 Introduction(p. 1)
• 1.1 Multivariate Statistics: Why?(p. 1)
• 1.1.1 The Domain of Multivariate Statistics: Numbers of IVs and DVs(p. 1)
• 1.1.2 Experimental and Nonexperimental Research(p. 2)
• 1.1.3 Computers and Multivariate Statistics(p. 4)
• 1.1.4 Garbage In, Roses Out?(p. 5)
• 1.2 Some Useful Definitions(p. 5)
• 1.2.1 Continuous, Discrete, and Dichotomous Data(p. 5)
• 1.2.2 Samples and Populations(p. 7)
• 1.2.3 Descriptive and Inferential Statistics(p. 7)
• 1.2.4 Orthogonality: Standard and Sequential Analyses(p. 8)
• 1.3 Linear Combinations of Variables(p. 10)
• 1.4 Number and Nature of Variables to Include(p. 11)
• 1.5 Statistical Power(p. 11)
• 1.6 Data Appropriate for Multivariate Statistics(p. 12)
• 1.6.1 The Data Matrix(p. 12)
• 1.6.2 The Correlation Matrix(p. 13)
• 1.6.3 The Variance-Covariance Matrix(p. 14)
• 1.6.4 The Sum-of-Squares and Cross-Products Matrix(p. 14)
• 1.6.5 Residuals(p. 16)
• 1.7 Organization of the Book(p. 16)
• 2 A Guide to Statistical Techniques: Using the Book(p. 17)
• 2.1 Research Questions and Associated Techniques(p. 17)
• 2.1.1 Degree of Relationship among Variables(p. 17)
• 2.1.2 Significance of Group Differences(p. 19)
• 2.1.3 Prediction of Group Membership(p. 23)
• 2.1.4 Structure(p. 25)
• 2.1.5 Time Course of Events(p. 26)
• 2.2 Some Further Comparisons(p. 27)
• 2.3 A Decision Tree(p. 28)
• 2.4 Technique Chapters(p. 31)
• 2.5 Preliminary Check of the Data(p. 32)
• 3 Review of Univariate and Bivariate Statistics(p. 33)
• 3.1 Hypothesis Testing(p. 33)
• 3.1.1 One-Sample z Test as Prototype(p. 33)
• 3.1.2 Power(p. 36)
• 3.1.3 Extensions of the Model(p. 37)
• 3.1.4 Controversy Surrounding Significance Testing(p. 37)
• 3.2 Analysis of Variance(p. 37)
• 3.2.1 One-Way Between-Subjects ANOVA(p. 39)
• 3.2.2 Factorial Between-Subjects ANOVA(p. 42)
• 3.2.3 Within-Subjects ANOVA(p. 43)
• 3.2.4 Mixed Between-Within-Subjects ANOVA(p. 46)
• 3.2.5 Design Complexity(p. 47)
• 3.2.6 Specific Comparisons(p. 49)
• 3.3 Parameter Estimation(p. 53)
• 3.4 Effect Size(p. 54)
• 3.5 Bivariate Statistics: Correlation and Regression(p. 56)
• 3.5.1 Correlation(p. 56)
• 3.5.2 Regression(p. 57)
• 3.6 Chi-Square Analysis(p. 58)
• 4 Cleaning Up Your Act: Screening Data Prior to Analysis(p. 60)
• 4.1 Important Issues in Data Screening(p. 61)
• 4.1.1 Accuracy of Data File(p. 61)
• 4.1.2 Honest Correlations(p. 61)
• 4.1.3 Missing Data(p. 62)
• 4.1.4 Outliers(p. 72)
• 4.1.5 Normality, Linearity, and Homoscedasticity(p. 78)
• 4.1.6 Common Data Transformations(p. 86)
• 4.1.7 Multicollinearity and Singularity(p. 88)
• 4.1.8 A Checklist and Some Practical Recommendations(p. 91)
• 4.2 Complete Examples of Data Screening(p. 92)
• 4.2.1 Screening Ungrouped Data(p. 92)
• 4.2.2 Screening Grouped Data(p. 105)
• 5 Multiple Regression(p. 117)
• 5.1 General Purpose and Description(p. 117)
• 5.2 Kinds of Research Questions(p. 118)
• 5.2.1 Degree of Relationship(p. 119)
• 5.2.2 Importance of IVs(p. 119)
• 5.2.3 Adding IVs(p. 119)
• 5.2.4 Changing IVs(p. 120)
• 5.2.5 Contingencies among IVs(p. 120)
