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A Theory Of Inferred Causation
, 1991
"... This paper concerns the empirical basis of causation, and addresses the following issues: 1. the clues that might prompt people to perceive causal relationships in uncontrolled observations. 2. the task of inferring causal models from these clues, and 3. whether the models inferred tell us anything ..."
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Cited by 215 (35 self)
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This paper concerns the empirical basis of causation, and addresses the following issues: 1. the clues that might prompt people to perceive causal relationships in uncontrolled observations. 2. the task of inferring causal models from these clues, and 3. whether the models inferred tell us anything useful about the causal mechanisms that underly the observations. We propose a minimalmodel semantics of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covariations following standard norms of inductive reasoning. We also establish a sound characterization of the conditions under which such a distinction is possible. We provide an effective algorithm for inferred causation and show that, for a large class of data the algorithm can uncover the direction of causal influences as defined above. Finally, we address the issue of nontemporal causation.
On the multivariate asymptotic distribution of sequential chisquare statistics
 Psychometrika
, 1985
"... The multivariate asymptotic distribution of sequential Chisquare test statistics is investigated. It is shown that: (a) when sequential Chisquare statistics are calculated for nested models on the same data, the statistics have an asymptotic intercorrelation which may be expressed in closed form, ..."
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Cited by 35 (1 self)
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The multivariate asymptotic distribution of sequential Chisquare test statistics is investigated. It is shown that: (a) when sequential Chisquare statistics are calculated for nested models on the same data, the statistics have an asymptotic intercorrelation which may be expressed in closed form, and which is, in many cases, quite high; and (b) sequential Chisquare difference tests are asymptotically independent. Some Monte Carlo evidence on the applicability of the theory is provided. Key words: Asymptotic distribution theory, sequential Chisquare tests. 1.
Application of covariance structure modeling in psychology: cause for concern? Psychol
 Bull
, 1990
"... Methods of covariance structure modeling are frequently applied in psychological research. These methods merge the logic of confirmatory factor analysis, multiple regression, and path analysis within a single data analytic framework. Among the many applications are estimation of disattenuated corre ..."
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Cited by 31 (0 self)
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Methods of covariance structure modeling are frequently applied in psychological research. These methods merge the logic of confirmatory factor analysis, multiple regression, and path analysis within a single data analytic framework. Among the many applications are estimation of disattenuated correlation and regression coefficients, evaluation of multitraitmultimethod matrices, and assessment of hypothesized causal structures. Shortcomings of these methods are commonly acknowledged in the mathematical literature and in textbooks. Nevertheless, serious flaws remain in many published applications. For example, it is rarely noted that the fit of a favored model is identical for a potentially large number of equivalent models. A review of the personality and social psychology literature illustrates the nature of this and other problems in reported applications of covariance structure models. A principal goal of experimentation in psychology is to provide a basis for inferring causation. Among the tools used to achieve this goal are the active manipulation and control of independent variables, random assignment to experimental treatments, and appropriate methods of data analysis. Causal infer
The Foundations of Causal Inference
 SUBMITTED TO SOCIOLOGICAL METHODOLOGY.
, 2010
"... This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM) – a natural generalization of ..."
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Cited by 10 (2 self)
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This paper reviews recent advances in the foundations of causal inference and introduces a systematic methodology for defining, estimating and testing causal claims in experimental and observational studies. It is based on nonparametric structural equation models (SEM) – a natural generalization of those used by econometricians and social scientists in the 195060s, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring the effects of potential interventions (also called “causal effects” or “policy evaluation”), as well as direct and indirect effects (also known as “mediation”), in both linear and nonlinear systems. Finally, the paper clarifies the role of propensity score matching in causal analysis, defines the relationships between the structural and
The path analysis controversy: A new statistical approach to strong apWALLER AND MEEHL336 praisal of verisimilitude
 Psychological Methods
, 2002
"... A new approach for using path analysis to appraise the verisimilitude of theories is described. Rather than trying to test a model’s truth (correctness), this method corroborates a class of path diagrams by determining how well they predict intradata relations in comparison with other diagrams. The ..."
