Determining the Impact of Repeated Measures on Power For Varying Numbers of Intermediate Measurements

In terms of the methodology used in this project, we will determine the impact of the power of formal comparisons between groups, which are based on the final measurement when the intermediate measures are included in the analysis via a linear mixed model, which has been included as a module in ST6034.[1] This impact of power has been assessed for the various intermediate measurements. Suppose the assumptions of independence are violated. In that case, we will have the model containing observations from each of the groups, and It would be reasonable to assume freedom within each of these subjects. However, this model can be developed further, and when fitted, adjustments are made to account for the lack of independence. The methodology is known as Repeated Measures ANOVA.[2] Even with these adjustments made to the subjects, these Repeated Measures ANOVA models don’t impose compound symmetry on the correlation within these subjects. We will know the relationship and the different factors that affect power. We will also be understanding the interrelationship between the statistical significance, power, and effect size. We will look at the values which determine the power with relation to the other variables. We will look at the mean and standard deviation of the sample size with the other variables known. The learning process with the hypothesis, sample subjects, the sample distributions, the calculation of the hypothesis tests, the confidence intervals, and the effect size determine the power. We will also establish relationships with the relational studies with the statistics to analyze the data with the correlational model or the experimental designs.