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15 - Planning Data Analysis

Published online by Cambridge University Press:  19 September 2019

Joanna M. Setchell
Joanna M. Setchell
Affiliation:
Durham University
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Summary

We use statistical analyses to test our predictions using the measures we collect for our sample. Like all aspects of study design, we need to think carefully about our choice of analytical approach. Planning our data analysis in detail, before we collect your data, helps to determine what data we need to collect. It is very common to rush past the analysis plan and dive straight into collecting data. This is partly because statistics are not intuitive and can be intimidating. However, statistical analysis is an integral part of study design. We must understand statistics to understand the strengths, limitations, and potential biases of any research. This may seem daunting, but our understanding of statistics determines the quality of a study. The more we think about this now, the better our study will be. I begin this chapter with how to determine what sort of analyses we need and the need to consult a statistician when we design a study. Next, I cover problems associated with multiple testing and assessing multiple predictor variables. I explain how to prepare an analysis plan and suggest pre-registration.

Keywords

data analysisstatisticsstatistical analysisnull hypothesis statistical testingp valuepre-registration
Type
Chapter
Information
Studying Primates
How to Design, Conduct and Report Primatological Research
 , pp. 185 - 206
Publisher: Cambridge University Press
Print publication year: 2019

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References

15.13 Further Reading

Barnard, C, Gilbert, F, McGregor, P. 2017. Asking Questions in Biology: A Guide to Hypothesis Testing, Experimental Design and Presentation in Practical Work and Research Projects. 5th edn. Harlow, Essex: Benjamin Cummings. Chapter 3 covers statistical analysis and hypothesis testing. Also includes quick test-finders at the end of the book, linking statistical tests to study design. Recommends step-wise regression but don’t follow this advice.Google Scholar
Beckerman, A, Petchey, O, Childs, D. 2017. Getting Started with R. Oxford: Oxford University Press. An introductory guide to the standard software for statistical analyses and graphical presentation of data. Includes how to import, explore, plot, and analyse data.Google Scholar
Burnham, KP, Anderson, DR. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. 2nd edn. New York: Springer-Verlag. An introduction to information-theoretic approaches to model selection and multi-model inference.Google Scholar
Darlington, RB, Smulders, TV. 2001. Problems with residual analysis. Animal Behaviour 62: 599–602. https://doi.org/10.1006/anbe.2001.1806. Explains sources of bias in residual analysis, and recommends multiple regression.Google Scholar
Field, A, Hole, G. 2003. How to Design and Report Experiments. London: Sage Publications Ltd. Part 2 covers analysing and interpreting data.Google Scholar
Field, A, Miles, J, Field, Z. 2012. Discovering Statistics Using R. SAGE Publications Ltd. Includes the logic behind tests, how to do them, and how to report the results, with worked examples. Uses theory for what I term a hypothesis and hypothesis for what I term predictions.Google Scholar
Freckleton, RP. 2002. On the misuse of residuals in ecology: regression of residuals vs. multiple regression. Journal of Animal Ecology 71: 542–545. https://doi.org/10.1046/j.1365-2656.2002.00618.x. Explains why we shouldn’t treat the residuals from regression as data in further analysis.CrossRefGoogle Scholar
Garamszegi, LZ. 2011. Special Issue on ‘Model selection, multimodel inference and information-theoretic approaches in behavioural ecology’. Behavioral Ecology and Sociobiology 65: 1–116. A collection of articles from authors with different viewpoints on how to analyse data with multiple predictors. Essential reading if you intend to use an information-theory approach.CrossRefGoogle Scholar
Grueber, CE, Nakagawa, S, Laws, RJ, Jamieson, IG. 2011. Multimodel inference in ecology and evolution: Challenges and solutions. Journal of Evolutionary Biology 24: 699–711. https://doi.org/10.1111/j.1420-9101.2010.02210.x. An overview of multi-model inference, common challenges faced when using the information theoretic approach and potential solutions where they exist.Google Scholar
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Janson, C. 2012. Reconciling rigor and range: observations, experiments, and quasi-experiments in field primatology. International Journal of Primatology 33: 520–541. https://doi.org/10.1007/s10764–011-9550-7. Includes the use of random-effects models to account for repeated and uneven sampling of the same individuals.CrossRefGoogle Scholar
Kronmal, RA. 1993. Spurious correlation and the fallacy of the ratio standard revisited Journal of the Royal Statistical Society. Series A (Statistics in Society). 156: 379–392. http://dx.doi.org/10.2307/2983064. Shows that using ratios in regression analyses can lead to incorrect or misleading inferences.CrossRefGoogle Scholar
Kroodsma, DE, Byers, BE, Goodale, E, Johnson, S, Liu, W-C. 2001. Pseudoreplication in playback experiments, revisited a decade later. Animal Behaviour 61: 1029–1033. https://doi.org/10.1006/anbe.2000.1676. Examines the issue of pseudo-replication in playback experiments and the need for multiple stimuli to represent a class of stimuli.CrossRefGoogle Scholar
Mangiafico, SS. 2015. An R Companion for the Handbook of Biological Statistics, version 1.3.2. rcompanion.org/rcompanion/. Pdf version: rcompanion.org/documents/RCompanionBioStatistics.pdf. Notes on getting started in R, and R code for many of the examples given in McDonald’s Handbook of Biological Statistics (below).Google Scholar
McDonald, JH. 2014. Handbook of Biological Statistics. 3rd edn. Baltimore, MD: Sparky House Publishing. Available free online at http://biostathandbook.com/ and as a pdf. A very useful introduction to statistics, including links to spreadsheets to calculate simple tests.Google Scholar
McElreath, R. 2015. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Boca Raton, FL: CRC Press. An excellent, pragmatic introduction to the reasoning underlying Bayesian inference, from basic probability to generalised linear multilevel modelling. Recorded lectures available online.Google Scholar
McGregor, PK. 2000. Playback experiments: Design and analysis. Acta Ethologica 3: 3–8. https://doi.org/10.1007/s102110000023. Examines issues in the design of playback experiments, including pseudoreplication.Google Scholar
Mundry, R, Nunn, R. 2009. Stepwise model fitting and statistical inference: Turning noise into signal pollution. American Naturalist 173: 119–123. https://doi.org/10.1086/593303. Shows why we should not use significance tests based on stepwise procedures.Google Scholar
Mundry, R, Sommer, C. 2007. Discriminant function analysis with non-independent data: Consequences and an alternative. Animal Behaviour 74: 965–976. https://doi.org/10.1016/j.anbehav.2006.12.028. Explains that including non-independent data in a discriminant function analysis yields incorrect results, and provides a permutation-based test that copes with such datasets.CrossRefGoogle 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. Includes the idea of a decision tree defining a sequence of tests and decision rules at each stage of analysis.Google Scholar
Ruxton, GB, Neuhäuser, M. 2010. When should we use one-tailed hypothesis testing? Methods in Ecology and Evolution 1: 114–117. https://doi.org/10.1111/j.2041-210X.2010.00014.x. Highlights how one-tailed tests are often used without clear justification.Google Scholar
Stamp, Dawkins M. 2007. Observing Animal Behaviour: Design and Analysis of Quantitative Data. Oxford: Oxford University Press. Chapter 4 addresses independence of data and matching each case to itself.Google Scholar
Waller, B, Warmelink, L, Liebal, K, Micheletta, , Slocombe, KE. 2013. Pseudoreplication: A widespread problem in primate communication research. Animal Behaviour 86: 483–488. https://doi.org/10.1016/j.anbehav.2013.05.038. Highlights the problem of pseudoreplication in primate communication studies.Google Scholar

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