Statistical Analysis Resources

Explore our expanding collection of statistical analysis resources designed to support your research efforts and enhance your ability to critically evaluate evidence.

Do you know of a relevant resource that would benefit others? Do you want to showcase your own work in this field? Submit relevant resources to Lucy Pratt and contribute to our collaborative knowledge hub.

Statistical Analysis Resources

• Cheek, C. Rajkumar, How to present statistics in medical journals, Age and Ageing, Volume 43, Issue 3, May 2014, Pages 306–308
  Advice on presenting statistics in papers, including the common pitfalls to avoid.
• Medical Statistics Made Easy
  One to two pages per statistical test, tells you what you need about key statistical principles without the complicated maths explanation. Ideal for non-statisticians who need to understand how statistics are used and applied in medicine and medical research.

• Parametric and Nonparametric: Demystifying the Terms
  Nonparametric statistical procedures rely on no or few assumptions about the shape or parameters of the population distribution from which the sample was drawn.

• Altman D G, Bland J M. Parametric v non-parametric methods for data analysis (BMJ  2009)
  Methods for analysing continuous data fall into two classes, distinguished by whether or not they make assumptions about the distribution of the data.
• Nahm FS. What the P values really tell us. Korean J Pain. 2017 Oct
  To support the significance of a quantitative study’s conclusion, the concept of “statistical significance”, typically assessed with an index referred as P value is commonly used. But what does the P value really tell us? The author of this easy-to-follow paper explains.

• Taherdoost, H. (2017) Determining Sample Size; How to Calculate Survey Sample Size – Determining Sample Size; How to Calculate Survey Sample Size by Hamed Taherdoost :: SSRN
  This study presents a summary of how to calculate the survey sample size in social research and information system research, but this can also be applied to health research.

• Marshall, G. and Jonker, L. (2010) An introduction to descriptive statistics: A review and practical guide – ScienceDirect (accessible via most libraries/universities, behind a paywall)
  This paper explains descriptive statistical concepts, including levels of measurement, measures of central tendency (average), and dispersion (spread) and the concept of normal distribution.

• Marshall, G. and Jonker, L. (2011) An introduction to inferential statistics: A review and practical guide – Radiography (radiographyonline.com) (accessible via most libraries/universities, behind a paywall)
  This second paper looks at common statistical tests that comprise inferential statistics and explains the use of parametric and nonparametric statistics.

• Gilbert, G. E. and Prion, S. (2016) Making Sense of Methods and Measurement: Parametric and Nonparametric Data Analysis – Clinical Simulation In Nursing (nursingsimulation.org) (accessible via most libraries/universities, behind a paywall)
  This paper illustrates the differences between parametric and nonparametric data, offers some suggestions about how to assess a data set for normalcy, and provides examples of common parametric tests and their nonparametric replacements.
• Gisev, N., Bell, J. S. and Chen, T. F. (2013) Interrater agreement and interrater reliability: Key concepts, approaches, and applications – ScienceDirect (accessible via most libraries/universities, behind a paywall)
  This paper highlights the key differences between interrater agreement and interrater reliability and describes the key concepts and approaches to evaluating these.
• Harpe, S. E. (2015) How to analyze Likert and other rating scale data – ScienceDirect (accessible via most libraries/universities, behind a paywall)
  This article identifies the characteristics of a true Likert scale and explain the situations when parametric analytical techniques are potentially appropriate for rating scale data or when nonparametric techniques are preferred.

• Norman, G. (2010) Likert scales, levels of measurement and the “laws” of statistics | Advances in Health Sciences Education (springer.com) (accessible via most libraries/universities, behind a paywall)
  This paper looks at the criticisms of parametric statistics and argues that many of them are unfounded.
• Van Stralen, K. J., Jager, K. J., Zoccali, C. and Dekker, F. W. (2008) Agreement between methods – Kidney International (kidney-international.org)
  This article looks at the comparison between new tests and measurement methods already in use and discusses the differences between correlation coefficient and Bland-Altman plots.

• Van Stralen, K. J., Stel, V. S., Reitsma, J. B., Dekker, F. W., Zoccali, C. and Jager, K. J. (2009) Diagnostic methods I: sensitivity, specificity, and other measures of accuracy – Kidney International (kidney-international.org)
  This paper focuses on the usefulness of diagnostic tests by explaining the different measures of accuracy, the interpretation of test results, and the implementation of a diagnostic strategy.

• O’Leary, S., Lund, M., Ytre-Hauge, T. J., Holm, S. R., Naess, K., Nagelstad Dalland, L., and McPhail, S. M. (2014) Pitfalls in the use of kappa when interpreting agreement between multiple raters in reliability studies – Physiotherapy (physiotherapyjournal.com)
  This article compares different reliability coefficients (exact agreement, and variations of the kappa (generalised, Cohen’s and Prevalence Adjusted and Biased Adjusted (PABAK))).