16 May HOW LONG DOES IT TAKE BEFORE THE PERSON RETURNS TO A NORMAL STATE?
In an independent groups design, different participants are assigned to each of the conditions using random assignment. This means that the decision to assign an individual to a particular condition is completely random and beyond the control of the researcher. For example, you could ask for the participant’s month of birth; individuals born in odd-numbered months would be assigned to one group and those born in even-numbered months would be assigned to the other group. In practice, researchers use a sequence of random numbers to determine assignment. Such numbers come from a random number generator such as Research Randomizer, available online at http://www.randomizer.org or QuickCalcs at http://www.graphpad.com/quickcalcs/randomize1.cfm; Excel can also generate random numbers. These programs allow you to randomly determine the assignment of each participant to the various groups in your study. Random assignment will prevent any systematic biases, and the groups can be considered equivalent in terms of participant characteristics such as income, intelligence, age, personality, and political attitudes. In this way, participant differences cannot be an explanation for results of the experiment. As we noted in Chapter 4, in an experiment on the effects of exercise on anxiety, lower levels of Page 169anxiety in the exercise group than in the no-exercise group cannot be explained by saying that people in the groups are somehow different on characteristics such as income, education, or personality.
An alternative procedure is to have the same individuals participate in all of the groups. This is called a repeated measures experimental design.
REPEATED MEASURES DESIGN
Consider an experiment investigating the relationship between the meaningfulness of material and the learning of that material. In an independent groups design, one group of participants is given highly meaningful material to learn and another group receives less meaningful material. For example, the meaningful material might include a story relating the material to a real-life event. In a repeated measures design, the same individuals participate in both conditions. Thus, participants might first read low-meaningful material and take a recall test to measure learning; the same participants would then read high-meaningful material and take the recall test. You can see why this is called a repeated measures design; participants are repeatedly measured on the dependent variable after being in each condition of the experiment.
Advantages and Disadvantages of Repeated Measures Design
The repeated measures design has several advantages. An obvious one is that fewer research participants are needed, because each individual participates in all conditions. When participants are scarce or when it is costly to run each individual in the experiment, a repeated measures design may be preferred. In much research on perception, for instance, extensive training of participants is necessary before the actual experiment can begin. Such research often involves only a few individuals who participate in all conditions of the experiment.
An additional advantage of repeated measures designs is that they are extremely sensitive to finding statistically significant differences between groups. This is because we have data from the same people in both conditions. To illustrate why this is important, consider possible data from the recall experiment. Using an independent groups design, the first three participants in the high-meaningful condition had scores of 68, 81, and 92. The first three participants in the low-meaningful condition had scores of 64, 78, and 85. If you calculated an average score for each condition, you would find that the average recall was a bit higher when the material was more meaningful. However, there is a lot of variability in the scores in both groups. You certainly are not finding that everyone in the high-meaningful condition has high recall and everyone in the other condition has low recall. The reason for this variability is that people differ—there are individual differences in recall abilities, so there is a range of scores in both conditions. This is part of “random error” in the scores that we cannot explain.
Page 170However, if the same scores were obtained from the first three participants in a repeated measures design, the conclusions would be much different. Let’s line up the recall scores for the two conditions:
With a repeated measures design, the individual differences can be seen and explained. It is true that some people score higher than others because of individual differences in recall abilities, but now you can much more clearly see the effect of the independent variable on recall scores. It is much easier to separate the systematic individual differences from the effect of the independent variable: Scores are higher for every participant in the high-meaningful condition. As a result, we are much more likely to detect an effect of the independent variable on the dependent variable.
The major problem with a repeated measures design stems from the fact that the different conditions must be presented in a particular sequence. Suppose that there is greater recall in the high-meaningful condition. Although this result could be caused by the manipulation of the meaningfulness variable, the result could also simply be an order effect—the order of presenting the treatments affects the dependent variable. Thus, greater recall in the high-meaningful condition could be attributed to the fact that the high-meaningful task came second in the order of presentation of the conditions. Performance on the second task might improve merely because of the practice gained on the first task. This improvement is in fact called a practice effect, or learning effect. It is also possible that a fatigue effect could result in a deterioration in performance from the first to the second condition as the research participant becomes tired, bored, or distracted.
It is also possible for the effect of the first treatment to carry over to influence the response to the second treatment—this is known as a carryover effect. Suppose the independent variable is severity of a crime. After reading about the less severe crime, the more severe one might seem much worse to participants than it normally would. In addition, reading about the severe crime might subsequently cause participants to view the less severe crime as much milder than they normally would. In both cases, the experience with one condition carried over to affect the response to the second condition. In this example, the carryover effect was a psychological effect of the way that the two situations contrasted with one another.
A carryover effect may also occur when the first condition produces a change that is still influencing the person when the second condition is introduced. Suppose the first condition involves experiencing failure at an important Page 171task. This may result in a temporary increase in stress responses. How long does it take before the person returns to a normal state? If the second condition is introduced too soon, the stress may still be affecting the participant.
There are two approaches to dealing with order effects. The first is to employ counterbalancing techniques. The second is to devise a procedure in which the interval between conditions is long enough to minimize the influence of the first condition on the second.
Counterbalancing
Complete counterbalancing In a repeated measures design, it is very important to counterbalance the order of the conditions. With complete counterbalancing, all possible orders of presentation are included in the experiment. In the example of a study on learning high- and low-meaningful material, half of the participants would be randomly assigned to the low-high order, and the other half would be assigned to the high-low order. This design is illustrated as follows:
By counterbalancing the order of conditions, it is possible to determine the extent to which order is influencing the results. In the hypothetical memory study, you would know whether the greater recall in the high-meaningful condition is consistent for both orders; you would also know the extent to which a practice effect is responsible for the results.
Counterbalancing principles can be extended to experiments with three or more groups. With three groups, there are 6 possible orders (3! = 3 × 2 × 1 = 6). With four groups, the number of possible orders increases to 24 (4! = 4 × 3 × 2 × 1 = 24); you would need a minimum of 24 participants to represent each order, and you would need 48 participants to have only two participants per order. Imagine the number of orders possible in an experiment by Shepard and Metzler (1971). In their basic experimental paradigm, each participant is shown a three-dimensional object along with the same figure rotated at one of 10 different angles ranging from 0 degrees to 180 degrees (see the sample objects illustrated in Figure 8.3). Each time, the participant presses a button when it is determined that the two figures are the same or different. The dependent variable is reaction time—the amount of time it takes to decide whether the figures are the same or different. The results show that reaction time becomes longer as the angle of rotation increases away from the original. In this experiment with 10 conditions, there are 3,628,800 possible orders! Fortunately, there are alternatives to complete counterbalancing that still allow researchers to draw valid conclusions about the effect of the independent variable without running some 3.6 million tests.
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