• 5.2.6 Comparing Sets of IVs(p. 120)
• 5.2.7 Predicting DV Scores for Members of a New Sample(p. 120)
• 5.2.8 Parameter Estimates(p. 121)
• 5.3 Limitations to Regression Analyses(p. 121)
• 5.3.1 Theoretical Issues(p. 122)
• 5.3.2 Practical Issues(p. 123)
• 5.4 Fundamental Equations for Multiple Regression(p. 128)
• 5.4.1 General Linear Equations(p. 129)
• 5.4.2 Matrix Equations(p. 131)
• 5.4.3 Computer Analyses of Small-Sample Example(p. 134)
• 5.5 Major Types of Multiple Regression(p. 136)
• 5.5.1 Standard Multiple Regression(p. 136)
• 5.5.2 Sequential Multiple Regression(p. 138)
• 5.5.3 Statistical (Stepwise) Regression(p. 138)
• 5.5.4 Choosing among Regression Strategies(p. 143)
• 5.6 Some Important Issues(p. 144)
• 5.6.1 Importance of IVs(p. 144)
• 5.6.2 Statistical Inference(p. 146)
• 5.6.3 Adjustment of R[superscript 2](p. 153)
• 5.6.4 Suppressor Variables(p. 154)
• 5.6.5 Regression Approach to ANOVA(p. 155)
• 5.6.6 Centering when Interactions and Powers of IVs Are Included(p. 157)
• 5.6.7 Mediation in Causal Sequences(p. 159)
• 5.7 Complete Examples of Regression Analysis(p. 161)
• 5.7.1 Evaluation of Assumptions(p. 161)
• 5.7.2 Standard Multiple Regression(p. 167)
• 5.7.3 Sequential Regression(p. 174)
• 5.7.4 Example of Standard Multiple Regression with Missing Values Multiply Imputed(p. 179)
• 5.8 Comparison of Programs(p. 188)
• 5.8.1 SPSS Package(p. 188)
• 5.8.2 SAS System(p. 191)
• 5.8.3 SYSTAT System(p. 194)
• 6 Analysis of Covariance(p. 195)
• 6.1 General Purpose and Description(p. 195)
• 6.2 Kinds of Research Questions(p. 198)
• 6.2.1 Main Effects of IVs(p. 198)
• 6.2.2 Interactions among IVs(p. 198)
• 6.2.3 Specific Comparisons and Trend Analysis(p. 199)
• 6.2.4 Effects of Covariates(p. 199)
• 6.2.5 Effect Size(p. 199)
• 6.2.6 Parameter Estimates(p. 199)
• 6.3 Limitations to Analysis of Covariance(p. 200)
• 6.3.1 Theoretical Issues(p. 200)
• 6.3.2 Practical Issues(p. 201)
• 6.4 Fundamental Equations for Analysis of Covariance(p. 203)
• 6.4.1 Sums of Squares and Cross Products(p. 204)
• 6.4.2 Significance Test and Effect Size(p. 208)
• 6.4.3 Computer Analyses of Small-Sample Example(p. 209)
• 6.5 Some Important Issues(p. 211)
• 6.5.1 Choosing Covariates(p. 211)
• 6.5.2 Evaluation of Covariates(p. 212)
• 6.5.3 Test for Homogeneity of Regression(p. 213)
• 6.5.4 Design Complexity(p. 213)
• 6.5.5 Alternatives to ANCOVA(p. 221)
• 6.6 Complete Example of Analysis of Covariance(p. 223)
• 6.6.1 Evaluation of Assumptions(p. 223)
• 6.6.2 Analysis of Covariance(p. 230)
• 6.7 Comparison of Programs(p. 240)
• 6.7.1 SPSS Package(p. 240)
• 6.7.2 SAS System(p. 240)
• 6.7.3 SYSTAT System(p. 240)
• 7 Multivariate Analysis of Variance and Covariance(p. 243)
• 7.1 General Purpose and Description(p. 243)
• 7.2 Kinds of Research Questions(p. 247)
• 7.2.1 Main Effects of IVs(p. 247)
• 7.2.2 Interactions among IVs(p. 247)
• 7.2.3 Importance of DVs(p. 247)
• 7.2.4 Parameter Estimates(p. 248)
• 7.2.5 Specific Comparisons and Trend Analysis(p. 248)
• 7.2.6 Effect Size(p. 248)
• 7.2.7 Effects of Covariates(p. 248)
