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.
Lawshe (1975) published a table of critical values computed by a colleague, Lowell Schipper, without saying how. Wilson et al. (2012) read the step from .78 at nine experts to .75 at eight as an anomaly, and found that the table matches a normal approximation at a two-tailed .05 level rather than the one-tailed .05 it was labeled with. Ayre and Scally (2014) then derived exact binomial values, which are the ones used here, and showed that the step is not an anomaly: .78 is 8 of 9 and .75 is 7 of 8, the counts the exact test also requires.
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. In a judge-by-item table every column is an item; a column whose name looks like a rater ID (such asexpertorrater_id) stops the function, so remove it, or rename an item that has such a name.- 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. By default the names of
essentialare used when every count has a distinct, non-blank name, andItem1,Item2, and so on otherwise.
Value
A data.frame containing item, ne, effective N, CVR, exact
p value, critical essential count/CVR, and pass. With very few
experts no count can reach alpha (4 of 4 gives p = .0625), so the
critical count and CVR are NA and pass is FALSE; the printout says
so.
It prints as a formatted table in APA style; the values themselves are
unrounded, and as.data.frame() returns the plain data frame.
References
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
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
Examples
cvr(essential = c(8, 10, 5), N = 12)
#> <contentvalid_cvr> Content validity ratio (CVR)
#> Lawshe (1975).
#>
#> Item Essential CVR p Needed Meets
#> Item1 8/12 .33 .194 10 no
#> Item2 10/12 .67 .019 10 yes
#> Item3 5/12 -.17 .806 10 no
#>
#> Needed: essential ratings the exact one-sided binomial test requires at
#> alpha = .05 (Ayre & Scally, 2014).
#>
#> See as.data.frame(x) for the unrounded values.