By Barbara M. Byrne
Modeled after Barbara Byrne’s different best-selling structural equation modeling (SEM) books, this useful consultant studies the elemental techniques and functions of SEM utilizing Mplus types five & 6. the writer studies SEM purposes in line with genuine info taken from her personal learn. utilizing non-mathematical language, it's written for the beginner SEM person. With every one software bankruptcy, the writer "walks" the reader via all steps occupied with checking out the SEM version including:
- an clarification of the problems addressed
- illustrated and annotated trying out of the hypothesized and submit hoc models
- explanation and interpretation of all Mplus enter and output files
- important caveats pertinent to the SEM software less than study
- a description of the knowledge and reference upon which the version used to be based
- the corresponding information and syntax records on hand at http://www.psypress.com/sem-with-mplus/datasets .
The first chapters introduce the basic innovations of SEM and critical fundamentals of the Mplus software. the rest chapters concentrate on SEM purposes and comprise a number of SEM types offered in the context of 3 sections: Single-group analyses, Multiple-group analyses, and different vital themes, the latter of together with the multitrait-multimethod, latent development curve, and multilevel models.
Intended for researchers, practitioners, and scholars who use SEM and Mplus, this e-book is a perfect source for graduate point classes on SEM taught in psychology, schooling, enterprise, and different social and future health sciences and/or as a complement for classes on utilized facts, multivariate facts, intermediate or complicated information, and/or study layout. acceptable for people with constrained publicity to SEM or Mplus, a prerequisite of uncomplicated information via regression research is recommended.
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Additional info for Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming
Included here is a description of each goodness-of-fit statistic reported by Mplus, together with a complete breakdown of its computation. In Chapter 4, we examine another first-order CFA model, albeit this time, as it relates to the hypothesized structure of a well-established measuring instrument. In contrast to the model tested in Chapter 3, the one tested here exhibits strong evidence of model misspecification. As such, I subsequently walk you through various stages of the post hoc model-fitting process in an attempt to establish a better-fitting model that is not only statistically viable and substantively meaningful, but also addresses the issue of scientific parsimony.
If goodness-of-fit is adequate, the model argues for the plausibility of postulated relations among variables; if it is inadequate, the tenability of such relations is rejected. Several aspects of SEM set it apart from the older generation of multivariate procedures. First, as noted above, it takes a confirmatory, rather than exploratory, approach to the data analysis (although aspects of the latter can be addressed). Furthermore, by demanding that the pattern of intervariable relations be specified a priori, SEM lends itself well to the analysis of data for inferential purposes.
The one-way arrows leading from the ERROR callouts to each of the observed variables indicate the impact of measurement error (random and unique) on the observed variables. Likewise, the single-headed arrow leading from the RESIDUAL callout to the endogenous factor represents the impact of error in the prediction of PEER RELATIONS. , structural) equations. Because (a) regression equations represent the influence of one or more variables on another, and (b) this influence, conventionally in SEM, is symbolized by a single-headed arrow pointing from the variable of influence to the variable of interest, we can think of each equation as summarizing the impact of all relevant variables in the model (observed and unobserved) on one specific variable (observed or unobserved).