Monthly Archives: October 2017

Practical Guide: Logistic Regression

Posted on by

Hilbe, J. M. (2015) Practical Guide to Logistic Regression

This short book shows the reader how to model a binary response variable using basic logistic regression models – and despite its modest size, Joseph M. Hilbe manages to introduce the reader to logistic models with single or multiple predictors as well as to grouped and Bayesian logistic regression.

Hilbe suggests that the book would be appropriate for someone who has completed a basic course in statistics which includes linear regression. I would agree with this assessment, though I would also recommend working through an introductory tutorial on R. That said, this book is written in an exceptionally clear style which means that the reader can expect a treatment of the subject which is concise but comprehensible.

An additional selling point of this text is that it introduces new R functions which can be applied in one’s own work, as well as equivalent SAS and Stata code. The provision of complete code in the book and on a dedicated website will also be of benefit to readers who wish to spend more time learning about logistic regression models than hacking code.

Indeed, the emphasis on understanding logistic regression modelling rather than on the mechanistic application of techniques is one of the great strengths of the book. Anyone who reads this book will therefore feel that they have a good understanding of this subject which can be consolidated both by analysis of their own data and by further reading.

Review originally published in Reviews. Significance,13:2 45. doi: 10.1111/j.1740-9713.2016.00885.x

Social Physics: A New Science

Posted on by

Pentland, A. (2014) Social Physics: How Good Ideas Spread  – the Lessons from a New Science

Alex Pentland’s book is a hugely readable introduction to “social physics”, which the author defines “as a quantitative social science that describes reliable, mathematical connections between information and idea flow on the one hand and people’s behaviour on the other”. In contradistinction to what the author defines as conventional “individual-centric economic and policy thinking”, Pentland suggests that the primary drivers of cultural evolution in our wired world are “social learning” and “social pressure”.

Pentland entertainingly describes a range of studies which he and colleagues have conducted that are both interesting and counterintuitive. He shows, for example, how equal “conversational turn-taking” is the most important factor in predicting “group intelligence”. Other studies focus on trading and the determinants of political opinion. Indeed, there seems to be nothing which is outside of the purview of social physics.

But Pentland’s enthusiasm for his subject carries an overtone of hubris. For Pentland, constructs like “market”, “class” and “capital” should be replaced by the concepts he outlines in the book. Moreover, he gives a very partial interpretation of history since the Enlightenment, which is puzzling because he simultaneously extols the virtues of Adam Smith and John Locke while suggesting that conventional economic concepts are redundant.

In order to gain a more nuanced view of what drives cultural, social and economic evolution, my advice would be to imagine Pentland in a dialogue with economists, historians, sociologists and philosophers and then to form your own view of the truth of the claims made in this book.

Review originally published in Reviews. Significance, 12:6 45. doi: 10.1111/j.1740-9713.2015.00871.x

SSD for R and Single-Subject Data

Posted on by

Auerbach, C., Zeitlin, W. (2014) SSD for R: An R Package for Analyzing Single-Subject Data

This work is short but, in spite of its brevity, Charles Auerbach and Wendy Zeitlin’s book describes how to analyse single-subject data using their own package, SSD for R. They introduce its functions as well as providing advice on how to analyse baseline and intervention phase data.

I thought that their discussion of serial dependency was particularly well done, as was their emphasis on how to use SSD for R to visualise data. Other chapters provide introductions to statistical testing and to the analysis of group data.

Readers should note that the book does not deal with single-subject methodology in any depth, so additional resources will be needed in order to make best use of the package. Fortunately, the authors include useful references for those who need information on specific research designs.

R newbies may need to read an introductory R text as the book’s scope is understandably restricted to providing information about the package. But Auerbach and Zeitlin write well and the content does not demand much in the way of prior statistical knowledge or IT skills.

Statisticians may not need to avail themselves of this book, but practitioners who are working in applied disciplines such as social work, psychology and medicine will find it very appealing.

Review originally published in Reviews. Significance, 12:4 45. doi: 10.1111/j.1740-9713.2015.00846.x

The Wellbeing of Nations

Posted on by

Allin, P., Hand, D.J. (2014) The Wellbeing of Nations: Meaning, Motive and Measurement 

This book shows how it is possible to measure national wellbeing, as well as explaining the motivation for doing so. With a title which pays homage to Adam Smith’s classic, The Wealth of Nations, Allin and Hand explain why it is important to move beyond economic measures like GDP in order to measure wellbeing – an objective in which they succeed admirably.

By drawing on research from disciplines as diverse as philosophy, economics, psychology, social policy and journalism, the authors convincingly argue that one can measure wellbeing. Indeed, their assessment is a welcome antidote to the scepticism of those who believe that economic measures are all that matter.

One might imagine that this book will primarily appeal to official statisticians, who may be tasked with collecting national wellbeing data, but such a view would be unwarranted.

Admittedly, there is much discussion of the role of national statistics offices, and much of the book seems to be a dialogue between the authors and prominent theorists, with the recommendations of the Stiglitz, Sen and Fitoussi Commission being particularly noteworthy throughout.

However, this book will appeal to a broad audience. Although there are brief discussions of technical topics like measurement theory, the book will be useful to researchers across a range of disciplines and the interested general reader.

Review originally published in Reviews. Significance, 12:3 44{45. doi: 10.1111/j.1740-9713.2015.00833.x

Using R for Introductory Statistics

Posted on by

Versani, J (2013) Using R for Introductory Statistics (Second Edition)

This book has a laudable aim: to introduce R and topics from an introductory statistics curriculum to students “outside of a classroom environment”. Now in its second edition, the book introduces the reader to exploratory data analysis and manipulation, statistical inference and statistical models. Particular attention is given to thoroughly learning base R before extending R’s capabilities with packages.

Author John Verzani includes information on computationally intensive approaches and manages to explain these topics with interesting, topical and challenging examples. The text includes a plethora of exercises which encourage the reader to test their understanding of the material as well as a useful appendix on R programming and a valuable bibliography.

Although informative, I don’t think this text will be useful for readers without any previous exposure to either statistical computing or statistics. The text does begin simply enough, but my impression is that the reader will need to refer to additional resources. I’m therefore not convinced by claims that the book may be used without a teacher. Indeed, the fact that the solutions to exercises are only available to those who adopt the book as a course text suggests that the book is intended for use by university teachers rather than autodidacts.

In short, a stimulating read for the classroom-based student, but too challenging for a neophyte learner studying at home.

Review originally published in Reviews. Significance, 12:2 44{45. doi: 10.1111/j.1740-9713.2015.00818.x