Repeated Measures Design Quiz

Study Notes

Repeated measures design refers to an experimental design where the same participants are measured multiple times under different conditions.
Repeated measures designs can be used to determine whether a treatment has an effect on a dependent variable.
Repeated measures designs have a potential for confounding variables if the order of conditions affects the results.

Variability in scores refers to the extent to which scores on a variable differ from one another.
Variability in scores can create difficulties in determining the effectiveness of the independent variable on the dependent variable.
Repeated within-subjects designs can control for variability in scores due to individual differences.

Repeated measures designs help to remove variability in scores, allowing for a clearer understanding of the influence of the IV on the DV.
With reduced variability, the influence of the Independent Variable (IV) on the Dependent Variable (DV) is more apparent.
This allows for greater reliability in determining the true effects of the IV.

Repeated measures designs provide a better method for examining the effects of the Independent Variable (IV) on the Dependent Variable (DV) compared to other designs.
This is because of the lack of overlapping conditions in repeated measures designs.
Repeated measures are not perfect, and there are potential confounds to consider.

Repeated measures designs are unable to fully eliminate random error in experiments.
However, these designs allow for a clearer distinction between random and systematic error.
With this clearer distinction, researchers can more accurately determine the true effects of the Independent Variable (IV) on the Dependent Variable (DV).

Repeated measures designs are particularly relevant in examining the effects on dependent variables that are susceptible to influences of individual differences.
In these situations, repeated measures designs can be employed to control for these variations and provide a more accurate understanding of the true effects of the independent variable.

A potential confound in repeated measures designs is that the order of conditions could impact the results.
This can be addressed through counterbalancing where the order of conditions varies across participants.
Counterbalancing aims to distribute the potential effects of order across all conditions and participants.

Researchers like Hans and Conde Saffer have explored the potential effects of the order of conditions in repeated measures designs.
They have also examined how potential "carry-over" effects from previous conditions can influence the independent variable's effectiveness on participants in later stages of the experiment.
Their work demonstrates the crucial need to consider potential confounders in repeated measures designs and to employ counterbalancing strategies to minimize their impact.

There are concerns about participant behavior in repeated measures designs because participants may observe others in the experiment.
If participants know the study's aim, they may change their behavior in subsequent conditions, impacting the study's validity.
To minimize such effects, researchers can employ various techniques like blind studies, where participants are unaware of the experiment's purpose.