Comparing groups: randomization and bootstrap methods using R
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2011
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | "This book, written by three behavioral scientists for other behavioral scientists, addresses common issues in statistical analysis for the behavioral and educational sciences. Modern Statistical & Computing Methods for the Behavioral and Educational Sciences using R emphasizes the direct link between scientific research questions and data analysis. Purposeful attention is paid to the integration of design, statistical methodology, and computation to propose answers to specific research questions. Furthermore, practical suggestions for the analysis and presentation of results, in prose, tables and/or figures, are included. Optional sections for each chapter include methodological extensions for readers desiring additional technical details. Rather than focus on mathematical calculations like so many other introductory texts in the behavioral sciences, the authors focus on conceptual explanations and the use of statistical computing. Statistical computing is an integral part of statistical work, and to support student learning in this area, examples using the R computer program are provided throughout the book. Rather than relegate examples to the end of chapters, the authors interweave computer examples with the narrative of the book. Topical coverage includes an introduction to R, data exploration of one variable, data exploration of multivariate data - comparing two groups and many groups, permutation and randomization tests, the independent samples t-Test, the Bootstrap test, interval estimates and effect sizes, power, and dependent samples"-- Provided by publisher. Includes bibliographical references (p. 287 - 298) |
| Beschreibung: | XXXII, 298 S. ill. 25 cm |
| ISBN: | 9780470621691 |
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| 100 | 1 | |a Zieffler, Andrew |d 1974- |e Verfasser |0 (DE-588)1013682084 |4 aut | |
| 245 | 1 | 0 | |a Comparing groups |b randomization and bootstrap methods using R |c Andrew S. Zieffler ; Jeffrey R. Harring ; Jeffrey D. Long |
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2011 | |
| 300 | |a XXXII, 298 S. |b ill. |c 25 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a "This book, written by three behavioral scientists for other behavioral scientists, addresses common issues in statistical analysis for the behavioral and educational sciences. Modern Statistical & Computing Methods for the Behavioral and Educational Sciences using R emphasizes the direct link between scientific research questions and data analysis. Purposeful attention is paid to the integration of design, statistical methodology, and computation to propose answers to specific research questions. Furthermore, practical suggestions for the analysis and presentation of results, in prose, tables and/or figures, are included. Optional sections for each chapter include methodological extensions for readers desiring additional technical details. Rather than focus on mathematical calculations like so many other introductory texts in the behavioral sciences, the authors focus on conceptual explanations and the use of statistical computing. Statistical computing is an integral part of statistical work, and to support student learning in this area, examples using the R computer program are provided throughout the book. Rather than relegate examples to the end of chapters, the authors interweave computer examples with the narrative of the book. Topical coverage includes an introduction to R, data exploration of one variable, data exploration of multivariate data - comparing two groups and many groups, permutation and randomization tests, the independent samples t-Test, the Bootstrap test, interval estimates and effect sizes, power, and dependent samples"-- Provided by publisher. | ||
