Tools of the Trade: Resources for Social Scientists

As part of our “Tools of the Trade” blog series, we’re highlighting resources for social science scholars and educators to aid in your research, writing, and prep work this summer. Look no further for a refresher of methods that you can use in your own work or share with your students.

How to Think Critically

Critical Thinking: Tools for Evaluating Research by Peter Nardi

This book prepares readers to thoughtfully interpret information and develop a sophisticated understanding of our increasingly complex and multi-mediated world. Peter M. Nardi’s approach helps students sharpen critical thinking skills and improve analytical reasoning, enabling them to ward off gullibility, develop insightful skepticism, and ask the right questions about material online, in the mass media, or in scholarly publications. Students will learn to understand common errors in thinking; create reliable and valid research methodologies; understand social science concepts needed to make sense of popular and academic claims; and communicate, apply, and integrate the methods learned in both research and daily life.

Stat-Spotting: A Field Guide to Identifying Dubious Data, Updated and Expanded by Joel Best

Are four million women really battered to death by their husbands or boyfriends each year? Is methamphetamine our number one drug problem today? Alarming statistics bombard our daily lives. But all too often, even the most respected publications present numbers that are miscalculated, misinterpreted, hyped, or simply misleading. This new edition contains revised benchmark statistics, updated resources, and a new section on the rhetorical uses of statistics, complete with new problems to be spotted and new examples illustrating those problems. Joel Best’s bestseller exposes questionable uses of statistics and guides the reader toward becoming a more critical, savvy consumer of news, information, and data. See also Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists, Updated Edition.


Data Mining for the Social Sciences: An Introduction by Paul Attewell and David Monaghan

We live in a world of big data: the amount of information collected on human behavior is staggering, and exponentially greater than at any time in the past. Powerful algorithms can churn through seas of data to uncover patterns. This book discusses how data mining substantially differs from conventional statistical modeling. The authors empower social scientists to tap into these new resources and incorporate data mining methodologies in their analytical toolkits. This book demystifies the process by describing the diverse set of techniques available, discussing the strengths and weaknesses of various approaches, and giving practical demonstrations of how to carry out analyses using tools in various statistical software packages.

The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies, With a New Introduction by Charles C. Ragin

The Comparative Method proposes a synthetic strategy, based on an application of Boolean algebra, that combines the strengths of both qualitative and quantitative sociology. Elegantly accessible and germane to the work of all the social sciences, and now updated with a new introduction, this book will continue to garner interest, debate, and praise.

“While not everyone will agree, all will learn from this book. The result will be to intensify the dialogue between theory and evidence in comparative research, furthering a fruitful symbiosis of ‘quantitative’ and ‘qualitative’ methods.”—Theda Skocpol, Harvard University

Time Series Analysis in the Social Sciences: The Fundamentals by Youseop Shin

 This book is a practical and highly readable, focusing on fundamental elements of time series analysis that social scientists need to understand so they can employ time series analysis for their research and practice. Through step-by-step explanations and using monthly violent crime rates as case studies, this book explains univariate time series from the preliminary visual analysis through the modeling of seasonality, trends, and residuals, to the evaluation and prediction of estimated models. It also explains smoothing, multiple time series analysis, and interrupted time series analysis. With a wealth of practical advice and supplemental data sets, this flexible and friendly text is suitable for all students and scholars in the social sciences.

Regression Models for Categorical, Count, and Related Variables: An Applied Approach by John P. Hoffmann

Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, and criminologists counting the number of offenses people commit are all interested in outcomes that are not continuous but must measure and analyze these events and phenomena in a discrete manner.

The book addresses logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques.

A companion website includes downloadable versions of all the data sets used in the book.

Presenting Your Data

Principles of Data Management and Presentation by John P. Hoffmann

The world is saturated with data in words, tables, and graphics. Assuming only that students have some familiarity with basic statistics and research methods, this book provides a comprehensive set of principles for understanding and using data as part of a research, including:
• how to narrow a research topic to a specific research question
• how to access and organize data that are useful for answering a research question
• how to use software such as Stata, SPSS, and SAS to manage data
• how to present data so that they convey a clear and effective message

A companion website includes material to enhance the learning experience—specifically statistical software code and the datasets used in the examples, in text format as well as Stata, SPSS, and SAS formats.


ASA Conference: Author Sessions

We’re excited to attend this year’s American Sociological Association conference in Seattle, WA from August 20 – August 23. Below is a list of just some sessions featuring our wonderful UC Press authors! See the full online program schedule at ASA’s site. #ASA2016, #ASA16

And take a look at some of our ASA award-winning authors’ and their titles.

morris 2Aldon Morris, The Scholar Denied: W. E. B. Du Bois and the Birth of Modern Sociology

  • Saturday, 8/20, 12:30 – 2:10pm, Plenary Session: Protesting Racism
  • Monday, 8/22, 2:30 – 4:10pm: Author Meets Critic, The Scholar Denied



Sanyu MojolaSanyu Mojola, Love, Money, and HIV: Becoming a Modern African Woman in the Age of AIDS

  • Saturday, 8/20, 10:20am – 12:10pm, Section on Aging and the Life, Behaving Well: The Transition to Respectable Womanhood in Rural South Africa


John.Iceland.PhotoJohn Iceland, Poverty in America: A Handbook, A Portrait of America: The Demographic Perspective, and Where we Live Now: Immigration and Race in the United States.   

