By Randall E. Schumacker, Richard G. Lomax

ISBN-10: 1138811904

ISBN-13: 9781138811904

Noted for its crystal transparent factors, this ebook is taken into account the main complete introductory textual content to structural equation modeling (SEM). famous for its thorough assessment of uncomplicated suggestions and a large choice of versions, this ebook higher prepares readers to use SEM to various examine questions. Programming info and using algebra are stored to a minimal to aid readers simply grab the innovations to allow them to behavior their very own research and critique comparable learn. that includes a better emphasis on statistical strength and version validation than different texts, each one bankruptcy good points key suggestions, examples from a variety of disciplines, tables and figures, a precis, and routines.

Highlights of the greatly revised 4th variation include:

-Uses assorted SEM software program (not simply Lisrel) together with Amos, EQS, LISREL, Mplus, and R to illustrate applications.

-Detailed creation to the statistical tools regarding SEM together with correlation, regression, and issue research to maximise realizing (Chs. 1 – 6).

-The five step method of modeling information (specification, id, estimation, trying out, and amendment) is now lined in additional element and sooner than the modeling chapters to supply a extra coherent view of ways to create types and interpret effects (ch. 7).

-More dialogue of speculation trying out, strength, sampling, influence sizes, and version healthy, serious subject matters for starting modelers (ch. 7).

- every one version bankruptcy now specializes in one strategy to increase knowing by means of offering extra description, assumptions, and interpretation of effects, and an workout relating to research and output (Chs. eight -15).

-The use of SPSS AMOS diagrams to explain the theoretical models.

-The key positive factors of every of the software program programs (Ch. 1).

-Guidelines for reporting SEM learn (Ch. 16). which gives entry to facts units that may be used with any application, hyperlinks to different SEM examples, similar readings, and magazine articles, and extra.

Reorganized, the hot variation starts off with a extra particular advent to SEM together with a number of the software program applications on hand, via chapters on facts access and modifying, and correlation that's serious to knowing how lacking facts, non-normality, size, and restrict of diversity in rankings impacts SEM research. a number of regression, course, and issue versions are then reviewed and exploratory and confirmatory issue research is brought. those chapters reveal how saw variables percentage variance in defining a latent variables and introduce how size errors will be faraway from saw variables. bankruptcy 7 info the five SEM modeling steps together with version specification, identity, estimation, trying out, and amendment in addition to a dialogue of speculation checking out and the similar problems with strength, and pattern and impact sizes.Chapters eight to fifteen supply finished introductions to varied SEM types together with a number of crew, Second-Order CFA, Dynamic issue, Multiple-Indicator Multiple-Cause, combined Variable and combination, Multi-Level, Latent progress, and SEM interplay types. all of the five SEM modeling steps is defined for every version besides an software. bankruptcy routines offer perform with and improve figuring out of the research of every version. The ebook concludes with a assessment of SEM directions for reporting learn.

Designed for introductory graduate classes in structural equation modeling, issue research, complex, multivariate, or utilized data, quantitative thoughts, or records II taught in psychology, schooling, enterprise, and the social and healthcare sciences, this useful ebook additionally appeals to researchers in those disciplines. necessities contain an creation to intermediate information that covers correlation and regression principles.

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Additional info for A Beginner’s Guide to Structural Equation Modeling

Example text

If skewed non-normal data are present for a continuous variable, then consider a linear transformation or use of an asymptotic variance–covariance matrix. Structural equation modeling (SEM) was introduced initially as a way of analyzing a covariance or correlation matrix. Typically, one would read this matrix into an SEM program and estimate the model parameter estimates using maximum likelihood estimation. If raw data were available without missing values, one would first estimate an asymptotic covariance matrix to obtain robust estimates of standard errors and chi-squares.

For interval data, it is wise to have an arbitrary zero point whenever possible. This aids our interpretation in many statistical applications. Our use of different variables requires us to be aware of their properties of scale and what mathematical operations are possible and meaningful, especially in SEM, where variance–covariance matrices are used. Different types of correlation coefficients will produce different types of variance–covariance matrices (Pearson, polychoric, polyserial) among variables depending upon the level of measurement, and this creates a unique problem in SEM.

Basically, researchers should know their data characteristics. Data screening is a very important first step in structural equation modeling. In the next chapter, we provide specific examples to illustrate the importance of the topics covered in this chapter. 6 lists the data editing issues along with suggestions for correcting any of these issues in the data. 6:╇ Data Editing Issues with Suggestions Issue Suggestions Measurement scale Restriction of range Missing data Need to take the measurement scale of the variables into account when computing statistics such as means, standard deviations, and correlations.

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A Beginner’s Guide to Structural Equation Modeling by Randall E. Schumacker, Richard G. Lomax

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