Qmatrix validation for the (sequential) GDINA model based on PVAF (de la Torre & Chiu, 2016; Najera, Sorrel, & Abad, 2019), stepwise Wald test (Ma & de la Torre, 2020) or mesa plot (de la Torre & Ma, 2016). All these methods are suitable for dichotomous and ordinal response data. If too many modifications are suggested based on the default PVAF method, you are suggested to try the stepwise Wald test method or predicted cutoffs. You should always check the mesa plots for further examination.
Qval(GDINA.obj, method = "PVAF", eps = 0.95, digits = 4, wald.args = list()) # S3 method for Qval extract(object, what = c("sug.Q", "varsigma", "PVAF", "eps", "Q"), ...) # S3 method for Qval summary(object, ...)
GDINA.obj  An estimated model object of class 

method  which Qmatrix validation method is used? Can be either 
eps  cutoff value for PVAF from 0 to 1. Default = 0.95. Note that it can also be 1, indicating the predicted cutoff based on Najera, P., Sorrel, M., and Abad, P. (2019). 
digits  How many decimal places in each number? The default is 4. 
wald.args  a list of arguments for the stepwise Wald test method.

object 

what  argument for S3 method 
...  additional arguments 
An object of class Qval
. Elements that can be
extracted using extract
method include:
suggested Qmatrix
original Qmatrix
varsigma index
PVAF
extract
: extract various elements from Qval
objects
summary
: print summary information
de la Torre, J. & Chiu, CY. (2016). A General Method of Empirical Qmatrix Validation. Psychometrika, 81, 253273.
de la Torre, J., & Ma, W. (2016, August). Cognitive diagnosis modeling: A general framework approach and its implementation in R. A Short Course at the Fourth Conference on Statistical Methods in Psychometrics, Columbia University, New York.
Ma, W., & de la Torre, J. (2020). An empirical Qmatrix validation method for the sequential GDINA model. British Journal of Mathematical and Statistical Psychology, 73, 142163.
Najera, P., Sorrel, M., & Abad, P. (2019). Reconsidering Cutoff Points in the General Method of Empirical QMatrix Validation. Educational and Psychological Measurement.
if (FALSE) { ################################ # # Binary response # ################################ dat < sim10GDINA$simdat Q < sim10GDINA$simQ Q[10,] < c(0,1,0) # Fit the GDINA model mod1 < GDINA(dat = dat, Q = Q, model = "GDINA") # Qvalidation using de la Torre and Chiu's method pvaf < Qval(mod1,method = "PVAF",eps = 0.95) pvaf extract(pvaf,what = "PVAF") #See also: extract(pvaf,what = "varsigma") extract(pvaf,what = "sug.Q") # Draw mesa plots using the function plot plot(pvaf,item=10) #The stepwise Wald test stepwise < Qval(mod1,method = "wald") stepwise extract(stepwise,what = "PVAF") #See also: extract(stepwise,what = "varsigma") extract(stepwise,what = "sug.Q") #Set eps = 1 to determine the cutoff empirically pvaf2 < Qval(mod1,method = "PVAF",eps = 1) pvaf2 ################################ # # Ordinal response # ################################ seq.est < GDINA(sim20seqGDINA$simdat,sim20seqGDINA$simQ,sequential = TRUE) stepwise < Qval(seq.est, method = "wald", eps = 1) }