| R.ETL {betafunctions} | R Documentation |
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)