An extraneous variable is any factor other than the variables being studied that could potentially influence the outcomes of the research study in an experiment.
If extraneous variables are not controlled or accounted for, they can lead to inaccurate conclusions about the relationship between the independent and dependent variables under investigation. Uncontrolled extraneous variables can also introduce various research biases into the work, particularly selection bias.
By failing to identify and control extraneous variables, alternative explanations for the findings cannot be ruled out. The observed effects may be partially or wholly caused by these external variables rather than solely due to the independent variable manipulated by the researcher. This undermines the internal validity of the experiment.
| Research Question | Potential Extraneous Variables |
| Does a new teaching method improve student test scores? | Teacher experience, class size, student motivation, socioeconomic status |
| Does a medication reduce anxiety symptoms? | Diet, exercise, sleep quality, existing medical conditions, stress levels |
| Does retail store music influence customer spending? | Time of day, day of week, store layout, product pricing, customer age/gender |
| Does fertilizer increase crop yield? | Soil quality, rainfall, pest infestations, temperature, seed quality |
| Does a web redesign increase user engagement? | Device type, browser, internet speed, user demographics, existing brand awareness |
| Does playing brain games improve cognitive abilities in older adults? | Education level, baseline cognitive function, physical activity, nutrition |
Why do extraneous variables matter?
Extraneous variables provide alternative explanations for your results. This can threaten the internal validity of your study. When not properly accounted for, this type of variable can also introduce many biases to your research, particularly various forms of selection bias such as:
- Sampling bias of ascertainment bias: This occurs when certain members of the intended population are less likely to be included in the sample than others.
- Attrition bias: This arises when participants who drop out of a study systematically differ from those who remain.
- Survivorship bias: Researchers draw conclusions by only focusing on examples of “survivors” or successful individuals rather than examining the group as a whole.
- Nonresponse bias: People who do not respond to a survey differ in meaningful ways from those who do respond.
- Undercoverage bias: Some members of the population are inadvertently excluded and not represented in the sample.
In an experiment, the goal is to manipulate an independent variable and observe its effects on a dependent variable. However, if extraneous variables are not controlled or accounted for, they can provide plausible alternative explanations for any observed findings, undermining the ability to make valid causal inferences about the independent variable’s impact on the dependent variable.
Example: Experimental study
In a study examining the effects of clothing on cognitive performance, you design an experiment to test whether wearing a white lab coat (independent variable) improves scores on a test of scientific reasoning (dependent variable).
You recruit participants from a local university and randomly assign them to one of two groups:
- Experimental Group: Participants are instructed to wear a white lab coat during the study session.
- Control Group: Participants are instructed to wear a casual jacket or sweater during the study session.
To ensure equivalence between the groups, you use randomized assignment and also collect data on potential extraneous variables like:
- Prior academic performance (GPA)
- Motivation levels
- Existing scientific knowledge
- Gender
- Time of day tested
After having participants wear the assigned coats, you administer the same scientific reasoning test to all participants under similar controlled conditions (same room, instructions, etc.).
Some potential extraneous variables that could threaten the internal validity include:
- Stereotype threat – Wearing a lab coat may trigger anxiety or negative stereotypes about scientific ability for some participants.
- Demand characteristics – Participants may alter their effort levels based on perceived expectations when wearing a lab coat.
- Test-taking environment – Factors like room temperature, noise levels, time of day could unequally influence performance.
- Participant fatigue or hunger levels when tested.
To account for these issues, you would want standardized testing conditions, use random assignment, and potentially measure/statistically control for variables like stereotype vulnerability, test motivation, and fatigue.
Example: Extraneous variables
In your experiment investigating whether wearing a white lab coat improves scores on a scientific reasoning test, there are several potential extraneous variables that could affect the results:
- Participant’s major (e.g., STEM majors may have stronger scientific backgrounds compared to humanities majors)
- Participant’s pre-existing interest or attitude toward science (those highly interested may be more engaged)
- Demographic variables like gender or family educational background (which could relate to science exposure/preparation)
- Time of day testing occurred (morning vs evening could impact cognitive performance)
- Specific environment or setting where the experiment took place (factors like noise, temperature, etc.)
If these extraneous variables systematically differ between your experimental group (wearing lab coats) and control group (wearing casual coats), it becomes difficult to conclusively attribute any differences in scientific reasoning scores solely to the lab coat manipulation.
Extraneous vs. confounding variables
An extraneous variable is any variable that is not the independent or dependent variable in a study but may still influence the dependent variable. A confounding variable is a specific type of extraneous variable that varies systematically with the independent variable. All confounding variables are extraneous variables, but not all extraneous variables are confounding variables.
