Simple Qualitative Analysis The analysis of the Studio to School E-journal post #10 is a great example of qualitative analysis in its simplest form. To analyze and summarize the posts, I looked at fourteen different written reflections containing 300 words to 750 words each and focused on one question. In total, analysis took me 5-6 hours from first reading to final results. I used no software designed for qualitative analysis, just pencil and paper and Word. This is qualitative analysis at its most basic.

Step one: Tidy the data I made a list of all of the programs who had submitted reflections and printed them out so that it was easier for me to read through them.

Step two: Revisiting the question (Time Spent: 20 minutes)The original question for Reflection 10 was “Imagine that it is the year 2020. What will arts education look like in your community?” We asked this question to discover what lasting changes and impacts grantees hope to see in and from their programs. As I read through the reflections, I looked for specific answers to that question. If I was going to do top down (deductive) buckets, I would have created a list of them based on the question. I chose not to do that here because it was just one question and I didn’t have a good idea about what I might see.

Step three: Initial read through and bottom up-buckets (Time spent: 1 hour)I read through all the entries and made short notes of ideas or snippets of text that provided answers or insights into the

Tidy your data

Revisit the

question- Top down buckets

Reading- Bottom

up buckets

Put data in

buckets

Analyzing what is in

your buckets

Check your work

question. My goal was capture as many of the “big ideas” as I could. I asked myself “What are all of the possible answers to the question that this person is trying to share with me?” When I’m analyzing qualitative data without pre-set list of things I’m looking for, focusing on the question is how I keep the project from ballooning to an unmanageable size, even if data is left behind. If unrelated data is too interesting to let slip away, I make a separate note to remind myself to come back to it later. To the right, you can see the original list of “big ideas” I came up with.

Step four: Putting the data into buckets (Time spent: 1-2 hours) My next step was to use my big ideas as buckets into which I could sort my data. (Note: you may have heard buckets referred to as codes and this process referred to as coding.) I opened a blank document and type in all my buckets. I started going back through the reflections again, this time making more specific notes. I included more complex ideas, salient quotes and noted which reflection the notes were from.

Step five: Analyzing what is in each bucket(Time spent: 2-3 hours)At this point I start moving from big ideas to themes. When I looked at big ideas, I was trying to capture all the idea. In themes, I am starting to analysis and look for common ideas. Looking at my original list of buckets and the 6 themes included in the final summary, you can see that they are not the same list. As I was analyzing the posts, I let my buckets shift and change, combining some of them together. For example, as I came across ideas that were closely related, I put them together under a new theme. In other cases, I ended up splitting one bucket into two. Here is a chart of how five different buckets contributed to two new buckets.

When I looked at “Excitement about where students are at”, programs talked about hopes they had for participating students. Those hopes included ideas that overlapped with ideas from my “Normal” theme, which focused on things that would be considered normal in the future. Grantees hoped that students would see arts programing and arts participation as a normal part of life. “Value of arts is seen” also overlapped with “Excitement about students”; grantees hoped that that the students would value the arts. One grantees pointed out that the middle school students would be graduating in 2020, which ended up giving the theme its name. The theme represents several ideas about grantee hopes for students who are currently participating in programming. When I looked at what was left in the “Normal” and “Value of arts is seen”, they contained a lot of ideas about school culture. I also realized that the ideas I collected under the heading of “Other Student Skills” were not so much about student skill, but about the teachers integrating arts into the classroom. These ideas combined with some ideas from “Sustainability” to create a new theme “School Culture Includes the Arts.” (Note: “Sustainability” and “Value of Arts” is seen also contributed to the “Communities that advocate for the arts” theme.)

I did all this work in a Word document, where I cut and paste and moved snippets around while I worked. The analysis process is almost like a puzzle where I was trying to find a way for the key themes to fit together. This is the toughest part of qualitative analysis and the most subjective. Especially if you are working with a lot of data, it can take a lot of time.

Step six: Checking my work (Time spent: 1-2 hours) Once the final themes had solidified, I went back and checked my work. I started by making sure I could see all the original big ideas in the final write up, either as main themes supporting ideas. I wanted to make sure nothing had been left out. Next, I went back to the reflections and looked at which grantees had spoken about each theme. I wanted to make sure that my summary was an accurate reflection of their writing; that I hadn’t lost any important meaning in doing the analysis. Finally, I sent my summary to my colleague Madeline Baars, who had also read the reflections. She checked that my summary reflected what she took away from her review of the reflections. In an ideal world, she would have done her own coding and analysis and we would have compared notes before writing it up, but this simpler process sufficed for the task of summarizing this set of e-journal posts.