Description Usage Arguments Details Author(s) References See Also Examples
Plots difficulty and (generalized) discrimination or criterion validity for items of the multiitem measurement test using the ggplot2 package. Difficulty and discrimination/validity indices are plotted for each item, items are ordered by their difficulty.
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Data 
numeric: binary or ordinal data 
item.names 
character: the names of items. If not specified, the names
of 
discrim 
character: type of discrimination index to be calculated.
Possible values are 
k 
numeric: number of groups to which data may be divided by the total
score to estimate discrimination using 
l 
numeric: lower group. Default value is 1. See Details. 
u 
numeric: upper group. Default value is 3. See Details. 
maxscore 
numeric: maximal scores of items. If single number is provided, the same maximal score is used for all items. If missing, vector of achieved maximal scores is calculated and used in calculations. 
minscore 
numeric: minimal scores of items. If single number is provided, the same maximal score is used for all items. If missing, vector of achieved maximal scores is calculated and used in calculations. 
bin 
logical: should the ordinal data be binarized? Default value is

cutscore 
numeric: cutscore used to binarize 
average.score 
logical: should average score of the item be displayed
instead of difficulty? Default value is 
thr 
numeric: value of discrimination threshold. Default value is 0.2.
With 
criterion 
numeric or logical vector: values of criterion. If supplied,

val_type 
character: criterion validity measure. Possible values are

data 
deprecated. Use argument 
Discrimination is calculated using method specified in
discrim
. Default option "ULI"
calculates difference in ratio of
correct answers in upper and lower third of students. "RIT"
index
calculates correlation between item score and test total score. "RIR"
index calculates correlation between item score and total score for the rest
of the items. With option "none"
, only difficulty is displayed.
"ULI"
index can be generalized using arguments k
, l
and
u
. Generalized ULI discrimination is then computed as follows: The
function takes data on individuals, computes their total test score and then
divides individuals into k
groups. The lower and upper group are
determined by l
and u
parameters, i.e. lth and uth group
where the ordering is defined by increasing total score.
For ordinal data, difficulty is defined as relative score (achieved 
minimal)/(maximal  minimal). Minimal score can be specified by
minscore
, maximal score can be specified by maxscore
. Average
score of items can be displayed with argument average.score = TRUE
.
Note that for binary data difficulty estimate is the same as average score of
the item.
Note that all correlations are estimated using Pearson correlation coefficient.
Adela Hladka
Institute of Computer Science of the Czech Academy of Sciences
hladka@cs.cas.cz
Lubomir Stepanek
Charles University
Jana Vorlickova
Institute of Computer Science of the Czech Academy of Sciences
Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
martinkova@cs.cas.cz
Allen, M. J., & Yen, W. M. (1979). Introduction to measurement theory. Monterey, CA: Brooks/Cole.
Martinkova, P., Stepanek, L., Drabinova, A., Houdek, J., Vejrazka, M., & Stuka, C. (2017). Semirealtime analyses of item characteristics for medical school admission tests. In: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems.
discrim
for calculation of discrimination
gDiscrim
for calculation of generalized ULI
ggplot
for general function to plot a "ggplot"
object
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45  # loading 100item medical admission test datasets
data(dataMedical, dataMedicalgraded)
# binary dataset
dataBin < dataMedical[, 1:100]
# ordinal dataset
dataOrd < dataMedicalgraded[, 1:100]
# DDplot of binary dataset
DDplot(dataBin)
## Not run:
# DDplot of binary dataset without threshold
DDplot(dataBin, thr = NULL)
# compared to DDplot using ordinal dataset and 'bin = TRUE'
DDplot(dataOrd, bin = TRUE)
# compared to binarized dataset using bin = TRUE and cutscore equal to 3
DDplot(dataOrd, bin = TRUE, cutscore = 3)
# DDplot of binary data using generalized ULI
# discrimination based on 5 groups, comparing 4th and 5th
# threshold lowered to 0.1
DDplot(dataBin, k = 5, l = 4, u = 5, thr = 0.1)
# DDplot of ordinal dataset using ULI
DDplot(dataOrd)
# DDplot of ordinal dataset using generalized ULI
# discrimination based on 5 groups, comparing 4th and 5th
# threshold lowered to 0.1
DDplot(dataOrd, k = 5, l = 4, u = 5, thr = 0.1)
# DDplot of ordinal dataset using RIT
DDplot(dataOrd, discrim = "RIT")
# DDplot of ordinal dataset using RIR
DDplot(dataOrd, discrim = "RIR")
# DDplot of ordinal dataset displaying only difficulty
DDplot(dataBin, discrim = "none")
# DDplot of ordinal dataset displaying difficulty estimates
DDplot(dataOrd)
# DDplot of ordinal dataset displaying average item scores
DDplot(dataOrd, average.score = TRUE)
# item difficulty / criterion validity plot for data with criterion
data(GMAT, package = "difNLR")
DDplot(GMAT[, 1:20], criterion = GMAT$criterion, val_type = "simple")
## End(Not run)

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