Applied multivariate statistics for the social sciences: analyses with SAS and IBM's SPSS
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York ; London
Routledge
2016
|
| Edition: | Sixth edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Previous edition by James P. Stevens. - Includes index |
| Physical Description: | xx, 793 Seiten Diagramme |
| ISBN: | 9780415836661 |
Staff View
MARC
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| 245 | 1 | 0 | |a Applied multivariate statistics for the social sciences |b analyses with SAS and IBM's SPSS |c Keenan A. Pituch and James P. Stevens |
| 250 | |a Sixth edition | ||
| 264 | 1 | |a New York ; London |b Routledge |c 2016 | |
| 300 | |a xx, 793 Seiten |b Diagramme | ||
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CONTENTS
Preface XV
1. Introduction 1
î.i Introduction 1
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 Importance 10
1.5 Outliers 12
1.6 Missing Data 18
1.7 Unit or Participant Nonresponse 31
1.8 Research Examples for Some Analyses
Considered in This Text 32
1.9 The SAS and SPSS Statistical Packages 35
1.10 SAS and SPSS Syntax 35
1.11 SAS and SPSS Syntax and Data Sets on the Internet 36
1.12 Some Issues Unique to Multivariate Analysis 36
1.13 Data Collection and Integrity 37
1.14 Internal and External Validity 39
1.15 Conflict of Interest 40
1.16 Summary 40
1.17 Exercises 41
2. Matrix Algebra 44
2.1 Introduction 44
2.2 Addition, Subtraction, and Multiplication of a
Matrix by a Scalar 47
2.3 Obtaining the Matrix of Variances and Covariances 50
2.4 Determinant of a Matrix 52
2.5 Inverse of a Matrix 55
2.6 SPSS Matrix Procedure 58
60
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175
CONTENTS
2.7 SAS IML Procedure
2.8 Summary
2.9 Exercises
Multiple Regression for Prediction
3.1 Introduction
3.2 Simple Regression
3.3 Multiple Regression for Two Predictors: Matrix Formulation
3.4 Mathematical Maximization Nature of
Least Squares Regression
3.5 Breakdown of Sum of Squares and F Test for
Multiple Correlation
3.6 Relationship of Simple Correlations to Multiple Correlation
3.7 Multicollinearity
3.8 Model Selection
3.9 Two Computer Examples
3.10 Checking Assumptions for the Regression Model
3.11 Model Validation
3.12 Importance of the Order of the Predictors
3.13 Other Important Issues
3.14 Outliers and Influential Data Points
3.15 Further Discussion of the Two Computer Examples
3.16 Sample Size Determination for a Reliable Prediction Equation
3.17 Other Types of Regression Analysis
3.18 Multivariate Regression
3.19 Summary
3.20 Exercises
Two-Group Multivariate Analysis of Variance
4.1 Introduction
4.2 Four Statistical Reasons for Preferring a Multivariate Analysis
4.3 The Multivariate Test Statistic as a Generalization of
the Univariate t Test
4.4 Numerical Calculations for a Two-Group Problem
4.5 Three Post Hoc Procedures
4.6 SAS and SPSS Control Lines for Sample Problem
and Selected Output
4.7 Multivariate Significance but No Univariate Significance
4.8 Multivariate Regression Analysis for the Sample Problem
4.9 Power Analysis
4.10 Ways of Improving Power
4.11 A Priori Power Estimation for a Two-Group MANOVA
4.12 Summary
4.13 Exercises
K-Group MANOVA: A Priori and Post Hoc Procedures
5.1 Introduction
contents n m m ix
5.2 Multivariate Regression Analysis for a Sample Problem 176
5.3 Traditional Multivariate Analysis of Variance 177
5.4 Multivariate Analysis of Variance for Sample Data 179
5.5 Post Hoc Procedures 184
5.6 The Tukey Procedure 187
5.7 Planned Comparisons 193
5.8 Test Statistics for Planned Comparisons 196
5.9 Multivariate Planned Comparisons on SPSS MANOVA 198
5.10 Correlated Contrasts 204
5.11 Studies Using Multivariate Planned Comparisons 208
