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The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations
 Journal of Personality and Social Psychology
, 1986
"... In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptua ..."
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Cited by 1245 (1 self)
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In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators. The purpose of this analysis is to distinguish between the properties of moderator and mediator variables in such a way as to clarify the different ways in which conceptual variables may account for differences in peoples ' behavior. Specifically, we differentiate between two oftenconfused functions of third variables: (a) the moderator function of third variables, which
Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance
 Psychological Bulletin
, 1989
"... Addresses issues related to partial measurement in variance using a tutorial approach based on the LISREL confirmatory factor analytic model. Specifically, we demonstrate procedures for (a) using "sensitivity analyses " to establish stable and substantively wellfitting baseline models, (b) determin ..."
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Cited by 32 (2 self)
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Addresses issues related to partial measurement in variance using a tutorial approach based on the LISREL confirmatory factor analytic model. Specifically, we demonstrate procedures for (a) using "sensitivity analyses " to establish stable and substantively wellfitting baseline models, (b) determining partially invariant measurement parameters, and (c) testing for the invariance of factor covariance and mean structures, given partial measurement invariance. We also show, explicitly, the transformation of parameters from an all^fto an ally model specification, for purposes of testing mean structures. These procedures are illustrated with multidimensional selfconcept data from low ( « = 248) and high (n = 582) academically tracked high school adolescents. An important assumption in testing for mean differences is that the measurement (Drasgow & Kanfer, 1985; Labouvie,
Principles and practice in reporting structural equation analyses
 Psychological Methods
, 2002
"... Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of ident ..."
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Cited by 29 (0 self)
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Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of identifiability. Nonnormality and missing data problems should also be addressed. A complete set of parameters and their standard errors is desirable, and it will often be convenient to supply the correlation matrix and discrepancies, as well as goodnessoffit indices, so that readers can exercise independent critical judgment. A survey of fairly representative studies compares recent practice with the principles of reporting recommended here. Structural equation modeling (SEM), also known as path analysis with latent variables, is now a regularly used method for representing dependency (arguably “causal”) relations in multivariate data in the behavioral and social sciences. Following the seminal work of Jöreskog (1973), a number of models for linear structural relations have been developed
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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Cited by 7 (0 self)
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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.
Identification and likelihood inference for recursive linear models with correlated errors
, 2007
"... In recursive linear models, the multivariate normal joint distribution of all variables exhibits a dependence structure induced by recursive systems of linear structural equations. Such models appear in particular in seemingly unrelated regressions, structural equation modelling, simultaneous equati ..."
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Cited by 2 (0 self)
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In recursive linear models, the multivariate normal joint distribution of all variables exhibits a dependence structure induced by recursive systems of linear structural equations. Such models appear in particular in seemingly unrelated regressions, structural equation modelling, simultaneous equation systems, and in Gaussian graphical modelling. We show that recursive linear models that are ‘bowfree’ are wellbehaved statistical models, namely, they are everywhere identifiable and form curved exponential families. Here, ‘bowfree ’ refers to models satisfying the condition that if a variable x occurs in the structural equation for y, then the errors for x and y are uncorrelated. For the computation of maximum likelihood estimates in ‘bowfree ’ recursive linear models we introduce the Residual Iterative Conditional Fitting (RICF) algorithm. Compared to existing algorithms RICF is easily implemented requiring only least squares computations, has clear convergence properties, and finds parameter estimates in closed form whenever possible. 1
Exploring the Success Factors of State Website Functionality: An Empirical Investigation
"... Analyzing information technology (IT) success factors is not a recent academic and practical interest. The last two decades have been rich in studies exploring the phenomenon of IT success/failure in both private and public organizations. However, many of the previous studies hypothesized only direc ..."
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Analyzing information technology (IT) success factors is not a recent academic and practical interest. The last two decades have been rich in studies exploring the phenomenon of IT success/failure in both private and public organizations. However, many of the previous studies hypothesized only direct effects and did not allow more complex relationships between different categories of factors. Based on Fountain’s technology enactment theory, this study develops a model to explore the influence of organizational, institutional, and contextual factors on the functionality of egovernment state websites in the US. Data about all 50 states were gathered from available published sources and the theoretical model was evaluated using partial least squares (PLS). Organizational factors such as size of the IT organization, budget structure, IT training, inhouse development, outsourcing, and marketing strategy were found to have a significant direct effect on state website functionality. The availability of resources for state government agencies represented by the overall size of the state economy also has a significant direct influence. Institutional arrangements, political orientation, and demographic factors have an indirect effect on state website functionality.
Generalization of the Tetrad Representation Theorem
 Preliminary Papers of the Fifth International Workshop on Artificial Intelligence and
, 1993
"... The tetrad representation theorem, due to Spirtes, Glymour, and Scheines (1993), gives a graphical condition necessary and sufficient for the vanishing of tetrad differences in a linear correlation structure. This note simplifies their proof and generalizes the theorem. This generalization can stren ..."
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The tetrad representation theorem, due to Spirtes, Glymour, and Scheines (1993), gives a graphical condition necessary and sufficient for the vanishing of tetrad differences in a linear correlation structure. This note simplifies their proof and generalizes the theorem. This generalization can strengthen procedures used to search for structural equation models for large data sets.  1  1 Introduction In a linear "structural equation" model, it is assumed that there is a set of variables V , and for each variable X i in V , there is a unique associated error term E i with nonzero variance. For each variable X i in V a linear equation relates X i to a subset of V (excluding X i ) and its error term E i ; the variables that do not appear in the equation for X i are assumed to have coefficients fixed at zero. We assume that the error terms are jointly independent (although in what follows, this assumption can easily be relaxed.) Associated with each such set of equations is a direct...
ORGANIZATION SCIENCE Vol. 1. No.1. Printed in U.S.A. AT THE CROSSROADS: A MULTIPLELEVEL EXPLANATION OF INDIVIDUAL ATTAINMENT*
"... Individual attainment within organizational careers, or career mobility, has been explained by individual attributes and by demographic processes. These seemingly unrelated views can be reconciled by suggesting that employees develop a shared perception of their organization's career hierarchy, and ..."
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Individual attainment within organizational careers, or career mobility, has been explained by individual attributes and by demographic processes. These seemingly unrelated views can be reconciled by suggesting that employees develop a shared perception of their organization's career hierarchy, and that this shared perception produces systematic managerial selection preferences that influence individual attainment. A study that examines the first part of this process is presented. The results, based on questionnaire data from an electric utility, suggest that managers do develop a shared perception of their organization's career hierarchy. However, managers ' perceptions are not unanimous, and the analysis examines two explanations for perceptual variation. The implications of the proposed connection for further development of a multiplelevel explanation of individual attainment are discussed.