Q-matrix validation
Q-matrix validation for the (sequential) G-DINA 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 class 'Qval'
extract(object, what = c("sug.Q", "varsigma", "PVAF", "eps", "Q"), ...)
## S3 method for class 'Qval'
summary(object, ...)GDINA.obj |
An estimated model object of class |
method |
which Q-matrix 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 Q-matrix
original Q-matrix
varsigma index
PVAF
extract: extract various elements from Qval objects
summary: print summary information
Wenchao Ma, The University of Alabama, wenchao.ma@ua.edu
Jimmy de la Torre, The University of Hong Kong
de la Torre, J. & Chiu, C-Y. (2016). A General Method of Empirical Q-matrix Validation. Psychometrika, 81, 253-273.
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 Q-matrix validation method for the sequential G-DINA model. British Journal of Mathematical and Statistical Psychology, 73, 142-163.
Najera, P., Sorrel, M., & Abad, P. (2019). Reconsidering Cutoff Points in the General Method of Empirical Q-Matrix Validation. Educational and Psychological Measurement.
## Not run: ################################ # # Binary response # ################################ dat <- sim10GDINA$simdat Q <- sim10GDINA$simQ Q[10,] <- c(0,1,0) # Fit the G-DINA model mod1 <- GDINA(dat = dat, Q = Q, model = "GDINA") # Q-validation 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) ## End(Not run)
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