Which of the following terms are also ways of describing observer bias select all that apply 1 point research bias experimenter bias perception bias spectator bias?

“Explicit bias” refers to the attitudes and beliefs we have about a person or group on a conscious level. Much of the time, these biases and their expression arise as the direct result of a perceived threat. When people feel threatened, they are more likely to draw group boundaries to distinguish themselves from others.

Why it’s important:

People are more likely to express explicit biases when they perceive an individual or group to be a threat to their well being. Research has shown that white people are more likely to express anti-Muslim prejudice when they perceive national security to be at risk and express more negative attitudes towards Asian Americans when they perceive an economic threat. When people perceive their biases to be valid, they are more likely to justify unfair treatment or even violence. This unfair treatment can have long-term negative impacts on its victims’ physical and mental health.

What can be done about it:

Expressions of explicit of bias (discrimination, hate speech, etc.) occur as the result of deliberate thought. Thus, they can be consciously regulated. People are more motivated to control their biases if there are social norms in place which dictate that prejudice is not socially acceptable. As we start forming our biases at an early age, it is important that we reinforce norms in our homes, schools, and in the media that promote respect for one’s own and other groups. Research shows that emphasizing a common group identity (such as “we are all Americans”) can help reduce interracial tensions that may arise between majority and minority ethnic groups in the U.S. Also, when conducted under the right conditions, studies show intergroup contact between people of different races can increase trust and reduce the anxiety that underlies bias.

Learn more:

More information about explicit bias and the way it shapes the lives of black men and boys can be found in our report Transforming Perception.

The observer expectancy effect arises due to demand characteristics, which are subtle cues given by the researcher to the participant about the nature of the study, as well as confirmation bias, which is when the researcher collects and interprets data in a way that confirms their hypothesis and ignores information that contradicts it.

Demand characteristics

Demand characteristics are a form of response bias that may give rise to the observer expectancy effect. Typically seen in psychological research, demand characteristics are subtle cues from the experimenter that may give the participants some idea of what the study is about. While some information about the study must be divulged to the participants for ethical reasons, it is ideal for participants to know as little as possible about the nature of the research being done. The more participants know, the more likely it is that they will try to “help” the researcher by behaving in the way they think the researcher wants them to. Unfortunately, when this happens, the data collected is inaccurate and therefore not very informative.

Many things can act as demand characteristics. Any verbal or non-verbal communication between the participant and the experimenter, the appearance of the room where the study is being held, and any knowledge the participant might have of the kind of work the lab does could all suggest certain behaviors to the participants. Naturally, demand characteristics cannot be eliminated completely, but their effects can be minimized.

Participants may provide biased responses in studies due to social desirability – wanting to give a good impression of themselves. However, it has been shown that, when participants have some knowledge of the researcher’s hypothesis, they are more likely to respond in a way that they think will benefit the researcher, whether or not it makes them look good. They do not want to provide “bad” information that would ruin the study or disprove the researcher’s hypothesis.1

Despite the good intentions of many participants, demand characteristics give rise to the observer expectancy effect, which compromises the accuracy of the study. Accurate data that yield no significant results are more informative and more valuable than inaccurate data that yield significant results.

Confirmation bias

Another factor that contributes to the observer expectancy effect is confirmation bias. Researchers are highly motivated to find evidence in support of their hypothesis, particularly now, when it is so difficult to get papers published in reputable journals. The intense motivation to collect data in support of a hypothesis can cause researchers to selectively notice and remember information that aligns with their hypothesis. This biased interpretation of data is referred to as confirmation bias.

The motivated search for information that confirms their hypothesis can lead experimenters to interpret participants’ behavior in a way that confirms their hypothesis. However, confirmation bias not only affects how we interpret data; it influences how we collect the data in the first place. As such, researchers may ask participants leading questions, which prompt a specific response, or even treat participants in a way that elicits the desired behavior. Of course, the data collected under such conditions is not accurate and therefore not helpful in the progression of knowledge.

Robert Rosenthal is one of the researchers most closely associated with the observer expectancy effect. He wrote many papers on the topic, the first of which was written in collaboration with Kermit L. Fode in 1963, and titled “The effect of experimenter bias on the performance of the albino rat”.2 The aim of this paper was to demonstrate the ease with which an experimenter can influence a participant to exhibit a certain behavior. Rosenthal and Fode provided evidence for this through a now highly renowned study. The participants were experimental psychology students, each of whom was given a group of five rats, which they were supposed to teach to navigate a maze to reach the darker of two platforms. Each participant was then told that they were either working with particularly bright or particularly dull rats, although there were no significant differences between any of the rats. At a later point, when the rats were tested on their ability to navigate the maze, those who had been randomly labeled as “bright” performed better than those who had been randomly labeled as “dull”. The students working with the rats had been biased by these labels. This caused them to treat the rats differently, ultimately resulting in the animals conforming to their expectations in a sort of self-fulfilling prophecy. This early example of the effects of experimenter bias prompted further research on the subject and helped raise awareness for the effect among investigators.

The classic example of the observer expectancy effect is the study of Clever Hans. Hans wasn’t your typical participant, mostly because he wasn’t human. Hans was a horse living in Germany in the late 19th and early 20th centuries. What earned him the title of “Clever” were his owner’s claims that he had near-human intelligence. Hans and his owner, Wilhelm von Osten, gave several performances, in which Hans displayed his many impressive abilities. He was seemingly able to perform basic arithmetic, identify colors, read, and recognize musical notes.3

Interestingly, von Osten never displayed any explicit signals cueing Hans to answer in any particular way. This caused many people to be drawn in by the act and believe that Hans was exactly as clever as he was cracked up to be.4 However, not everyone was so sure. German biologist and psychologist, Oscar Pfungst, launched a study into the so-called “Clever Hans Phenomenon” in 1907, and found that Hans only answered correctly when the questioner, usually his owner, van Osten, knew the correct answer.5 He concluded that Hans was not answering the question, but simply responding to subtle cues, likely unconsciously made, from the questioner.6

This is an exemplary instance of the observer expectancy effect, as Hans’ questioner influenced the horse to behave in a way that conformed with his expectations. In a situation where the questioner was not present, Hans would not have responded as he did in their presence. As such, this is a good example of how experimenter expectations can heavily influence participant behavior.

What it is

The observer expectancy effect describes how the perceived expectations of an observer can influence the people being observed, particularly in the context of research.

Why it happens

The observer expectancy effect arises due to demand characteristics, which are subtle cues given by the researcher to the participant about the nature of the study, as well as confirmation bias, which is when the researcher collects and interprets data in a way that confirms their hypothesis and ignores information that contradicts it.

Example 1 – Clever Hans

One of the earliest recorded cases of the observer expectancy effect was that of Clever Hans, a German horse in the late 19th and early 20th centuries. Many people believed he had near-human intelligence because he was able to answer mathematical and vocabulary questions. However, it was later shown that Hans only ever answered correctly when the person asking the question also knew the response; he was responding to subtle cues from the questioner and not actually answering the question. This illustrated how the expectations of the experimenter can heavily influence participant behavior.

Example 2 – Teacher expectations

Teachers often form expectations for how their students will perform throughout the school year. This can result in a self-fulfilling prophecy where the teacher consciously or unconsciously influences their students to behave in a way that aligns with their expectations.

How to avoid it

Researchers can avoid the observer expectancy effect by using a double-blind design, in which neither the participants nor the experimenters know which participants are in the experimental condition and which are in the control condition. This way, the experimenter’s expectations will not influence participant behavior.

Which of the following terms are also way of describing observer bias?

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