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Research Methods for the Biosciences 3rd Revised edition [Minkštas viršelis]

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(Senior Lecturer, Human Nutrition, University of Worcester), (Senior Lecturer, Genetics, University of Worcester), (Senior Lecturer, Environmental Science, University of Worcester), (Senior Lecturer, Computing, University of Worcester)
  • Formatas: Paperback / softback, 488 pages, aukštis x plotis x storis: 251x198x19 mm, weight: 906 g, 93 line drawings
  • Išleidimo metai: 10-Nov-2016
  • Leidėjas: Oxford University Press
  • ISBN-10: 0198728492
  • ISBN-13: 9780198728498
Kitos knygos pagal šią temą:
Research Methods for the Biosciences 3rd Revised editionDidesnis paveikslėlis
  • Formatas: Paperback / softback, 488 pages, aukštis x plotis x storis: 251x198x19 mm, weight: 906 g, 93 line drawings
  • Išleidimo metai: 10-Nov-2016
  • Leidėjas: Oxford University Press
  • ISBN-10: 0198728492
  • ISBN-13: 9780198728498
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780198728498.html
Raktažodžiai:
Scientific research is a proven and powerful tool for discovering the answers to biological questions. As such, today's students need to be well-versed in experimental design, analysis, and the communication of research. Furthermore, they must appreciate how all of these aspects are interlinked--how, for example, statistics can be used to inform the design of a particular experiment. As a resource which skillfully integrates all of the key aspects relating to biological research, Research Methods for the Biosciences is the perfect guide for undergraduates.

The exceptionally clear layout takes students through choosing a project and planning their research; collecting, evaluating, and analyzing their data; and finally reporting their results. Research methods, which can often seem abstract, are brought to life through the use of examples taken from real undergraduate research. Friendly guidance and advice is provided throughout the text, and little prior knowledge or mathematical experience is required. Since statistics is a subject best learned through doing, frequent worked examples appear throughout Part Two 'Handling your data', showing step-by-step how to carry out the various statistical tests. In addition, online software walkthroughs and video screencasts clearly demonstrate how to use software such as SPSS, Minitab, Excel and R to carry out statistical analyses.



Online Resource Centre

The Online Resource Centre to accompany Research Methods for the Biosciences features:

For students: · New video screencasts showing how to carry out statistical tests using software · Statistical software walkthroughs for SPSS, Excel, and Minitab · Additional statistical tests not included in the main text · Full details of calculations given in the in-text boxes · Interactive and printable decision tree, to help you design your experiment · Interactive and printable risk assessment form · Integrative exercises to help students test their understanding of the topics in the book

For lecturers: · A test bank of questions · Figures from the book available to download
List of boxes xvi
Preface xviii
Acknowledgements xxii
Section 1 Planning your experiment
1 Where do I begin?
3(17)
1.1 Aims and objectives
3(3)
1.1.1 The aim
4(1)
1.1.2 The objective
4(2)
1.2 Data, items, and observations
6(1)
1.3 Populations
7(1)
1.4 Sample
7(3)
1.4.1 Representative samples
8(1)
1.4.2 How do youobtain a-representative sample?
8(2)
1.5 Population parameters and sample statistics
10(1)
1.5.1 Mathematical notation for populations and samples
10(1)
1.5.2 Calculations
10(1)
1.6 Treatments
11(1)
1.7 Variation and variables
12(4)
1.7.1 Variation
12(1)
1.7.2 Variables that are designed to be part of your experiment
12(1)
1.7.3 Confounding variation: confounding variables
13(1)
1.7.4 Confounding variation: sampling error
14(1)
1.7.5 Minimizing the effect of confounding variation
15(1)
1.8 Hypotheses
16(4)
1.8.1 Null hypotheses
16(1)
1.8.2 Information-theoretic models
17(3)
2 Planning your experiment
20(25)
2.1 Evaluating published research
21(3)
2.1.1 What are the aim and objective(s)?
22(1)
2.1.2 Strengths of the experimental design
22(1)
2.1.3 Weaknesses of the experimental design
22(2)
2.2 Have ago
24(16)
2.2.1 Identification of a research topic
25(2)
2.2.2 Aim and objectives
27(1)
