Applied multivariate statistics for the social sciences /
CD-ROM contains: Data sets.
| Κύριος συγγραφέας: | |
|---|---|
| Μορφή: | Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Mahwah, N.J. :
L. Erlbaum,
2002.
|
| Έκδοση: | 4η έκδ. |
| Θέματα: | |
| Διαθέσιμο Online: | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=63477 |
Πίνακας περιεχομένων:
- 1.2 Type I Error, Type II Error, and Power 3
- 1.3 Multiple Statistical Tests and the Probability of Spurious Results 6
- 1.4 Statistical Significance Versus Practical Significance 9
- 1.5 Outliers 12
- 1.6 Research Examples for Some Analyses Considered in This Text 17
- 1.7 SAS and SPSS Statistical Packages 24
- 1.8 SPSS for Windows
- Releases 9.0 and 10.0 34
- 1.9 Data Files 36
- 1.10 Data Editing 40
- 1.11 SPSS Output Navigator 45
- 1.12 Some Issues Unique to Multivariate Analysis 48
- 1.13 Data Collection and Integrity 49
- Appendix 1 Defining a Measure of Statistical Distance 50
- Appendix 2 Milk Data 52
- Chapter 2 Matrix Algebra
- 2.2 Addition, Subtraction, and Multiplication of a Matrix by a Scalar 59
- 2.3 Obtaining the Matrix of Variances and Covariances 62
- 2.4 Determinant of a Matrix 64
- 2.5 Inverse of a Matrix 70
- 2.6 Eigenvalues 73
- 2.7 SPSS Matrix Procedure 75
- 2.8 SAS IML Procedure 76
- Chapter 3 Multiple Regression
- 3.2 Simple Regression 82
- 3.3 Multiple Regression for Two Predictors
- Matrix Formulation 86
- 3.4 Mathematical Maximization Nature of Least Squares Regression 88
- 3.5 Breakdown of Sum of Squares in Regression and F Test for Multiple Correlation 89
- 3.6 Relationship of Simple Correlations to Multiple Correlation 91
- 3.7 Multicollinearity 91
- 3.8 Model Selection 93
- 3.9 Two Computer Examples 98
- 3.10 Checking Assumptions for the Regression Model 110
- 3.11 Model Validation 113
- 3.12 Importance of the Order of the Predictors in Regression Analysis 119
- 3.13 Other Important Issues 121
- 3.14 Outliers and Influential Data Points 125
- 3.15 Further Discussion of the Two Computer Examples 138
- 3.16 Sample Size Determination for a Reliable Prediction Equation 143
- 3.17 Logistic Regression 146
- 3.18 Other Types of Regression Analysis 155
- 3.19 Multivariate Regression 155
- 3.20 Summary of Important Points 159
- Chapter 4 Two-Group Multivariate Analysis Of Variance
- 4.2 Four Statistical Reasons for Preferring a Multivariate Analysis 174
- 4.3 Multivariate Test Statistic as a Generalization of Univariate t 175
- 4.4 Numerical Calculations for a Two-Group Problem 177
- 4.5 Three Post Hoc Procedures 181
- 4.6 SAS and SPSS Control Lines for Sample Problem and Selected Printout 183
- 4.7 Multivariate Significance but No Univariate Significance 184
- 4.8 Multivariate Regression Analysis for the Sample Problem 188
- 4.9 Power Analysis 192
- 4.10 Ways of Improving Power 195
- 4.11 Power Estimation on SPSS MANOVA 197
- 4.12 Multivariate Estimation of Power 197
- Chapter 5 K-Group Manova: A Priori And Post Hoc Procedures
- 5.2 Multivariate Regression Analysis for a Sample Problem 209
- 5.3 Traditional Multivariate Analysis of Variance 210
- 5.4 Multivariate Analysis of Variance for Sample Data 212
- 5.5 Post Hoc Procedures 217
- 5.6 Tukey Procedure 222
- 5.7 Planned Comparisons 225
- 5.8 Test Statistics for Planned Comparisons 228
- 5.9 Multivariate Planned Comparisons on SPSS MANOVA 231
- 5.10 Correlated Contrasts 235
- 5.11 Studies Using Multivariate Planned Comparisons 241
- 5.12 Stepdown Analysis 243
- 5.13 Other Multivariate Test Statistics 243
- 5.14 How Many Dependent Variables for a MANOVA? 245
- 5.15 Power Analysis
- A Priori Determination of Sample Size 245
- Appendix Novince (1977) Data for Multivariate Analysis of Variance Presented in Tables 5.3 and 5.4 249
- Chapter 6 Assumptions In Manova
- 6.2 ANOVA and MANOVA Assumptions 257
- 6.3 Independence Assumption 258
- 6.4 What Should Be Done With Correlated Observations? 260
- 6.5 Normality Assumption 261
- 6.6 Multivariate Normality 262
- 6.7 Assessing Univariate Normality 263
- 6.8 Homogeneity of Variance Assumption 268
- 6.9d Homogeneity of the Covariance Matrices 269
- 6.10 General Procedure for Assessing Violations in MANOVA 276
- Appendix Multivariate Test Statistics for Unequal Covariance Matrices 279
- Chapter 7 Discriminant Analysis
- 7.2 Descriptive Discriminant Analysis 286
- 7.3 Significance Tests 287
- 7.4 Interpreting the Discriminant Functions 288
- 7.5 Graphing the Groups in the Discriminant Plane 289
- 7.6 Rotation of the Discriminant Functions 296
