Applied multivariate statistical concepts:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Taylor & Francis
2017
|
| Ausgabe: | First published |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | xii, 647 Seiten Illustrationen, Diagramme |
| ISBN: | 9780415842365 9780415842358 |
Internformat
MARC
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| 245 | 1 | 0 | |a Applied multivariate statistical concepts |c Debbie L. Hahs-Vaughn |
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Datensatz im Suchindex
| _version_ | 1804177456535437312 |
|---|---|
| adam_text | BRIEF CONTENTS
Preface xi
Acknowledgments xiii
1 Multivariate Statistics 1
2 Univariate and Bivariate Statistics Review 9
3 Data Screening 35
4 Multiple Linear Regression 57
5 Logistic Regression 117
6 Multivariate Analysis of Variance: Single Factor,
Factorial, and Repeated Measures Designs 169
7 Discriminant Analysis 273
8 Cluster Analysis 335
9 Exploratory Factor Analysis 362
10 Path Analysis, Confirmatory Factor Analysis, and
Structural Equation Modeling 441
11 Multilevel Linear Modeling 505
12 Propensity Score Analysis 571
Appendix A: An Introduction to Matrix Algebra 599
Appendix B: Answers to Odd-Numbered Conceptual
Computational Questions 609
635
Index
DETAILED CONTENTS
Preface xi
Acknowledgments xiii
i Multivariate Statistics 1
u What Are Multivariate Statistics? 2
1.2 Decision Rules 2
1.3 Coverage of the Textbook 4
1.4 Layout of the Textbook 6
1.5 Overarching Goal of the Textbook 6
2 Univariate and Bivariate Statistics Review 9
2.1 Fundamental Concepts 10
2.2 Foundational Univariate Statistics 14
2.3 Foundational Bivariate Statistics 21
3 Data Screening 35
3.1 Independence 36
3.2 Variance 41
3.3 Normality 43
3.4 Linearity 51
3.5 Noncollinearity 52
4 Multiple Linear Regression 57
4.1 What Multiple Linear Regression Is and How
It Works 58
4.2 Mathematical Introduction Snapshot 83
4.3 Computing Multiple Linear Regression Using SPSS 87
4.4 Data Screening 96
4.5 Power Using G*Power 104
4.6 Research Question Template and Example Write-Up 107
117
118
138
139
150
160
163
169
170
188
191
227
251
260
273
275
292
293
315
324
328
335
336
345
346
358
358
362
363
381
383
427
433
441
442
465
466
DETAILED CONTENTS
Logistic Regression
5.1 What Logistic Regression Is and How It Works
5.2 Mathematical Introduction Snapshot
5.3 Computing Logistic Regression Using SPSS
5.4 Data Screening
5.5 Power Using G*Power
5.6 Research Question Template and Example Write-Up
Multivariate Analysis of Variance: Single Factor, Factorial,
and Repeated Measures Designs
6.1 What Multivariate Analysis of Variance Is and How
It Works
6.2 Mathematical Introduction Snapshot
6.3 Computing MANOVA Using SPSS
6.4 Data Screening
6.5 Power Using G*Power
6.6 Research Question Template and Example Write-Up
Discriminant Analysis
7.1 What Discriminant Analysis Is and How It Works
7.2 Mathematical Introduction Snapshot
7.3 Computing Discriminant Analysis Using SPSS
7.4 Data Screening
7.5 Power Using G*Power
7.6 Research Question Template and Example Write-Up
Cluster Analysis
8.1 What Cluster Analysis Is and How It Works
8.2 Mathematical Introduction Snapshot
8.3 Computing Cluster Analysis Using SPSS
8.4 Data Screening
8.5 Research Question Template and Example Write-Up
Exploratory Factor Analysis
9.1 What Exploratory Factor Analysis Is and How It Works
9.2 Mathematical Introduction Snapshot
9.3 Computing EFA Using SPSS
9.4 Data Screening
9.5 Research Question Template and Example Write-Up
