demand characteristics and investigator effects

demand characteristics

Demand characteristics are cues in an experimental situation that reveal the aim or expectations of the study to participants.
This can cause them to change their natural behaviour — either helping or hindering the researcher.

why they are a problem

They reduce internal validity because the behaviour you observe isn’t genuine, it’s influenced by participants trying to:

• Please the researcher (the “good participant effect”)

• Do the opposite (the “screw-you effect”)

• Behave normally (the “apprehensive participant” — acts in a socially desirable way to avoid embarrassment)

examples

• In Milgram’s obedience study, participants may have guessed the shocks weren’t real.

• In a memory study, if participants guess the aim (“I’m supposed to recall words”), they might try extra hard.

how to control demand characteristics

• Deception: Don’t tell participants the true aim (ethical issues must be justified).

• Single-blind design: Participants don’t know which condition they’re in or the true aim.

• Use of control groups: Helps compare genuine vs expected behaviour.

• Post-experimental interviews: Check if participants guessed the aim.

investigator effects

Investigator effects occur when the researcher’s behaviour, appearance, or expectations unintentionally influence participants’ responses.

This is a form of researcher bias.

types of investigator effects

• Conscious or unconscious behaviour:

  • Tone of voice, facial expression, or body language giving away expectations.

• Design bias:

  • Selecting biased samples or creating leading questions (e.g., “How much do you agree that…”).

• Recording bias:

  • Researcher interprets or records data to fit expectations.

why they are a problem

They reduce internal validity, because changes in the dependent variable might be caused by the researcher, not the independent variable.

how to control investigator effects

• Double-blind design:

  • Neither participant nor researcher knows which condition participants are in.

• Standardised instructions/procedures:

  • Same instructions and environment for all participants.

• Use of automated data collection:

  • e.g., computers to measure reaction time instead of human judgment.

• Training or awareness:

  • Researchers trained to avoid influencing behaviour.

example

• If a researcher smiles more at participants in one condition, they may perform better simply due to positive reinforcement.

• In a clinical interview, if a psychologist expects a diagnosis, they might interpret ambiguous answers as confirming it.