| 500 | |a Includes bibliographical references (p. 287 - 298) | ||
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Statistik | |
| 650 | 4 | |a Bootstrap (Statistics) | |
| 650 | 4 | |a Random data (Statistics) | |
| 650 | 4 | |a Psychology |x Data processing | |
| 650 | 4 | |a R (Computer program language) | |
| 650 | 4 | |a Distribution (Probability theory) | |
| 650 | 7 | |a SOCIAL SCIENCE / Statistics |2 bisacsh | |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-024785805 | ||
Datensatz im Suchindex
| _version_ | 1804148890389184512 |
|---|---|
| adam_text | CONTENTS
List of Figures
xiii
List of Tables
xxi
Foreword
xxiii
Preface
xxv
Acknowledgments
xxxi
1
An Introduction to
R
1
1.1 Getting Started
2
1.1.1 Windows OS
2
1.1.2
MacOS 2
1.1.3 Add-On Packages
2
1.2
Arithmetic:
R
as a Calculator
4
1.3
Computations in R: Functions
4
1.4
Connecting Computations
7
1.4.1
Naming Conventions
8
1.5
Data Structures: Vectors
9
1.5.1
Creating Vectors in
R
9
1.5.2 Computation with Vectors
11
13.3
Character and Logical Vectors
12
VÍ
CONTENTS
1.6
Getting Help
13
1.7
Alternative Ways to Run
R
14
1.8
Extension: Matrices and Matrix Operations
14
1.8.1
Computation with Matrices
15
1.9
Further Reading
18
Problems
19
Data Representation and Preparation
21
2.1
Tabular Data
23
2.1.1
External Formats for Storing Tabular Data
23
2.2
Data Entry
24
2.2.1
Data
Codebooks
25
2.3
Reading Delimited Data into
R
25
2.3.1
Identifying the Location of a File
26
2.3.2
Examining the Data in a Text Editor
28
2.3.3
Reading Delimited Separated Data: An Example
28
2.4
Data Structure: Data Frames
29
2.4.1
Examining the Data Read into
R
29
2.5
Recording Syntax using Script Files
33
2.5.1
Documentation File
34
2.6
Simple Graphing in
R
34
2.6.1
Saving Graphics to Insert into a Word-Processing File
35
2.7
Extension: Logical Expressions and Graphs for Categorical
Variables
37
2.7.1
Logical Operators
38
2.7.2
Measurement Level and Analysis
40
2.7.3
Categorical Data
42
2.7.4
Plotting Categorical Data
44
2.8
Further Reading
45
Problems
46
Data Exploration: One Variable
49
3.1
Reading In the Data
50
3.2
Nonparametric Density Estimation
52
3.2.1
Graphically Summarizing the Distribution
52
3.2.2
Histograms
52
3.2.3
Kernel Density Estimators
53
3.2.4
Controlling the Density Estimation
53
CONTENTS
VII
3.2.5
Plotting the Estimated Density
55
3.3
Summarizing the Findings
58
3.3.1
Creating a Plot for Publication
59
3.3.2
Writing Up the Results for Publication
61
3.4
Extension: Variability Bands for Kernel Densities
62
3.5
Further Reading
62
Problems
63
Exploration of Multivariate Data: Comparing Two Groups
65
4.1
Graphically Summarizing the Marginal Distribution
66
4.2
Graphically Summarizing Conditional Distributions
66
4.2.1
Indexing: Accessing Individuals or Subsets
68
4.2.2
Indexing Using a Logical Expression
69
4.2.3
Density Plots of the Conditional Distributions
70
4.2.4
Side-by-Side
B ox-and-
Whiskers Plots
70
4.3
Numerical Summaries of Data: Estimates of the Population
Parameters
72
4.3.1
Measuring Central Tendency
73
4.3.2
Measuring Variation
74
4.3.3
Measuring Skewness
76
4.3.4
Kurtosis
78
4.4
Summarizing the Findings
80
4.4.1
Creating a Plot for Publication
80
4.4.2
Using Color
81
4.4.3
Selecting a Color Palette
85
4.5
Extension: Robust Estimation
87
4.5.1
Robust Estimate of Location: The Trimmed Mean
87
4.5.2
Robust Estimate of Variation: The Winsorized Variance
89
4.6
Further Reading
91
Problems
91
Exploration of Multivariate Data: Comparing Many Groups
93
5.1
Graphing Many Conditional Distributions
94
5.1.1
Panel Plots
96
5.1.2
Side-by-Side Box-and-Whiskers Plots
97
5.2
Numerically Summarizing the Data
100
5.3
Summarizing the Findings
101
5.3.1
Writing Up the Results for Publication
102
VIU
CONTENTS
5.3.2
Enhancing a Plot with a Line
102
5.4
Examining Distributions Conditional on Multiple Variables
103
5.5
Extension: Conditioning on Continuous Variables
107