- 7.7 Stepwise Discriminant Analysis 296
- 7.8 Two Other Studies That Used Discriminant Analysis 297
- 7.9 Classification Problem 301
- 7.10 Linear vs. Quadratic Classification Rule 316
- 7.11 Characteristics of a Good Classification Procedure 316
- Chapter 8 Factorial Analysis Of Variance
- 8.2 Advantages of a Two-Way Design 322
- 8.3 Univariate Factorial Analysis 324
- 8.4 Factorial Multivariate Analysis of Variance 331
- 8.5 Weighting of the Cell Means 332
- 8.6 Three-Way MANOVA 335
- Chapter 9 Analysis Of Covariance
- 9.2 Purposes of Covariance 340
- 9.3 Adjustment of Posttest Means and Reduction of Error Variance 342
- 9.4 Choice of Covariates 345
- 9.5 Assumptions in Analysis of Covariance 347
- 9.6 Use of ANCOVA With Intact Groups 350
- 9.7 Alternative Analyses for Pretest-Posttest Designs 351
- 9.8 Error Reduction and Adjustment of Posttest Means for Several Covariates 353
- 9.9 MANCOVA
- Several Dependent Variables and Several Covariates 354
- 9.10 Testing the Assumption of Homogeneous Regression Hyperplanes on SPSS 355
- 9.11 Two Computer Examples 356
- 9.12 Bryant-Paulson Simultaneous Test Procedure 361
- Chapter 10 Stepdown Analysis
- 10.2 Four Appropriate Situations for Stepdown Analysis 375
- 10.3 Controlling on Overall Type I Error 376
- 10.4 Stepdown F's for Two Groups 377
- 10.5 Comparison of Interpretation of Stepdown F's vs.
- Univariate F's 379
- 10.6 Stepdown F's for k Groups
- Effect of Within and Between Correlations 381
- Chapter 11 Confirmatory And Exploratory Factor Analysis
- 11.2 Nature of Principal Components 386
- 11.3 Three Uses for Components as a Variable Reducing Scheme 388
- 11.4 Criteria for Deciding on How Many Components to Retain 389
- 11.5 Increasing Interpretability of Factors by Rotation 391
- 11.6 What Loadings Should Be Used for Interpretation? 393
- 11.7 Sample Size and Reliable Factors 395
- 11.8 Four Computer Examples 395
- 11.9 Communality Issue 409
- 11.11 Exploratory and Confirmatory Factor Analysis 411
- 11.12 PRELIS 415
- 11.13 A LISREL Example Comparing Two A Priori Models 419
- 11.14 Identification 427
- 11.15 Estimation 429
- 11.16 Assessment of Model Fit 430
- 11.17 Model Modification 435
- 11.18 LISREL 8 Example 437
- 11.19 EQS Example 445
- 11.20 Some Caveats Regarding Structural Equation Modeling 449
- Chapter 12 Canonical Correlation
- 12.2 Nature of Canonical Correlation 472
- 12.3 Significance Tests 473
- 12.4 Interpreting the Canonical Variates 475
- 12.5 Computer Example Using SAS CANCORR 476
- 12.6 A Study That Used Canonical Correlation: Relationship Between Student Needs and Teacher Ratings 479
- 12.7 Using SAS for Canonical Correlation on Two Sets of Factor Scores 481
- 12.8 Redundancy Index of Stewart and Love 483
- 12.9 Rotation of Canonical Variates 485
- 12.10 Obtaining More Reliable Canonical Variates 485
- Chapter 13 Repeated Measures Analysis
- 13.2 Single-Group Repeated Measures 496
- 13.3 Multivariate Test Statistic for Repeated Measures 497
- 13.4 Assumptions in Repeated Measures Analysis 500
- 13.5 Computer Analysis of the Drug Data 502
- 13.6 Post Hoc Procedures in Repeated Measures Analysis 506
- 13.7 Should We Use the Univariate or Multivariate Approach? 509
- 13.8 Sample Size for Power = .80 in Single-Sample Case 510
- 13.9 Multivariate Matched Pairs Analysis 512
- 13.10 One Between and One Within Factor
- A Trend Analysis 512
- 13.11 Post Hoc Procedures for the One Between and One Within Design 519
- 13.12 One Between and Two Within Factors 521
- 13.13 Two Between and One Within Factors 526
- 13.14 Two Between and Two Within Factors 532
- 13.15 Totally Within Designs 532
- 13.16 Planned Comparisons in Repeated Measures Designs 534
- 13.17 Profile Analysis 536
- 13.18 Doubly Multivariate Repeated Measures Designs 538
- Chapter 14 Categorical Data Analysis: The Log Linear Model
- 14.2 Sampling Distributions: Binomial and Multinomial 561
- 14.3 Two Way Chi Square
- Log Linear Formulation 564
- 14.4 Three-Way Tables 567
- 14.5 Model Selection 576
- 14.6 Collapsibility 578
- 14.7 Odds (Cross-Product) Ratio 582
- 14.8 Normed Fit Index and Residual Analysis 583
- 14.9 Residual Analysis 584
- 14.10 Cross-Validation 585
- 14.11 Higher Dimensional Tables
- Model Selection
- 14.12 Contrasts for the Log Linear Model 586
- 14.13 Log Linear Analysis for Ordinal Data 590
- 14.14 Sampling and Structural (Fixed) Zeros 595
- Appendix A Statistical Tables 614
- Appendix B Data Sets 634
- Appendix C Obtaining Nonorthogonal Contrasts in Repeated Measures Designs 653.
