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In a good research environment, participants of a study will provide honest responses concerning their thoughts and opinions about a particular topic. However, this is not obtainable in all studies.

Sometimes participants will try to shape their answers to fit into what the researcher wants. Participants may also be influenced to change their responses due to the environment or the type of experiment. This phenomenon is known as participants bias or response bias. 

In previous articles, we have discussed systematic errors in research. In this article, we will look at research errors from the angle of the participants. How can they affect the outcome of a study and what are the best ways to avoid participants' bias? But first, let us look at the definition of participants' bias.

What is Participant Bias?

Sometimes when conducting research, you may realize that your research participants are exhibiting characters that suggest that they're trying to influence their behavior. It may be unclear whether this is because they have knowledge of the research or the researcher's result of interest.

Participants' bias happens when the participants involved in research respond in a manner that suggests they are trying to match up with the desired result of the researcher. This means that the respondent starts to exhibit unusual characters from what they would normally do or how they would normally react.

The occurrence of participants bias can be detrimental to the research because while the independent variables may appear to have an influence on the dependent variables, it may be revealed that it was the participants' bias all along. This may be another source of confounding variables.

The research results may still show that the conclusions drawn by the researcher based on the research are correct. This is known as internal validity. Now the impact of this is that it may be difficult for the researcher to determine if, truly, participant bias is occurring in the research. This will also hamper any attempt to correct it ultimately.

The researcher being aware of participants' bias and finding control measures from the beginning of the research can be beneficial to the positive outcome of the research, same as for all other research biases or errors.  Researchers should know that no study will be perfect, but being prepared and cautious can get you close to your desired result.

We will further discuss some ways in which participant bias occurs, and what we can do to reduce its effects. 

Read: Leading Questions: Definitions, Types, and Examples

Effects and Implications of Participant Bias

Unfortunately, subject bias is detrimental to an experiment because it jeopardizes the external validity of that study. The results of a study are only externally valid if the independent variables truly have an influence on the dependent variables, and if the influence is not from other causal factors. 

In the case of participants' bias, another cause-and-effect factor that could occur is the research participants trying to act in a way that drives the perceived desired outcome of the researcher.

Let us consider this example:

A researcher goes to a paper company and requests that the CEO allow him to conduct an experiment with the company's employees for a period of one week. He aims to study whether the employees' productivity will increase by having nap pods in a quiet and undisturbed room where they can take a fifteen to twenty minutes power nap during the week.

In the course of his study, some employees could guess what the aim of the research was. Now, unknown to the researcher, some employees put in more effort into their work so that the power nap could seem influential and perhaps adopted.

These participants' bias not only led to a wrong conclusion from the researcher but also invalidated the test. Because it is unclear whether the power nap (independent variable) has any influence on the increased productivity (dependent variables).

Read: Sampling Bias: Definition, Types + [Examples]

Causes of Participant Bias

Participants bias occur because of many reasons, some of which are:

1. Participant fatigue: when the respondents are tired of the survey task. In the eyes of the participants, the data quality will start to reduce. This will cause the participants' attention to reducing, especially during the latter sections of the survey. Tired participants may keep answering "don't know," or choose a response in a "straight-line" 

2. Another cause of participants' bias is the wording of the questions in the survey. The tone of the researcher is important in determining how the participants will respond. 

3. Sometimes, the participants may have a desire to be good experimental participants. This may cause them to provide responses considered socially desirable. 

How to Detect Participant Bias

It is quite difficult to detect the presence of biases in research. It can be more difficult to prevent biases and correct them. The researcher should ensure that there is a high-reliability level in all the research. The researcher should also ensure that they are familiar with all the necessary information about the participants. 

We can also employ biosensors with all the above information so that participants' biases can be quickly detected and eliminated. This will guarantee that the remaining results from the research are trustworthy because they are true. 

Read: Undercoverage Bias: Definition, Examples in Survey Research

Types of Biases Closely Related to Participant Bias

  • Acquiescence Bias

Acquiescence bias or ‘Yes’ bias happens when a participant feels inclined to respond to a research question in a positive or agreeable way. This can sometimes occur when the participants select an option that seems “right” even if they don't necessarily agree with it.

"acquiescence-bias"

For example, when a custom bias or satisfaction survey was conducted, some of the participants may have selected “Very satisfied” because it appears pleasing to the researcher or seems to be the most positive option on the list even against their true feelings. Acquiescence bias can also happen in a study if the respondents are fatigued and put fewer thoughts into answering the questions posed to them. 

