Longitudinal data analysis for the behavioral sciences using R:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Los Angeles [u.a.]
SAGE
2012
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXII, 542 S. graph. Darst. |
| ISBN: | 9781412982689 |
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| 653 | |a Longitudinal method | ||
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Datensatz im Suchindex
| _version_ | 1804149176318033920 |
|---|---|
| adam_text | Titel: Longitudinal data analysis for the behavioral sciences using R
Autor: Long, Jeffrey D.
Jahr: 2012
Detailed Contents
About the Author xv
Preface xvii
1 Introduction 1
1.1 Statistical Computing 3
1.2 Preliminary Issues 3
1.2.1 Means Versus Correlations 3
1.2.2 Measurement Issues 7
1.2.3 Response Variable Assumptions 8
1.3 Conceptual Overview of Linear Mixed Effects Regression 8
1.3.1 Goals of Inference 9
1.3.2 Random Effects 13
1.3.3 How Important Are Random Effects? 18
1.4 Traditional Approaches 19
1.5 MPLS Data Set 21
1.6 Statistical Strategy 23
1.7 LMER and Multimodel Inference 27
1.7.1 Statistical Hypotheses 27
1.8 Overview of the Remainder of the Book 32
2 Brief Introduction to R 33
2.1 Obtaining and Installing R 34
2.2 Functions and Packages 35
2.3 Essential Syntax 36
2.3.1 Prompt Versus Script Files 36
2.3.2 Input and Output Appearance in This Book 36
2.3.3 Quitting R 37
2.3.4 Terminating a Process 37
2.3.5 Basic Calculations 38
2.3.6 Objects 38
2.3.7 Concatenation 39
2.3.8 Statistical Functions 40
2.4 Data Types 41
2.4.1 Missing Values 43
2.5 Matrices, Data Frames, and Lists 44
2.5.1 Vector 44
2.5.2 Matrix 45
2.5.3 Data Frame 45
2.5.4 List 46
2.6 Indexing 49
2.6.1 Matrix and Data Frame 49
2.6.2 Vector 50
2.6.3 List 51
2.6.4 Sorting 52
2.6.5 Recoding 52
2.6.6 Saving Objects 53
2.6.7 Loading and Listing Objects 53
2.7 User-Defined Functions 54
2.8 Repetitive Operations 55
2.8.1 rdplyO 55
2.8.2 for() Loop 57
2.9 Linear Regression 59
2.10 Getting Help 61
2.11 Summary of Functions 61
Data Structures and Longitudinal Analysis 63
3.1 Longitudinal Data Structures 63
3.1.1 Wide Format 64
3.1.2 Long Format 64
3.2 Reading an External File 65
3.2.1 Reading a Text File With read, table () 65
3.2.2 Displaying the Data Frame 68
3.2.3 Converting and Recoding Variables 70
3.3 Basic Statistics for Wide-Format Data 72
3.3.1 Means, Variances, and Correlations 73
3.3.2 Missing Data Statistics 74
3.3.3 Conditioning on Static Predictors 75
3.4 Reshaping Data 76
3.4.1 Wide to Long Format 76
3.4.2 Long to Wide Format 79
3.5 Basic Statistics for Long-Format Data 80
3.5.1 Means, Variances, and Correlations 80
3.5.2 Missing Data Statistics 82
3.5.3 Conditioning on Static Predictors 82
3.6 Data Structures and Balance on Time 84
3.7 Missing Data in LMER Analysis 85
3.7.1 Retain or Omit Missing Data Rows? 88
3.8 Missing Data Concepts 89
3.8.1 Missing Completely at Random 90
3.8.2 Missing at Random 91
3.8.3 Not Missing at Random 92
3.8.4 Missing Data Mechanisms
and Statistical Analysis 93
3.8.5 Missing Data Simulation 94
3.8.6 LMER Analysis 97
3.9 Extensions to More Complex Data Structures* 100
3.9.1 Multiple Dynamic Variables * 100
3.9.2 Unbalanced Data* 102
Graphing Longitudinal Data 105
4.1 Graphing and Statistical Strategy 105
4.2 Graphing With ggplot2 106
4.2.1 Graph Components 107
4.2.2 Layering 107
4.3 Graphing Individual-Level Curves 109
