Sources of Bias in Research

Introduction to Bias in Research

  • Importance of recognizing various sources of bias

  • Commitment to maintaining sound research minimizes bias

Sources of Bias

1. Researcher Bias

  • Researchers may unintentionally influence the outcomes of their studies.

  • Example: Hawthorne Effect

    • Definition: Behavioral change in participants due to awareness of being observed.

    • Origin: Derived from the studies conducted at Western Electric’s Hawthorne Works factory.

    • Experiment Details:

    • Researchers altered conditions like lighting and breaks to measure productivity.

    • Results showed increased productivity with every change made (lighting, breaks).

    • Realization: Productivity changes were not due to environmental changes but rather to the awareness of observation.

2. Participant Bias

  • Participants may alter their behavior due to awareness or expectations of the experiment.

  • Demand Characteristics

    • Definition: Unintentional cues in the experimental design leading participants to guess the study's purpose.

    • Example:

    • Experiment on spatial navigation using a maze

    • Unnoticed muddy footprints created by a previous participant leading the next participant, invalidating spatial navigation results.

3. Clever Hans Effect

  • Origin: A horse named Clever Hans purported to perform mathematical feats through trained behavior.

  • Research Investigation

    • Psychologist Oscar Funst's experiments sought to uncover whether Hans could actually perform math.

    • Findings:

    • When isolated, wearing blinders, or when the questioner didn’t know the answer, Hans performed worse than expected.

    • Conclusion: Cues from the audience and questioner influenced the horse’s responses, showcasing how biases can manifest even in animal studies.

4. Social Desirability Bias

  • Definition: Participants respond in a manner they believe is more favorable or acceptable socially.

  • Common in surveys and questionnaires, especially regarding personal topics (e.g., drug use, sexual history).

  • Implications:

    • Non-honest responses skew data accuracy, affecting the validity of research findings.

    • Example: Hypothetical scenarios involving public figures like Donald Trump responding to question about personal attributes.

  • Solution: Ensuring anonymity and confidentiality in surveys improves response honesty.

5. Observer Expectancy Effect

  • Researchers’ expectations can inadvertently affect participant behavior.

  • Example of Teacher Favoritism

    • Study where teachers were told certain students had special potential led to those students performing better.

    • Result: Teachers’ altered behavior towards students labeled as 'special' led to improved performance
      despite being randomly selected.

  • Similar findings in animal studies with rats, where labeled 'bright' rats performed better due to experimenters’ biased expectations.

6. Placebo Effect

  • Definition: Improvement in participants’ condition due to belief that they received treatment, despite actual lack of medicinal effect.

  • Evidence of physiological change versus subjective improvement.

  • Implications include expecting improvement and its association with stress relief.

  • Example: Placebos used in treating pain show measurable brain changes; some conditions show no physiological change.

  • Influence of conditioning effects and color associations on medication efficacy (e.g., red pills as stimulants, blue pills as depressants).

  • Nocebo Effect

    • Definition: Negative side effects experienced by individuals overly aware of potential risks.

    • Conclusion: Preoccupation with side effects heightens stress and likelihood of experiencing them.

Conclusion

  • Recognition and control of biases critical for valid experimental design and sound research outcomes.

  • Research integrity requires continuous examination of both researcher and participant influences upon study results.