2.2.3 What is/are the statistical population(s)? Will I sample from the population(s)?
28(1)
2.2.4 Which variables am I investigating?
29(1)
2.2.5 Are there any potential sources of confounding variation?
30(2)
2.2.6 Will I need replicates?
32(2)
2.2.7 Will I need any controls?
34(1)
2.2.8 How will I analyse my data?
35(2)
2.2.9 Do I need to take action to ensure that I comply with UK law?
37(1)
2.2.10 Are there any causes of possible bias? Have I made any assumptions?
38(1)
2.2.11 Will I repeat the investigation?
39(1)
2.2.12 Back to the beginning
39(1)
2.3 Managing research
40(5)
2.3.1 Time management
41(1)
2.3.2 Space management
42(1)
2.3.3 Data management
42(3)
3 Questionnaires, focus groups, and interviews
45(12)
3.1 What is a questionnaire, interview, or focus group?
46(1)
3.1.1 Questionnaires
46(1)
3.1.2 Focus groups
46(1)
3.1.3 Interviews
46(1)
3.2 Closed and open questions
46(6)
3.2.1 Closed questions
50(2)
3.2.2 Open questions
52(1)
3.3 Phrasing questions
52(1)
3.4 Your participants
53(1)
3.5 Sample sizes and data analysis
53(4)
3.5.1 Closed questions
54(1)
3.5.2 Open questions
55(1)
3.5.3 Achieving the required sample size
55(2)
4 Research, the law, and you
57(40)
4.1 About the law
58(3)
4.1.1 International law
58(1)
4.1.2 National law
58(3)
4.2 Ethics
61(1)
4.3 Intellectual property rights
61(1)
4.4 Health and safety
62(10)
4.4.1 Hazard identification and rating
63(2)
4.4.2 Activity
65(1)
4.4.3 Probability of harm
66(1)
4.4.4 Minimizing the risk
67(4)
4.4.5 Risk evaluation
71(1)
4.4.6 Emergencies
71(1)
4.4.7 Action
72(1)
4.5 Access and sampling
72(11)
4.5.1 Access
72(3)
4.5.2 Theft
75(1)
4.5.3 Plants, animals, and other organisms
76(3)
4.5.4 Protection in special areas
79(3)
4.5.5 Movement, import, export, and control
82(1)
4.5.6 Permits and licences
83(1)
4.6 Animal welfare
83(3)
4.6.1 Protection of Animals Act 1911
84(1)
4.6.2 Animals (Scientific Procedures) Act 1986 (as amended)
85(1)
4.6.3 Animal Welfare Act (2006) and Animal Health and Welfare (Scotland) Act 2006
86(1)
4.7 Genetically modified organisms (GMO)
86(1)
4.8 Working with humans
86(5)
4.8.1 Informed consent
87(2)
4.8.2 Special cases: children and vulnerable people
89(1)
4.8.3 Equality
90(1)
4.8.4 Anonymity, confidentiality, information storage, and dissemination
90(1)
4.9 Discussion topics
91(6)
Section 2 Handling Our data
5 What to do with raw data
97(37)
5.1 Types of data
98(2)
5.2 Distributions of data
100(6)
5.2.1 Normal distribution
100(2)
5.2.2 Binomial distribution
102(1)
5.2.3 Poisson distribution
103(2)
5.2.4 Exponential distribution
105(1)
5.3 Summary statistics
106(2)
5.4 Estimates of the central tendency
108(7)
5.4.1 Mode
108(1)
5.4.2 Median
109(1)
5.4.3 Mean
109(4)
5.4.4 Skew and kurtosis
113(2)
5.5 Estimates of variation
115(6)
5.5.1 Range
116(1)
5.5.2 I nterquartile range and percentiles
116(1)
5.5.3 Variance and standard deviation
116(5)
5.6 Coefficient of variation
121(1)
5.7 Confidence limits
121(1)
5.8 Parametric data
122(4)
