(Crocker) defines the standard error of measurement as the average standard deviation of the error distributions for a large number of repeated test situations, and the standard error is important in order to produce a confidence interval around the observed test scores that have a known probability to contain the observed test scores, and the standard error of the measurement has a value for the population of subjects in Traditional measurement theory, and it is important in many contexts when we aim to measure individual differences.
Hello Ali,
You can estimate the SEM using a very basic standard formula, which is standard deviation of your sample scores multiplied by the squares root of 1 minus the reliability of your scores as follows: SEM = SD √1 - r
Usually SE = SD/ square root of n. The formula given by Giovane R. Sousa is not conventional and does not provide an absolute mathematical value, but rather an estimate based on test consistency, but it's still okay :)
2 comments:
1. It depends on what you want to measure Jochen Wilhelm provides the most general definition. Giovane R. Sousa needs to parse his equation better because as is it is not clear what terms are under the square root symbol. Andrew Paul McKenzie Pegman is largely correct for most common measurements.
2 Thus you need you need to supply context for your question to get a really helpful answer.
Best wishes, D. Booth
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