An extraneous variable, also sometimes referred to as a confounding variable or concomitant variable, is any variable present in an experiment that the researcher doesn’t directly control. These variables can potentially influence the outcome (dependent variable) and threaten the validity of the cause-and-effect relationship you’re trying to establish between the independent and dependent variables.
The Problem with Extraneous Variables:
Imagine you’re conducting an experiment to see if a new fertilizer increases plant growth (dependent variable). You manipulate the fertilizer amount (independent variable), but other factors like sunlight exposure, pot size, or watering frequency (extraneous variables) could also affect plant growth. If these extraneous variables aren’t controlled for, it becomes difficult to know for sure if the observed increase in growth is truly due to the fertilizer or these other factors.
Types of Extraneous Variables:
- Participant Characteristics: These are inherent qualities of the participants in your study, such as age, gender, intelligence level, or prior experience.
- Environmental Variables: These are aspects of the physical environment where the experiment takes place, like lighting, temperature, noise level, or testing room.
- Experimenter Effects: Unintentional behaviors of the researcher that might influence participant responses, such as body language, tone of voice, or expectations.
- Demand Characteristics: Cues in the experiment that might lead participants to behave in a certain way because they think that’s what the researcher wants. For instance, if participants know the study is about the effectiveness of a new fertilizer, they might be more careful watering the plants in the fertilizer group.
How to Deal with Extraneous Variables:
- Identification: Consider potential extraneous variables based on your research question and prior research findings.
- Control: There are various strategies to control for extraneous variables:
- Randomization: Randomly assigning participants to experimental and control groups helps to balance out the influence of extraneous variables across these groups. This is because it’s unlikely that all participants in one group will share the same extraneous variable characteristics.
- Matching: Matching participants on characteristics related to the extraneous variable can create more comparable experimental and control groups.
- Standardization: Keeping environmental conditions consistent across all experimental groups minimizes the influence of extraneous environmental variables.
- Statistical Control: Statistical techniques can sometimes be used to account for the influence of extraneous variables during data analysis.
- Acknowledging Limitations: If extraneous variables cannot be fully controlled for, researchers should acknowledge these limitations in their study design and discuss the potential impact on the interpretation of the results.
In Conclusion:
Extraneous variables are a significant consideration in experimental research design. By being aware of their potential influence and implementing strategies to control for them, researchers can increase the internal validity of their experiments, meaning the results more accurately reflect the true cause-and-effect relationship between the independent and dependent variables.