Acknowledging and managing bias
Ethical research requires you to reflect, identify and respond to your biases. Let’s see why.
Let’s say you have a hypothesis (theory) which you have turned over in your mind for countless hours. You suspect your friends and family are tired of you talking about it (and are all secretly hoping your research will be over sometime soon). You’re so committed to this hypothesis and can’t wait to prove it.
Beware…this scenario is ripe for bias.
The problem with bias
It’s human to want to see results that we think should be there, even when they aren’t. Bias can be intentional, but usually, it’s not. For this reason, it’s essential to actively work toward identifying any biases, be transparent about them and put strategies in place to avoid them twisting the accuracy of your research.
An ethical approach requires you to maintain objectivity when you design and interpret your research.
The problem with unrecognised and unchecked bias is its detrimental effects on the reliability and validity of your research. You may remember the terms reliability and validity from earlier courses in this series? Let’s take a moment to quickly refresh.
Reliability refers to the accuracy of the research. In other words, if the same experiment continually produces the same results, the research is seen to be reliable.
Validity is said to be achieved when the research is testing what it sets out to test.
In research, bias occurs when ‘systematic error [is] introduced into sampling or testing by selecting or encouraging one outcome or answer over others’ (Merriam-Webster). If we do not specifically plan to manage bias, the way we design the research, choose the participants, carry out the study and analyse the results may compromise both the reliability and the validity of the work.
Case study example: Hormone Replacement Therapy (HRT)
Pannucci & Wilkins (2010) cite an example of research bias in relation to studies conducted on the effects of hormone replacement therapy (HRT). Research prior to 1998, indicated a decreased risk of heart disease among postmenopausal women using HRT however, in newer studies (designed to rigorously minimise bias), the opposite was found to be true (Pannucci & Wilkins, 2010).
Reflexivity in research
The beauty of research lies in its potential to reveal truth. As human beings, we bring our imperfect selves into the process and sometimes ‘truth’ can inadvertently take a detour.
If you are not consciously aware of and attuned to your own values, beliefs, knowledge and biases - they will impact on the credibility of your findings. ‘Reflexivity’ requires you to identify the assumptions and preconceived ideas you personally bring into your research project. You must then reflect on the way these preconceptions have the potential to skew the results of your research and take action. Remember, it’s ‘natural’ and ‘human’ to have biases, our job is to identify and reflect honestly on these influences.
Rather than seeing such influences as potential contamination of the data to be avoided or allowed for…prospective reflexivity seeks to help researchers grow their capacity to understand the significance of the knowledge, feelings, and values that they brought into the field, to the research questions that they came to formulate, to the analytical lenses that they chose to employ, and to their findings.
(Attia & Edge, 2017).
You may recall we have visited the issue of researcher bias in previous courses in this series. Take a few minutes to revise these steps to refresh your knowledge of bias and strategies to help manage it, before completing the task below.
In course 1, Why Research Matters, step 1.11 looked at the power of prejudice and predisposition and in course 2, Why Experience Matters: Qualitative research, introduced practical strategies for incorporating reflexivity in research, in step 1.8 Time for an identity audit.
What question are you thinking about researching? Do you already have a confident hunch about what the results of your research might reveal? After revisiting the steps from previous courses in this series, use the comments link and post a description of any assumptions, beliefs and personal factors that might have the potential to affect any part of your research.
Attia, M. & Edge, J. (2017). Be(com)ing a reflexive researcher: a developmental approach to research methodology. Open Review of Educational Research, 4(1).
Pannucci, C. J., & Wilkins, E. G. (2010). Identifying and Avoiding Bias in Research. Plastic and Reconstructive Surgery, 126(2).
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