GRM {exametrika} | R Documentation |
Graded Response Model (GRM)
Description
Implements Samejima's (1969) Graded Response Model (GRM), which is an Item Response Theory model for ordered categorical response data. The model estimates discrimination parameters and category threshold parameters for each item. It is widely used in psychological measurement, educational assessment, and other fields that deal with multi-step rating scales.
Usage
GRM(U, na = NULL, Z = NULL, w = NULL, verbose = TRUE)
Arguments
U |
Either an object of class "exametrika" or raw data. When raw data is given,
it is converted to the exametrika class using the |
na |
Specifies numbers or characters to be treated as missing values. |
Z |
Missing indicator matrix of type matrix or data.frame. 1 indicates observed values, 0 indicates missing values. |
w |
Item weight vector |
verbose |
Logical; if TRUE, shows progress of iterations (default: TRUE) |
Value
A list of class "exametrika" and "GRM" containing the following elements:
- testlength
Length of the test (number of items)
- nobs
Sample size (number of rows in the dataset)
- params
Matrix containing the estimated item parameters
- EAP
Ability parameters of examinees estimated by EAP method
- MAP
Ability parameters of examinees estimated by MAP method
- PSD
Posterior standard deviation of the ability parameters
- ItemFitIndices
Fit indices for each item. See also
ItemFit
- TestFitIndices
Overall fit indices for the test. See also
TestFit
References
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 34(4, Pt. 2), 1-100.
Examples
# Apply GRM to example data
result <- GRM(J5S1000)
print(result)
plot(result, type = "IRF")
plot(result, type = "IIF")
plot(result, type = "TIF")