Experimental design for the life sciences:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY, United States of America
Oxford University Press
[2016]
|
| Ausgabe: | Fourth edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | xviii, 202 Seiten Illustrationen, Diagramme 25 cm |
| ISBN: | 9780198717355 |
Internformat
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| 100 | 1 | |a Ruxton, Graeme D. |e Verfasser |0 (DE-588)1071366084 |4 aut | |
| 245 | 1 | 0 | |a Experimental design for the life sciences |c Graeme D. Ruxton, University of St Andrews, Nick Colegrave, University of Edinburgh |
| 250 | |a Fourth edition | ||
| 264 | 1 | |a New York, NY, United States of America |b Oxford University Press |c [2016] | |
| 264 | 4 | |c © 2016 | |
| 300 | |a xviii, 202 Seiten |b Illustrationen, Diagramme |c 25 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 505 | 8 | |a Bibliogr. p. [196]-200. Index | |
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| 650 | 7 | |a Sciences de la vie / Expériences / Manuels d'enseignement supérieur |2 ram | |
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Datensatz im Suchindex
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Contents
1 Why you should care about design 1
1.1 Why experiments need to be designed 1
1.2 The costs of poor design 3
1.2.1 Time and money 3
1.2.2 Ethical issues 3
1.3 The relationship between experimental design
and statistics 4
1.4 Why good experimental design is particularly
important to life scientists 5
1.4.1 Random variation 5
1.4.2 Confounding factors 6
1.4.3 Simpson’s paradox 7
1.5 Subjects, experimental units, samples, and terminology 9
Summary 11
2 Starting with a well-defined hypothesis 13
2.1 Why your study should be focused: questions,
hypotheses, and predictions 13
2.1.1 An example of moving from a question to hypotheses,
and then to an experimental design 14
2.1.2 An example of multiple hypotheses 16
2.1.3 Where do (good) ideas come from in the first place? 18
2.2 Producing the strongest evidence with which
to challenge a hypothesis 20
2.2.1 Indirect measures 20
2.2.2 Considering all possible outcomes of an experiment 21
2.2.3 Satisfying sceptics 22
2.3 Controls 23
2.3.1 Different types of control 23
2.3.2 Making sure the control is as effective as possible 26
2.3.3 The ethics of controlling 27
2.3.4 Situations where a control is not required 27
2.4 The importance of a pilot study and preliminary data 28
2.4.1 Making sure that you are asking a sensible question 28
2.4.2 Making sure that your techniques work 29
Summary 31
3 Selecting the broad design of your study 32
3.1 Experimental manipulation versus natural variation 32
3.1.1 An example hypothesis that could be tackled by
either manipulation or correlation 32
3.1.2 Arguments for doing a correlational study 33
3.1.3 Arguments for doing a manipulative study 35
3.1.4 Situations where manipulation is impossible 38
3.2 Deciding whether to work in the field or the laboratory 41
3.3 In vivo versus in vitro studies 43
3.4 There is no perfect study 43
Summary 44
4 Between-individual variation, replication,
and sampling 46
4.1 Between-individual variation 46
4.2 Replication 47
4.3 Selecting a sample 57
4.3.1 Simple random sampling 57
4.3.2 Remember, you want a representative sample 58
4.3.3 Stratified sampling 59
4.3.4 Cluster sampling 61
4.3.5 Convenience sampling 63
4.3.6 Self-selection 64
Summary 65
5 Pseudoreplication
67
5.1 Explaining what independence and pseudoreplication are 57
5.2 Common sources of pseudoreplication 70
5.2.1 A shared enclosure 70
5.2.2 The common environment 71
5.2.3 Relatedness 72
5.2.4 A pseudorepticated stimulus 72
5.2.5 Individuals are part of the environment too 72
5.2.6 Pseudoreplication of measurements through time 73
5.2.7 Species comparisons and pseudoreplication 73
5.3 Dealing with non-independence 74
