score_neo: Score the NEO Five-Factor Inventory (NEO-FFI) scale (Costa &...

In PennStateDEPENdLab/dependlab: Useful functions for the UNC Chapel Hill DEPENd Lab (Director: Michael Hallquist)

Score the NEO Five-Factor Inventory (NEO-FFI) scale (Costa & McCrae, 1992) This is for the 60-item version

Description

Score the NEO Five-Factor Inventory (NEO-FFI) scale (Costa & McCrae, 1992) This is for the 60-item version

Usage

score_neo(
  df,
  item_prefix = "NEO",
  max_impute = 0.2,
  drop_items = FALSE,
  keep_reverse_codes = FALSE,
  min_value = 1,
  max_value = 5,
  add_alphas = TRUE
)

Arguments

df	a data.frame containing the 60 NEO items to be scored
item_prefix	a character prefix of the items names in df to be scored. Default: "NEO"
max_impute	the proportion of missingness [0..1) or number [1..n] of missing values per scale. Below this threshold, the person subscale mean will be imputed for missing items.
drop_items	whether to remove the item-level data from the df. Default: FALSE
keep_reverse_codes	whether to retain the reverse coded items (suffix "r")
min_value	the minimum value for the item anchors, used in reverse scoring. Default: 1
max_value	the highest value for the item anchors, used in reverse scoring. Default: 5
add_alphas	whether to compute coefficient alpha for subscales and return a column attribute. Default: TRUE

Details

Adds five columns, NEO_neurot, NEO_extra, NEO_open, NEO_agree, and NEO_cons, to df containing the NEO-FFI scales, respectively.

Note: the default NEO scoring uses the mean of the items for the scales.

Note: the code assumes that pasting together the item_prefix and the numbers 1:60 will yield the 60 items from the test.

Author(s)

Michael Hallquist

PennStateDEPENdLab/dependlab documentation built on Sept. 13, 2024, 4:48 a.m.