Standardize Raw Cognitive Scores to Z-Scores — std

Calculate z-scores as (raw score - average) / sd. Available for a wide array of cognitive scores both using normative data, and regression based models. A wrapper around std_scores_using_norms(), std_scores_using_regression() and std_scores_using_T().

Usage

std_scores(
  raw_scores,
  var_name,
  education,
  age,
  sex = c("m", "f"),
  delay = NULL,
  race = NULL,
  method = c("norms", "regression", "T-score"),
  version = c("nacc", "updated"),
  print_messages = T
)

Arguments

raw_scores: Numeric vector of raw scores
var_name: String with name of the variable. Used to get correct means and standard deviations, or regression coefficients.
education: Numeric vector with years of education. Used for subset of variables only.
age: Numeric vector with ages in years
sex: Character vector with sex of participants. Must be either "m" (for male) or "f" (for female). Used for subset of variables only
delay: Only for standardizing MEMUNITS ("Logical Memory, Delayed") in which case it is the time of delay in minutes
race: Character vector with race of participants. Must be either "White" or "Other". Used only for updated regression models.
method: String specifying method to use for standardization.
version: String specifying version to use. "nacc" = 2020 numbers published by NACC. "updated" = numbers calculated using data freeze from June of 2024.
print_messages: Logical (default: TRUE); should messages be printed?

Examples

std_scores(
  raw_scores = c(21, 15),
  var_name = "MOCATOTS",
  sex = c("m", "f"),
  age = c(57, 70),
  education = c(15, 13),
  method = "regression",
  version = "nacc"
)
#> [1] -2.324629 -4.209920