score: Score CREDI response data

In marcus-waldman/credi: Caregiver Reported Early Development Instruments (CREDI) Multi-Dimensional Item Factor Analysis Scoring

Score CREDI response data

Description

This function calculates the posterior density function and provides CREDI Overall, Short Form, and domain-specific scores. CREDI variables must be input according to standardized variable names. Four types of naming conventions are supported by this package:

• "Long Form (October 2017)" variable names starting with "LF"

• "Short Form" variables starting with "CREDI_A01", "CREDI_B01", etc.

• "Long Form (April 2017)" variables starting with "CREDI_LM", "CREDI_LC", etc.

• "CREDI Pilot 4" variables starting with "QC", "QS", and "QM"

The function also requires a unique ID variable and an AGE variable. Only scores with a non-missing AGE variables will be scored.

Usage

score(
  data = NULL,
  reverse_code = TRUE,
  interactive = TRUE,
  min_items = 5,
  dscore = FALSE
)

Arguments

data	(data.frame) Defaults to NULL. Response data. If NULL, then user is prompted to identify a .csv file with response data. Defaults to NULL.
reverse_code	(Logical) Defaults to TRUE. If TRUE, then reverse coding is automated to appropriately handle the negatively worded items LF9, LF102, LFMH1, LFMH2, LFMH3, LFMH4, LFMH5, LFMH7, LFMH8, & LFMH9. If FALSE, then the package assumes items are already reverse coded and no changes are applied prior to scoring.
interactive	(Logical) Defaults to TRUE. If TRUE, the user may be prompted with caution messages regarding whether scoring should be continued, where to save the scores, where to save a logfile, etc. If FALSE, continuation is assumed and scores and the user is not prompted to save scores or a logfile.
min_items	(integer) Defaults to 5. The minimum number of scale-specific items (e.g. SEM, MOT, etc.) required for a score to be calculated.
dscore	(Logical) Defaults to FALSE. If TRUE, calculate GSED d-score and DAZ in addition to CREDI scores.

Examples

#Create a sample dataframe
dat <- data.frame(
ID = 1:3, AGE = c(3,5,4), LF1 = c(1,0,NA), LF2 = c(0,0,0), 
LF3 = c(1,0,1), LF4 = c(1,1,1), LF5 = c(1,0,0))

#Score the dat
scored_dat <- credi::score(data = dat, reverse_code = FALSE, interactive = FALSE, min_items = 5)

#Print out domain scores:
scored_dat$scores[,c("MOT", "LANG", "SEM", "COG", "OVERALL")]
#MOT   LANG    SEM    COG OVERALL
#43.489 45.968 44.626 45.091  40.079
#42.058 45.049 43.755 44.250  38.16
#One observation did not have at least 5 items responded to, so is not included in the results

marcus-waldman/credi documentation built on Nov. 17, 2023, 2:49 p.m.