Social Dynamics Models and Methods
Published on 28. August 1984
302 pages
978-0-323-15690-5 (ISBN)
Description
Social Dynamics: Models and Methods focuses on sociological methodology and on the practice of sociological research. This book is organized into three parts encompassing 16 chapters that deal with the basic principles of social dynamics. The first part of this book considers the development of models and methods for causal analysis of the actual time paths of change in attributes of individual and social systems. This part also discusses the applications in which the use of dynamic models and methods seems to have enhanced the capacity to formulate and test sociological arguments. These models and methods are useful for answering questions about the detailed structure of social change processes. The second part explores the formulation of the continuous-time models of change in both quantitative and qualitative outcomes and the development of suitable methods for estimating these models from the kinds of data commonly available to sociologists. The third part describes a stochastic framework for analyzing both qualitative and quantitative outcome of social changes. This part also discusses the sociologists' perspective on the empirical study of social change processes. This text will be of great value to sociologists and sociological researchers.
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01/1984
Academic Press
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Content
¿Preface Acknowledgments Part I Introduction 1 Why Dynamic Analysis? 1.1 Static Analysis for Studying Change 1.2 Dynamic Analysis for Studying Static Relationships 1.3 Other Obstacles to Dynamic Analysis 1.4 Conclusions 2 Varieties of Temporal Analysis: Overview and Critique 2.1 Observation Plans 2.2 Panel Analysis of Qualitative Outcomes 2.3 Event-History Analysis 2.4 Panel Analysis of Quantitative Outcomes 2.5 Time-Series Analysis 2.6 Conclusions Part II Qualitative Outcomes 3 Fundamentals of Event-History Analysis 3.1 Event-History Data 3.2 Terms for Populations of Event Histories 3.3 Conclusions 4 Models of Change in Qualitative Variables 4.1 Reasons for Continuous-Time Stochastic Models 4.2 Models of Event Histories 4.3 Implications of Semi-Markov Models 4.4 Particular Models 4.5 Conclusions 5 Estimation Using Censored Event Histories 5.1 The Censoring Problem 5.2 Maximum-Likelihood Estimation 5.3 ML Estimation of Right-Censored Event Histories 5.4 ML Estimation of Left-Censored Event Histories 5.5 ML Estimators for a Single Constant Rate 5.6 Two Pseudo-ML Estimators 5.7 A Moment Estimator 5.8 Monte Carlo Results on Effects of Censoring 5.9 Measurement Error in Dates 5.10 Monte Carlo Results on Measurement Error 5.11 Markov Models with Multiple Outcomes 5.12 Conclusions 6 Models for Heterogeneous Populations 6.1 Parameterizing Observed Heterogeneity 6.2 An Example: NIT Effects on Marital Stability 6.3 Incorporating Unobserved Heterogeneity 6.4 An Example: Unobserved Heterogeneity in Job-Shift Rates 6.5 Misspecification of the Disturbance's Distribution 6.6 Conclusions 7 Time Dependence: Parametric Approaches 7.1 Sources of Time Dependence 7.2 Periodic Shifts in Parameters and Causal Variables 7.3 Linearly Changing Causal Variables 7.4 Time as a Proxy for Unobserved Change Processes 7.5 Conclusions 8 Time Dependence: A Partially Parametric Approach 8.1 Proportional Rates 8.2 Partial Likelihood 8.3 Monte Carlo Study of PL and ML Estimators 8.4 PL Estimation of a Hazard Function Illustrated 8.5 Handling of Ties 8.6 Intermittently Measured Explanatory Variables 8.7 Estimating the Nuisance and Survivor Functions 8.8 Sources of Variation in the Nuisance Function 8.9 Multiple Outcomes 8.10 PL Estimation of Transition Rates Illustrated 8.11 Repeatable Events 8.12 Conclusions 9 Systems of Qualitative Variables 9.1 Modeling Strategies 9.2 An Example: Marital Status and Employment Statuses 9.3 Consequences of Ignoring Interdependence 9.4 Conclusions 10 A Comparison of Approaches 10.1 Cross-Sectional Analysis 10.2 Event-Count and Event-Sequence Analysis 10.3 Panel Analysis 10.4 An Example: Formal Political Structure 10.5 How Well Do These Models Fit? 10.6 Conclusions Part III Quantitative Outcomes 11 Linear Deterministic Models 11.1 Linear Models for Rates of Change 11.2 Time Paths of Changes: Integral Equations 11.3 An Example: Organizational Growth and Decline 11.4 Linear Systems 11.5 Integral Equations for Linear Systems 11.6 Qualitative Stability 11.7 Organizational Growth and Decline Reconsidered 11.8 Conclusions Appendix 12 Linear Stochastic Models 12.1 Need for Stochastic Models 12.2 Stochastic Differential Equations 12.
