When a researcher’s assumptions, views, or preconceptions impact what they see or report in an experiment, it is known as observer bias. It frequently impacts research in which participants are informed of the study’s objectives and assumptions. Detection bias or ascertainment bias are other terms for observer bias.

In observational research, observer bias is very probable. However, it can impact other forms of research in which data is physically obtained or recorded.

What is observational research?

Observational studies frequently monitor individual activities or collect data without influencing the result or scenario. Many scientific domains, like healthcare, psychiatry, behavioural science, and anthropology employ observational studies.

Whether you utilize qualitative or quantitative research methodologies, observer bias can arise.

Subjective methods

When collecting your data, you must analyze them using subjective research methods.

Your life experiences, beliefs, or sentiments can impact how you see and understand others’ behaviour in any experiments study. For example, they may cause you to consider some things while dismissing others that are equally essential.

Example of subjective methods

You conduct an observational study to understand how young children respond to a new toy. Then, you and another researcher partner the kids up and give them each a new toy. You and the other researcher now watch how frequently the kids share or spend hours interacting with it.

You record and analyze various interactions among the youngsters, concluding that they spent much of their time sharing the item and interacting positively. On the other hand, your co-worker opposes, claiming that most of their interactions were unfriendly.

Your expectations for the study might bias the outcomes. There’s a chance you’re unconsciously programmed only to see whatever you want to see.

Objective methods

Even if you utilize more objective measuring methods (medical equipment and imaging), observer bias could still affect your research.

Since humans can perceive data differently, research outcomes might change amongst observers.

Example of objective methods

A blood pressure monitor is used to check the blood pressure of individuals. While taking blood pressure readings, you prefer to round up to the nearest full number, but your partner rounds down.

Observer bias can be caused by inadequate training, lack of control, or insufficient processes or protocols.

Observer drift

You grow more accustomed to the methods you gather data, and you may become less cautious while collecting or logging measurements. Observer drift occurs when observers deviate from established methods in predictable ways, resulting in varied ratings of the same occurrences over time.

Example of observer drift

Even if your instruction book offers different rules for evaluating blood pressure, you gradually tend to flag only severe high blood pressure values as “high.”

Minimizing observer bias

It’s critical to research, so that observer bias is minimized. However, while you still can strive to limit observer bias in your research, you may not be able to eradicate it.

• Employ masking

Masking, also known as blinding, ensures that both respondents and observers are uninformed of the study’s objectives.

It can help to eliminate several of the research expectations that come with understanding the study’s goal, making observers less likely to be prejudiced in one direction or the other.

Masking may be done by including other individuals in your research as observers and providing them with a cover narrative that will deceive them about the genuine goal of your research.

• Make use of triangulation.

To ensure that your conclusions are trustworthy, use several observers, sources of information, or study methodologies. It’s usually a good idea to utilize triangulation to double-check your numbers and make sure they match up.

It’s very crucial to include several observers and strive to employ various data gathering methods for the same observations to prevent observer bias. Whenever data from multiple observers or methodologies converge, the possibility of bias is reduced, and you may be more specific with your conclusions.

• Multiple observers

With several observers, you can ensure that your records are consistent and unlikely to be distorted by the biases of any single observer.

It’s critical to assess and keep good interrater reliability if you have several observers. The consistency with which multiple observers evaluate the same observation is referred to as interrater reliability.

You may compare data from several observers, assess interrater reliability, and define a goal to fulfil with quantitative data. Observers are often trained in methods until they can consistently generate the same or comparable observations for each event in training.

• Observers should be trained.

It’s a good idea to educate all observers before undertaking any research to ensure that everyone gathers and reports data in the same way.

It’s critical to calibrate your procedures such that different individuals record the same observation with little or no change. To maintain high interrater reliability and prevent observer drift, you can adjust your methods between observers at essential moments throughout the research.

• Standardize the procedures

It’s best to construct systematic and easy-to-understand standardized processes or guidelines for all observers. If your research is about behaviours, for instance, include all the behaviours that observers should be aware of.

Please make a video or written record of these methods so you may refer to them at any time during the entire study to jog your memory.

Other types of biases

Observer bias is linked to many different types of study bias.

• Observer-expectancy effect

When investigators affect the outcomes of their research by interacting with participants, this is known as the observer-expectancy effect.

Researchers may unwittingly indicate their own opinions and perceptions about the research through demand characteristics, influencing respondents.

The observer-expectancy effect is also known as:

• Experimenter-expectancy effect
• Pygmalion effect
• Rosenthal effect

Example of the observer-expectancy effect

You’re testing the effectiveness of a particular back pain reliever. You conduct a study with two groups:

• The real therapy with the novel painkiller is given to Group A.

• Group B is given a placebo instead of a drug.

The investigators know whether you’re in Group A or B; however, the individuals don’t. While completing a poll on their degree of back discomfort, you unconsciously evaluate the two groups accordingly. You position your queries more critically for Group B than Group A, as if you anticipate the respondents to still be in agony.

Actor–observer bias

The actor-observer bias is a type of attributional observer bias. You describe the reason for something different based on if you’re the actor or the observer in a given circumstance.

As a participant in a scenario, you may be tempted to blame your actions on other causes. However, being an observer, you can instead ascribe another person’s conduct to internal causes, even though it’s the same for everyone. The actor-observer bias is an issue in social psychology.

Example of the actor-observer bias

You’re functioning as an apprentice and are stuck in a rut. You blame it on the cold, the absence of sunlight and your long drive to work (external factors).

You see another co-worker thinking the same way a few days later, but you blame it on their attitude, work habits, and lack of motivation (internal factors).

Hawthorne Effect 

Whenever some research participants think they are being observed, they tend to be more productive to do better. It is known as the Hawthorne effect. It outlines what individuals who are being monitored in research may unwittingly do.

It was named after Hawthorne Works, a corporation where productivity was said to enhance owing to the involvement of observers, independent of the experimental treatment.

Example of the Hawthorne effect

You’re the middle school administrator, and a teachers’ instructional management abilities have been criticized. You chose to spend the day observing their sessions to see if the concerns were valid.

Because both the instructor and the students are mindful that the administrator is watching them, the students act correctly, and the teacher works harder. Therefore, the observations do not correspond to regular classroom behaviour patterns.

Experimenter bias

Experimenter bias encompasses all forms of researcher biases that may affect their study. Observer bias, actor-observer bias, observer expectancy effects, and other biases are examples. The experimenter effect is another name for experimenter bias.

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