Case Study

Explain why simply running two separate simple correlation analyses (income-happiness and health-happiness) would be insufficient to address the researcher's concern. How does multiple regression help the researcher comprehend the unique influence of each predictor in this scenario?

Case context: A developmental psychologist is studying the factors that contribute to subjective well-being in young adults. She measures annual income, self-reported physical health, and overall happiness. She notices a problem: participants with higher incomes also report significantly better physical health, meaning income and health are correlated. She wants to know if they both independently contribute to happiness or if the relationship is entirely due to this overlap.

Question: Explain why simply running two separate simple correlation analyses (income-happiness and health-happiness) would be insufficient to address the researcher's concern. How does multiple regression help the researcher comprehend the unique influence of each predictor in this scenario?

Sample answer: Separate simple correlations cannot account for the shared relationship (correlation) between income and health, meaning any observed relationship with happiness could be confounded by the other variable. Multiple regression solves this by including both income and health as predictor variables in a single model. This allows the researcher to statistically control for their correlation, revealing whether the unique portion of income (independent of health) and the unique portion of health (independent of income) predict happiness.

Key points:

  • Simple correlation cannot control for the correlation between income and health.
  • Separate analyses leave the confound between the two predictors unaddressed.
  • Multiple regression includes both income and health as predictor variables simultaneously.
  • It statistically controls for one predictor while examining the other.
  • It identifies the unique contribution of each predictor (unrelated to the other) to happiness.

Rubric: The student must show comprehension by explaining that separate correlation analyses fail to isolate the independent contribution of each predictor due to their overlap. The student must explain that multiple regression controls for the shared variance to reveal whether the unique, non-overlapping part of each predictor relates to happiness.

0

1

Updated 2026-05-27

Contributors are:

Who are from:

Tags

KPU

Research Methods in Psychology - 4th American Edition @ KPU

Related