Evidence map›Paper›PMID 39731263›Full record

ArticleMultivariate behavioral research

MIIVefa: An R Package for a New Type of Exploratory Factor Anaylysis Using Model-Implied Instrumental Variables.

Lan Luo, Kathleen M Gates, Kenneth A Bollen

Abstract read
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In one paragraph

Article in Multivariate behavioral research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Lan LuoDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Kathleen M GatesDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Kenneth A BollenDepartment of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Funding

Measuring the individual: Personalized latent variable models from ecological momentary assessmentsR21MH119572 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BOLLEN, KENNETH, GATES, KATHLEEN · 2019 to 2020
$416k
NIMH NIH HHS R21 MH119572
6 · The paper itself

Abstract

We present the R package MIIVefa, designed to implement the MIIV-EFA algorithm. This algorithm explores and identifies the underlying factor structure within a set of variables. The resulting model is not a typical exploratory factor analysis (EFA) model because some loadings are fixed to zero and it allows users to include hypothesized correlated errors such as might occur with longitudinal data. As such, it resembles a confirmatory factor analysis (CFA) model. But, unlike CFA, the MIIV-EFA algorithm determines the number of factors and the items that load on these factors directly from the data. We provide both simulation and empirical examples to illustrate the application of MIIVefa and discuss its benefits and limitations.

Indexed as

AlgorithmsModels, StatisticalSoftwareComputer SimulationFactor Analysis, StatisticalHumansdata-driven approachExploratory factor analysismodel implied instrumental variablesR package

Identifiers

PMID39731263
PMCPMC12189262

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.