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Related Experiment Videos

The intervention selection bias: an underrecognized confound in intervention research.

Robert E Larzelere1, Brett R Kuhn, Byron Johnson

  • 1Department of Psychology, Munroe-Meyer Institute, University of Nebraska Medical Center, Omaha, NE 68198-5450, USA. rlarzelere@unmc.edu

Psychological Bulletin
|February 26, 2004
PubMed
Summary

Selection bias threatens intervention research validity but is often overlooked. Controlling this bias requires robust study designs like randomized trials and careful evaluation of alternative explanations for reliable causal conclusions.

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Area of Science:

  • Health Services Research
  • Psychology
  • Medical Research

Background:

  • Selection bias poses a significant threat to the internal validity of intervention research.
  • This bias is frequently underestimated and inadequately managed across various research domains.

Purpose of the Study:

  • To highlight the pervasive issue of selection bias in intervention studies.
  • To discuss methods for controlling selection bias and ensuring research validity.
  • To emphasize the importance of considering alternative explanations in causal inference.

Main Methods:

  • Illustrative examples from parental, medical, and psychological interventions.
  • Discussion of control strategies including randomized controlled trials and quasi-experimental designs.

Related Experiment Videos

  • Mention of statistical innovations for enhancing weaker research designs.

    • Main Results:

    • Selection bias impacts diverse fields such as parental support, medical treatments, and mental health interventions.
    • Randomized and strong quasi-experimental designs offer the most effective control for selection bias.
    • Advanced statistical techniques can improve the validity of studies with inherent design limitations.

    Conclusions:

    • Increased awareness and systematic evaluation of selection bias are crucial for accurate intervention research.
    • Researchers must rigorously assess alternative explanations to establish credible causal links.
    • Prioritizing robust methodologies is essential for advancing scientific understanding in intervention effectiveness.