Biomeasurement: a student's guide to biological statistics
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford [u.a.]
Oxford University Press
2014
|
| Ausgabe: | third edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXVIII, 333 Seiten Diagramme |
| ISBN: | 9780199650446 |
Internformat
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| 245 | 1 | 0 | |a Biomeasurement |b a student's guide to biological statistics |c Dawn Hawkins |
| 250 | |a third edition | ||
| 264 | 1 | |a Oxford [u.a.] |b Oxford University Press |c 2014 | |
| 300 | |a XXVIII, 333 Seiten |b Diagramme | ||
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Datensatz im Suchindex
| _version_ | 1804152095520063488 |
|---|---|
| adam_text | Titel: Biomeasurement
Autor: Hawkins, Dawn
Jahr: 2014
Contents
Preface
Acknowledgements
Using this book
Why am I reading this book?
Book and chapter aims 1
1.1 My lecturer is a sadist! 1
1.2 Doing science: the big picture 3
1.2.1 Descriptive questions 3
1.2.2 Questions answered using a research hypothesis 4
Stage 1: developing research hypotheses 4
Stage 2: generating predictions 5
Stage 3: testing predictions 6
1.3 The process in practice 6
1.4 Essential skills for doing science 7
Analysing data 7
Developing hypotheses and predictions 7
Experimental design 8
Taking measurements 8
Critical evaluation 8
Health, safety, and ethical assessment 8
1.5 Types of data analysis 9
Checklist of key points 10
Self-help questions 10
Getting to grips with the basics
Chapter aims 11
2.1 Populations and samples 11
2.1.1 The sampling process 12
Sample size 13
Replication and pseudoreplication 13
Random sampling and bias 14
2.1.2 Sample error 14
2.2 Variation and variables 15
2.2.1 Identifying variables 17
2.2.2 Dependent and independent variables 17
2.2.3 Manipulated versus natural Variation in independent variables 18
2.2.4 Lack of independence between variables 18
XIV CONTENTS
2.3 Understandingdata 19
2.3.1 Related and unrelated data 19
Related data: repeated and matched 20
Unrelated data 21
Distinguishing related and unrelated data 22
2.3.2 Levels of measurement 22
Nominal (categories) 22
Ordinal (ranks) 23
Scale (counts and measures) 23
2.4 Demystifyingformulae 24
2.4.1 Squiggles, lines, and letters 24
2.4.2 Doing things in order 25
Checklist of key points 26
Self-help questions 27
Describing a Single sample
Chapter aims 29
3.1 The Single sample 29
3.2 Descriptive statistics 30
3.2.1 Central tendency 30
Mean (y) 30
Median 31
Mode 31
3.2.2 Variability 32
Range 32
Interquartile ränge 33
Variance (s2) 33
Standard deviation (s) 36
3.3 Frequencydistributions 36
3.3.1 For nominal, ordinal, and discrete scale data 36
3.3.2 For continuous scale data 37
3.3.3 The normal distribution 39
3.3.4 Discrete data and the normal distribution 41
3.3.5 Otherdistributions: binomial and Poisson 41
3.4 Pies, boxes, and errors 41
3.4.1 Pie charts as alternatives tofrequency-distribution charts 41
3.4.2 Understandingboxplots 42
3.4.3 Introducing error bars 43
3.5 Exampledata: ranger patrol tusk records 43
3.6 Worked example: using SPSS 45
3.6.1 Descriptive statistics and frequency distributions 45
For nominal, ordinal, and discrete scale data 45
For continuous scale data 47
3.6.2 Pie charts 51
3.6.3 Boxplots 53
CONTENTS XV
Checklist of key points 54
Self-help questions 56
Inferring and estimating
Chapter aims 57
4.1 Overview ofinferential statistics 57
4.1.1 Why we need inferential statistics-a reminder 57
4.1.2 Uncertainty and probability 58
4.2 Inferring through estimation 59
4.2.1 Standard error (of the mean, Sy-) 59
4.2.2 Confidence intervals (ofthe mean) 61
4.2.3 Error bars revisited 62
4.2.4 Comparingsamples 63
4.3 Example data: ground squirrels 64
4.4 Worked example: using SPSS 65
4.4.1 Standard error and confidence intervals 66
4.4.2 Errorplots 66
Checklist of key points 69
