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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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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
Bayesian Estimation and Testing of Structural Equation Models
 Psychometrika
, 1999
"... The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameter ..."
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Cited by 37 (10 self)
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The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples. If the prior distribution over the parameters is uninformative, the posterior is proportional to the likelihood, and asymptotically the inferences based on the Gibbs sample are the same as those based on the maximum likelihood solution, e.g., output from LISREL or EQS. In small samples, however, the likelihood surface is not Gaussian and in some cases contains local maxima. Nevertheless, the Gibbs sample comes from the correct posterior distribution over the parameters regardless of the sample size and the shape of the likelihood surface. With an informative prior distribution over the parameters, the posterior can be used to make inferences about the parameters of underidentified models, as we illustrate on a simple errorsinvariables model.
Can We Ever Escape From Data Overload? A Cognitive Systems Diagnosis
 Cognition, Technology and Work
, 2002
"... gence in circumscribed, cooperative roles to aid human observers in organizing, selecting, managing, and interpreting data. CHARACTERIZATIONS OF DATA OVERLOAD Data overload is the problem of our age  generic yet surprisingly resistant to different avenues of attack. In order to make progress on in ..."
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Cited by 28 (3 self)
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gence in circumscribed, cooperative roles to aid human observers in organizing, selecting, managing, and interpreting data. CHARACTERIZATIONS OF DATA OVERLOAD Data overload is the problem of our age  generic yet surprisingly resistant to different avenues of attack. In order to make progress on innovating solutions to data overload in a particular setting, we need to identify the root issues that make data overload a challenging problem everywhere and to understand why proposed solutions have broken down or produced limited success in operational settings. There are three basic ways that the data overload problem has been characterized (Woods, Patterson, and Roth, 1998): 1. As a clutter problem where there is too much data: therefore, we can solve data overload by reducing the number of data units that are displayed. This has not proven to be a fruitful direction in solving data overload because it misrepresents the design problem, is based on erroneous assumptions a
What Is a Theory of Mental Representation
 Mind
, 1992
"... you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact inform ..."
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Cited by 10 (2 self)
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you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at
Latent variables, causal models and overidentifying constraints
 Journal of Econometrics
, 1988
"... When is a statistical dependency between two variables best explained by the supposition that one of these variables causes the other, as opposed to the supposition that there is a (possibly unmeasured) common cause acting on both variables? In this paper, we describe an approach towards model speci ..."
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When is a statistical dependency between two variables best explained by the supposition that one of these variables causes the other, as opposed to the supposition that there is a (possibly unmeasured) common cause acting on both variables? In this paper, we describe an approach towards model specification developed more fully in our book Discovering Cuud Structure, and illustrate its application to the aforementioned question. Briefly, the approach is to determine constraints satisfied by the variancecovariance matrix of a sample, and then to conduct a quasiautomated search for the causal specifications that will best explain those constraints, 1.
Inductive Process Modeling
"... Abstract. In this paper, we pose a novel research problem for machine learning that involves constructing a process model from continuous data. We claim that casting learned knowledge in terms of processes with associated equations is desirable for scientific and engineering domains, where such nota ..."
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Abstract. In this paper, we pose a novel research problem for machine learning that involves constructing a process model from continuous data. We claim that casting learned knowledge in terms of processes with associated equations is desirable for scientific and engineering domains, where such notations are commonly used. We also argue that existing induction methods are not well suited to this task, although some techniques hold partial solutions. In response, we describe an approach to learning process models from timeseries data and illustrate its behavior in three domains. In closing, we describe open issues in process model induction and encourage other researchers to tackle this important problem.
Considering the major arguments against random assignment: An analysis of the intellectual culture surrounding evaluation in American schools of education
 In R. Boruch & F. Mosterller (Eds.), Education
, 2001
"... Paper presented at the Harvard Faculty Seminar on Experiments in Education. ..."
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Paper presented at the Harvard Faculty Seminar on Experiments in Education.
A Study of Causal Discovery With Weak Links and Small Samples
, 1997
"... Weak causal relationships and small sample size pose two significant difficulties to the automatic discovery of causal models from observational data. This paper examines the influence of weak causal links and varying sample sizes on the discovery of causal models. The experimental results illustrat ..."
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Cited by 5 (1 self)
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Weak causal relationships and small sample size pose two significant difficulties to the automatic discovery of causal models from observational data. This paper examines the influence of weak causal links and varying sample sizes on the discovery of causal models. The experimental results illustrate the effect of larger sample sizes for discovering causal models reliably and the relevance of the strength of causal links and the complexity of the original causal model. We present indicative evidence of the superior robustness of MML (Minimum Message Length) methods to standard significance tests in the recovery of causal links. The comparative results show that the MMLCI (the MML Causal Inducer) causal discovery system finds better models than TETRAD II given small samples from linear causal models. The experimental results also reveal that MMLCI finds weak links with smaller sample sizes than can TETRAD II.
Conducting tetrad tests of model fit and contrasts of tetradnested models: a new SAS macro
 Struct. Equ. Model
"... This article describes a SAS macro to assess model fit of structural equation models by employing a test of the modelimplied vanishing tetrads. Use of this test has been limited in the past, in part due to the lack of software that fully automates the test in a userfriendly way. The current SAS m ..."
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This article describes a SAS macro to assess model fit of structural equation models by employing a test of the modelimplied vanishing tetrads. Use of this test has been limited in the past, in part due to the lack of software that fully automates the test in a userfriendly way. The current SAS macro provides a straightforward method for researchers touse thevanishing tetrads impliedbymodels toassess the fitof (a) structural equation models containing continuous endogenous variables; (b) structural equation models containing continuous endogenous variables nested for vanishing tetrads; and (c) structural equation models containing dichotomous, ordinal, or censored endogenous variables. Besides providing an alternative assessment of model fit to the usual likelihoodratio test (LRT), thevanishing tetrads testoccasionallyprovidesastatistical assessment of competing models nested for vanishing tetrads but not nested for the LRT. The macro permits formal comparisons between tetradnested structural equation models containing dichotomous, ordinal, or censored endogenous variables. A key focus of structural equation modeling (SEM) is the assessment of model fit. The usual test applied for assessing model fit is the likelihoodratio chisquare test
Automated search for causal relations: Theory and practice
 Heuristics, Probability and Causality: A Tribute to Judea Pearl
, 2010
"... The rapid spread of interest in the last two decades in principled methods of search or estimation of causal relations has been driven in part by technological developments, especially the changing nature of modern data collection and storage techniques, and the increases in the speed and storage ca ..."
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The rapid spread of interest in the last two decades in principled methods of search or estimation of causal relations has been driven in part by technological developments, especially the changing nature of modern data collection and storage techniques, and the increases in the speed and storage capacities of computers. Statistics books from 30 years