ArticlePsychometrika2020
A Note on Exploratory Item Factor Analysis by Singular Value Decomposition.
Article in Psychometrika, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Spectral Clustering with Likelihood Refinement for High-Dimensional Latent Class Recovery.Psychometrika · 2026Article
- Exploratory General-Response Cognitive Diagnostic Models with Higher-Order Structures.Psychometrika · 2025Article
- A multidimensional Bayesian IRT method for discovering misconceptions from concept test data.Frontiers in psychology · 2025Article
- A Spectral Method for Identifiable Grade of Membership Analysis with Binary Responses.Psychometrika · 2024Article
- A Note on Improving Variational Estimation for Multidimensional Item Response Theory.Psychometrika · 2024Article
- Rotation to Sparse Loadings UsingPsychometrika · 2023Article
- Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework.Psychometrika · 2022Article
- Using EM Algorithm for Finite Mixtures and Reformed Supplemented EM for MIRT Calibration.Psychometrika · 2021Article
- A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis.Psychometrika · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We revisit a singular value decomposition (SVD) algorithm given in Chen et al. (Psychometrika 84:124-146, 2019b) for exploratory item factor analysis (IFA). This algorithm estimates a multidimensional IFA model by SVD and was used to obtain a starting point for joint maximum likelihood estimation in Chen et al. (2019b). Thanks to the analytic and computational properties of SVD, this algorithm guarantees a unique solution and has computational advantage over other exploratory IFA methods. Its computational advantage becomes significant when the numbers of respondents, items, and factors are all large. This algorithm can be viewed as a generalization of principal component analysis to binary data. In this note, we provide the statistical underpinning of the algorithm. In particular, we show its statistical consistency under the same double asymptotic setting as in Chen et al. (2019b). We also demonstrate how this algorithm provides a scree plot for investigating the number of factors and provide its asymptotic theory. Further extensions of the algorithm are discussed. Finally, simulation studies suggest that the algorithm has good finite sample performance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.