This function computes item fit statistics for outputs generated by estimation functions in the package, including AlphaNP, AlphaMLE, ParMLE, andJMLE. The function currently provides the RMSEA and Chi-square item fit statistics.
ItemFit(x, model=NULL, par=NULL)The output from the function (The list of all outputs).
This needs to be additionally specified only when x is output from AlphaNP. Currently support five models: "DINA", "DINO", "NIDA", "GNIDA", and "RRUM".
This needs to be additionally specified only when x is output from AlphaNP. A list of parameters.
DINA & DINO --- par$slip: a vector of slipping parameters for each item;
par$guess: a vector of guessing parameters for each item.
NIDA --- par$slip: a vector of slipping parameters for each attribute;
par$guess: a vector of guessing parameters for each attribute.
GNIDA --- par$slip: a matrix (items by attributes) of slipping parameters;
par$guess: a matrix (items by attributes) of guessing parameters.
RRUM --- par$pi: a vector of pi parameters for each item;
par$r: a matrix (items by attributes) of r parameters.
The model-based root mean square error of approximation (Kunina-Habenicht et al., 2012) of each item based on the estimated or given item parameter, Q-vector, and alpha matrix.
The Q1 Chi-square statistic (Wang et al., 2015; Yen, 1981) of each item based on the estimated or given item parameter, Q-vector, and alpha matrix.
The p-values for the Chi-square statistic for each item.
The degrees of freedom for the Chi-square statistic for each item.
Kunina-Habenicht, O., Rupp, A. A., & Wilhelm, O. (2012). The Impact of Model Misspecification on Parameter Estimation and Item-Fit Assessment in Log0Linear Diagnostic Classification Models. Journal of Educational Measurement, 49(1), 59-81.
Wang, C., Shu, Z., Shagn, Z., & Xu, G. (2015). Assessing Item-Level Fit for the DINA Model. Applied Psychological Measurement, 1-14.
Yen, W. M. (1981). Using Simulation Results to Choose a Latent Trait Model. Applied Psychological Measurement, 5, 245-262.
# NOT RUN {
# See examples in AlphaNP, AlphaMLE, ParMLE, and JMLE.
# }
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