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[null,"1.","Record Nr.",null,"UNINA9910477245403321"]
[null,null,"Autore",null,"Beaumont Thomas"]
[null,null,"Titolo",null,"Fellow Travellers : Communist Trade Unionism and Industrial Relations on the French Railways, 1914-1939 / / Thomas Beaumont"]
[null,null,"Pubbl/distr/stampa",null,"Liverpool : , : Liverpool University Press, , 2019"]
[null,null,"Descrizione fisica",null,"1 online resource (x, 271 pages)"]
[null,null,"Collana",null,"Studies in labour history (Liverpool University Press)"]
[null,null,"Disciplina",null,"331.88113850967"]
[null,null,"Soggetti",null,"Railroads - Employees - Labor unions"]
[{"$nulls":4},"Communism - France - History - 20th century"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Acknowledgements -- Introduction -- Chapter 1: Railway Workers at War -- Chapter 2: Railway Workers and the 'Apres Guerre' -- Chapter 3: Railway Workers and the Communist Choice -- Chapter 4: Stabilisation -- Chapter 5: International Connections -- Chapter 6: 'Hostile Participants': Communists and Railway Industrial Relations in the Class against Class era, 1928-1934 -- Chapter 7: Railway Workers and the Popular Front: Victory to Defeat, 1936-1939 -- Conclusion -- Bibliography."]
[null,null,null,"Sommario/riassunto","Fellow Travellers examines the shifting practices and strategies adopted by Communist militants as they sought to build and maintain support on the railways. In a period in which the Communist party struggled to establish a foothold in many French workplaces, activists on the railways bucked the trend and set down deep and lasting roots of support. They maintained this support even through the sectarian period of the Comintern's shift to class against class, deepening their participation within railway industrial relations and gaining the experience of engagement with managers and state officials upon which they would build during the years of the Popular Front. Here France's railway employees joined alongside their fellow workers in shaping a new social contract for workers, extending the principle of democratic representation into the workplace. While the Popular Front experiment proved shortlived, its influence was long lasting. In the post Liberation period, the key tenets of the Popular Front experience re-emerged within the nationalised SNCF, shaping the particular character of railway industrial relations - the peculiar mix of collaboration and "]
[{"$nulls":4},"hostile confrontation between management and workforce that continues to make the French railways one of the most contested sectors of the modern French economy."]
[null,"2.","Record Nr.",null,"UNINA9910821778903321"]
[null,null,"Autore",null,"Lee Sik-Yum"]
[null,null,"Titolo",null,"Basic and advanced Bayesian structural equation modeling : with applications in the medical and behavioral sciences / / Sik-Yum Lee and Xin-Yuan Song"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, : Wiley, 2012"]
[null,null,"ISBN",null,"1-118-35887-2"]
[{"$nulls":4},"1-280-87995-5"]
[{"$nulls":4},"9786613721266"]
[{"$nulls":4},"1-118-35880-5"]
[{"$nulls":4},"1-118-35888-0"]
[{"$nulls":4},"1-118-35943-7"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (397 p.)"]
[null,null,"Collana",null,"Wiley series in probability and statistics"]
[null,null,"Classificazione",null,"MAT029000"]
[null,null,"Disciplina",null,"519.5/3"]
[null,null,"Soggetti",null,"Structural equation modeling"]
[{"$nulls":4},"Bayesian statistical decision theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Basic and Advanced Bayesian Structural Equation Modeling; Contents; About the authors; Preface; 1 Introduction; 1.1 Observed and latent variables; 1.2 Structural equation model; 1.3 Objectives of the book; 1.4 The Bayesian approach; 1.5 Real data sets and notation; Appendix 1.1: Information on real data sets; References; 2 Basic concepts and applications of structural equation models; 2.1 Introduction; 2.2 Linear SEMs; 2.2.1 Measurement equation; 2.2.2 Structural equation and one extension; 2.2.3 Assumptions of linear SEMs; 2.2.4 Model identification; 2.2.5 Path diagram"]
[{"$nulls":4},"2.3 SEMs with fixed covariates 2.3.1 The model; 2.3.2 An artificial example; 2.4 Nonlinear SEMs; 2.4.1 Basic nonlinear SEMs; 2.4.2 Nonlinear SEMs with fixed covariates; 2.4.3 Remarks; 2.5 Discussion and conclusions; References; 3 Bayesian methods for estimating structural equation models; 3.1 Introduction; 3.2 Basic concepts of the Bayesian estimation and prior distributions; 3.2.1 Prior distributions; 3.2.2 Conjugate prior distributions in Bayesian analyses of SEMs; 3.3 Posterior analysis using Markov chain Monte Carlo methods; 3.4 Application of Markov chain Monte Carlo methods"]
[{"$nulls":4},"3.5 Bayesian estimation via WinBUGS Appendix 3.1: The gamma, "]
[{"$nulls":4},"inverted gamma, Wishart, and inverted Wishart distributions and their characteristics; Appendix 3.2: The Metropolis-Hastings algorithm; Appendix 3.3: Conditional distributions [Ω|Y,θ] and [θ|Y,Ω]; Appendix 3.4: Conditional distributions [Ω|Y,θ] and [θ|Y,Ω] in nonlinear SEMs with covariates; Appendix 3.5: WinBUGS code; Appendix 3.6: R2WinBUGS code; References; 4 Bayesian model comparison and model checking; 4.1 Introduction; 4.2 Bayes factor; 4.2.1 Path sampling; 4.2.2 A simulation study; 4.3 Other model comparison statistics"]
[{"$nulls":4},"4.3.1 Bayesian information criterion and Akaike information criterion 4.3.2 Deviance information criterion; 4.3.3 Lν-measure; 4.4 Illustration; 4.5 Goodness of fit and model checking methods; 4.5.1 Posterior predictive p-value; 4.5.2 Residual analysis; Appendix 4.1: WinBUGS code; Appendix 4.2: R code in Bayes factor example; Appendix 4.3: Posterior predictive p-value for model assessment; References; 5 Practical structural equation models; 5.1 Introduction; 5.2 SEMs with continuous and ordered categorical variables; 5.2.1 Introduction; 5.2.2 The basic model; 5.2.3 Bayesian analysis"]
[{"$nulls":4},"5.2.4 Application: Bayesian analysis of quality of life data 5.2.5 SEMs with dichotomous variables; 5.3 SEMs with variables from exponential family distributions; 5.3.1 Introduction; 5.3.2 The SEM framework with exponential family distributions; 5.3.3 Bayesian inference; 5.3.4 Simulation study; 5.4 SEMs with missing data; 5.4.1 Introduction; 5.4.2 SEMs with missing data that are MAR; 5.4.3 An illustrative example; 5.4.4 Nonlinear SEMs with nonignorable missing data; 5.4.5 An illustrative real example"]
[{"$nulls":4},"Appendix 5.1: Conditional distributions and implementation of the MH algorithm for SEMs with continuous and ordered categorical variables"]
[null,null,null,"Sommario/riassunto","\"This book introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject's recent advances\"--"]