LEADER 05466nam 22006734a 450 001 9910143577103321 005 20170815113201.0 010 $a1-280-40940-1 010 $a9786610409402 010 $a0-470-32167-9 010 $a0-471-74609-6 010 $a0-471-74608-8 035 $a(CKB)1000000000355717 035 $a(EBL)257069 035 $a(OCoLC)475972754 035 $a(SSID)ssj0000301350 035 $a(PQKBManifestationID)11273062 035 $a(PQKBTitleCode)TC0000301350 035 $a(PQKBWorkID)10263495 035 $a(PQKB)11257895 035 $a(MiAaPQ)EBC257069 035 $a(EXLCZ)991000000000355717 100 $a20050330d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLatent curve models$b[electronic resource] $ea structural equation perspective /$fKenneth A. Bollen, Patrick J. Curran 210 $aHoboken, N.J. $cWiley-Interscience$dc2006 215 $a1 online resource (307 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-45592-X 320 $aIncludes bibliographical references (p. 263-273) and indexes. 327 $aLatent Curve Models; Contents; Preface; 1 Introduction; 1.1 Conceptualization and Analysis of Trajectories; 1.1.1 Trajectories of Crime Rates; 1.1.2 Data Requirements; 1.1.3 Summary; 1.2 Three Initial Questions About Trajectories; 1.2.1 Question 1: What Is the Trajectory for the Entire Group?; 1.2.2 Question 2: Do We Need Distinct Trajectories for Each Case?; 1.2.3 Question 3: If Distinct Trajectories Are Needed, Can We Identify Variables to Predict These Individual Trajectories?; 1.2.4 Summary; 1.3 Brief History of Latent Curve Models; 1.3.1 Early Developments: The Nineteenth Century 327 $a1.3.2 Fitting Group Trajectories: 1900-19371.3.3 Fitting Individual and Group Trajectories: 1938-1950s; 1.3.4 Trajectory Modeling with Latent Variables: 1950s-1984; 1.3.5 Current Latent Curve Modeling: 1984-present; 1.3.6 Summary; 1.4 Organization of the Remainder of the Book; 2 Unconditional Latent Curve Model; 2.1 Repeated Measures; 2.2 General Model and Assumptions; 2.3 Identification; 2.4 Case-By-Case Approach; 2.4.1 Assessing Model Fit; 2.4.2 Limitations of Case-by-Case Approach; 2.5 Structural Equation Model Approach; 2.5.1 Matrix Expression of the Latent Curve Model 327 $a2.5.2 Maximum Likelihood Estimation2.5.3 Empirical Example; 2.5.4 Assessing Model Fit; 2.5.5 Components of Fit; 2.6 Alternative Approaches to the SEM; 2.7 Conclusions; Appendix 2A: Test Statistics, Nonnormality, and Statistical Power; 3 Missing Data and Alternative Metrics of Time; 3.1 Missing Data; 3.1.1 Types of Missing Data; 3.1.2 Treatment of Missing Data; 3.1.3 Empirical Example; 3.1.4 Summary; 3.2 Missing Data and Alternative Metrics of Time; 3.2.1 Numerical Measure of Time; 3.2.2 When Wave of Assessment and Alternative Metrics of Time Are Equivalent 327 $a3.2.3 When Wave of Assessment and Alternative Metrics of Time Are Different3.2.4 Reorganizing Data as a Function of Alternative Metrics of Time; 3.2.5 Individually Varying Values of Time; 3.2.6 Summary; 3.2.7 Empirical Example: Reading Achievement; 3.3 Conclusions; 4 Nonlinear Trajectories and the Coding of Time; 4.1 Modeling Nonlinear Functions of Time; 4.1.1 Polynomial Trajectories: Quadratic Trajectory Model; 4.1.2 Polynomial Trajectories: Cubic Trajectory Models; 4.1.3 Summary; 4.2 Nonlinear Curve Fitting: Estimated Factor Loadings; 4.2.1 Selecting the Metric of Change 327 $a4.3 Piecewise Linear Trajectory Models4.3.1 Identification; 4.3.2 Interpretation; 4.4 Alternative Parametric Functions; 4.4.1 Exponential Trajectory; 4.4.2 Parametric Functions with Cycles; 4.4.3 Nonlinear Transformations of the Metric of Time; 4.4.4 Nonlinear Transformations of the Repeated Measures; 4.5 Linear Transformations of the Metric of Time; 4.5.1 Logic of Recoding the Metric of Time; 4.5.2 General Framework for Transforming Time; 4.5.3 Summary; 4.6 Conclusions; Appendix 4A: Identification of Quadratic and Piecewise Latent Curve Models; 4A.1 Quadratic LCM; 4A.2 Piecewise LCM 327 $a5 Conditional Latent Curve Models 330 $aAn effective technique for data analysis in the social sciences The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories. The authors synthesize a vast amount of research and find 410 0$aWiley series in probability and statistics. 