<img height="1" width="1" style="display:none" src="https://www.facebook.com/tr?id=648880075207035&amp;ev=PageView&amp;noscript=1">

Webinar Recording

Analyzing the 2016 General Social Survey with BayesiaLab's Unsupervised Learning Algorithms

Recorded on February 16, 2018

 

Webinar Materials

Abstract

In this webinar, we explore the high-dimensional relationships between hundreds of variables from the 2016 General Social Survey.[1] Our objective is to employ one of BayesiaLab's Unsupervised Learning algorithms to generate a Bayesian network model that approximates the joint probability distribution of the underlying survey responses. 

Once we have this representation, we can address research questions that specifically require computing the joint probability. In this context, a prototypical—albeit theoretical—issue would be how to best approximate the diversity of many voters through a small number of elected representatives. This translates directly into a variable and data clustering task, which will be at the core of our presentation. Our objective will be to trade off the faithfulness of voter representation with the number of variable/data clusters to be generated.

Workflow to be presented in the webinar:

  • Transform original GSS data in SPSS format with Stat/Transfer 14.
  • Import data, variables labels, and value labels into BayesiaLab.
  • Define variable classes, e.g., demographics, voting behavior, etc.
  • Perform Unsupervised Learning.
  • Optimize Bayesian network model with Structural Coefficient Analysis.
  • Identify Latent Factors through BayesiaLab's Variable Clustering.
  • Cluster voters into segments with Data Clustering (Multi-Net).

[1] Smith, Tom W, Peter Marsden, Michael Hout, and Jibum Kim. General Social Surveys, 1972-2014 [machine-readable data file] /Principal Investigator, Tom W. Smith; Co-Principal Investigator, Peter V. Marsden; Co-Principal Investigator, Michael Hout; Sponsored by National Science Foundation. --NORC ed.-- Chicago: NORC at the University of Chicago [producer]; Storrs, CT: The Roper Center for Public Opinion Research, University of Connecticut [distributor], 2015.

1 data file (57,061 logical records) + 1 codebook (3,567p.).  (National Data Program for the Social Sciences, No. 22).

BayesiaLab Courses

December 10–12, 2019 New York, NY, USA 3-Day Introductory Course
February 5–7, 2020 Singapore 3-Day Introductory Course
February 10–12, 2020 Sydney, NSW, Australia 3-Day Introductory Course
March 3–5, 2020 Dubai, UAE 3-Day Introductory Course
March 9–11, 2020 Dubai, UAE 3-Day Advanced Course
March 24–26, 2020 Boston, MA, USA 3-Day Introductory Course
April 7–9, 2020 Paris, France 3-Day Introductory Course
May 6–8, 2020 Seattle, WA, USA 3-Day Introductory Course
May 11–13, 2020 Seattle, WA, USA 3-Day Advanced Course
June 15–17, 2020 Paris, France 3-Day Advanced Course
October 5–7, 2020 Toronto, ON, Canada 3-Day Introductory Course
October 13–15, 2020 Toronto, ON, Canada 3-Day Advanced Course

Seminars, Webinars, and Conferences

December 12, 2019
2 p.m. – 5 p.m. (EST, UTC-05)
Free Seminar in New York, NY Artificial Intelligence for Judicial Reasoning
January 21, 2020
2 p.m. – 5 p.m. (EST, UTC-05)
Free Seminar in Washington, DC Artificial Intelligence for Judicial Reasoning
January 28, 2020, 11 a.m. – 12 p.m. (CST, UTC-06) Live Webinar Bayesian Parameter Estimation for Individualized Drug Dosing
January 30, 2020
2 p.m. – 5 p.m. (CST, UTC-06)
Free Seminar in Chicago, IL Artificial Intelligence for Judicial Reasoning
Please check out our archive of recordings of previous events

8th Annual BayesiaLab Conference

October 5–7, 2020 Toronto, ON, Canada 3-Day Introductory Course
October 8–9, 2020 Toronto, ON, Canada 8th Annual BayesiaLab Conference
October 13–15, 2020 Toronto, ON, Canada 3-Day Advanced Course