Species loss revisited

Among his books are The Economics of Population Growth; Effort, Opportunity, and Wealth;and Population Matters: People, Resources, Environment, and Immigration (the latter was published by Transaction).     

Using the Delphi process to analyze social policy implementation: A post hoc case from vocational rehabilitation

This study uses a Policy Delphi to discern differences in perspective among and within groups responsible for formulating and implementing vocational rehabilitation policy. Four groups of players were chosen for our analysis: government officials, academics, directors of rehabilitation centers, and the staff who interface with program participants. Significant differences were found between the groups regarding the relative importance of possible legislative goals. This suggests that the failure of vocational rehabilitation policy to promote a work agenda may be attributed to a lack of consensus among policy implementors. The Delphi technique could help policy planners understand the different perspectives within the implementation community, and hence craft more realistic legislation.     

Social Support, Risk-Level and Safety Actions Following Acute Assessment of Suicidal Youth

The purpose of the pilot study was to examine the interrelationship between social support, risk-level, and safety actions for 2 groups of suicidal adolescents (50 attempters and 50 ideators), who had presented for an acute assessment at an outpatient mental health service. A social support model was proposed in which it was thought that information related to patients' social support would impact upon the types of risk-level and safety actions made by clinicians. Data was collected from a total of 100 patient files, utilizing the acute assessment reports (e.g., reports assessing risk of self-harm). Findings show that groups differed significantly on indices of negative support severity, positive support, and risk-level. Limited support was found for the proposed social support model. Limitations and implications for future research are discussed.     

Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation

Bayesian simulation is increasingly exploited in the socialsciences for estimation and inference of model parameters. Butan especially useful (if often overlooked) feature of Bayesiansimulation is that it can be used to estimate any function ofmodel parameters, including "auxiliary" quantities such as goodness-of-fitstatistics, predicted values, and residuals. Bayesian simulationtreats these quantities as if they were missing data, samplingfrom their implied posterior densities. Exploiting this principlealso lets researchers estimate models via Bayesian simulationwhere maximum-likelihood estimation would be intractable. Bayesiansimulation thus provides a unified solution for quantitativesocial science. I elaborate these ideas in a variety of contexts:these include generalized linear models for binary responsesusing data on bill cosponsorship recently reanalyzed in PoliticalAnalysis, item—response models for the measurement ofrespondent's levels of political information in public opinionsurveys, the estimation and analysis of legislators' ideal pointsfrom roll-call data, and outlier-resistant regression estimatesof incumbency advantage in U.S. Congressional elections