5.5.1
Scatterplots of the Conditional Distributions
110
5.6
Further Reading
112
Problems
113
Randomization and Permutation Tests
115
6.1
Randomized Experimental Research
118
6.2
Introduction to the Randomization Test
119
6.3
Randomization Tests with Large Samples: Monte Carlo
Simulation
122
6.3.1
Rerandomization of the Data
124
6.3.2
Repeating the Randomization Process
125
6.3.3
Generalizing Processes: Functions
126
6.3.4
Repeated Operations on Matrix Rows or Columns
127
6.3.5
Examining the Monte Carlo Distribution and Obtaining
the/7-Value
127
6.4
Validity of the Inferences and Conclusions Drawn from a
Randomization Test
130
6.4.1
Exchangeability
130
6.4.2
Nonexperimental Research: Permutation Tests
131
6.4.3
Nonexperimental, Nongeneralizable Research
131
6.5
Generalization from the Randomization Results
132
6.6
Summarizing the Results for Publication
133
6.7
Extension: Tests of the Variance
133
6.8
Further Reading
134
Problems
135
Bootstrap Tests
137
7.1
Educational Achievement of Latino Immigrants
138
7.2
Probability Models: An Interlude
140
7.3
Theoretical Probability Models in
R
141
7.4
Parametric Bootstrap Tests
143
7.4.1
Choosing a Probability Model
144
7.4.2
Standardizing the Distribution of Achievement Scores
144
7.5
The Parametric Bootstrap
146
CONTENTS
ІХ
7.5.1
The Parametric Bootstrap: Approximating the
Distribution of the Mean Difference
146
7.6
Implementing the Parametric Bootstrap in
R
148
7.6.1
Writing a Function to Randomly Generate Data for the
boot() Function
148
7.6.2
Writing a Function to Compute a Test Statistic Using
the Randomly Generated Data
150
7.6.3
The Bootstrap Distribution of the Mean Difference
151
7.7
Summarizing the Results of the Parametric Bootstrap Test
154
7.8
Nonparametric Bootstrap Tests
154
7.8.1
Using the Nonparametric Bootstrap to Approximate
the Distribution of the Mean Difference
157
7.8.2
Implementing the Nonparametric Bootstrap in
R
158
7.9
Summarizing the Results for the Nonparametric Bootstrap Test
160
7.10
Bootstrapping Using a Pivot Statistic
161
7.10.1
Student s r-Statistic
161
7.11
Independence Assumption for the Bootstrap Methods
164
7.12
Extension: Testing Functions
166
7.12.1
Ordering a Data Frame
166
7.13
Further Reading
168
Problems
168
Philosophical Considerations
171
8.1
The Randomization Test vs. the Bootstrap Test
172
8.2
Philosophical Frameworks of Classical Inference
173
8.2.1
Fisher s Significance Testing
174
8.2.2
Neyman-Pearson Hypothesis Testing
175
8.2.3
p-Values
176
Bootstrap Intervals and Effect Sizes
179
9.1
Educational Achievement Among Latino Immigrants: Example
Revisited
180
9.2
Plausible Models to Reproduce the Observed Result
180
9.2.1
Computing the Likelihood of Reproducing the
Observed Result
181
9.3
Bootstrapping Using an Alternative Model
185
9.3.1
Using
R
to Bootstrap under the Alternative Model
187
X
CONTENTS
9.3.2
Using the Bootstrap Distribution to Compute the
Interval Limits
190
9.3.3
Historical Interlude: Student s Approximation for the
Interval Estimate
190
9.3.4
Studentized Bootstrap Interval
191
9.4
Interpretation of the Interval Estimate
191
9.5
Adjusted Bootstrap Intervals
192
9.6
Standardized Effect Size: Quantifying the Group Differences in
a Common Metric
192
9.6.1
Effect Size as Distance—Cohen s
б
193
9.6.2
Robust Distance Measure of Effect
195
9.7
Summarizing the Results
197
9.8
Extension: Bootstrapping the Confidence Envelope for a Q-Q
Plot
197
9.9
Confidence Envelopes
198
9.10
Further Reading
202
Problems
204
10
Dependent Samples
205
10.1
Matching: Reducing the Likelihood of
Nonequivalent
Groups
206
10.2
Mathematics Achievement Study Design
206
10.2.1
Exploratory Analysis
209
10.3
Randomization/Permutation Test for Dependent Samples
211
10.3.1
Reshaping the Data
212
10.3.2
Randomization Test Using the Reshaped Data
214
10.4
Effect Size
216
10.5
Summarizing the Results of a Dependent Samples Test for
Publication
217
10.6
To Match or Not to Match
...