606 $aLatent structure analysis 606 $aLatent variables 608 $aElectronic books. 615 0$aLatent structure analysis. 615 0$aLatent variables. 676 $a519.5/35 676 $a621.384135015118 700 $aBollen$b Kenneth A$0144978 701 $aCurran$b Patrick J.$f1965-$0502145 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143577103321 996 $aLatent curve models$9731606 997 $aUNINA LEADER 00912nam a2200229 i 4500 001 991002032659707536 008 050621s1992 it 0 ita d 020 $a8834820762 035 $ab14110040-39ule_inst 040 $aDip.to Scienze dell'economia$bita 082 04$a341.484 100 1 $aPanzera, Antonio Filippo$0227859 245 13$aLa circolazione delle persone fra Italia e stati confinanti :$bprofili giuridici /$cAntonio Filippo Panzera 260 $aTorino :$bGiappichelli,$cc1992 300 $a137 p. ;$c24 cm 650 04$aLibertà di circolazione 907 $a.b14110040$b02-04-14$c22-04-13 912 $a991002032659707536 945 $aLE025 ECO 341 PAN01.01$g1$i2025000262263$lle025$og$pE8.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i15502302$z22-04-13 996 $aCircolazione delle persone fra Italia e stati confinanti$9264616 997 $aUNISALENTO 998 $ale025$b22-04-13$cm$da $e-$fita$git $h3$i0 LEADER 02700oam 22005055 450 001 9910792508503321 005 20170528080238.0 010 $a1-4648-0820-1 024 7 $a10.1596/978-1-4648-0819-7 035 $a(CKB)3710000001078679 035 $a(MiAaPQ)EBC4816178 035 $a(The World Bank)210819 035 $a(US-djbf)210819 035 $a(EXLCZ)993710000001078679 100 $a20020129d2017 uf 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMining in Africa : $eAre Local Communities Better Off? /$fPunam Chuhan-Pole 210 1$aWashington, D.C. :$cThe World Bank,$d2017. 215 $a1 online resource (212 pages) 225 1 $aAfrica Development Forum 311 $a1-4648-0819-8 320 $aIncludes bibliographical references at the end of each chapters and index. 330 3 $aThis study focuses on the local and regional impact of large-scale gold mining in Africa in the context of a mineral boom in the region since 2000. It contributes to filling a gap in the literature on the welfare effects of mineral resources, which, until now, has concentrated more on the national or macroeconomic impacts. Economists have long been intrigued by the paradox that a rich endowment of natural resources may retard economic performance, particularly in the case of mineral-exporting developing countries. Studies of this phenomenon, known as the "resource curse", examine the economy-wide consequences of mineral exports. Africa's resource boom has lifted growth, but has been less successful in improving people's welfare. Yet much of the focus in academic and policy circles has been on appropriate management of the macro-fiscal and governance risks that have historically undermined development outcomes. This study focuses instead on the fortune of local communities where resources are located. It aims to better inform public policy and corporate behavior on the welfare of communities in Africa in which the extraction of resources takes place. 410 0$aWorld Bank e-Library. 606 $aMines and mineral resources$zAfrica 606 $aMineral industries$zAfrica 607 $aAfrica$2fast 615 0$aMines and mineral resources 615 0$aMineral industries 676 $a553.096 700 $aChuhan-Pole$b Punam$01471704 702 $aChuhan-Pole$b Punam 702 $aLewin$b Michael 712 02$aWorld Bank, 712 02$aAgence franc?aise de de?veloppement, 801 0$bDJBF 801 1$bDJBF 906 $aBOOK 912 $a9910792508503321 996 $aMining in Africa$93795574 997 $aUNINA