![]() ![]() What kinds of things might cause the distributions for each of the different experimental conditions to be different from one another ( between treatments variability)? Now let's consider the sources of variance in our design. Why would we decide to design our experiment as a repeated measures design instead of a between subjects? By using participants as their own comparison group we can remove the variance due to subjects from the overall variance due to treatments. The difference is whether or not the data in the different experimental conditions are independent or not. So when is an ANOVA the appropriate analysis? Check the decision tree.įind the string of decisions that lead to a 1-way between groups Analysis of Variance.įind the string of decisions that lead to a 1-way within groups (repeated measures) Analysis of Variance. What this means is that the everybody in the experiment participates in all levels of the factor (independent variable). This design is referred to as a within-subjects or repeated measures ANOVA design. ![]() ![]() But that's not the appropriate analysis to do because the data in the different conditions are not independent. One might be tempted to analyze this data set using the ANOVA process that we discussed last time. The scores are the number of items recalled in a memory test (out of a possible 10 items). After 4 weeks you test their memory, and again after 16 weeks. Then you have each start taking the new drug each week. So you give a group of 5 individuals a pre-test for memory. You (the researcher) believe that the drug should improve memory performance, and that continued usage of the drug should lead to continued memory performance. Suppose that you are testing the long term effects of a new memory drug. Psycholog圓40: ANOVA2 Psychology 340 SyllabusĬonsider the following research description: ![]()
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