Self-help questions 70
Overview of null hypothesis significance testing
Chapter aims 71
5.1 Four Steps of null hypothesis significance testing 72
5.1.1 Construct a (Statistical) null hypothesis (H0) 72
5.1.2 Decide on a critical significance level ( x) 73
5.1.3 Calculateyour statistic 74
5.1.4 Reject or accept the null hypothesis 74
Step 4: using critical-value tables 74
Step 4: using P values on Computer Output 75
5.2 Error and power 76
5.2.1 Implications for decision making 77
5.3 Parametric and nonparametric 77
5.3.1 Comparison of parametric and nonparametric 77
5.3.2 Checking criteria for parametric tests using the normal distribution 78
5.3.3 Choosing between parametric and nonparametric 79
5.3.4 Transformation 79
5.4 One- and two-tailed tests 80
5.5 Effect sizes and their confidence intervals 81
5.5.1 Uses of effect size 81
5.5.2 Ways ofmeasuring effect size 81
5.6 Limitations, extensions, and alternatives to null hypothesis
significance testing (NHST) 82
5.6.1 Limitations of NHST 82
5.6.2 Extensions and alternatives to NHST 83
XVI CONTENTS
Checklist of key points 84
Self-help questions 85
Tests on frequencies
Chapter aims 87
6.1 Introduction tochi-square tests 87
6.1.1 Only use frequency data 87
6.1.2 Types of chi-square test 89
6.1.3 When to use chi-square with caution 89
6.1.4 Alternatives to chi-square tests 90
6.1.5 Names and Symbols for the chi-square statistic 90
6.2 Example data 91
6.2.1 One-way Classification: Mendel s peas 91
6.2.2 Two-way Classification: Mikumi s elephants 92
6.3 One-way Classification chi-square test 94
6.3.1 When to use 94
6.3.2 Four steps 95
Using critical-value tables 96
Using P values on Computer Output 96
6.3.3 Worked examples: by hand 96
With expected according to a 1:1:1:1 Mendelian ratio
(test of homogeneity) 97
With expected according to a 9:3:3:1 Mendelian ratio 98
6.3.4 Worked example: using SPSS 99
With expected according to a 1:1:1:1 Mendelian ratio (test of homogeneity) 99
With expected according to a 9:3:3:1 Mendelian ratio 102
6.3.5 Literature link: weary lettuces 104
6.4 Two-way Classification chi-square test 105
6.4.1 When to use 105
6.4.2 Four steps 106
Using critical-value tables 107
Using P values on Computer Output 107
6.4.3 Worked example: by hand 107
6.4.4 Worked example: using SPSS 109
6.4.5 Literature link: treatment alliance 111
Checklist of key points 114
Self-help questions 115
Tests of difference: two unrelated samples
Chapter aims 117
7.1 Introduction to the t-and Mann-Whitney U tests H7
7.1.1 Variables and levels of measurement needed 117
7.1.2 Comparison of t-and Mann-Whitney U tests H8
7.1.3 The t-test and the parametric criteria 118
7.1.4 Alternatives to t-and Mann-Whitney U tests H9
7.2 Example data: dem bones H9
CONTENTS XVII
7.3 t-Test 121
7.3.1 When to use 121
7.3.2 Four steps ofat-test 121
Using critical-value tables 122
Using P values on Computer Output 122
7.3.3 Worked example: by hand 122
7.3.4 Worked example: using SPSS 125
7.3.5 Literature link: Silicon and sorghum 126
7.4 Mann-Whitney U test 128
7.4.1 When to use 128
7.4.2 Four steps of a Mann-Whitney U test 128
Using critical-value tables 129
Using P values on Computer Output 130
7.4.3 Worked example: by hand 130
7.4.4 Worked example: using SPSS 133
7.4.5 Literature link: Alzheimers disease 135
Checklist of key points 136
Self-help questions 136
Tests of difference: two related samples
Chapter aims 138
8.1 Introduction topaired t- and Wilcoxon signed-rank tests 138
8.1.1 Variablesand levels of measurement needed 139
8.1.2 Comparison of paired t- and Wilcoxon signed-rank tests 139
8.1.3 The paired t-test and the parametric criteria 140
8.1.4 Alternatives to and extensions ofthe paired t- and Wilcoxon signed-rank tests 140
8.2 Example data: bighorn ewes 140
8.3 Paired t-test 143
8.3.1 When to use 143
8.3.2 Four steps ofa paired t-test 143
