By John C. Loehlin, A. Alexander Beaujean
Latent Variable types: An advent to issue, course, and Structural Equation Analysis introduces latent variable types by using course diagrams to provide an explanation for the relationships within the versions. This technique is helping much less mathematically-inclined readers to understand the underlying kinfolk between course research, issue research, and structural equation modeling, and to establish and perform such analyses. This revised and accelerated 5th version back includes key chapters on direction research, structural equation versions, and exploratory issue research. additionally, it includes new fabric on composite reliability, versions with specific information, the minimal ordinary partial process, bi-factor versions, and speaking approximately latent variable models.
The casual writing kind and the varied illustrative examples make the ebook available to readers of various backgrounds. Notes on the finish of every chapter expand the dialogue and supply extra technical element and references. in addition, so much chapters include a longer instance during which the authors paintings via one of many chapter’s examples intimately to assist readers in carrying out comparable analyses with their very own information. The booklet and accompanying site offer the entire information for the book’s examples in addition to syntax from latent variable courses so readers can mirror the analyses. The publication can be utilized with any of various computing device courses, yet designated awareness is paid to LISREL and R.
An vital source for complicated scholars and researchers in several disciplines within the behavioral sciences, schooling, enterprise, and health and wellbeing sciences, Latent Variable Models is a pragmatic and readable reference for these looking to comprehend or behavior an research utilizing latent variables.
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Additional resources for Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis
E is hypothesized as influencing A and B, and F as influencing C and D. In the path diagram there are five unknowns, the paths a, b, c, and d, and the correlation e between the two latent variables. There are six equations, shown to the right of the diagram, based on the six correlations between pairs of observed variables. 60 for rAB ). Because there are more equations than unknowns, one might expect that a single exact solution would not be available, and indeed this is the case. An iterative least squares solution, carried out in a way discussed in the next chapter, yielded the values shown to the far right of Fig.
The sociologist O. D. Duncan (1966) is usually credited with rediscovering path analysis for social scientists; Werts and Linn (1970) wrote a paper calling psychologists’ attention to the method. For an annotated bibliography on the history of path analysis, see Wolfle (2003). Factor analysis. Wolfle (1940) wrote one of the first histories of the field. Maxwell (1977) has a brief account of some of the early history, which Mulaik (1986) updates. Carroll (1993) provides a history as it relates to the study of cognitive ability.
The rule for expressing the value of a compound path between two variables in terms of concrete path coefficients (stated for a vertically oriented path diagram) is: The value of a compound path between two variables is equal to the product of the raw-score path coefficients and the topmost variance or covariance in the path. The tracing of compound paths according to the tracing rules, and adding compound paths together to yield the overall covariance, proceed in just the same way with raw-score as with standardized coefficients.
Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis by John C. Loehlin, A. Alexander Beaujean