There are two general approaches to removing confounds: 
• Randomize participants between conditions of the experiment 
• Experimentally control for potential confounds

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A confounding variable is an extraneous variable that differs on average across levels of the independent variable, meaning it varies systematically alongside it. Because this variable differs systematically across conditions just like the independent variable, it provides a plausible alternative explanation for any observed difference in the dependent variable, making it difficult to establish a clear cause-and-effect relationship. For instance, if an extraneous variable like intelligence is substantially lower in one group than another, it becomes a confounder that confuses the results.

Confounding Variable

Two approaches to removing confounds

A classic example of a spurious correlation is the observed relationship between ice cream sales and crime rates. While data shows both increase concurrently, one does not cause the other. The association is more likely explained by a third, confounding variable: temperature. During warm weather, more people are outside, leading to increased social interactions and potential for crime. Simultaneously, warm weather drives up the demand for cool treats like ice cream. Therefore, the temperature independently influences both variables, creating a statistical correlation without a direct causal link.

Example of Spurious Correlation: Ice Cream Sales and Crime Rates

Based on the research finding described in the case study, identify a likely third variable that is influencing both the number of firefighters and the amount of property damage. Explain how this variable creates the misleading impression of a direct relationship between the two.

Analyzing a Research Finding

Based on the study described, identify a likely third variable that could be influencing both the number of books in the home and the child's grades, and explain how this variable could account for the observed correlation.

Interpreting Correlational Research

A between-subjects experiment is an experimental design approach wherein each participant is exposed to only a single level of the independent variable. This method differs fundamentally from within-subjects designs, as individuals are tested in just one specific condition rather than across all experimental variations.

Between-Subjects Experiment

In a hypothetical study evaluating the effect of mood on memory, participants in a positive mood condition might score higher on a memory task than those in a negative mood condition. However, if participants in the positive mood condition also possess substantially higher intelligence quotients (IQs) on average than those in the negative mood group, IQ acts as a confounding variable. This systematic difference provides an alternative explanation for the results, making it unclear whether the positive mood or the higher intelligence caused the superior memory performance.

Example of a Confounding Variable: Mood and Memory

While quasi-experimental research manipulates an independent variable before measuring a dependent variable, it still cannot definitively establish cause-and-effect relationships. The absence of random assignment or counterbalancing means researchers cannot confidently rule out confounding variables—pre-existing differences between conditions—as the true cause of any observed outcomes.

Causal Limitations of Quasi-Experimental Research

What type of variable is an extraneous factor that differs on average across levels of the independent variable, providing a plausible alternative explanation for any observed difference?

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