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Multilevel Modeling of Social Problems : A Causal Perspective / by Robert B. Smith

Publisher	(Dordrecht : Springer Netherlands : Imprint: Springer)
Year	2011
Edition	1st ed. 2011.
Authors	*Smith, Robert B author SpringerLink (Online service)

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Links to the text	Location	Volume	Call No.	Barcode No.	Status	Comments	ISBN	Printed	Restriction	Reserve
Links to the text	Library Off-campus access			OB00166587		Springer Humanities, Social Sciences and Law eBooks (電子ブック)	9789048198559

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Material Type	E-Book
Media type	機械可読データファイル
Size	XXXIX, 535 p : online resource
Notes	Contents -- Preface and Acknowledgements -- List of Tables and Figures -- Overview of Book Chapters -- Part 1, Introductory Essays -- 1.Contextual Analysis and Multilevel Models -- 2.Stable Association and Potential Outcomes -- 3.Dependency Networks -- 4.Uses for Multilevel Models -- Part 2, Contextual Studies -- 5.Global Human Development -- 6.A Globalized Conflict -- 7.Will Claims Workers Dislike a Fraud Detector? -- Part 3, Evaluative Research -- 8.Target, Matched, and Not-Matched Schools -- 9.Using Propensity Scores -- Part 4, Research Summaries -- 10.Gatekeepers and Sentinels -- 11.Childhood Vaccinations and Autism -- 12.Conclusion: Gauging Causality in Multilevel Models -- Acronyms -- Glossary -- References -- Index 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 via meta-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 HTTP:URL=https://doi.org/10.1007/978-90-481-9855-9
Subjects	LCSH:Sociology—Methodology LCSH:Social sciences—Statistical methods FREE:Sociological Methods FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
Classification	LCC:HM511-538 DC23:301.01
ID	8000019700
ISBN	9789048198559