15.14
Causa e Efeito
Embora as variáveis às vezes sejam correlacionadas porque uma causa a outra, também pode ser que algum outro fator, uma variável de confusão, esteja r…
Em alguns casos, a relação entre dois itens parece clara e intuitiva, como quando há um jogo de esportes, as pessoas pedem pizza mesmo que esteja ficando tarde. Um membro do grupo jura que comer várias fatias antes de se retirar para a noite lhe dá pesadelos.
Agora, a única maneira de responder a esta pergunta "Comer pizza antes de dormir faz com que alguém tenha mais pesadelos?" é projetar um experimento.
Em um tipo de projeto experimental, uma relação de causa e efeito, um pesquisador pode determinar se a manipulação de uma variável independente - neste caso, comer pizza antes de dormir - causa um efeito particular - mudanças na variável dependente, o número de pesadelos que ocorrem durante a noite.
Eles poderiam designar metade dos participantes para o grupo experimental, que recebe a manipulação experimental - a atribuição de comer três fatias de pizza antes de ir para a cama - e a segunda metade para o grupo de controle, que é instruído a não comer nada.
Eles também podem tomar certas medidas para controlar variáveis confusas que podem produzir explicações alternativas.
Por exemplo, ao atribuir aleatoriamente os participantes a diferentes grupos - usando um método baseado em probabilidade - o pesquisador pode garantir que os participantes sejam igualmente combinados em possíveis fatores de confusão, por exemplo, sua história e predisposição para pesadelos, o tempo que levam para adormecer e até mesmo sua qualidade geral de sono.
Além disso, o pesquisador pode conduzir o experimento em um ambiente de laboratório onde ainda mais fatores podem ser controlados, como o ambiente de sono e o que eles assistem antes de dormir.
No final, a única maneira de estabelecer causalidade entre duas variáveis é executando experimentos sólidos!
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.