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Autor(en): 
  • Robert B. Smith
  • Multilevel Modeling of Social Problems: A Causal Perspective 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


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    Lieferstatus:   i.d.R. innert 14-24 Tagen versandfertig
    Veröffentlichung:  März 2011  
    Genre:  Soziologie 
    ISBN:  9789048198542 
    EAN-Code: 
    9789048198542 
    Verlag:  Springer Netherlands 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 241 mm / B 160 mm / D 35 mm 
    Gewicht:  1021 gr 
    Seiten:  576 
    Zus. Info:  HC gerader Rücken kaschiert 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Uniquely focusing on intersections of social problems, multilevel statistical modeling, and causality, the substantively and methodologically integrated chapters of this book clarify basic strategies for developing and testing multilevel linear models (MLMs), and drawing casual inferences from such models. These models are also referred to as hierarchical linear models (HLMs) or mixed models. The statistical modeling of multilevel data structures enables researchers to combine contextual and longitudinal analyses appropriately. But researchers working on social problems seldom apply these methods, even though the topics they are studying and the empirical data call for their use. By applying multilevel modeling to hierarchical data structures, this book illustrates how the use of these methods can facilitate social problems research and the formulation of social policies. It gives the reader access to working data sets, computer code, and analytic techniques, while at the same time carefully discussing issues of causality in such models. This book innovatively: . Develops procedures for studying social, economic, and human development. . Uses typologies to group (i.e., classify or nest) the level of random macro-level factors. . Estimates models with Poisson, binomial, and Gaussian end points using SAS's generalized linear mixed models (GLIMMIX) procedure. . Selects appropriate covariance structures for generalized linear mixed models. . Applies difference-in-differences study designs in the multilevel modeling of intervention studies. . Calculates propensity scores by applying Firth logistic regression to Goldberger-corrected data. . Uses the Kenward-Rogers correction in mixed models of repeated measures. . Explicates differences between associational and causal analysis of multilevel models. . Consolidates research findings viameta-analysis and methodological critique. . Develops criteria for assessing a study's validity and zone of causality. Because of its social problems focus, clarity of exposition, and use of state-of-the-art procedures, policy researchers, methodologists, and applied statisticians in the social sciences (specifically, sociology, social psychology, political science, education, and public health) will find this book of great interest. It can be used as a primary text in courses on multilevel modeling or as a primer for more advanced texts.
      
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