By J. (John) Scott Long

ISBN-10: 080392044X

ISBN-13: 9780803920446

A statistical procedure that might attract teams particularly - people who find themselves at the moment utilizing the extra conventional means of exploratory issue research and people who have an interest within the research of covariance buildings, generally called the LISREL version. the 1st crew will locate that this method could be extra acceptable to the research in their study difficulties whereas the second one crew will locate that confirmatory issue research is an invaluable first step to knowing the LISREL version. This ebook, and its spouse quantity, Covariance constitution versions, are designed to be learn consecutively. The proofs offered are uncomplicated, however the reader needs to suppose happy with matrix algebra that allows you to comprehend the version.

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Extra info for Confirmatory factor analysis: a preface to LISREL

Example text

Hagenaars, Social Sciences, Tilburg University Sally Jackson, Communications, University of Arizona Richard M. Jaeger, Education, University of North Carolina, Greensboro Gary King, Department of Government, Harvard University Roger E. Kirk, Psychology, Baylor University Helena Chmura Kraemer, Psychiatry and Behavioral Sciences, Stanford University Peter Marsden, Sociology, Harvard University Helmut Norpoth, Political Science, SUNY, Stony Brook Frank L. Schmidt, Management and Organization, University of Iowa Herbert Weisberg, Political Science, The Ohio State University Publisher Sara Miller McCune, Sage Publications, Inc.

Page 24 Since it was assumed that the qi's are measured as deviations from their means, qij = COV(qi,qj) and qii = COV(qi,qi ) = VAR(qi). , Q = Q'). Matrices such as Q are called variance/covariance matrices, or simply covariance matrices. A number of definitions and assumptions can now be stated. 1. The population covariance matrix for the observed variables contained in x is defined as S = E(xx'), a (q × q) symmetric matrix. The (i,j)th element of S, sij, is the population value of the covariance between xi and xj, and can be defined as sij = E(xixj).

However, if there is an effect of the method of measurement, then each observed variable would load on both the factor for that particular trait and the factor for the particular method used. The MMMT model attempts to disentangle the effects of different substantive concepts from the methods of measurement used to measure those concepts. This model can be easily formulated as a confirmatory factor model, as our next example illustrates. For more details on the MMMT model, see Alwin (1974); Kenny (1979); and Sullivan and Feldman (1979).

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