Using critical value tables 144
Using P values on Computer Output 144
8.3.3 Worked example: by hand 144
8.3.4 Worked example: using SPSS 147
8.3.5 Literature link: slug slime 148
8.4 Wilcoxon signed-rank test 150
8.4.1 When to use 150
8.4.2 Four steps ofa Wilcoxon signed-rank test 151
Using critical-value tables 152
Using P values on Computer output 152
8.4.3 Worked example: by hand 152
8.4.4 Worked example: using SPSS 154
8.4.5 Literature link: head injuries 156
Checklist of key points 157
Self-help questions 158
XVÜi CONTENTS
9 Tests of difference: more than two samples
Chapter aims 160
9.1 Introduction to one-way and Kruskal-WallisAnova tests 160
9.1.1 Variables and levels of measurement needed 161
9.1.2 Comparison of one-way and Kruskal-Wallis Anovas 162
9.1.3 One-way Anova and the parametric criteria 162
9.1.4 Alternatives to and extensions of one-way and Kruskal-Wallis Anovas 162
9.1.5 The language of Anova 163
9.1.6 Multiple comparisons 164
9.2 Example data: nitrogen levels in reeds 164
9.3 One-way Anova test 165
9.3.1 When to use 166
9.3.2 Four steps of a one-way Anova 167
Using critical-value tables 168
Using P values on Computer Output 168
9.3.3 Worked example: using SPSS 169
9.3.4 Literature link: running rats 171
9.4 Kruskal-Wallis test 171
9.4.1 When to use 172
9.4.2 Four steps ofa Kruskal-Wallis test 172
Using critical-value tables 173
Using P values on Computer output 174
9.4.3 Worked example: using SPSS 174
9.4.4 Literature link: cooperating long-tailed tits 175
9.5 Model I and model II Anova 176
Checklist of key points 177
Seif- help questions 177
10 Tests of relationship: regression
Chapter aims 179
10.1 Introduction to bivariate linear regression 179
10.1.1 Variables and levels of measurement needed 180
10.1.2 Linear model: scary-not! 181
10.1.3 The three regression questions 182
10.1.4 Added extras: how much is explained, and prediction 182
10.1.5 Regression and the parametric criteria 183
10.1.6 Alternatives to and extensions of bivariate linear regression and Anova 184
10.2 Example data: species richness 185
10.3 Regression test 186
10.3.1 When to use 186
10.3.2 Four Steps of a regression test 186
Using critical-value tables 187
Using P values on Computer output 18
10.3.3 Worked example: using SPSS for a regression test 1 °°
10.3.4 Worked example: using SPSS to get the added extras 190
CONTENTS XIX
10.3.5 Reporting bivariate linear regression results 193
10.3.6 Literature link: nodules 193
10.4 Model I and model II regression 194
Checklist of key points 195
Self-help questions 196
11 Tests of relationship: correlation
Chapter aims 198
11.1 Introduction to the Pearson and Spearman correlation tests 198
11.1.1 Variables and levels of measurement needed 199
11.1.2 Comparison of Pearson s and Spearman s tests 199
11.1.3 Pearson and the parametric criteria 201
11.1.4 The correlation coefficient 202
11.1.5 Partial, multiple, and multivariate correlation 202
11.2 Example data: eyeballs 203
11.3 Pearson correlation test 204
11.3.1 When to use 204
11.3.2 Four steps ofa Pearson correlation test 206
Using critical-value tables . 207
Using P values on Computer output 207
11.3.3 Worked example: using SPSS 207
11.3.4 Literature link: male sacrifice 209
11.4 Spearman correlation test 210
11.4.1 When to use 210
11.4.2 Four steps ofa Spearman correlation test 211
Using critical-value tables 212
Using P values on Computer Output 212
11.4.3 Worked example: using SPSS 212
11.4.4 Literature link: defoliating ryegrass 214
11.5 Comparison of correlation and regression 215
Checklist of key points 215
Self-help questions 216
12 Introducing the generalized linear model: general linear model
Chapter aims 218
12.1 Introduction to the general linear model 219
12.1.1 Variablesand levels of measurement 220
12.1.2 The linear model revisited 221
12.1.3 The language ofGLMs 224
