plotDistractorAnalysis {ShinyItemAnalysis} | 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 splitted. |
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 splitted 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
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)