Literature DB >> 25080868

Sparse Versus Simple Structure Loadings.

Nickolay T Trendafilov1, Kohei Adachi.   

Abstract

The component loadings are interpreted by considering their magnitudes, which indicates how strongly each of the original variables relates to the corresponding principal component. The usual ad hoc practice in the interpretation process is to ignore the variables with small absolute loadings or set to zero loadings smaller than some threshold value. This, in fact, makes the component loadings sparse in an artificial and a subjective way. We propose a new alternative approach, which produces sparse loadings in an optimal way. The introduced approach is illustrated on two well-known data sets and compared to the existing rotation methods.

Mesh:

Year:  2014        PMID: 25080868     DOI: 10.1007/s11336-014-9416-y

Source DB:  PubMed          Journal:  Psychometrika        ISSN: 0033-3123            Impact factor:   2.500


  6 in total

1.  Regularized Structural Equation Modeling.

Authors:  Ross Jacobucci; Kevin J Grimm; John J McArdle
Journal:  Struct Equ Modeling       Date:  2016-04-12       Impact factor: 6.125

2.  A Penalized Likelihood Method for Structural Equation Modeling.

Authors:  Po-Hsien Huang; Hung Chen; Li-Jen Weng
Journal:  Psychometrika       Date:  2017-04-17       Impact factor: 2.500

3.  Approximated Penalized Maximum Likelihood for Exploratory Factor Analysis: An Orthogonal Case.

Authors:  Shaobo Jin; Irini Moustaki; Fan Yang-Wallentin
Journal:  Psychometrika       Date:  2018-06-06       Impact factor: 2.500

4.  Sparse Exploratory Factor Analysis.

Authors:  Nickolay T Trendafilov; Sara Fontanella; Kohei Adachi
Journal:  Psychometrika       Date:  2017-07-13       Impact factor: 2.500

5.  Simplifying the Assessment of Measurement Invariance over Multiple Background Variables: Using Regularized Moderated Nonlinear Factor Analysis to Detect Differential Item Functioning.

Authors:  Daniel J Bauer; William C M Belzak; Veronica Cole
Journal:  Struct Equ Modeling       Date:  2019-09-05       Impact factor: 6.125

6.  A Guide for Sparse PCA: Model Comparison and Applications.

Authors:  Rosember Guerra-Urzola; Katrijn Van Deun; Juan C Vera; Klaas Sijtsma
Journal:  Psychometrika       Date:  2021-06-29       Impact factor: 2.290

  6 in total

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