An important source of experimental error for a repeated-measures design is the problem of carryover effects from one treatment or condition to the next (Elmes, Kantowitz, & Roedinger, 2006). Describe carryover effects and discuss ways to combat them.

Question

What will be an ideal response?

Answer 1

Ans: Varies.

Carryover effects will cause results to be confounded by the order of presentation of the conditions. Under these circumstances, a researcher will not be able to attribute the results of a study to a particular treatment or condition. True differences in treatment, or true differences between or among levels of the independent variable, may be masked by the order in which the levels of the independent variable were presented. Carryover effects can also arise from rather routine factors, such as fatigue participants might experience by the end of an experiment, or from familiarity or practice effects participants might experience over the course of a study. There are several strategies to combat carryover effects. One way is to randomizing the order in which participants receive treatments. Randomization ensures that any differences in treatment orders are due to chance only. Another technique is counterbalancing the order of treatments which ensures that each treatment or level of the independent variable occurs in each time period of the experiment. Researchers will frequently use what is called a Latin-square design as a way to combat confounds of carryover effects in within-subjects experiments. A Latin-square design is an incomplete counterbalancing arrangement in which each possible order of treatments occurs equally often across research participants.