5.8.1 What are parametric data and why do we need them?
123(1)
5.8.2 How to confirm that data are parametric
123(1)
5.8.3 Using the shape of the distribution to confirm that your data are parametric
123(2)
5.8.4 Using statistical tests to confirm that your data are parametric
125(1)
5.9 Non-parametric data
126(1)
5.9.1 Ranking data
126(1)
5.10 Transforming data
127(7)
5.10.1 How to choose a suitable transformation
128(1)
5.10.2 How to carry out the transformation
128(1)
5.10.3 Did the transformation work, and what to do if it didn't
128(2)
5.10.4 How to report analyses that have used transformed data
130(4)
6 An introduction to hypothesis testing
134(26)
6.1 What is hypothesis testing?
135(11)
6.1.1 Extreme rare values in a population
135(4)
6.1.2 What are null and alternate hypotheses?
139(1)
6.1.3 Testing the hypotheses
140(2)
6.1.4 Choosing the correct critical value
142(1)
6.1.5 Choosing a distribution
142(1)
6.1.6 Choosing the p value
142(3)
6.1.7 Overview of hypothesis testing
145(1)
6.2 How to choose a sample size
146(3)
6.2.1 A representative sample
146(1)
6.2.2 Meeting the criteria of statistical tests
146(1)
6.2.3 Maximum sample sizes
146(1)
6.2.4 Power calculations
147(1)
6.2.5 Ethics
148(1)
6.2.6 Legal and practical constraints
149(1)
6.3 Select and correctly phrase the hypotheses to be tested
149(4)
6.3.1 Types of hypothesis
149(2)
6.3.2 General and specific hypotheses
151(1)
6.3.3 How to write hypotheses
151(2)
6.4 Using tables of critical values
153(4)
6.4.1 p values
153(1)
6.4.2 Degrees of freedom
154(2)
6.4.3 More than two criteria
156(1)
6.4.4 Interpolation
156(1)
6.5 What does this mean in real terms?
157(3)
7 Which statistical test should I choose?
160(23)
7.1 Designing an experiment and analysing your data
161(13)
7.1.1 Key to determine the correct statistical test
162(6)
7.1.2 Supporting explanations and examples
168(6)
7.2 Experimental design and statistics
174(2)
7.2.1 General and specific hypothesis
175(1)
7.2.2 Controls
175(1)
7.2.3 Replication and sample sizes
175(1)
7.2.4 Matched data or repeated measures
176(1)
7.2.5 Confounding variables
176(1)
7.3 The critical reader of statistics and experimental design
176(7)
7.3.1 The title and introduction
177(1)
7.3.2 The method
177(1)
7.3.3 Tables and figures
178(1)
7.3.4 Your evaluation
179(4)
8 Hypothesis testing: Do my data fit an expected ratio?
183(17)
8.1 Which ratios can we fit?
184(1)
8.2 Expected values
185(1)
8.3 Chi-squared goodness-of-fit test: one sample
185(4)
8.3.1 Key trends and experimental design
186(1)
8.3.2 Using this test
187(1)
8.3.3 The general calculation
187(2)
8.4 How to check whether your data have a normal distribution using the chi-squared goodness-of-fit test
189(4)
8.4.1 Key trends and experififental design
190(1)
8.4.2 Using this test
190(1)
8.4.3 The general calculation
190(3)
8.5 Replication in a goodness-of-fit test
193(7)
8.5.1 Key trends and experimental design
194(1)
8.5.2 Using this test
195(1)
8.5.3 The calculation
195(5)
9 Hypothesis testing: Associations and relationships
200(50)
9.1 Associations and relationships
201(1)
9.2 Modelling the association
201(1)
9.3 Chi-squared test for association
202(5)
9.3.1 Key trends and experimental design
204(1)
9.3.2 Using this test
205(1)
9.3.3 The calculation
205(2)
9.4 The problem with small numbers and limited designs
207(3)
9.4.1 Your sample size is small
208(1)
9.4.2 Your expected values are less than five
208(2)
9.4.3 Chi-squared test for a 2 x 2 contingency table
210(1)
9.5 Correlations
210(4)
9.6 Spearman's rank correlation
214(3)
9.6.1 Key trends and experimental design
