Center for Social Development and Education Blog

Building a Qualitative Archive in QDA Miner

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A word cloud representing the frequency of key word occurrence in the archive.

A few years ago, researchers at CSDE began the process of creating a qualitative archive that encompassed all interviews and focus groups conducted throughout the Unified Champion Schools (UCS) Evaluation, which began in 2008. The goal was to bring together the perspectives of those variously involved with UCS to gain a more holistic and longitudinal perspective. By quickly analyzing all 1,700 of our transcripts (totaling almost 4 million words), the software allows us to see broader trends before beginning an in-depth qualitative analysis. Now that the archive is complete, we’re looking back at how we built it and what we learned along the way! 

To start, we began an arduous process of reading each transcript and demarcating any text said by the interviewer. By bracketing the interviewer’s questions, we essentially created two archives at once: one reflecting the kinds of questions and themes that were prevalent across projects and years and another that reflects the topics that participants were passionate about discussing. This feature allows us to not only analyze trends across participant interviews, but also to evaluate our own research practices and better understand any thematic biases caused by an interviewer emphasis. 

Once the transcripts were ready, we discussed which variables they would have and how those variables would enhance our analysis. Some variables, like school name, grade level, or participant role were recorded manually next to the transcript’s file name in Excel. This allowed QDA Miner to attach the variables to the transcripts as we imported them. We also included several variables from the National Center for Education Statistics (NCES) by linking them through each school’s identification number. These variables ranged from the number of students receiving free and reduced lunch to the locale classification of the school (city, suburb, town, etc.). As NCES releases new data, we will be able to update the archive by adding new variables linked to the school ID. Analysis using these variables allows us to compare between the characteristics of a school and the occurrence of various themes. 

Together, the transcripts and variables illustrate trends in key word frequency and occurrence across participant roles, years, locations, and more. Using the archive we can quickly see the most common topics from a certain project, stakeholder, or locale and how participants related them to each other. Stay tuned to see how we used the archive for the annual UCS evaluation and plans for future research! 

By Anika Lanser, Research Assistant at the Center for Social Development and Education 

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