Using Lisrel For Structural Equation Modeling
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Using LISREL for Structural Equation Modeling
A highly readable introduction, Using LISREL for Structural Equation Modeling is for researchers and graduate students in the social sciences who want or need to use structural equation modeling techniques to answer substantive research questions. Author E. Kevin Kelloway provides an overview of structural equation modeling including the theory and logic of structural equation models (SEMs), assessing the "fit" of SEMs to the data, and implementation of SEMs in the LISREL environment. Specific applications of SEMs are considered, including confirmatory factor analysis, observed variable path analysis, and latent variable path analysis. A sample application including the source code, printout, and results section is presented for each type of analysis. Tricks of the trade for structural equation modeling are presented, including the use of single-indicator latent variable and reducing the cognitive complexity of models.
Structural Equation Modeling With Lisrel, Prelis, and Simplis
This book illustrates the ease with which various features of LISREL 8 and PRELIS 2 can be implemented in addressing research questions that lend themselves to SEM. Its purpose is threefold: (a) to present a nonmathmatical introduction to basic concepts associated with SEM, (b) to demonstrate basic applications of SEM using both the DOS and Windows versions of LISREL 8, as well as both the LISREL and SIMPLIS lexicons, and (c) to highlight particular features of the LISREL 8 and PRELIS 2 progams that address important caveats related to SEM analyses. This book is intended neither as a text on the topic of SEM, nor as a comprehensive review of the many statistical funcitons available in the LISREL 8 and PRELIS 2 programs. Rather, the intent is to provide a practical guide to SEM using the LISREL approach. As such, the reader is "walked through" a diversity of SEM applications that include both factor analytic and full latent variable models, as well as a variety of data management procedures.
Basic Principles of Structural Equation Modeling
Author: Ralph O. Mueller
language: en
Publisher: Springer Science & Business Media
Release Date: 1999-06-04
During the last two decades, structural equation modeling (SEM) has emerged as a powerful multivariate data analysis tool in social science research settings, especially in the fields of sociology, psychology, and education. Although its roots can be traced back to the first half of this century, when Spearman (1904) developed factor analysis and Wright (1934) introduced path analysis, it was not until the 1970s that the works by Karl Joreskog and his associates (e. g. , Joreskog, 1977; Joreskog and Van Thillo, 1973) began to make general SEM techniques accessible to the social and behavioral science research communities. Today, with the development and increasing avail ability of SEM computer programs, SEM has become a well-established and respected data analysis method, incorporating many of the traditional analysis techniques as special cases. State-of-the-art SEM software packages such as LISREL (Joreskog and Sorbom, 1993a,b) and EQS (Bentler, 1993; Bentler and Wu, 1993) handle a variety of ordinary least squares regression designs as well as complex structural equation models involving variables with arbitrary distributions. Unfortunately, many students and researchers hesitate to use SEM methods, perhaps due to the somewhat complex underlying statistical repre sentation and theory. In my opinion, social science students and researchers can benefit greatly from acquiring knowledge and skills in SEM since the methods-applied appropriately-can provide a bridge between the theo retical and empirical aspects of behavioral research.