An extraneous variable is any uncontrolled factor that can affect the outcome of an experiment. These variables are crucial in the work of statisticians and scientists.
Extraneous variables are unwanted factors in a study that, if not accounted for, can negatively impact the data collected by confounding the results.
These factors can prevent researchers from identifying a direct causal relationship between the manipulated independent variables (IVs) and the measured dependent variables (DVs) in an investigation.
To ensure that changes in the IV directly affect changes in the DV, potential confounds must be identified and controlled or eliminated; poor control will result in less reliable findings.
Extraneous Variable
An extraneous variable is any factor other than the independent variable that can influence an experiment’s dependent variables. These variables can affect the controlled conditions and alter the interpretation and results of the experiment.
To ensure accurate results, it’s essential to control these unexpected variables. Extraneous variables can include natural characteristics of the participant, such as age or gender, as well as environmental features like noise or lighting.
Example of Extraneous Variable
Robert conducted a study to examine how lack of sleep affects college students. In this study, the amount of sleep is the independent variable, while the college students’ performance or behavior is the dependent variable.
An extraneous variable could include other factors that affect sleep, such as living in a loud dormitory or experiencing a smoke detector malfunction one night.
To control these extraneous variables, Robert might ask students to sleep in a quiet location for the duration of the experiment.
Key Considerations in Controlling Extraneous Variables
When controlling extraneous variables, three key considerations are crucial:
- Participant Variables: Minimize differences between participants, such as their developmental stage (e.g., age) or abilities (e.g., IQ).
- Researcher Variables: Ensure factors like researcher behavior, appearance, or gender are consistent throughout the experiment, as these can affect participant responses.
- Situational Variables: Control the experimental setting by maintaining consistent light, sound, and temperature levels.
Methods for Controlling Extraneous Variables in Research
There are four main approaches to controlling the effect of extraneous variables in research.
Randomization
This approach involves randomly assigning treatments to experimental groups, ensuring that extraneous factors are equally distributed among all groups. However, this technique is only effective when the sample size is very large.
Matching
Another important technique is to match the different groups based on confounding variables such as gender, age, and income. This ensures that these variables are evenly distributed among the groups. However, it can be challenging to match all groups perfectly, and sometimes the matched characteristics may not be relevant to the dependent variable.
Use of Experimental Designs
In certain studies, the design of the experiment itself can play a crucial role in minimizing or eliminating the impact of extraneous variables. Proper experimental design can help control these variables more effectively.
Statistical Control
When the aforementioned methods do not produce significant outcomes, statistical control can be employed. Techniques such as Analysis of Covariance (ANOVA) can help reduce the impact of extraneous factors on the study, making it easier to draw causal inferences.
These four methods can be used individually or collectively to reduce the effect of extraneous variables. The effectiveness of these methods depends on the researcher’s expertise in understanding and applying them appropriately to achieve the best possible results.
How to Control Extraneous Variables?
Controlling extraneous variables is crucial for conducting reliable research. Follow these steps to manage extraneous variables in your experiments:
1. Identify the Types of Extraneous Variables in Your Study
Begin by analyzing each part of your research process to identify any potential extraneous variables. For example, when conducting a survey, carefully review each question for demand characteristics, which are clues that might reveal the study’s purpose. If your experiment is outdoors during summer, choose a shaded location to ensure participants focus on the study rather than being affected by the weather.
2. Selecting Methods to Control Extraneous Variables
Once you’ve identified the extraneous variables affecting your study, choosing an appropriate control method is essential. Various methods correspond to specific categories of variables:
- Random Sampling: This method addresses participant variables by ensuring equal chances of selection. For instance, when forming control and experimental groups, randomly draw names to ensure each participant has an equal opportunity to be assigned to either group.
- Standardized Procedures: Targeting situational and demand characteristic variables, this method standardizes procedures during study design. Maintaining consistent conditions, such as controlling room temperature, prevents environmental factors from influencing participant responses.
- Counterbalancing: Addressing participant variables like the sequence of events, counterbalancing involves varying the order of tasks among different participant groups. For example, one group might start with “step one” while another begins with “step two,” ensuring each condition’s effect is measured independently.
- Masking (Double-Blind Method): Used to manage experimenter variables, masking involves employing individuals unaware of the research’s purpose to administer the experiment. This double-blind approach minimizes bias by preventing experimenters from influencing participant behavior based on their knowledge of the study’s objectives.
3. Implement the method of control
The last step in managing extraneous variables is to put your chosen control methods into action. By effectively implementing these strategies, you can minimize the influence of extraneous variables on your study’s outcomes.