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Aiken's V for expert content-relevance ratings

Source: R/aikens_v.R
aikens_v.Rd

Computes Aiken's (1980) V per item for bounded ordinal expert ratings. By default, confidence intervals use the score method described by Penfield and Giacobbi (2004). Percentile bootstrap intervals remain available for compatibility and sensitivity analysis.

Usage

aikens_v(
  ratings,
  lo,
  hi,
  ci = c("score", "none", "bootstrap"),
  B = 500,
  alpha = 0.05,
  seed = NULL,
  na.rm = FALSE
)

Arguments

ratings

Matrix/data.frame with judges in rows and items in columns. Every column is an item; a column whose name looks like a rater ID (such as expert or rater_id) stops the function, so remove it, or rename an item that has such a name.

lo, hi

Lowest and highest points of the rating scale. Both are required: V rescales the mean rating by the range of the scale, so the same ratings give a different V on a 1-4 scale than on a 1-5 scale.

ci

Confidence-interval method: "score" (default), "bootstrap", or "none".

B

Number of bootstrap replicates when ci = "bootstrap".

alpha

Two-sided CI alpha level; .05 gives a 95% interval.

seed

Optional integer seed for bootstrap reproducibility. The random-number stream of the session is left as it was.

na.rm

Logical. If FALSE (default), missing ratings are an error. If TRUE, item-specific effective judge counts are used.

Value

A data.frame with item, effective judge count N, number missing, Aiken's V, and (when requested) ci_low and ci_high. It prints as a formatted table in APA style; the values themselves are unrounded, and as.data.frame() returns the plain data frame.

References

Aiken, L. R. (1980). Content validity and reliability of single items or questionnaires. Educational and Psychological Measurement, 40(4), 955–959. doi:10.1177/001316448004000419

Penfield, R. D., & Giacobbi, P. R., Jr. (2004). Applying a score confidence interval to Aiken's item content-relevance index. Measurement in Physical Education and Exercise Science, 8(4), 213–225. doi:10.1207/S15327841MPEE0804_3

Examples

R <- matrix(c(4,4,3,4, 4,3,4,4, 3,3,4,4), nrow = 4)
colnames(R) <- c("Item1", "Item2", "Item3")
aikens_v(R, lo = 1, hi = 4)
#> <contentvalid_aiken> Aiken's V
#> Aiken (1980).
#> 
#>   Item   Experts    V      95% CI
#>   Item1        4  .92  [.65, .99]
#>   Item2        4  .92  [.65, .99]
#>   Item3        4  .83  [.55, .95]
#> 
#> Scale: 1 to 4.
#> Interval: Penfield-Giacobbi score (Penfield & Giacobbi, 2004).
#> 
#> See as.data.frame(x) for the unrounded values.