5.12 Other Multivariate Test Statistics 210
5.13 How Many Dependent Variables for a MANOVA? 211
5.14 Power Analysis—A Priori Determination of Sample Size 211
5.15 Summary 213
5.16 Exercises 214
6. Assumptions in MANOVA 219
6.1 Introduction 219
6.2 ANOVA and MANOVA Assumptions 220
6.3 Independence Assumption 220
6.4 What Should Be Done With Correlated Observations? 222
6.5 Normality Assumption 224
6.6 Multivariate Normality 225
6.7 Assessing the Normality Assumption 226
6.8 Homogeneity of Variance Assumption 232
6.9 Homogeneity of the Covariance Matrices 233
6.10 Summary 240
6.11 Complete Three-Group MANOVA Example 242
6.12 Example Results Section for One-Way MANOVA 249
6.13 Analysis Summary 250
Appendix 6.1 Analyzing Correlated Observations 255
Appendix 6.2 Multivariate Test Statistics for Unequal
Covariance Matrices 259
6.14 Exercises 262
7. Factorial ANOVA and MANOVA 265
7.1 Introduction 265
7.2 Advantages of a Two-Way Design 266
7.3 Univariate Factorial Analysis 268
7.4 Factorial Multivariate Analysis of Variance 277
7.5 Weighting of the Ceil Means 280
7.6 Analysis Procedures for Two-Way MANOVA 280
7.7 Factorial MANOVA With SeniorWISE Data 281
7.8 Example Results Section for Factorial MANOVA With
SeniorWise Data 290
7.9 Three-Way MANOVA 292
294
298
299
301
301
302
303
307
308
311
312
314
315
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329
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373
CONTENTS
7.10 Factorial Descriptive Discriminant Analysis
7.11 Summary
7.12 Exercises
Analysis of Covariance
8.1 Introduction
8.2 Purposes of ANCOVA
8.3 Adjustment of Posttest Means and Reduction of Error Variance
8.4 Choice of Covariates
8.5 Assumptions in Analysis of Covariance
8.6 Use of ANCOVA With Intact Groups
8.7 Alternative Analyses for Pretest-Posttest Designs
8.8 Error Reduction and Adjustment of Posttest Means for
Several Covariates
8.9 MANCOVA—Several Dependent Variables and
Several Covariates
8.10 Testing the Assumption of Homogeneous
Hyperplanes on SPSS
8.11 Effect Size Measures for Group Comparisons in
MANCOVA/ANCOVA
8.12 Two Computer Examples
8.13 Note on Post Hoc Procedures
8.14 Note on the Use of MVMM
8.15 Example Results Section for MANCOVA
8.16 Summary
8.17 Analysis Summary
8.18 Exercises
Exploratory Factor Analysis
9.1 Introduction
9.2 The Principal Components Method
9.3 Criteria for Determining How Many Factors to Retain
Using Principal Components Extraction
9.4 Increasing Interpretability of Factors by Rotation
9.5 What Coefficients Should Be Used for Interpretation?
9.6 Sample Size and Reliable Factors
9.7 Some Simple Factor Analyses Using Principal
Components Extraction
9.8 The Communality Issue
9.9 The Factor Analysis Model
9.10 Assumptions for Common Factor Analysis
9.11 Determining How Many Factors Are Present With
Principal Axis Factoring
9.12 Exploratory Factor Analysis Example With Principal Axis
Factoring
9.13 Factor Scores
CONTENTS
Ll □ ■ xi
9.14 Using SPSS in Factor Analysis 376
9.15 Using SAS in Factor Analysis 378
9.16 Exploratory and Confirmatory Factor Analysis 382
9.17 Example Results Section for EFA of Reactions-to-
Tests Scale 383
9.18 Summary 385
9.19 Exercises 387
10. Discriminant Analysis 391
10.1 Introduction 391
10.2 Descriptive Discriminant Analysis 392
10.3 Dimension Reduction Analysis 393
10.4 Interpreting the Discriminant Functions 395
10.5 Minimum Sample Size 396
10.6 Graphing the Groups in the Discriminant Plane 397
10.7 Example With SeniorWISE Data 398
10.8 National Merit Scholar Example 409
10.9 Rotation of the Discriminant Functions 415
10.10 Stepwise Discriminant Analysis 415
10.11 The Classification Problem 416
10.12 Linear Versus Quadratic Classification Rule 425
10.13 Characteristics of a Good Classification Procedure 425
