Coding Qualitative Data

Kaplan, Rebecca G.

Citeable URL: https://scholar.colorado.edu/concern/articles/jq085k88m

Abstract

Objectives To experience an abbreviated version of the process involved in coding qualitative data To discover some of the challenges that arise within the coding process To discuss possibilities for navigating challenges Materials Small sticky notes (3 per participant) Pens Flip chart paper/whiteboard/chalkboard Markers/dry erase markers/chalk Prepare Before Write the simulation research question: “How are teachers depicted in popular movies?” on the flip chart paper/whiteboard/chalkboard. Warm Up Write a word or phrase that describes or shows how teachers are depicted in movies (e.g., on a sticky note, I might write “insecure” or “wise” or “doesn’t care about students”). Write one word or phrase per sticky, on 3 different stickies. Stick the stickies on the flip chart paper/whiteboard/chalkboard. Experience Everyone gather around the board where the sticky notes represent our “dataset.” Designate a volunteer to be the sorter and move the stickies where participants suggest, using the following guiding question: Which words seem to go together? Then, move the words that fit together into a cluster. Next, code the dataset. What codes or names can we give to the clusters? Write a code name next to each cluster of sticky notes, or data. Next, analyze and create claims. What can we state, and know is true, based on our dataset? Look back at our research question, “How are teachers depicted in popular movies?” We’re creating claims from our dataset that answer the research question. Note: “Claim” is a word to add to your group’s glossary. Whether you have a “word wall” where you continually add glossary words and definitions, or if you have students keep track of these words in a notebook, it can be useful to call out glossary terms and discuss them as your group encounters them. It’s possible each cluster will be a claim, but it’s also possible that more than one cluster will support a single claim. The facilitator may want to model one claim to start the process. Think aloud to the group how you came up with the claim, including which data points support it and how it answers the research question. Participants then volunteer possible claims and discuss these as a group using the following guiding questions: Does the claim connect to our research question? Is the claim supported by the data? Reflect Facilitate a discussion using the following reflection question: Think about the process we just used. What challenges came up and how did we navigate them? Summarize Coding data may at first seem like a simple process of categorization, but it can get complicated fast. The following questions or concerns can arise: Why did we put the data where we did? Would it have changed our claims if we labeled our codes differently? Did our codes create our claims, or did the data? What do we really know from our data? How much do our own thoughts about the topic changed the way we read and coded our data? These and many other questions often arise during this activity, and it helps to be able to come back to the decisions we made with the simulation dataset when we have similar dilemmas arise when coding our real dataset. Demonstrate Individually, in small groups, or as a large group, consider the following questions and take notes: Think about the data we have collected thus far for our PAR work. Plan a process for coding our data. How should we start? What materials should we use? For instance, do we want to code materials on the computer? Do we need to use Google Documents and if so how do we want to use them? If we are coding printed materials, do we need colored highlighters, scissors, or other materials?

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