Lecture 4- correlational research

Correlational Research Overview

Focuses on determining associations or relationships between variables.
Involves investigating how changes in one variable relate to changes in another.
Key elements assessed: direction and strength of the relationship.
Examples of relationships:
- Increased age may be associated with decreased muscle mass.
- Decreased body image relates to decreased self-esteem.
- Higher CGPA correlates with increased salary offers.
Recognized as non-experimental, meaning it cannot establish causation.

Non-experimental Design:
- Variables measured rather than manipulated.
- Do not have independent or dependent variables; generally labeled as Variable 1 and Variable 2.
- Common measurement scales include interval and ratio.

Causation Limitations:
- Cannot infer causation due to:
  - Third-variable problem: An unseen variable may affect both observed variables (e.g., teachers’ salaries can be affected by other economic factors).
  - Directionality problem: Cannot determine the cause-and-effect relationship from correlation (e.g., is employee satisfaction leading to productivity or vice versa?).

Linear Correlation:
- Describes relationships that can be represented as a straight line.
Correlation Coefficient:
- Ranges from -1 to +1, indicating strength and direction of the relationship:
  - +1 indicates a perfect positive correlation.
  - -1 indicates a perfect negative correlation.
  - 0 indicates no correlation.

Positive Correlation:
- Both variables increase or decrease together.
- Example relationship: Hours spent studying correlating positively with academic performance.
Negative Correlation:
- One variable increases while the other decreases.
- Example relationship: Increased TV hours correlate negatively with academic performance.