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When & Where
Thu, February 27, 2020 - 11:00 AM to 1:00 PM
D-Lab Convening Room

This workshop will cover the main types of weighting, to correct for bias in sample data. These types of weights are designed to compensate for different selection probabilities, to correct for non-response, and to post-stratify data to match the demographic distributions found in census data or other criterion distributions. We will also discuss the loss in precision (the increase in the size of confidence intervals) that may result from weighting.

Prior knowledge: This workshop presupposes some basic knowledge of sampling.   Attend if you are interested in learning about the process of weighting sample data to correct for bias.

Primary Tool: 
Training Host: 
D-lab Facilitator: 
Evan Muzzall
Format Detail: 
Hands-on, interactive
Participant Technology Requirement: 

Basic Competency

These workshops are designed for participants with beginner fluency. They already have a little coding, tool or method experience but need to learn more intermediate applications such as conditional subsetting and appropriate data visualizations for their research. 

Examples: Introduction to Pandas, R-wrang, Data Visualization with Python, R-graphics, Survey Sampling, Weighting Data, Introduction to Qualtrics, Finding Health Statistics and Data, Data Viz Theory and Best Practices, Python Machine Learning, Machine Learning in R, Intro to Computational Text Analysis, Geospatial Fundamentals in Python/sf/QGIS/ArcGIS, Intermediate Tableau

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