The correlation between pretest and posttest scores within the treatment group provides an estimate of the consistency of the treatment effect across individuals. In treatment outcome studies it is unlikely that the treatment effect will be exactly the same for every individual. The error term will be relatively large when the effect of treatment is not the same for each person. Error will be small to the extent that the effect of the treatment is the same for each individual (i.e., the gain score is the same for each person). j is the mean gain score for a particular treatment group. In a gain score analysis X ij is the observed gain score and M. That is, SSerror is the sum of the squared differences between a score and the group mean for that score. Recall that in an analysis of variance the sums of squares for error is defined as The sums of squares for the within cells error term is the amount of error in the gain scores. If the treatment main effect is significant, then we reject the null hypothesis. The null hypothesis of no difference in improvement between the treatment and control groups can be tested by an analysis of variance on the gain scores using treatment (treatment vs. But, a gain score analysis does not control for the differences in pretest scores between the two groups. The gain score controls for individual differences in pretest scores by measuring the posttest score relative to the each person's pretest score. In our example the dependent variable is trait anxiety so we expect that successful treatment would lead to lower anxiety. When you compute a gain score in this manner a positive gain score indicates that the posttest score was greater than the pretest score, a negative gain score indicates that the posttest score was less than the pretest score. The SPSS syntax for computing the gain score is as follows: The improvement (gain) from pretest to posttest can be computed for each participant by subtracting each person's pretest score from his or her posttest score. The general approach to a gain score analysis is: (a) to compute the gain score, and then (b) analyze those gain scores in an analysis of variance with treatment as the between-subjects factor. It will be shown that the treatment by time interaction effect in the 2 x 2 analysis of variance yields identical statistical results to the treatment main effect in the gain score analysis. If the interaction is significant, then the change between pretest and posttest is not the same in the two treatment conditions. control) as a between subjects factor and time (pretest vs. This analysis of difference scores is also called a gain score analysis.Īnother way of answering this question is by looking at the interaction effect in a 2 x 2 analysis of variance (ANOVA) with treatment (treatment vs. If the treatment main effect is significant, then the change from pretest to posttest is not the same in the two groups. The question can be answered by computing the difference between the pretest and posttest scores for each person and then analyzing those differences in a oneway ANOVA using treatment (treatment vs. The question of interest is whether the improvement in scores from pretest to posttest is greater for the treatment group than it is for the control group. The data that we displayed as a scattergram in the analysis of covariance notes are redisplayed here using the pretest and posttest means within each treatment condition. Hypothetical pretest and posttest trait anxiety means for a two group design are shown in Figure 1. Those procedures were used to analyze the differences in posttest scores after any pretest score differences were "held constant." In this set of notes we will take a different approach and look at the change from the pretest and posttest scores. In previous sets of notes in this series we analyzed a pretest-posttest, two-group, quasi-experimental design using blocking, matching, and analysis of covariance procedures.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |