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Exploratory factor analysis—Parameter estimation and scores prediction with high-dimensional data
2016
Journal of Multivariate Analysis
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. • For large p, factor scores can be estimated with high precision (n need not be large). • For large p, an old iteration method converges fast and with no inadmissible values. a b s t r a c t In an approach ...
aiming at high-dimensional situations, we first introduce a distribution-free approach to parameter estimation in the standard random factor model, that is shown to lead to the same estimating equations ...
Acknowledgment We are grateful to an anonymous referee for valuable and constructive criticism. ...
doi:10.1016/j.jmva.2016.02.013
fatcat:onux6lsiozhvxazj2oria6rrfe
Bayesian exploratory factor analysis
2014
Journal of Econometrics
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Bayesian Exploratory Factor Analysis * This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. ...
The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. JEL Classification: C11, C38, C63 ...
Classical Exploratory Factor Analysis is widely used to boil down high dimensional data on psychological traits to interpretable scales. ...
doi:10.1016/j.jeconom.2014.06.008
pmid:25431517
pmcid:PMC4242469
fatcat:7xtvkc2opbfd3axcypiak3lpdu
A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2001.07859v3 fatcat:ufjvykj6kjd7tajochixhalcsqOther Versions
In this work, we investigate a deep learning-based VI algorithm for exploratory item factor analysis (IFA) that is computationally fast even in large data sets with many latent factors. ...
We provide a real data application that recovers results aligning with psychological theory across random starts. ...
Other marginal likelihood-based parameter estimation methods for exploratory IFA avoid approximating high-dimensional integrals and are therefore more computationally efficient. ...
arXiv:2001.07859v4
fatcat:5hitjvjpurdrxfshevgtskqlzq
Exploratory Tobit Factor Analysis for Multivariate Censored Data
2001
Multivariate Behavioral Research
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In addition, the factor model parameters lend themselves to substantive interpretation and graphical display. The models are estimated with simulated maximum likelihood. ...
Such models are particularly useful in the exploratory analysis of multivariate censored data and the identification of latent variables from behavioral data. ...
With our model one can deal with exploratory and confirmatory ML factor analysis of data with mixed type variables, in which a (large) number of observations equal to zero. ...
doi:10.1207/s15327906mbr3601_03
fatcat:jwpfqivgavcdbjoksv7jsaysxa
Exploratory Factor Analysis of Data on a Sphere
[article]
2021
arXiv
pre-print
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We develop exploratory factor analysis of the projected normal distribution to explain the variability in such data using a few easily interpreted latent factors. ...
Data on high-dimensional spheres arise frequently in many disciplines either naturally or as a consequence of preliminary processing and can have intricate dependence structure that needs to be understood ...
ACKNOWLEDGMENTS The research was supported in part by the United States Department of Agriculture (USDA)/National Institute of Food and Agriculture (NIFA), Hatch projects IOW03617 and IOW03717. ...
arXiv:2111.04940v1
fatcat:ftpxt6jtz5h6bjlgn26gz4gxuu
Simultaneous Parameter Estimation in Exploratory Factor Analysis: An Expository Review
2010
International Statistical Review
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The classical exploratory factor analysis (EFA) finds estimates for the factor loadings matrix and the matrix of unique factor variances which give the best fit to the sample correlation matrix with respect ...
Common factor scores can be obtained as a function of these estimates and the data. ...
Acknowledgements The authors are grateful to an anonymous reviewer and the Editor for their helpful comments on the first draft of this paper. ...
doi:10.1111/j.1751-5823.2010.00120.x
fatcat:bm55wle4w5d3pmlgew6besblza
High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm
2009
Psychometrika
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A Metropolis-Hastings Robbins-Monro (MH-RM) algorithm for high-dimensional maximum marginal likelihood exploratory item factor analysis is proposed. ...
It is shown that when the dimensionality is high, MH-RM has advantages over existing methods such as numerical quadrature based EM algorithm. ...
JML computations iterate between two stages that are similar to the first and last stages in MH-RM: (1) replacing the unobserved factor scores with modal estimates given current item parameters, and (2 ...
doi:10.1007/s11336-009-9136-x
fatcat:52s3nwrd6vb6hkl2s3uzzmyzpu
Exploratory Factor Analysis of Wireline Logs Using a Float-Encoded Genetic Algorithm
2017
Mathematical Geosciences
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With suitably chosen genetic operators, 82 the factor loadings and scores are estimated in a convergent iterative procedure. ...
Factor analysis (FA) is applicable to reduce 64 the dimensionality of statistical problems and extract non-measurable information 65 from large-scale data sets (Lawley and Maxwell 1962). ...
