Iterative Analysis of Interviews About Cycling in Copenhagen, Denmark © 2021 SAGE Publications, Ltd. All Rights Reserved. This PDF has been generated from SAGE Research Methods Datasets. SAGE SAGE Research Methods Datasets Part 2021 SAGE Publications, Ltd. All Rights Reserved. 1 Iterative Analysis of Interviews About Cycling in Copenhagen, Denmark Student Guide Introduction This dataset demonstrates how to conduct an iterative analysis on interview data. In iterative analysis, the researcher alternates between emergent meanings found in data and existing concepts, theories, and ideas from the academic literature (Tracy, 2020). Iterative analysis is useful for developing new insight into a topic that builds on an existing body of research. These data are drawn from field research conducted by Elizabeth Wilhoit Larson. Following research on bike commuters in the American Midwest, where biking to work is unusual, Larson was interested in studying a place where biking is normal. This led her to Copenhagen, Denmark, a city with world-class cycling infrastructure where about half of its 1 million residents use bicycles every day (Denmark, 2016). The goal of this study was to understand how communication plays a role in making cycling in Copenhagen an orderly and organized experience. Semi-structured interviews with residents of Copenhagen and ethnographic observations were used to collect data. Semi-Structured Interviews In qualitative research, interviews are seen as a way to co-construct knowledge through the interaction between a researcher and participant. The researcher is not seen as extracting knowledge from the participant, but knowledge and Page 2 of 11 Iterative Analysis of Interviews About Cycling in Copenhagen, Denmark SAGE SAGE Research Methods Datasets Part 2021 SAGE Publications, Ltd. All Rights Reserved. 1 data are created through conversation. Sidney Webb and Beatrice Webb (1932) have defined a research interview as “a conversation with a purpose.” In a semi- structured interview, the researcher has something in mind that they would like to learn from a research participant. To this end, the researcher comes prepared with some questions they plan to ask. However, the interview is only semi-structured because the interviewer might ask additional questions, change the order that questions are asked in, or not ask certain questions based on the progression of the interview. The researcher seeks to understand the world from the participant’s point of view and uses the interview questions to help the participant articulate their perspective. With semi-structured interviews, the researcher makes in-the- moment decisions about how to advance the interview and which questions to ask in order to best understand the participant’s experience. Semi-structured interviews also allow the participant to guide the interview more, potentially resulting in a more complex understanding of participant’s experiences (Tracy, 2020). Data Exemplar: Semi-Structured Interviews on Cycling With Residents of Copenhagen, Denmark Data collection took place in Copenhagen, Denmark, one of the best cities in the world for cycling (Københavns Kommune, 2017). I had previously studied people who ride bikes to work in the American Midwest—where riding bikes for transportation is unusual (Wilhoit & Kisselburgh, 2015), and wanted to understand what makes a city like Copenhagen so good for biking, particularly in terms of how rules and norms for cycling are communicated. I collected data through participant observation. I spent January–July 2013 in Copenhagen and collected extensive ethnographic data during that time, both biking around the city myself as an active participant and observing others as they navigated the cycling infrastructure and culture of the city. I wrote field notes after most bike rides I took (Emerson et al., 2011). Additionally, I conducted semi-structured interviews (n = 23) with residents Page 3 of 11 Iterative Analysis of Interviews About Cycling in Copenhagen, Denmark SAGE SAGE Research Methods Datasets Part 2021 SAGE Publications, Ltd. All Rights Reserved. 1 of Copenhagen, so that I could better understand the experience of cycling. I also conducted one interview with an official from the cycling division of the city government. In the interviews, I asked questions about their cycling routines, challenges they encountered, how they learned the rules and norms of cycling in Copenhagen, and their reflections on the culture of cycling in Denmark. In this dataset, I focus on the interview data. Iterative Analysis Iterative analysis is a framework for analyzing qualitative data in a way that alternates between meanings emerging from the data (emic readings) and use of existing literature, theories, and explanations (etic readings). Iterative analysis has been synthesized by Tracy (2020), drawing on the work of other scholars like Dennis Gioia and colleagues (see Gioia et al., 2013) and Srivastava and Hopwood (2009). Tracy was motivated to articulate this form of analysis because she noted that there is a difference between