Data cleaning

You collected your quantitative data, now comes the fun part: finding out how your study came out! I will start the discussion of analytic strategies with the basics of quantitative data cleaning and analysis. Start by thinking about how data gets into your SPSS or other statistical software program, there are several options. The first option is to use Survey Monkey or other surveying software sites. They allow you to directly download your data into an SPSS file. You will need to double check everything is as you expect, but in general, the data do tend to be accurate.

The second option is the old-fashioned way of entering the data by hand. This is commonly used when you have used paper surveys/ instruments. The concern for this method is it is easy to mistype and introduce errors into your data set. It is a good idea to have someone check your work when entering data. I also recommend doing some descriptive statistics, looking at the range of the scores for each variable to make sure they are as expected (e.g., if you are expecting scores ranging between 1-5 a 55 tells you have entered a wrong number).

What do I mean by data cleaning? There are many definitions, but I am talking about a two-step process. First, double-check all cells are filled, you will probably discover some are not and decisions will need to be made on this. The second step is carefully checking the statistical assumptions of your variables and looking for extreme scores.

Why are these steps necessary? Because the results of your study will only be as accurate as the data you analyze. Therefore, it is very important to take the time to check your data carefully, so you know your results are valid and accurate. I want to refer you to a great book that much of my advice over the next few topics will be based: Osborn's (2013) Best Practices in Data Cleaning. 

Next time we will consider missing data. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu