Improving statistical literacy

Improving statistical literacy

This section is intended to bring together resources targeted towards improved statistical literacy throughout the NIHR.  There is much already on the internet targeted towards better understanding of statistics but efforts within this group will focus upon bringing these together in a readily located place.

Students 4 Best Evidence has a website with a library of resources that you may find of interest (one general, and one with nursing resources).  You can find the libraries here and here.

These learning resources haven’t been formally evaluated (with the exception of Know Your Chances, which has been shown in two randomized trials to improve peoples’ understanding of risk in the context of health care choices). However, you will see a comment attached to most of the resources, where one of our working group members has reviewed the resource and given their personal thoughts’.

Books

  • An Introduction to Medical Statistics by Martin Bland

“Good introductory text – accompanying workbook is great and contains answers.”

  • Essential Medical Statistics (Second edition), by Betty Kirkwood and Jonathan A.C. Sterne

“Good introductory text – helpful for those who don’t like mathematical formulas.”

  • Oxford Handbook of Medical Statistics by Janet Peacock and Philip Peacock

“Excellent book covering a wide variety of topics. It’s always the first book I pick up when I want to double check something.”

  • Statistical Questions in Evidence Based Medicine by Janet Peacock and Martin Bland

“Excellent book that encourages the reader to critically appraise published papers and contains answers to the questions. It’s also a good revision tool.”

  • Practical Statistics for Medical Research by Doug Altman
  • Medical Statistics at a Glance, Second edition by Aviva Petrie and Caroline Sabin

“Good introductory text and the flowchart at the back of the book is very helpful!”

  • Multilevel and longitudinal modelling using Stata (Volumes I and II, Third Edition) – by Sophia Rabe-Hesketh & Anders Skrondal

“A great text for explaining hierarchical modelling theory, as well as thorough interpretation of the code and output.”

  • Negative Binomial Regression (Second edition), by Joseph M. Hilbe

“Best text for negative binomial regression it includes detailed appendices which contain excellent interpretation of interactions.”

  • Discovering Structural Equation Modeling Using Stata, by Alan C. Acock

“A good first guide into the world of Structural equation Modelling. Should ideally be used in conjunction with another introductory text as its main focus is applying the techniques.”

Other helpful links

“A book about how to understand medical health information through statistics.”

  • Cochrane Training website. Features a range of learning resources and workshops on statistics (among many other topics). A simple search e.g. ‘statistics’ should bring up relevant materials. A number of these are open access.

“For researchers in eyes and vision, there is the series that has been put together by the Ophthalmic Statistics group (please look at the Ophthalmology Research section).”

“I finally understand the difference between MCAR and MAR after reading this paper! The layout is different to anything I’ve seen in the published literature which makes it very easy to read and approachable.”

“Explains latent class trajectory analysis from first principles and includes relevant published examples.”

“Contains excellent annotated output, along with the relevant code, for various statistical techniques. The annotated output comes from a variety of statistical analysis software such as Stata, SAS, SPSS, Mplus and R.”

If you have come across a particularly helpful statistical resource, please do get in touch and if you would like to participate in these endeavours please contact Catey Bunce (King’s College London) at nihr-stats@kcl.ac.uk.

Section leaders

Group Co-leader, Improving Statistical Literacy

Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford.

Group Co-leader, Improving Statistical Literacy

Centre for Biostatistics, University of Manchester

Working group members

University of Oxford

University of Sheffield

King's College London

King's College London

Centre for Biostatistics, University of Manchester

King's College London

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