• 7.2.8 Repeated-Measures Analysis of Variance(p. 249)
• 7.3 Limitations to Multivariate Analysis of Variance and Covariance(p. 249)
• 7.3.1 Theoretical Issues(p. 249)
• 7.3.2 Practical Issues(p. 250)
• 7.4 Fundamental Equations for Multivariate Analysis of Variance and Covariance(p. 253)
• 7.4.1 Multivariate Analysis of Variance(p. 253)
• 7.4.2 Computer Analyses of Small-Sample Example(p. 261)
• 7.4.3 Multivariate Analysis of Covariance(p. 264)
• 7.5 Some Important Issues(p. 268)
• 7.5.1 MANOVA vs. ANOVAs(p. 268)
• 7.5.2 Criteria for Statistical Inference(p. 269)
• 7.5.3 Assessing DVs(p. 270)
• 7.5.4 Specific Comparisons and Trend Analysis(p. 273)
• 7.5.5 Design Complexity(p. 274)
• 7.6 Complete Examples of Multivariate Analysis of Variance and Covariance(p. 277)
• 7.6.1 Evaluation of Assumptions(p. 277)
• 7.6.2 Multivariate Analysis of Variance(p. 285)
• 7.6.3 Multivariate Analysis of Covariance(p. 296)
• 7.7 Comparison of Programs(p. 307)
• 7.7.1 SPSS Package(p. 307)
• 7.7.2 SAS System(p. 310)
• 7.7.3 SYSTAT System(p. 310)
• 8 Profile Analysis: The Multivariate Approach to Repeated Measures(p. 311)
• 8.1 General Purpose and Description(p. 311)
• 8.2 Kinds of Research Questions(p. 312)
• 8.2.1 Parallelism of Profiles(p. 312)
• 8.2.2 Overall Difference among Groups(p. 313)
• 8.2.3 Flatness of Profiles(p. 313)
• 8.2.4 Contrasts Following Profile Analysis(p. 313)
• 8.2.5 Parameter Estimates(p. 313)
• 8.2.6 Effect Size(p. 314)
• 8.3 Limitations to Profile Analysis(p. 314)
• 8.3.1 Theoretical Issues(p. 314)
• 8.3.2 Practical Issues(p. 315)
• 8.4 Fundamental Equations for Profile Analysis(p. 316)
• 8.4.1 Differences in Levels(p. 316)
• 8.4.2 Parallelism(p. 318)
• 8.4.3 Flatness(p. 321)
• 8.4.4 Computer Analyses of Small-Sample Example(p. 323)
• 8.5 Some Important Issues(p. 329)
• 8.5.1 Univariate vs. Multivariate Approach to Repeated Measures(p. 329)
• 8.5.2 Contrasts in Profile Analysis(p. 331)
• 8.5.3 Doubly-Multivariate Designs(p. 339)
• 8.5.4 Classifying Profiles(p. 345)
• 8.5.5 Imputation of Missing Values(p. 345)
• 8.6 Complete Examples of Profile Analysis(p. 346)
• 8.6.1 Profile Analysis of Subscales of the WISC(p. 346)
• 8.6.2 Doubly-Multivariate Analysis of Reaction Time(p. 360)
• 8.7 Comparison of Programs(p. 371)
• 8.7.1 SPSS Package(p. 373)
• 8.7.2 SAS System(p. 373)
• 8.7.3 SYSTAT System(p. 374)
• 9 Discriminant Analysis(p. 375)
• 9.1 General Purpose and Description(p. 375)
• 9.2 Kinds of Research Questions(p. 378)
• 9.2.1 Significance of Prediction(p. 378)
• 9.2.2 Number of Significant Discriminant Functions(p. 378)
• 9.2.3 Dimensions of Discrimination(p. 379)
• 9.2.4 Classification Functions(p. 379)
• 9.2.5 Adequacy of Classification(p. 379)
• 9.2.6 Effect Size(p. 379)
• 9.2.7 Importance of Predictor Variables(p. 380)
• 9.2.8 Significance of Prediction with Covariates(p. 380)
• 9.2.9 Estimation of Group Means(p. 380)
• 9.3 Limitations to Discriminant Analysis(p. 381)
• 9.3.1 Theoretical Issues(p. 381)
• 9.3.2 Practical Issues(p. 381)
• 9.4 Fundamental Equations for Discriminant Analysis(p. 384)
