Description Details Author(s) References
Create, Simulate, Fit, Solve k-Cube Thurstonian IRT Models.
Package: | kcirt |
Type: | Package |
Version: | 0.6.0 |
Date: | 2014-04-22 |
License: | GPL (>= 2) |
Use kcirt.model
to define a k-Cube Thurstonian IRT model. The function kcirt.sim
generates a random realization. The function kcirt.fitEE
uses an expectation-expectation volley to approximately locate mu
and Lambda
and predict the states, Eta
. The function kcirt.fitMSS
makes use of metaheuristic stochastic search to further refine the predictions/estimates.
The system of interest is defined as
y_i* = Δ μ + Δ Λ S η_i + Δ ε_i
y = 1, if y* > 0
y = 0, otherwise
Y = (y_1, y_2, ..., y_N)
where
y_i is the (column) response vector for observation i.
Δ is the Delta matrix.
μ is the column vector of item means (aka, 'utilities').
Λ is the hyperparameter matrix (aka, 'loadings').
S is the Slot matrix.
η_i is the row vector of latent states (aka, 'constructs', or 'scales') for observation i.
ε_i ~ N[0, Σ_s] is a column vector of system shocks for observation i.
Dave Zes, Jimmy Lewis, Dana Landis @ Korn/Ferry International
<zesdave@gmail.com>
Brown, A., & Maydeu-Olivares, A. (2012, November 12). How IRT Can Solve Problems of Ipsative Data in Forced-Choice Questionnaires. Psychological Methods. Advance online publication. doi: 10.1037/a0030641
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