plotDistractorAnalysis: Plot item distractor analysis

Description

Plots graphical representation of item distractor analysis with proportions and optional number of groups.

Usage

plotDistractorAnalysis(
  Data,
  key,
  num.groups = 3,
  item = 1,
  item.name,
  multiple.answers = TRUE,
  criterion = NULL,
  crit.discrete = FALSE,
  cut.points,
  data,
  matching,
  match.discrete
)

Arguments

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 key must be a vector of the same length as ncol(Data). In case it is not provided, criterion needs to be specified.
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 criterion discrete? Default value is FALSE.
cut.points: numeric: numeric vector specifying cut points of criterion.
data: deprecated. Use argument Data instead.
matching: deprecated. Use argument criterion instead.
match.discrete: deprecated. Use argument crit.discrete instead.

Author

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

Details

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.

Examples

Run this code

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)

if (FALSE) {
# 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))
}