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Case Study

Explain why multiple regression is a suitable choice in this situation. Describe how the regression model will handle caffeine intake while evaluating the relationship between screen time and sleep quality, and what the concept of 'statistical control' means in this context.

Case context: A researcher is studying the relationship between screen time (X1X_{1}) and sleep quality (YY). A colleague suggests that caffeine intake (X2X_{2}) is a major confounding factor that could explain the association. To address this, the researcher decides to use multiple regression instead of simple correlation.

Question: Explain why multiple regression is a suitable choice in this situation. Describe how the regression model will handle caffeine intake while evaluating the relationship between screen time and sleep quality, and what the concept of 'statistical control' means in this context.

Sample answer: Multiple regression is suitable because it can evaluate the unique contribution of screen time to sleep quality over and above the contribution of caffeine intake. By including both screen time and caffeine intake as predictor variables in the model, the researcher can statistically control for caffeine. This means the analysis estimates the relationship between screen time and sleep quality while holding the effect of caffeine constant.

Key points:

  • Explains that multiple regression evaluates the unique contribution of screen time over and above caffeine.
  • Describes statistical control as accounting for or holding constant the effect of caffeine.
  • Demonstrates understanding of how multiple regression isolates the specific relationship of interest.

Rubric: The response should demonstrate understanding of how multiple regression addresses confounding variables. It must explain that caffeine is controlled statistically, meaning the model assesses the unique relationship between screen time and sleep quality after accounting for caffeine.

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Updated 2026-05-26

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Research Methods in Psychology - 4th American Edition @ KPU

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