Bias in Qualitative Studies

Jane asks: "how do you avoid bias in qualitative interviewing and analysis of data?" 

Great question, Jane! There are some safeguards built into the process, if you follow the protocols (e.g., see Creswell & Plano, 2011). Here are some general guidelines. First, make sure that your research question are neutral, so you are not predicting a particular outcome. For example, "What is the experience of homeless people in dealing with the medical community?" is more neutral and less biased than "Do homeless people believe that the medical community is dangerous?" 

Second, when you write your interview questions, remember they must follow from your research questions and they must be neutral. You should not lead the participant into a particular belief that you have about them, instead you want them to indicate whether they believe the  issue is important. An example would be, "tell me about your last medical visit." This is much more neutral and more likely to get you their opinions than "do you think that all doctors are dangerous?" 

The third way to reduce your bias is to keep a journal where you write down your opinions and insights. Here is where you get a chance to note your biases and internal beliefs. Do not express them to your participants. 

Fourth, when analyzing data it is always a good idea to have a second independent person go through and code your data, or at least double check your coding of responses. This is not a job for a spouse or close family member, instead consider a dissertation peer. If you don’t know anyone appropriate, check if your committee can suggest anyone. You want them to not know how you are expecting the data to come out, keep them neutral so they can spot any biases you have introduced. 

Next time I will post an updated blog index. Do you have an issue or a question that you would like me to discuss in a future post? Would you like to be a guest writer? Send me your ideas! leann.stadtlander@waldenu.edu 

Creswell, J. W. & Plano Clark, V.L. (2011) Designing and conducting mixed methods research, 3^rd ed. Los Angeles: Sage.