5.3.1 Independence of replicates is a biological issue 74
5.4 Accepting that sometimes replication is impractical 76
5.5 Pseudoreplication, third variables, and
confounding variables 77
5.6 Cohort effects, confounding variables, and
cross-sectional studies 78
Summary 79
6 Sample size, power, and efficient design 81
6.1 Selecting the appropriate number of replicates 81
6.1.1 Educated guesswork 82
6.1.2 Formal power analysis 82
6.2 Factors affecting the power of an experiment 82
6.3 Working out the power of a planned study 84
6.3.1 Determining the likely effect size 85
6.3.2 How much variation? 86
6.3.3 Experimental design 87
6.3.4 How many replicates? 87
6.3.5 Imaginary experiments 88
6.4 Improving the power of a study 91
6.4.1 Reducing random variation 92
6.4.2 Dealing with variation through design 94
6.4.3 Increasing the effect size 95
6.4.4 Consider all other ways of increasing power before
increasing sample sizes 97
6.5 Comparing the power of different planned studies 98
Summary 100
7 The simplest type of experimental design:
completely randomized, single-factor 101
7.1 Completely randomized single-factor designs 102
7.2 Randomization 102
7.2.1 Randomizing study subjects 102
7.2.2 Randomizing other aspects of your study 103
7.2.3 Haphazard allocation 104
7.2.4 Balanced and unbalanced allocation 105
7.3 Factors with more than one level 106
7.4 Advantages and disadvantages of complete randomization 107
Summary 108
8 Experiments with several factors
(factorial designs) 109
8.1 Randomized designs with more than one factor 109
8.2 Interactions 110
8.3 Confusing levels and factors 117
8.4 Split-plot designs (sometimes called split-unit designs) 119
8.5 Latin square designs 121
8.6 Thinking about the statistics 123
Summary 125
9 Beyond complete randomization: blocking
ana covariates 127
9.1 The concept of blocking on a particular variable 128
9.2 Blocking on individual characters, space, and time 131
9.3 The advantages and disadvantages of blocking 132
9.4 Paired designs 133
9.5 How to select blocks 133
9.6 Covariates 134
9.7 Interactions between covariates and factors 138
Summary 140
10 Within-subject designs 141
10.1 What do we mean by a within-subject design? 141
10.2 The advantages of a within-subject design 142
10.3 The disadvantages of a within-subject design 142
10.3.1 Period effects 142
10.3.2 Carry-over effects 144
10.4 Isn’t repeatedly measuring the same individual
pseudoreplication? 146
10.5 With multiple treatments, within-subject experiments
can take a long time 147
10.6 Which sequences should you use? 147
10.7 Within-subject designs and randomized
block designs 148
10.8 It is possible to design experiments that mix
within-subject and between-subject effects 149
Summary 150
XVIII
Contents
11 Taking measurements 152
11.1 Calibration 153
11.2 Inaccuracy and imprecision 154
11.2.1 Subsampling: more woods or more trees? 157
11.3 Sensitivity and specificity 159
11.4 Intra-observer variability 162
11.4.1 Describing the problem 162
11.4.2 Tackling the problem 163
11.4.3 Repeatability 163
11.4.4 Remember, you can be consistent but still consistently wrong 164
11.5 Inter-observer variability 166
11.5.1 Describing the problem 166
11.5.2 Tackling the problem 166
11.6 Deciding howto measure 167
11.6.1 Defining categories 167
11.6.2 How fine-scale to make measurements of continuous variables 168
11.6.3 Observer bias and blinding 168
11.6.4 Allocation concealment 171
11.6.5 Floor and ceiling effects 172
11.6.6 Observer effects 173
11.7 Pitfalls to avoid when recording data 174
11.7.1 Don’t try to record too much information at once 175
11.7.2 Beware of shorthand codes 175
11.7.3 Keep more than one copy of your data 175
11.7.4 Write out your experimental protocol formally and
in detail, and keep a detailed field journal or lab book 176
11.7.5 Don't overwork 176
