Research Bias 101: Selection Bias, Analysis Bias and Procedural Bias Explained (With Examples)

In this video, we are going to unpack the  thorny topic of research bias. Also sometimes called researcher bias. We will explain  what it is look at some common types of research bias and share some tips to help  you minimise the potential biases in your research.

If you are currently working on a  dissertation, thesis or research project be sure to grab our free dissertation templates to  help fast-track your write-up. These tried and tested templates provide a detailed roadmap  to guide you through each chapter section by section. If that sounds helpful you can  find the link in the description below.

So what exactly is research bias? Well, simply  put research bias is when the researcher, that is you, intentionally or unintentionally  skews the process of a systematic inquiry which then of course skews the outcomes of the study.  In other words, research bias is what happens when you affect the results of your research by  influencing how you arrive at them.

For example, if you plan to research the effects of remote  working arrangements across all levels of an organisation but your sample consisted  mostly of management-level respondents you run into a form of research bias. In  this case excluding input from lower-level staff in other words not getting input from all  levels of staff means that the results of the study would be biased in favour of a certain  perspective that of management. Of course, if your research aims and research questions  were only interested in the perspectives of managers this sampling approach would not be a  problem.

But that is not the case here as there is a misalignment between the research aims and  the sample. By the way, if you want to learn more about research aims and research questions check  out our video covering that up here. You can also find the link in the description below.

Now it  is important to remember that research bias is not always deliberate or intended. Quite often  it is just the result of a poorly designed study or practical changes in terms of getting a  well-rounded suitable sample. While perfect objectivity is the ideal some level of bias is  generally unavoidable when you are undertaking a study.

That said as a savvy researcher it  is your job to reduce potential sources of research bias as much as possible. To minimise  potential bias you first need to know what to look for. So next up I will unpack three common  types of research bias we see at Grad Coach when reviewing students' projects.

These include  selection bias, analysis bias and procedural bias. Keep in mind that there are many different  forms of bias that can creep into your research. So do not take this as a comprehensive list it is  just a useful starting point.

To learn more about other potential biases you can check out the  Grad Coach blog. Link down in the description. First up we have selection bias.

The example I  mentioned earlier about only surveying management as opposed to all levels of employees is a  prime example of this type of research bias. In other words, selection bias occurs when  your studies' design automatically excludes a relevant group from the research process and  therefore negatively impacts the quality of the results. With selection bias, the results of your  study will be biased towards the group that it includes or favours meaning that you are likely  to arrive at prejudiced results.

For example, research into government policies that  only includes participants who voted for a specific party is going to produce skewed  results as the views of those who voted for other parties will be excluded. Selection bias  commonly occurs in quantitative research as the sampling strategy adopted can have a major  impact on the statistical results. That said selection bias does of course also come up in  qualitative research as there is still plenty of room for skewed samples.

So it is important to  pay close attention to the makeup of your sample and make sure that you adopt a sampling strategy  that aligns with your research aims. Of course, you will seldom achieve a perfect sample  and that is okay but you need to be aware of how your sample may be skewed and factor  this into your thinking when you analyse the result in data. If you are enjoying this video  so far please help us out by hitting that like button.

You can also subscribe for loads of  plain language actionable advice. If you are new to research check out our free dissertation  writing course which covers everything you need to get started on your research project.  As always links in the description.

Next up we have analysis bias. Analysis bias  occurs when the analysis itself emphasises or discounts certain data points so as to favour  a particular result. Often the researchers own expected result or hypothesis.

In other words,  analysis bias happens when you prioritise the presentation of data that supports a certain idea  or hypothesis rather than presenting all the data indiscriminately. For example, if your study was  looking into consumer perceptions of a specific product you might present more analysis of data  that reflects positive sentiment toward the product and give less real estate to the analysis  that reflects negative sentiment. In other words, you would cherry-pick the data that suits  your desired outcomes and as a result, you would create a bias in terms of information  conveyed by the study.

Although this kind of bias is common in quantitative research it can  just as easily occur in qualitative studies given the amount of interpretive power the  researcher has. This may not be intentional or even noticed by the researcher given the inherent  subjectivity in qualitative research. As humans, we naturally search for and interpret information  in a way that confirms or supports our prior beliefs or values.

In psychology, this is called  confirmation bias. So do not make the mistake of thinking that analysis bias is always intentional  and that you do not need to worry about it because you are an honest researcher it really can creep  up on anyone. To reduce the risk of analysis bias a good starting point is to determine your data  analysis strategy in as much detail as possible before you even collect your data.

In other words,  decide in advance how you will prepare the data, which analysis method you will use and be aware  of how different analysis methods can favour different types of data. Also, take the time  to reflect on your own preconceived notions and expectations regarding the analysis outcomes  so that you are fully aware of the potential influence you may have on the analysis and  therefore hopefully, you can minimise it. Last but definitely not least we have procedural  bias which is also sometimes referred to as administration bias.

Procedural bias is one of  the easier biases to overlook so it is important to understand what it is and how to avoid it. This  type of bias occurs when the administration of the study especially the data collection aspect  has an impact on either who responds or how they respond. A practical example of procedural  bias would be when participants in a study are required to provide information under some form  of constraint.

For example, participants might be given insufficient time to complete a survey  resulting in incomplete or hastily filled out forms that do not necessarily reflect how they  really feel. This can happen really easily if, for example, you innocently ask your participants  to fill out a survey during their lunch break. Another form of procedural bias can happen  when you improperly incentivise participation in a study.

For example, offering a reward for  completing a survey or interview might incline participants to provide false or inaccurate  information just to get through the process as fast as possible and collect their reward. It  could also potentially attract a particular type of respondent a freebie seeker resulting in a  skewed sample that does not really reflect your demographic of interest. The format of your  data collection method can also potentially contribute to procedural bias if, for example,  you decide to host your survey or interviews online this could unintentionally exclude  people who are not particularly tech-savvy, do not have a suitable device or just do not have  a reliable internet connection.

On the flip side, some people might find in-person interviews a bit  intimidating compared to online ones at least or they might find the physical environment  in which they are being interviewed to be uncomfortable or awkward. Maybe the boss is  peering into the meeting room, for example. Now, although procedural bias is more common in  qualitative research it can come up in any form of fieldwork where you are actively collecting  data from study participants.

So it is important to consider how your data is being collected and  how this might impact respondents. Simply put you need to take the respondent's viewpoint and think  about the challenges they might face no matter how small or trivial these might seem. So it is always  a good idea to have an informal discussion with a handful of potential respondents before you  start collecting data and ask for their input regarding your proposed plan upfront.

Okay, so let  us do a quick recap. Research bias refers to any instance where the researcher or the research  design negatively influences the quality of a study's results whether intentionally or not. So  three common types of research bias we looked at are selection bias where a skewed sample leads to  skewed results, analysis bias where the analysis method and or approach leads to biased results  and procedural bias where the administration of the study especially the data collection aspect  has an impact on who respondents are and how they respond.

As I mentioned there are many other  forms of research bias but we can only cover a handful here. So be sure to familiarise  yourself with as many potential sources of bias as possible to minimise the risk of research  bias in your study. Now as I mentioned earlier you can learn more about all of this on the Grad  Coach blog.

The link is in the description. If you got value from this video please hit  that like button to help more students find this content. For more videos like this check  out the Grad Coach channel and subscribe for plain language actionable research tips and  advice every week.

Also if you are looking for one-on-one support with your dissertation,  thesis or research project be sure to check out our private coaching service where we hold  your hand throughout the research process step by step. You can learn more about that and book  a free initial consultation at gradcoach. com.