Continuity and change have been major concerns of the social and behavioral sciences -- in the study of human development and in the study of processes that unfold in various ways across time. There has been a veritable explosion of techniques for studying change over time which have fundamentally changed how we need to think of and study change. Unfortunately, many of the old precepts and beliefs are still among us. The field of methodology for the study of change is itself ready to change. Recently, there have been many analytic and conceptual developments questioning our cherished beliefs about the study of change. As such, how are individuals to think about issues and correctly analyze change? The chapters in this volume address these issues. Divided into two sections, this book deals with designs that analyze change in multiple subjects, and with change in single subjects and an interacting system. Papers presented in this volume are accessible to scientists who are not methodologists. The character of the papers are more like primers than basic treatises on methodology, written for other methodologists. It is time that people stop thinking in rigid ways about how to study change and be introduced to a range of many possibilities. Change, stability, order and chaos are elusive concepts. The pursuit of the laws of change must be approached in as flexible and creative a fashion as possible. This book should help to lead the way. --- from the publisher Contents: Preface. Part I: Analyzing Change in Multiple Subjects. D. Rogosa, Myths and Methods: "Myths About Longitudinal Research" plus Supplemental Questions. G.P. Sackett, J.W. Shortt, Hierarchical Regression Analysis with Repeated Data Measures. G.R. Patterson, Orderly Change in a Stable World: The Antisocial Trait as a Chimera. M. Stoolmiller, Using Latent Growth Curve Models to Study Developmental Processes. E.R. Anderson, Accelerating and Maximizing Information from Short-Term Longitudinal Research. S.W. Raudenbush, Hierarchical Linear Models to Study the Effects of Social Context on Development. J.B. Willett, J.D. Singer, Investigating Onset, Cessation, Relapse, and Recovery: Using Discrete-Time Survival Analysis to Examine the Occurrence and Timing of Critical Events. M. Stoolmiller, L. Bank, Autoregressive Effects in Structural Equation Models: We See Some Problems. Part II: Analyzing Change in a Single Subject or an Interacting System. R. Bakeman, L.B. Adamson, P. Strisik, Lags and Logs: Statistical Approaches to Interaction (SPSS Version). W.A. Griffin, Assessing State Changes in Micro-Social Interaction: An Introduction to Event-History Analysis. W. Gardner, On the Reliability of Sequential Data: Measurement, Meaning, and Correction. J. Crosbie, Interrupted Time-Series Analysis with Short Series: Why It Is Problematic; How It Can Be Improved. M.B. Priestly, Current Developments in Time-Series Modeling. J. Murray, Nonlinear Dynamics and Chaos. G.L. Baker, The Chaotic Pendulum: Model and Metaphor. |
