LEADER 05568nam 2200757 450 001 9910131296503321 005 20220718132848.0 010 $a1-118-95782-2 010 $a1-118-95783-0 035 $a(CKB)3710000000391757 035 $a(EBL)1895808 035 $a(OCoLC)904194680 035 $a(SSID)ssj0001461986 035 $a(PQKBManifestationID)12551069 035 $a(PQKBTitleCode)TC0001461986 035 $a(PQKBWorkID)11478792 035 $a(PQKB)11040724 035 $a(PQKBManifestationID)16051824 035 $a(PQKB)23749949 035 $a(DLC) 2015008108 035 $a(Au-PeEL)EBL1895808 035 $a(CaPaEBR)ebr11041411 035 $a(CaONFJC)MIL770076 035 $a(CaSebORM)9781119993438 035 $a(MiAaPQ)EBC1895808 035 $a(PPN)191912093 035 $a(EXLCZ)993710000000391757 100 $a20150416h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMeta-analysis $ea structural equation modeling approach /$fMike W. L. Cheung 205 $a1st edition 210 1$aChichester, England ;$aWest Sussex, England :$cWiley,$d2015. 210 4$dİ2015 215 $a1 online resource (403 p.) 300 $aDescription based upon print version of record. 311 $a1-118-95781-4 311 $a1-119-99343-1 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $a""Cover ""; ""Title Page ""; ""Copyright ""; ""Contents ""; ""Preface ""; ""Acknowledgments ""; ""List of abbreviations ""; ""List of figures ""; ""List of tables ""; ""Chapter 1 Introduction ""; ""1.1 What is meta-analysis? ""; ""1.2 What is structural equation modeling? "" 327 $a""1.3 Reasons for writing a book on meta-analysis and structural equation modeling """"1.3.1 Benefits to users of structural equation modeling and meta-analysis ""; ""1.4 Outline of the following chapters ""; ""1.4.1 Computer examples and data sets used in this book "" 327 $a""1.5 Concluding remarks and further readings """"References ""; ""Chapter 2 Brief review of structural equation modeling ""; ""2.1 Introduction ""; ""2.2 Model specification ""; ""2.2.1 Equations ""; ""2.2.2 Path diagram ""; ""2.2.3 Matrix representation ""; ""2.3 Common structural equation models "" 327 $a""2.3.1 Path analysis """"2.3.2 Confirmatory factor analysis ""; ""2.3.3 Structural equation model ""; ""2.3.4 Latent growth model ""; ""2.3.5 Multiple-group analysis ""; ""2.4 Estimation methods, test statistics, and goodness-of-fit indices ""; ""2.4.1 Maximum likelihood estimation "" 327 $a""2.4.2 Weighted least squares """"2.4.3 Multiple-group analysis ""; ""2.4.4 Likelihood ratio test and Wald test ""; ""2.4.5 Confidence intervals on parameter estimates ""; ""2.4.6 Test statistics versus goodness-of-fit indices ""; ""2.5 Extensions on structural equation modeling "" 327 $a""2.5.1 Phantom variables "" 330 $aPresents a novel approach to conducting meta-analysis using structural equation modeling. Structural equation modeling (SEM) and meta-analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta-analytic data within the SEM framework, and illustrates how to conduct meta-analysis using the metaSEM package in the R statistical environment. Meta-Analysis: A Structural Equation Modeling Approach begins by introducing the impo 606 $aStatistics 606 $aMeta-analysis 606 $aResearch$xStatistical methods 606 $aSampling (Statistics) 615 0$aStatistics. 615 0$aMeta-analysis. 615 0$aResearch$xStatistical methods. 615 0$aSampling (Statistics) 676 $a001.4/22 700 $aCheung$b Mike W. L.$0902637 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910131296503321 996 $aMeta-analysis$92017812 997 $aUNINA LEADER 02796nam0 2200481 i 450 001 VAN0103915 005 20220221040853.537 017 70$2N$a978-3-319-09063-4 100 $a20151130d2014 |0itac50 ba 101 $aeng 102 $aCH 105 $a|||| ||||| 200 1 $aModeling, simulation and optimization of complex processes - HPSC 2012$eproceedings of the 5. international conference on High Performance Scientific Computing, march 5-9, 2012, Hanoi, Vietnam$fHans Georg Bock ... [et al.] editors 210 $aCham$cSpringer$d2014 215 $aVIII, 272 p.$cill.$d24 cm 500 1$3VAN0241051$aModeling, simulation and optimization of complex processes HPSC 2012$92544804 606 $a65L05$xNumerical methods for initial value problems [MSC 2020]$3VANC023029$2MF 606 $a93B30$xSystem identification [MSC 2020]$3VANC023144$2MF 606 $a49J15$xExistence theories for optimal control problems involving ordinary differential equations [MSC 2020]$3VANC024587$2MF 606 $a49K15$xOptimality conditions for problems involving ordinary differential equations [MSC 2020]$3VANC024589$2MF 606 $a68W10$xParallel algorithms in computer science [MSC 2020]$3VANC027694$2MF 606 $a65K05$xNumerical mathematical programming methods [MSC 2020]$3VANC028868$2MF 606 $a34B15$xNonlinear boundary value problems for ordinary differential equations [MSC 2020]$3VANC029108$2MF 606 $a35Q92$xPDEs in connection with biology, chemistry and other natural sciences [MSC 2020]$3VANC031096$2MF 606 $a49Mxx$xNumerical methods in optimal control [MSC 2020]$3VANC031384$2MF 606 $a70E60$xRobot dynamics and control of rigid bodies [MSC 2020]$3VANC031396$2MF 610 $aApplications in science and technology$9KW:K 610 $aMathematical modeling$9KW:K 610 $aNumerical simulation$9KW:K 610 $aOptimization and optimal control$9KW:K 610 $aSoftware development and parallel computing$9KW:K 620 $aCH$dCham$3VANL001889 702 1$aBock$bHans Georg$3VANV081005 712 12$aInternational conference on High Performance Scientific Computing$d5.$f2012$eHanoi, Vietnam$3VANV081006 712 $aSpringer $3VANV108073$4650 801 $aIT$bSOL$c20240614$gRICA 856 4 $uhttp://dx.doi.org/10.1007/978-3-319-09063-4$zE-book ? 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