(25) Twelve Steps of Data Cleaning: Strategies for Dealing with Dirty Evaluation Data (October 16, 2013: 8am-3pm)

Session Number: 25
Track: Workshops
Session Type: Pre-Conference
Primary Presenter: Jennifer Morrow
Additional Presenter: Gary Skolits
Time: Oct 16, 2013 (08:00 AM - 03:00 PM)
Room: Columbia Section 3

Schedule: Full Day, Wednesday 8-3
Level: Intermediate
Speakers: None


Evaluation data, like a lot of research data, can be messy. Rarely are evaluators given data that is ready to be analyzed. Missing data, coding mistakes and outliers are just some of the problems that evaluators should address prior to conducting analyses for their evaluation report. Even though data cleaning is an important step to data analysis, the topic has received little attention in the literature, and the resources that are available in the literature tend to be complex and not always user friendly.

In this workshop, you will go step-by-step through the data cleaning process and learn suggestions for what to do at each step.

You will learn:

  • The recommended 12 steps for cleaning dirty evaluation data;
  • Suggestions for ways to deal with messy data at each step;
  • Methods for reviewing analysis outputs and making decisions regarding data cleaning options.

Jennifer Morrow is an Associate Professor in Evaluation, Statistics and Measurement at the University of Tennessee with more than 15 years of experience teaching statistics at the undergraduate and graduate level. Gary Skolits, also a Professor in Evaluation, Statistics and Measurement at the University of Tennessee, has more than 21 years of experience training novice evaluators and analyzing quantitative data. The presenters are working on a publication (article and book) on the 12-step data cleaning process.

Session 25: Data Cleaning
Prerequisite: Basic understanding of quantitative analysis

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