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 standard qualitative
protocols (e.g., see Creswell & Plano, 2011). Here are some general
guidelines. First, make sure your research questions 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 the medical community is
dangerous?"
Second, when you write your
interview questions, remember they must follow from your research questions, be
open-ended, and they must be neutral. You should not lead the participant into
a particular belief you have about them, instead you want the participant to
say or indicate whether 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 all doctors are
ageist?"
The third way to reduce your bias
is to keep a journal (or field notes) 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
do not know anyone, check if your committee can suggest a student. You want the
independent coder 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 we will consider "easy"
interview subjects. 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
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