• 9.4.1 Derivation and Test of Discriminant Functions(p. 384)
• 9.4.2 Classification(p. 387)
• 9.4.3 Computer Analyses of Small-Sample Example(p. 389)
• 9.5 Types of Discriminant Function Analyses(p. 395)
• 9.5.1 Direct Discriminant Analysis(p. 395)
• 9.5.2 Sequential Discriminant Analysis(p. 396)
• 9.5.3 Stepwise (Statistical) Discriminant Analysis(p. 396)
• 9.6 Some Important Issues(p. 397)
• 9.6.1 Statistical Inference(p. 397)
• 9.6.2 Number of Discriminant Functions(p. 398)
• 9.6.3 Interpreting Discriminant Functions(p. 398)
• 9.6.4 Evaluating Predictor Variables(p. 401)
• 9.6.5 Effect Size(p. 402)
• 9.6.6 Design Complexity: Factorial Designs(p. 403)
• 9.6.7 Use of Classification Procedures(p. 404)
• 9.7 Complete Example of Discriminant Analysis(p. 407)
• 9.7.1 Evaluation of Assumptions(p. 407)
• 9.7.2 Direct Discriminant Analysis(p. 412)
• 9.8 Comparison of Programs(p. 430)
• 9.8.1 SPSS Package(p. 430)
• 9.8.2 SAS System(p. 430)
• 9.8.3 SYSTAT System(p. 436)
• 10 Logistic Regression(p. 437)
• 10.1 General Purpose and Description(p. 437)
• 10.2 Kinds of Research Questions(p. 439)
• 10.2.1 Prediction of Group Membership or Outcome(p. 439)
• 10.2.2 Importance of Predictors(p. 439)
• 10.2.3 Interactions among Predictors(p. 440)
• 10.2.4 Parameter Estimates(p. 440)
• 10.2.5 Classification of Cases(p. 440)
• 10.2.6 Significance of Prediction with Covariates(p. 440)
• 10.2.7 Effect Size(p. 441)
• 10.3 Limitations to Logistic Regression Analysis(p. 441)
• 10.3.1 Theoretical Issues(p. 441)
• 10.3.2 Practical Issues(p. 442)
• 10.4 Fundamental Equations for Logistic Regression(p. 444)
• 10.4.1 Testing and Interpreting Coefficients(p. 445)
• 10.4.2 Goodness-of-Fit(p. 446)
• 10.4.3 Comparing Models(p. 448)
• 10.4.4 Interpretation and Analysis of Residuals(p. 448)
• 10.4.5 Computer Analyses of Small-Sample Example(p. 449)
• 10.5 Types of Logistic Regression(p. 453)
• 10.5.1 Direct Logistic Regression(p. 454)
• 10.5.2 Sequential Logistic Regression(p. 454)
• 10.5.3 Statistical (Stepwise) Logistic Regression(p. 454)
• 10.5.4 Probit and Other Analyses(p. 456)
• 10.6 Some Important Issues(p. 457)
• 10.6.1 Statistical Inference(p. 457)
• 10.6.2 Effect Size for a Model(p. 460)
• 10.6.3 Interpretation of Coefficients Using Odds(p. 461)
• 10.6.4 Coding Outcome and Predictor Categories(p. 464)
• 10.6.5 Number and Type of Outcome Categories(p. 464)
• 10.6.6 Classification of Cases(p. 468)
• 10.6.7 Hierarchical and Nonhierarchical Analysis(p. 468)
• 10.6.8 Importance of Predictors(p. 469)
• 10.6.9 Logistic Regression for Matched Groups(p. 469)
• 10.7 Complete Examples of Logistic Regression(p. 469)
• 10.7.1 Evaluation of Limitations(p. 470)
• 10.7.2 Direct Logistic Regression with Two-Category Outcome and Continuous Predictors(p. 474)
• 10.7.3 Sequential Logistic Regression with Three Categories of Outcome(p. 481)
• 10.8 Comparisons of Programs(p. 499)
• 10.8.1 SPSS Package(p. 499)
• 10.8.2 SAS System(p. 504)
• 10.8.3 SYSTAT System(p. 504)
• 11 Survival/Failure Analysis(p. 506)
• 11.1 General Purpose and Description(p. 506)