4.3.1 Superimposed Individual Curves 109
4.3.2 Facet Plots of Individual Curves 112
4.3.3 Selecting Subsets 113
4.3.4 Graphing Fitted Curves 115
4.4 Graphing Group-Level Curves 122
4.4.1 Curve of the Means 123
4.4.2 Graphing Fitted Curves 126
4.4.3 Graphing Individual-Level
and Group-Level Curves 129
4.5 Conditioning on Static Predictors 130
4.5.1 Categorical Static Predictors 132
4.5.2 Quantitative Static Predictors 139
4.6 Customizing Graphs* 143
4.6.1 Customizing Axes* 143
4.6.2 Customizing Facets* 144
4.6.3 Customizing the Legend* 144
4.7 Summary of ggplot2 Components 145
Introduction to Linear Mixed Effects Regression 147
5.1 Traditional Regression and the Linear Model 148
5.2 Regression Examples 150
5.2.1 Single Quantitative Predictor 150
5.2.2 Analysis of Covariance 154
5.2.3 Interaction Model 158
5.3 Linear Mixed Effects Regression 160
5.3.1 LMER as a Multilevel Model 163
5.3.2 Random Effects as Errors 167
5.3.3 Assumptions Regarding Random
Effects and Random Error 168
5.3.4 Random Effects and Correlated
Observations 169
5.4 Estimating the LMER Model 170
5.4.1 Time as a Predictor 170
5.4.2 Anchoring the Intercept 175
5.5 LMER With Static Predictors 177
5.5.1 Intercept Effects 177
5.5.2 Slope and Intercept Effects 178
5.5.3 Initial Status as a Static Predictor 179
5.5.4 Extensions to More Complex Models 180
5.5.5 Summary of lmer () Syntax 181
5.6 Additional Details of LMER* 181
5.6.1 General Form of the LMER Model* 182
5.6.2 Variance-Covariance Matrix
Among Repeated Measures* 184
5.6.3 Importance of Random Effects* 186
5.6.4 Working With Matrices in R* 187
6 Overview of Maximum Likelihood Estimation 191
6.1 Conceptual Overview 192
6.2 Maximum Likelihood and LM 194
6.2.1 Several Unknown Parameters 202
6.2.2 Exhaustive Search and
Numerical Methods 204
6.2.3 Restricted Maximum Likelihood 207
6.2.4 Extracting the Log-Likelihood
and the Deviance 208
6.2.5 Comparing Models 208
6.3 Maximum Likelihood and LMER 212
6.3.1 LMER Deviance Function 214
6.3.2 ML Standard Errors 216
6.3.3 Additional SE Details 220
6.3.4 Default lmer () Output 222
6.3.5 Assumptions Regarding Missing Data 223
6.4 Additional Details of ML for LMER* 224
7 Multimodel Inference and Akaike s Information Criterion 227
7.1 Obj ects of Inference 228
7.2 Statistical Strategy 232
7.3 AIC and Predictive Accuracy 235
7.3.1 Extension to LMER 243
7.3.2 AIC Corrected 245
7.4 AICc and Effect Size 246
7.4.1 Delta 246
7.4.2 Weight of Evidence 248
7.4.3 Evidence Ratio 252
7.5 AICc and Multimodel Inference 254
7.5.1 Contrast With NHST 255
7.6 Example of Multimodel Analysis 260
7.6.1 Guidelines for Model Formulation 260
7.6.2 Example Set of Models 261
7.6.3 Bar Graphs of Results 264
7.6.4 Interpretation of Global Results 265
7.6.5 Details of Models 268
7.6.6 Comments Regarding the
Multimodel Approach 273
7.6.7 Post Hoc Models 273
7.7 Example Write-up 275
7.8 Parametric Bootstrap of the Evidence Ratio* 277
7.8.1 Performing the Parametric Bootstrap* 278
7.8.2 Caveats Regarding the
Parametric Bootstrap* 282
7.9 Bayesian Information Criterion* 282
Likelihood Ratio Test 285
8.1 Why Use the Likelihood Ratio Test? 286
8.2 Fisher and Neyman-Pearson 288
8.3 Evaluation of Two Nested Models 291
8.3.1 Calibratingp-Values Based on
Predictive Accuracy 295
8.4 Approaches to Testing Multiple Models 301
8.5 Step-Up Approach 302
