• Summary
  • Contents
  • Subject index

With the availability of software programs such as LISREL, EQS, and AMOS modeling techniques have become a popular tool for formalized presentation of the hypothesized relationships underlying correlational research and for testing the plausibility of hypothesizing for a particular data set. The popularity of these techniques, however, has often led to misunderstandings of them, particularly by students being exposed to them for the first time. Through the use of careful narrative explanation, Basics of Structural Equation Modeling describes the logic underlying structural equation modeling (SEM) approaches, describes how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores the various methodologies for analyzing structural equation data.

Effects of Random and Nonrandom Error on Path Models
Effects of random and nonrandom error on path models

As has been mentioned throughout this book, the term path analysis refers only to a restricted subset of path models. In this chapter, extensions from the subset of models that can be called path analysis ...

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