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Content Validity Index (CVI)

Source: R/cvi.R
cvi.Rd

Computes item-level Content Validity Index (I-CVI), scale-level average CVI (S-CVI/Ave), universal-agreement CVI (S-CVI/UA), and the modified kappa described by Polit, Beck, and Owen (2007).

For each item, modified kappa adjusts I-CVI for chance agreement using the probability of observing exactly A agreements among N judges:

$$P_c = {N \choose A}(0.5)^N$$

and

$$k^* = (I_CVI - P_c) / (1 - P_c).$$

Usage

cvi(binary, na.rm = FALSE)

Arguments

binary

Matrix/data.frame with judges in rows and items in columns, coded 1 = relevant and 0 = not relevant.

na.rm

Logical. If FALSE (default), missing ratings are an error. If TRUE, missing ratings are removed itemwise and each item's effective judge count is reported in N.

Value

A classed list with:

  • item_level: item, A, N, I_CVI, Pc, kappa_mod

  • scale_level: S_CVI_Ave and S_CVI_UA

References

Polit, D. F., Beck, C. T., & Owen, S. V. (2007). Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Research in Nursing & Health, 30(4), 459-467. doi:10.1002/nur.20199

Examples

M <- matrix(
  c(1,1,1,1, 1,1,1,0, 1,1,0,0),
  nrow = 4,
  dimnames = list(NULL, c("Item1", "Item2", "Item3"))
)
cvi(M)
#> Content Validity Index (CVI)
#> ----------------------------
#> Items analyzed: 3 
#> Judges per item: 4 
#> S-CVI/Ave: 0.750 
#> S-CVI/UA : 0.333 
#> 
#> Item-level results (modified kappa is chance-corrected):
#>   item A N I_CVI    Pc kappa_mod
#>  Item1 4 4  1.00 0.062     1.000
#>  Item2 3 4  0.75 0.250     0.667
#>  Item3 2 4  0.50 0.375     0.200
#> 
#> Interpretation should consider panel size, item purpose, and qualitative expert feedback;
#> CVI statistics alone do not establish comprehensive content validity.