Calculate the Cutoff Score for SE of Items — calculate

This function allows you to bootstrap samples across various sample sizes when the data (optionally) has repeated measures items.

Usage

calculate_cutoff(population, grouping_items, score, minimum, maximum)

Arguments

population: The population data set or the pilot dataset
grouping_items: The names of columns to group your data by for the cutoff calculation, usually this column is the item column
score: The column of the score you wish to calculate for your cutoff score SE
minimum: The minimum possible value for your score, used to calculate the proportion of variability in your items
maximum: The maximum possible value for your score, used to calculate the proportion of variability in your items

Value

se_items: The standard errors for each of your items.
sd_items: The standard deviation of the standard errors of your items.
cutoff: The cutoff score for your estimation of sample size by item.
prop_var: The proportion of variability found in your items, used to calculate the revised sample from simulations.

Examples

# step 1 create data like what I think I'll get or use your own
pops <- simulate_population(mu = 4, mu_sigma = .2, sigma = 2,
 sigma_sigma = .2, number_items = 30, number_scores = 20,
 smallest_sigma = .02, min_score = 1, max_score = 7, digits = 0)
# step 2 calculate our cut off score
cutoff <- calculate_cutoff(population = pops,
  grouping_items = "item",
  score = "score",
  minimum = 1,
  maximum = 7)

cutoff$se_items
#>  [1] 0.4316431 0.3670652 0.3947018 0.3797506 0.3670652 0.3627381 0.4558104
#>  [8] 0.3361939 0.4369451 0.2111996 0.3980214 0.5099020 0.2892822 0.4441135
#> [15] 0.3871284 0.3361939 0.4142209 0.3734618 0.3101358 0.3015312 0.3656285
#> [22] 0.4524786 0.4198997 0.4784844 0.3762698 0.4495612 0.4000000 0.4255028
#> [29] 0.4472136 0.3661679
cutoff$sd_items
#> [1] 0.06233727
cutoff$cutoff
#>       40% 
#> 0.3751466 
cutoff$prop_var
#> [1] 0.02077909