Beyond data collection, coding, and analysis, good interview research requires analysts to clean the data, organize the interviews, and reflect on the analysis.
The purpose of cleaning data is to ensure that the data analysis is as efficient as possible. For example, consider analyzing only the interview respondent's answers and not the interviewer's questions using the Word Cloud or Concepts tools. This task may mean analyzing a modified interview transcript with only the respondent's answers so that the analysis does not include the interviewer's words.
Organizing the interviews also helps to ensure a smooth data analysis process. There are various ways to organize the data effectively, but one good organization strategy is giving each interview respondent their own document in an ATLAS.ti project. Using the Code-Document Table tool, you can determine the frequency of codes associated with each interview to identify differences in themes or patterns among interview respondents.
Reflecting on respondent bias
It is also essential to consider the inherent biases in any set of qualitative data. Interviews involve interactions between people. As a result, there is always the possibility that respondents could provide answers they think the interviewer would like to hear. Some examples of social desirability bias include situations where the respondent says they always eat healthy food or donate to charity.
Respondents may experience a tendency to adjust their answers toward such socially desirable practices. This bias can also occur in focus group discussions, especially when respondents feel pressured by others in the group to express more desirable opinions than they otherwise would. Bias and subjectivity are unavoidable in qualitative research, but what is important is that researchers carefully reflect on how biases can affect respondents' perspectives and the resulting data analysis.