In The Following Observational Studies Describe Changes

In The Following Observational Studies Describe Changes That Could Be

In the following observational studies, describe changes that could be made to the data collection process that would result in an experiment rather than an observation study. Also, offer suggestions about unseen biases or lurking variables that may be present in the studies as they are described here.

a. In a sample of 50 members of a local health club, it was observed that 12 members meet weekly with a physical fitness trainer and their average body mass index (BMI) is less than that of the other 38 members who do not meet with a trainer.

b. In a sample of 12 bank tellers at a local branch office, it was observed that the 7 tellers who have completed the advanced training program offered by the bank have a lower average error rate in processing transactions than the remaining 5 tellers.

Paper For Above instruction

The primary distinction between observational studies and controlled experiments lies in the manipulation of variables and the control over confounding factors. Observational studies involve recording and analyzing data without intervention or manipulation, which can leave room for biases and lurking variables. Transforming these into experiments requires deliberate changes to the data collection process, specifically involving experimental manipulation and randomization to establish causality more convincingly.

Part A: Health Club and BMI

In the initial observational study involving the health club members, the key variable of interest is the association between meeting with a trainer and BMI levels. To convert this into an experiment, researchers could randomly assign members to two groups: one that meets weekly with a trainer and a control group that does not. Randomization ensures that confounding variables, such as age, gender, baseline fitness level, or motivation, are evenly distributed across groups, reducing biases.

Specifically, the researchers could first collect baseline BMI and other pertinent health data, then randomly assign participants to either intervention or control groups. The intervention group would undergo a structured program of trainer meetings, while the control group would not. After a predetermined period, the BMI of both groups would be measured and compared statistically. This process isolates the effect of trainer meetings, establishing causality rather than mere correlation.

Unseen biases in the original study include selection bias, as the sample may not be representative of the broader population, and self-selection bias — members who seek trainer meetings might be inherently more health-conscious or motivated. Lurking variables such as diet, physical activity outside the gym, or genetic factors could also influence BMI independently of trainer meetings.

Part B: Bank Tellers and Training Program

In the observational study involving bank tellers, the association between advanced training completion and lower error rates is noted. To transition from observation to experiment, bank management could randomly assign tellers to two groups: those who participate in the training program and those who do not. Ensuring random assignment minimizes selection bias, as factors such as prior experience, aptitude, or motivation are evenly distributed.

The experimental setup would involve randomly selecting tellers from the workforce, with one group receiving the training and the other serving as a control. Post-training, error rates would be monitored over a substantial period to evaluate the training's effect more reliably. This randomization and controlled intervention help establish a causal link between training and error reduction rather than inferring causality from mere association.

Biases potentially present in the observational approach include confounding variables like individual skill levels or workload variations, which might influence error rates independently. Also, the small sample size raises concerns about statistical significance and generalizability.

Potential Biases and Lurking Variables

In both studies, several biases and lurking variables may distort the observed relationships. Selection bias can occur if the samples are not representative; for instance, health club members who meet with trainers may differ systematically from those who do not. Similarly, bank tellers who opt into training may have varying intrinsic motivation or abilities, affecting error rates independently.

Unseen confounders include lifestyle factors such as diet, sleep, stress levels, and physical activity outside the studied context, which may influence BMI and error rates. In the health study, genetic predispositions and baseline fitness levels could skew results. For the bank study, factors like experience levels or workload intensity could explain error differences.

Furthermore, the original studies are susceptible to measurement bias if data collection methods are inconsistent or subjective. Recall bias or reporting bias may also distort findings if participants self-report behaviors or outcomes inaccurately.

Conclusion

Transforming observational studies into experiments enhances the ability to infer causality by controlling for confounding variables through randomization and deliberate intervention. Recognizing and accounting for unseen biases and lurking variables is crucial when interpreting the results, as they can influence the validity of the conclusions. Proper experimental design, including random assignment, baseline measurements, and adequate sample sizes, helps mitigate these issues and produce more reliable, generalizable findings.

References

• Fisher, R. A. (1935). The Design of Experiments. Edinburgh: Oliver & Boyd.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.
• Finkelstein, R. (2012). Experimental Design & Data Analysis for Biologists. Springer.
• Pocock, S. J. (2013). Clinical Trials: A Practical Approach. John Wiley & Sons.
• Levin, K. A. (2006). Study Design III: Cross-sectional studies. Evidence-Based Dentistry, 7(1), 24-25.
• Cook, T. D., & Campbell, D. T. (1979). Quasi-Experimentation: Design & Analysis Issues for Field Settings. Houghton Mifflin.
• Rubin, D. B. (2006). Matched Sampling for Causal Effects. Cambridge University Press.
• Shadish, W. R., & Cook, T. D. (2007). Understanding Causal Inference in Education Research. Journal of School Psychology, 45(4), 377–395.
• Hedges, L. V., & Hedberg, E. C. (2007). Methodology in Educational Research. Sage Publications.
• DeMets, D. L., & Emens, J. S. (2005). Adaptive designs for clinical trials: Why use them? Clinical Pharmacology & Therapeutics, 77(3), 239-247.