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https://github.com/inuyasha2012/pypsy

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) .
https://github.com/inuyasha2012/pypsy

classical-test-theory cognitive-diagnostic-models computerized-adaptive-testing education factor-analysis item-response-theory psychology psychometrics questionnaire structural-equation-modeling survey

Last synced: 3 months ago
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Host: GitHub
URL: https://github.com/inuyasha2012/pypsy
Owner: inuyasha2012
License: mit
Created: 2017-08-28T13:56:59.000Z (almost 7 years ago)
Default Branch: master
Last Pushed: 2018-11-16T10:32:53.000Z (over 5 years ago)
Last Synced: 2024-02-16T06:02:53.239Z (4 months ago)
Topics: classical-test-theory, cognitive-diagnostic-models, computerized-adaptive-testing, education, factor-analysis, item-response-theory, psychology, psychometrics, questionnaire, structural-equation-modeling, survey
Language: Python
Homepage:
Size: 189 KB
Stars: 201
Watchers: 19
Forks: 70
Open Issues: 3
Metadata Files:
- Readme: README.rst
- Changelog: HISTORY.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE

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README

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pypsy

=====

`中文 <./README_ZH.rst>`_

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.

unidimensional item response theory

-----------------------------------

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）

--------------

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.

--------------

Cognitive diagnosis model

-------------------------

models

~~~~~~

-  Dina

-  ho-dina

parameter estimation algorithms

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-  EM algorithm

-  MCMC algorithm

-  maximum likelihood estimation (only for estimating skill parameters

   of subjects)

--------------

Structural equation model

-------------------------

-  contains three parameter estimation methods(ULS, ML and GLS).

-  based on gradient descent

--------------

Confirmatory factor analysis

----------------------------

-  can be used for continuous data, binary data and ordered data.

-  based on gradient descent

-  binary and ordered data based on Polychoric correlation matrix.

--------------

Factor analysis

---------------

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

--------------

Adaptive test

-------------

model

~~~~~

Thurston IRT model (multidimensional item response theory model for

personality test)

Algorithm

~~~~~~~~~

Maximum information method for multidimensional item response theory

--------------

Require

-------

-  numpy

-  progressbar2

--------------

How to use it

-------------

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 `__

-  `Higher-order latent trait models for cognitive

   diagnosis `__

-  `Full-Information Item Factor

   Analysis. `__

-  `Multidimensional adaptive

   testing `__

-  `Derivative free gradient projection algorithms for rotation `__