ItemAnalysis {ShinyItemAnalysis} | R Documentation |
Compute traditional item analysis indices
Description
Computes various traditional item analysis indices including difficulty, discrimination and item validity. For ordinal items, the function returns scaled values for some of the indices. See the details below.
Usage
ItemAnalysis(
Data,
minscore = NULL,
maxscore = NULL,
cutscore = NULL,
criterion = NULL,
k = NULL,
l = NULL,
u = NULL,
bin = "deprecated"
)
Arguments
Data |
matrix or data.frame of items to be examined. Rows represent respondents, columns represent items. |
minscore , maxscore |
integer, theoretical minimal/maximal score. If not provided, these are computed on observed data. Automatically recycled to the number of columns of the data. |
cutscore |
integer If provided, the input data are binarized accordingly. Automatically recycled to the number of columns of the data. |
criterion |
vector of criterion values. |
k , l , u |
Arguments passed on to |
bin |
deprecated, use |
Details
For calculation of generalized ULI index, it is possible to specify a custom
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 cutscore
is provided, item analysis is conducted on binarized data;
values greater or equal to cut-score are set to 1
, other values are set to
0
. Both the minscore
and maxscore
arguments are then ingored and set to
0 and 1, respectively.
Value
A data.frame
with following columns:
Difficulty |
average score of the item divided by its range. |
Mean |
average item score. |
SD |
standard deviation of the item score. |
Cut.score |
cut-score specified in |
obs.min |
observed minimal score. |
Min.score |
minimal score specified in |
obs.max |
observed maximal score. |
Max.score |
maximal score specified in |
Prop.max.score |
proportion of maximal scores. |
RIT |
item-total correlation (correlation between item score and overall test score). |
RIR |
item-rest correlation (correlation between item score and overall test score without the given item). |
ULI |
upper-lower index using the standard parameters (3 groups, comparing 1st and 3rd). |
Corr.criterion |
correlation between item score and criterion
|
gULI |
generalized ULI. |
Alpha.drop |
Cronbach's alpha without given item. |
Index.rel |
Gulliksen's (1950) item reliability index. |
Index.val |
Gulliksen's (1950) item validity index. |
Perc.miss |
Percentage of missed responses on the particular item. |
Perc.nr |
Percentage of respondents that did not reached the item
nor the subsequent ones, see |
Author(s)
Patricia Martinkova
Institute of Computer Science of the Czech
Academy of Sciences
martinkova@cs.cas.cz
Jan Netik
Institute of Computer Science of the Czech Academy of
Sciences
netik@cs.cas.cz
Jana Vorlickova
Institute of Computer Science of the Czech Academy of
Sciences
Adela Hladka
Institute of Computer Science of the Czech Academy of
Sciences
hladka@cs.cas.cz
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. https://doi.org/10.15439/2017F380
Gulliksen, H. (1950). Theory of mental tests. John Wiley & Sons Inc. https://doi.org/10.1037/13240-000
See Also
DDplot()
, gDiscrim()
, recode_nr()
Examples
## Not run:
# binary dataset
dataBin <- dataMedical[, 1:100]
# ordinal dataset
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, criterion = StudySuccess))
# item analysis for binary data
head(ItemAnalysis(dataOrd))
# item analysis for binary data using also study success
head(ItemAnalysis(dataOrd, criterion = StudySuccess))
# including also item analysis for binarized data
head(ItemAnalysis(dataOrd,
criterion = StudySuccess, k = 5, l = 4, u = 5,
maxscore = 4, minscore = 0, cutscore = 4
))
## End(Not run)