Computes Lawshe's CVR and exact one-sided binomial inference following the critical-value logic revisited by Ayre and Scally (2014). Input may be either counts of experts marking each item essential or a judge-by-item 0/1 matrix.
Arguments
- essential
Numeric/integer vector of essential counts, or a matrix/data frame with judges in rows, items in columns, coded
1 = essentialand0 = not essential.- N
Panel size. Required for count-vector input. May be a scalar or a vector matching
essential. Ignored for matrix input, where effective N is calculated itemwise.- alpha
One-sided exact alpha level. Default
.05.- na.rm
Logical; for matrix input, permit itemwise missing ratings.
- item_names
Optional item names for count-vector input.
Value
A data.frame containing item, ne, effective N, CVR, exact
p-value, critical essential count/CVR, and pass.
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
Ayre, C., & Scally, A. J. (2014). Critical values for Lawshe's content validity ratio: Revisiting the original methods of calculation. Measurement and Evaluation in Counseling and Development, 47(1), 79-86. doi:10.1177/0748175613513808
Examples
cvr(essential = c(8, 10, 5), N = 12)
#> item ne N cvr p_value critical_ne critical_cvr pass
#> 1 Item1 8 12 0.3333333 0.19384766 10 0.6666667 FALSE
#> 2 Item2 10 12 0.6666667 0.01928711 10 0.6666667 TRUE
#> 3 Item3 5 12 -0.1666667 0.80615234 10 0.6666667 FALSE