Missing values with ordinary (not repeated measures) ANOVA

Note that one value is blank. It is fine to have some missing values for ordinary (but not repeated measures) ANOVA, but you must have at least one value in each row for each data set.

The following table cannot be analyzed by two-way ANOVA because there are no data for treated women. Since this example will be analyzed by ordinary two-way ANOVA,  it doesn't matter much that there are only two (not three) replicates for control men and treated men. When you want to use repeated-measures ANOVA, missing values are not allowed.

If you are entering mean, SD (or SEM) and n, You must never leave n blank or enter zero, but it is ok if n is not always the same.

Missing values with repeated measures ANOVA

The rules regarding missing values and repeated measures two-way ANOVA are:

•Prism can compute repeated measures two-way ANOVA fine if alternative treatments were given to different numbers of subjects.

•Prism cannot compute repeated measures two-way ANOVA if values at some time points are missing.

For the examples shown below, assume that the matched values are stacked into a subcolumn, which is the most common way to enter data.

In the top table, column A has data in three subcolumns, while column B had data in only two subcolumns. This is not a problem, and repeated measures ANOVA will work fine. Note that it is not possible to format a table, in this case, with three subcolumns for data set A, and two for data set B. Prism always creates tables with the same number of subcolumns in each data set, but it is ok to simply leave subcolumns blank.

Prism cannot perform repeated measures ANOVA from the second data table below. The problem is that the value in row 2 of subcolumn 2 of data set B is missing. Computing repeated measures ANOVA with missing values is far from straightforward, and Prism doesn't attempt it.