mirt: gen.difficulty – R documentation

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gen.difficulty

Generalized item difficulty summaries

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

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

## 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

Multidimensional Item Response Theory

v1.33.2

GPL (>= 3)

Authors

Phil Chalmers [aut, cre] (<https://orcid.org/0000-0001-5332-2810>), Joshua Pritikin [ctb], Alexander Robitzsch [ctb], Mateusz Zoltak [ctb], KwonHyun Kim [ctb], Carl F. Falk [ctb], Adam Meade [ctb], Lennart Schneider [ctb], David King [ctb], Chen-Wei Liu [ctb], Ogreden Oguzhan [ctb]

Initial release