What is extraneous vs confounding variable?
Here's a breakdown of extraneous and confounding variables, with links as requested:
Extraneous Variables
- Definition: An extraneous variable is any variable that is not the independent variable (the variable you are manipulating) that could influence the dependent variable (the variable you are measuring). They are unwanted variables that can add error to an experiment.
- Impact: Extraneous variables can make it difficult to determine if the changes in the dependent variable are truly due to the independent variable, or if they are due to the presence of the extraneous variable.
- Control: Researchers try to control for extraneous variables to minimize their impact. This can be done through various methods like:
- Holding them constant: Keeping certain variables the same across all conditions.
- Random assignment: Randomly assigning participants to different groups to distribute extraneous variables evenly.
- Counterbalancing: Varying the order of conditions to control for order effects.
Confounding Variables
- Definition: A confounding variable is a specific type of extraneous variable that is related to both the independent variable and the dependent variable. This makes it difficult to separate the effect of the independent variable from the effect of the confounding variable.
- Impact: Confounding variables present a serious problem because they provide an alternative explanation for the results. They can lead to incorrect conclusions about the relationship between the independent and dependent variables.
- Example: Imagine a study that found a correlation between ice cream sales and crime rates. It would be tempting to conclude that ice cream causes crime, or vice versa. However, the confounding variable is likely temperature. Higher temperatures lead to both increased ice cream sales and increased crime rates.
- Important Note: Not all extraneous variables are confounding variables. An extraneous variable is only a confounding variable if it is systematically related to both the independent and dependent variables.
- Control: Confounding variables can be hard to deal with. Careful experimental design and statistical control are useful.
- Statistical control: Regression and ANCOVA are methods to statistically control the effects of confounding variables.
Key Differences Summarized
| Feature | Extraneous Variable | Confounding Variable |
|---|
| Definition | Any variable other than the IV that can influence the DV | A variable related to both the IV and DV |
| Impact | Adds error, makes it harder to see true effect of IV | Provides an alternative explanation, biases results |
| Relationship to IV & DV | May or may not be related to IV | Systematically related to both IV and DV |
Links