SimDichoDif | R Documentation |
Function to generate DIF for dichotomous items using the 2PL model.
SimDichoDif(It, ItDIFa, ItDIFb, NR, NF,
a = rep(1, It), b,
Ga = rep(0, length(ItDIFa)), Gb = rep(0, length(ItDIFb)),
D = 1, thR = NULL, thF = NULL,
muR = 0, muF = 0, sigR = 1, sigF = 1)
It |
It: Number of items |
ItDIFa |
Vector of integers specifying which items have DIF for a parameters. |
ItDIFb |
Vector of integers specifying which items have DIF for b parameters. |
NR |
Number of respondents for reference group. |
NF |
Number of respondents for focal group (generalize to multiple focal groups). |
a |
Item slope for reference group. |
b |
Item difficulty for reference group. |
Gb |
Vector of difference in b's for focal group(s). |
Ga |
Vector of difference in a's for focal group(s). |
D |
Scaling parameter for 2PL. Defaults to 1. |
thR |
Optional vector of latent variable values for reference group. |
thF |
Optional vector of latent variable values for focal group. |
muR |
Mean of latent variable for reference group. Used if latent scores not supplied. |
muF |
Mean of latent variable for reference group. Used if latent scores not supplied. |
sigR |
Standard deviation of latent variable for reference group. Used if latent scores not supplied. |
sigF |
Standard deviation of latent variable for reference group. Used if latent scores not supplied. |
This function is based on the 2PL model to test uniform, non-uniform of both DIF. To use the Rasch model, please restrict a parameter to 1.
A list with several arguments:
data |
the matrix with DIF items. |
ipars |
the item parameters. |
thetas |
the person parameters. |
Sebastien Beland
Faculte des sciences de l'education
Universite de Montreal (Canada)
sebastien.beland@umontreal.ca
Carl F. Falk
Department of Psychology
McGill University (Canada)
carl.falk@mcgill.ca, https://www.mcgill.ca/psychology/carl-f-falk
Berger, M., & Tutz, G. (2016). Detection of Uniform and Nonuniform Differential Item Functioning by Item-Focused Trees. Journal of Educational and Behavioral Statistics, 41(6), 559–592. https://doi.org/10.3102/1076998616659371
## Not run:
# test to generate UDIF
It <- 15 # number of items
ItDIFa <- NULL
ItDIFb <- c(1,3)
NR <- 100 # number of responses for group 1 (reference)
NF <- 100 # number of responses for group 2 (focal)
a <- rep(1,It) # for tests: runif(It,0.2,.5)
b <- rnorm(It,1,.5)
Gb <- rep(2,2) # Group value for U-DIF
Ga <- 0 # Group value for NU-DIF: need to be fix to 0 for U-DIF
#Type <- "UDIF"
#seed <- 1
Out1 <- SimDichoDif(It,ItDIFa,ItDIFb,NR,NF,a,b,Ga,Gb)
Out1
Out1$ipars
# Test to generate NUDIF
It <- 15 # Nb of items with DIF
ItDIFa <- c(1,3)
ItDIFb <- c(1,3)
NR <- 100 # N for Ref.
NF <- 100 # N for Focal
a <- rep(1,It) # For Rasch or any value for 1PL
b <- rnorm(It,1,.5) # Item difficulties from random normal
Gb <- rep(.8,2) # Group value for U-DIF
Ga <- rep(1.2,2) # Group value for NU-DIF
#Type <- "NUDIF"
#seed <- 1
Out2 <- SimDichoDif(It,ItDIFa,ItDIFb,NR,NF,a,b,Ga,Gb)
Out2
Out2$ipars
# Generates a mix of UDIF and NUDIF
It <- 15 # Nb of items with DIF
ItDIFa <- c(1)
ItDIFb <- c(1,3)
NR <- 100 # N for Ref.
NF <- 100 # N for Focal
a <- rep(1,It) # For Rasch or any value for 1PL
b <- rnorm(It,1,.5) # Item difficulties from random normal
Gb <- rep(.8,2) # Group value for U-DIF
Ga <- 1.2 # Group value for NU-DIF
#Type <- "NUDIF"
#seed <- 1
Out3 <- SimDichoDif(It,ItDIFa,ItDIFb,NR,NF,a,b,Ga,Gb)
Out3
Out3$ipars
## End(Not run)
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