Guide 
The Ultimate Data Cleaning And Tidying Checklist
In theory, data cleaning is super simple. You check your data for integrity issues that could play havoc with analysis, then you tidy up the data set so the errors are removed.
In practice, it is far more complex. From missing variables to poorly ordered categories, there are literally hundreds of potential issues in your data to check for. Then even when you do detect ‘dirty data’ - how do you know what to do with it?
That’s why Displayr created the Ultimate Data Cleaning and Tidying Checklist. It’s your exhaustive list of all the possible data issues to keep an eye out for, as well as a quick fix on how to solve them. Get started now!
What you'll get from this guide
You’ll learn how to identify and resolve:
- File quality issues
- Respondent quality issues
- Non-representative samples
- Data capture errors
- Missing data
- Outliers
- Data quality problems
More about this guide
Data cleaning is a hot topic. With more sources than ever and the rise of generative AI tools, the survey data that you need to analyze as a researcher is getting messier and messier.
And it’s causing confusion amongst even the most experienced researchers. Do you need to tidy those labels? What should you do with identical responses? Would it be easier to just start over with a new file?
That’s why it’s important to have a clear framework to guide your data cleaning – that’s exactly what this checklist is designed to do. The checklist is your data quality cheat sheet - with almost 50 of the top causes of ‘dirty data’, as well as the appropriate fix for each issue.
Get started today
