calculate_proportion: Simulate Normal Population Scores for Multiple Items

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

Simulate Normal Population Scores for Multiple Items

Description

This function allows you to create normal populations for data that would include repeated measures items. Additionally, the data can be rounded and/or truncated to ensure it matches a target scale - for example, a 1-7 type rating scale.

Usage

calculate_proportion(samples, cutoff, grouping_items = NULL, score)

Arguments

samples	The bootstrapped samples from your population
cutoff	The cutoff score for an item to be well measured from the standard errors of your items
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

Value

summary_DF

Summary of the proportion of items below the standard error cutoff score.

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

# step 3 simulate samples
samples <- simulate_samples(start = 20, stop = 100,
 increase = 5, population = pops,
 replace = TRUE, grouping_items = "item")

# step 4 and 5
proportion_summary <- calculate_proportion(samples = samples,
 cutoff = cutoff$cutoff,
 grouping_items = "item",
 score = "score")
proportion_summary

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