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CDM (version 7.4-19)

data.sda6: Dataset SDA6 (Jurich & Bradshaw, 2014)

Description

This is a simulated dataset of the SDA6 study according to informations given in Jurich and Bradshaw (2014).

Usage

data(data.sda6)

Arguments

Format

The datasets contains 17 items observed at 1710 students.

The format is:

List of 2 $ data : num [1:1710, 1:17] 0 1 0 1 0 0 0 0 1 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:17] "MCM01" "MCM03" "MCM13" "MCM17" ... $ q.matrix:'data.frame': ..$ CM: int [1:17] 1 1 1 1 0 0 0 0 0 0 ... ..$ II: int [1:17] 0 0 0 0 1 1 1 1 0 0 ... ..$ PP: int [1:17] 0 0 0 0 0 0 0 0 1 1 ... ..$ DG: int [1:17] 0 0 0 0 0 0 0 0 0 0 ...

The meaning of the skills is

CM -- Critique Methods

II -- Identify Improvements

PP -- Protect Participants

DG -- Discern Generalizability

References

Jurich, D. P., & Bradshaw, L. P. (2014). An illustration of diagnostic classification modeling in student learning outcomes assessment. International Journal of Testing, 14, 49-72.

Examples

Run this code
# NOT RUN {
data(data.sda6, package="CDM")

data <- data.sda6$data
q.matrix <- data.sda6$q.matrix

#*** Model 1a: LCDM with gdina
mod1a <- CDM::gdina( data, q.matrix, rule="ACDM", linkfct="logit",
                  reduced.skillspace=FALSE )
summary(mod1a)

#*** Model 1b: estimate LCDM with gdm
mod1b <- CDM::gdm( data, q.matrix=q.matrix, theta.k=c(0,1) )
summary(mod1b)

#*** Model 2: LCDM with hierarchy II > CM
B <- "II > CM"
ss2 <- CDM::skillspace.hierarchy(B=B, skill.names=colnames(q.matrix ) )
mod2 <- CDM::gdina( data, q.matrix, rule="ACDM", linkfct="logit",
                skillclasses=ss2$skillspace.reduced,
                reduced.skillspace=FALSE )
summary(mod2)

#*** Model 3: LCDM with hierarchy II > CM and DG > CM
B <- "II > CM
      DG > CM"
ss2 <- CDM::skillspace.hierarchy(B=B, skill.names=colnames(q.matrix ) )
mod3 <- CDM::gdina( data, q.matrix, rule="ACDM", linkfct="logit",
               skillclasses=ss2$skillspace.reduced,
               reduced.skillspace=FALSE )
summary(mod3)

# model comparisons
anova(mod1a,mod2)
anova(mod1a,mod3)
# model fit
summary( CDM::modelfit.cor.din(mod1a))
summary( CDM::modelfit.cor.din(mod2) )
summary( CDM::modelfit.cor.din(mod3) )
# }

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