Latent Variable Models and Factor Analysis: A Unified Approach

By David J. Bartholomew, M. Knott, Irini Moustaki. 2011

676 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/37500af3be052cad10ed445466393700e2de61c2

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Citations in the corpus

Introduction to Latent Class Analysis With Applications (2017, background): “Bartholomew, Knott, and Moustaki (2011) provided a framework that identifies four main types of LVMs.”
Measurement bias and effect restoration in causal inference (2014, methodology): “…in § 3 can be considered an extension of the identification of latent structure models , known methods of estimation and variance analysis used in latent structure analysis (e.g., Bartholomew et al., 2011; Hu, 2008) are applicable to the estimands derived in this paper under most situations.”
On Group Comparisons With Logistic Regression Models (2020, background): “…for them is also familiar: use multiple observed indicators that are all regarded as imperfect measures of the latent variable, and define and estimate latent variable measurement models that represent this situation (see e.g., Bartholomew, Knott, and Moustaki 2011, for an overview of such models).”
Variational learning for latent Gaussian model of discrete data (2012, methodology): “LGMs include popular models such as factor analysis and probabilistic principal components analysis for continuous data [Bartholomew et al., 2011; Tipping and Bishop, 1999], binary and multinomial factor analysis for discrete data [Collins et al.”
So Many Variables: Joint Modeling in Community Ecology. (2015, methodology): “The most common example of a latent variable model is factor analysis, and LVMs are sometimes even called factor analytic models [30].”
Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB (2024, unknown): “Given these definitions, the multivariate random effect is distributed as which is a reduced-rank approximation of b j ∼ N ( 0 , Σ ), as often seen in factor analytic models (Bartholomew et al. 2011; Niku et al. 2017).”
Enhancing Interpretability in Factor Analysis by Means of Mathematical Optimization (2019, background): “In this paper, we focus on latent variable models to uncover the hidden information enclosed in raw data, Anderson (2003), Bartholomew, Knott, and Moustaki (2011), and Blei (2014).”
Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications (2017, methodology): “Latent factor models are one of the major statistical tools for multivariate data analysis that have received many applications in social and behavioral sciences (Anderson, 2003; Bartholomew et al., 2011).”
Partial least squares path modeling: Time for some serious second thoughts (2016, methodology): “PLSc starts by estimating factor loadings using a modified version of the minimum residuals (MINRES) estimator, an early factor analysis technique (Harman and Jones, 1966) that is now largely obsolete (Bartholomew et al., 2011, sec.”
Variational Approximations for Generalized Linear Latent Variable Models (2017, unknown): “In both settings, we assessed performance based on computation time, and the di ff erence between the true and estimated param-13 eter values / latent variables as calculated using the symmetric Procrustes error (see Chapter 8.4, Bartholomew et al., 2011).”
The Impact of Entrepreneurial Business Networks on Firms’ Performance Through a Mediating Role of Dynamic Capabilities (2019, methodology): “Tabachnick and Fidell described that skewness and kurtosis deviation from normality typically do not make any substantive differences in data analysis when the population size is over 200 [87,88].”
A Brief Overview of Unsupervised Neural Speech Representation Learning (2022, background): “These more closely follow the assumption of local independence which states that observed variables are conditionally independent given the local (LOC, table 2) and/or global (GLB, table 2) latent variables (Bartholomew, Knott, and Moustaki 2011).”
Item Response Theory—A Statistical Framework for Educational and Psychological Measurement (2021, unknown): “Speciﬁcally, we will show that these two types of models can be viewed as special cases of a general linear latent variable modeling framework (GLLVM, Chapter 2, Bartholomew et al., 2011), where IRT models focus on categorical items while linear factor models concern continuous variables.”
The design of international trade agreements: Introducing a new dataset (2013, background): “6 Latent trait analysis is a type of factor analysis for binary data (Bartholomew et al. 2011).”
Mathematics for Machine Learning (2020, methodology): “If we allow each observation dimension d to have a different variance σ(2) d, we obtain factor analysis factor analysis (FA) (Spearman, 1904; Bartholomew et al., 2011).”
Strategic and tactical price decisions in hotel revenue management (2016, methodology): “This is a statistical method for finding groups or subtypes of related cases in multivariate data (for review, see Bartholomew et al., 2011).”
Latent Variable Models (2019, background): “Generally, latent variable models can be classified into four categories depending on whether the observable variables and latent variables are categorical or continuous: a) both latent variables and observed variables are continuous; b) both latent variables and observed variables are categorical; ”