Exact power for the item-sort target-count rule

Computes the exact probability that an item will meet the Howard-Melloy target-count criterion for a planned judge sample size and an assumed true target-assignment probability. This is a binomial calculation, not a simulation.

Usage

sort_power(N, true_p, p0 = 0.5, alpha = 0.05)

Arguments

N

Positive integer judge sample size(s).

true_p

Assumed true probability that a judge assigns the item to its intended construct. May be scalar or vector.

p0

Null target-assignment probability. Default 0.5.

alpha

Significance level. Default 0.05.

Value

An object of class contentvalid_sort_power containing an exact planning table.

Examples

sort_power(N = c(20, 30, 40), true_p = .70)
#> Exact item-sort planning analysis
#> ---------------------------------
#> Retention rule: p0 = 0.50, alpha = 0.050
#> 
#>   N true_p critical_n_target minimum_observed_psa power
#>  20    0.7                15                0.750 0.416
#>  30    0.7                20                0.667 0.730
#>  40    0.7                26                0.650 0.807
#> 
#> Power is the exact probability of reaching the required target-assignment count
#> under the assumed true target-assignment probability.
sort_power(N = 30, true_p = c(.60, .70, .80))
#> Exact item-sort planning analysis
#> ---------------------------------
#> Retention rule: p0 = 0.50, alpha = 0.050
#> 
#>   N true_p critical_n_target minimum_observed_psa power
#>  30    0.6                20                0.667 0.291
#>  30    0.7                20                0.667 0.730
#>  30    0.8                20                0.667 0.974
#> 
#> Power is the exact probability of reaching the required target-assignment count
#> under the assumed true target-assignment probability.