what many researchers say they are doing for their analysis and what they actually do. Tracy (2020) writes, “I would estimate at least 80% of qualitative articles say, ‘I used a version of grounded theory and the constant comparative method for analyzing my qualitative data.’” However, she continues to state that most scholars who claim to use grounded theory to analyze their data are not actually using this method. Grounded theory (Glaser & Strauss, 1967) is an inductive approach to data analysis in which one’s findings emerge from the data rather than questions the researcher had going into the project. Tracy (2020) has explained that most researchers who claim to be doing grounded theory are actually drawing on existing frameworks, theories, and questions in their data analysis. Based on this observation, Tracy (2020) synthesized various approaches to data analysis to develop iterative analysis, which she suggests more closely matches what most qualitative researchers actually do when they analyze data. Iterative Page 4 of 11 Iterative Analysis of Interviews About Cycling in Copenhagen, Denmark SAGE SAGE Research Methods Datasets Part 2021 SAGE Publications, Ltd. All Rights Reserved. 1 analysis is a reflexive process. Through repeated engagement with the data, researchers gradually gain a more focused understanding of the data (Srivastava & Hopwood, 2009). Iterative data analysis begins with coding. Coding involves assigning a word or phrase to a portion of data to capture the essence of that segment of data. Primary-cycle coding involves examining the data and assigning emergent codes that represent the data (Tracy, 2020). As a researcher gets further into the data analysis process, they will create a codebook that summarizes and defines each code. Once primary-cycle coding has been completed, the researcher moves to secondary-cycle coding (Tracy, 2020). In this cycle of coding, the researcher moves beyond purely descriptive codes to categorize, combine, and synthesize the first-level codes. It is in the second- level coding that the researcher will often turn to disciplinary concepts as codes, specifically looking for concepts from relevant theories in the data. Through activities like memoing, finding additional data that might counter one’s emerging findings, and proposing possible interpretations of the data, the researcher creates an in-depth understanding of the data and relationships between codes in this stage. At this point, the researcher can return to initial research questions and begin to formulate answers. Stage 1: Organizing and Preparing Data The first step of data analysis is simply to gather and organize one’s data (Tracy, 2020). This might involve transcribing interviews, organizing field notes, or deciding which documents should be part of the analysis. For this project, organizing the data involved transcribing the interviews. Additionally, I used software to aid my analysis, and I needed to import the interview transcripts into the software. Stage 2: Primary-Cycle Coding The next step is to begin coding the data. In primary-cycle coding, the researcher Page 5 of 11 Iterative Analysis of Interviews About Cycling in Copenhagen, Denmark SAGE SAGE Research Methods Datasets Part 2021 SAGE Publications, Ltd. All Rights Reserved. 1 seeks to identify meaning in a unit of data (Saldaña, 2016). While it is important to be open to many possible meanings in the data, the researcher will often have their initial research question or goal in mind at this stage. The table below shows some examples of how I identified first-order codes. As I read through the data, I sought to capture the essence of different parts of the data. The amount of data that gets a code can vary in length—sometimes one word will be coded, while other times it is an entire paragraph. As you can see below, the codes are very open-ended and emergent, and they encapsulate what is in the data. For this project, I used NVivo 11, a qualitative data analysis software, to help me code. Software is useful because it can help you manage a large number of codes and then sort by them. For example, once I was done with coding, I could easily pull up a list of all data that I had coding with, “everyone bikes” to see how often participants talked about this concept. However, you can also code manually by writing codes on printed transcripts or in documents. Many researchers find it useful to print out the transcripts and use different colored pens to mark different codes. Looking at the codes below, it is also important to note that a code does not need to be one word. A code can be a word, a phrase, or even a sentence that gets at the meaning present. At some point during the coding process, it becomes important for the researcher to create a codebook that lists and defines the codes used. A codebook helps the researcher to code consistently across the data and is especially useful if multiple researchers are working together. Data (Participant names are pseudonyms) Codes Researcher: So then when you moved to Copenhagen, tell me about getting A.