8.5.1 Order of Testing 306
8.5.2 Comments on the Step-Up Approach 307
8.6 Top-Down Approach 307
8.7 Comparison of Approaches 310
8.8 Parametric Bootstrap* 312
8.8.1 Comments on the Parametric Bootstrap* 315
8.9 Planning a Study* 316
8.9.1 Comment on the Procedure* 320
Selecting Time Predictors 321
9.1 Selection of Time Transformations 322
9.2 Group-Level Selection of Time Transformations 325
9.3 Multimodel Inference 326
9.3.1 Analysis Without Static Predictors 327
9.3.2 Analysis With Static Predictors 329
9.4 Likelihood Ratio Test 333
9.4.1 Analysis Without Static Predictors 333
9.4.2 Analysis With Static Predictors 335
9.5 Cautions Concerning Group-Level Selection 335
9.6 Subject-Level Selection of Time Transformations 336
9.6.1 Level 1 Polynomial Model 336
9.6.2 Missing Data 337
9.6.3 Subject-Level Fits 338
9.6.4 Pooled Measures of Fit 346
9.6.5 Clustering of Subject Curves* 350
10 Selecting Random Effects 357
10.1 Automatic Selection of Random Effects 358
10.2 Random Effects and Variance Components 359
10.2.1 Restricted Maximum Likelihood 361
10.2.2 Random Effects and Correlated Data 363
10.3 Descriptive Methods 371
10.3.1 OLS Estimates 372
10.3.2 Examining Residuals 376
10.3.3 Residuals and Normality 382
10.4 Inferential Methods 383
10.4.1 Likelihood Ratio Test 383
10.4.2 AICc 393
10.5 Variance Components and Static Predictors 394
10.6 Predicted Random Effects 394
10.6.1 Evaluating the Normality Assumption 397
10.6.2 Predicted Values for an Individual 399
11 Extending Linear Mixed Effects Regression 405
11.1 Graphing Fitted Curves 405
11.2 Static Predictors With Multiple Levels 409
11.2.1 Evaluating Sets of Dummy Variables 415
11.2.2 Evaluating Individual Dummy Variables 416
11.3 Interactions Among Static Predictors 420
11.3.1 Static Predictor Interactions With lmer () 422
11.3.2 Interpreting Interactions 424
11.3.3 Nonlinear Static Predictor Effects 426
11.4 Indexes of Absolute Effect Size in LMER 427
11.4.1 Alternative Indexes 429
11.5 Additional Transformations 433
11.5.1 Time Units and Variances 434
11.5.2 Transforming for Standardized Change 437
11.5.3 Standardizing and Compositing 439
12 Modeling Nonlinear Change 443
12.1 Data Set and Analysis Strategy 444
12.2 Global Versus Local Models 448
12.3 Polynomials 449
12.3.1 Mean-Corrected Polynomials 454
12.3.2 Orthogonal Polynomials 454
12.3.3 The poly () Function 455
12.3.4 Polynomial Example 459
12.4 Alternatives to Polynomials 460
12.5 Trigonometric Functions 461
12.6 Fractional Polynomials 466
12.6.1 First-Order Fractional Polynomials 467
12.6.2 Second-Order Fractional Polynomials 471
12.6.3 Static Predictors 474
12.6.4 Caveats Regarding the Use of
Fractional Polynomials 475
12.7 Spline Models 478
12.7.1 Linear Spline Models 479
12.7.2 Higher Order Regression Splines 485
12.8 Additional Details* 486
12.8.1 Computing Orthogonal Polynomials* 486
12.8.2 General Form of Fractional Polynomials* 488
13 Advanced Topics 489
13.1 Dynamic Predictors 490
13.1.1 Dynamic Predictor as a Single Effect 493
13.1.2 Dynamic Predictor With a Time Variable 496
13.2 Multiple Response Variables 500
13.2.1 Reading and Mathematics 500
13.2.2 Analyzing Two Responses With lmer () 501
13.3 Additional Levels of Nesting 507
13.3.1 Three-Level Model 508
13.3.2 Static Predictors in Three-Level Models 513
Appendix: Soft Introduction to Matrix Algebra 515
A.l Matrices 515