• 11.2 Kinds of Research Questions(p. 507)
• 11.2.1 Proportions Surviving at Various Times(p. 507)
• 11.2.2 Group Differences in Survival(p. 508)
• 11.2.3 Survival Time with Covariates(p. 508)
• 11.3 Limitations to Survival Analysis(p. 509)
• 11.3.1 Theoretical Issues(p. 509)
• 11.3.2 Practical Issues(p. 509)
• 11.4 Fundamental Equations for Survival Analysis(p. 511)
• 11.4.1 Life Tables(p. 511)
• 11.4.2 Standard Error of Cumulative Proportion Surviving(p. 513)
• 11.4.3 Hazard and Density Functions(p. 514)
• 11.4.4 Plot of Life Tables(p. 515)
• 11.4.5 Test for Group Differences(p. 515)
• 11.4.6 Computer Analyses of Small-Sample Example(p. 517)
• 11.5 Types of Survival Analyses(p. 524)
• 11.5.1 Actuarial and Product-Limit Life Tables and Survivor Functions(p. 524)
• 11.5.2 Prediction of Group Survival Times from Covariates(p. 524)
• 11.6 Some Important Issues(p. 535)
• 11.6.1 Proportionality of Hazards(p. 535)
• 11.6.2 Censored Data(p. 537)
• 11.6.3 Effect Size and Power(p. 538)
• 11.6.4 Statistical Criteria(p. 539)
• 11.6.5 Predicting Survival Rate(p. 540)
• 11.7 Complete Example of Survival Analysis(p. 541)
• 11.7.1 Evaluation of Assumptions(p. 543)
• 11.7.2 Cox Regression Survival Analysis(p. 551)
• 11.8 Comparison of Programs(p. 559)
• 11.8.1 SAS System(p. 559)
• 11.8.2 SPSS Package(p. 559)
• 11.8.3 SYSTAT System(p. 566)
• 12 Canonical Correlation(p. 567)
• 12.1 General Purpose and Description(p. 567)
• 12.2 Kinds of Research Questions(p. 568)
• 12.2.1 Number of Canonical Variate Pairs(p. 568)
• 12.2.2 Interpretation of Canonical Variates(p. 569)
• 12.2.3 Importance of Canonical Variates(p. 569)
• 12.2.4 Canonical Variate Scores(p. 569)
• 12.3 Limitations(p. 569)
• 12.3.1 Theoretical Limitations(p. 569)
• 12.3.2 Practical Issues(p. 570)
• 12.4 Fundamental Equations for Canonical Correlation(p. 572)
• 12.4.1 Eigenvalues and Eigenvectors(p. 573)
• 12.4.2 Matrix Equations(p. 575)
• 12.4.3 Proportions of Variance Extracted(p. 579)
• 12.4.4 Computer Analyses of Small-Sample Example(p. 580)
• 12.5 Some Important Issues(p. 586)
• 12.5.1 Importance of Canonical Variates(p. 586)
• 12.5.2 Interpretation of Canonical Variates(p. 587)
• 12.6 Complete Example of Canonical Correlation(p. 587)
• 12.6.1 Evaluation of Assumptions(p. 588)
• 12.6.2 Canonical Correlation(p. 595)
• 12.7 Comparison of Programs(p. 604)
• 12.7.1 SAS System(p. 604)
• 12.7.2 SPSS Package(p. 604)
• 12.7.3 SYSTAT System(p. 606)
• 13 Principal Components and Factor Analysis(p. 607)
• 13.1 General Purpose and Description(p. 607)
• 13.2 Kinds of Research Questions(p. 610)
• 13.2.1 Number of Factors(p. 610)
• 13.2.2 Nature of Factors(p. 611)
• 13.2.3 Importance of Solutions and Factors(p. 611)
• 13.2.4 Testing Theory in FA(p. 611)
• 13.2.5 Estimating Scores on Factors(p. 611)
• 13.3 Limitations(p. 611)
• 13.3.1 Theoretical Issues(p. 611)
• 13.3.2 Practical Issues(p. 612)
• 13.4 Fundamental Equations for Factor Analysis(p. 615)
• 13.4.1 Extraction(p. 616)
• 13.4.2 Orthogonal Rotation(p. 620)
• 13.4.3 Communalities, Variance, and Covariance(p. 621)
