gen.difficulty: Generalized item difficulty summaries

Description Usage Arguments Author(s) References Examples

View source: R/gen.difficulty.R

Description

Function provides the four generalized item difficulty representations for polytomous response models described by Ali, Chang, and Anderson (2015). These estimates are used to gauge how difficult a polytomous item may be.

Usage

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gen.difficulty(mod, type = "IRF", interval = c(-30, 30), ...)

Arguments

mod

a single factor model estimated by mirt

type

type of generalized difficulty parameter to report. Can be 'IRF' to use the item response function (default), 'mean' to find the average of the difficulty estimates, 'median' the median of the difficulty estimates, and 'trimmed' to find the trimmed mean after removing the first and last difficulty estimates

interval

interval range to search for 'IRF' type

...

additional arguments to pass to uniroot

Author(s)

Phil Chalmers rphilip.chalmers@gmail.com

References

Ali, U. S., Chang, H.-H., & Anderson, C. J. (2015). Location indices for ordinal polytomous items based on item response theory (Research Report No. RR-15-20). Princeton, NJ: Educational Testing Service. http://dx.doi.org/10.1002/ets2.12065

Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi: 10.18637/jss.v048.i06

Examples

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## Not run: 

mod <- mirt(Science, 1)
coef(mod, simplify=TRUE, IRTpars = TRUE)$items

gen.difficulty(mod)
gen.difficulty(mod, type = 'mean')

# also works for dichotomous items (though this is unnecessary)
dat <- expand.table(LSAT7)
mod <- mirt(dat, 1)
coef(mod, simplify=TRUE, IRTpars = TRUE)$items

gen.difficulty(mod)
gen.difficulty(mod, type = 'mean')


## End(Not run)

mirt documentation built on June 29, 2021, 1:06 a.m.