
The function mcdina
implements the multiple choice DINA model
(de la Torre, 2009; see also Ozaki, 2015; Chen & Zhou, 2017)
for multiple groups. Note that the dataset must contain
integer values
mcdina(dat, q.matrix, group=NULL, itempars="gr", weights=NULL,
skillclasses=NULL, zeroprob.skillclasses=NULL,
reduced.skillspace=TRUE, conv.crit=1e-04,
dev.crit=0.1, maxit=1000, progress=TRUE)# S3 method for mcdina
summary(object, digits=4, file=NULL, ...)
# S3 method for mcdina
print(x, ...)
A list with following entries
Data frame with item parameters
Individual posterior distribution
Individual likelihood
List with information criteria
Used Q-matrix
Array of item-category probabilities
Array of item parameters
Array of standard errors of item parameters
Data frame containing item definitions
Array of expected counts
Deviance
Probabilities of latent classes
Splitted attribute pattern
Marginal skill probabilities
Classified skills for each student (MLE)
Classified skills for each student (MAP)
Classified skills for each student (EAP)
Used dataset
Used skill classes
Used group identifiers
Data frame containing definitions of each item category
Data frame containing the relation of each latent class and each item category
Number of iterations
Used specification of item parameter estimation type
Logical indicating whether convergence was achieved.
A required
A required matrix specifying which item category is intended to measure which skill.
The Q-matrix has data.cdm01$q.matrix
for the layout of such a
Q-matrix.
An optional vector of group identifiers for multiple group estimation.
A character or a character vector of length "gr"
, for group-invariant item parameters choose "jo"
.
An optional vector of sample weights.
An optional matrix for determining the skill space. The argument can be used
if a user wants less than the prespecified number of
An optional vector of integers which indicates which skill classes should have
zero probability. Default is NULL
(no skill classes with zero probability).
An optional logical indicating whether the skill space should be reduced to cover only bivariate associations among skills (see Xu & von Davier, 2008).
Convergence criterion for change in item parameter values
Convergence criterion for change in deviance values
Maximum number of iterations.
An optional logical indicating whether the function should print the progress of iteration in the estimation process.
Object of class mcdina
.
Number of digits to display in summary.mcdina
Optional file name for a file in which summary
should be sinked.
Object of class mcdina
Further arguments to be passed.
The multiple choice DINA model defines for each item category
The multiple choice DINA model estimates the item response function as
Chen, J., & Zhou, H. (2017) Test designs and modeling under the general nominal diagnosis model framework. PLoS ONE 12(6), e0180016.
de la Torre, J. (2009). A cognitive diagnosis model for cognitively based multiple-choice options. Applied Psychological Measurement, 33, 163-183.
Ozaki, K. (2015). DINA models for multiple-choice items with few parameters: Considering incorrect answers. Applied Psychological Measurement, 39(6), 431-447.
Xu, X., & von Davier, M. (2008). Fitting the structured general diagnostic model to NAEP data. ETS Research Report ETS RR-08-27. Princeton, ETS.
See din
for estimating the DINA/DINO model and gdina
for estimating the GDINA model.