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]

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]

Shmueli, Galit. To Explain or to Predict?. Statist. Sci. 25 (2010), no. 3, 289–310. doi:10.1214/10-STS330. https://projecteuclid.org/euclid.ss/1294167961

Fiedler, K. (2011). Voodoo Correlations Are Everywhere—Not Only in Neuroscience. Perspectives on Psychological Science, 6(2), 163–171. https://doi.org/10.1177/1745691611400237

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.

Gelman, A., & Carlin, J. (2014). Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors. Perspectives on Psychological Science, 9(6), 641–651. https://doi.org/10.1177/1745691614551642

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]

Understanding p values

Greenland, S., Senn, S.J., Rothman, K.J. et al. (2016) Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol 31: 337. https://doi.org/10.1007/s10654-016-0149-3

Ronald L. Wasserstein & Nicole A. Lazar (2016) The ASA’s Statement on p-Values: Context, Process, and Purpose, The American Statistician, 70:2, 129-133, DOI: 10.1080/00031305.2016.1154108

A world without mindless dichotomies (p<0.05)

Meehl, P.E. (1997) The Problem Is Epistemology, Not Statistics: Replace Significance Tests by Confidence Intervals and Quantify Accuracy of Risky Numerical Predictions. In: Harlow, L., Mulaik, S.A. and Steiger, J.H., Eds., What If There Were No Significance Tests? Erlbaum, Mahwah, NJ, 393-425.

Cumming, G. (2014). The New Statistics: Why and How. Psychological Science, 25(1), 7–29. https://doi.org/10.1177/0956797613504966

Kruschke, J.K. & Liddell, T.M. (2018) The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychon Bull Rev 25: 178. https://doi.org/10.3758/s13423-016-1221-4

Gelman, A. (2018). The Failure of Null Hypothesis Significance Testing When Studying Incremental Changes, and What to Do About It. Personality and Social Psychology Bulletin, 44(1), 16–23. https://doi.org/10.1177/0146167217729162

Blakeley B. McShane, David Gal, Andrew Gelman, Christian Robert, Jennifer L. Tackett (2018) Abandon Statistical Significance. arXiv [there is no need for arbitrary thresholds, let’s embrace uncertainty!]

Valentin Amrhein​, David Trafimow & Sander Greenland (2018) Inferential statistics as descriptive statistics: there is no replication crisis if we don’t expect replication. PeerJ Preprints [scientific life without zombie thresholds – refreshing]

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.

Sassenhagen, J. & Alday, P.M. (2016) A common misapplication of statistical inference: nuisance control with null-hypothesis significance tests. Brain and Language

Beyond power calculations: planning for precision

Gelman, A. & Carlin, J. (2014) Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors. Perspect Psychol Sci, 9, 641-651.

Maxwell, S.E., Kelley, K. & Rausch, J.R. (2008) Sample size planning for statistical power and accuracy in parameter estimation. Annu Rev Psychol, 59, 537-563.

Peters, G.-J.Y. & Crutzen, R. (2017) Knowing exactly how effective an intervention, treatment, or manipulation is and ensuring that a study replicates: accuracy in parameter estimation as a partial solution to the replication crisis. PsyArXiv. doi:10.31234/osf.io/cjsk2.

Rothman, K.J. & Greenland, S. (2018) Planning Study Size Based on Precision Rather Than Power. Epidemiology, 29, 599-603.

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]

 

 

Advertisements

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 )

Google photo

You are commenting using your Google 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 )

Connecting to %s