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Updated16 mai 2018
The Stata module "Raschpower"
raschpower allows estimating the power of the Wald test comparing the means of two groups of patients in the context of the Rasch model or the Partial Credit Model. The estimation is based on the estimation of the variance of the difference of the means based on the Cramer-Rao bound.
Type "findit raschpower" or "ssc install raschpower" directly from your Stata browser.
Syntax (version 3.2)
raschpower [, n0(#) n1(#) gamma(#) var(#) d(matrix) Method(string) ]
- n0(#): indicates the numbers of individuals in the first group [100 by default]
- n1(#): indicates the numbers of individuals in the second group [100 by default]
- gamma(#): indicates the group effect (difference between the two means) [0.5 by default]
- var(#): indicates the value of the variance of the latent trait [1 by default]
- d(matrix): is a matrix containing the item parameters [one row per item, one column per positive modality - (-1.151, -0.987\-0.615, -0.325\-0.184, -0.043\0.246, 0.554\0.782, 1.724) by default]
- Method(string): indicates the method for constructing data. method may be GH, MEAN, MEAN+GH or POPULATION+GH [default is method(GH) if number of patterns<500, method(MEAN+GH) if 500<=number of patterns<10000, method(MEAN) if 10000<=number of patterns<1000000, method(POPULATION+GH) otherwise].
- GH: The probability of all possible response patterns is estimated by Gauss-Hermite quadratures.
- MEAN: The mean of the latent trait for each group is used instead of Gauss-Hermite quadratures.
- MEAN+GH: In a first step, the MEAN method is used to determine the most probable patterns. In a second step, the probability of response patterns is estimated by Gauss-Hermite quadratures on the most probable patterns.
- POPULATION+GH: The most frequent response patterns are selected from a simulated population of 1,000,000 of individuals. The probability of the selected response patterns is estimated by Gauss-Hermite quadratures.
raschpower, n0(200) n1(200) gamma(0.4) var(1.3)
. raschpower, n0(127) n1(134) gamma(0.23) d(diff) var(2.58)