LEADER 03587nam 22005291 450 001 9910791728503321 005 20170308170817.8 010 $a1-5063-2035-X 010 $a1-4833-4510-6 010 $a1-4522-5020-0 035 $a(CKB)2560000000089869 035 $a(EBL)997200 035 $a(OCoLC)809774252 035 $a(MiAaPQ)EBC997200 035 $a(OCoLC)811140594 035 $a(CaToSAGE)SAGE000001280 035 $a(StDuBDS)EDZ0000159001 035 $a(PPN)198710445 035 $a(EXLCZ)992560000000089869 100 $a20170308d1998 fy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBasics of structural equation modeling /$fGeoffrey M. Maruyama 210 1$aLos Angeles, CA :$cSAGE Publications, Inc.,$d1998. 215 $a1 online resource (311 pages) $cillustrations 300 $aDescription based upon print version of record. 311 $a1-322-30717-2 311 $a0-8039-7409-4 320 $aIncludes bibliographical references (p. 299-305) and indexes. 327 $aBackground. What does it mean to model hypothesized causal processes with nonexperimental data? -- History and logic of structural equation modeling -- Basic approaches to modeling with single observed measures of theoretical variables. The basics: path analysis and partitioning of variance -- Effects of collinearity on regression and path analysis -- Effects of random and nonrandom error on path models -- Recursive and longitudinal models: where causality goes in more than one direction and where data are collected over time -- Factor analysis and path modeling. Introducing the logic of factor analysis and multiple indicators to path modeling -- Latent variable structural equation models. Putting it all together: latent variable structural equation modeling -- Using latent variable structural equation modeling to examine plausibility of models -- Logic of alternative models and significance tests -- Variations on the basic latent variable structural equation model -- Wrapping up -- Appendix A: A brief introduction to matrix algebra and structural equation modeling. 330 8 $aWith the availability of software programs such as LISREL, EQS, and AMOS modeling techniques have become a popular tool for formalized presentation of the hypothesized relationships underlying correlational research and for testing the plausibility of hypothesizing for a particular data set. The popularity of these techniques, however, has often led to misunderstandings of them, particularly by students being exposed to them for the first time. Through the use of careful narrative explanation, Basics of Structural Equation Modeling describes the logic underlying structural equation modeling (SEM) approaches, describes how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores the various methodologies for analyzing structural equation data. 606 $aStructural equation modeling 606 $aStructural equation modeling$xComputer programs 615 0$aStructural equation modeling. 615 0$aStructural equation modeling$xComputer programs. 676 $a519.535 700 $aMaruyama$b Geoffrey M.$0496041 801 0$bCaToSAGE 801 1$bCaToSAGE 801 2$bUtOrBLW 906 $aBOOK 912 $a9910791728503321 996 $aBasics of structural equation modeling$9748778 997 $aUNINA