Standard Errors of Measurement (test scores)
These functions will calculate the three Standard Errors of Measurement as described by Dudek(1979). They are useful in constructing CI about observed scores, true scores and predicting observed scores on parallel measures.
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Standard Deviation in tests scores on test x
Standard Deviation in tests scores on parallel test y = x
Reliability of test x
Dudek (1979) notes that in practice, individuals often misinterpret the SEM. In fact, most textbooks misinterpret these measures. The SE.Meas (s*sqrt(1-rxx)) is useful in the construction of CI about observed scores, but should not be interpreted as indicating the TRUE SCORE is necessarily included in the CI. The SE.Est (s*sqrt(rxx*(1-rxx))) is useful in the construction of CI about the TRUE SCORE. The estimate of a CI for a TRUE SCORE also requires the calculation of a TRUE SCORE (due to regression to the mean) from observed scores. The SE.Pred (sy*sqrt(1-rxx^2)) is useful in predicting the score on a parallel measure (Y) given a score on test X. SE.Pred is usually used to estimate the score of a re-test of an individual.
The returned value is the appropriate standard error
Since strictly parallel tests have the same SD, s and sy are equivalent in these functions.
SE.Meas() is used by
CI.obs. SE.Est() is used by
CI.tscore. You must use
Est.true to first compute the estimated true score from an observed score accounting for regression
to the mean.
Thomas D. Fletcher email@example.com
Dudek, F. J. (1979). The continuing misinterpretation of the standard error of measurement. Psychological Bulletin, 86, 335-337.
Lord, F. M. & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). New York: McGraw-Hill.
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