ShinyItemAnalysis (version 1.3.6)

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.

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.

See Also

DistractorAnalysis, distractor.analysis

Examples

# NOT RUN {
# loading 100-item medical admission test datasets
data(dataMedical, dataMedicaltest, dataMedicalkey)
data <- dataMedicaltest[, 1:100]
dataBin <- dataMedical[, 1:100]
key <- unlist(dataMedicalkey)

# distractor plot for items 48, 57 and 32 displaying distractors only
plotDistractorAnalysis(data, key, item = 48, multiple.answers = FALSE)
# correct answer B does not function well
plotDistractorAnalysis(data, key, item = 57, multiple.answers = FALSE)
# all options function well, thus the whole item discriminates well
plotDistractorAnalysis(data, key, item = 32, multiple.answers = FALSE)
# functions well, thus the whole item discriminates well
# }
# NOT RUN {
# distractor plot for items 48, 57 and 32 displaying all combinations
plotDistractorAnalysis(data, key, item = 48)
plotDistractorAnalysis(data, key, item = 57)
plotDistractorAnalysis(data, key, item = 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, 100, 10))
# }