15.14
Causa y Efecto
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actuall…
En algunos casos, la relación entre dos elementos parece clara e intuitiva, como cuando hay un juego de deportes, la gente pide pizza aunque se esté haciendo tarde. Un miembro del grupo jura que comer varias rebanadas antes de retirarse a dormir le da pesadillas.
Ahora, la única forma de responder a esta pregunta "¿Comer pizza justo antes de ir a dormir hace que alguien tenga más pesadillas?" es diseñar un experimento.
En un tipo de diseño experimental, una relación de causa y efecto, un investigador puede determinar si la manipulación de una variable independiente (en este caso, comer pizza antes de acostarse) causa un efecto particular: cambios en la variable dependiente, el número de pesadillas que ocurren a lo largo de la noche.
Podrían asignar la mitad de los participantes al grupo experimental, al que se le asigna la manipulación experimental (la tarea de comer tres rebanadas de pizza justo antes de acostarse) y la segunda mitad al grupo de control, al que se le indica que no coma nada.
También pueden tomar ciertas medidas para controlar las variables de confusión que pueden producir explicaciones alternativas.
Por ejemplo, al asignar aleatoriamente a los participantes a diferentes grupos, utilizando un método basado en probabilidades, el investigador puede asegurarse de que los participantes coincidan por igual en los posibles factores de confusión, por ejemplo, su historial y predisposición a las pesadillas, el tiempo que les lleva conciliar el sueño e incluso su calidad general del sueño.
Además, el investigador podría llevar a cabo el experimento en un entorno de laboratorio donde se puedan controlar aún más factores, como el entorno del sueño y lo que ven antes de dormir.
Al final, la única forma de establecer la causalidad entre dos variables es mediante la realización de experimentos sonoros.
View the full transcript and gain access to JoVE Core videos
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.