data.fims.Aus.Jpn: Dataset FIMS Study with Responses of Australian and Japanese...
In alexanderrobitzsch/TAM: Test Analysis Modules

data.fims.Aus.Jpn.scored

R Documentation

Dataset FIMS Study with Responses of Australian and Japanese Students

Description

Dataset FIMS study with raw responses (data.fims.Aus.Jpn.raw) or scored responses (data.fims.Aus.Jpn.scored) of Australian and Japanese Students.

Usage

data(data.fims.Aus.Jpn.raw)
data(data.fims.Aus.Jpn.scored)

Format

A data frame with 6371 observations on the following 16 variables.

SEX: Gender: 1 – male, 2 – female
M1PTI1: A Mathematics item
M1PTI2: A Mathematics item
M1PTI3: A Mathematics item
M1PTI6: A Mathematics item
M1PTI7: A Mathematics item
M1PTI11: A Mathematics item
M1PTI12: A Mathematics item
M1PTI14: A Mathematics item
M1PTI17: A Mathematics item
M1PTI18: A Mathematics item
M1PTI19: A Mathematics item
M1PTI21: A Mathematics item
M1PTI22: A Mathematics item
M1PTI23: A Mathematics item
country: Country: 1 – Australia, 2 – Japan

Examples

## Not run: 
data(data.fims.Aus.Jpn.scored)
#*****
# Model 1: Differential Item Functioning Gender for Australian students

# extract Australian students
scored <- data.fims.Aus.Jpn.scored[ data.fims.Aus.Jpn.scored$country==1, ]

# select items
items <- grep("M1", colnames(data.fims.Aus.Jpn.scored), value=TRUE)
##   > items
##    [1] "M1PTI1"  "M1PTI2"  "M1PTI3"  "M1PTI6"  "M1PTI7"  "M1PTI11" "M1PTI12"
##    [8] "M1PTI14" "M1PTI17" "M1PTI18" "M1PTI19" "M1PTI21" "M1PTI22" "M1PTI23"

# Run partial credit model
mod1 <- TAM::tam.mml(scored[,items])

# extract values of the gender variable into a variable called "gender".
gender <- scored[,"SEX"]
# computes the test score for each student by calculating the row sum
# of each student's scored responses.
raw_score <- rowSums(scored[,items] )

# compute the mean test score for each gender group: 1=male, and 2=female
stats::aggregate(raw_score,by=list(gender),FUN=mean)
# The mean test score is 6.12 for group 1 (males) and 6.27 for group 2 (females).
# That is, the two groups performed similarly, with girls having a slightly
# higher mean test score. The step of computing raw test scores is not necessary
# for the IRT analyses. But it's always a good practice to explore the data
# a little before delving into more complex analyses.

# Facets analysis
# To conduct a DIF analysis, we set up the variable "gender" as a facet and
# re-run the IRT analysis.
formulaA <- ~item+gender+item*gender    # define facets analysis
facets <- as.data.frame(gender)         # data frame with student covariates
# facets model for studying differential item functioning
mod2 <- TAM::tam.mml.mfr( resp=scored[,items], facets=facets, formulaA=formulaA )
summary(mod2)

## End(Not run)

alexanderrobitzsch/TAM documentation built on Feb. 21, 2024, 5:59 p.m.