Book contents
- Studying Primates
- Studying Primates
- Copyright page
- Dedication
- Contents
- Preface
- Acknowledgements
- 1 Asking Questions about Primates
- 2 Ethics in Primatology
- 3 Keeping Science Healthy: Research Integrity
- 4 Inclusive Science
- 5 Understanding Statistical Evidence
- 6 Communicating Ideas in Writing
- 7 Introduction to the Primates
- 8 Why Study Primates?
- 9 Identifying a Research Question
- 10 Finding Out What We Know
- 11 Reading Journal Articles
- 12 Formulating Hypotheses and Predictions and Designing a Study
- 13 Observing and Manipulating
- 14 Choosing Measures
- 15 Planning Data Analysis
- 16 Sampling and Statistical Power
- 17 Checking Feasibility and Finalising Your Plans
- 18 Writing a Research Proposal
- 19 Collecting Data
- 20 Conducting Fieldwork
- 21 Analysing and Interpreting Data
- 22 Writing a Scientific Report
- 23 Submitting to a Peer-Reviewed Journal
- 24 Presenting Your Work at a Conference
- 25 Conclusions
- Index
- References
21 - Analysing and Interpreting Data
Published online by Cambridge University Press: 19 September 2019
Book contents
- Studying Primates
- Studying Primates
- Copyright page
- Dedication
- Contents
- Preface
- Acknowledgements
- 1 Asking Questions about Primates
- 2 Ethics in Primatology
- 3 Keeping Science Healthy: Research Integrity
- 4 Inclusive Science
- 5 Understanding Statistical Evidence
- 6 Communicating Ideas in Writing
- 7 Introduction to the Primates
- 8 Why Study Primates?
- 9 Identifying a Research Question
- 10 Finding Out What We Know
- 11 Reading Journal Articles
- 12 Formulating Hypotheses and Predictions and Designing a Study
- 13 Observing and Manipulating
- 14 Choosing Measures
- 15 Planning Data Analysis
- 16 Sampling and Statistical Power
- 17 Checking Feasibility and Finalising Your Plans
- 18 Writing a Research Proposal
- 19 Collecting Data
- 20 Conducting Fieldwork
- 21 Analysing and Interpreting Data
- 22 Writing a Scientific Report
- 23 Submitting to a Peer-Reviewed Journal
- 24 Presenting Your Work at a Conference
- 25 Conclusions
- Index
- References
Summary
Data analysis and interpretation allow you to test your predictions and interpret your results. This is an exciting time and can be daunting because it’s a big change from data collection. It’s very unlikely that you will have collected exactly the data you set out to collect, but your analysis plan will keep you on track and avoid the dangers of aimlessly exploring your dataset. You will probably need further statistical advice at this stage. This chapter guides you through data preparation, initial data analysis, hypothesis testing, calculating your effect sizes and confidence intervals, interpreting your results and extrapolating from them.
Keywords
- Type
- Chapter
- Information
- Studying PrimatesHow to Design, Conduct and Report Primatological Research, pp. 263 - 270Publisher: Cambridge University PressPrint publication year: 2019
References
21.8 Further Reading
Benjamini, Y, Hochberg, Y. 1995. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57: 289–300. https://doi.org/10.2307/2346101. Proposes the false discovery rate and a procedure to control it.Google Scholar
Ihle, M, Winney, IS, Krysalli, A, Croucher, M. 2017. Striving for transparent and credible research: Practical guidelines for behavioral ecologists. Behavioral Ecology 28: 348–354. https://doi.org/10.1093/beheco/arx003. An example of the application of open (transparent) practices to a field.Google Scholar
Mundry, R, Fischer, J. 1998. Use of statistical programs for nonparametric tests of small samples often leads to incorrect P values: Examples from Animal Behaviour. Animal Behaviour 56: 256–259. https://doi.org/10.1006/anbe.1998.0756. Highlights the need for exact (rather than asymptotic) test procedures for non-parametric statistics.Google Scholar
Neuhauser, M, Ruxton, GD. 2009. Round your numbers in rank tests: exact and asymptotic inference and ties. Behavioral Ecology and Sociobiology 64: 297–303. https://doi.org/10.1007/s00265–009-0843-1. Warns against pseudo-precision in ranking data.Google Scholar
Nosek, BA, Ebersole, CR, DeHaven, AC, Mellor, DT. 2018. The preregistration revolution. Proceedings of the National Academy of Sciences of the United States of America 115: 2600–2606. https://doi.org/10.1073/pnas.1708274114. Introduces preregistration and addresses challenges to it, including how to handle changes to the methods and violations of assumptions.Google Scholar
Parker, TH, Bowman, SD, Nakagawa, S, Gurevitch, J, Mellor, DT, Rosenblatt, RP, DeHaven, AC. 2018. Tools for Transparency in Ecology and Evolution. http://doi.org/10.17605/OSF.IO/G65CB. A checklist of questions to maximise transparency in science.Google Scholar
Pike, N, 2011. Using false discovery rates for multiple comparisons in ecology and evolution. Methods in Ecology and Evolution 2: 278–282. https://doi.org/10.1111/j.2041-210X.2010.00061.x. Discusses the advantages and disadvantages of analyses based on the false discovery rate and provides spreadsheet programmes to calculate false discovery rates.Google Scholar