An introduction to structural equation models.

By D. Francis. 1988

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Citations in the corpus

Causal inference by using invariant prediction: identification and confidence intervals (2015, methodology): “We consider linear Gaussian structural equation models (SEMs) [e.g. Wright, 1921, Duncan, 1975].”
Fairness in Decision-Making - The Causal Explanation Formula (2018, methodology): “The path analysis method gained momentum in the social sciences during 1960’s, becoming extremely popular in the form of the mediation formula in which the total effect of X on Y is decomposed into direct and indirect components (Baron and Kenny 1986; Bollen 1989; Duncan 1975; Fox 1980).”
Causal inference in statistics: An overview (2009, methodology): “It is based on the Structural Causal Model (SCM) developed in (Pearl, 1995a, 2000a) which combines features of the structural equation models (SEM) used in economics and social science (Goldberger, 1973; Duncan, 1975), the potentialoutcome framework of Neyman (1923) and Rubin (1974), and the graphic”
lavaan.survey: An R Package for Complex Survey Analysis of Structural Equation Models (2014, background): “The main development of SEM has been in social science ﬁelds such as psychology (Ullman and Bentler 2003), education (Kaplan 2008), and sociology (Duncan 1975; Saris and Stronkhorst 1984), while more recently SEM is ﬁnding applications in other ﬁelds such as ecology and biology (Grace 2006) and…”
Introduction to Structural Equation Modeling Using IBM SPSS Statistics and EQS (2012, background): “Wright was a biometrician and it is amazing that his work was more or less unknown to scientists outside this area, until it was taken up by social researchers in the 1960s (Blalock, 1961, 1971; Duncan, 1975).”
Causes of Effects and Effects of Causes (2015, methodology): “Galles and Pearl (1998) and Halpern (1998) have given a complete axiomatization of structural counterfactuals, embracing both recursive and nonrecursive models (see also Pearl 2009:chap.”
EIGHT MYTHS ABOUT CAUSALITY AND STRUCTURAL EQUATION MODELS (2013, unknown): “Much research concentrated on simultaneous equations without latent variables (e.g., Duncan 1975; Fox 1980; Baron and Kenny 1986).”
Statistical Techniques Employed in AERJ and JCP Articles from 1988 to 1997: A Methodological Review (2001, methodology): “tional Research Journal (AERJ) from 1969 to 1978, Willson (1980) found that ANOVA methods made up only 34% of the total techniques used but were included in 56% of the articles surveyed. Goodwin and Goodwin (1985) examined several articles published in AERJ from 1979 to 1983 and found a decreased us”
3. The Foundations of Causal Inference (2010, methodology): “We start with recursive linear models,6 in the style of Wright (1923), Blalock (1964), and Duncan (1975) and, 6 By “recursive” we mean systems free of feedback loops.”
Causation, prediction, and search (1993, background): “The same moral is if anything more vivid in another linear model built from the same empirical study by Duncan, Featherman and Duncan (1972). They developed the following model of socioeconomic background and occupational achievement, where FE signifies father's education, FO father's occupational s”
On the nature and direction of relationships between constructs and measures. (2000, background): “Moreover, a particular pattem of covariances may be consistent with numerous causal structures (Bollen & Ting, 1993; Duncan, 1975; Lee & Hershberger, 1990; MacCallum, Wegener, Uchino, & Fabrigar, 1993).”
The shadow economy in three Mediterranean countries: France, Spain and Greece. A MIMIC approach (2007, methodology): “In this section we explain the theoretical model applied to estimate the underground economy, bearing in mind the observation of Duncan (1975), who points out: “The meaning of the latent variable depends completely on how correctly, precisely and comprehensively the causal and indicator variables co”
Multivariate Statistical Analysis (2011, unknown): “[ 17 ] Duncan, O.D. (1975). Introduction to structural equation models.”