That is the Question
218
10.7
Extension: Block Bootstrap
220
10.8
Further Reading
223
Problems
224
11
Planned Contrasts
227
11.1
Planned Comparisons
228
11.2
Examination of Weight Loss Conditioned on Diet
228
11.2.1
Exploration of Research Question
1 229
11.2.2
Exploration of Research Question
2 230
CONTENTS
ХІ
11.2.3
Exploration
of Research Question
3 231
11.3
From Research Questions to Hypotheses
232
11.4
Statistical Contrasts
233
11.4.1
Complex Contrasts
236
11.5
Computing the Estimated Contrasts Using the Observed Data
237
11.6
Testing Contrasts: Randomization Test
239
11.7
Strength of Association: A Measure of Effect
240
11.7.1
Total Sum of Squares
241
11.8
Contrast Sum of Squares
243
11.9
Eta-Squared for Contrasts
243
11.10
Bootstrap Interval for Eta-Squared
244
11.11
Summarizing the Results of a Planned Contrast Test Analysis
245
11.12
Extension: Orthogonal Contrasts
245
11.13
Further Reading
251
Problems
251
12
Unplanned Contrasts
253
12.
1 Unplanned Comparisons
254
12.2
Examination of Weight Loss Conditioned on Diet
254
12.3
Omnibus Test
257
12.3.1
Statistical Models
257
12.3.2
Postulating a Statistical Model to Fit the Data
258
12.3.3
Fitting a Statistical Model to the Data
260
12.3.4
Partitioning Variation in the Observed Scores
262
12.3.5
Randomization Test for the Omnibus Hypothesis
268
12.4
Group Comparisons After the Omnibus Test
269
12.5
Ensemble- Adjusted p- values
270
12.5.1
False Discovery Rate
272
12.6
Strengths and Limitations of the Four Approaches
273
12.6.1
Planned Comparisons
273
12.6.2
Omnibus Test Followed by Unadjusted Group
Comparisons
274
12.6.3
Omnibus Test Followed by Adjusted Group
Comparisons
274
12.6.4
Adjusted Group Comparisons without the Omnibus Test
275
12.6.5
Final Thoughts
276
12.7
Summarizing the Results of Unplanned Contrast Tests for
Publication
276
XII CONTENTS
12.8
Extension:
Plots of the Unplanned Contrasts
276
12.8.1
Simultaneous Intervals
280
12.9
Further Reading
282
Problems
283
References
285
|
| any_adam_object | 1 |
| author | Zieffler, Andrew 1974- Harring, Jeffrey 1964- Long, Jeffrey D. 1964- |
| author_GND | (DE-588)1013682084 (DE-588)1013682122 (DE-588)1013682173 |
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| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/4 |
| dewey-search | 519.5/4 |
| dewey-sort | 3519.5 14 |
| dewey-tens | 510 - Mathematics |
| discipline | Sprachwissenschaft Informatik Soziologie Mathematik Literaturwissenschaft |
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| id | DE-604.BV039927464 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:14:19Z |
| institution | BVB |
| isbn | 9780470621691 |
| language | English |
| lccn | 2010054064 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024785805 |
| oclc_num | 747927470 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-188 |
| owner_facet | DE-355 DE-BY-UBR DE-188 |
| physical | XXXII, 298 S. ill. 25 cm |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Wiley |
| record_format | marc |
| spelling | Zieffler, Andrew 1974- Verfasser (DE-588)1013682084 aut Comparing groups randomization and bootstrap methods using R Andrew S. Zieffler ; Jeffrey R. Harring ; Jeffrey D. Long Hoboken, NJ Wiley 2011 XXXII, 298 S. ill. 25 cm txt rdacontent n rdamedia nc rdacarrier "This book, written by three behavioral scientists for other behavioral scientists, addresses common issues in statistical analysis for the behavioral and educational sciences. Modern Statistical & Computing Methods for the Behavioral and Educational Sciences using R emphasizes the direct link between scientific research questions and data analysis. Purposeful attention is paid to the integration of design, statistical methodology, and computation to propose answers