Observer bias is when the results of an experiment are influenced by a researcher's expectations. For example, If you expect that a certain place will be dirty and smelly, you might be more likely to notice trash or bad smells than if you expected the place to be clean and pleasant.

Observer bias mostly occurs in the observational study but it can happen in other forms of studies. In this article, we will discuss observer bias, the types, and the implications in research.

What is Observer Bias?

Observer bias is defined as when an observer's expectations about a person, object, or event influence their observations. In other words, observer bias is a type of bias that occurs when the person writing the content has personal feelings or perspectives that affect their ability to impartially discuss the subject. 

Observer bias is a problem in research studies where the information collected about participants or events is affected by the fact that the researcher has expectations about what is going to happen. For example, if a researcher is trying to figure out whether there are more men or women in a public park, and she tries to count every person that passes her but happens to miss some of the people who are wearing hats or backpacks because she doesn't expect them to be women (even though they might be), that's observer bias.

Read: Research Bias: Definition, Types + Examples

Other types of observer bias include confirmation bias, in which someone interprets information to favor their existing beliefs; selectivity bias, in which a researcher uses only data that supports his or her hypothesis; and recall bias, in which someone's memory skews their interpretation of events.

For example, if you were writing about a restaurant that you've never visited but had heard good things about, your perceptions might be skewed in a positive direction, affecting your ability to write objectively and factually about it. Observer bias is also known as the experimenter effect, and it can result in misleading information.

Effect and Implications of Observer Bias

When observer bias is present in a study, it affects the data collection itself. The results fail to accurately represent what exists in reality because they are influenced by the researcher's own biases and prejudices. 

Researchers can combat this type of bias by taking some steps. They should make sure that any surveys or questionnaires are designed so as not to lead respondents to answer questions in a particular way desired by the researcher. 

Read: Selection Bias in Research: Types, Examples & Impact

Also, observer bias can lead to inaccurate data sets, which can be damaging to scientific research and public policy decisions. The presence of observer bias in your study can lead to negative outcomes for the people who are involved, including misinterpretation of their behavior and biased treatment from the researchers.

Also, note that observer bias can take many forms intentional or not but it essentially means that an observer's actions or presence alter what's being observed.

Types (or related biases) to Observer Bias

1. Observer-expectancy effect: The observer-expectancy effect can occur in several different ways such as when the researcher unconsciously treats participants in different groups differently, leading to unequal results between groups (for instance, if they are more likely to ask questions or give directions to one group).

When the researcher deliberately treats participants differently because they have formed a hypothesis and want to test it. It could also be when the researcher influences participants' behavior by changing their body language, posture, tone of voice, or appearance in certain ways.

Read: What is Participant Bias? How to Detect & Avoid It

One example of observer bias is when doctors expect a certain outcome based on their previous experiences and then unconsciously influence their patients to achieve the expected outcome (like saying "I know this will hurt" when giving an injection). This phenomenon is called expectancy bias.

2. Actor–observer bias: The Actor–observer bias refers to the phenomenon in which we attribute our actions to external factors (e.g., "I failed the test because it was too hard"), but attribute other people's actions to their internal traits (e.g., "She failed the test because she is dumb").

To understand this, let's break it down:

  • The "actor" is the person who is performing the behavior.
  • The "observer" is someone who is watching someone else's behavior.
  • The "attribution" refers to what we think "caused" a person's behavior.

It helps us understand why we are inclined to blame others for things that happen, even when we would not blame ourselves for acting in the same way.

Read: What is Publication Bias? (How to Detect & Avoid It)

For example, if you get a bad grade on a test, you will likely attribute your failure to some factor outside of yourself: maybe the teacher is biased against you, or maybe the questions were harder than usual. If you see someone else fail a test, however, you will likely attribute their failure to something inside of them: maybe they didn't study hard enough, or maybe they are just not as smart as everyone else.

3. Hawthorne effect: The Hawthorne effect is a type of observer bias that can occur in research studies in which the behavior of one or more people being observed changes simply because they are being observed. The name comes from a series of experiments conducted at Western Electric Company's Hawthorne Works factory in Cicero, Illinois during the 1920s and 1930s. During these experiments, researchers sought to determine whether changes in lighting would improve productivity. 

They found that productivity increased while they were conducting the study, even when they tried different lighting scenarios and light levels decreased. This led them to believe that, instead of it being the lights that caused the increase in productivity, it was simply the fact that people were getting more attention from their supervisors than normal that was improving productivity.

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

4. Experimenter bias: The experimenter bias of the observer bias is that the researcher believes what they are looking for, and therefore will find it. Before you begin a research study, you may have already formulated an idea of what the results will be. 

If this is the case, you're setting yourself up for some observer bias. When you have a pre-determined idea of what the results will be, and you conduct a study to test your theory, if you don't get the exact results that confirm your theory, you may be tempted to twist those results to make them more in line with your predictions. 

So if you're doing a study on whether or not taking folic acid while pregnant helps reduce the risk of having an autistic child, and you hypothesize that it does help reduce that risk but after conducting your study you find out it doesn't you may decide that folic acid does reduce the risk, but only when combined with another vitamin.

How to Minimize Observer Bias

There are several ways to minimize observer bias in research studies such as using the triangulation method, the masking method, and by using multiple observers. Other ways you can use to minimize observer bias include:

  1. Assign random subjects to each group instead of choosing subjects yourself
  2. Use blind experiments, where your subjects don't know which treatment they're getting.
  3. Use double-blind experiments, where neither you nor your subjects know which treatments are being used or which group they belong to.
  4. You can also videotape the research project and ask an outside party to analyze it.
  5. Having multiple researchers involved in the project will make it more difficult for one researcher's biases to greatly affect the project's outcome.
Read: Undercoverage Bias: Definition, Examples in Survey Research

It's hard to control all factors, but you should do your due diligence and try. For example, if you're running an experiment on whether or not a certain treatment helps people lose weight, it might help to use a blind study where half the participants see a doctor who tells them they have a high chance of losing weight even though those people are actually in a control group.

Another good way to minimize observer bias is to ensure that the subject doesn't know they're being observed. That way, their behavior won't be affected by how they think the observer wants them to act.

Examples of Observer Bias

Here are some examples of observer bias.

Example 1

Let us consider a study that attempts to show that taking a certain vitamin increases concentration levels in participants. If the researchers expect to find that result, they might be more likely to encourage participants who are taking the vitamin to talk about how much better they're doing on their tests or point out that they seem more alert and focused. 

They might even suggest that their behavior seems like it has changed since starting their vitamin regimen. In contrast, if the researchers expected to see no change in performance as a result of taking the vitamin, they might pay less attention when participants who are taking the vitamin speak up or act differently, or they might try to play down any comments by suggesting that it's probably "just a fluke." 

In this way, their expectations can affect the results of the study allowing them to "prove" something that wasn't true in the first place.

Example 2

If you're studying how cats react to music, and you expect that they'll be drawn to classical music because it's soothing, you might interpret their behaviors as being attracted to classical music. But if you're playing the same song over and over again while you're observing them, they might just be reacting to the noise. 

If you didn't expect them to like classical music, though, you would be more likely to observe their behavior with less bias and more objectivity.

Conclusion

To minimize observer bias, you should try to make sure that your observations are as consistent as possible from person to person, and you should be careful not to allow your personal feelings about participants in your research to affect how you conduct the study.



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