  • Social desirability bias

This occurs when respondents choose answers based on what they perceive as socially acceptable. This may cause participants to provide answers which either show an increased number of “desirable” answers or a decreased number of “undesirable” answers. Survey questions that center around health, income, politics, and religion are most likely to be affected by the participants' social bias. For example, if a participant is asked to answer the question “How frequently do you drink alcohol?” the participants might answer with a lower frequency, which may not be true. 

  • Confirmation bias

Confirmation Bias is known as the human tendency to want to know, to go after information that agrees with their pre-existing beliefs while ignoring the information that does not. Confirmation bias affects the research analysis process. This is because the researcher may ignore all contradictory data and use the participants' data to confirm the validity of the original hypothesis. 

  • Habituation Bias

Habituation bias happens when the participants can get affected by habituation factors which are when questions are repeated or similar to each other. Habituation bias reduces assertiveness and causes the participants to respond to similar questions they have initially responded to. For example, a survey with questions such as “On a scale of 1-5, how likely are you to use this product”, will likely suffer from habituation bias. 

  • The halo effect

We tend to overlook the misgivings of those we like and find the best in them regardless, and this bias also happens in an experiment. To measure the thoughts of an individual, a researcher should expect that if the participant has a positive feeling about a thing, the participant will have the same feeling towards things associated with what they like. For example, a participant that likes football may like other sports associated with the ball. Such as volleyball, handball, and even basketball.

Read: Survey Errors To Avoid: Types, Sources, Examples, Mitigation

How to Reduce, Avoid, or Correct Participant Bias 

We can reduce or avoid participant bias in the following ways;

1. To reduce acquiescence bias, the researcher should analyze the questions and adjust anyone that may appear as a favorable answer. This leading answer also includes “Yes/No”, “True/False”, and “Agree/Disagree”. Dual negative-positive scale types of answers are helpful in reducing this type of bias. It also makes the results more comparable across boards.

2. To reduce social desirability bias, researchers should make the participants anonymous and assure the participants of confidentiality. The researcher should also construct the wordings of the questions in a non-suggestive manner.

3. Confirmation bias effects can be reduced if the researchers have an open mind and take into consideration all the data when analyzing existing hypotheses. The researcher should also know that the hypotheses can be proven wrong in the analysis. 

4. The habituation bias can be prevented when the researcher differentiates how they word the questions and use a tone that is more engaging and can keep the participants active.

5. The halo effect bias can be avoided if the researcher refrains from shaping the participants’ experiences before the study material is administered to the participants. Therefore, it is important that the researcher provides the participant with only the needed information for the task at hand. Also, a large number of participants should be introduced to increase the likelihood of obtaining unbiased data from the population.

Read: Type I vs Type II Errors: Causes, Examples & Prevention

Examples of Participant Bias

Example 1:

A researcher selects a sample population of 500 field managers and conducts a survey poll in relation to their workloads. The field managers that have a higher workload may be unable to respond to the survey because they have limited time to answer it, while the managers' lower workload may also decline in providing suitable answers to the survey. 

This may be because of fear that their senior colleagues or supervisors may perceive them as surplus employees if they are not anonymous and the survey responses are compromised and revealed, maybe now or in the future. Therefore, participants that consent to be a part of the survey may give a false response to their true feelings or thoughts.

This may lead to erroneous conclusions being made from the research.

Example 2:

A study was conducted among medical staff of a particular institution. The study aimed to observe how well the medical staff adhere to the hand washing rule.

In the first experiment, the percentage of medical staff that complied with the hand-washing rule was 27%. After a one-week interval, the second testing took place, surprising the percentage rose to 55%, which was a 28% increase. The question is why? What ignited the change in behavior?

The outcome of the hand-washing research shows that the medical staff knew when they were being watched. Therefore, the compliance percentage for the hand-washing rule rose to 55%. Which is far greater than when the staff were not being watched. In this example, we can see that the respondent influenced the result of the research because they were aware of what the researcher was looking out for. 

Conclusion

Participant bias can occur because of an error from the researcher, which provides cues about the details or aim of the research to the participants and then influences them to act in unusual ways that can jeopardize the outcome of the experiment. But, it can also occur without an error from the researcher.

Hence, researchers must learn to be careful and not show any form of bias or try to, directly and indirectly, influence the behavior of the participants. If this is to happen, participants' bias may be undetected.

Researchers should look out for any unusual characters in the participants and correct them as quickly as detected. If all the aforementioned is in check, then you can expect your findings to be accurate.

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