How is test re-test reliability calculated? Without the confidence that the measure you’ve chosen is reliable, it is difficult to ascertain whether differences in performance pre and post-intervention are genuinely due to the intervention provided and not an artefact of the tool.Ī tool with low reliability can therefore mask the true effects of an intervention, which could have serious ramifications on the conclusions drawn, and therefore the future progression of that intervention. Often your aim in research will be to evaluate the impact of an intervention on an individual’s performance. Without good reliability, it is difficult for you to trust that the data provided by the measure is an accurate representation of the participant’s performance rather than due to irrelevant artefacts in the testing session such as environmental, psychological or methodological processes. Often, test re-test reliability analyses are conducted over two time-points (T1, T2) over a relatively short period of time, to mitigate against conclusions being due to age-related changes in performance, as opposed to poor test stability. Having good test re-test reliability signifies the internal validity of a test and ensures that the measurements obtained in one sitting are both representative and stable over time. Why is it important to choose measures with good reliability? ![]() Tools which do provide such consistency are regarded as having high test re-test reliability, and therefore appropriate for use in longitudinal research. In an experiment with multiple time points, you would hope that the measurement tool chosen could consistently reproduce the same result over all the visits providing all other variables remain the same. appropriately measure the construct or domain in question), and that they could also reliably replicate the result more than once in the same situation and population. When you come to choose the measurement tools for your experiment, it is important to check that they are valid (i.e.
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