Demand Characteristics: Definition, Examples, & Control

Demand characteristics refer to clues or signals in an experimental setting that hint to participants about the experimenter’s expectations, leading them to behave in a certain way to match these expectations, potentially biasing the results.

Unfortunately, research participants often act and speak in ways inconsistent with how they would otherwise normally behave in the real world. This is a result of demand characteristics (Orne, 1959).

Demand characteristics are subtle hints that suggest to the participant what the experimenter predicts or hopes to find in the study.

This motivates the participant to conform to the experimenter’s expectations, thus changing the outcome of the experiment (Orne, 2009).

It is important to note that participants may change their behavior in either a conscious (intentional) or unconscious (unintentional) way, and this behavior may or may not be what the experimenter was actually hoping for.

Regardless, feeding into these demand characteristics can have a huge impact on the results of any psychological study. 

a red tick in a box on paper

Unlike other fields of science that rely on cells or bacteria samples for experiments, psychology experiments require participation from real, everyday people.

During these studies, participants often have to answer questionnaires or partake in simulations to help the researchers draw broader conclusions on how the human brain operates and how people act in certain situations.

As such, any participant in a study must be truthful and accurate when filling out a self-report questionnaire, being interviewed, or partaking in a role-play situation, so as not to skew the data or lead the researcher to draw faulty conclusions. 

Types of demand characteristics

There are many different types of demand characteristics that can bear weight on the outcome of an experiment. These characteristics provide clues regarding the overall research hypothesis.

Here are some of the most common types of demand characteristics:

Study rumors

Information relevant to the experiment but learned about outside of the experiment itself (Orne, 1962).

For example, a study may ask a participant to read a story at the beginning and, unbeknownst to the participant, prompt the participant to recall details from the story after completing a series of other unrelated tasks.

If rumors circulate that this is the procedure, a participant may pay extra close attention and make an effort to remember the details, thus skewing the results of the study. 

Lab setting

The specific location in which the experiment is being performed (Orne, 1962). For example, if a study is conducted in a laboratory setting, as opposed to a more natural setting, such as a gym or a classroom, it may place hidden demands on the participant to respond in a certain way. 

Order of procedure

The order of the actual questions in the experiment may bias the participants to answer in a certain way (Orne, 1962).

For example, if demographic questions are asked at the beginning, a participant will become more cognizant of their own demographics, such as their race, gender, or socioeconomic status, and may subconsciously (or consciously) behave or respond to subsequent questions in a way that adheres to stereotypes or expectations about that group.

In psychology, this is called a self-fulfilling prophecy (Merton, 1948), whereby exposure to a person’s beliefs about the expectations of their group leads them to act in such a way that conforms to these expectations.

Explicit or implicit communication

Any form of communication, whether intentional or unintentional, can affect the outcome of the study (Orne, 1962).

For example, the researcher might consciously or unconsciously smile or frown during the experiment, suggesting to the participant that their answers are what the researcher did or did not hope for, thus affecting the participant’s future responses. 

These four types of demand characteristics are by no means the only ones that can occur in research studies. Even the title of an experiment or tools such as video cameras can create hidden demands for how a participant should act during the study.

And so, with all of these types of characteristics in mind, it is important to consider how the presence of these hidden cues may affect the participant and, as a consequence, the results of the study.

How do demand characteristics affect participants?

Beyond just being present in the study, demand characteristics bear weight on the performance of participants.

There’s a famous psychology study called the white bear problem where participants were told explicitly not to think of a white bear, causing them to actually be more likely to think of one, a process referred to as ironic process theory (Wegner, 1994).

In other words, conscious attempts to suppress certain thoughts make them more likely to surface. And this is what can happen with demand characteristics. Once a participant becomes aware of their presence, it becomes increasingly hard to ignore them.

For example, if a participant realizes that the experimenter is smiling, it’s hard to stop noticing it and try to act in an honest manner. 

Here are four specific ways in which demand characteristics affect the behavior of participants once they become aware of the demand characteristics:

Good-participant role

These participants will try to assist the researcher with their findings, acting in such a way that supports what the participant believes to be the hypothesis. Acting like this provides the participant with a sense of fulfillment (Nichols & Maner, 2008). 

Negative-participant role

These participants will do the exact opposite: they will actively try to sabotage the results of the study by lying in their responses or acting in ways they otherwise would not have (Weber & Cook, 1972).  

Apprehensive subject

These participants try to produce the most socially desirable answers to avoid being judged by the experimenter. This phenomenon is called the social desirability bias (Paulhus, 1984), whereby participants over-report more socially desirable attitudes and behaviors (Weber & Cook, 1972).

For example, if filling out a self-worth questionnaire, participants may report more positive feelings about themself than they actually have. 

Faithful subject

This participant follows the instructions of the study exactly as they are presented (Weber & Cook, 1972). However, knowing what we do about the challenges of avoiding demand characteristics once we are aware of them is definitely the most challenging role to take on. 

Why do demand characteristics matter?

Demand characteristics matter greatly because they can alter the results of a research study. As mentioned, once a participant becomes aware of these hidden demands, it becomes incredibly difficult to not have them affect their behavior and responses.

Thus, these characteristics pose a threat to both the internal and external validity of the study (Spencer, 1978).

With the presence of these demands, it becomes harder to say whether the independent variable truly is responsible for the change in the dependent variable or if the participant’s altered behavior played a role, thus affecting the internal validity of the study.

Similarly, because of the potential influence of these demand characteristics, the findings cannot be generalized to populations outside of the research study itself, impacting the study’s external validity.

How To Reduce Demand Characteristics

Don’t give up hope just yet. Even though demand characteristics are relatively prevalent in psychology studies, it does not mean we can’t combat them.

There are many methods researchers can take to help reduce the impact these demand characteristics have on the results of a study, such as:

Deception to conceal demand characteristics

Trying to deceive participants about the study’s meaning helps prevent them from behaving in a certain way (Orne, 2009).

That being said, researchers have to be careful because deception can sometimes be an unethical practice, so researchers must make sure to debrief the participant on the actual purpose of the study at the end. Researchers also often include filler questions to distract the participant from the true research purpose.

For example, they may insert a memory test in the middle of the experiment to make participants think the researcher is studying memory when in reality, the experiment has nothing to do with memory.

Between-subjects design

In a between-subjects design, participants are either part of the experiment or control group instead of receiving both treatments in a within-groups design.

A within-groups design makes it easier for the participant to discern what the study is about because they can see the differences between the two groups, so a between-subjects design is better for combating demand characteristics (Rubin & Badea, 2010). 

Implicit measures

Implicit measures can help reduce the impact of demand characteristics (Orne, 2009).

Because they are subconscious tasks, participants have way less control over their answers, so they can’t try to either confirm or sabotage the researcher’s hypothesis.

These measures also help circumvent social desirability bias because participants can’t explicitly respond in ways that they think are desirable.

Frequently Asked Questions

Is participant bias the same as demand characteristics? 

Participant bias occurs when a participant consciously or unconsciously responds in a way that they think the researcher wants them to (Brito, 2017).

As such, demand characteristics are often the cause of participant bias, placing hidden demands that biases the participant and alters their behavior. 

Is participant bias the same as expectancy effects? 

Participant bias and expectancy effects are similar but not the exact same. Participant bias, on the one hand, involves the participant changing their behavior to what they think the researcher wants rather than acting as they normally would.

Expectancy effects, on the other hand, occur when the researcher’s own cognitive biases affect the way they interact with the participant (Harris & Rosenthal, 1985). This, in turn, may produce participant bias, altering the participant’s behavior whether knowingly or unknowingly. 

Is participant bias the same as the placebo effect? 

The placebo effect occurs when the participant thinks they had the treatment effect when, in fact, they did not (Sliwinski & Elkins, 2013). As such, they alter their behavior under the wrongful assumption that they have been assigned a certain condition in the experiment.

This is a type of participant bias, where participants change their behavior because of the specific reason that they think they are in the treatment or experimental group when they are actually in the control group. 

Is participant bias the same as reactivity (observer effect)? 

In psychology, expectancy effects are another name for the observer effect, both of which are a form of reactivity (Harris & Rosenthal, 1985).

As such, while participant bias refers to the participant responding in a way they think the researcher wants, the observer effect occurs when the researcher themselves acts in a way that biases the participant’s behavior.

Is the Hawthorne Effect the same as demand characteristics?

The Hawthorne Effect occurs when a participant alters their behavior after becoming aware that they are actively being observed (Landsberger, 1958).

A common example in the literature demonstrated that when medical workers were aware that they were being watched, they were much more likely to regularly use antiseptic hand rub when washing their hands (Eckmanns et al., 2006). 

While the Hawthorne effect is more about observation, demand characteristics are the clues that reveal what the experimenter wants.

The key difference is that the Hawthorne effect doesn’t necessarily result in the behavior that the researcher wants. It just results in altered behavior as a result of being watched. 

What is the difference between demand characteristics and social desirability?

As mentioned, social desirability refers to the bias of participants to act or respond in a way that they perceive to be socially desirable.

While certain demand characteristics may produce social desirability bias, the key difference is that demand characteristics typically involve the participant trying to ascertain the research hypothesis, whereas social desirability bias can result in behavior that may or may not coincide with the researcher’s aims. 

Are demand characteristics a confounding variable?

Confounding variables are variables other than the independent variable that affect the outcome of the dependent variable (VanderWeele & Shpitser, 2013).