• 13.4.4 Factor Scores(p. 622)
• 13.4.5 Oblique Rotation(p. 625)
• 13.4.6 Computer Analyses of Small-Sample Example(p. 628)
• 13.5 Major Types of Factor Analyses(p. 633)
• 13.5.1 Factor Extraction Techniques(p. 633)
• 13.5.2 Rotation(p. 637)
• 13.5.3 Some Practical Recommendations(p. 642)
• 13.6 Some Important Issues(p. 643)
• 13.6.1 Estimates of Communalities(p. 643)
• 13.6.2 Adequacy of Extraction and Number of Factors(p. 644)
• 13.6.3 Adequacy of Rotation and Simple Structure(p. 646)
• 13.6.4 Importance and Internal Consistency of Factors(p. 647)
• 13.6.5 Interpretation of Factors(p. 649)
• 13.6.6 Factor Scores(p. 650)
• 13.6.7 Comparisons among Solutions and Groups(p. 651)
• 13.7 Complete Example of FA(p. 651)
• 13.7.1 Evaluation of Limitations(p. 652)
• 13.7.2 Principal Factors Extraction with Varimax Rotation(p. 657)
• 13.8 Comparison of Programs(p. 671)
• 13.8.1 SPSS Package(p. 674)
• 13.8.2 SAS System(p. 675)
• 13.8.3 SYSTAT System(p. 675)
• 14 Structural Equation Modeling(p. 676)
• 14.1 General Purpose and Description(p. 676)
• 14.2 Kinds of Research Questions(p. 680)
• 14.2.1 Adequacy of the Model(p. 680)
• 14.2.2 Testing Theory(p. 680)
• 14.2.3 Amount of Variance in the Variables Accounted for by the Factors(p. 680)
• 14.2.4 Reliability of the Indicators(p. 680)
• 14.2.5 Parameter Estimates(p. 680)
• 14.2.6 Intervening Variables(p. 681)
• 14.2.7 Group Differences(p. 681)
• 14.2.8 Longitudinal Differences(p. 681)
• 14.2.9 Multilevel Modeling(p. 681)
• 14.3 Limitations to Structural Equation Modeling(p. 682)
• 14.3.1 Theoretical Issues(p. 682)
• 14.3.2 Practical Issues(p. 682)
• 14.4 Fundamental Equations for Structural Equations Modeling(p. 684)
• 14.4.1 Covariance Algebra(p. 684)
• 14.4.2 Model Hypotheses(p. 686)
• 14.4.3 Model Specification(p. 688)
• 14.4.4 Model Estimation(p. 690)
• 14.4.5 Model Evaluation(p. 694)
• 14.4.6 Computer Analysis of Small-Sample Example(p. 696)
• 14.5 Some Important Issues(p. 709)
• 14.5.1 Model Identification(p. 709)
• 14.5.2 Estimation Techniques(p. 713)
• 14.5.3 Assessing the Fit of the Model(p. 715)
• 14.5.4 Model Modification(p. 721)
• 14.5.5 Reliability and Proportion of Variance(p. 728)
• 14.5.6 Discrete and Ordinal Data(p. 729)
• 14.5.7 Multiple Group Models(p. 730)
• 14.5.8 Mean and Covariance Structure Models(p. 731)
• 14.6 Complete Examples of Structural Equation Modeling Analysis(p. 732)
• 14.6.1 Confirmatory Factor Analysis of the WISC(p. 732)
• 14.6.2 SEM of Health Data(p. 750)
• 14.7 Comparison of Programs(p. 773)
• 14.7.1 EQS(p. 773)
• 14.7.2 LISREL(p. 773)
• 14.7.3 AMOS(p. 780)
• 14.7.4 SAS System(p. 780)
• 15 Multilevel Linear Modeling(p. 781)
• 15.1 General Purpose and Description(p. 781)
• 15.2 Kinds of Research Questions(p. 784)
• 15.2.1 Group Differences in Means(p. 784)
• 15.2.2 Group Differences in Slopes(p. 784)
• 15.2.3 Cross-Level Interactions(p. 785)
• 15.2.4 Meta-Analysis(p. 785)
• 15.2.5 Relative Strength of Predictors at Various Levels(p. 785)
• 15.2.6 Individual and Group Structure(p. 785)
