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Visualize your research findings, without the bias

08 April 2019 - BY Dr Alexandra James

Visualizing your research can help your findings come alive. But how do you avoid preempting or inserting suggestions or bias into those findings?


Cast your thoughts to one of your research projects. 

Perhaps this was a particularly satisfying project that you’ve already completed with great success. Or, it may be the elusive project that you have on hold, waiting for just the right time to embark upon.

Trace the research process in your mind with me. Think about the methods design, the sample population, perhaps even your mode of analysis.

Imagine the data collection process, the feeling of anticipation as you analyze (through tools such as NVivo) and generate findings that produce answers to our burning research questions.

Picture the way your data will produce results and drive forward new knowledge in your field.

But wait.

Stop.

In visualizing findings, we helped them come alive, but did we just pre-empt these findings?

Was our research design influenced by what we anticipated our findings to be? 

Risking confirmation bias

It should come as no surprise that holding preconceived ideas in relation to our research has the potential to produce problematic results. Known as hypothesis and orientation bias, this can foreseeably impact data collection and subsequent research outcomes.

With respect to specific pharmaceutical trials, Ted Kaptchuk asserted that ‘research outcomes seem to be affected by what the researcher is looking for’. 

In donning blinkers, I argue that we also risk confirmation bias throughout the analysis of our data. It is not a stretch of the imagination to consider that researchers may be more attuned to results which marry up with their pre-held convictions.  

The closer we are to our research subject, the greater the possibility that we come equipped with ‘insider’ knowledge on the topic.

However, this too could restrict our inquiry to questions we don’t know the answers to, rather than opening investigations to topics which researchers ‘don’t know they don’t know’.   

Interviewing the interviewer

Practical solutions to these concerns are addressed by Ronald Chenail.

He suggests that opening ourselves up to peer evaluation via pilot studies or a process of ‘interviewing the interviewer’ can effectively test the research process.

This enables the researcher to improve the data collection method and, through the process of reflection, reveal researcher perspectives which could otherwise lead to bias. 

Journaling throughout this exercise may further enable researchers to identify and interrogate thinking patterns and impressions which may adversely impact on their own neutrality. 

The importance of researcher flexibility

From the outset of my own research trajectory, my mentors emphasized the importance of reflexivity in research. This was an invaluable lesson which has guided my relationship to, and interaction with, my research participants and data.

It involves actively ‘identifying preconceptions brought into the project by the researcher’ and serves to acknowledge the role of the researcher within the production of knowledge.

In addition to ensuring an ethical approach, reflexivity has arguably made me a better researcher, open to the possibility of new research avenues.

The effect of your own assumptions

Practically, the application may involve intentionally investigating the potential for alternative conclusions within our research, or ensuring that others have the capacity to audit our data.

Ethical researchers routinely navigate a host of potential errors which threaten the validity of research. It is arguably impossible to remove all of our subjectivities, expectations and imaginations from the research process.

Indeed, I would discourage you from trying to do so.

Visualizing your data can aid and inspire your research process and help to ensure you are transparent and aware of the effect of your own assumptions.

For as Malterud argues ‘preconceptions are not the same as bias, unless the researcher fails to mention them’.  


Learn more about mitigating the risk of bias, and being transparent in your research, with NVivo.