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The major advantage offered by PSPP consists in its ability to handle very large volumes of data, even if the provided data is larger than the virtual memory of your Mac. Thanks to PSPP you can perform descriptive statistics, T-tests, anova, linear and logistic regression, factor analysis, cluster analysis as well as non-parametric tests.

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A Factor Analysis class

Project description

FactorAnalyzer

This is a Python module to perform exploratory and factor analysis (EFA), with severaloptional rotations. It also includes a class to perform confirmatory factoranalysis (CFA), with certain pre-defined constraints. In expoloratory factor analysis,factor extraction can be performed using a variety of estimation techniques. Thefactor_analyzer package allows users to perfrom EFA using either (1) a minimumresidual (MINRES) solution, (2) a maximum likelihood (ML) solution, or (3) a principalfactor solution. However, CFA can only be performe using an ML solution.

Both the EFA and CFA classes within this package are fully compatible with scikit-learn.Portions of this code are ported from the excellent R library psych, and the sempackage provided inspiration for the CFA class.

Please see the official documentation for additional details.

Description

Exploratory factor analysis (EFA) is a statistical technique used toidentify latent relationships among sets of observed variables in adataset. In particular, EFA seeks to model a large set of observedvariables as linear combinations of some smaller set of unobserved,latent factors. The matrix of weights, or factor loadings, generatedfrom an EFA model describes the underlying relationships between eachvariable and the latent factors.

Confirmatory factor analysis (CFA), a closely associated technique, isused to test an a priori hypothesis about latent relationships among setsof observed variables. In CFA, the researcher specifies the expected patternof factor loadings (and possibly other constraints), and fits a model accordingto this specification.

Typically, a number of factors (K) in an EFA or CFA model is selectedsuch that it is substantially smaller than the number of variables. Thefactor analysis model can be estimated using a variety of standardestimation methods, including but not limited MINRES or ML.

Factor loadings are similar to standardized regression coefficients, andvariables with higher loadings on a particular factor can be interpretedas explaining a larger proportion of the variation in that factor. In thecase of EFA, factor loading matrices are usually rotated after the factoranalysis model is estimated in order to produce a simpler, more interpretablestructure to identify which variables are loading on a particular factor.

Two common types of rotations are:

1. The varimax rotation, which rotates the factor loading matrix soas to maximize the sum of the variance of squared loadings, whilepreserving the orthogonality of the loading matrix.
2. The promax rotation, a method for oblique rotation, which buildsupon the varimax rotation, but ultimately allows factors to becomecorrelated.

This package includes a factor_analyzer module with a stand-aloneFactorAnalyzer class. The class includes fit() and transform()methods that enable users to perform factor analysis and score new datausing the fitted factor model. Users can also perform optional otationson a factor loading matrix using the Rotator class.

The following rotation options are available in both FactorAnalyzerand Rotator:

1. varimax (orthogonal rotation)
2. promax (oblique rotation)
3. oblimin (oblique rotation)
4. oblimax (orthogonal rotation)
5. quartimin (oblique rotation)
6. quartimax (orthogonal rotation)
7. equamax (orthogonal rotation)

In adddition, the package includes a confirmatory_factor_analyzermodule with a stand-alone ConfirmatoryFactorAnalyzer class. Theclass includes fit() and transform() that enable users to performconfirmatory factor analysis and score new data using the fitted model.Performing CFA requires users to specify in advance a model specificationwith the expected factor loading relationships. This can be done usingthe ModelSpecificationParser class.

Note that the ConfirmatoryFactorAnalyzer class is very experimental at this point,so use it with caution, especially if your data are highly non-normal.

Examples

Exploratory factor analysis example.

Confirmatory factor analysis example.

Requirements

• Python 3.4 or higher
• numpy
• pandas
• scipy
• scikit-learn

Contributing

Contributions to factor_analyzer are very welcome. Please file an issueon GitHub, or contact jbiggs@ets.org if you would like to contribute.

Installation

You can install this package via pip with:

$ pip install factor_analyzer

Alternatively, you can install via conda with:

$ conda install -c ets factor_analyzer

Project details

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0.3.2

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