Because of this, Prism would assume that the variation between pseudo-replicates represented variation between animals. Prism would think that there were twelve total animals (as described above) - not six. Performing a standard two-way ANOVA in this scenario (with no data treatment) would provide misleading results. You must be careful when analyzing data from this type of experiment with Prism due to the problem of pseudo-replication. ![]() These duplicate values for each animal are known as pseudo-replicates. In this situation, the value in row 1, column A:Y1 (23) came from the same animal as the value in row 1, column A:Y2 (24). Let's say each treatment combination has only one animal and measurements were made in duplicate (two measurements per animal). Of course, this would not be a repeated measures experiment.Īnother situation could be that the experiment that generated the data above was performed using six animals. In this case, each cell would represent a different animal. Perhaps one of the simplest explanations for the table above is that this experiment was performed using twelve animals, with one measurement per animal. These data could have come from four distinct experimental designs. ![]() The table above shows example data testing the effects of three doses of drugs in control and treated animals. Your choice will not affect the ANOVA results, but the choice is important as it affects the appearance of graphs. You need to decide which factor is defined by rows, and which by columns. Each data set (column) represents a different level of one factor, and each row represents a different level of the other factor. You use rows and columns to designate the different groups (levels) of each factor. Two within-subject variables (both factors are repeated measures) One data table can correspond to four experimental designs.One between-subject variable and one within subject variable. ![]()
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