My review of David Abernathy’s book Using Geodata and Geolocation in the Social Sciences has been published in the February 2018 issue of Significance magazine – the magazine of the Royal Statistical Society and the American Statistical Association. Why not take a look?
Hammersley, M. (2014) The limits of social science. Causal explanation and value relevance
In this short book, Hammersley argues for a social science which eschews grand theorising in favour of the explanation of social phenomena. Drawing inspiration from Max Weber and referring to a range of social theorists and philosophers, Hammersley encourages social scientists to re-think what they are actually doing as researchers in order to create a social science which generates knowledge which is both reliable and valid. Some readers might, of course, reply that there are no problems with social research as an intellectual endeavour, but Hammersley’s purpose seems to be to awake us from our slumbers. This is a task in which he partially succeeds. Hammersley is not, for example, opposed to causal analysis in the social sciences, but argues that we should raise our game by adopting ‘within-case and cross-case analysis’. He also prioritises explanation over theorising with the proviso that ‘all purpose’ explanations are not possible because explanations are ‘always answers to particular questions’. He also argues that value conclusions cannot be derived from evidence, and offers convincing arguments why this might be the case. The consequence of Hammersley’s position is that social research should be limited to making ‘factual’ statements rather than ‘value’ claims. Although much of the book is theoretical, the author grounds his views by referring to social mobility research and to work on the English riots of 2011.
What I most enjoyed about this book is that Hammersley encourages the reader to think hard about social research practice. He is, for example, unconvinced by the view that there is a direct relationship between research and policy outcomes. On the contrary, he says that the relationship is ‘highly mediated and contingent’. Moreover, he recognises that different social science disciplines employ different methods of explanation. One has only to think of the very different approaches of the experimental psychologist and of the historian to appreciate that he has a point. But such explanatory pluralism in the social sciences has a disturbing consequence. If there is no agreed threshold which all social scientists have to meet in order to generate valid and reliable knowledge, then how do these disciplines differ from vocations like investigative or data journalism? In addition, Hammersley draws a sharp distinction between ‘facts’ which are of interest to the social scientist and ‘value claims’ which should be of interest to policymakers and think tanks. If true, it is very hard to see how social researchers can make the case for funding their work in a cultural environment which does not recognise that knowledge has value in itself. Hammersley recognises this point but does not offer any solutions.
This book is not a paean to social science as it is currently practised and will be, to use Hammersley’s own word, a ‘deflationary’ read for some. If, however, you want to read something which may question your preconceptions, this book is a good place to begin.
Review originally published in Research Matters, December 2015
Charmaz, K. (2014) Constructing Grounded Theory
If you need a clear introduction to grounded theory, then you will find it here. Charmaz describes grounded theory’s genesis, and explains how to code, write and sort memos and engage in theoretical sampling. This second edition includes new material on interviewing and symbolic interactionism.
She supports what she is saying by referring to her own research and that of others working in diverse fields. She manages to convey the excitement of conducting a grounded theory study which will, I’m sure, make readers think how they can apply her techniques. Information is easy to locate as main points are presented throughout the text. This means that the reader can either read the text linearly or source what they want later.
It succeeds as a book about methods but it is much more than this. Charmaz skilfully situates grounded theory within its historical context by showing how Glaser and Strauss – the pioneers of this approach – were influenced by the ‘Columbia University positivism’ of Paul Lazarsfeld and Robert Merton and the ‘Chicago school pragmatism and field research’ of sociologists such as Herbert Blumer. She devotes an entire chapter to symbolic interactionism – a ‘theoretical perspective that views human actions as constructing self, situation and society’. She also shows how her own ‘constructivist’ approach to grounded theory contrasts with that of ‘objectivist’ theorists who adopt the position of a neutral observer and consider that they are studying worlds which are entirely external to themselves. For Charmaz, meaning does not exclusively inhere in the data, which is a position which may be troubling to those who assume a clear separation between ‘facts’ and ‘values’.
Although convinced that symbolic interactionism and grounded theory are a ‘theory-method package’, she readily concedes that grounded theory may be used with other theoretical perspectives. As she would say, theoretical ‘purity fosters preconception’. Although one might think that her meditations on ontology and epistemology may be heavy going, her writing is simple and informal, and she always shows how her theoretical views connect to the practical business of doing research. These sections require careful study but are the most rewarding.
This is an excellent book. It is easy to read, gives lots of practical advice and is quite profound. If you are serious about studying the conceptual universes and the interior worlds of research participants in a way which recognises that the researcher is intimately involved in the construction and analysis of data, this is a book which will make you re-think how you conduct research.
Review originally published in Research Matters, September 2015
Corti, L., Van den Eynden, V., Bishop, L., Woollard, M. (2014) Managing and sharing research data: a guide to good practice
This is a guide to best practice for researchers who want to supplement existing data management skills and those who want to develop data management skills for the first time.
Written by members of the UK’s Data Archive, the authors describe those skills which will be needed to ensure that data is open and reusable, and collected, stored and shared in ways which respect ethical practice and relevant legislation. The authors also make a convincing case for why data sharing is beneficial, and present counter arguments to some of the more common reasons which are given for not sharing data.
The authors introduce the reader to the research data life cycle and approaches to research data management planning as well as referring to specific skills and software which the researcher could usefully acquire. There are, for example, very clear introductions to version control systems and to the encryption of sensitive data using open source software. I particularly enjoyed the chapter about formatting and organising data, which contains a section on how to organise data files logically. The book is written in very clear prose making the more technical topics accessible to the non-specialist. Moreover, the text is supplemented by case studies, exercises and useful references as well as a website.
The authors manage to successfully combine a discussion of abstract topics such as metadata with grounded examples of how these topics could be applied in practice. For the purposes of this review, I read the text sequentially but I think that one could usefully refer to particular chapters or sections in order to fill specific knowledge gaps. Indeed, I found myself repeatedly returning to particular sections of the text to reinforce my understanding of key concepts.
To conclude, this book fills a gap in the market and will, I’m sure, be read by researchers in any discipline where data management skills are needed. I would recommend this book without hesitation. Well written, informative and, with its commitment to transparency and data sharing, commendable.
Review originally published in Research Matters, March 2015
Hill, C.A., Dean E., Murphy J. (2014) Social media, sociality and survey research
This book has been written because of the writers’ awareness that declining response rates and inadequate sampling frames present a challenge to all social researchers who wish to collect survey data which is ‘accurate, timely and accessible’. Primarily written by researchers from RTI International, the book is a compendium of chapters which describe how the researchers have incorporated social media data into their research projects. The authors suggest that the book is intended for survey and market researchers, as well as students in survey methodology and market research and I agree that this book will be useful for this constituency.
The writers don’t argue for the replacement of the more familiar survey modes but suggest that postal, web-based and telephone surveys can be supplemented by the imaginative use of social media. Indeed, they recognise that social media data has its own limitations and does not fit easily into designs where precise estimates are needed.
The writers define social media as ‘a collection of websites and web-based systems that allow for mass interaction, conversation, and sharing among members of a network’ and refer to web 2.0 with its user generated content. The book covers a diverse range of topics which include how to predict sentiments and emotions using consistent methods, how to pre-test questionnaires use Skype and Second Life and how to develop innovative research by using social media to collect ideas from large groups of people. There is also a chapter on how to apply the principles of the games designer to market research so that participation in research is more enjoyable.
Athough very wide ranging, the book retains its coherence because it is organised around the idea of a ‘sociality hierarchy’ which can be broken down into broadcast, conversational and community levels. The authors also consistently avoid the use of technical language and include a useful set of references – many of which are downloadable – at the end of each chapter.
This book is a must read for any researcher who wants to make use of social media data; it is incisive, instructive, easy to read and, above all, fascinating.
Review originally published in Research Matters, June 2014
Borgatti, S.P., Everett, M.G. and Johnson J.C. (2013) Analyzing social networks
This book takes the reader on a tour of key theoretical concepts in social network analysis. It is divided into four sections: introduction, research methods, core concepts and measures and a final section which deals with what the writers describe as ‘three cross-cutting chapters’ on ‘affiliation type data’, ‘large networks’ and ‘ego network data’. Although primarily theoretical, the book refers to interesting empirical work across the social sciences and health care in order to illustrate core concepts. It introduces readers to software – UCINET and NetDraw – which they can use to analyse and visualise network data but refers to a dedicated website for readers who require a software tutorial.
There is much to commend in this book. The authors provide a clear introduction to graph theory and matrix algebra for non-mathematicians. There is also an interesting introduction to core concepts like ‘centrality’, ‘sub-group’ and ‘equivalence’ and a fascinating discussion of how hypothesis testing is possible with network data when the assumptions of standard inferential tests are violated. The authors also provide invaluable advice on how best to lay out network diagrams in order to make interpretation easier.
However, I think that how information is presented may need to be reviewed. The authors assume that readers are familiar with research terminology without necessarily defining their terms. Although this is a reasonable assumption if the book is for established researchers, beginners may need to refer to an introductory research methods textbook in order to take full advantage of the material. Borgatti et al. also state that a sequential reading of each chapter isn’t needed although this suggestion doesn’t work for readers who assume that a book will begin with straightforward material before moving to advanced topics. A glossary would be useful.
This is an informative book for established social researchers with some prior exposure to social network analysis. Aspirant social network analysts may find the book a little too advanced.
Review originally published in Research Matters, March 2014
Field, A., Miles J., Field, Z. (2012) Discovering statistics using R
This book teaches statistics by using R – the free statistical environment and programming language. It will be of use to undergraduate and postgraduate students and professional researchers across the social sciences, including material which ranges from the introductory to the advanced. Divided into four levels of difficulty with ‘Level 1’ representing introductory material and ‘Level 4’ the most advanced material, it may be read from beginning to end or with reference to particular techniques. An understanding of the advanced material may require knowing the material in earlier chapters. There is a comprehensive glossary of specialised terms and a selection of statistical tables in the appendix. There is also material on the publisher’s companion website and on the principal author’s own web pages.
The main strength of this book is that it presents a lot of information in an accessible, engaging and irreverent way. The style is informal with interesting excursions into the history of statistics and psychology. There are entertaining references to research papers which illustrate the methods explained, and are also very entertaining. The authors manage to pull off the Herculean task of teaching statistics through the medium of R. This is an achievement when one considers that R can be difficult to use for researchers who have never manipulated data from the command line. Another plus point is that the authors describe how to ‘extend’ R’s capabilities with ‘packages’. This is a massive time saver for any researcher who does not know which package is required in order to extend R’s base system to conduct a particular test. Field et al. also succeed in placing many of the statistical procedures to which they allude within the framework of the ‘general linear model’ giving the book a sense of theoretical coherence.
But I think that the book would have benefited from an explanation of how R fits into the wider ‘tool chain’ of public domain programs which can be used to produce a publication-ready paper. Moreover, some of the exemplars of R code may not work or may be illustrative of deprecated techniques but the principal author is maintaining an errata file on his own website. Nevertheless, I would recommend this book to students, academics and applied researchers. Although heavily weighted towards the interests of psychological researchers, it would not be too difficult to transfer the techniques to a different area of expertise. All in all, an invaluable resource.
Review originally published in Research Matters, December 2013