The key distinction is that confounding variables are specifically related to both the independent and dependent variables, introducing ambiguity about the true causal relationship. Extraneous variables, on the other hand, are any external factors that may influence the dependent variable, regardless of their relationship with the independent variable.
| Attribute | Extraneous Variables | Confounding Variables |
| Definition | Any variable other than the independent and dependent variables that may influence the dependent variable | A specific type of extraneous variable that varies systematically with the independent variable |
| Relationship to Variables | May or may not be related to the independent variable | Related to both the independent and dependent variables |
| Effect | Threatens internal validity by providing alternative explanations for results | Introduces ambiguity about the true causal relationship between variables |
| Impact | Any uncontrolled extraneous variable can impact the dependent variable | Can lead to spurious associations or misleading correlations |
| Characterization | General external factors | Represents a “third variable” problem |
| Examples | Participant demographics, environmental factors, researcher biases | Age, gender, socioeconomic status, pre-existing conditions |
| Handling | Must be controlled or accounted for in research design and analysis | Must be controlled, measured, or statistically accounted for |
Example: Confounding vs. extraneous variables
In your study investigating whether wearing a white lab coat improves scores on a scientific reasoning test, having participants who are currently employed in scientific or medical professions would be a potential confounding variable.
Employment in these fields is likely correlated with both regularly wearing lab coats as part of the job duties and having higher existing scientific knowledge/reasoning abilities. Lab coat familiarity and stronger scientific backgrounds could lead to higher test scores, regardless of your experimental manipulation.
Therefore, the effect of wearing a lab coat on scientific reasoning scores may be confounded or obscured by differences in occupational experience between your experiment and control groups. Isolating the true causal impact becomes difficult.
On the other hand, variables like participants’ general interests in science topics or their undergraduate major would be considered extraneous variables. While these factors could influence scientific reasoning ability, they are not directly related to the act of wearing a lab coat itself.
For example, a physics major may outperform an English major on the reasoning test due to more scientific training. However, this ability difference exists regardless of whether they wore a lab coat during testing.
To account for these issues, you would want to:
- Collect data on participant occupations and academic backgrounds
- Attempt to balance these factors across conditions through randomization and screening
- Potentially statistically control for occupational differences as a confounding variable
This allows you to isolate and examine the unique effect of wearing a lab coat, separate from general scientific abilities which may differ across groups for reasons unrelated to your manipulation.
Types and controls of extraneous variables
Extraneous variables can take different forms and arise from various sources in research studies. Here are some common types of extraneous variables and examples, along with potential controls or techniques to address them:
Demand characteristics
Demand characteristics refer to cues or situational factors that can influence participants’ behavior or responses based on their interpretation of the study’s purpose or the experimenter’s expectations.
Example: Demand Characteristics
In a study investigating the effects of caffeine on task performance, participants may believe that the researchers expect them to perform better after consuming caffeine. This expectation could lead participants to exert more effort, regardless of the actual effects of caffeine, thereby influencing the results.
- Potential Controls: Use double-blind procedures, provide convincing cover stories, and minimize demand characteristics through careful instructions and experimental procedures.
Experimenter effects
Experimenter effects refer to unintentional influences that researchers or experimenters may have on participants’ behavior or responses due to their personal characteristics, biases, or expectations.
Example: Experimenter Effects
In a study on teaching methods, the enthusiasm, experience, or personal traits of the instructor implementing a new teaching technique could impact student performance and learning outcomes, rather than the teaching method itself.
- Potential Controls: Use standardized procedures, train experimenters thoroughly, use double-blind techniques, and counterbalance or randomize experimenter assignments.
Situational variables
Situational variables refer to aspects of the physical environment or context in which the study takes place that could influence participant behavior or responses.
Example: Situational Variables
In a study examining the effects of background noise on cognitive task performance, variables such as room temperature, lighting conditions, or the presence of distractions could impact participants’ concentration and performance levels.
- Potential Controls: Standardized testing environments, control for potential confounding factors, counterbalance conditions, or use within-subjects designs.
Participant variables
Participant variables refer to individual characteristics or traits of the participants that could influence their behavior or responses in the study.
Example: Participant Variables
In a study investigating the effects of exercise on mood, variables such as participants’ baseline fitness levels, sleep patterns, or stress levels could impact their mood and emotional states, regardless of the exercise intervention.
- Potential Controls: Collect data on relevant participant variables, use random assignment, match or block participants based on relevant characteristics, or statistically control for participant variables in analyses.