By Tenko Raykov, George A. Marcoulides
During this ebook, authors Tenko Raykov and George A. Marcoulides introduce scholars to the fundamentals of structural equation modeling (SEM) via a conceptual, nonmathematical technique. For ease of knowing, the few mathematical formulation awarded are utilized in a conceptual or illustrative nature, instead of a computational one. that includes examples from EQS, LISREL, and Mplus, a primary path in Structural Equation Modeling is a superb beginner’s advisor to studying easy methods to manage enter records to slot the main primary kinds of structural equation types with those courses. the elemental principles and techniques for engaging in SEM are self sufficient of any specific software program. Highlights of the second one variation contain: • evaluation of latent swap (growth) research types at an introductory point • assurance of the preferred Mplus application • up-to-date examples of LISREL and EQS • A CD that comprises the entire text’s LISREL, EQS, and Mplus examples. a primary direction in Structural Equation Modeling is meant as an introductory publication for college students and researchers in psychology, schooling, enterprise, drugs, and different utilized social, behavioral, and future health sciences with constrained or no prior publicity to SEM. A prerequisite of simple records via regression research is usually recommended. The ebook often attracts parallels among SEM and regression, making this past wisdom valuable.
Read or Download A First Course in Structural Equation Modeling, 2nd edition PDF
Similar mathematics books
- Primzahltests für Einsteiger: Zahlentheorie - Algorithmik - Kryptographie
- Modern Mathematics: 1900 to 1950 (Pioneers in Mathematics, Volume 4)
- Lie Algebras (Interscience Tracts in Pure and Applied Mathematics Number 10)
- Fuzzy Logic: A Practical Approach
- The Handy Math Answer Book (2nd Edition) (The Handy Answer Book Series)
Extra resources for A First Course in Structural Equation Modeling, 2nd edition
At each step, the method specific distance—that is, fit function value—between S and S(g) with the new parameter values, should be smaller than this distance with the parameter values available at the preceding step. This principle is followed until no further improvement in the fit function can be achieved. , Appendix to this chapter). , start values for all parameters. Quite often, these values can be automatically calculated by the SEM software used, although researchers can provide their own initial values if they so choose with some complicated models.
FUNDAMENTALS OF STRUCTURAL EQUATION MODELING interested in retaining a proposed model whose validity is the essence of a pertinent null hypothesis. In other words, statistically speaking, when using SEM one is usually ‘interested’ in not rejecting the null hypothesis. However, recall from introductory statistics that not rejecting a null hypothesis does not mean that it is true. Similarly, because model testing in SEM involves testing the null hypothesis that the model is capable of perfectly reproducing with certain values of its unknown parameters the population matrix of observed variable interrelationship indices, not rejecting a fitted model does not imply that it is the true model.
Tabachnick & Fidell, 2001; Khattree & Naik, 1999), examining multinormality involves several steps. The simplest way to assess univariate normality, an implication of multivariate normality, is to consider skewness and kurtosis, and statistical tests are available for this purpose. Skewness is an index that reflects the lack of symmetry of a univariate distribution. Kurtosis has to do with the shape of the distribution in terms of its peakedness relative to a corresponding normal distribution.
A First Course in Structural Equation Modeling, 2nd edition by Tenko Raykov, George A. Marcoulides