week 13
Fyafee
olp_4404-5504_analyzing_qualitative_data.doc1
OLP 4404/5504 Analyzing Qualitative Data
The purpose of analyzing your data is to extract as much data-based meaning out of the data as possible. (i.e. what is the story the data tells you.)
As with anything, qualitative data analysis is a rich field of study…you can one or more semesters studying qualitative data analysis! Here is a high-level process for approaching the qualitative data analysis for your evaluations.
1. Read, read, read your data. You need to be familiar with your data in order to do an effective job analyzing your data. A quick read through won’t do it. (we often say you want to “marinate” yourself in the data.)
2. Reduce your data by coding it into themes and categories. At a high level, I suggest the magic marker approach or the scissors approach. The scissors approach is my personal favorite and the one most students find the easiest. (The more data you have, the more important it is that you use a structured approach to your analysis vs. a quick read and a “gut” analysis of the qualitative data.)
a. Print or photocopy your data so you are working with a copy of the data…not the original.
b. Start with one qualitative question.
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c. Marker approach |
c. Scissors approach |
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Read through the data several times to become familiar with the data. Go through the data and highlight each piece of data (sentence or an piece of feedback) with a marker (let’s say yellow). As you see data that seems similar or fits in the same category, highlight that data with the same color of marker (yellow). Continue through all your data with that one color of marker (yellow) and highlight only the data that seems similar. Look through all the items you highlighted (yellow). Can this data be reduced further? If so, go through the highlighted data again with a different color of marker and highlight into smaller categories (blue). (i.e. some of the yellow data will remain yellow, and some will be re-categorized into blue). Now review the two colors of data (yellow, blue) can this data be further reduced into more categories? If so, pick another color of marker and go through the highlighted data. Name the category(s). Go back to your unhighlighted data and continue with a different color of marker (green). Repeat steps ##-## until all the data is coded into categories. Consider starting over with another copy of the same data to see if there are different categories that do a better job of representing the data. |
With scissors cut each piece individual data from that question into a strip. On a big table (or the floor) take each piece of data/strip of paper and physically sort it into categories/piles of data. Use paper/post-it notes to create category names for each pile so you know what to sort where. Once all the data is sorted, start with one of the categories/piles and read through all the data in that category. Ask yourself if this category is reduced as much as possible or if you can create more than one meaningful category from this category. If so, sort the data into more than one pile and re-label the categories/piles. Do the same for all the categories/piles. Consider capturing the data and categories (either start over with a new copy of the data or take photos of these data and categories) and then starting over to see if there are different categories that do a better job of representing the data. |
NOTE: data that does not fit into a category still counts. (It is not OK to eliminate data.) You will need to determine how you will describe the uncategorized data as well as the categorized data.
d. Repeat step c for each qualitative question.
e. Determine the best way to present what the data says – the facts of the data (list, table, charts and pictures) in addition to written description.
f. Explain what the data means – tell the story of the data.
· In order to tell the story of the data you are adding your data-based interpretation of the data…but it must always be based on the data (you do not just add your opinion).
· You may decide to interpret the data within questions and across questions.
· The story of the data is typically presented from the most important to the least important theme or category.
· Do not just summarize the data, synthesize the data so you can tell the story.
