What is the difference between replication and repeated measurements?
Repeated measures involves measuring the same cases multiple times. So, if you measured the chips, then did something to them, then measured them again, etc it would be repeated measures. Replication involves running the same study on different subjects but identical conditions.
Why do we need to gather replicate repeat measures?
Getting the same result when an experiment is repeated is called replication. Replication is important in science so scientists can “check their work.” The result of an investigation is not likely to be well accepted unless the investigation is repeated many times and the same result is always obtained.
Can a replicate be used as a Pseudoreplication?
Ideally, one replicate from each treatment ought to be grouped together into a block, and each treatment repeated in many different blocks. Repeated measures (e.g. from the same individual or the same spatial location) are not replicates (this is probably the commonest cause of pseudoreplication in statistical work).
What’s the difference between replication and repeated data?
Running your experiment again is a replication; it gets you a second data point. In a biomedical context, you run an experiment and get N data. Someone else replicates your experiment on a new sample with another N ′ data.
What are the subscripts in repeated measure analysis?
Similar to the preceding section on two-factor ANOVA, c denotes the number of repeated columns (repeated measures), r the number of independent measures, and w the number of replications. Subscript designations appear in Table 13.15. Table 13.15. Subscript Designations k = 1, 2, …, w.
When to use repeated measure analysis of variance?
Correlated data analyses can sometimes be handled by repeated measures analysis of variance (ANOVA). When the data are balanced and appropriate for ANOVA, statistics with exact null hypothesis distributions (as opposed to asymptotic, likelihood based) are available for testing.