Doing better statistics in human-computer interaction:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2019
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xvi, 235 Seiten Diagramme |
| ISBN: | 9781108710596 9781108482523 |
Staff View
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Record in the Search Index
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|---|---|
| adam_text | Contents
List of Figures page xi
List of Tables xiv
Acknowledgements XV
Getting Started I
PARTI WHY WE USE STATISTICS 9
i How Statistics Support Science 11
1*1 The Problem of Induction 12
1.2 Severe Testing 15
1.3 Evidence in HCI 17
1.4 New Experimentalism in HCI 18
1.5 Big Data 21
1,6 Conclusions 22
2 Testing the Null 25
2.1 The Basics of NHST 2 6
2.2 Going beyond / -Values 30
2.3 NHST and Severe Testing 33
2.4 Honesty in Statistics 35
3 Constraining Bayes 38
3.1 Defining Probability 40
3.2 Plausibility 4*
3.3 Unconstrained Bayes 44
3.4 The Bayesian Critique of Frequentism 47
3*5 Being Careful: A Response to the Critique 48
3.6 So, Frequentist or Bayesian? 50
4 Effects: What Tests Test 53
4.1 Location 55
4.2 Dominance 58
4.3 Variation 59
4.4 Estimation and Significance 60
VÜ
viii Contents
4.5 Big, Small and Zero Effects 62
4.6 Choosing Tests to See Effects 64
P ART II HOW TO USE STATISTICS 69
5 Planning Your Statistical Analysis 71
5.1 Principle 1: Articulation 73
5.2 Principle 2: Simplicity 75
5.3 Principle 3: Honesty 77
5.4 Conclusions 79
6 A Cautionary Tail: Why You Should Not Do a One-Tailed Test So
6.1 A Tale of Two Tails 81
6.2 One-Tail Bad, Two-Tails Better 82
7 Is This Normal? 86
7.1 What Makes Data Normal? S6
7.2 The Problems of Non-normal Data 90
7.3 Testing for Normality 91
7.4 Implications 93
8 Sorting Out Outliers 95
8.1 Detecting Oudiers 96
8.2 Sources and Remedies for Outliers 98
8.2.1 Errors in Data 99
8.2.2 Mischievous Participants 99
8.2.3 Faulty Study Design 100
8.2.4 Natural Variation 101
8.3 Conclusions 102
9 Power and Two Types of Error 104
9.1 Type I and Type II Errors 105
9.2 Defining Power 106
9.3 Power and Sample Sizes 108
9.4 Power and the Quality of Tests no
9.5 Summary 112
10 Using Non-Parametric Tests 1x4
10.1 The Mechanics of Ranks 115
10.2 Analysing Errors 117
10.2.1 Type I Errors 117
10.2.2 Type II Errors 119
10.3 Practical Use 120
10.4 Reporting Non-Parametric Tests 122
10.5 Summary 123
Contents
IX
11 A Robust ¿-Test
11.1 A Traditional ¿-Test
11.2 Simple Solutions?
11.3 Location, Location, Location
11.4 Trimmed and Winsorized Means
11.5 M-Estimators
11.6 Back to ¿-Tests
11.7 Overall Advice
12 The ANOVA Family and Friends
12.1 What ANOVA Does
12.2 Is ANOVA Robust?
12.3 Robust Alternatives to ANOVA
12.3.1 Non-Parametric Alternatives
12.3.2 Changes of Location
12.3.3 Do Something Else
12.4 Summary
13 Exploring, Over-Testing and Fishing
13.1 Exploring After a Severe Test
13.2 Exploratory Studies
13.3 Over-Testing
13.3.1 ANOVA Can (Sometimes) Help
13.3.2 Planned Comparisons
13.3.3 The Bonferroni Correction
13.3.4 Bayesian Methods Can Over-Test Too
13.4 Fishing
13.5 Some Rules of Exploration
14 When Is a Correlation Not a Correlation?
14.1 Defining Correlation
14.2 Outlying Points
14.3 Clusters
14.4 Avoiding Problems
14.5 A Final Warning
15 What Makes a Good Likert Item?
15.1 Some Important Context
15.2 Should Items Have a Midpoint?
15.3 How Many Options?
15.4 Label All Options or Just End-Points?
15.5 The Final Story?
16 The Meaning of Factors
16.1 From Concepts to Items
16.2 From Items to Factors
125
126
130
131
132
134
135
136
139
140
145
*47
*47
149
150
151
155
156
*57
*59
160
161
161
162
162
164
167
168
170
*7*
173
*74
175
*76
178
180
181
182
186
188
190
X
Contents
16.2.1 The Methods of Factor Analysis
16.2.2 Finding Factors
16-3 From Factors to Concepts?
16.4 What Does It Mean?
17 Unreliable Reliability: The Problem of Cronbachs Alpha
17.1 Reliability and Validity
17*2, A Simple Model
17.3 When a Is Low
17.4 When Of Is Too High
17.5 Beyond a
18 Tests for Questionnaires
18.1 Testing Likert Items
18.1.1 Type I Analysis
18.1.2 Power Analysis
18.1.3 Which Test for Likert Items?
18.2 Questionnaire Data
18.2.1 Type I Analysis
18.2.2 Power Analysis
18.2.3 Which Test for Questionnaires?
18.3 One Final Observation
192
194
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205
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Index
231
|
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| language | English |
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| spelling | Cairns, Paul 1971- Verfasser (DE-588)136963447 aut Doing better statistics in human-computer interaction Paul Cairns Cambridge Cambridge University Press 2019 xvi, 235 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Statistik (DE-588)4056995-0 gnd rswk-swf Mensch-Maschine-Kommunikation (DE-588)4125909-9 gnd rswk-swf Statistik (DE-588)4056995-0 s Mensch-Maschine-Kommunikation (DE-588)4125909-9 s DE-604 Erscheint auch als Onlineausgabe 978-1-108-68513-9 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030934037&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cairns, Paul 1971- Doing better statistics in human-computer interaction Statistik (DE-588)4056995-0 gnd Mensch-Maschine-Kommunikation (DE-588)4125909-9 gnd |
| subject_GND | (DE-588)4056995-0 (DE-588)4125909-9 |
| title | Doing better statistics in human-computer interaction |
| title_auth | Doing better statistics in human-computer interaction |
| title_exact_search | Doing better statistics in human-computer interaction |
| title_full | Doing better statistics in human-computer interaction Paul Cairns |
| title_fullStr | Doing better statistics in human-computer interaction Paul Cairns |
| title_full_unstemmed | Doing better statistics in human-computer interaction Paul Cairns |
| title_short | Doing better statistics in human-computer interaction |
| title_sort | doing better statistics in human computer interaction |
| topic | Statistik (DE-588)4056995-0 gnd Mensch-Maschine-Kommunikation (DE-588)4125909-9 gnd |
| topic_facet | Statistik Mensch-Maschine-Kommunikation |
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