Social Physics: A New Science

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

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

Using R for Introductory Statistics

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

Text Mining with ‘tm’

It is possible to identify top level categories in qualitative data analysis by using text mining methods. One can count the frequency of terms or words in a text or texts. Words which occur frequently may be top level classifications or themes.

Text mining involves the creation of a corpus or collection of texts for analysis, some initial work to preprocess the corpus so that punctuation, capitalisation and numbers are removed as well as common words which are, ipso facto, very frequent in any text. A document term matrix is then created where the documents in the corpus are represented by rows and the words by columns. Analysis could then include identification of frequent terms and a ‘frequency of frequencies’ i.e. how many words occur in a corpus at specific frequencies?

For further detail, check out Kailash Awati’s Gentle Introduction to Text Mining with R here and an RStudio resource here which describe how to text mine with R’s tm package. The RStudio link also includes additional links to books on text and data mining as well as material on ‘clustering’ methods.

Both tutorials assume that R is already installed. If this is not the case, go to The R Project for Statistical Computing here and follow the instructions for your system.

R binaries are available for Windows, Mac and Linux distributions.


The R package RQDA may be one alternative for qualitative researchers who do not have access to, or do not wish to use, proprietary CAQDAS software. RQDA allows the user to import text files, create codes and file categories and to visualise file categories with  sociograms.

It’s also possible to run the package from the command line and to export RQDA data to LaTeX.

Further information is available from:

  • the RQDA site;
  • the RQDA User Manual;
  • and Metin Caliskan’s excellent YouTube tutorials.



Research as activism

JustPublics@365 have produced some interesting skills guides for scholars who wish to build an audience for their work beyond academia. Example guides include a Social Media Toolkita report: Engaging Academics and Reimagining Scholarly Communication for the Public Good and thought provoking material on altmetrics.

The site is particularly interesting because of the collaborations which it encourages between scholars, activists and journalists in the pursuit of social justice.

So why not take a look at their resources here ?