12.1.4 Questions and extras 224
12.1.5 Types of sums of Squares 225
12.1.6 Model assumptions and the parametric criteria 226
Normalityof error 226
Homogeneity ofvariance 226
Linearity 226
XX CONTENTS
12.2 Example data: watered willows 227
12.3 Using the general linear model 230
12.3.1 When to use 230
12.3.2 GLM and the four steps 231
12.3.3 Worked example: using SPSS 232
Looking at the model overall (answering question 2a, Section 12.1.4) 232
Looking at individual explanatory variables (answering
question 2b, Section 12.1.4) 234
Looking at R2 (answering question 3, Section 12.1.4) 236
Using coefficients (answering question 1, Section 12.1.4) 237
Using coefficients (making predictions) 239
Checking model assumptions 239
12.3.4 Reporting results from GLM 240
12.3.5 Literature link: brains and booze 243
12.4 Interaction 244
12.4.1 Worked example: Using SPSS, interaction 246
12.5 Random factors and mixed modeis 247
12.6 The multiple model approach 248
12.6.1 Finding the best model 248
12.6.2 Report results from multiple modeis 249
12.7 The general and generalized linear modeis compared 250
Checklist of key points 251
Self-help questions 253
13 More on the generalized linear model: logistic and loglinear
modeis
Chapter aims 254
13.1 Introduction to the logistic and loglinear modeis 254
13.1.1 Variables and levels of measurement 255
13.1.2 Link functions revisited 256
13.1.3 Questions and extras 257
13.1.4 Model assumptions and overdispersion 257
13.2 Example data: urban birds 258
13.3 Using the binary logistic model 259
13.3.1 When to use 259
13.3.2 Binary logistic modeis and the four steps 260
13.3.3 Worked example: using SPSS 260
Answering question 3: How good is the model? 266
Answering question 2a: Is the model significant overall? 266
Answering question 2b: Are individual explanatory variables significant? 267
Answering question 1: What is the model? 268
Effect size 269
Checking for overdispersion 269
13.3.4 Literature link: Death by AMI 270
CONTENTS XXI
13.4 Using the loglinear model 271
13.4.1 When to use 271
13.4.2 Loglinear modeis and the four steps 271
13.4.3 Worked example: using SPSS 272
Answering question 3: How good is the model? 276
Answering question 2a: Is the model significant overall? 276
Answering question 2b: Are individual explanatory variables significant? 277
Answering question 1: What is the model? 278
Effect size 279
Checking for overdispersion 280
13.4.4 Literature link: seacows 281
13.5 The general, binary logistic, and loglinear modeis compared 281
13.6 Alternatives and extensions 281
Checklist of key points 283
Self-help questions 284
14 Choosing the right test and graph
Chapter aims 285
14.1 Introduction to choosing 285
14.2 Whichtest? 286
14.3 Which graph? 288
14.4 Worked examples: graphs using SPSS 289
14.4.1 Pie charts 290
14.4.2 Boxplots 290
Unrelated samples 290
Related samples 292
14.4.3 Errorplots 294
Unrelated samples 294
Related samples 295
14.4.4 Scatterplots 296
Checklist of key points 299
Self-help questions 300
Answers to self-help questions
Chapter 1 301
Chapter 2 301
Chapter 3 301
Chapter 4 301
Chapter 5 302
Chapter 6 302
Chapter 7 302
Chapter 8 302
xxii CONTENTS
Chapter 9 303
Chapter 10 303
Chapter 11 303
Chapter 12 303
Chapter 13 304
Chapter 14 304
Appendix I How to enter data into SPSS
Name 305
Type, width, and decimals 305
Label 305
Values 305
Missing 306
Columns 306
Align 306
Measure 306
Role 307
Appendix II Statistical tables of critical values
X2 308
t 309
U 310
T 312
F 314
H 315
r
316
rs 317
Appendix III Summary guidance on reporting Statistical results
Descriptive statistics 318
Statistical tests 318
Effect size 319
Appendix IV Statistics and experimental design
Designs with control groups 320
Balanced and unbalanced design 320
Completely randomized designs 320
One-way or one factor designs 320
