Generalized latent trait models

By Irini Moustaki, M. Knott. 2000

276 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ec0d4b872a61c75d66c6625c4a2eeeb722af3b8e

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In Experimental and Cognitive Psychology

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Bayesian Structural Equation Modeling (2007, background): “Potentially, one can also define separate generalized linear models for each of the observed variables in the measurement model (as in Sammel, Ryan, Legler, 1997; Moustaki and Knott, 2000; Dunson, 2000; 2003) to allow a broader class of measurement models.”
Beta-Negative Binomial Process and Poisson Factor Analysis (2011, background): “There has been some work on exponential family latent factor models that incorporate Gaussian latent variables (Dunson, 2000, 2003; Moustaki and Knott, 2000; Sammel et al., 1997), but computation tends to be prohibitive in high-dimensional settings and the Gaussian assumption is restrictive for…”
The α and the ω of Congeneric Test Theory: An Extension of Reliability and Internal Consistency to Heterogeneous Tests (2005, background): “The results presented here are confined to congeneric test theory and do not generalize to reliability (Bartholomew, 1996, pp. 31-36) for the larger class of general linear latent variable models (GLLVMs) (Bartholomew & Knott, 1999; Mellenbergh, 1994; Moustaki & Knott, 2000).”
Factor analysis with (mixed) observed and latent variables in the exponential family (2001, methodology): “Note that previous work, for example Moustaki and Knott (2000), was limited to factor models with only two factors because of the limitations of numerical integration.”
Factor Analysis with Categorical Indicators: A Comparison Between Traditional and Latent Class Approaches (2005, background): “Depending on whether the latent variable is treated as continuous or discrete, one obtains a generalized LT (Moustaki & Knott, 2000) or LC (Vermunt & Magidson, 2001) model.”
Factor Models for Ordinal Variables With Covariate Effects on the Manifest and Latent Variables: A Comparison of LISREL and IRT Approaches (2004, methodology): “The measurement model is fitted first, and factor scores (latent scores) (Moustaki & Knott, 2000) are computed and used as dependent variables for further analysis.”
Estimation of generalized linear latent variable models (2004, methodology): “Moustaki (1996) and Moustaki and Knott (2000) proposed the use of a simple Gauss–Hermite quadrature (GHQ) as a numerical approximation method.”
Item Response Theory—A Statistical Framework for Educational and Psychological Measurement (2021, unknown): “The GLLVM framework was ﬁrst introduced in Bartholomew (1984), and this framework has been further uniﬁed and extended in a wider sense in Moustaki and Knott (2000) and Rabe-Hesketh and Skrondal (2004).”
Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications (2017, methodology): “…model (e.g. Anderson, 2003; Bartholomew et al., 2008), multidimensional item response theory model for binary responses that plays a key role in educational assessment (e.g. Reckase, 2009), and the Poisson model that is widely used to analyze multivariate count (e.g. Moustaki and Knott, 2000).”
Latent Variable Theory (2008, background): “…have several distinct systems of continuous latent variables that themselves define latent classes (Lubke & Muthén, 2005; Rost, 1990), and at the observed front these models may also relate to a mixture of categorical and continuous observed variables (e.g., Moustaki, 1996; Moustaki & Knott, 2000).”
Bias in Factor Score Regression and a Simple Solution (2011, methodology): “In theory, simultaneous estimation such as that based on maximum likelihood estimation is applicable to SEM with mixed continuous and nominal observed variables (Moustaki & Knott, 2000; Sammel, Ryan & Legler, 1997).”