Interpret a statistic using Colquitt et al. (2019) norms

Classifies one or more Psa, Csv, HTC, or HTD values using the empirical percentile bands from Colquitt et al. (2019). These norms were derived from scale-level averages and from naive judges representative of substantive study populations. They should therefore be treated as contextual norms, not pass/fail rules.

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.

When judge_type = "expert", the Colquitt classification is deliberately not applied because the authors caution against using their norms for expert judges.

Usage

interpret_colquitt(
  value,
  statistic = c("psa", "csv", "htc", "htd"),
  orbiting_r = NULL,
  judge_type = c("naive", "expert")
)

Arguments

value

Numeric value(s) to interpret.

statistic

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

orbiting_r

Optional scalar, or a vector matching value, containing the average focal-orbiting correlation. NULL uses the overall norms.

judge_type

Either "naive" or "expert".

Value

A data.frame with the value, benchmark set, interpretation, and an applicability flag. 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

interpret_colquitt(.84, "psa")
#> <contentvalid_colquitt> Benchmark bands
#> Colquitt et al. (2019).
#> 
#>   Statistic  Value  Band    Benchmarks
#>   Psa          .84  Strong  Overall (not correlation-normed)
#> 
#> Empirical percentile norm from scale-level averages; not a universal cutoff.
#> 
#> See as.data.frame(x) for the unrounded values.
interpret_colquitt(.70, "csv", orbiting_r = .40)
#> <contentvalid_colquitt> Benchmark bands
#> Colquitt et al. (2019).
#> 
#>   Statistic  Value  Band    Benchmarks
#>   Csv          .70  Strong  More moderate focal-orbiting correlation (.35-.50)
#> 
#> Empirical percentile norm from scale-level averages; not a universal cutoff.
#> 
#> See as.data.frame(x) for the unrounded values.