10.14 Analysis Summary of Descriptive Discriminant Analysis 426
10.15 Example Results Section for Discriminant Analysis of the
National Merit Scholar Example 427
10.16 Summary 429
10.17 Exercises 429
11. Binary Logistic Regression 434
11.1 introduction 434
11.2 The Research Example 435
11.3 Problems With Linear Regression Analysis 436
11A Transformations and the Odds Ratio With a
Dichotomous Explanatory Variable 438
11.5 The Logistic Regression Equation With a Single
Dichotomous Explanatory Variable 442
11.6 The Logistic Regression Equation With a Single
Continuous Explanatory Variable 443
11.7 Logistic Regression as a Generalized Linear Model 444
11.8 Parameter Estimation 445
11.9 Significance Test for the Entire Model and Sets of Variables 447
11.10 McFadden’s Pseudo R- Square for Strength of Association 448
11.11 Significance Tests and Confidence Intervals for
Single Variables 450
11.12 Preliminary Analysis 451
11.13 Residuals and influence 451
453
457
458
461
463
465
466
468
471
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576
CONTENTS
11.14 Assumptions
11.15 Other Data Issues
11.16 Classification
11.17 Using SAS and SPSS for Multiple Logistic Regression
11.18 Using SAS and SPSS to Implement the Box-Tidwell
Procedure
11.19 Example Results Section for Logistic Regression
With Diabetes Prevention Study
11.20 Analysis Summary
11.21 Exercises
Repeated-Measures Analysis
12.1 Introduction
12.2 Single-Group Repeated Measures
12.3 The Multivariate Test Statistic for Repeated Measures
12.4 Assumptions in Repeated-Measures Analysis
12.5 Computer Analysis of the Drug Data
12.6 Post Hoc Procedures in Repeated-Measures Analysis
12.7 Should We Use the Univariate or Multivariate Approach?
12.8 One-Way Repeated Measures—A Trend Analysis
12.9 Sample Size for Power = .80 in Single-Sample Case
12.10 Multivariate Matched-Pairs Analysis
12.11 One-Between and One-Within Design
12.12 Post Hoc Procedures for the One-Between and
One-Within Design
12.13 One-Between and Two-Within Factors
12.14 Two-Between and One-Within Factors
12.15 Two-Between and Two-Within Factors
12.16 Totally Within Designs
12.17 Planned Comparisons in Repeated-Measures Designs
12.18 Profile Analysis
12.19 Doubly Multivariate Repeated-Measures Designs
12.20 Summary
12.21 Exercises
Hierarchical Linear Modeling
13.1 Introduction
13.2 Problems Using Single-Level Analyses of
Multilevel Data
13.3 Formulation of the Multilevel Model
13.4 Two-Level Model—General Formation
13.5 Example 1: Examining School Differences in
Mathematics
13.6 Centering Predictor Variables
13.7 Sample Size
13.8 Example 2: Evaluating the Efficacy of a Treatment
13.9 Summary
CONTENTS
O El ■ Xiii
14. Multivariate Multilevel Modeling 578
14.1 Introduction 578
14.2 Benefits of Conducting a Multivariate Multilevel
Analysis 579
14.3 Research Example 580
14.4 Preparing a Data Set for MVMM Using SAS and SPSS 581
14.5 Incorporating Multiple Outcomes in the Level-1 Model 584
14.6 Example 1: Using SAS and SPSS to Conduct Two-Level
Multivariate Analysis 585
14.7 Example 2: Using SAS and SPSS to Conduct
Three-Level Multivariate Analysis 595
14.8 Summary 614
14.9 SAS and SPSS Commands Used to Estimate All
Models in the Chapter 615
15. Canonical Correlation 618
15.1 Introduction 618
15.2 The Nature of Canonical Correlation 619
15.3 Significance Tests 620
15.4 Interpreting the Canonical Variates 621
15.5 Computer Example Using SAS CANCORR 623
15.6 A Study That Used Canonical Correlation 625
15.7 Using SAS for Canonical Correlation on
Two Sets of Factor Scores 628
15.8 The Redundancy Index of Stewart and Love 630
15.9 Rotation of Canonical Variates 631
15.10 Obtaining More Reliable Canonical Variates 632
15.11 Summary 632
15.12 Exercises 634
