pwr.rasch {pwrRasch} | R Documentation |
Simulation to Estimate Statistical Power of a Rasch Model Test
Description
This function conducts a simulation to estimate statistical power of a Rasch model test for user-specified item and person parameters.
Usage
pwr.rasch(b, ipar = list(), ppar = list("rnorm(b, mean = 0, sd = 1.5)",
"rnorm(b, mean = 0, sd = 1.5)"), runs = 1000, H0 = TRUE,
sig.level = 0.05, method = c("loop", "vectorized"), output = TRUE)
Arguments
b |
Either a vector or an integer indicating the number of observations in each group. |
ipar |
Item parameters in both groups specified in a list. |
ppar |
Person parameters specified by a distribution for each group. |
runs |
Number of simulation runs. |
H0 |
If |
sig.level |
Nominal significance level. |
method |
Simulation method: for-loop or vectorized. |
output |
If |
Details
The F-test in a three-way analysis of variance design (A \succ \mathbf{B}) x C
(A > B) x C
with mixed classification (fixed factor A = subgroup, random factor B = testee,
and fixed factor C = items) is used to simulate statistical power of a
Rasch model test. This approach using a F-distributed statistic, where
the sample size directly affects the degree of freedom enables determination
of the sample size according to a given type I and type II risk, and according
to a certain effect of model misfit which is of practical relevance.
Note, that this approach works as long as there exists no main effect of
A (subgroup). Otherwise an artificially high type I risk of the A x C interaction
F-test results - that is, the approach works as long as no statistically significant
main effect of A occurs.
Value
Returns a list with following entries:
b | number of observations in each group |
ipar | item parameters in both subgroups |
c | number of items |
ppar | distribution of person parameters |
runs | number of simulation runs |
sig.level | nominal significance level |
H0.AC.p | p-values of the interaction A x C in the null hypothesis condition (if H0 = TRUE ) |
H1.AC.p | p-values of the interaction A x C in the alternative hypothesis condition |
power | estimated statistical power |
type1 | estimated significance level |
Author(s)
Takuya Yanagida takuya.yanagida@univie.ac.at, Jan Steinfeld jan.steinfeld@univie.ac.at
References
Kubinger, K. D., Rasch, D., & Yanagida, T. (2009). On designing data-sampling for Rasch model calibrating an achievement test. Psychology Science Quarterly, 51, 370-384.
Kubinger, K. D., Rasch, D., & Yanagida, T. (2011). A new approach for testing the Rasch model. Educational Research and Evaluation, 17, 321-333.
See Also
Examples
## Not run:
# item parameters
ipar2 <- ipar1 <- seq(-3, 3, length.out = 20)
# model differential item function (DIF)
ipar2[10] <- ipar1[11]
ipar2[11] <- ipar1[10]
# simulation for b = 200
pwr.rasch(200, ipar = list(ipar1, ipar2))
# simulation for b = 100, 200, 300, 400, 500
pwr.rasch(seq(100, 500, by = 100), ipar = list(ipar1, ipar2))
# simulation for b = 100, 200, 300, 400, 500
# uniform distribution [-3, 3] of person parameters
pwr.rasch(200, ipar = list(ipar1, ipar2), ppar = list("runif(b, -3, 3)", "runif(b, -3, 3)"))
## End(Not run)