Which of the following is listed as a study-factor confounding variable in intervention research?

Which of the following is listed as a study-factor confounding variable in intervention research?

• A. Hawthorne effect
• B. Placebo effect
• C. Regression to the mean
• D. Selection bias

Answer: (A)

The key idea is that some biases come from the way the study is conducted, not from the treatment itself. The Hawthorne effect fits here because simply being part of a study and being observed tends to change participants’ behavior, which can alter outcomes regardless of the intervention. This makes it a study-factor confounding variable: the study conditions themselves are driving a change in the measured effect. The other options don’t fit as study-factor confounding in the same way. The placebo effect arises from participants’ expectations about the treatment they’re receiving, which is more about perception of the treatment than about the study process itself. Regression to the mean is a statistical artifact that occurs when extreme measurements tend to move toward the average on repeated testing, not a bias introduced by study conditions. Selection bias stems from systematic differences in who participates or is assigned to groups, a design issue related to allocation rather than a bias introduced by being studied.

The key idea is that some biases come from the way the study is conducted, not from the treatment itself. The Hawthorne effect fits here because simply being part of a study and being observed tends to change participants’ behavior, which can alter outcomes regardless of the intervention. This makes it a study-factor confounding variable: the study conditions themselves are driving a change in the measured effect.

The other options don’t fit as study-factor confounding in the same way. The placebo effect arises from participants’ expectations about the treatment they’re receiving, which is more about perception of the treatment than about the study process itself. Regression to the mean is a statistical artifact that occurs when extreme measurements tend to move toward the average on repeated testing, not a bias introduced by study conditions. Selection bias stems from systematic differences in who participates or is assigned to groups, a design issue related to allocation rather than a bias introduced by being studied.