214(1)
9.6.2 Using this test
214(2)
9.6.3 The calculation
216(1)
9.7 Pearson's product moment correlation
217(4)
9.7.1 Key trends and experimental design
218(1)
9.7.2 Using this test
218(1)
9.7.3 The calculation
218(3)
9.8 Coefficient of determination
221(1)
9.9 Regressions
221(2)
9.10 Model I: simple linear regression: only one y for each x
223(7)
9.10.1 Key trends and experimental design
225(1)
9.10.2 Using this test
225(1)
9.10.3 The calculation
226(4)
9.11 Model I: linear regression: more than one y for each value of x, with equal replicates
230(8)
9.11.1 Key trends and experimental design
232(1)
9.11.2 Using this test
232(2)
9.11.3 The calculation
234(4)
9.12 Model II: principal axis regression
238(4)
9.12.1 Key trends and experimental design
239(1)
9.12.2 Using this test
239(1)
9.12.3 The calculation
240(2)
9.13 Model II: ranged principal axis regression
242(8)
9.13.1 Key trends and experimental design
243(1)
9.13.2 Using this test
243(1)
9.13.3 The calculation
244(6)
10 Hypothesis testing: Do my samples come from the same population? Parametric data
250(75)
10.1 Two sample z-test for unmatched data
252(6)
10.1.1 Key trends and experimental design
254(1)
10.1.2 Using this test
254(3)
10.1.3 The calculation
257(1)
10.2 Two-sample Student's t-test for unmatched data
258(4)
10.2.1 Key trends and experimental design
259(1)
10.2.2 Using this test
260(1)
10.2.3 The calculation
260(2)
10.3 Two-sample unequal variance t-test for unmatched data
262(3)
10.3.1 Key trends and experimental design
262(1)
10.3.2 Using this test
262(1)
10.3.3 The calculation
263(2)
10.4 Two-sample z- and t-tests for matched data
265(4)
10.4.1 Key trends and experimental design
266(1)
10.4.2 Using this test
267(1)
10.4.3 The calculation
267(2)
10.5 One-sample t-test
269(3)
10.5.1 Key trends and experimental design
270(1)
10.5.2 Using this test
270(1)
10.5.3 The calculation
271(1)
10.6 Introduction to parametric ANOVAs
272(2)
10.7 One-way parametric ANOVA with equal numbers of replicates
274(5)
10.7.1 Key trends and experimental design
275(1)
10.7.2 Using this test
276(1)
10.7.3 The calculation
277(2)
10.8 Tukey's test following a parametric one-way ANOVA with equal replicates
279(4)
10.8.1 Key trends and experimental design
280(1)
10.8.2 Using this test
280(1)
10.8.3 The calculation
281(1)
10.8.4 Reporting your findings
282(1)
10.9 One-way parametric ANOVA with unequal replicates
283(3)
10.9.1 Key trends and experimental design
284(1)
10.9.2 Using this test
284(1)
10.9.3 The calculation
285(1)
10.10 Tukey-Kramer test following a parametric one-way ANOVA with unequal replicates
286(3)
10.10.1 Key trends and experimental design
286(1)
10.10.2 Using this test
286(1)
10.10.3 The calculation
286(3)
10.10.4 Reporting your findings
289(1)
10.11 ANOVAs for more than one treatment variable
289(5)
10.11.1 Randomized orthogonal designs
289(1)
10.11.2 Confounding variables as a treatment variable
290(1)
10.11.3 More than one confounding variable: a Latin square
290(1)
10.11.4 Linear confound ing,yariables
291(1)
10.11.5 Repeated measures as a second treatment variable
292(1)
10.11.6 Fixed and nested models
292(1)
10.11.7 Interactions
293(1)
10.12 Two-way parametric ANOVA with equal replicates
294(6)
10.12.1 Key trends and experimental design
295(1)
10.12.2 Using this test
296(1)
10.12.3 The calculation
296(4)
10.13 Tukey's test following a parametric two-way ANOVA with equal replicates
300(2)
10.13.1 Key trends and experimental design
300(1)
10.13.2 Using this test
301(1)
10.13.3 The calculation
301(1)
10.14 Two-way parametric ANOVA with unequal replicates
