Calculate the Cutoff Score for SE of Items
calculate_cutoff.Rd
This function allows you to bootstrap samples across various sample sizes when the data (optionally) has repeated measures items.
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$se_items
#> [1] 0.3544826 0.4032761 0.3831998 0.3598245 0.3769685 0.4110321 0.4772454
#> [8] 0.3500000 0.3656285 0.4161225 0.3618301 0.2835397 0.4261208 0.3913539
#> [15] 0.3802700 0.4110321 0.4000000 0.4031129 0.2809757 0.4524786 0.3811340
#> [22] 0.3371709 0.3940345 0.3077935 0.3845708 0.4184873 0.3524351 0.4346808
#> [29] 0.3439324 0.3620119
cutoff$sd_items
#> [1] 0.04449697
cutoff$cutoff
#> 40%
#> 0.3724325
cutoff$prop_var
#> [1] 0.01483232