Big Data is not only about data: The two cultures of modelling

The contribution of Big Data to social science is not limited to data availability but includes the introduction of analytical approaches that have been developed in computer science, and in particular in machine learning. This brings about a new ‘culture’ of statistical modelling that bears considerable potential for the social scientist. This argument is illustrated with a brief discussion of model-based recursive partitioning which can bridge the theory and data-driven approach. Such a method is an example of how this new approach can help revise models that work for the full dataset: it can be used for evaluating different models, a traditional weakness of the ‘traditional’ statistical approach used in social science.

[1]  Roger Burrows,et al.  The Coming Crisis of Empirical Sociology , 2007, Sociology.

[2]  Eliot R. Smith,et al.  Agent-Based Modeling: A New Approach for Theory Building in Social Psychology , 2007, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[3]  Nicola Perra,et al.  Social Phenomena: From Data Analysis to Models , 2015 .

[4]  Trevor Hastie,et al.  Computer Age Statistical Inference: Algorithms, Evidence, and Data Science , 2016 .

[5]  C. Hamlin,et al.  Ways of Knowing: A New History of Science, Technology and Medicine , 2001 .

[6]  Lada A. Adamic,et al.  Computational Social Science , 2009, Science.

[7]  Albert-Lszl Barabsi,et al.  Network Science , 2016, Encyclopedia of Big Data.

[8]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[9]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001, Statistical Science.

[10]  Giuseppe Porro,et al.  Measuring Social Well Being in The Big Data Era: Asking or Listening? , 2015, ArXiv.

[11]  Ralph Schroeder,et al.  Big Data and the brave new world of social media research , 2014, Big Data Soc..

[12]  R. Michael Alvarez,et al.  Computational Social Science: Discovery and Prediction , 2016, Analytical Methods for Social Research.

[13]  A. Pentland,et al.  Computational Social Science , 2009, Science.

[14]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[15]  Richard A. Berk,et al.  An Introduction to Ensemble Methods for Data Analysis , 2004 .

[16]  Gabrielle Durepos Reassembling the Social: An Introduction to Actor‐Network‐Theory , 2008 .

[17]  K. Hornik,et al.  Model-Based Recursive Partitioning , 2008 .

[18]  Roger Burrows,et al.  After the crisis? Big Data and the methodological challenges of empirical sociology , 2014 .

[19]  D. Hand,et al.  Local Versus Global Models for Classification Problems , 2003 .

[20]  R. Kitchin,et al.  Big Data, new epistemologies and paradigm shifts , 2014, Big Data Soc..

[21]  Claus Boyens,et al.  Handbook of Computational Statistics , 2005 .

[22]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .

[23]  Claudio Cioffi-Revilla,et al.  Introduction to Computational Social Science: Principles and Applications , 2017 .

[24]  K. Hornik,et al.  Generalized M‐fluctuation tests for parameter instability , 2007 .

[25]  Andrew Abbott,et al.  Time Matters: On Theory and Method , 2001 .

[26]  A. Tversky,et al.  Choices, Values, and Frames , 2000 .

[27]  S. Halford,et al.  Big Data: Methodological Challenges and Approaches for Sociological Analysis , 2014 .

[28]  J. Overhage,et al.  Sorting Things Out: Classification and Its Consequences , 2001, Annals of Internal Medicine.