simulate_population: 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

simulate_population(
  mu = NULL,
  mu_sigma = NULL,
  sigma = NULL,
  sigma_sigma = NULL,
  number_items = NULL,
  number_scores = NULL,
  smallest_sigma = NULL,
  min_score = NULL,
  max_score = NULL,
  digits = NULL
)

Arguments

mu	The population mean for the items.
mu_sigma	The amount of variability for each of the means (i.e., the standard deviation of the item's means)
sigma	The population standard deviation for each item.
sigma_sigma	The standard deviation of the standard deviations for the items (i.e., heterogeneity)
number_items	The number of items to generate
number_scores	The number of scores for each item to generate
smallest_sigma	The smallest possible standard deviation for an item that is acceptable (default is sigma_sigma/10)
min_score	If you want to truncate scores, what is the smallest possible score?
max_score	If you want to truncate scores, what is the largest possible score?
digits	If you want to round scores, how many digits should it be rounded to?

Value

population

The population scores with the number of items and scores specified

Examples

simulate_population(mu = 4, mu_sigma = .2, sigma = 2,
 sigma_sigma = .2, number_items = 30, number_scores = 1000,
 smallest_sigma = .02, min_score = 1, max_score = 7,
 digits = 0)

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