View source: R/plotDistractorAnalysis.R
| plotDistractorAnalysis | R Documentation |
Plots graphical representation of item distractor analysis with proportions and optional number of groups.
plotDistractorAnalysis(
Data,
key,
num.groups = 3,
item = 1,
item.name,
multiple.answers = TRUE,
criterion = NULL,
crit.discrete = FALSE,
cut.points,
data,
matching,
match.discrete
)
Data |
character: data matrix or data.frame with rows representing unscored item response from a multiple-choice test and columns corresponding to the items. |
key |
character: answer key for the items. The |
num.groups |
numeric: number of groups to which are the respondents split. |
item |
numeric: the number of the item to be plotted. |
item.name |
character: the name of the item. |
multiple.answers |
logical: should be all combinations plotted (default) or should be answers split into distractors. See Details. |
criterion |
numeric: numeric vector. If not provided, total score is calculated and distractor analysis is performed based on it. |
crit.discrete |
logical: is |
cut.points |
numeric: numeric vector specifying cut points of
|
data |
deprecated. Use argument |
matching |
deprecated. Use argument |
match.discrete |
deprecated. Use argument |
This function is a graphical representation of the
DistractorAnalysis() function. In case that no criterion is
provided, the scores are calculated using the item Data and
key. The respondents are by default split into the
num.groups-quantiles and the proportions of respondents in each
quantile are displayed with respect to their answers. In case
that criterion is discrete (crit.discrete = TRUE),
criterion is split based on its unique levels. Other cut points
can be specified via cut.points argument.
If multiple.answers = TRUE (default) all reported combinations
of answers are plotted. If multiple.answers = FALSE all
combinations are split into distractors and only these are then
plotted with correct combination.
Adela Hladka
Institute of Computer Science of the Czech Academy of Sciences
hladka@cs.cas.cz
Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
martinkova@cs.cas.cz
DistractorAnalysis()
Data <- dataMedicaltest[, 1:100]
DataBin <- dataMedical[, 1:100]
key <- dataMedicalkey
# distractor plot for items 48, 57 and 32 displaying distractors only
# correct answer B does not function well:
plotDistractorAnalysis(Data, key, item = 48, multiple.answers = FALSE)
# all options function well, thus the whole item discriminates well:
plotDistractorAnalysis(Data, key, item = 57, multiple.answers = FALSE)
# functions well, thus the whole item discriminates well:
plotDistractorAnalysis(Data, key, item = 32, multiple.answers = FALSE)
## Not run:
# distractor plot for items 48, 57 and 32 displaying all combinations
plotDistractorAnalysis(Data, key, item = c(48, 57, 32))
# distractor plot for item 57 with all combinations and 6 groups
plotDistractorAnalysis(Data, key, item = 57, num.group = 6)
# distractor plot for item 57 using specified criterion and key option
criterion <- round(rowSums(DataBin), -1)
plotDistractorAnalysis(Data, key, item = 57, criterion = criterion)
# distractor plot for item 57 using specified criterion without key option
plotDistractorAnalysis(Data, item = 57, criterion = criterion)
# distractor plot for item 57 using discrete criterion
plotDistractorAnalysis(Data, key,
item = 57, criterion = criterion,
crit.discrete = TRUE
)
# distractor plot for item 57 using groups specified by cut.points
plotDistractorAnalysis(Data, key, item = 57, cut.points = seq(10, 96, 10))
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.