• 15.2.7 Path Analysis at Individual and Group Levels(p. 786)
• 15.2.8 Analysis of Longitudinal Data(p. 786)
• 15.2.9 Multilevel Logistic Regression(p. 786)
• 15.2.10 Multiple Response Analysis(p. 786)
• 15.3 Limitations to Multilevel Linear Modeling(p. 786)
• 15.3.1 Theoretical Issues(p. 786)
• 15.3.2 Practical Issues(p. 787)
• 15.4 Fundamental Equations(p. 789)
• 15.4.1 Intercepts-Only Model(p. 792)
• 15.4.2 Model with a First-Level Predictor(p. 799)
• 15.4.3 Model with Predictors at First and Second Levels(p. 807)
• 15.5 Types of MLM(p. 814)
• 15.5.1 Repeated Measures(p. 814)
• 15.5.2 Higher-Order MLM(p. 819)
• 15.5.3 Latent Variables(p. 819)
• 15.5.4 Nonnormal Outcome Variables(p. 820)
• 15.5.5 Multiple Response Models(p. 821)
• 15.6 Some Important Issues(p. 822)
• 15.6.1 Intraclass Correlation(p. 822)
• 15.6.2 Centering Predictors and Changes in Their Interpretations(p. 823)
• 15.6.3 Interactions(p. 826)
• 15.6.4 Random and Fixed Intercepts and Slopes(p. 826)
• 15.6.5 Statistical Inference(p. 830)
• 15.6.6 Effect Size(p. 832)
• 15.6.7 Estimation Techniques and Convergence Problems(p. 833)
• 15.6.8 Exploratory Model Building(p. 834)
• 15.7 Complete Example of MLM(p. 835)
• 15.7.1 Evaluation of Assumptions(p. 835)
• 15.7.2 Multilevel Modeling(p. 840)
• 15.8 Comparison of Programs(p. 852)
• 15.8.1 SAS System(p. 852)
• 15.8.2 SPSS Package(p. 856)
• 15.8.3 HLM Program(p. 856)
• 15.8.4 MLwiN Program(p. 857)
• 15.8.5 SYSTAT System(p. 857)
• 16 Multiway Frequency Analysis(p. 858)
• 16.1 General Purpose and Description(p. 858)
• 16.2 Kinds of Research Questions(p. 859)
• 16.2.1 Associations among Variables(p. 859)
• 16.2.2 Effect on a Dependent Variable(p. 860)
• 16.2.3 Parameter Estimates(p. 860)
• 16.2.4 Importance of Effects(p. 860)
• 16.2.5 Effect Size(p. 860)
• 16.2.6 Specific Comparisons and Trend Analysis(p. 860)
• 16.3 Limitations to Multiway Frequency Analysis(p. 861)
• 16.3.1 Theoretical Issues(p. 861)
• 16.3.2 Practical Issues(p. 861)
• 16.4 Fundamental Equations for Multiway Frequency Analysis(p. 863)
• 16.4.1 Screening for Effects(p. 864)
• 16.4.2 Modeling(p. 871)
• 16.4.3 Evaluation and Interpretation(p. 874)
• 16.4.4 Computer Analyses of Small-Sample Example(p. 880)
• 16.5 Some Important Issues(p. 887)
• 16.5.1 Hierarchical and Nonhierarchical Models(p. 887)
• 16.5.2 Statistical Criteria(p. 888)
• 16.5.3 Strategies for Choosing a Model(p. 889)
• 16.6 Complete Example of Multiway Frequency Analysis(p. 890)
• 16.6.1 Evaluation of Assumptions: Adequacy of Expected Frequencies(p. 890)
• 16.6.2 Hierarchical Log-Linear Analysis(p. 891)
• 16.7 Comparison of Programs(p. 908)
• 16.7.1 SPSS Package(p. 911)
• 16.7.2 SAS System(p. 912)
• 16.7.3 SYSTAT System(p. 912)
• 17 An Overview of the General Linear Model(p. 913)
• 17.1 Linearity and the General Linear Model(p. 913)
• 17.2 Bivariate to Multivariate Statistics and Overview of Techniques(p. 913)
• 17.2.1 Bivariate Form(p. 913)
• 17.2.2 Simple Multivariate Form(p. 914)
• 17.2.3 Full Multivariate Form(p. 917)
• 17.3 Alternative Research Strategies(p. 918)