to specific research questions. Furthermore, practical suggestions for the analysis and presentation of results, in prose, tables and/or figures, are included. Optional sections for each chapter include methodological extensions for readers desiring additional technical details. Rather than focus on mathematical calculations like so many other introductory texts in the behavioral sciences, the authors focus on conceptual explanations and the use of statistical computing. Statistical computing is an integral part of statistical work, and to support student learning in this area, examples using the R computer program are provided throughout the book. Rather than relegate examples to the end of chapters, the authors interweave computer examples with the narrative of the book. Topical coverage includes an introduction to R, data exploration of one variable, data exploration of multivariate data - comparing two groups and many groups, permutation and randomization tests, the independent samples t-Test, the Bootstrap test, interval estimates and effect sizes, power, and dependent samples"-- Provided by publisher. Includes bibliographical references (p. 287 - 298) Datenverarbeitung Statistik Bootstrap (Statistics) Random data (Statistics) Psychology Data processing R (Computer program language) Distribution (Probability theory) SOCIAL SCIENCE / Statistics bisacsh Statistik (DE-588)4056995-0 gnd rswk-swf Große Abweichung (DE-588)4330658-5 gnd rswk-swf Statistik (DE-588)4056995-0 s Große Abweichung (DE-588)4330658-5 s DE-604 Harring, Jeffrey 1964- Verfasser (DE-588)1013682122 aut Long, Jeffrey D. 1964- Verfasser (DE-588)1013682173 aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024785805&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zieffler, Andrew 1974- Harring, Jeffrey 1964- Long, Jeffrey D. 1964- Comparing groups randomization and bootstrap methods using R Datenverarbeitung Statistik Bootstrap (Statistics) Random data (Statistics) Psychology Data processing R (Computer program language) Distribution (Probability theory) SOCIAL SCIENCE / Statistics bisacsh Statistik (DE-588)4056995-0 gnd Große Abweichung (DE-588)4330658-5 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4330658-5 |
| title | Comparing groups randomization and bootstrap methods using R |
| title_auth | Comparing groups randomization and bootstrap methods using R |
| title_exact_search | Comparing groups randomization and bootstrap methods using R |
| title_full | Comparing groups randomization and bootstrap methods using R Andrew S. Zieffler ; Jeffrey R. Harring ; Jeffrey D. Long |
| title_fullStr | Comparing groups randomization and bootstrap methods using R Andrew S. Zieffler ; Jeffrey R. Harring ; Jeffrey D. Long |
| title_full_unstemmed | Comparing groups randomization and bootstrap methods using R Andrew S. Zieffler ; Jeffrey R. Harring ; Jeffrey D. Long |
| title_short | Comparing groups |
| title_sort | comparing groups randomization and bootstrap methods using r |
| title_sub | randomization and bootstrap methods using R |
| topic | Datenverarbeitung Statistik Bootstrap (Statistics) Random data (Statistics) Psychology Data processing R (Computer program language) Distribution (Probability theory) SOCIAL SCIENCE / Statistics bisacsh Statistik (DE-588)4056995-0 gnd Große Abweichung (DE-588)4330658-5 gnd |
| topic_facet | Datenverarbeitung Statistik Bootstrap (Statistics) Random data (Statistics) Psychology Data processing R (Computer program language) Distribution (Probability theory) SOCIAL SCIENCE / Statistics Große Abweichung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024785805&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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