Multi-way or multi-factor designs 321
Fully crossed designs 321
CONTENTS XXIII
Incomplete designs 321
Blocking 321
Paired design 321
Covariate 321
Within-subject designs 322
Split-plot designs 322
Selected further reading
Next steps... 323
For when you are feeling stronger... 324
Online... 325
References 326
Index 329
|
| any_adam_object | 1 |
| author | Hawkins, Dawn |
| author_GND | (DE-588)1077774036 |
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| author_role | aut |
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| author_variant | d h dh |
| building | Verbundindex |
| bvnumber | BV041782303 |
| callnumber-first | Q - Science |
| callnumber-label | QH323 |
| callnumber-raw | QH323.5 |
| callnumber-search | QH323.5 |
| callnumber-sort | QH 3323.5 |
| callnumber-subject | QH - Natural History and Biology |
| classification_rvk | WC 7000 |
| classification_tum | BIO 110f |
| ctrlnum | (OCoLC)879834838 (DE-599)BVBBV041782303 |
| dewey-full | 570.72/7 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 570 - Biology |
| dewey-raw | 570.72/7 |
| dewey-search | 570.72/7 |
| dewey-sort | 3570.72 17 |
| dewey-tens | 570 - Biology |
| discipline | Biologie Informatik |
| edition | third edition |
| format | Book |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:05:16Z |
| institution | BVB |
| isbn | 9780199650446 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027228089 |
| oclc_num | 879834838 |
| open_access_boolean | |
| owner | DE-20 DE-11 DE-19 DE-BY-UBM DE-29T |
| owner_facet | DE-20 DE-11 DE-19 DE-BY-UBM DE-29T |
| physical | XXVIII, 333 Seiten Diagramme |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Oxford University Press |
| record_format | marc |
| spelling | Hawkins, Dawn Verfasser (DE-588)1077774036 aut Biomeasurement a student's guide to biological statistics Dawn Hawkins third edition Oxford [u.a.] Oxford University Press 2014 XXVIII, 333 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Biowissenschaften Life sciences Research Methodology Life sciences Statistical methods Biostatistik (DE-588)4729990-3 gnd rswk-swf Biomathematik (DE-588)4139408-2 gnd rswk-swf 1\p (DE-588)4123623-3 Lehrbuch gnd-content Biostatistik (DE-588)4729990-3 s DE-604 Biomathematik (DE-588)4139408-2 s HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027228089&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hawkins, Dawn Biomeasurement a student's guide to biological statistics Biowissenschaften Life sciences Research Methodology Life sciences Statistical methods Biostatistik (DE-588)4729990-3 gnd Biomathematik (DE-588)4139408-2 gnd |
| subject_GND | (DE-588)4729990-3 (DE-588)4139408-2 (DE-588)4123623-3 |
| title | Biomeasurement a student's guide to biological statistics |
| title_auth | Biomeasurement a student's guide to biological statistics |
| title_exact_search | Biomeasurement a student's guide to biological statistics |
| title_full | Biomeasurement a student's guide to biological statistics Dawn Hawkins |
| title_fullStr | Biomeasurement a student's guide to biological statistics Dawn Hawkins |
| title_full_unstemmed | Biomeasurement a student's guide to biological statistics Dawn Hawkins |
| title_short | Biomeasurement |
| title_sort | biomeasurement a student s guide to biological statistics |
| title_sub | a student's guide to biological statistics |
| topic | Biowissenschaften Life sciences Research Methodology Life sciences Statistical methods Biostatistik (DE-588)4729990-3 gnd Biomathematik (DE-588)4139408-2 gnd |
| topic_facet | Biowissenschaften Life sciences Research Methodology Life sciences Statistical methods Biostatistik Biomathematik Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027228089&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hawkinsdawn biomeasurementastudentsguidetobiologicalstatistics |