GDINA (version 2.9.4)

MCmodel: Multiple-choice models

Description

This function estimates the multiple-choice DINA model (de la Torre, 2009).

Usage

MCmodel(
  dat,
  Qc,
  model = "MCDINA",
  key = NULL,
  conv.crit = 0.001,
  maxitr = 2000,
  conv.type = "pr",
  SE = FALSE
)

Value

an object of class MCmodel with the following components:

prob.parm

A list of success probabilities for each reduced latent class on each item (IRF)

prob.se

A list of standard errors of item parameters

attribute

A list of estimated attribute profiles including EAP, MLE and MAP estimates.

testfit

A list of test fit statistics including deviance, number of parameters, AIC and BIC

R

expected # of individuals in each latent group choosing each option

lik

posterior probability

itr

Total # of iterations

Arguments

dat

A required \(N \times J\) data matrix of N examinees to J items. Values must be 1, 2,... representing nominal categories. Missing values are currently not allowed.

Qc

A required category and attribute association matrix. The first column gives the item number, which must be numeric (i.e., 1,2,...) and match the number of column in the data. The second column indicates the coded category of each item. The number in the second column must match with the number in the data, but if a category is not coded, it should not be included in the Q-matrix. Entry 1 indicates that the attribute is measured by the category, and 0 otherwise. Note that the MC-DINA model assumes that the category with the largest number of 1s is the key and that the coded distractors should allow to assign examinees uniquely.

model

MCDINA only currently. Other MC models may be incorporated.

key

a numeric vector giving the key of each item. See Examples. NULL by default indicating the coded category requiring the largest number of 1s is the key.

conv.crit

The convergence criterion for max absolute change in conv.type for two consecutive iterations.

maxitr

The maximum iterations allowed.

conv.type

convergence criteria; Can be pr or LL, indicating category response function, or -2 times log-likelihood,respectively.

SE

logical; estimating standard error of item parameters? Default is FALSE.

Author

Wenchao Ma, The University of Alabama,

References

De La Torre, J. (2009). A cognitive diagnosis model for cognitively based multiple-choice options. Applied Psychological Measurement, 33, 163--183.

Ma, W., & de la Torre, J. (2020). GDINA: An R Package for Cognitive Diagnosis Modeling. Journal of Statistical Software, 93(14), 1-26.

See Also

GDINA for G-DINA model

Examples

if (FALSE) {
 # check the format of the data
 # Entry 0 is not allowed
 head(sim10MCDINA$simdat)

 #---------------------------------
 # check the format of the Q-matrix
 #---------------------------------
 # Take item 1 as an example:
 # category 2 has a q-vector (1,0,0)
 # category 1 has a q-vector (0,1,0)
 # category 4 has a q-vector (1,1,0)
 # category 3 is not included in the Q-matrix because it is not coded
 # the order of the coded categories in the Q-matrix doesn't matter

 sim10MCDINA$simQ
  #     Item coded cat A1 A2 A3
  #        1         2  1  0  0
  #        1         1  0  1  0
  #        1         4  1  1  0
  #...
 est <- MCmodel(sim10MCDINA$simdat,sim10MCDINA$simQ)
 est
 est$testfit

 #--------------------------------------
 # Distractors involving more attributes
 #--------------------------------------
 # some distractors may involve attributes that are not invovled by the key option
 # this is not allowed by the "original" MC-DINA (de la Torre, 2009) but is allowed
 # in the current implementation

 # Users need to specify the key for each item to appropriate handle such an issue
 # Note item 1 below: category 1 is the key (as indicated in the key argument below)
 # The distractor (category 4) involves an attribute not included by the key option

 Qc <- matrix(c(1,	1,	1,	1,	0,
                1,	2,	0,	1,	0,
                1,	3,	1,	0,	0,
                1,	4,	1,	0,	1,
                2,	1,	1,	0,	0,
                2,	3,	1,	1,	0,
                2,	2,	1,	1,	1,
                3,	4,	1,	1,	1,
                3,	2,	1,	1,	0,
                3,	3,	0,	1,	1,
                4,	1,	0,	1,	1,
                4,	2,	0,	0,	1,
                5,	1,	1,	0,	0,
                6,	3,	0,	1,	0,
                7,	2,	0,	0,	1,
                8,	4,	1,	0,	0,
                9,	1,	0,	1,	0,
                10, 4,	0,	0,	1),ncol = 5,byrow = TRUE)

 est2 <- MCmodel(sim10MCDINA2$simdat,Qc, key = c(1,2,4,1,1,3,2,4,1,4))
 est2
 est2$prob.parm
 est2$testfit
 est2$attribute
}