  • Sunday 8/21, 8:30 – 9:30am, Section on Community and Urban Sociology Refereed Roundtable Session, Hispanic Concentrated Poverty in Traditional and New Destinations, 2010-2014


Paul.Attewell.PhotoPaul Attwell, Data Mining for the Social Sciences: An Introduction 

  • Saturday 8/20, 8:30 – 10:10am, Sociology of Higher Education, Class Inequality among College Graduates.
  • Sunday 8/21, 12:30 – 2:10pm, Inequality and Privilege in Education, The Earnings Payoff from Attending a Selective College
  • Monday 8/22, 10:30am – 12:10pm, College Affordability, Who Suffers and Who Benefits from Student Loans.


Joachim J. Savelsberg, Representing Mass Violence: Conflicting Responses to Human Rights Violations in Darfur

  • Sunday 8/21, 10:30am – 12:10pm, Section on Political Sociology: How Political Culture Matters; Collective Memories, Political Culture, and Policy: The Case of Irish Humanitarianism


SoyerheadshotMichaela Soyer, A Dream Denied: Incarceration, Recidivism, and Young Minority Men in America

  • Saturday 8/20, 8:30 – 10:10am, Section on Aging and the Life Course, Exploring Life Course and Network Mechanisms Underlying Prison-based Therapeutic Communities


Data Mining for the Social Sciences

By David Monaghan, co-author of Data Mining for the Social Sciences: An Introduction

This guest post is published in advance of the American Sociological Association conference in Chicago.Check back every day for new posts through the end of the conference on Tuesday, August 25th. 

Many social scientists focus on qualitative methods for their inquiry and analysis. Do you have any examples of how qualitative researchers have employed data mining techniques to assist them in their work? 

Usually, in the social sciences, when we say “quantitative” we mean analysis of numbers, and when we say “qualitative” we mean  the analysis of words (and sometimes images, sound data, etc.)  The fact is that just as our stocks of numeric data have exploded in recent decades, so too do we now have far, far more data pertaining to the social world that is “qualitative” in nature.  One great example is twitter feeds.  Over 300 million people use Twitter at this point worldwide to discuss everything from the details of their personal relationships to politics to media to promoting their own businesses or artistic careers.  Twitter data can be “scraped” and then analyzed, and this is tremendous amount of real-time data on the social world.  This field is, at this point, wide open – we have only just begun to think about how this data might be best analyzed.  The rules that we rely on in standard statistical analyses, which presume that data come from a random sample, clearly won’t hold very well here.  And this is a big-data problem par excellence – with this amount of data, standard “qualitative” interpretive techniques won’t work either.  This is an instance of the worlds of qualitative research and computationally-intensive analytic methods meeting.  And it is certainly not just Twitter – the same can be said of google searches, of Facebook posts, of the massive numbers of books and other texts which have been digitized.

What are some of the most compelling ways you’ve seen social scientists use data mining in their research?

Social scientists, as a whole, have been rather slow to embrace data mining.  In all honesty, social scientists – and sociologists in particular – have spent far more time (and text) discussing the sociological implications of “Big Data” and computationally intensive methods, and the fact that social scientists should be getting into these areas, then they have actually performing 9780520280984useful or interesting analyses.  At this point, the most interesting “social science” work done with data mining methods has been done by computer scientists.  There are probably a number of reasons for this.  Perhaps it is because social scientists are not comfortable with the methods themselves or the software necessary to use them.  To some degree, these methods have been dismissed as unscientific “data dredging”.  It is probably harder to get an article that uses these methods into a top journal, because the norms for how to present these sorts of analyses haven’t really been developed yet.  But I think, most importantly, it is because the type of data we now use is neatly fitted to a certain type of social-science question, and in order to profitably use computationally-intensive methods, we need to be using different sorts of data (particularly data that is very wide or long) and to be asking different sorts of questions.

What do you think are the most important lessons you have learned about data mining that you would like students of sociology to know?

I think there is a lot of mystique surrounding data mining, in the lay public and even among a lot of social scientists.  Data mining methods are discussed as something almost magical, a way of “discovering structure in data” uncovering otherwise hidden knowledge.  At the same time and as a corollary, it is presumed that these methods are very abstract, difficult to understand, difficult to use.  I think the most important thing, first and foremost, is to puncture this mythology.  Data mining methods are not magical ways of automatically uncovering knowledge.  Like traditional techniques, they are computational tools, and what they tell you depends on what you tell them to do.  And they are not particularly hard to understand or inaccessible.  In fact, a fair number of methods – like decision trees or association rule mining, to give examples – in fact use very simple algorithms.  It is just that they perform fairly simple mathematical operations a huger number of times.  And increasingly, software has been developed that makes these methods accessible to  people other than computer scientists.  Our world has, as has been noted ad nauseum, become much more data driven than ever in the past. These sorts of methods are being increasingly applied to analyze the massive stocks of data we find in our possession.  So it is all the more important that people understand them. The good news is, this is very, very possible.

David B. Monaghan is a doctoral candidate in Sociology at the Graduate Center of the City University of New York, and has taught courses on quantitative research methods, demography, and education. His research is focused on the relationship between higher education and social stratification.