By J. (John) Scott Long
Whereas many readers can be unusual with the complete complexity of the covariance constitution version, many could have mastered at the least one in all its elements - every one of that's a strong and famous statistical method in its personal correct. the 1st is the confirmatory issue version often utilized in psychometrics; the second one, the structural equation version, is commonplace to econometricians. The dialogue during this quantity could be rather precious for estimating versions with equality constraints and correlated mistakes throughout a few yet now not all equations. the ultimate bankruptcy incorporates a consultant to suitable software program programs.
Read or Download Covariance Structure Models: An Introduction to LISREL (Quantitative Applications in the Social Sciences) PDF
Similar probability & statistics books
Graphical Methods in Applied Mathematics
Writer: London, Macmillan and Co. , constrained booklet date: 1909 matters: arithmetic picture tools Notes: this is often an OCR reprint. there is typos or lacking textual content. There aren't any illustrations or indexes. for those who purchase the overall Books variation of this publication you get loose trial entry to Million-Books.
Stochastic Processes: A Survey of the Mathematical Theory
This publication is the results of lectures which I gave dur ing the tutorial yr 1972-73 to third-year scholars a~ Aarhus collage in Denmark. the aim of the e-book, as of the lectures, is to survey a number of the major issues within the glossy concept of stochastic approaches. In my earlier ebook chance: !
A Handbook of Numerical and Statistical Techniques with Examples Mainly from the Life Sciences
This guide is designed for experimental scientists, rather these within the existence sciences. it truly is for the non-specialist, and even though it assumes just a little wisdom of information and arithmetic, people with a deeper figuring out also will locate it precious. The ebook is directed on the scientist who needs to unravel his numerical and statistical difficulties on a programmable calculator, mini-computer or interactive terminal.
"Starting from the preliminaries via stay examples, the writer tells the tale approximately what a pattern intends to speak to a reader concerning the unknowable mixture in a true state of affairs. the tale develops its personal common sense and a motivation for the reader to place up with, herein follows. quite a few highbrow techniques are set forth, in as lucid a way as attainable.
- Schrödinger Diffusion Processes (Probability and its Applications)
- Simulation and the Monte Carlo Method
- Extremes in Random Fields: A Theory and its Applications
- Lévy Matters I: Recent Progress in Theory and Applications: Foundations, Trees and Numerical Issues in Finance
Additional info for Covariance Structure Models: An Introduction to LISREL (Quantitative Applications in the Social Sciences)
Sample text
The first component is the confirmatory factor model considered in psychometrics; the second component is the structural equation model considered in econometrics. This monograph is designed to take advantage of a reader's familiarity with one or both of these components. The reader is assumed to be familiar with the confirmatory factor model and the mathematical tools presented in a companion volume in the Sage Series on Quantitative Applications in the Social Sciences: Confirmatory Factor Analysis: A Preface to LISREL.
The observed x's and y's can be correlated. Their covariances are contained in the (q × p) matrix Sxy, whose (i,j)th element is the covariance between xi and yj. Similarly, the exogenous x-factors and endogenous h-factors can be correlated. Their covariances are contained in the (s × r) matrix COV(x,h), or the (r × s) matrix COV(h,x). Just as the unique factors are assumed to be uncorrelated with the common factors in their own factor equation, they are assumed to be uncorrelated with the common factors in the other equation.
3 would be written as h1 = g11x1 + g12x2 + z1 More realistic models are constructed using multiple equation systems, both with and without reciprocal causation. 2. This model states that the observed variable h1 is causally determined by the observed variables h2, x1, and x2. That these three variables do not perfectly explain h1 is reflected by the error in equation z1. Similarly, h2 is causally determined by h1, x2 and x3, with an error in equation z2. These structural relations would be written as In its general form the structural equations are written as Page 16 where h is a vector of observed dependent variables measured without error; x is a vector of observed independent variables measured without error; z is a vector of errors in equations; B is a matrix of coefficients relating the dependent variables to one another; and G is a matrix of coefficients relating the independent variables to the dependent variables.