By Randall E. Schumacker, Richard G. Lomax
This best-seller introduces readers to structural equation modeling (SEM) to allow them to behavior their very own research and critique comparable learn. famous for its obtainable, utilized procedure, chapters conceal simple innovations and practices and laptop input/output from the unfastened pupil model of Lisrel 8.8 within the examples. every one bankruptcy positive factors an overview, key techniques, a precis, a number of examples from numerous disciplines, tables, and figures, together with course diagrams, to help with conceptual understanding.
The ebook first experiences the fundamentals of SEM, information entry/editing, and correlation. subsequent the authors spotlight the fundamental steps of SEM: version specification, identity, estimation, checking out, and amendment, via concerns on the topic of version healthy and gear and pattern measurement. Chapters 6 via 10 keep on with the stairs of modeling utilizing regression, direction, confirmatory issue, and structural equation versions. subsequent readers discover a bankruptcy on reporting SEM examine together with a record to steer decision-making, via one on version validation. Chapters thirteen via sixteen offer examples of assorted SEM version purposes. The booklet concludes with the matrix method of SEM utilizing examples from prior chapters.
Highlights of the hot version include:
* an internet site with uncooked information units for the book's examples and workouts to allow them to be used with any SEM application, the entire book's workouts, hotlinks to comparable web content, and solutions to all the routines for Instructor’s only
* New troubleshooting find out how to handle the main often encountered problems
* Examples now reference the unfastened pupil model of Lisrel 8.8
* improved assurance of complicated types with extra on multiple-group, multi-level, & combination modeling (Chs. thirteen & 15), second-order and dynamic issue types (Ch. 14), and Monte Carlo equipment (Ch. 16)
* elevated assurance of pattern dimension and tool (Ch. five) and reporting examine (Ch. 11)
* New magazine article references support readers greater comprehend released learn (Chs. thirteen – 17) and 25 % new routines with solutions to part within the booklet for scholar review.
Designed for introductory graduate point classes in structural equation modeling or issue research taught in psychology, schooling, enterprise, and the social and healthcare sciences, this sensible publication additionally appeals to researchers in those disciplines. An knowing of correlation is thought. To entry the web site stopover at the e-book web page or the Textbook source web page at http://www.psypress.com/textbook-resources/ for extra info.
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Extra resources for A beginner's guide to structural equation modeling
SEM software programs estimate coefficients based on the user-specified theoretical model, or implied model, but also must work with the saturated and independence models. A saturated model is the model with all parameters indicated, while the independence model is the null model or model with no parameters estimated. A saturated model with p observed variables has p (p + 3)/2 free parameters [Note: Number of independent elements in the symmetric covariance matrix = p(p + 1)/2. Number of means = p, so total number of independent elements = p (p + 1)/2 + p = p (p + 3)/2].
The Import Data option permits inputting raw data files or SPSS saved files. dat, is in the PRELIS folder (PR2EX). When selecting this file, you will need to know the number of variables in the file. sav, is in the SPSS folder (SPSSEX). Once you open this file, a PRELIS system file is created. indb 16 3/22/10 3:25:13 PM Data Entry and Data Editing Issues 17 Once the PRELIS system file becomes active, then it needs to be saved for future use. 00 will appear. ). psf) activates a pull-down menu that permits data editing features, data transformations, statistical analysis of data, graphical display of data, multilevel modeling, and many other related features.
D. ), Structural equation models in the social sciences (pp. 85–112). New York: Seminar. Keesling, J. W. (1972). Maximum likelihood approaches to causal flow analysis. Unpublished doctoral dissertation. Chicago: University of Chicago. Lawley, D. N. (1958). Estimation in factor analysis under various initial assumptions. British Journal of Statistical Psychology, 11, 1–12. Parkerson, J. , Lomax, R. , Schiller, D. , & Walberg, H. J. (1984). Exploring causal models of educational achievement. Journal of Educational Psychology, 76, 638–646.