Establishing Cause-Effect Relationships
Cause-Effect Relationships
The relationship between two variables where one variable (the independent variable ) directly influences or causes a change in the other variable (the dependent variable).
The Role of Experiments in Establishing Causality
- Experiments are the gold standard for establishing causality because they allow researchers to manipulate one variable while controlling others.
- This manipulation helps determine if changes in the independent variable lead to changes in the dependent variable.
A classic example is Newcomer et al. (1999), which investigated the effects of cortisol on memory.
This study used a double-blind, placebo-controlled experiment to establish a cause-and-effect relationship between cortisol levels and memory performance.
Key Features of Experiments
Manipulation of Variables
- Independent Variable (IV): The variable that is manipulated by the researcher.
- Dependent Variable (DV): The variable that is measured to see if it is affected by the IV.
In Newcomer et al. (1999), the IV was the level of cortisol administered, and the DV was memory performance.
Randomization
- Random Assignment: Participants are randomly assigned to different conditions to ensure that groups are equivalent at the start of the experiment.
In Newcomer et al. (1999), participants were randomly assigned to one of three conditions:
- High cortisol group: Received 160 mg of cortisol per day.
- Low cortisol group: Received 40 mg of cortisol per day.
- Control group: Received a placebo.
Control Groups
- Control Group: A group that does not receive the experimental treatment, used as a baseline to compare the effects of the IV.
Standardization
- Standardized Procedures: Ensuring that all participants are treated the same way, except for the manipulation of the IV.
In Newcomer et al. (1999), all participants took the same memory test under similar conditions.
How Experiments Establish Causality
- By manipulating the IV and controlling other variables, experiments can demonstrate a direct relationship between the IV and DV.
- This control helps rule out confounding variables that could otherwise explain the results.
In Newcomer et al. (1999), the researchers controlled for factors like age and health status, ensuring that differences in memory performance were due to cortisol levels.
Strengths and Limitations of Experiments
Strengths
- Control Over Variables: Experiments allow researchers to isolate the effects of the IV.
- Replication: Standardized procedures make experiments easy to replicate.
- Causality: Experiments are the only method that can establish a cause-and-effect relationship.
Limitations
- Ecological Validity: Laboratory experiments may not reflect real-world conditions.
- Ethical Concerns: Manipulating variables can sometimes harm participants.
- Demand Characteristics: Participants may guess the aim of the study and alter their behavior.
In Newcomer et al. (1999), the artificial setting and the use of a memory test may not fully capture how cortisol affects memory in everyday life.
Common Misconceptions About Experiments
"Experiments Always Prove Causation"
- While experiments are designed to establish causality, they are not infallible.
- Factors like low ecological validity, ethical constraints, or individual differences can limit the generalizability of findings.
For example, the results of Newcomer et al. (1999) may not apply to populations with different age ranges or health conditions.
"Control Groups Are Always Perfect"
- Control groups help isolate variables, but they are not immune to confounding factors.
- Researchers must carefully design experiments to minimize these risks.
- If participants in the control group of Newcomer et al. (1999) had higher baseline stress levels, this could have influenced the results.
What are other factors that influence the ability to establish a cause-effect relationship?
- Statistical Significance: through hypothesis testing, researchers can find out the probability that the results were due to chance. The lower this probability is, the stronger the relationship between these two variables are.
- Complexity: this is when researchers acknowledge that confounding variables exist and interact with variables involved in the experiment. This helps us create more accurate ideas of causality.
Validity
- Validity refers to the accuracy of the results. It is linked to establishing a cause-effect relationship in experiments.
- Internal Validity: Does manipulating the independent variable actually cause the dependent variable to change?
- External Validity: Can we generalize the results to other populations?
- Mundane Realism: How similar is the experiment to real-life situations?
Reflection
- How do experiments establish causality?
- What are the strengths and limitations of using experiments to study psychological phenomena?
- How does the use of control groups enhance the validity of experimental findings?