302(2)
10.14.1 Key trends and experimental design
303(1)
10.14.2 Using this test
303(1)
10.14.3 The calculation
303(1)
10.15 Two-way parametric ANOVA with no replicates
304(6)
10.15.1 Key trends and experimental design
305(1)
10.15.2 Using this test
306(1)
10.15.3 The calculation
307(3)
10.16 Two-way nested parametric ANOVA with equal replicates
310(5)
10.16.1 Key trends and experimental design
311(1)
10.16.2 Using this test
312(1)
10.16.3 The calculation
312(3)
10.16.4 Tukey's test for nested parametric ANOVAs
315(1)
10.17 Factorial three-way parametric ANOVA with no replicates
315(10)
10.17.1 Key trends and experimental design
318(1)
10.17.2 Using this test
318(1)
10.17.3 The calculation
318(5)
10.17.4 Tukey's test and a three-way parametric ANOVA
323(2)
11 Hypothesis testing: Do my samples come from the same population? Non-parametric data
325(44)
11.1 Mann-Whitney U test
327(6)
11.1.1 Key trends and experimental design
328(1)
11.1.2 Using this test
328(1)
11.1.3 The calculation
329(4)
11.2 Wilcoxon's signed ranks test for matched pairs test
333(5)
11.2.1 Key trends and experimental design
334(1)
11.2.2 Using this test
335(1)
11.2.3 The calculation
335(3)
11.3 One-way non-parametric ANOVA (Kruskal-Wall is test)
338(5)
11.3.1 Key trends and experimental design
339(1)
11.3.2 Using this test
340(1)
11.3.3 The calculation
341(2)
11.4 Post hoc test following a non-parametric one-way ANOVA
343(4)
11.4.1 Key trends and experimental design
344(1)
11.4.2 Using this test
344(1)
11.4.3 The calculation
345(2)
11.5 Two-way non-parametric ANOVA
347(9)
11.5.1 Key trends and experimental design
348(1)
11.5.2 Using this test
349(1)
11.5.3 The calculation
349(7)
11.6 Post hoc test following a two-way non-parametric ANOVA
356(7)
11.6.1 Key trends and experimental design
357(1)
11.6.2 Using this test
358(5)
11.6.3 The calculation
363(1)
11.7 Scheirer-Ray-Hare test
363(6)
11.7.1 Key trends and experimental design
363(1)
11.7.2 Using this test
363(1)
11.7.3 The calculation
364(5)
Section 3 Reporting your results
12 Reporting your research
369(44)
12.1 Writing a research paper or report
370(30)
12.1.1 General format
370(1)
12.1.2 Title
371(1)
12.1.3 Abstract
372(1)
12.1.4 Keywords
372(1)
12.1.5 Acknowledgements
373(1)
12.1.6 Introduction
373(4)
12.1.7 Method
377(3)
12.1.8 Results: text
380(1)
12.1.9 Results: tables
381(3)
12.1.10 Results: figures
384(6)
12.1.11 Results: statistics
390(2)
12.1.12 Discussion
392(1)
12.1.13 References: Harvard system
393(6)
12.1.14 Appendix
399(1)
12.1.15 Your approach to writing a report
399(1)
12.2 Producing a poster
400(6)
12.2.1 Poster content
400(4)
12.2.2 Poster construction
404(1)
12.2.3 Principles of layout on a'poster
404(1)
12.2.4 Laying out a poster
405(1)
12.2.5 Additional material
406(1)
12.3 Presentations
406(7)
12.3.1 The role of the presenter
406(2)
12.3.2 Visual slide presentation
408(2)
12.3.3 Preparation
410(1)
12.3.4 Nerves
410(3)
Appendix A How to choose a research project 413(4)
Appendix B Maths and statistics 417(12)
Appendix C Quick reference guide for choosing a statistical test 429(3)
Appendix D Tables of critical values for statistical tests 432(18)
Glossary 450(6)
References 456(1)
Index 457
Debbie Holmes, Diana Dine, Peter Moody, and Laurence Trueman are all senior lecturers in the Department of Applied Sciences, Geography and Archaeology at the University of Worcester. Between them, the authors have wide experience of teaching across a broad range of biological and environmental science-based subject areas, and developing web-based resources to support their undergraduate teaching.