Can you do a repeated measures ANCOVA?
The repeated measures ANCOVA is similar to the dependent sample t-Test, and the repeated measures ANOVA because it also compares the mean scores of one group to another group on different observations. The repeated measures ANCOVA can correct for the individual differences or baselines.
Can you use ANOVA for repeated measures?
Introduction. Repeated measures ANOVA is the equivalent of the one-way ANOVA, but for related, not independent groups, and is the extension of the dependent t-test. A repeated measures ANOVA is also referred to as a within-subjects ANOVA or ANOVA for correlated samples.
How do you calculate df for repeated measures?
The calculation of df2 for a repeated measures ANOVA with one within-subjects factor is as follows: df2 = df_total – df_subjects – df_factor, where df_total = number of observations (across all levels of the within-subjects factor, n) – 1, df_subjects = number of participants (N) – 1, and df_factor = number of levels ( …
Should I use repeated measures ANOVA or ANCOVA?
Analyzing the Pre-Post Data The advisor insisted that this was a classic pre-post design, and that the way to analyze pre-post data is not with a repeated measures ANOVA, but with an ANCOVA. The advisor said repeated measures ANOVA is only appropriate if the outcome is measured multiple times after the intervention.
How do you calculate SSM?
Therefore, the easiest way to calculate SSM is to: Calculate the difference between the mean of each group and the grand mean. Square each of these differences. Multiply each result by the number of participants within that group (nk).
How do you calculate df?
The most commonly encountered equation to determine degrees of freedom in statistics is df = N-1. Use this number to look up the critical values for an equation using a critical value table, which in turn determines the statistical significance of the results.
How is df total calculated?
The degrees of freedom is equal to the sum of the individual degrees of freedom for each sample. Since each sample has degrees of freedom equal to one less than their sample sizes, and there are k samples, the total degrees of freedom is k less than the total sample size: df = N – k.