Social Dynamics Models and Methods

1st Edition - August 1, 1984
Editor: Nancy Brandon Tuma
Language: English
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 1 5 6 9 0 - 5

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… Read more

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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.

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

12.3 Complicating the Noise Process

12.4 Diffusion Processes

12.5 Boundary Behavior

12.6 Systems of Equations

12.7 Conclusions

13 Estimation of Linear Models

13.1 Time-Series versus Panel Data

13.2 Two Ways to Estimate a Dynamic Model

13.3 Scalar Models

13.4 Autocorrelation of Disturbances

13.5 Pooled Cross-Section and Time-Series Estimators

13.6 Monte Carlo Studies of Pooled Estimators

13.7 Measurement Error

13.8 Unequally Spaced Observations

13.9 Linear Systems

13.10 Conclusions

14 Deterministic Nonlinear Models

14.1 Scalar Models

14.2 Models of Systems

14.3 Competition Models

14.4 Exact Discrete Approximations

14.5 An Example: National Expansion of Education

14.6 Qualitative Stability

14.7 Cyclic Behavior: Predator-Prey Interactions

14.8 Conclusions

15 Stochastic Nonlinear Models

15.1 Stochastic Integrals: The Nonlinear Case

15.2 Geometric Brownian Motion

15.3 The Itŏ Transformation Formula

15.4 Conclusions

16 Coupled Qualitative and Quantitative Processes

16.1 Quality and Quantity

16.2 Approaches

16.3 Conclusions

References

Author Index

Subject Index