R.ETL: Model Implied Reliability from Livingston and Lewis'...

In betafunctions: Functions for Working with Two- And Four-Parameter Beta Probability Distributions and Psychometric Analysis of Classifications

Model Implied Reliability from Livingston and Lewis' "Effective Test Length".

Description

Calculate model-implied reliability given mean, variance, the minimum and maximum possible scores, and the effective test length.

Usage

R.ETL(mean, variance, min = 0, max = 1, ETL)

Arguments

mean	The mean of the observed-score distribution.
variance	The variance of the observed-score distribution.
min	The lower-bound (minimum possible value) of the observed-score distribution. Default is 0 (assuming observed scores represent proportions).
max	The upper-bound (maximum possible value) of the observed-score distribution. Default is 1 (assuming observed scores represent proportions).
ETL	The effective test length as defined by Livingston and Lewis (1995).

Value

An estimate of the reliability of a test, given the effective test length, mean, variance, and minimum and maximum possible scores of the observed-score distribution..

References

Livingston, Samuel A. and Lewis, Charles. (1995). Estimating the Consistency and Accuracy of Classifications Based on Test Scores. Journal of Educational Measurement, 32(2).

Examples

# Generate some fictional data. Say, 100 individuals take a test with a
# maximum score of 100 and a minimum score of 0.
set.seed(1234)
testdata <- rbinom(100, 100, rBeta.4P(100, .25, .75, 5, 3))
hist(testdata, xlim = c(0, 100))

# From the data-generating script above, the effective test length is 100.
# To estimate and retrieve the model-implied reliability using R.ETL():
R.ETL(mean = mean(testdata), variance = var(testdata), min = 0, max = 100,
ETL = 100)

betafunctions documentation built on May 29, 2024, 1:13 a.m.