Path Analysis, Confirmatory Factor Analysis, and
Structural Equation Modeling
10.1 What Path Analysis and Confirmatory Factor Analysis
Are and How They Work
10.2 Mathematical Introduction Snapshot
10.3 Computing Path Analysis and Confirmatory Factor
Analysis Using LISREL
DETAILED CONTENTS B B ■ ÎX
10.4 Data Screening 494
10.5 Power 494
10.6 Research Question Template and Example Write-Up 496
11 Multilevel Linear Modeling 505
11.1 What Multilevel Linear Modeling Is and How It Works 507
11.2 Mathematical Introduction Snapshot 530
11.3 Computing Multilevel Modeling Using HLM 531
11.4 Data Screening 553
11.5 Power Using Optimal Design 559
11.6 Research Question Template and Example Write-Up 561
12 Propensity Score Analysis 571
12.1 What Propensity Score Analysis Is and How It Works 572
12.2 Mathematical Introduction Snapshot 582
12.3 Computing Propensity Score Analysis Using R 582
12.4 Example Write-Up 594
Appendix A: An Introduction to Matrix Algebra 599
A.l Matrices 600
A.2 Calculations With Matrices 600
A.3 Types of Matrices 602
A.4 Matrices and Multivariate Statistics 605
Appendix B: Answers to Odd-Numbered Conceptual
Computational Questions 609
635
Index
BRIEF CONTENTS
Preface xi
Acknowledgments xiii
1 Multivariate Statistics 1
2 Univariate and Bivariate Statistics Review 9
3 Data Screening 35
4 Multiple Linear Regression 57
5 Logistic Regression 117
6 Multivariate Analysis of Variance: Single Factor,
Factorial, and Repeated Measures Designs 169
7 Discriminant Analysis 273
8 Cluster Analysis 335
9 Exploratory Factor Analysis 362
10 Path Analysis, Confirmatory Factor Analysis, and
Structural Equation Modeling 441
11 Multilevel Linear Modeling 505
12 Propensity Score Analysis 571
Appendix A: An Introduction to Matrix Algebra 599
Appendix B: Answers to Odd-Numbered Conceptual
Computational Questions 609
635
Index
DETAILED CONTENTS
Preface xi
Acknowledgments xiii
i Multivariate Statistics 1
u What Are Multivariate Statistics? 2
1.2 Decision Rules 2
1.3 Coverage of the Textbook 4
1.4 Layout of the Textbook 6
1.5 Overarching Goal of the Textbook 6
2 Univariate and Bivariate Statistics Review 9
2.1 Fundamental Concepts 10
2.2 Foundational Univariate Statistics 14
2.3 Foundational Bivariate Statistics 21
3 Data Screening 35
3.1 Independence 36
3.2 Variance 41
3.3 Normality 43
3.4 Linearity 51
3.5 Noncollinearity 52
4 Multiple Linear Regression 57
4.1 What Multiple Linear Regression Is and How
It Works 58
4.2 Mathematical Introduction Snapshot 83
4.3 Computing Multiple Linear Regression Using SPSS 87
4.4 Data Screening 96
4.5 Power Using G*Power 104
4.6 Research Question Template and Example Write-Up 107
117
118
138
139
150
160
163
169
170
188
191
227
251
260
273
275
292
293
315
324
328
335
336
345
346
358
358
362
363
381
383
427
433
441
442
465
466
DETAILED CONTENTS
Logistic Regression
5.1 What Logistic Regression Is and How It Works
5.2 Mathematical Introduction Snapshot
5.3 Computing Logistic Regression Using SPSS
5.4 Data Screening
5.5 Power Using G*Power
5.6 Research Question Template and Example Write-Up
Multivariate Analysis of Variance: Single Factor, Factorial,
and Repeated Measures Designs
6.1 What Multivariate Analysis of Variance Is and How
It Works
6.2 Mathematical Introduction Snapshot
6.3 Computing MANOVA Using SPSS
6.4 Data Screening
6.5 Power Using G*Power
6.6 Research Question Template and Example Write-Up
Discriminant Analysis
7.1 What Discriminant Analysis Is and How It Works
