Colquitt et al. (2019) empirical content-validation benchmarks

Returns the empirical interpretation bands proposed by Colquitt et al. (2019) for Psa, Csv, HTC, or HTD. The benchmarks were created from scale-level averages for 112 scales and are percentile-based norms, not universal psychometric cutoffs.

Colquitt et al. (2019) built these norms from tasks in which naive judges saw three definitions, the focal construct and two orbiting constructs, and either sorted each item into one of them (Psa, Csv) or rated it against each on a 7-point scale (HTC, HTD). They did not examine tasks offering more or fewer definitions. sort_validity() and rating_validity() therefore add a caution, this package's own, when a study offers a different number.

If orbiting_r is supplied, the correlation-conditional benchmark set is selected. Otherwise the overall, non-correlation-normed criteria are used.

The published table contains a few rounded boundary overlaps/gaps. This implementation treats each printed lower bound as the start of its category and assigns categories from strongest to weakest, yielding deterministic interpretation at rounded boundaries.

Usage

colquitt_benchmarks(
  statistic = c("psa", "csv", "htc", "htd"),
  orbiting_r = NULL
)

Arguments

statistic

One of "psa", "csv", "htc", or "htd".

orbiting_r

Optional average correlation between the focal scale and its orbiting scales.

Value

A data.frame describing the selected benchmark set and its lower cutpoints. 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

Examples

colquitt_benchmarks("psa")
#> <contentvalid_colquitt_norms> Benchmarks for Psa
#> Colquitt et al. (2019). Benchmark set: Overall (not correlation-normed).
#> 
#>   Band         Percentile  Minimum
#>   Very Strong  80th-99th       .91
#>   Strong       60th-79th       .82
#>   Moderate     40th-59th       .72
#>   Weak         20th-39th       .39
#>   Lack of      0th-19th       none
#> 
#> A scale-level mean at or above a band's minimum falls in that band. The bands
#> are percentiles of published scales, not validity cutoffs.
#> 
#> See as.data.frame(x) for the unrounded values.
colquitt_benchmarks("csv", orbiting_r = .40)
#> <contentvalid_colquitt_norms> Benchmarks for Csv
#> Colquitt et al. (2019). Benchmark set: More moderate focal-orbiting
#> correlation (.35-.50).
#> 
#>   Band         Percentile  Minimum
#>   Very Strong  80th-99th       .83
#>   Strong       60th-79th       .61
#>   Moderate     40th-59th       .52
#>   Weak         20th-39th       .01
#>   Lack of      0th-19th       none
#> 
#> A scale-level mean at or above a band's minimum falls in that band. The bands
#> are percentiles of published scales, not validity cutoffs.
#> 
#> See as.data.frame(x) for the unrounded values.