Description Usage Arguments Details Value Author(s) References See Also Examples
ItemAnalysis
function computes various traditional item analysis indices
including difficulty, discrimination and item validity. For ordinal items the difficulty and
discrimination indices take into account minimal item score as well as range.
1 2  ItemAnalysis(data, y = NULL, k = 3, l = 1, u = 3,
maxscore, minscore, cutscore, add.bin = FALSE)

data 
matrix or data.frame of items to be examined. Rows represent respondents, columns reperesent items. 
y 
vector of criterion values. 
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 or vector: maximal score in ordinal items. If missing, vector of obtained maximal scores is imputed. See Details. 
minscore 
numeric or vector: minimal score in ordinal items. If missing, vector of obtained minimal scores is imputed. See Details. 
cutscore 
numeric or vector: cut score used for binarization of ordinal data. If missing, vector of maximal scores is imputed. See Details. 
add.bin 
logical: If TRUE, indices are printed also for binarized data. See Details. 
For ordinal items the difficulty and discrimination indices take into account minimal item score as well as range.
For calculation of discimination ULI index, it is possible to
specify the number of groups k
, and which two groups l
and u
are to be compared.
In ordinal items, difficulty is calculated as difference of average score divided by range
(maximal possible score maxscore
minus minimal possible score minscore
).
If add.bin
is set to TRUE
, item analysis of binarized data is
included in the output table. In such a case, cutscore
is used for binarization.
When binarizing the data, values greater or equal to cutscore are set to 1
,
other values are set to 0
.
ItemAnalysis
function computes various traditional item analysis indices. Output
is a data.frame
with following columns:

item difficulty based on ratio of correct answers 



standard deviation of the item 

proportion of correct answers 

minimal score specified in 

maximal score specified in 

observed minimal score 

observed maximal score 

cutscore specified in 

generalized ULI 

dscrimination with ULI 

correlation between item score and overall test score 

correlation between item score and overall test score 

correlation of item score with criterion 

item reliability index 

item reliability index (scored without item) 

item validity index 

correlation between item and criterion 

Cronbach's alpha without given item 
With add.bin == TRUE
, indices based on binarized data set are also provided
and marked with bin
.
Patricia Martinkova
Institute of Computer Science, The Czech Academy of Sciences
[email protected]
Jana Vorlickova
Institute of Computer Science, The Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
Adela Drabinova
Institute of Computer Science, The Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
[email protected]
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. http://dx.doi.org/10.15439/2017F380
Allen, M. J. & Yen, W. M. (1979). Introduction to measurement theory. Monterey, CA: Brooks/Cole.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  ## Not run:
# loading 100item medical admission test data sets
data(dataMedical, dataMedicalgraded)
# binary data set
dataBin < dataMedical[, 1:100]
# ordinal data set
dataOrd < dataMedicalgraded[, 1:100]
# study success is the same for both data sets
StudySuccess < dataMedical[, 102]
# item analysis for binary data
head(ItemAnalysis(dataBin))
# item analysis for binary data using also study success
head(ItemAnalysis(dataBin, y = StudySuccess))
# item analysis for binary data
head(ItemAnalysis(dataOrd))
# item analysis for binary data using also study success
head(ItemAnalysis(dataOrd, y = StudySuccess))
# including also item analysis for binarized data
head(ItemAnalysis(dataOrd, y = StudySuccess, k = 5, l = 4, u = 5,
maxscore = 4, minscore = 0, cutscore = 4, add.bin = TRUE) )
## End(Not run)

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