16. Structural Equation Modeling 639
16.1 Introduction 639
16.2 Notation, Terminology, and Software 639
16.3 Causal Inference 642
16.4 Fundamental Topics in SEM 643
16.5 Three Principal SEM Techniques 663
16.6 Observed Variable Path Analysis 663
16.7 Observed Variable Path Analysis With the Mueller
Study 668
16.8 Confirmatory Factor Analysis 689
16.9 CFA With Reactions-to-Tests Data 691
16.10 Latent Variable Path Analysis 707
16.11 Latent Variable Path Analysis With Exercise Behavior
Study 711
16.12 SEM Considerations 719
16.13 Additional Models in SEM 724
16.14 Final Thoughts 726
XIV 11 m m CONTENTS
Appendix 16.1 Abbreviated SAS Output for Final Observed
Variable Path Model 734
Appendix 16.2 Abbreviated SAS Output for the Final
Latent Variable Path Model for Exercise Behavior 736
Appendix A: Statistical Tables 747
Appendix B: Obtaining Nonorthogonal Contrasts in Repeated Measures Designs 763
Detailed Answers 771
Index 785 |
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| author | Pituch, Keenan A. Stevens, James Paul |
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| discipline | Soziologie Psychologie Wirtschaftswissenschaften |
| edition | Sixth edition |
| format | Book |
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| id | DE-604.BV042952028 |
| illustrated | Not Illustrated |
| indexdate | 2025-04-10T04:00:27Z |
| institution | BVB |
| isbn | 9780415836661 |
| language | English |
| lccn | 015017536 |
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| physical | xx, 793 Seiten Diagramme |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Routledge |
| record_format | marc |
| spellingShingle | Pituch, Keenan A. Stevens, James Paul Applied multivariate statistics for the social sciences analyses with SAS and IBM's SPSS Sozialwissenschaften Multivariate analysis Social sciences Statistical methods Sozialwissenschaften (DE-588)4055916-6 gnd Multivariate Analyse (DE-588)4040708-1 gnd |
| subject_GND | (DE-588)4055916-6 (DE-588)4040708-1 |
| title | Applied multivariate statistics for the social sciences analyses with SAS and IBM's SPSS |
| title_auth | Applied multivariate statistics for the social sciences analyses with SAS and IBM's SPSS |
| title_exact_search | Applied multivariate statistics for the social sciences analyses with SAS and IBM's SPSS |
| title_full | Applied multivariate statistics for the social sciences analyses with SAS and IBM's SPSS Keenan A. Pituch and James P. Stevens |
| title_fullStr | Applied multivariate statistics for the social sciences analyses with SAS and IBM's SPSS Keenan A. Pituch and James P. Stevens |
| title_full_unstemmed | Applied multivariate statistics for the social sciences analyses with SAS and IBM's SPSS Keenan A. Pituch and James P. Stevens |
| title_short | Applied multivariate statistics for the social sciences |
| title_sort | applied multivariate statistics for the social sciences analyses with sas and ibm s spss |
| title_sub | analyses with SAS and IBM's SPSS |
| topic | Sozialwissenschaften Multivariate analysis Social sciences Statistical methods Sozialwissenschaften (DE-588)4055916-6 gnd Multivariate Analyse (DE-588)4040708-1 gnd |
| topic_facet | Sozialwissenschaften Multivariate analysis Social sciences Statistical methods Multivariate Analyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028378129&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT pituchkeenana appliedmultivariatestatisticsforthesocialsciencesanalyseswithsasandibmsspss AT stevensjamespaul appliedmultivariatestatisticsforthesocialsciencesanalyseswithsasandibmsspss |
Indexes
Sonderstandort Fakultät
| Call Number: |
2000 QH 234 P692(6) |
|---|---|
| Copy 1 | nicht ausleihbar Checked out – Due: 31.12.2099 Recall This |