Plot Hinkin-Tracey rating evidence — plot.contentvalid

Provides three complementary views of a construct-rating pretest. "item" reproduces the original one-index plot, "map" places HTC against HTD to show correspondence and distinctiveness jointly, and "profile" draws a target-versus- strongest-competitor gap plot on the original response scale, first item at the top, with a dashed gap for items to review. The profile view is a graphical analogue of the mean-rating tables used in Hinkin and Tracey (1999). Both ends of a gap are means over the judges who rated the item against every construct, the judges the tests use. An item without a decision has no gap: a cross marks its mean target rating.

The key sits above the data, in as many rows as the figure's width needs. Where a vertical axis title would not fit the figure's height, as the HTD title does at 7 by 4 inches, the axis shows the index's name alone and the key's heading gives the full definition. The item plot widens its bottom margin for long item names, shortening a name in the middle with "..." when it would take more than about 40% of the figure's height.

Usage

# S3 method for class 'contentvalid_rating'
plot(
  x,
  metric = c("htc", "htd"),
  type = c("item", "map", "profile"),
  label = c("review", "all", "none"),
  show_legend = TRUE,
  ...
)

Arguments

x: A contentvalid_rating object.
metric: Either "htc" or "htd" for type = "item".
type: One of "item", "map", or "profile".
label: Which item labels to draw on the map: "review" (default), "all", or "none".
show_legend: Logical; draw the compact plot key. Default TRUE.
...: Additional graphical arguments passed to graphics::plot().

Value

The input object invisibly.

References

Hinkin, T. R., & Tracey, J. B. (1999). An analysis of variance approach to content validation. Organizational Research Methods, 2(2), 175–186. doi:10.1177/109442819922004

Examples

set.seed(12)
d <- expand.grid(item = c("A1", "A2", "B1"), rater = 1:20,
                 construct = c("A", "B", "C"))
d$target_construct <- ifelse(d$item == "B1", "B", "A")
d$rating <- ifelse(d$construct == d$target_construct,
                   pmin(5, pmax(1, round(rnorm(nrow(d), 4.5, .6)))),
                   pmin(5, pmax(1, round(rnorm(nrow(d), 2.0, .7)))))
fit <- rating_validity(d, scale_min = 1, scale_max = 5)
plot(fit)

plot(fit, type = "map")

plot(fit, type = "profile")