What Is the Hawthorne Effect? | Definition & Examples

The Hawthorne effect refers to people’s tendency to behave differently when they become aware that they are being observed. As a result, what is observed may not represent “normal” behavior, threatening the internal and external validity of your research.

The Hawthorne effect is also known as the observer effect and is closely linked with observer bias.

Example: Hawthorne effect
You are researching the smoking rates among bank employees as part of a smoking cessation program. You collect your data by watching the employees during their work breaks.

If employees are aware that you are observing them, this can affect your study’s results. For example, you may record higher or lower smoking rates than are genuinely representative of the population under study.

Like other types of research bias, the Hawthorne effect often occurs in observational and experimental study designs in fields like medicine, organizational psychology, and education.

What is the Hawthorne effect?

The Hawthorne effect occurs when a participant’s behavior changes as a result of being observed, rather than as a result of an intervention.

In other words, when groups or individuals realize they are being observed, they may change their behavior. This change can be positive or negative, depending on the research context. For example, people participating in a nutrition-related experiment may improve their diet solely because they are taking part in the experiment.

It’s important to note that participants must be aware that they are under observation for this effect to occur. Thus, the Hawthorne effect is a subtype of performance bias. 

The first study that identified the Hawthorne effect took place in the 1920s at the Hawthorne Works, a Western Electric plant in Cicero, IL. The objective was to investigate whether manipulating working conditions, such as lighting, increased worker productivity.

Initially, results suggested that productivity improved whenever any changes to those variables were made—including negative changes like reduced lighting. However, any change in productivity disappeared when the experiments stopped.

Ultimately, it was concluded that productivity improved because workers were responding to the increased attention from supervisors, not to the changes in working conditions. This came to be known as the Hawthorne effect.

Example of the Hawthorne effect

Changes in behavior attributed to Hawthorne effect can seriously distort your conclusions, especially in terms of any assertions made about causal relationships between variables. This affects the internal validity of the study.

Relatedly, a Hawthorne effect can also compromise your ability to make generalizations. This affects the external validity of your study.

Example: Hawthorne effect
A doctor examining a patient with Alzheimer’s disease decides to prescribe a treatment to improve their memory, planning to re-examine the patient after six months.

In the follow-up visit, the patient appears worse. The doctor has read many published Alzeheimer’s medication trials where patients who were prescribed active medication (i.e., not a placebo) often appeared better or more stable. Due to this, the doctor concludes that if the patient is worse, then it has to be a treatment failure. The doctor then decides to stop the medication.

However, the doctor hasn’t taken into account the possible impact of a Hawthorne effect on trial results. People participating in clinical trials often appear to do better than those in routine practice solely because of their participation in the study. In reality, the increased attention and interaction with doctors and nurses at regular intervals may be what leads to their better health outcomes.

For this reason, the Hawthorne effect may affect the generalizability of clinical research to everyday practice.

Criticism of the Hawthorne effect

Recent research into the original studies at Hawthorne Works has shown that the findings were flawed or overstated. In particular, significant differences between control groups and experimental groups led to the introduction of confounding variables that experimenters were unaware of at the time. It is highly likely that other factors also played a role in the original study.

Because the original study that coined the name “Hawthorne effect” has drawn criticism, some researchers propose the general term “participant reactivity” to describe the same phenomenon.

Regardless of the validity of the initial studies being challenged, the phenomenon does still occur. In other words, this doesn’t mean that being observed does not affect behavior. It just shows that the Hawthorne studies’ data are not the best representation of this phenomenon.

Ultimately, it may be hard to determine exactly how participant awareness impacts study results. However, researchers must keep this in mind when designing studies or interpreting results with human-centered research.

Other explanations of the Hawthorne effect

There are a few other factors to keep in mind that can also explain behavioral changes in study participants. These include:

Performance feedback

Participants who receive feedback may also have improved performance. For instance, in the context of employee productivity, increased attention from researchers can result in increased productivity. In other words, employees with regular access to information about their individual daily output or performance may perform differently to those who don’t.

Demand characteristics

Demand characteristics are subtle cues that can reveal the study’s research objectives to the participants. This awareness may lead them to change their behavior. For example, participants may feel motivated to please the researcher.

Novelty effect

A temporary improvement in performance resulting from participation in a research study for the first time is known as a novelty effect. This improvement can also occur when a new element, technology, feature, or process is introduced into an experimental setting.

Because participants are unfamiliar with the new element, increased interest can result in an initial increase in performance or productivity. For example, students often perform better when a learning experience is new. However, the novelty effect wears off with time.

How to reduce the Hawthorne effect

The Hawthorne effect cannot be entirely avoided in research using participant observation or experimental research. However, there are a few things you can do to reduce it:

  • Invest in interpersonal relationships at the study site. Sustaining contact with participants over time reduces participant reactivity and improves the quality of data collection.
  • Give participants tasks unrelated to the purposes of the study. This can mask the research objectives from the participants. However, be sure to consider whether this is ethical to do.
  • Whenever possible, opt for a naturalistic or covert observation. In this way, you can observe people in their natural surroundings without being seen. The downside here is that your ability to draw conclusions about causal relationships or generalize to other contexts is limited. There are also implications for participant privacy and informed consent here.

Other types of research bias

Frequently asked questions about research bias

What are threats to external validity?

There are seven threats to external validity: selection bias, history, experimenter effect, Hawthorne effect, testing effect, aptitude-treatment and situation effect.

What is performance bias in research?

Performance bias is a general term describing the effects of unequal treatment between study groups. As a result, study participants alter their behavior. There are two subtypes of performance bias—namely, the Hawthorne (or observer) effect and the John Henry effect.

What are demand characteristics?

In research, demand characteristics are cues that might indicate the aim of a study to participants. These cues can lead to participants changing their behaviors or responses based on what they think the research is about.

Demand characteristics are common problems in psychology experiments and other social science studies because they can cause a bias in your research findings.

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Kassiani Nikolopoulou

Kassiani has an academic background in Communication, Bioeconomy and Circular Economy. As a former journalist she enjoys turning complex scientific information into easily accessible articles to help students. She specializes in writing about research methods and research bias.