15.14
Causa ed effetto
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 alcuni casi, la relazione tra due elementi sembra chiara e intuitiva, come quando c'è una partita sportiva, le persone ordinano la pizza anche se si sta facendo tardi. Un membro del gruppo giura che mangiare diverse fette prima di ritirarsi per la serata gli fa venire gli incubi.
Ora, l'unico modo per rispondere a questa domanda "Mangiare la pizza prima di andare a dormire provoca a qualcuno di avere più incubi?" è progettare un esperimento.
In un tipo di disegno sperimentale, una relazione causa-effetto, un ricercatore può determinare se la manipolazione di una variabile indipendente – in questo caso, mangiare la pizza prima di coricarsi – provoca un effetto particolare – cambiamenti nella variabile dipendente, il numero di incubi che si verificano durante la notte.
Potrebbero assegnare metà dei partecipanti al gruppo sperimentale, a cui viene data la manipolazione sperimentale – l'assegnazione di mangiare tre fette di pizza prima di andare a letto – e la seconda metà al gruppo di controllo, a cui viene ordinato di non mangiare nulla.
Possono anche adottare determinate misure per controllare le variabili confondenti che possono produrre spiegazioni alternative.
Ad esempio, assegnando in modo casuale i partecipanti a gruppi diversi, utilizzando un metodo basato sulla probabilità, il ricercatore può garantire che i partecipanti siano equamente abbinati su potenziali fattori confondenti, ad esempio, la loro storia e predisposizione agli incubi, il tempo necessario per addormentarsi e persino la qualità generale del sonno.
Inoltre, il ricercatore potrebbe condurre l'esperimento in un ambiente di laboratorio in cui è possibile controllare ancora più fattori, come l'ambiente del sonno e ciò che guarda prima di dormire.
Alla fine, l'unico modo per stabilire la causalità tra due variabili è eseguire esperimenti validi!
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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.