11.7.6 Take care to check computers and automated data collection 176
Summary 177
Sample answers to self-test questions 179
Flow chart on experimental design 191
Bibliography 196
Index 201
Contents
1 Why you should care about design 1
1.1 Why experiments need to be designed 1
1.2 The costs of poor design 3
1.2.1 Time and money 3
1.2.2 Ethical issues 3
1.3 The relationship between experimental design
and statistics 4
1.4 Why good experimental design is particularly
important to life scientists 5
1.4.1 Random variation 5
1.4.2 Confounding factors 6
1.4.3 Simpson’s paradox 7
1.5 Subjects, experimental units, samples, and terminology 9
Summary 11
2 Starting with a well-defined hypothesis 13
2.1 Why your study should be focused: questions,
hypotheses, and predictions 13
2.1.1 An example of moving from a question to hypotheses,
and then to an experimental design 14
2.1.2 An example of multiple hypotheses 15
2.1.3 Where do (good) ideas come from in the first place? 18
2.2 Producing the strongest evidence with which
to challenge a hypothesis 20
2.2.1 Indirect measures 20
2.2.2 Considering all possible outcomes of an experiment 21
2.2.3 Satisfying sceptics 22
2.3 Controls 23
2.3.1 Different types of control 23
2.3.2 Making sure the control is as effective as possible 26
2.3.3 The ethics of controlling 27
2.3.4 Situations where a control is not required 27
2.4 The importance of a pilot study and preliminary data 28
2.4.1 Making sure that you are asking a sensible question 28
2.4.2 Making sure that your techniques work 29
Summary 31
3 Selecting the broad design of your study 32
3.1 Experimental manipulation versus natural variation 32
3.1.1 An example hypothesis that could be tackled by
either manipulation or correlation 32
3.1.2 Arguments for doing a correlational study 33
3.1.3 Arguments for doing a manipulative study 35
3.1.4 Situations where manipulation is impossible 38
3.2 Deciding whether to work in the field or the laboratory 41
3.3 In vivo versus in vitro studies 43
3.4 There is no perfect study 43
Summary 44
4 Between-individual variation, replication,
and sampling 46
4.1 Between-individual variation 46
4.2 Replication 47
4.3 Selecting a sample 57
4.3.1 Simple random sampling 57
4.3.2 Remember, you want a representative sample 58
4.3.3 Stratified sampling 59
4.3.4 Cluster sampling 61
4.3.5 Convenience sampling 63
4.3.6 Self-selection 64
Summary 65
5 Pseudoreplication 67
5.1 Explaining what independence and pseudoreplication are 67
5.2 Common sources of pseudoreplication 70
5.2.1 A shared enclosure 70
5.2.2 The common environment 71
5.2.3 Reiatedness ' 72
5.2.4 A pseudoreplicated stimulus 72
5.2.5 Individuals are part of the environment too 72
5.2.6 Pseudoreplication of measurements through time 73
' 5.2.7 Species comparisons and pseudoreplication 73
5.3 Dealing with non-independence 74
, 5.3.1 Independence of replicates is a biologicalissue 74
5.4 Accepting that sometimes replication is impractical 76
5.5 Pseudoreplication, third variables, and
confounding variables 77
5.6 Cohort effects, confounding variables, and
cross-sectional studies 78
Summary 79
6 Sample size, power, and efficient design 81
6.1 Selecting the appropriate number of replicates 81
6.1.1 Educated guesswork 82
6.1.2 Formal power analysis 82
6.2 Factors affecting the power of an experiment 82
6.3 Working out the power of a planned study 84
6.3.1 Determining the likely effect size 85
6.3.2 How much variation? 86
6.3.3 Experimental design 87
6.3.4 How many replicates? 87
6.3.5 Imaginary experiments 88
6.4 Improving the power of a study 91
6.4.1 Reducing random variation 92
6.4.2 Dealing with variation through design 94
6.4.3 Increasing the effect size 95
6.4.4 Consider all other ways of increasing power before
increasing sample sizes 97
6.5 Comparing the power of different planned studies 98
