CDM (version 2.7-7)

data.dtmr: DTMR Fraction Data (Bradshaw et al., 2014)

Description

This is a simulated dataset of the DTMR fraction data described in Bradshaw, Izsak, Templin and Jacobson (2014).

Usage

data(data.dtmr)

format

The format is: List of 2 $ data : num [1:5000, 1:27] 0 0 0 0 0 1 0 0 1 1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:27] "M1" "M2" "M3" "M4" ... $ q.matrix:'data.frame': ..$ RU : int [1:27] 1 0 0 1 1 0 1 0 0 0 ... ..$ PI : int [1:27] 0 0 1 0 0 1 0 0 0 0 ... ..$ APP: int [1:27] 0 1 0 0 0 0 0 1 1 1 ... ..$ MC : int [1:27] 0 0 0 0 0 0 0 0 0 0 ... The attribute definition are as follows RU: {Referent units} PI: {Partitioning and iterating attribute} APP: {Appropriateness attribute} MC: {Multiplicative Comparison attribute}

source

Simulated dataset according to Bradshaw et al. (2014).

References

Bradshaw, L., Izsak, A., Templin, J., & Jacobson, E. (2014). Diagnosing teachers' understandings of rational numbers: Building a multidimensional test within the diagnostic Classification framework. Educational Measurement: Issues and Practice, xx, xxx-xxx.

Examples

data(data.dtmr)
data <- data.dtmr$data
q.matrix <- data.dtmr$q.matrix
I <- ncol(data)

#*** Model 1: LCDM
# define item wise rules
rule <- rep( "ACDM" , I )
names(rule) <- colnames(data)
rule[ c("M14","M17") ] <- "GDINA2"
# estimate model
mod1 <- gdina( data , q.matrix , linkfct="logit" , rule= rule)  
summary(mod1)

#*** Model 2: DINA model
mod2 <- gdina( data , q.matrix , rule="DINA" )  
summary(mod2)

#*** Model 3: RRUM model
mod3 <- gdina( data , q.matrix , rule="RRUM" )  
summary(mod3)

#--- model comparisons

# LCDM vs. DINA
anova(mod1,mod2)
##       Model   loglike Deviance Npars      AIC      BIC    Chisq df  p
##   2 Model 2 -76570.89 153141.8    69 153279.8 153729.5 1726.645 10  0
##   1 Model 1 -75707.57 151415.1    79 151573.1 152088.0       NA NA NA

# LCDM vs. RRUM
anova(mod1,mod3)
##       Model   loglike Deviance Npars      AIC      BIC    Chisq df  p
##   2 Model 2 -75746.13 151492.3    77 151646.3 152148.1 77.10994  2  0
##   1 Model 1 -75707.57 151415.1    79 151573.1 152088.0       NA NA NA

#--- model fit
summary( modelfit.cor.din( mod1 ) )
##   Test of Global Model Fit
##          type   value       p
##   1   max(X2) 7.74382 1.00000
##   2 abs(fcor) 0.04056 0.72707
##   
##   Fit Statistics
##                       est
##   MADcor          0.00959
##   SRMSR           0.01217
##   MX2             0.75696
##   100*MADRESIDCOV 0.20283
##   MADQ3           0.02220