• 18 Time-Series Analysis (available online at www.ablongman.com/tabachnick5e)(p. 1)
• 18.1 General Purpose and Description(p. 1)
• 18.2 Kinds of Research Questions(p. 3)
• 18.2.1 Pattern of Autocorrelation(p. 5)
• 18.2.2 Seasonal Cycles and Trends(p. 5)
• 18.2.3 Forecasting(p. 5)
• 18.2.4 Effect of an Intervention(p. 5)
• 18.2.5 Comparing Time Series(p. 5)
• 18.2.6 Time Series with Covariates(p. 6)
• 18.2.7 Effect Size and Power(p. 6)
• 18.3 Assumptions of Time-Series Analysis(p. 6)
• 18.3.1 Theoretical Issues(p. 6)
• 18.3.2 Practical Issues(p. 6)
• 18.4 Fundamental Equations for Time-Series ARIMA Models(p. 7)
• 18.4.1 Identification ARIMA (p, d, q) Models(p. 8)
• 18.4.2 Estimating Model Parameters(p. 16)
• 18.4.3 Diagnosing a Model(p. 19)
• 18.4.4 Computer Analysis of Small-Sample Time-Series Example(p. 19)
• 18.5 Types of Time-Series Analyses(p. 27)
• 18.5.1 Models with Seasonal Components(p. 27)
• 18.5.2 Models with Interventions(p. 30)
• 18.5.3 Adding Continuous Variables(p. 38)
• 18.6 Some Important Issues(p. 41)
• 18.6.1 Patterns of ACFs and PACFs(p. 41)
• 18.6.2 Effect Size(p. 44)
• 18.6.3 Forecasting(p. 45)
• 18.6.4 Statistical Methods for Comparing Two Models(p. 45)
• 18.7 Complete Example of a Time-Series Analysis(p. 47)
• 18.7.1 Evaluation of Assumptions(p. 48)
• 18.7.2 Baseline Model Identification and Estimation(p. 48)
• 18.7.3 Baseline Model Diagnosis(p. 49)
• 18.7.4 Intervention Analysis(p. 55)
• 18.8 Comparison of Programs(p. 60)
• 18.8.1 SPSS Package(p. 61)
• 18.8.2 SAS System(p. 61)
• 18.8.3 SYSTAT System(p. 61)
• Appendix A A Skimpy Introduction to Matrix Algebra(p. 924)
• A.1 The Trace of a Matrix(p. 925)
• A.2 Addition or Subtraction of a Constant to a Matrix(p. 925)
• A.3 Multiplication or Division of a Matrix by a Constant(p. 925)
• A.4 Addition and Subtraction of Two Matrices(p. 926)
• A.5 Multiplication, Transposes, and Square Roots of Matrices(p. 927)
• A.6 Matrix "Division" (Inverses and Determinants)(p. 929)
• A.7 Eigenvalues and Eigenvectors: Procedures for Consolidating Variance from a Matrix(p. 930)
• Appendix B Research Designs for Complete Examples(p. 934)
• B.1 Women's Health and Drug Study(p. 934)
• B.2 Sexual Attraction Study(p. 935)
• B.3 Learning Disabilities Data Bank(p. 938)
• B.4 Reaction Time to Identify Figures(p. 939)
• B.5 Field Studies of Noise-Induced Sleep Disturbance(p. 939)
• B.6 Clinical Trial for Primary Biliary Cirrhosis(p. 940)
• B.7 Impact of Seat Belt Law(p. 940)
• Appendix C Statistical Tables(p. 941)
• C.1 Normal Curve Areas(p. 942)
• C.2 Critical Values of the t Distribution for [alpha] = .05 and .01, Two-Tailed Test(p. 943)
• C.3 Critical Values of the F Distribution(p. 944)
• C.4 Critical Values of Chi Square (X[superscript 2])(p. 949)
• C.5 Critical Values for Squared Multiple Correlation (R[superscript 2]) in Forward Stepwise Selection(p. 950)
• C.6 Critical Values for F[subscript MAX] (S[superscript 2 subscript MAX]/S[superscript 2 subscript MIN]) Distribution for [alpha] = .05 and .01(p. 952)
• References(p. 953)
• Index(p. 963)