Summary 100
7 The simplest type of experimental design:
completely randomized, single-factor 101
7.1 Completely randomized single-factor designs 102
7.2 Randomization 102
7.2.1 Randomizing study subjects 102
7.2.2 Randomizing other aspects of your study 103
7.2.3 Haphazard allocation 104
7.2.4 Balanced and unbalanced allocation 105
7.3 Factors with more than one level 106
7.4 Advantages and disadvantages of complete randomization 107
Summary 108
8 Experiments with several factors
(factorial designs) 109
8.1 Randomized designs with more than one factor 109
8.2 Interactions 110
8.3 Confusing levels and factors 117
8.4 Split-plot designs (sometimes called split-unit designs) 119
8.5 Latin square designs 121
8.6 Thinking about the statistics 123
Summary 125
9 Beyond complete randomization: blocking
ana covariates 127
9.1 The concept of blocking on a particular variable 128
9.2 Blocking on individual characters, space, and time 131
9.3 The advantages and disadvantages of blocking 132
9.4 Paired designs 133
9.5 How to select blocks 133
9.6 Covariates 134
9.7 Interactions between covariates and factors 138
Summary 140
10 Within-subject designs 141
10.1 What do we mean by a within-subject design? 141
10.2 The advantages of a within-subject design 142
10.3 The disadvantages of a within-subject design 142
10.3.1 Period effects 142
10.3.2 Carry-over effects 144
10.4 Isn’t repeatedly measuring the same individual
pseudoreplication? 146
10.5 With multiple treatments, within-subject experiments
can take a long time 147
10.6 Which sequences should you use? 147
10.7 Within-subject designs and randomized
block designs 148
10.8 It is possible to design experiments that mix
within-subject and between-subject effects 149
Summary 150
Contents
11 Taking measurements 152
11.1 Calibration 153
11.2 Inaccuracy and imprecision 154
11.2.1 Subsampling: more woods or more trees? 157
11.3 Sensitivity and specificity 159
11.4 Intra-observer variability 162
11.4.1 Describing the problem 152
11.4.2 Tackling the problem 163
11.4.3 Repeatability 163
11.4.4 Remember, you can be consistent but still consistently wrong 164
11.5 Inter-observer variability 166
11.5.1 Describing the problem 166
11.5.2 Tackling the problem 166
11.6 Deciding how to measure 167
11.6.1 Defining categories 167
11.6.2 How fine-scale to make measurements of continuous variables 168
11.6.3 Observer bias and blinding 168
11.6.4 Allocation concealment 171
11.6.5 Floor and ceiling effects 172
11.6.6 Observer effects 173
11.7 Pitfalls to avoid when recording data 174
1171 Don’t try to record too much information at once 175
117.2 Beware of shorthand codes 175
11.7.3 Keep more than one copy of your data 175
117.4 Write out your experimental protocol formally and
in detail, and keep a detailed field journal or lab book 176
11.7.5 Don’t overwork 176
11.7.6 Take care to check computers and automated data collection 176
Summary 177
Sample answers to self-test questions 179
Flow chart on experimental design 191
Bibliography 196
Index 201 |
| any_adam_object | 1 |
| author | Ruxton, Graeme D. Colegrave, Nick |
| author_GND | (DE-588)1071366084 (DE-588)135605946 |
| author_facet | Ruxton, Graeme D. Colegrave, Nick |
| author_role | aut aut |
| author_sort | Ruxton, Graeme D. |
| author_variant | g d r gd gdr n c nc |
| building | Verbundindex |
| bvnumber | BV043680202 |
| classification_rvk | CM 3500 WC 7600 |
| classification_tum | BIO 655f BIO 040f BIO 355f |
| contents | Bibliogr. p. [196]-200. Index |
| ctrlnum | (OCoLC)953289817 (DE-599)BVBBV043680202 |
| dewey-full | 570.724 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 570 - Biology |
| dewey-raw | 570.724 |
| dewey-search | 570.724 |
| dewey-sort | 3570.724 |
| dewey-tens | 570 - Biology |
| discipline | Biologie Psychologie |
| edition | Fourth edition |
| format | Book |
