Illustrates the usefulness of multilevel modeling for the quantification of effects and causal inference.Each core chapter begins with a pressing social problem that motivates theoretical analysis, gathering of relevant data and application of appropriate statistical procedures.This book uniquely focuses on the intersection of social problems, multilevel modeling, and causality.
Illustrates the usefulness of multilevel modeling for the quantification of effects and causal inference.
Each core chapter begins with a pressing social problem that motivates theoretical analysis, gathering of relevant data and application of appropriate statistical procedures.
This book uniquely focuses on the intersection of social problems, multilevel modeling, and causality.
Includes supplementary material: sn.pub/extras
Robert B. Smith (Ph.D. Columbia University, 1971) taught political sociology, research methods, and theory development at the University of California, Santa Barbara. His research there focused on the social consequences of war, generalizations of path analysis, and computer simulations of social processes. Since then, he has worked extensively in applied research. His publications include articles on political and social processes, and on multilevel models bearing on human development. He was the primary editor of the three volumes of the A Handbook of Social Science Methods, which link qualitative and quantitative methods, and he is the author of Cumulative Social Inquiry: Transforming Novelty into Innovation. His recent research at the Cambridge-MIT Institute assesses student exchange programs and pedagogical experiments. As senior statistician he worked on software for randomized trials, exact statistics, and Bayesian simulations at Cytel Inc., and is an advisory editor of Quality & Quantity. He was a Fulbright lecturer in structural sociology at Ghent University, Belgium, and he has served as president of the Boston chapter of the American Statistical Association. He resides in Cambridge, Massachusetts where he directs his social structural research.
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.|
Contemporary societal problems are complex, intractable, and costly. Aiming to ameliorate them, social scientists formulate policies and programs, and conduct research testing the efficacy of the interventions. All too often the results are disappointing; partly because the theories guiding these studies are inappropriate, the study designs are flawed, and the empirical databases covering their research questions are sparse. This book confronts these problems of research by following this process: analyze the roots of the social problem both theoretically and empirically; formulate a study design that captures the nuances of the problem; gather appropriate empirical data operationalizing the study design; model these data using multilevel statistical methods to uncover potential causes and any biases to their implied effects; use the results by refining theory and by formulating evidence-based policy recommendations for implementation and testing.
Applying this process, the chapters focus on these social problems: political extremism; global human development; violence against religious minorities; computerization of work; reform of urban schools; and the utilization and costs of health care. Because these chapters exemplify the usefulness of multilevel modeling for the quantification of effects and causal inference, they can serve as vivid exemplars for the teaching of students. This use of examples reverses the usual procedure for introducing statistical methods. Rather than beginning with a new statistical model bearing on statistical theory and searching for illustrative data, each core chapter begins with a pressing social problem. The specific problem motivates theoretical analysis, gathering of relevant data, and application of appropriate statistical procedures. Readers can use the provided data sets and syntaxes to replicate, critique, and advance the analyses, thereby developing their ability to produce future applications of multilevel modeling.
The chapters address the multilevel data structures of these social problems by grouping observations on the micro units (level-1) by more macro-units (level-2) (e.g., school children are grouped by their classroom), and by conducting multilevel statistical modeling in contextual, longitudinal, and meta-analyses. Each core chapter applies a qualitative typology to nest the variance between the macro units, thereby crafting a "mixed-methods" approach that combines qualitative attributes with quantitative measures
ContentsPreface and AcknowledgementsList of Tables and Figures Overview of BookChaptersPart 1, Introductory Essays1.Contextual Analysis and Multilevel Models 2.Stable Association and Potential Outcomes3.Dependency Networks4.Uses for Multilevel ModelsPart 2, Contextual Studies5.Global Human Development6.A Globalized Conflict7.Will Claims Workers Dislike a Fraud Detector?Part 3, Evaluative Research 8.Target, Matched, and Not-Matched Schools 9.Using Propensity ScoresPart 4, Research Summaries10.Gatekeepers and Sentinels11.Childhood Vaccinations and Autism12.Conclusion: Gauging Causality in Multilevel ModelsAcronymsGlossaryReferencesIndex
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.
Uniquely focusing on intersections of social problems, multilevel statistical modeling, and causality; These models are also referred to as hierarchical linear models (HLMs) or mixed models.
This book details basic strategies for developing and testing multilevel linear models (MLMs) and drawing casual inferences from such models. It develops procedures for studying social, economic and human development.
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 ofcausality.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.
Contents.- Preface and Acknowledgements.- List of Tables and Figures.- Overview of BookChapters.- 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.
Robert B. Smith (Ph.D. Columbia University, 1971) taught political sociology, research methods, and theory development at the University of California, Santa Barbara. His research there focused on the social consequences of war, generalizations of path analysis, and computer simulations of social processes. Since then, he has worked extensively in applied research. His publications include articles on political and social processes, and on multilevel models bearing on human development. He was the primary editor of the three volumes of the A Handbook of Social Science Methods, which link qualitative and quantitative methods, and he is the author of Cumulative Social Inquiry: Transforming Novelty into Innovation. His recent research at the Cambridge-MIT Institute assesses student exchange programs and pedagogical experiments. As senior statistician he worked on software for randomized trials, exact statistics, and Bayesian simulations at Cytel Inc., and is an advisory editor of Quality & Quantity. He was a Fulbright lecturer in structural sociology at Ghent University, Belgium, and he has served as president of the Boston chapter of the American Statistical Association. He resides in Cambridge, Massachusetts where he directs his social structural research.
Über den Autor
Robert B. Smith (Ph.D. Columbia University, 1971) taught political sociology, research methods, and theory development at the University of California, Santa Barbara. His research there focused on the social consequences of war, generalizations of path analysis, and computer simulations of social processes. Since then, he has worked extensively in applied research. His publications include articles on political and social processes, and on multilevel models bearing on human development. He was the primary editor of the three volumes of the A Handbook of Social Science Methods, which link qualitative and quantitative methods, and he is the author of Cumulative Social Inquiry: Transforming Novelty into Innovation. His recent research at the Cambridge-MIT Institute assesses student exchange programs and pedagogical experiments. As senior statistician he worked on software for randomized trials, exact statistics, and Bayesian simulations at Cytel Inc., and is an advisory editor of Quality & Quantity. He was a Fulbright lecturer in structural sociology at Ghent University, Belgium, and he has served as president of the Boston chapter of the American Statistical Association. He resides in Cambridge, Massachusetts where he directs his social structural research.
Inhaltsverzeichnis
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
Klappentext
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 ofcausality.
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.
Illustrates the usefulness of multilevel modeling for the quantification of effects and causal inference.
n
Each core chapter begins with a pressing social problem that motivates theoretical analysis, gathering of relevant data and application of appropriate statistical procedures.
n
This book uniquely focuses on the intersection of social problems, multilevel modeling, and causality.
n
Includes supplementary material: sn.pub/extras
n
