gDiscrim: Generalized Item Discrimination

Description Usage Arguments Details Note Author(s) References See Also Examples

View source: R/gDisrim.R

Description

gDiscrim function computes various generalizations of discrimination index ULI. It enumerates the ablitity of item to distinguish between individuals from upper (U) vs. lower (L) ability groups, i.e. between respondents with high vs. low overall score on the test. Number of groups, as well as upper and lower groups can be specified by user. Maximal and minimal score in ordinal data sets can be specified by user.

Usage

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gDiscrim(x, k = 3, l = 1, u = 3, maxscore, minscore)

Arguments

x

matrix or data.frame of items to be examined. Rows represent persons, columns reperesent items.

k

numeric: number of groups to which may be data.frame x divided by the total score. Default value is 3. See Details.

l

numeric: lower group. Default value is 1. See Details.

u

numeric: upper group. Default value is 3. See Details.

maxscore

numeric: maximal score in ordinal items. If missing, vector of obtained maximal scores is imputed. See Details.

minscore

numeric: minimal score in ordinal items. If missing, vector of obtained minimal scores is imputed. See Details.

Details

The function computes total test scores for all respondents and then divides the respondents into k groups. The lower and upper groups are determined by l and u parameters, i.e. l-th and u-th group where the ordering is defined by increasing total score.

In ordinal items, difficulty is calculated as difference of average score divided by range (maximal possible score maxscore minus minimal possible score minscore for given item).

Discrimination is calculated as difference in difficulty between upper and lower group.

Note

gDiscrim is used by DDplot function.

Author(s)

Adela Drabinova
Institute of Computer Science, The Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
[email protected]

Lubos Stepanek
First Faculty of Medicine, Charles University
[email protected]

Jana Vorlickova
Institute of Computer Science, The Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University

Patricia Martinkova
Institute of Computer Science, The Czech Academy of Sciences
[email protected]

References

Martinkova, P., Stepanek, L., Drabinova, A., Houdek, J., Vejrazka, M., & Stuka, C. (2017). Semi-real-time analyses of item characteristics for medical school admission tests. In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems. http://dx.doi.org/10.15439/2017F380

See Also

DDplot

Examples

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## Not run: 
# loading 100-item medical admission test data sets
data(dataMedical, dataMedicalgraded)
# binary data set
dataBin <- dataMedical[, 1:100]
# ordinal data set
dataOrd <- dataMedicalgraded[, 1:100]

# ULI for first 5 items for binary data set
# compare to psychometric::discrim(x)
gDiscrim(dataBin)[1:5]
# generalized ULI using 5 groups, compare 4th and 5th for binary data set
gDiscrim(dataBin, k = 5, l = 4, u = 5)[1:5]

# ULI for first 5 items for ordinal data set
gDiscrim(dataOrd)[1:5]
# generalized ULI using 5 groups, compare 4th and 5th for binary data set
gDiscrim(dataOrd, k = 5, l = 4, u = 5)[1:5]
# maximum (4) and minimum (0) score are same for all items
gDiscrim(dataOrd,  k = 5, l = 4, u = 5, maxscore = 4, minscore = 0)[1:5]

## End(Not run)

patriciamar/ShinyItemAnalysis documentation built on Jan. 14, 2019, 8:53 p.m.