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| id | DE-604.BV043680202 |
| illustrated | Illustrated |
| indexdate | 2025-02-21T11:01:05Z |
| institution | BVB |
| isbn | 9780198717355 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029093101 |
| oclc_num | 953289817 |
| open_access_boolean | |
| owner | DE-29T DE-355 DE-BY-UBR DE-11 DE-M49 DE-BY-TUM DE-703 DE-634 |
| owner_facet | DE-29T DE-355 DE-BY-UBR DE-11 DE-M49 DE-BY-TUM DE-703 DE-634 |
| physical | xviii, 202 Seiten Illustrationen, Diagramme 25 cm |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Oxford University Press |
| record_format | marc |
| spelling | Ruxton, Graeme D. Verfasser (DE-588)1071366084 aut Experimental design for the life sciences Graeme D. Ruxton, University of St Andrews, Nick Colegrave, University of Edinburgh Fourth edition New York, NY, United States of America Oxford University Press [2016] © 2016 xviii, 202 Seiten Illustrationen, Diagramme 25 cm txt rdacontent n rdamedia nc rdacarrier Bibliogr. p. [196]-200. Index Sciences de la vie / Simulation, Méthodes de / Manuels d'enseignement supérieur ram Sciences de la vie / Expériences / Manuels d'enseignement supérieur ram Experiment (DE-588)4015999-1 gnd rswk-swf Biowissenschaften (DE-588)4129772-6 gnd rswk-swf Biologie (DE-588)4006851-1 gnd rswk-swf Versuchsplanung (DE-588)4078859-3 gnd rswk-swf Biologie (DE-588)4006851-1 s Biowissenschaften (DE-588)4129772-6 s Experiment (DE-588)4015999-1 s Versuchsplanung (DE-588)4078859-3 s DE-188 Colegrave, Nick Verfasser (DE-588)135605946 aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029093101&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029093101&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ruxton, Graeme D. Colegrave, Nick Experimental design for the life sciences Bibliogr. p. [196]-200. Index Sciences de la vie / Simulation, Méthodes de / Manuels d'enseignement supérieur ram Sciences de la vie / Expériences / Manuels d'enseignement supérieur ram Experiment (DE-588)4015999-1 gnd Biowissenschaften (DE-588)4129772-6 gnd Biologie (DE-588)4006851-1 gnd Versuchsplanung (DE-588)4078859-3 gnd |
| subject_GND | (DE-588)4015999-1 (DE-588)4129772-6 (DE-588)4006851-1 (DE-588)4078859-3 |
| title | Experimental design for the life sciences |
| title_auth | Experimental design for the life sciences |
| title_exact_search | Experimental design for the life sciences |
| title_full | Experimental design for the life sciences Graeme D. Ruxton, University of St Andrews, Nick Colegrave, University of Edinburgh |
| title_fullStr | Experimental design for the life sciences Graeme D. Ruxton, University of St Andrews, Nick Colegrave, University of Edinburgh |
| title_full_unstemmed | Experimental design for the life sciences Graeme D. Ruxton, University of St Andrews, Nick Colegrave, University of Edinburgh |
| title_short | Experimental design for the life sciences |
| title_sort | experimental design for the life sciences |
| topic | Sciences de la vie / Simulation, Méthodes de / Manuels d'enseignement supérieur ram Sciences de la vie / Expériences / Manuels d'enseignement supérieur ram Experiment (DE-588)4015999-1 gnd Biowissenschaften (DE-588)4129772-6 gnd Biologie (DE-588)4006851-1 gnd Versuchsplanung (DE-588)4078859-3 gnd |
| topic_facet | Sciences de la vie / Simulation, Méthodes de / Manuels d'enseignement supérieur Sciences de la vie / Expériences / Manuels d'enseignement supérieur Experiment Biowissenschaften Biologie Versuchsplanung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029093101&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029093101&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT ruxtongraemed experimentaldesignforthelifesciences AT colegravenick experimentaldesignforthelifesciences |
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