7.2 Mathematical Introduction Snapshot
7.3 Computing Discriminant Analysis Using SPSS
7.4 Data Screening
7.5 Power Using G*Power
7.6 Research Question Template and Example Write-Up
Cluster Analysis
8.1 What Cluster Analysis Is and How It Works
8.2 Mathematical Introduction Snapshot
8.3 Computing Cluster Analysis Using SPSS
8.4 Data Screening
8.5 Research Question Template and Example Write-Up
Exploratory Factor Analysis
9.1 What Exploratory Factor Analysis Is and How It Works
9.2 Mathematical Introduction Snapshot
9.3 Computing EFA Using SPSS
9.4 Data Screening
9.5 Research Question Template and Example Write-Up
Path Analysis, Confirmatory Factor Analysis, and
Structural Equation Modeling
10.1 What Path Analysis and Confirmatory Factor Analysis
Are and How They Work
10.2 Mathematical Introduction Snapshot
10.3 Computing Path Analysis and Confirmatory Factor
Analysis Using LISREL
DETAILED CONTENTS B B ■ ÎX
10.4 Data Screening 494
10.5 Power 494
10.6 Research Question Template and Example Write-Up 496
11 Multilevel Linear Modeling 505
11.1 What Multilevel Linear Modeling Is and How It Works 507
11.2 Mathematical Introduction Snapshot 530
11.3 Computing Multilevel Modeling Using HLM 531
11.4 Data Screening 553
11.5 Power Using Optimal Design 559
11.6 Research Question Template and Example Write-Up 561
12 Propensity Score Analysis 571
12.1 What Propensity Score Analysis Is and How It Works 572
12.2 Mathematical Introduction Snapshot 582
12.3 Computing Propensity Score Analysis Using R 582
12.4 Example Write-Up 594
Appendix A: An Introduction to Matrix Algebra 599
A.l Matrices 600
A.2 Calculations With Matrices 600
A.3 Types of Matrices 602
A.4 Matrices and Multivariate Statistics 605
Appendix B: Answers to Odd-Numbered Conceptual
Computational Questions 609
635
Index
|
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| spelling | Hahs-Vaughn, Debbie L. aut Applied multivariate statistical concepts Debbie L. Hahs-Vaughn First published New York, NY Taylor & Francis 2017 xii, 647 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Multivariate analysis Textbooks Mathematical statistics Textbooks Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 s DE-604 Erscheint auch als Online-Ausgabe 978-1-315-81668-5 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029676650&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029676650&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hahs-Vaughn, Debbie L. Applied multivariate statistical concepts Multivariate analysis Textbooks Mathematical statistics Textbooks Multivariate Analyse (DE-588)4040708-1 gnd |
| subject_GND | (DE-588)4040708-1 |
| title | Applied multivariate statistical concepts |
| title_auth | Applied multivariate statistical concepts |
| title_exact_search | Applied multivariate statistical concepts |
| title_full | Applied multivariate statistical concepts Debbie L. Hahs-Vaughn |
| title_fullStr | Applied multivariate statistical concepts Debbie L. Hahs-Vaughn |
| title_full_unstemmed | Applied multivariate statistical concepts Debbie L. Hahs-Vaughn |
| title_short | Applied multivariate statistical concepts |
| title_sort | applied multivariate statistical concepts |
| topic | Multivariate analysis Textbooks Mathematical statistics Textbooks Multivariate Analyse (DE-588)4040708-1 gnd |
| topic_facet | Multivariate analysis Textbooks Mathematical statistics Textbooks Multivariate Analyse |
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