Essential readings

Here is a growing list of some of my favourite articles on data analysis; mostly articles I recommend to students or when reviewing or editing papers.

Review & position papers

Tukey, J.W. (1969) Analyzing Data – Sanctification or Detective Work? American Psychologist, 24, 83-91. [inspiring thoughts from a true visionary, hinting at what will become data science]

Button, K.S., Ioannidis, J.P., Mokrysz, C., Nosek, B.A., Flint, J., Robinson, E.S. & Munafo, M.R. (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nature reviews. Neuroscience, 14, 365-376.

Colquhoun, D. (2014) An investigation of the false discovery rate and the misinterpretation of p-values. R Soc Open Sci, 1, 140216.

Forstmeier, W., Wagenmakers, E.J. & Parker, T.H. (2016) Detecting and avoiding likely false-positive findings – a practical guide. Biol Rev Camb Philos Soc. [best summary of statistical, experimental and experimenter issues leading to false positives]

Problems with frequentist statistics

Wagenmakers, E.J. (2007) A practical solution to the pervasive problems of p values. Psychonomic bulletin & review, 14, 779-804. [one of the best summaries of problems with p values]

Solutions to common issues

Jaeger, T.F. (2008) Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models. J Mem Lang, 59, 434-446.

MacCallum, R.C., Zhang, S., Preacher, K.J. & Rucker, D.D. (2002) On the practice of dichotomization of quantitative variables. Psychological Methods, 7, 19-40.

Data visualisation

Allen, E.A., Erhardt, E.B. & Calhoun, V.D. (2012) Data visualization in the neurosciences: overcoming the curse of dimensionality. Neuron, 74, 603-608.

Anscombe, F.J. (1973) Graphs in Statistical Analysis. Am Stat, 27, 17-21.

Weissgerber, T.L., Milic, N.M., Winham, S.J. & Garovic, V.D. (2015) Beyond bar and line graphs: time for a new data presentation paradigm. PLoS Biol, 13, e1002128.

Weissgerber, T.L., Garovic, V.D., Winham, S.J., Milic, N.M. & Prager, E.M. (2016) Transparent reporting for reproducible science. J Neurosci Res, 94, 859-864.

Wilcox, R.R. (2006) Graphical methods for assessing effect size: Some alternatives to Cohen’s d. Journal of Experimental Education, 74, 353-367.

Data description

DeCarlo, L.T. (1997) On the meaning and use of kurtosis. Psychol. Meth., 2, 292-307.

Robust estimation

Hubert, M., Rousseeuw, P. J., & Van Aelst, S. (2008). High-breakdown robust multivariate methods. Statistical Science, 92-119. [includes robust alternatives to the Mahalanobis distance]

Wilcox, R.R. & Keselman, H.J. (2003) Modern Robust Data Analysis Methods: Measures of Central Tendency. Psychological Methods, 8, 254-274. [introduction to robust estimation – in particular how to deal with skewness and outliers]

 

 

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s