Question

1. Assess the implications for professional practice when a researcher implies causation after using correlation (e.g., bivariate correlation) analyses.

2. Explain why the results of bivariate correlation analyses are considered weak in terms of internal validity.

3. Explain how would you extend or modify a research design to examine a true cause-and-effect relationship.

Answer 1

1. Assuming correlation to be causation can be considered a logical fallacy. Two events that occur together may nor always share a cause and effect relationship. On the other hand, one of the variable can be a hint about what has caused the effect.

For example, assume that a study revealed that when the number of religious worship places and crimes in two cities were compared, the rate of crimes were higher in the city with more number of worship places. But here, we cannot conclude that as number of worship places increase, crime rate will also increase. The reason for increase in both variables can be increased population in the city. But the population variable is not studied in the research.

Thus, if a researcher falsely assumes cause and effect relationship in a correlational study, the real cause will not be recognized.