Social Dynamics Models and Methods

Social Dynamics Models and Methods

1st Edition - August 1, 1984

Write a review

  • Editor: Nancy Brandon Tuma
  • eBook ISBN: 9780323156905

Purchase options

Purchase options
DRM-free (PDF)
Sales tax will be calculated at check-out

Institutional Subscription

Free Global Shipping
No minimum order

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.

Table of Contents


  • 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


Product details

  • No. of pages: 302
  • Language: English
  • Copyright: © Academic Press 1984
  • Published: August 1, 1984
  • Imprint: Academic Press
  • eBook ISBN: 9780323156905

About the Editor

Nancy Brandon Tuma

Ratings and Reviews

Write a review

There are currently no reviews for "Social Dynamics Models and Methods"