Collective Action—and Then What?

Two aspects of the social psychology of collective action are of particular interest to social movement organizers and activists: how to motivate people to engage in collective action, and how to use collective action to create social change. The second question remains almost untouched within social psychology. The present article delineates research from political science and sociology concerning variables that moderate the effectiveness of collective action and maps these variables against intergroup research. Within intergroup social psychology, there is a theoretical literature on what needs to be done to achieve change (e.g., changing identification, social norms, or perceptions of legitimacy, stability, permeability). The article considers possible testable hypotheses concerning the outcomes of collective action which can be derived from intergroup research and from the synthesis of the three disciplines. For theoreticians and practitioners alike, a program of research which addresses the social-psychological outcomes of collective action and links these to identities, norms, intentions, and support for social change in bystanders, protagonists, and opponents has a great deal of interest.     

Gino Germant, 1911–1979: Sociologist from the other America

This personal statement was prepared in response to a series of questions put forth to me by Ana Germani. She is the daughter of Gino and a good friend in her own right. However, she is not responsible for any part of this narrative. Ana is preparing a full-scale biography of Gino, in which I am sure the shortcomings in this brief memoir will be properly corrected. - ILH Irving Louis Horowitz is Hannah Arendt distinguished professor emeritus of sociology and political science at Rutgers University, where he also serves as chairman of the board of Transaction Publishers. His most recent work is Behemoth: The History and Theory of Political Sociology.     

Why Frequency is Not Well Defined for Engineering Systems with Nonexponential Failure Times

Dr. Yellman proposes to define frequency as “a time‐rate of events of a specified type over a particular time interval.” We review why no definition of frequency, including this one, can satisfy both of two conditions: (1) the definition should agree with the ordinary meaning of frequency, such as that less frequent events are less likely to occur than more frequent events, over any particular time interval for which the frequencies of both are defined; and (2) the definition should be applicable not only to exponentially distributed times between (or until) events, but also to some nonexponential (e.g., uniformly distributed) times. We make the simple point that no definition can satisfy (1) and (2) by showing that any definition that determines which of any two uniformly distributed times has the higher “frequency” (or that determines that they have the same “frequency,” if neither is higher) must assign a higher frequency number to the distribution with the lower probability of occurrence over some time intervals. Dr. Yellman's proposed phrase, “time‐rate of events … over a particular time interval” is profoundly ambiguous in such cases, as the instantaneous failure rates vary over an infinitely wide range (e.g., from one to infinity), making it unclear which value is denoted by the phrase “time‐rate of events.”     

Community Resilience and Decision Theory Challenges for Catastrophic Events

Extreme and catastrophic events pose challenges for normative models of risk management decision making. They invite development of new methods and principles to complement existing normative decision and risk analysis. Because such events are rare, it is difficult to learn about them from experience. They can prompt both too little concern before the fact, and too much after. Emotionally charged and vivid outcomes promote probability neglect and distort risk perceptions. Aversion to acting on uncertain probabilities saps precautionary action; moral hazard distorts incentives to take care; imperfect learning and social adaptation (e.g., herd‐following, group‐think) complicate forecasting and coordination of individual behaviors and undermine prediction, preparation, and insurance of catastrophic events. Such difficulties raise substantial challenges for normative decision theories prescribing how catastrophe risks should be managed. This article summarizes challenges for catastrophic hazards with uncertain or unpredictable frequencies and severities, hard‐to‐envision and incompletely described decision alternatives and consequences, and individual responses that influence each other. Conceptual models and examples clarify where and why new methods are needed to complement traditional normative decision theories for individuals and groups. For example, prospective and retrospective preferences for risk management alternatives may conflict; procedures for combining individual beliefs or preferences can produce collective decisions that no one favors; and individual choices or behaviors in preparing for possible disasters may have no equilibrium. Recent ideas for building “disaster‐resilient” communities can complement traditional normative decision theories, helping to meet the practical need for better ways to manage risks of extreme and catastrophic events.     

Accommodating stochastic departures from percentile invariance in causal models

Summary.  Consider a clinical trial in which participants are randomized to a single-dose treatment or a placebo control and assume that the adherence level is accurately recorded. If the treatment is effective, then good adherers in the treatment group should do better than poor ad- herers because they received more drug; the treatment group data follow a dose–response curve. But, good adherers to the placebo often do better than poor adherers, so the observed adherence–response in the treatment group cannot be completely attributed to the treatment. Efron and Feldman proposed an adjustment to the observed adherence–response in the treatment group by using the adherence–response in the control group. It relies on a percentile invariance assumption under which each participant's adherence percentile within their assigned treatment group does not depend on the assigned group (active drug or placebo). The Efron and Feldman approach is valid under percentile invariance, but not necessarily under departures from it. We propose an analysis based on a generalization of percentile invariance that allows adherence percentiles to be stochastically permuted across treatment groups, using a broad class of stochastic permutation models. We show that approximate maximum likelihood estimates of the underlying dose–response curve perform well when the stochastic permutation process is correctly specified and are quite robust to model misspecification.     

Bootstrap techniques for proportional hazards models with censored observations

We propose a bootstrap technique for generating pseudo-samples from survival data containing censored observations. This simulation selects a survival time with replacement from the data and then assigns a covariate according to the model of proportional hazards. We also develop a constrained bootstrap technique in which every pseudo-sample has the same distribution of covariate values as does the original, observed data. We use these simulation techniques to estimate the bias and variance of regression coefficients and to approximate the significance levels of goodness-of-fit statistics for testing the assumption of the proportional hazards model.     

On a career in nonprofit management

Careers for Dreamers and Doers: A Guide to Management Careers in the Nonprofit Sector, edited by Lilly Cohen and Dennis R. Young. New York: Foundation Center, 1989, $24.95 paper. Doing Well by Doing Good: The First Complete Guide to Careers in the Nonprofit Sector, by Terry W. McAdam. New York: Penguin Books, 1988. $6.95 paper.