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Plot a difficulty-discrimination scatter for an mcq_analysis object

Source: R/plot.mcq_analysis.R
plot.mcq_analysis.Rd

Produces the classical item quality map: a scatterplot of item difficulty (x-axis) against item discrimination (y-axis), with reference lines marking conventional adequacy cutoffs. Items in the upper-middle region (medium difficulty, high discrimination) are performing well; items in the lower regions are candidates for revision.

Usage

# S3 method for class 'mcq_analysis'
plot(
  x,
  y = NULL,
  discrimination_metric = c("point_biserial", "discrimination_index"),
  label = c("flagged", "all", "none"),
  flag_threshold_difficulty = c(0.3, 0.9),
  flag_threshold_discrimination = 0.3,
  point_cex = 1.4,
  label_cex = 0.75,
  ...
)

Arguments

x

An object of class mcq_analysis.

y

Ignored. Present for S3 compatibility.

discrimination_metric

Which discrimination index to plot on the y-axis. One of "point_biserial" (default) or "discrimination_index".

label

One of "flagged" (default, label only problematic items), "all" (label every item), or "none" (no labels). Also accepts TRUE (= "all") or FALSE (= "none") for backwards compatibility.

flag_threshold_difficulty

Numeric vector of length 2 giving the informative difficulty range. Default c(0.30, 0.90).

flag_threshold_discrimination

Numeric. Discrimination cutoff below which an item is considered weak. Default 0.30.

point_cex

Numeric. Point size. Default 1.4.

label_cex

Numeric. Label text size. Default 0.75.

...

Additional graphical parameters passed to plot().

Value

The input mcq_analysis object, invisibly.

Details

By default, only flagged items (those falling outside the conventional adequacy region) are labeled, to keep the plot legible when many items cluster in the acceptable region. Use label = "all" to label every item, or label = "none" to suppress labels entirely.

Reference lines are drawn at conventional cutoffs from Ebel and Frisbie (1991): discrimination >= 0.30 (acceptable) and difficulty between 0.30 and 0.90 (informative range).

References

Ebel, R. L., & Frisbie, D. A. (1991). Essentials of educational measurement (5th ed.). Prentice Hall.

Examples

data(mcq_example)
result <- mcq_analysis(mcq_example$responses, mcq_example$key)
plot(result)

plot(result, label = "all")

plot(result, label = "none")