Exploratory latent structure analysis using both identifiable and unidentifiable models

By L. A. Goodman. 1974

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Citations in the corpus

Identifiability of parameters in latent structure models with many observed variables (2008, background): “For instance, Goodman [22] describes a 3-class model with four manifest binary variables and thus a parameter space of dimension 3(4) + 2 = 14.”
Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder (2016, background): “Identifiability of the diagrams reflects the well-known fact in latent class analysis that a latent class model is identified with at least three independent proxies of the latent factor (Kruskal, 1976; Goodman, 1974; Allman et al., 2009).”
AVOIDING BOUNDARY ESTIMATES IN LATENT CLASS ANALYSIS BY BAYESIAN POSTERIOR MODE ESTIMATION (2006, background): “In other words, responses are assumed to be mutually independent within latent classes, which is known as the local independence assumption (Goodman, 1974a, 1974b).”
poLCA: An R Package for Polytomous Variable Latent Class Analysis (2011, background): “Such constraints might, for example, require selected response probabilities to be set equal to one another across diﬀerent classes, across manifest variables within classes, or equal to ﬁxed constant values, as in Goodman (1974).”
Resource Efficiency and Circular Economy in European SMEs: Investigating the Role of Green Jobs and Skills (2021, background): “…Lazarsfeld and Henry (1968) to express latent attitudinal variables from dichotomous survey items, then they were extended to nominal variables by Goodman (1974a, 1974b), who also developed the maximum likelihood algorithm for estimating latent class models that serves as the basis for many…”
Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis (2013, background): “…(Lazarsfeld, 1950; Lazarsfeld & Henry, 1968), but were later extended to models with continuous (Gibson, 1959; Lazarsfeld & Henry, 1968), polytomous (Goodman, 1974a, 1974b; Haberman, 1979), and ordinal, rank, count and mixed scale (Muthén & Muthén, 1998–2010; Vermunt & Magidson, 2005) variables.”
Latent Class Analysis: An Alternative Perspective on Subgroup Analysis in Prevention and Treatment (2013, background): “LCA (Collins and Lanza 2010; Goodman 1974; Lazarsfeld and Henry 1968) is a mixture model that posits that there is an underlying unobserved categorical variable that divides a population into mutually exclusive and exhaustive latent classes.”
Latent Class Models (2004, unknown): “LC factor models were initially proposed by Goodman (Goodman, 1974b) in the context of confirmatory latent class analysis.”
Identifiability of restricted latent class models with binary responses (2016, background): “Identiﬁability is the prerequisite for most common statistical inferences, especially parameter estimation, and its study dates back to Koopmans (1950) and Koopmans and Reiersøl (1950); see also McHugh (1956); Rothenberg (1971); Goodman (1974); Gabrielsen (1978) for further developments.”