Hinkin-Tracey correspondence (HTC)

Computes the Hinkin-Tracey correspondence index for each item, named for the rating task of Hinkin and Tracey (1999). Following Colquitt et al. (2019), HTC is the average definitional-correspondence rating for the intended construct divided by a, the number of rating anchors. Ratings are internally shifted to a 1-to-a metric when a scale such as 0-to-4 is supplied, preserving the meaning of the published formula.

HTC describes definitional correspondence. Higher values indicate that judges see the item as more representative of its intended construct. Because the lowest rating is 1 on the 1-to-a metric, HTC runs from 1 / a to 1 (.20 to 1 on a five-point scale), not from 0.

Usage

htc(
  ratings,
  item_col = "item",
  rater_col = "rater",
  construct_col = "construct",
  rating_col = "rating",
  target_map = NULL,
  target_col = "target_construct",
  scale_min = 1,
  scale_max = 5
)

Arguments

ratings

A long-format data.frame containing item, rater, construct, and rating columns.

item_col, rater_col, construct_col, rating_col

Column names.

target_map

Optional named character vector/list mapping item to target.

target_col

Target column used when target_map is NULL.

scale_min, scale_max

Endpoints of the equally spaced integer rating scale (for example, 1 and 5).

Value

A data.frame with item-level target means, usable target-rating counts, and HTC. It prints as a formatted table in APA style; the values themselves are unrounded, and as.data.frame() returns the plain data frame.

References

Colquitt, J. A., Sabey, T. B., Rodell, J. B., & Hill, E. T. (2019). Content validation guidelines: Evaluation criteria for definitional correspondence and definitional distinctiveness. Journal of Applied Psychology, 104(10), 1243–1265. doi:10.1037/apl0000406

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

d <- expand.grid(item = "I1", rater = 1:4, construct = c("A", "B"))
d$rating <- c(5, 4, 5, 4, 2, 2, 1, 2)
htc(d, target_map = c(I1 = "A"), scale_min = 1, scale_max = 5)
#> <contentvalid_htc> Hinkin-Tracey correspondence (HTC)
#> Colquitt et al. (2019).
#> 
#>   Item  Target  Judges  Target mean  HTC
#>   I1    A            4         4.50  .90
#> 
#> HTC expresses the mean target rating as a share of the 5-point scale.
#> 
#> See as.data.frame(x) for the unrounded values.