Journal ArticleDOI
Effect Analysis in Structural Equation Models Extensions and Simplified Methods of Computation
TLDR
Efficient matrix methods of computation for effect decomposition is discussed and these methods are extended to models with unstandardized variables and to nonrecursive models.Abstract:
One of the great virtues of structural equation models is that they permit the quantification of causal and noncausal sources of statistical relationship. The present article discusses efficient ma...read more
Citations
More filters
Journal ArticleDOI
A comparison of methods to test mediation and other intervening variable effects.
TL;DR: A Monte Carlo study compared 14 methods to test the statistical significance of the intervening variable effect and found two methods based on the distribution of the product and 2 difference-in-coefficients methods have the most accurate Type I error rates and greatest statistical power.
Journal ArticleDOI
Structural Equations with Latent Variables.
Journal ArticleDOI
Linking Empowering Leadership and Employee Creativity: The Influence of Psychological Empowerment, Intrinsic Motivation, and Creative Process Engagement
Xiaomeng Zhang,Kathryn M. Bartol +1 more
TL;DR: In this article, a theoretical model linking empowering leadership with creativity via several intervening variables was built and tested, and they found that, as anticipated, empowering leadership positively affected psychological empowerment, which in turn influenced both intrinsic motivation and creative process engagement.
Journal ArticleDOI
12 Structural Equation Modeling in Management Research: A Guide for Improved Analysis
TL;DR: This chapter first provides a brief introduction to SEM, and discusses four issues related to the measurement component of such models, including how indicators are developed, types of relationships between indicators and latent variables, approaches for multidimensional constructs, and analyses needed when data from multiple time points or multiple groups are examined.
Book ChapterDOI
Eight myths about causality and structural equation models
Kenneth A. Bollen,Judea Pearl +1 more
TL;DR: In this paper, structural equation models (SEMs) and their role in causal analysis have been discussed and a variety of misunderstandings and myths about the nature of SEMs have emerged, and their repetition has led some to believe they are true.
References
More filters
Book
The American occupational structure
Peter M. Blau,Otis Dudley Duncan +1 more
TL;DR: The American Occupational Structure is renowned for its pioneering methods of statistical analysis as well as for its far-reaching conclusions about social stratification and occupational mobility in the United States.
Journal ArticleDOI
The Decomposition of Effects in Path Analysis
Duane F. Alwin,Robert M. Hauser +1 more
TL;DR: In this article, a general method for decomposing effects into their components by the systematic application of ordinary least squares regression is described, which involves successive computation of reduced-form equations, beginning with an equation containing only exogenous variables, and adding intervening variables in sequence from cause to effect.
Journal ArticleDOI
Path analysis: Sociological examples.
TL;DR: Path analysis focuses on the problem of interpretation and does not purpot to be a method for discovering causes as mentioned in this paper, but it may, nevertheless, be invaluable in rendering interpretations explicit, self-consistent, and susceptible to rejection by subsequent research.
Related Papers (5)
The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.
Reuben M. Baron,David A. Kenny +1 more