Lawshe's Content Validity Ratio (CVR)

Computes Lawshe's (1975) Content Validity Ratio for one or more items rated by an expert panel. Each expert classifies an item as "essential", "useful but not essential", or "not necessary"; CVR captures the proportion of experts endorsing "essential" relative to chance.

Usage

cvr(
  ratings,
  essential = 1,
  na.rm = FALSE,
  ci = FALSE,
  n_boot = 2000,
  ci_method = c("percentile", "bca"),
  conf_level = 0.95,
  seed = NULL
)

Arguments

ratings

A numeric matrix or data frame of expert ratings (rows = experts, columns = items). A numeric vector is also accepted, treated as a single item.

essential

Numeric vector. Rating value(s) that indicate an expert classified the item as "essential". Defaults to 1, matching Lawshe's (1975) original 3-point scale where 1 = essential, 2 = useful but not essential, 3 = not necessary. Pass a vector if multiple values count as essential.

na.rm

Logical. If TRUE, missing ratings are excluded when counting experts. Defaults to FALSE.

ci

Logical. If TRUE, returns a data frame with bootstrap confidence intervals alongside the point estimate. Defaults to FALSE (returns a numeric vector, identical to the package's pre-0.2.0 behaviour).

n_boot

Integer. Number of bootstrap replicates when ci = TRUE. Defaults to 2000 (Davison & Hinkley, 1997; Hesterberg, 2015).

ci_method

Character. One of "percentile" (default; Efron & Tibshirani, 1993) or "bca" (bias-corrected and accelerated; DiCiccio & Efron, 1996).

conf_level

Numeric. Confidence level between 0 and 1. Defaults to 0.95.

seed

Integer or NULL. If supplied, passed to set.seed() for reproducible bootstrap samples. Defaults to NULL.

Value

A named numeric vector of CVR values per item, ranging from -1 to +1. If ratings is a vector, returns a single numeric value.

Details

The formula is:

$$CVR = (n_e - N/2) / (N/2)$$

where \(n_e\) is the number of experts rating the item as essential and N is the total number of experts.

Use cvr_critical() to obtain the minimum CVR considered statistically significant for a given panel size, following the corrected critical values of Wilson, Pan, and Schumsky (2012).

References

Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563-575. doi:10.1111/j.1744-6570.1975.tb01393.x

Wilson, F. R., Pan, W., & Schumsky, D. A. (2012). Recalculation of the critical values for Lawshe's content validity ratio. Measurement and Evaluation in Counseling and Development, 45(3), 197-210. doi:10.1177/0748175612440286

See also

cvr_critical()

Examples

# 10 experts rating 3 items on Lawshe's 3-point scale
# (1 = essential, 2 = useful, 3 = not necessary)
ratings <- matrix(
  c(1, 1, 1, 1, 1, 1, 1, 1, 2, 2,    # 8 of 10 essential
    1, 1, 1, 2, 2, 2, 2, 3, 3, 3,    # 3 of 10 essential
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1),   # 10 of 10 essential
  nrow = 10,
  dimnames = list(NULL, paste0("item", 1:3))
)
cvr(ratings)
#> item1 item2 item3 
#>   0.6  -0.4   1.0 

# Compare to the critical value for N = 10
cvr_critical(10)
#> [1] 0.8

# With bootstrap confidence intervals
cvr(ratings, ci = TRUE, n_boot = 1000, seed = 1)
#>    item  cvr ci_lower ci_upper  ci_method conf_level n_boot
#> 1 item1  0.6        0      1.0 percentile       0.95   1000
#> 2 item2 -0.4       -1      0.2 percentile       0.95   1000
#> 3 item3  1.0        1      1.0 percentile       0.95   1000