score_bfi: Score The Big Five Inventory-2 - 60 items BFI-2 (Soto & John,...

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

Score The Big Five Inventory-2 – 60 items BFI-2 (Soto & John, 2017)

Description

Score The Big Five Inventory-2 – 60 items BFI-2 (Soto & John, 2017)

Usage

score_bfi(
  df,
  item_prefix = "BFI_",
  max_impute = 0.2,
  drop_items = FALSE,
  keep_reverse_codes = FALSE,
  min_value = 1,
  max_value = 5,
  bad_items = NULL,
  add_alphas = TRUE
)

Arguments

df	a data.frame containing the 60 BFI-2 items to be scored
item_prefix	a character prefix of the items names in df to be scored. Default: "BFI"
max_impute	the proportion of missingness [0..1) or number [1..n] of missing values per scale. Default: 0.2 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
bad_items	numeric value or vector of the items that need to be dropped before imputation or calculation of subscales
add_alphas	whether to compute coefficient alpha for subscales and return a column attribute. Default: TRUE

Details

Adds twenty-one columns, fifteen for facet scales, five for domain scales, and one total, to df containing the BFI-2 subscales, respectively.

Note: the default BFI-2 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.

See: https://www.colby.edu/wp-content/uploads/2013/08/bfi2-form.pdf

Author(s)

Michael Hallquist, Zach Vig

PennStateDEPENdLab/dependlab documentation built on Sept. 26, 2024, 8:40 p.m.