Data Cleaning: A Short Course

After taking this seminar, you will have the skills to:

• Clean different types of data step-by-step.
• Compile data.
• Label and name variables.
• Examine data.
• Solve data cleaning dilemmas.
• Alter variables.
• Incorporate new variables.
• Re-configure data.
• Re-examine data.
• Identify missing data.*
• Document and present your data.

*While this seminar will help you to identify missing data, it does not spend considerable time on how to address missing data. If you’d like to learn more about methods for handling missing data, check out Dr. Paul Allison’s Missing Data seminar.

The empirical examples and exercises in this course will emphasize Stata, but there will be equivalent code and examples presented/available for R. To fully benefit from the course, you should have access to a computer loaded with R or Stata. Whichever package you choose, you should already have a working understanding of the software and be able to complete basic functions in the software.

If you’d like to familiarize yourself with Stata basics before the seminar begins, we recommend following along with a “getting started” video like the one here.

Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy.

If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills.

This course is for anyone who works with data and wants to improve their data management practices. It will be useful for graduate students, junior scholars, and also provide insights to seasoned researchers.

Day 1: Getting Started with Data Cleaning

• Introduction to data cleaning
  • Types of data
  • Steps of data cleaning
  • Documentation
• Compiling data
• • Downloading/collecting
  • Merging
  • Appending
  • Reshaping

Day 2: Examining Data Quality

• Labeling and naming variables
• • Best practices for variable
• Examining data
• • Detecting anomalous data
    • Univariate statistics/graphs
    • Bivariate statistics/graphs
  • Detecting missing data
  • Creating indicator variables

Day 3: Recoding & Creating Variables

• Examining data, continued
  • Data cleaning dilemmas
• Altering variables
  • Recoding variables
  • Transforming variables
• New variables
• • Scale construction
  • Substantive variable combination

Day 4: Final steps for preparing data for analysis

• Re-configuring data
• Re-examining data
• Documentation and presentation of data cleaning
• Summary and conclusion

“Dr. Manago is very knowledge and presented the course in a way that made sense. We often learn statistics in pieces, so it was refreshing for her to present in the order that we would need to follow in the data cleaning process. The instructor provided so much detail about parts of the data cleaning process I had never learned before. It was refreshing, fun, and full of material to guide me in the data cleaning process.” 
  Sara Bryson, East Carolina University 

“The tips for organizing the cleaning steps were great, as were the applied examples.”
   Monique Ernst, National Institute of Health

“As someone who does not have much experience with R, I was surprised that I wasn’t lost or confused during the lecture. Dr. Manago gave a lot of useful information, some of which might not be valuable to me right now in this early stage of my career but I’m sure will be more valuable later in my career. I will certainly be looking back on these slides for guidance during future projects.”
  Mennefer Blue, Rush University Medical Center