While there are many reasons to run clinical studies, including testing the safety/effectiveness of new medications/medical devices and improving upon current treatments, clinical research studies also involve looking at patient psychology. The collection, analysis, and interpretation of this type of research help us gain insight into human behavior, emotion, and thought processes, which can be invaluable when improving medical treatments and protocols. Unfortunately, it’s not always easy for participants to be open, and honest about their innermost thoughts, due to a phenomenon called research participant bias. To get accurate, reliable, and unbiased results, it’s essential to take steps to minimize participant bias in clinical studies, which is what we’re going to outline today.
What Is Research Participant Bias in Clinical Studies?
Research participant bias or response bias is when a participant either knowingly or unknowingly deviates their response to correspond with the way they think the researcher wants them to, rather than responding naturally. Things that influence this are expectations, beliefs, or perceptions about the study and its participants, as well as, the environment, instructions given, or their need to please the researcher. Research participant bias can lead to inaccurate or misleading results, and can take many forms, including:
- Social Desirability Bias: this is when participants feel the need to respond in a socially desirable way that presents the best version of themselves or the version that is socially acceptable. This distorts the answer to what they believe is best due to the pressure to conform. This is prevalent when answering personal and sensitive questions about income, religion, and the quality of their person.
- The Halo Effect Bias: this is when participants overlook the faults of an individual, to only see the best version of them. If someone has a positive belief about someone else, they’re also likely to have a positive opinion about experiences associated with that individual. This works in reverse as well, where an individual reacts negatively towards something or someone if there is already a perceived negative association with it – whether that be an experience or even another individual. This type of bias has a carry-over effect. Which it directly impacts how one perceives the world.
- Yea-and Nay-Saying Acquiescence: this is a common bias that pops up in self-reported surveys where the participant completes the questionnaire on their own. Essentially, this bias is the increased tendency to choose an answer regularly; for example to say “yes” to both “yes” and “no” questions or to respond “yes” or “no” to all questions. A participant may do this to disrupt research, to please the researcher, or simply because of fatigue.
- Selection Bias: this is an experimental error that can occur if certain groups of participants are chosen for inclusion, who are not representative of the larger population, leading to distorted results.
- Demand Characteristics: this refers to when participants become aware of the researcher’s expectations and modify their responses accordingly, causing the results of the experiment to be biased. It can also come up if there are set expectations on participant performance or tasks that demand specific participant behavior.
- Top-Down Processing: when participants have the “big picture” of an idea or general concept from previous knowledge, and then break it down into specific information by pulling from a perceptual set of past experiences, expectations, or emotions. This can lead to a cognitive bias within the formation of opinions or cause preconceived notions on available evidence.
- Participant Reactivity: In this type of research participant bias is when individuals are aware that their behaviors are being observed and thus adjust their behavior to align with what they think the researcher is looking for.
Why Is Research Participant Bias Hard to Detect?
Research participant bias can be particularly difficult to detect as survey results and the conclusions can often appear valid, even if they are influenced by external factors. This makes it difficult to detect when bias has taken place, and limits the effectiveness of any attempts to correct it. As such, it’s essential for researchers to pay close attention to potential sources of participant bias, as noted above, and take steps to minimize the impact.
How Does The Lynn Health Science Institute Tackle Research Participant Bias?
The Lynn Health Science Institute has developed a range of methods and best practices to tackle research participant bias in our clinical studies. The key here is to control the environment in which the study takes place, using techniques like confidentiality agreements, so participants know their answers are private, and plain language consent forms to ensure each participant understands the study. In studies where social desirability bias or participant reactivity may occur, we use randomized response techniques (randomizing responses), and complete anonymity – where our researchers never meet the participants – to ensure that they can answer truthfully and honestly.
The institute emphasizes the importance of pilot-testing questionnaires to guarantee that only pertinent information is provided to participants, so as to not shape responses or to give extraneous detail that might allow participants to form ideas before facing the experimental materials (this avoids the halo effect bias). We also make sure that all information presented about our studies, including the advertising, asking of the questions, the formulation of questions, and the way the information is treated afterward, is free from judgment and is well-balanced (to prevent yea- and nay-saying bias, and top-down processing bias). We also use statistical analysis techniques such as sensitivity tests to identify potential sources of bias that may affect the data and corresponding conclusions.
Finally, we use large sample sizes to increase the accuracy of results and to avoid selection bias, while using different study types like single-blind, where participants do not know which intervention they are receiving or which group they belong to; double-blind, which is when participants and researchers are unaware of participants’ group assignments, and placebo’s, an inactive medication to which the participant is unaware of.
If you have any questions about how the Lynn Health Science Institute deals with research participant bias in our clinical studies, then contact us today here or by phone at 405-447-8839. Our team of experts is happy to answer all questions concerning the techniques used to guard against and handle participant bias.