Another category of alternative explanations goes under the name of maturation. Participants might have changed between the pretest and the posttest in ways that they were going to anyway because they are growing and learning. If it were a yearlong program, participants might become less impulsive or better reasoners and this might be responsible for the change.
Another alternative explanation for a change in the dependent variable in a pretest-posttest design is regression to the mean. This refers to the statistical fact that an individual who scores extremely on a variable on one occasion will tend to score less extremely on the next occasion. For example, a bowler with a long-term average of who suddenly bowls a will almost certainly score lower in the next game. Regression to the mean can be a problem when participants are selected for further study because of their extreme scores.
Imagine, for example, that only students who scored especially low on a test of fractions are given a special training program and then retested. Regression to the mean all but guarantees that their scores will be higher even if the training program has no effect. A closely related concept—and an extremely important one in psychological research—is spontaneous remission.
This is the tendency for many medical and psychological problems to improve over time without any form of treatment. The common cold is a good example. If one were to measure symptom severity in common cold sufferers today, give them a bowl of chicken soup every day, and then measure their symptom severity again in a week, they would probably be much improved.
This does not mean that the chicken soup was responsible for the improvement, however, because they would have been much improved without any treatment at all. The same is true of many psychological problems. A group of severely depressed people today is likely to be less depressed on average in 6 months. Thus one must generally be very cautious about inferring causality from pretest-posttest designs. Early studies on the effectiveness of psychotherapy tended to use pretest-posttest designs.
In a classic article, researcher Hans Eysenck summarized the results of 24 such studies showing that about two thirds of patients improved between the pretest and the posttest Eysenck, [3].
But Eysenck also compared these results with archival data from state hospital and insurance company records showing that similar patients recovered at about the same rate without receiving psychotherapy. This parallel suggested to Eysenck that the improvement that patients showed in the pretest-posttest studies might be no more than spontaneous remission.
Note that Eysenck did not conclude that psychotherapy was ineffective. You can read the entire article here: Classics in the History of Psychology. Subsequent research has focused more on the conditions under which different types of psychotherapy are more or less effective.
A variant of the pretest-posttest design is the interrupted time-series design. A time series is a set of measurements taken at intervals over a period of time. Because productivity increased rather quickly after the shortening of the work shifts, and because it remained elevated for many months afterward, the researcher concluded that the shortening of the shifts caused the increase in productivity.
Notice that the interrupted time-series design is like a pretest-posttest design in that it includes measurements of the dependent variable both before and after the treatment. It is unlike the pretest-posttest design, however, in that it includes multiple pretest and posttest measurements.
Figure 7. The dependent variable is the number of student absences per week in a research methods course. The treatment is that the instructor begins publicly taking attendance each day so that students know that the instructor is aware of who is present and who is absent. The top panel of Figure 7.
There is a consistently high number of absences before the treatment, and there is an immediate and sustained drop in absences after the treatment. The bottom panel of Figure 7. On average, the number of absences after the treatment is about the same as the number before. This figure also illustrates an advantage of the interrupted time-series design over a simpler pretest-posttest design. Further, for quasi-experimental methods to provide valid and unbiased estimates of program impacts, researchers must make more assumptions about the control group than in experimental methods.
For example, difference-in-differences relies on the equal trends assumption see Difference-in-Differences for more details , while matching assumes identical unobserved characteristics between the treatment and control groups. Jump to: navigation , search. Category : Quasi-Experimental Methods. At first glance, the regression discontinuity design strikes most people as biased because of regression to the mean. I had the distinct honor of co-authoring a paper with Donald T. Campbell that first described the Regression Point Displacement Design.
At the time of his death in Spring , we had gone through about five drafts each over a five year period. The paper includes numerous examples of this newest of quasi-experiments, and provides a detailed description of the statistical analysis of the regression point displacement design. There is one major class of quasi-experimental designs that are not included here — the interrupted time series designs.
I plan to include them in later rewrites of this material. We send an occasional email to keep our users informed about new developments on Conjoint. You can always unsubscribe later. Your email will not be shared with other companies.
0コメント