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Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods. [1] For instance, repeated measurements are collected in a longitudinal study in which change over time is assessed.
[1] [2] [3] Concurrent designs are the traditional approach to multiple baseline studies, where baseline measurements of all participants start at (roughly) the same moment in real time. This strategy is advantageous because it moderates several threats to validity , and history effects in particular.
A crossover trial has a repeated measures design in which each patient is assigned to a sequence of two or more treatments, of which one may be a standard treatment or a placebo. Nearly all crossover are designed to have "balance", whereby all subjects receive the same number of treatments and participate for the same number of periods.
In statistics, a mixed-design analysis of variance model, also known as a split-plot ANOVA, is used to test for differences between two or more independent groups whilst subjecting participants to repeated measures. Thus, in a mixed-design ANOVA model, one factor (a fixed effects factor) is a between-subjects variable and the other (a random ...
This design-based analysis was discussed and developed by Francis J. Anscombe at Rothamsted Experimental Station and by Oscar Kempthorne at Iowa State University. [19] Kempthorne and his students make an assumption of unit treatment additivity , which is discussed in the books of Kempthorne and David R. Cox .
However, repeat measurements are collected during a single experimental session, while replicate measurements are gathered across different experimental sessions. [2] Replication in statistics evaluates the consistency of experiment results across different trials to ensure external validity, while repetition measures precision and internal ...
A longitudinal study (or longitudinal survey, or panel study) is a research design that involves repeated observations of the same variables (e.g., people) over long periods of time (i.e., uses longitudinal data). It is often a type of observational study, although it can also be structured as longitudinal randomized experiment. [1]
Designed experiments with full factorial design (left), response surface with second-degree polynomial (right) In statistics, a full factorial experiment is an experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors.