The U.S. Health System and Immigration: An Institutional Interpretation1

We examine the institutions that comprise the U.S. health system and their relationship to a surging immigrant population. The clash between the system and this human flow originates in the large number of immigrants who are unauthorized, poor, and uninsured and, hence, unable to access a system largely based on ability to pay. Basic concepts from sociological theory are brought to bear on the analysis of this clash and its consequences. Data from a recently completed study of health institutions in three areas of the United States are used as an empirical basis to illustrate various aspects of this complex relation. Implications of our results for theory and future health policy are discussed.     

Some indications of limits to framing the policy preferences of the civically engaged: Interplay of social capital,race attitudes,and social justice frames

Our experiment, which tested support for a hypothetical social welfare program, found that the civically engaged as a whole were resistant to social justice framing employing universalistic versus particularistic standards. We suggest the lack of a framing effect was due to the use of a preexisting, shared “symbolic racism” frame. Social justice framing did succeed for those whose attitudes toward symbolic racism were ambivalent or neutral. Other factors including sex, income level, political participation, and ideology significantly influenced choice. These results provide some indications of limits to experimental framing of policy preferences of the civically engaged in their institutional settings.     

Conceptual, computational and inferential benefits of the missing data perspective in applied and theoretical statistical problems

This article advocates the following perspective: When confronting a scientific problem, the field of statistics enters by viewing the problem as one where the scientific answer could be calculated if some missing data, hypothetical or real, were available. Thus, statistical effort should be devoted to three steps:

1. formulate the missing data that would allow this calculation,
2. stochastically fill in these missing data, and
3. do the calculations as if the filled-in data were available.

This presentation discusses: conceptual benefits, such as for causal inference using potential outcomes; computational benefits, such as afforded by using the EM algorithm and related data augmentation methods based on MCMC; and inferential benefits, such as valid interval estimation and assessment of assumptions based on multiple imputation.     

Commentary on ‘Science,Measurement, and Validity: Is Completion of Samuel Messick's Synthesis Possible?’ by Keith A. Markus

Social Indicators Research -     

Private Bag: More on the Myths That Population and Immigration Are No Longer Problems

Population and Environment -     

An Evaluation of Stimulus Preference Assessment Methodology in Organizational Behavior Management

The organizational behavior management literature has yielded few stimulus preference assessment methodologies for use with employees. The current investigation compared three preference assessments (ranking, survey, and multiple stimulus without replacement procedures) found in the organizational behavior management literature for their ability to predict reinforcers for direct care staff members’ behavior. In the first experiment all assessments were effective for predicting reinforcers, but the results were limited by a lack of control items in the assessment. In the second experiment the survey and ranking assessments both proved to be effective for identifying reinforcers and neutral or ineffective stimuli. Implications of these results and directions for future research are discussed.     

Systematic analysis of multiple voting rules

For a class of voting rules, which includes Approval and Cumulative Voting, it is shown how to find and analyze all possible outcomes that arise with a specified profile, and, conversely, how to start with a potential region and determine whether there exist supporting profiles. The geometry of these regions is determined by the “Reversal symmetry” portion of a profile; i.e., components of the A\succ B\succ C, C\succ B\succ A{A\succ B\succ C, C\succ B\succ A} type.