As we know with demand characteristics, the experiment has caused them to implicitly or explicitly alter their behavior from what it would have been without the demand characteristics present.

Thus, demand characteristics are a type of confounding variable that has an impact on the results of the study beyond the independent variable. 

Are demand characteristics extraneous variables?

Demand characteristics are a type of extraneous variable that can affect the outcome of a study. They can serve to invalidate the results of a study altogether by providing an alternative explanation for the observed results (Kalton, 1968).

Demand characteristics can be classified as both an extraneous and a confounding variable that can greatly impact the outcome of an experiment. But luckily, we’ve discussed ways to help combat the impact demand characteristics can have on experiments.

Do demand characteristics affect internal validity? 

As mentioned earlier, demand characteristics make it extremely hard to know whether the independent variable truly is responsible for the change in the dependent variable or if the participant’s subsequent altered behavior played a role.

Therefore, demand characteristics can pose a great threat to the internal validity of an experiment and can, in extreme cases, completely invalidate the results altogether. 

Do demand characteristics affect ecological validity? 

Ecological validity refers to the extent to which the design of an experiment matches the participant’s real-world context.

Consequently, certain demand characteristics can affect the ecological validity of an experiment, such as an experimenter’s implicit or explicit behavior (and there isn’t ever an experimenter administering a research task in real life!).

The most obvious demand characteristic that can bear weight on the ecological validity of an experiment is the lab setting in which most experiments are conducted (Orne, 1968). As we discussed, the very environment of a study can signal certain clues that affect the way a participant behaves.

And a lab setting certainly does not mirror that of a real-world environment. Thus it becomes clear that demand characteristics can often (and in almost all cases do) affect ecological validity. 

References

Brito, C. F. (2017). Demonstrating experimenter and participant bias.

Eckmanns, T., Bessert, J., Behnke, M., Gastmeier, P., & Rüden, H. (2006). Compliance with antiseptic hand rub use in intensive care units the Hawthorne effect. Infection Control & Hospital Epidemiology, 27(9), 931-934.

Harris, M. J., & Rosenthal, R. (1985). Mediation of interpersonal expectancy effects: 31 meta-analyses. Psychological bulletin, 97(3), 363.

Kalton, G. (1968). Standardization: A technique to control for extraneous variables. Journal of the Royal Statistical Society: Series C (Applied Statistics), 17(2), 118-136.

Kihlstrom, J. F. (2021). Ecological validity and “ecological validity”. Perspectives on Psychological Science, 16(2), 466-471.

Landsberger, H. A. (1958). Hawthorne Revisited: Management and the Worker, Its Critics, and Developments in Human Relations in Industry.

Merton, R. K. (1948). The self-fulfilling prophecy. The antioch review, 8(2), 193-210.

Nichols, A. L., & Maner, J. K. (2008). The good-subject effect: Investigating participant demand characteristics. The Journal of general psychology, 135(2), 151-166.

Orne, M. T. (2009). Demand characteristics and the concept of quasi-controls. Artifacts in behavioral research: Robert Rosenthal and Ralph L. Rosnow’s classic books, 110, 110-137.

Orne, M. T. (1959). The demand characteristics of an experimental design and their implications. American Psychological Association, Cincinnati.

Orne, M. T., & Holland, C. H. (1968). On the ecological validity of laboratory deceptions. International Journal of Psychiatry, 6(4), 282-293.

Orne M.T. (1962) On the social psychology of the psychological experiment: With particular reference to demand characteristics and their implications. American Psychologist 17: 776–783.

Paulhus, D. L. (1984). Two-component models of socially desirable responding. Journal of personality and social psychology, 46(3), 598.

Rubin, M., & Badea, C. (2010). The central tendency of a social group can affect ratings of its intragroup variability in the absence of social identity concerns. Journal of Experimental Social Psychology, 46(2), 410-415.

Spencer, C. D. (1978). Two types of role playing: Threats to internal and external validity. American Psychologist, 33(3), 265.

VanderWeele, T. J., & Shpitser, I. (2013). On the definition of a confounder. Annals of statistics, 41(1), 196.

Weber, S. J., & Cook, T. D. (1972). Subject effects in laboratory research: an examination of subject roles, demand characteristics, and valid inference. Psychological bulletin, 77(4), 273.

Wegner, D. M. (1994). Ironic processes of mental control. Psychological review, 101(1), 34.

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Saul Mcleod, PhD

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Educator, Researcher

Saul Mcleod, Ph.D., is a qualified psychology teacher with over 18 years experience of working in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.


Charlotte Ruhl

Research Assistant & Psychology Graduate

BA (Hons) Psychology, Harvard University

Charlotte Ruhl, a psychology graduate from Harvard College, boasts over six years of research experience in clinical and social psychology. During her tenure at Harvard, she contributed to the Decision Science Lab, administering numerous studies in behavioral economics and social psychology.