Analysing and Interpreting Data

Joanna M. Setchell

doi:10.1017/9781108368513.022

21 - Analysing and Interpreting Data

Published online by Cambridge University Press: 19 September 2019

Joanna M. Setchell

Show author details

Joanna M. Setchell: Affiliation:
Durham University

Book contents

Get access

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

data preparation data analysis null hypothesis statistical testing effect sizes confidence intervals

Type: Chapter
Information: Studying Primates
How to Design, Conduct and Report Primatological Research
, pp. 263 - 270

DOI: https://doi.org/10.1017/9781108368513.022 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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