One-way repeated measures ANOVA is used to analyze the relationship between the independent variable and dependent variable. It is an extension of the correlated-groups t test, where the main advantage is controlling the disturbance variables or individual differences that could influence the dependent variable.
In contrast to the independent groups ANOVA (between-subjects), the repeated measures procedure is generally more powerful (ie. leads to a greater likelihood of rejecting a false null by hypothesis). Statistically, this is the case because unwanted variability (error) due to individual differences among participants can be quantified and removed from the denominator of the F-ratio. This procedure requires fewer participants, but it can give rise to research confounds such as order or practice effects and participant bias.
Total variability broken down into two components:
-Between subjects. Variability in scores due to individual differences among participants.
-Within subjects.
The within subjects variability is subdivided into the following components:
-Treatment. Variance among the treatment means (same as MS between in the independent groups ANOVA).
-Residual. Leftover or unwanted error variability (can be thought of an inconsistencies in the effects of the treatments across the participants).
It needs to input the X-data matrix (Size of matrix must be n-by-3;dependent variable=column 1, independent variable=column 2;subject=column 3) and the alpha- significance level (default = 0.05).
The output is a complete Analysis of Variance table and the strength of the relationship. |