Plot expert-panel content-validity results — plot.contentvalid

Draws a mode-specific evidence plot, one row per item with the first item at the top. Relevance mode shows Aiken's V and I-CVI side by side, each with its interval, and a dashed line at the I-CVI criterion when every item had the same number of experts. Essentiality mode shows each observed CVR against the CVR the exact test needs for that item. Congruence mode shows each item's index for its intended objective against the ioc_cut criterion (dashed), with the experts' mean ratings on the target (a triangle pointing up) and on the other objective with the highest mean (pointing down) in gray, when a target mapping is available; an item with no index is marked with a cross.

Filled and open symbols mean what the key says. In congruence mode, as in the item-sort and construct-rating figures, a filled index met the criterion and an open one fell below it. In relevance and essentiality mode they tell apart the two values drawn on each row: Aiken's V (filled) from I-CVI (open), and the observed CVR (filled) from the CVR the exact test needs (open). There, an item met the criterion when its I-CVI reaches the dashed criterion line, or its observed CVR reaches the CVR needed. The key sits above the first item and the reference lines, in as many rows as the figure's width needs. The left margin widens for long item names, and a name that would take more than about 40% of the figure's width is shortened in the middle with "...".

In relevance mode, type = "distribution" draws every expert's rating as a diverging stacked bar (Heiberger & Robbins, 2014), split at the relevance cut. Ratings below the cut extend left and ratings at or above it extend right, so the right-hand length is the item's I-CVI, read against the dashed criterion line. The number beside each bar is that I-CVI, and the symbol is the decision the fit made. It shows what the index cannot: two items with the same I-CVI, one rated relevant with 4s and the other with 3s.

Usage

# S3 method for class 'contentvalid_expert'
plot(
  x,
  show_legend = TRUE,
  type = c("item", "distribution"),
  apa = TRUE,
  labels = NULL,
  ...
)

Arguments

x: A contentvalid_expert object.
show_legend: Logical; draw the compact plot key. Default TRUE.
type: "item" (default) for the evidence plot described above, or "distribution" for the rating distributions (relevance mode only).
apa: Used by type = "distribution". TRUE (default) draws in gray, with darker meaning a higher rating, as an APA figure is printed. FALSE draws ratings below the cut in brown and ratings at or above it in teal, a colorblind-safe scheme for slides and posters. The symbol beside each bar carries the decision either way. The "item" plot is always gray.
labels: For type = "distribution", one label per rating category, lowest first, such as c("Not relevant", "Somewhat relevant", "Quite relevant", "Highly relevant"). Defaults to "Rated 1", "Rated 2", and so on.
...: Additional graphical arguments passed to graphics::plot().

Value

The input object invisibly.

References

Heiberger, R. M., & Robbins, N. B. (2014). Design of diverging stacked bar charts for Likert scales and other applications. Journal of Statistical Software, 57(5), 1–32. doi:10.18637/jss.v057.i05

Examples

relevance <- matrix(
  c(4,4,4,3, 4,4,3,4, 3,4,4,4, 4,3,4,4),
  nrow = 4,
  dimnames = list(NULL, paste0("Item", 1:4))
)
fit <- expert_validity(relevance, mode = "relevance", lo = 1, hi = 4,
                       agreement = "none")
plot(fit)

plot(fit, type = "distribution")

plot(fit, type = "distribution", apa = FALSE)

plot(expert_validity(c(10, 8, 6), mode = "essentiality", N = 12))