psychometrics package, including MIRT(multidimension item response theory), IRT(item response theory),GRM(grade response theory),CAT(computerized adaptive testing), CDM(cognitive diagnostic model), FA(factor analysis), SEM(Structural Equation Modeling) .
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中文 <./README_ZH.rst>
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psychometrics package, including structural equation model, confirmatory factor analysis, unidimensional item response theory, multidimensional item response theory, cognitive diagnosis model, factor analysis and adaptive testing. The package is still a doll. will be finished in future.
models
- binary response data IRT (two parameters, three parameters).
- grade respone data IRT (GRM model)
Parameter estimation algorithm
------------------------------
- EM algorithm (2PL, GRM)
- MCMC algorithm (3PL)
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Multidimensional item response theory (full information item factor analysis)
-----------------------------------------------------------------------------
Parameter estimation algorithm
The initial value ^^^^^^^^^^^^^^^^^
The approximate polychoric correlation is calculated, and the slope initial value is obtained by factor analysis of the polychoric correlation matrix.
EM algorithm ^^^^^^^^^^^^
E step uses GH integral.
M step uses Newton algorithm (sparse matrix is divided into non sparse matrix).
Factor rotation ^^^^^^^^^^^^^^^
Gradient projection algorithm
The shortcomings
GH integrals can only estimate low dimensional parameters.
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Cognitive diagnosis model
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models
~~~~~~
- Dina
- ho-dina
parameter estimation algorithms
EM algorithm
MCMC algorithm
maximum likelihood estimation (only for estimating skill parameters of subjects)
contains three parameter estimation methods(ULS, ML and GLS).
based on gradient descent
can be used for continuous data, binary data and ordered data.
based on gradient descent
binary and ordered data based on Polychoric correlation matrix.
For the time being, only for the calculation of full information item factor analysis, it is very simple.
The algorithm
principal component analysis
The rotation algorithm
gradient projection
model
Thurston IRT model (multidimensional item response theory model for
personality test)
Algorithm
Maximum information method for multidimensional item response theory
numpy
progressbar2
install
::
pip install psy
See demo
TODO LIST
---------
- theta parameterization of CCFA
- parameter estimation of structural equation models for multivariate
data
- Bayesin knowledge tracing (Bayesian knowledge tracking)
- multidimensional item response theory (full information item factor
analysis)
- high dimensional computing algorithm (adaptive integral, etc.)
- various item response models
- cognitive diagnosis model
- G-DINA model
- Q matrix correlation algorithm
- Factor analysis
- maximum likelihood estimation
- various factor rotation algorithms
- adaptive
- adaptive cognitive diagnosis
- other adaption model
- standard error and P value
- code annotation, testing and documentation.
Reference
---------
- `DINA Model and Parameter Estimation: A
Didactic <http://www.stat.cmu.edu/~brian/PIER-methods/For%202013-03-04/Readings/de%20la%20Torre-dina-est-115-30-jebs.pdf>`__
- `Higher-order latent trait models for cognitive
diagnosis <http://www.aliquote.org/pub/delatorre2004.pdf>`__
- `Full-Information Item Factor
Analysis. <http://conservancy.umn.edu/bitstream/11299/104282/1/v12n3p261.pdf>`__
- `Multidimensional adaptive
testing <http://media.metrik.de/uploads/incoming/pub/Literatur/1996_Multidimensional%20adaptive%20testing.pdf>`__
- `Derivative free gradient projection algorithms for rotation <https://cloudfront.escholarship.org/dist/prd/content/qt9938p4wc/qt9938p4wc.pdf>`__