<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).

Professional BayesiaLab Courses

February 27–28 & March 2, 2019 Dubai, UAE Introductory Course
March 5–7, 2019 Dubai, UAE Advanced Course
March 19–21, 2019 Washington, D.C. Introductory Course
April 2–4, 2019 Amsterdam, Netherlands Introductory Course
May 8–10, 2019 Singapore Introductory Course
May 13–15, 2019 Sydney, Australia Introductory Course
May 21–23, 2019 Paris, France Advanced Course (in French)
June 12–14, 2019 Seattle, WA Introductory Course
June 17–19, 2019 Seattle, WA Advanced Course

Upcoming Seminars, Webinars, and Conferences

Live Webinar February 21, 2019 11:00 a.m. – 12:00 p.m. (CST, UTC-6) Simulation Meta-Modeling with Bayesian Networks
Please check out our archive of recordings of previous events.

7th Annual BayesiaLab Conference

October 7–9, 2019 Durham, NC 3-Day Introductory Course
October 10–11, 2019 Durham, NC 7th Annual BayesiaLab Conference
October 14–16, 2019 Durham, NC 3-Day Advanced Course