Estimated parameters are: theoretical data calculated from the factor scores (TH), first scaled factor (FAC TO R 1 ), second and third factors (FAC TO R 2 , FAC TO R 3 ), shale volume estimated by Larionov ...
doi:10.1007/s11004-017-9714-x
fatcat:niwnyivoi5gdxg7zsci2vj2fhm
Fitting Exploratory Factor Analysis Models with High Dimensional Psychological Data
2021
Journal of Data Science
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The purpose of the current study was to investigate and compare some alternative approaches to fitting EFA in the case of small samples and high dimensional data. ...
Objectives: Exploratory Factor Analysis (EFA) is a very popular statistical technique for identifying potential latent structure underlying a set of observed indicator variables. ...
Methods for high dimensional factor analysis A number of statistical methods have been suggested for use in exploratory latent variable modeling with high dimensional datasets. ...
doi:10.6339/jds.201607_14(3).0008
fatcat:vs3kf3yxxrb4beka4rthqovuty
Exploratory factor analysis with structured residuals for brain network data
2020
Network Neuroscience
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Dimension reduction is widely used and often necessary to make network analyses and their interpretation tractable by reducing high dimensional data to a small number of underlying variables. ...
Techniques such as Exploratory Factor Analysis (EFA) are used by neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors. ...
of the high-dimensional raw data by the low-dimensional factors. ...
doi:10.1162/netn_a_00162
pmid:33688604
pmcid:PMC7935039
fatcat:hrqi527uirbltexo6b4x2t3xme
Exploratory Factor Analysis with Structured Residuals for Brain Imaging Data
[article]
2020
bioRxiv
pre-print
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Dimension reduction is widely used and often necessary to reduce high dimensional data to a small number of underlying variables -- factors or components -- to make data analyses and their interpretation ...
Here, a) we show the adverse consequences of ignoring such a priori structure in standard factor analysis, b) we propose a technique for Exploratory factor analysis with structured residuals (EFAST) which ...
Acknowledgements We would like to thank Yves Rosseel for valuable input and the development of key tools, Linda Geerligs for providing the functional connectivity data, and Jonathan Helm and Øystein Sørensen ...
doi:10.1101/2020.02.06.933689
fatcat:yisfvxs2fbdcdmlusmvpgbrlfe
Exploratory Structural Equation Modeling: An Integration of the Best Features of Exploratory and Confirmatory Factor Analysis
2014
Annual Review of Clinical Psychology
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Keywords exploratory and confirmatory factor analysis, exploratory structural equation models, exploratory structural equation model within confirmatory factor analysis, multiple-indicator multiple-cause ...
(MIMIC) models, multitrait-multimethod models, bifactor models Abstract Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), path analysis, and structural equation modeling (SEM) ...
, Andrew Martin, Benjamin Nagengast, Alexander Robitzsch, and Ulrich Trautwein) for helpful comments at earlier stages of this research. ...
doi:10.1146/annurev-clinpsy-032813-153700
pmid:24313568
fatcat:j6e57fni6bgb3oy2qpffwqkdg4
Modeling multiple phenotypes in wheat using data‐driven genomic exploratory factor analysis and Bayesian network learning
2021
Plant Direct
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This study shows that data-driven exploratory factor analysis can reveal smaller dimensional common latent phenotypes that are likely to give rise to numerous observed field phenotypes without relying ...
The objectives of this study were to illustrate the manner in which data-driven exploratory factor analysis can map observed phenotypes into a smaller number of latent variables and infer a genomic latent ...
Exploratory factor analysis estimates the phenotypefactor relationship from the data by allowing cross-loading. ...
doi:10.1002/pld3.304
pmid:33532691
pmcid:PMC7833463
fatcat:lllpeyy2kvbc5gj6ccoqhadhuq
Inferring constructs of effective teaching from classroom observations: An application of Bayesian exploratory factor analysis without restrictions
2015
Annals of Applied Statistics
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2015 pre-print arXiv:1511.05360v1 fatcat:fxdv6g6z4bfdra2j4fpujmgoaa
We make inferences about these constructs using a novel approach to Bayesian exploratory factor analysis (EFA) that, unlike commonly used approaches for identifying factor loadings in Bayesian EFA, is ...
constructs of high-quality teaching. ...
SUPPLEMENTARY MATERIAL Supplement to "Inferring constructs of effective teaching from classroom observations: An application of Bayesian exploratory factor analysis with- ...
doi:10.1214/15-aoas833
fatcat:qgy5yjuuvndkrlkh55yh5a2wuq
Determining Dimensionality of Exercise Readiness Using Exploratory Factor Analysis
2016
Journal of Sports Science and Medicine
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The purpose of this study was to assess construct dimensionality of exercise readiness using exploratory factor analysis. ...
A principal axis factor analysis was conducted with 41 items using oblique rotation (promax). ...
Whitney Breslin for her assistance with survey data collection. All procedures in the current study comply with U.S. laws and local institutional review board guidelines for scientific research. ...
pmid:27274659
pmcid:PMC4879435
fatcat:iswjeblksnalhfln7qsdudzfoi
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