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Cited by 9 (1 self)
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A new approach for using path analysis to appraise the verisimilitude of theories is described. Rather than trying to test a model’s truth (correctness), this method corroborates a class of path diagrams by determining how well they predict intradata relations in comparison with other diagrams. The observed correlation matrix is partitioned into disjoint sets. One set is used to estimate the model parameters, and a nonoverlapping set is used to assess the model’s verisimilitude. Computer code was written to generate competing models and to test the conjectured model’s superiority (relative to the generated set) using diagram combinatorics and is available on the Web
Psychological determinants of financial preparedness for retirement
 The Gerontologist
, 2000
"... Economists predict that in the coming decades an unprecedented number of American baby boomers will enter retirement lacking adequate resources. The present investigation was designed to examine the factors that influence individuals ’ financial preparedness for retirement. ..."
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Cited by 9 (1 self)
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Economists predict that in the coming decades an unprecedented number of American baby boomers will enter retirement lacking adequate resources. The present investigation was designed to examine the factors that influence individuals ’ financial preparedness for retirement.
The Adoption of Total Cost of Ownership for Sourcing Decisions – A Structural Equations Analysis
"... A structural equations analysis ..."
EIGHT MYTHS ABOUT CAUSALITY AND STRUCTURAL EQUATION
, 2012
"... Mulaik, Johannes Textor, and other researchers from SEMNET for their comments on and critiques of our paper. Bollen’s work was partially supported by NSF SES 0617276. 1 EIGHT MYTHS ABOUT CAUSALITY AND STRUCTURAL EQUATION MODELS Social scientists ’ interest in causal effects is as old as the social s ..."
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Cited by 2 (0 self)
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Mulaik, Johannes Textor, and other researchers from SEMNET for their comments on and critiques of our paper. Bollen’s work was partially supported by NSF SES 0617276. 1 EIGHT MYTHS ABOUT CAUSALITY AND STRUCTURAL EQUATION MODELS Social scientists ’ interest in causal effects is as old as the social sciences. Attention to the philosophical underpinnings and the methodological challenges of analyzing causality has waxed and waned. Other authors in this volume trace the history of the concept of causality in the social sciences and we leave this task to their skilled hands. But we do note that we are at a time when there is a renaissance, if not a revolution in the methodology of causal inference, and structural equation models play a major role in this renaissance. Our emphasis in this chapter is on causality and structural equation models (SEMs). If nothing else, the pervasiveness of SEMs justifies such a focus. SEM applications are published in numerous substantive journals. Methodological developments on SEMs regularly appear in journals such as Sociological Methods & Research, Psychometrika, Sociological Methodology, Multivariate Behavioral Research, Psychological Methods,
On the Use of Structural Equation Modeling
"... Structural equation modeling (SEM) is a multivariate technique suited for testing proposed relations between variables. In this article, the authors discuss the potential for SEM as a tool to advance health communication research both statistically and conceptually. Specifically, the authors discuss ..."
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Structural equation modeling (SEM) is a multivariate technique suited for testing proposed relations between variables. In this article, the authors discuss the potential for SEM as a tool to advance health communication research both statistically and conceptually. Specifically, the authors discuss the advantages that latent variable modeling in SEM affords researchers by extracting measurement error. In addition, they argue that SEM is useful in understanding communication as a complex set of relations between variables. Moreover, the authors articulate the possibility for examining communication as an agent, mediator, and an outcome. Finally, they reviewtheapplicationofSEMtorecursivemodels,interactions,andconfirmatoryfactoranalysis. Health communication researchers need an arsenal of theoretical, methodological, and data analytic skills to see a research project through from start to finish. While there exist an array of resources for theory and methodology (Crano &