Demand characteristics are cues in an experiment that influence participants' behavior based on their perceptions of the research aims.
Example: A horse might perform a task as expected because it picks up on cues from the researcher, which represents demand characteristics in action.
Addressing Demand Characteristics
To minimize the influence of demand characteristics, researchers employ various experimental designs:
Single Blind Design
In a single blind design, the participant is unaware of the research aims and/or which condition they are receiving.
Purpose: This design prevents participants from seeking cues about the aims and responding to them.
Double Blind Design
A double blind design entails that both the participant and the experimenter are 'blind' to the aims and/or hypothesis of the study.
Effectiveness: This approach reduces the likelihood of the experimenter unintentionally providing cues that could influence the participants' responses.
Experimental Realism
Experimental realism refers to the degree to which the experimental task engages the participant sufficiently, so they focus on the task rather than the observation aspect of the research.
High experimental realism enhances the validity of the study outcomes.
Extraneous Variables (EVs)
Participant Variables
Participant variables are characteristics of individual participants that may influence the results of an experiment. They differ from participant effects, which concern behavior influenced by demand characteristics.
Examples of participant variables:
Age
Intelligence
Motivation
Experience
Gender
Gender as a Participant Variable
Research suggests that gender can impact certain behaviors, such as conformity.
Example: Alice Eagly (1978) reported that women may be more conformist than men.
Implications: If one experimental condition has a disproportionate number of men or women, it could obscure the effects of the independent variable (IV).
Controlling Participant Variables
Participant variables act as extraneous variables only in an independent groups design.
In a repeated measures design, participant variables are controlled because the same participants are used across conditions.
In a matched pairs design, efforts are made to control participant variables to ensure they do not influence the results.
Situational Variables
Situational variables are aspects of the research setting that may affect participants' behavior, potentially serving as extraneous or confounding variables.
Example of a situational variable: Order effects.
Order effects occur when participants may perform better on subsequent tasks due to practice or familiarity (a confounding variable), rather than the IV's influence.
Situational variables are only considered confounding if they vary systematically with the IV.
Example: If all participants in one group are tested in the morning, and another group in the afternoon, this could influence the results related to the timing of testing rather than the experimental manipulation.