A.2 Transpose 517
A.3 Matrix Addition 518
A.4 Multiplication of a Matrix by a Scalar 518
A.5 Matrix Multiplication 518
A.6 Determinant 520
A.7 Inverse 521
A.8 Matrix Algebra and R Functions 523
References 525
Author Index 535
Subject Index 539
* Denotes optional section
|
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| dewey-ones | 300 - Social sciences |
| dewey-raw | 300.285/5133 |
| dewey-search | 300.285/5133 |
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| dewey-tens | 300 - Social sciences |
| discipline | Informatik Soziologie Psychologie |
| format | Book |
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| indexdate | 2024-07-10T00:18:52Z |
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| isbn | 9781412982689 |
| language | English |
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| spelling | Long, Jeffrey D. 1964- Verfasser (DE-588)1013682173 aut Longitudinal data analysis for the behavioral sciences using R Jeffrey D. Long Los Angeles [u.a.] SAGE 2012 XXII, 542 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Datenverarbeitung Sozialwissenschaften Längsschnittuntersuchung (DE-588)4034036-3 gnd rswk-swf Empirische Forschung (DE-588)4300400-3 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Verhaltenswissenschaften (DE-588)4133253-2 gnd rswk-swf Social sciences / Research Longitudinal method Mathematical statistics / Data processing R (Computer program language) Verhaltenswissenschaften (DE-588)4133253-2 s Empirische Forschung (DE-588)4300400-3 s Längsschnittuntersuchung (DE-588)4034036-3 s R Programm (DE-588)4705956-4 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025008067&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Long, Jeffrey D. 1964- Longitudinal data analysis for the behavioral sciences using R Datenverarbeitung Sozialwissenschaften Längsschnittuntersuchung (DE-588)4034036-3 gnd Empirische Forschung (DE-588)4300400-3 gnd R Programm (DE-588)4705956-4 gnd Verhaltenswissenschaften (DE-588)4133253-2 gnd |
| subject_GND | (DE-588)4034036-3 (DE-588)4300400-3 (DE-588)4705956-4 (DE-588)4133253-2 |
| title | Longitudinal data analysis for the behavioral sciences using R |
| title_auth | Longitudinal data analysis for the behavioral sciences using R |
| title_exact_search | Longitudinal data analysis for the behavioral sciences using R |
| title_full | Longitudinal data analysis for the behavioral sciences using R Jeffrey D. Long |
| title_fullStr | Longitudinal data analysis for the behavioral sciences using R Jeffrey D. Long |
| title_full_unstemmed | Longitudinal data analysis for the behavioral sciences using R Jeffrey D. Long |
| title_short | Longitudinal data analysis for the behavioral sciences using R |
| title_sort | longitudinal data analysis for the behavioral sciences using r |
| topic | Datenverarbeitung Sozialwissenschaften Längsschnittuntersuchung (DE-588)4034036-3 gnd Empirische Forschung (DE-588)4300400-3 gnd R Programm (DE-588)4705956-4 gnd Verhaltenswissenschaften (DE-588)4133253-2 gnd |
| topic_facet | Datenverarbeitung Sozialwissenschaften Längsschnittuntersuchung Empirische Forschung R Programm Verhaltenswissenschaften |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025008067&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT longjeffreyd longitudinaldataanalysisforthebehavioralsciencesusingr |