TAM (version 4.2-21)

data.fims.Aus.Jpn.scored: 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

See Also

http://www.edmeasurementsurveys.com/TAM/Tutorials/7DIF.htm

Examples

if (FALSE) {
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)
}