15.14
Ursache und Wirkung
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actuall…
In some cases, the relationship between two items seems clear and intuitive, like when there’s a sports game, people will order pizza even though it’s getting late. One member of the group swears that eating several slices before retiring for the evening gives him nightmares.
Now, the only way to answer this question "Does eating pizza right before going to sleep cause someone to have more nightmares?" is to design an experiment.
In one type of experimental design, a cause-and-effect relationship, a researcher can determine whether manipulating an independent variable—in this case, eating pizza before bedtime—causes a particular effect—changes in the dependent variable, the number of nightmares that occur throughout the night.
They could assign half of the participants to the experimental group, which is given the experimental manipulation—the assignment of eating three slices of pizza right before they go to bed—and the second half to the control group, which is instructed not to eat anything.
They can also take certain measures to control for confounding variables that may produce alternative explanations.
For example, by randomly assigning participants to different groups—using a probability-based method—the researcher can ensure that participants are equally matched on potential confounds, for instance, their history of and predisposition for nightmares, the time it takes them to fall asleep, and even their overall quality of sleep.
In addition, the researcher could conduct the experiment in a laboratory setting where even more factors can be controlled for, like the sleep environment and what they watch before sleeping.
In the end, the only way to establish causality between two variables is by running sound experiments!
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Q1: Why can't researchers establish causality from correlational data alone?
Correlational research identifies relationships between variables but cannot prove one causes the other. A confounding variable—a third factor—may actually explain the relationship. For example, ice cream sales and crime both increase in warm weather, but temperature causes both, not ice cream causing crime. Only experiments that manipulate variables and control for alternative explanations can establish true causality.
Q2: How do experimental designs establish cause and effect relationships?
Experimental designs establish cause and effect by manipulating an independent variable and measuring its effect on a dependent variable while controlling confounds. Researchers assign participants to experimental and control groups, then use random assignment to ensure equal matching on potential confounds. Laboratory settings allow researchers to control additional factors like environment and timing, enabling sound conclusions about causality.
Q3: What is a confounding variable and why does it matter in research?
A confounding variable is an unmeasured third factor that causes systematic changes in variables of interest, creating false correlations. For instance, both cereal consumption and healthy weight may correlate with overall health consciousness rather than cereal causing weight loss. Confounding variables produce alternative explanations for observed relationships, which is why controlling for them through random assignment and laboratory conditions is essential for valid causal claims.
Q4: What are illusory correlations and how do they affect our thinking?
Illusory correlations are false beliefs that relationships exist between two things when no actual relationship exists. For example, many people believe the full moon affects human behavior, but meta-analyses show no such relationship. Illusory correlations arise from confirmation bias—seeking evidence supporting a hunch while ignoring contradictory evidence—and can lead to prejudicial attitudes and discriminatory behavior toward certain groups.
Q5: How does random assignment help control for confounding variables?
Random assignment uses probability-based methods to distribute participants equally across experimental and control groups. This ensures participants are matched on potential confounds like predisposition for nightmares, sleep latency, and sleep quality. By randomly assigning rather than allowing self-selection, researchers reduce systematic bias and increase confidence that observed effects result from the independent variable manipulation, not pre-existing differences.
Q6: Why do people mistakenly claim causation from correlational findings?
People often mistake correlation for causation because the relationship seems intuitive or because advertisements and news stories make causal claims based on correlational data. Confirmation bias leads people to accept information supporting their hunches without scrutiny. Additionally, information that comes easily to mind feels more reliable, even if limited. These cognitive shortcuts are especially problematic in media, where misleading causal claims influence public perception and behavior.
Q7: What role does laboratory control play in establishing causality?
Laboratory settings allow researchers to control numerous environmental factors beyond the independent variable, such as sleep environment, pre-sleep activities, and timing conditions. This controlled environment eliminates alternative explanations for observed effects on the dependent variable. By isolating the experimental manipulation from confounding factors, laboratory experiments provide stronger evidence for cause-and-effect relationships than field studies where many variables remain uncontrolled.