Using R with multivariate statistics / Randall E. Schumacker, University of Alabama.

Format:

Book

Author/Creator:

Schumacker, Randall E.

Language:

English

Subjects (All):

Multivariate analysis--Data processing.

Multivariate analysis.

R (Computer program language).

Statistics--Data processing.

Statistics.

Physical Description:

xxi, 383 pages : illustrations, map ; 24 cm

Place of Publication:

Los Angeles : Sage, [2016]

Contents:

1 Introduction and Overview 1

Background 1

Persons of Interest 1

Factors Affecting Statistics 2

R Software 5

Web Resources 7

References 7

2 Multivariate Statistics: Issues and Assumptions 9

Issues 11

Assumptions 12

Normality 12

Determinant of a Matrix 16

Equality of Variance-Covariance Matrix 18

Box M Test 21

SPSS Check 23

Summary 23

Web Resources 24

References 24

3 Hotelling's T²: A Two-Group Multivariate Analysis 27

Overview 28

Assumptions 29

Univariate Versus Multivariate Hypothesis 30

Statistical Significance 32

Practical Examples Using R 33

Single Sample 33

Two Independent Group Mean Difference 36

Two Groups (Paired) Dependent Variable Mean Difference 42

Power and Effect Size 49

A Priori Power Estimation 50

Effect Size Measures 52

Reporting and Interpreting 54

Summary 54

Exercises 55

Web Resources 55

References 55

4 Multivariate Analysis of Variance 57

MANOVA Assumptions 58

Independent Observations 59

Normality 62

Equal Variance-Covariance Matrices 63

Summary 66

MANOVA Example: One-Way Design 66

MANOVA Example: Factorial Design 70

Effect Size 76

Reporting and Interpreting 78

Summary 79

Exercises 79

Web Resources 80

References 80

5 Multivariate Analysis of Covariance 81

Assumptions 82

Multivariate Analysis of Covariance 84

MANCOVA Example 85

Dependent Variable: Adjusted Means 87

Reporting and Interpreting 93

Propensity Score Matching 94

Summary 97

Web Resources 97

References 98

6 Multivariate Repeated Measures 99

Assumptions 101

Advantages of Repeated Measure Design 102

Multivariate Repeated Measure Examples 103

Single Dependent Variable 103

Several Dependent Variables: Profile Analysis 108

Doubly Multivariate Repeated Measures 114

Reporting and Interpreting Results 126

Summary 127

Exercises 128

Web Resources 128

References 128

7 Discriminant Analysis 131

Overview 133

Assumptions 133

Dichotomous Dependent Variable 134

Box M Test 135

Classification Summary 136

Cbi-Square Test 137

Polytomous Dependent Variable 138

Box M Test 139

Classification Summary 141

Chi-Square Test 142

Effect Size 142

Reporting and Interpreting 143

Summary 144

Exercises 144

Web Resources 145

References 145

8 Canonical Correlation 147

Overview 148

Assumptions 149

R Packages 150

CCA Package 152

Yacca Package 158

Canonical Correlation Example 158

Effect Size 165

Reporting and Interpreting 165

Summary 166

Exercises 167

Web Resources 168

References 168

9 Exploratory Factor Analysis 171

Overview 172

Types of Factor Analysis 173

Assumptions 173

Factor Analysis Versus Principal Components Analysis 176

EFA Example 178

R Packages 178

Data Set Input 179

Sample Size Adequacy 180

Number of Factors and Factor Loadings 183

Factor Rotation and Extraction: Orthogonal Versus Oblique Factors 190

Factor Scores 195

Graphical Display 201

Reporting and Interpreting 201

Summary 202

Exercises 203

Web Resources 203

References 204

Appendix: Attitudes Toward Educational Research Scale 205

10 Principal Components Analysis 207

Overview 208

Assumptions 209

Bartlett Test (Sphericity) 209

KMO Test (Sampling Adequacy) 210

Determinant of Correlation Matrix 210

Basics of Principal Components Analysis 211

Principal Component Scores 215

Principal Component Example 216

R Packages 216

Data Set 216

Assumptions 219

Number of Components 220

Reporting and Interpreting 226

Summary 227

Exercises 228

Web Resources 228

References 228

11 Multidimensional Scaling 229

Overview 231

Assumptions 232

Proximity Matrix 233

MDS Model 233

MDS Analysis 233

Sample Size 233

Variable Scaling 234

Number of Dimensions 234

R Packages 234

Goodness-of-Fit Index 236

MDS Metric Example 237

MDS Nonmetric Example 244

Reporting and Interpreting Results 251

Summary 252

Exercises 252

Web Resources 253

References 253

12 Structural Equation Modeling 255

Overview 257

Assumptions 258

Multivariate Normality 258

Positive Definite Matrix 261

Equal Variance-Covariance Matrices 263

Correlation Versus Covariance Matrix 264

Basic Correlation and Covariance Functions 265

Matrix Input Functions 267

Reference Scaling in SEM Models 270

R Packages 271

Finding R Packages and Functions 271

SEM Packages 273

CFA Models 275

Basic Model 275

Multiple Group Model 282

Structural Equation Models 290

Basic SEM Model 290

Longitudinal SEM Models 295

Reporting and Interpreting Results 310

Summary 311

Exercises 312

Web Resources 312

References 312.

Notes:

Includes bibliographical references and index.

ISBN:

9781483377964

1483377962

OCLC:

899229906