calculate_cutoff: Calculate the Cutoff Score for SE of Items

In SemanticPriming/semanticprimeR: Semantic Priming Across Many Languages and Related Projects

Calculate the Cutoff Score for SE of Items

Description

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$se_items
cutoff$sd_items
cutoff$cutoff
cutoff$prop_var

SemanticPriming/semanticprimeR documentation built on Dec. 5, 2024, 7:59 p.m.