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Aiken's V for expert content-relevance ratings

Source: R/aikens_v.R
aikens_v.Rd

Computes Aiken's 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 = 1,
  hi = 5,
  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.

lo, hi

Numeric lower and upper bounds of the rating 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.

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.

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)
#>    item N n_missing         V    ci_low   ci_high               ci_method
#> 1 Item1 4         0 0.9166667 0.6461201 0.9851349 Penfield-Giacobbi score
#> 2 Item2 4         0 0.9166667 0.6461201 0.9851349 Penfield-Giacobbi score
#> 3 Item3 4         0 0.8333333 0.5519691 0.9530349 Penfield-Giacobbi score