CDM (version 4.991-1)

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, 33, 2-14.

Examples

## Not run: 
# 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 <- CDM::gdina( data , q.matrix , linkfct="logit" , rule= rule)  
# summary(mod1)
# 
# #*** Model 2: DINA model
# mod2 <- CDM::gdina( data , q.matrix , rule="DINA" )  
# summary(mod2)
# 
# #*** Model 3: RRUM model
# mod3 <- CDM::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
# ## End(Not run)