1987•Educational and Psychological MeasurementRequires access

An Equation to Simplify Correction of Range Restricted Standard Deviations and Correlations when the Population Variance is Unknown

George M. Alliger

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Abstract

Estimates of population, or unrestricted, variance are generally required to correct correlations for restriction in range. Several articles have been concerned with the advantages of being able to correct correlations for variables that are restricted in range but for which the population variance is not known. Methods previously suggested use a tabled function, as per Cohen (1959), to estimate the population variance. This article presents an empirically derived formula that permits an accurate determination of corrections without the use of a table.

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What this paper is about

Estimates of population, or unrestricted, variance are generally required to correct correlations for restriction in range. Several articles have been concerned with the advantages of being able to correct correlations for variables that are restricted in range but for which the population variance is not known. Methods previously suggested use a tabled function, as per Cohen (1959), to estimate the population variance. This article presents an empirically derived formula that permits an accurate determination of corrections without the use of a table.

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Available abstract

Estimates of population, or unrestricted, variance are generally required to correct correlations for restriction in range. Several articles have been concerned with the advantages of being able to correct correlations for variables that are restricted in range but for which the population variance is not known. Methods previously suggested use a tabled function, as per Cohen (1959), to estimate the population variance. This article presents an empirically derived formula that permits an accurate determination of corrections without the use of a table.

Key concepts: Variance (accounting), Range (aeronautics), Statistics, Population variance, Population, Mathematics, Econometrics, Standard deviation

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