Exam 11: Causal Inference and Experimental Designs

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A study would be particularly vulnerable to the threat of statistical regression, also known as regression toward the mean, if it involves extreme groups or scores that are likely to be atypical or unstable over time. This threat is most pronounced in designs where participants are selected based on their extreme scores on a particular measure at one point in time.

Here are several factors that can make a study susceptible to statistical regression:

1. **Selection of Participants Based on Extreme Scores**: If participants are chosen because they have extremely high or low scores on a pre-test or initial measurement, there is a natural tendency for their scores to move closer to the average upon subsequent testing. This is not due to any treatment effect but simply a result of statistical phenomena.

2. **Short-Term Fluctuations**: If the initial measurement is influenced by short-term fluctuations or temporary conditions (e.g., mood, health, fatigue), these conditions are likely to be less extreme during subsequent measurements, causing scores to regress toward the mean.

3. **Measurement Error**: High levels of measurement error can exacerbate regression effects. If the initial extreme scores are partly due to measurement error, it is unlikely that this error will be exactly replicated in subsequent measurements, leading to a regression effect.

4. **Lack of a Control Group**: Without a control group, it is difficult to determine whether changes in scores are due to the treatment or simply due to regression toward the mean.

5. **Small Sample Sizes**: Studies with small sample sizes are more likely to have extreme scores by chance, and thus, are more susceptible to regression effects.

To mitigate the threat of statistical regression, researchers can:

- Use a control group that is also selected based on extreme scores to compare whether the treatment group's scores regress more or less than the control group's scores.
- Ensure that the selection of participants is not based solely on extreme scores, or use a wider range of initial scores.
- Increase the sample size to reduce the impact of chance fluctuations in scores.
- Use reliable measurement tools to minimize measurement error.
- Conduct a pre-test and post-test design and include a follow-up assessment to check the stability of the scores over time.

By being aware of and addressing these factors, researchers can design studies that are less vulnerable to the threat of statistical regression and can more accurately interpret the effects of the treatment or intervention being studied.