Research Methods Y1 - Correlations

Question 1

What is correlation?

Answer 1

Correlation illustrates the strength and direction of an association between two or more co-variables (things that are being measured).

Question 2

What are the three types of correlation?

Answer 2

Positive correlation: As one co-variable increases, so does the other (e.g., more people in a room → noise increases).
Negative correlation: As one co-variable increases, the other decreases (e.g., more caffeine → less sleep).
Zero correlation: No relationship between the variables (e.g., daily rainfall in Peru and the number of people in Manchester).

Question 3

How is correlation different from experiments?

Answer 3

In experiments, researchers deliberately manipulate an independent variable (IV) to measure its effect on the dependent variable (DV), allowing them to establish cause and effect. Correlation only measures the relationship between variables, meaning causal conclusions cannot be drawn.

Question 4

Evaluation (Strengths)

Answer 4

1. Point: Correlations are useful for identifying patterns or relationships between variables.
   Evidence: For example, a positive correlation might be found between caffeine consumption and self-reported anxiety.
   Explanation: This provides a valuable preliminary tool for researchers, as it highlights patterns that may warrant further experimental investigation. Without correlations, these associations may go unnoticed.
   Link: Therefore, correlations can guide researchers in designing more focused experimental studies.

Question 5

Evaluation (Strengths)

Answer 5

2. Point: Correlations are efficient and cost-effective to carry out.
   Evidence: Correlational research often uses secondary data, such as surveys or publicly available records, which minimizes time and financial costs.
   Explanation: This makes correlations a practical method when exploring relationships between variables without requiring controlled environments or manipulation.
   Link: Consequently, correlations are particularly suited for large-scale data analysis and exploratory studies.

Question 6

Evaluation (Limitations)

Answer 6

1. Point: Correlations cannot establish cause and effect.
   Evidence: For instance, while a negative correlation might be found between caffeine consumption and hours of sleep, it is impossible to determine whether caffeine causes sleep disturbances or other factors (like stress) influence both variables.
   Explanation: This limitation occurs because no deliberate manipulation of variables takes place, and confounding variables may affect the results.
   Link: As a result, researchers must be cautious about making causal claims based solely on correlational findings.

Question 7

Evaluation (Limitations)

Answer 7

2. Point: Correlations are subject to the issue of third-variable problems.
   Evidence: For example, a positive correlation between single-parent families and crime rates may be explained by a third factor, such as poverty, rather than the family structure itself.
   Explanation: This demonstrates how correlations can be misleading, as the apparent relationship between two variables may be spurious and influenced by external factors.
   Link: Consequently, researchers must account for potential confounding variables to avoid drawing invalid conclusions.