A metadata approach to statistical query processing

Concerning the task of integrating census and survey data from different sources as it is carried out by supranational statistical agencies, a formal metadata approach is investigated which supports data integration and table processing simultaneously. To this end, a metadata model is devised such that statistical query processing is accomplished by means of symbolic reasoning on machine-readable, operative metadata. As in databases, statistical queries are stated as formal expressions specifying declaratively what the intended output is; the operations necessary to retrieve appropriate available source data and to aggregate source data into the requested macrodata are derived mechanically. Using simple mathematics, this paper focuses particularly on the metadata model devised to harmonize semantically related data sources as well as the table model providing the principal data structure of the proposed system. Only an outline of the general design of a statistical information system based on the proposed metadata model is given and the state of development is summarized briefly.     

NEIGHBOUR ANALYSIS WITH ADJUSTMENT FOR INTERPLOT COMPETITION

In field experiments involving a large number of experimental plots, a neighbour analysis can be used to control environmental variation by estimating the trend within blocks. The effect of interplot competition is another important source of variation which has an influence on the estimation of treatment contrasts. To reduce the effect of the variation from these sources and to improve the precision of comparison between treatments, a spatial model is proposed for incorporating both trend effect and interplot competition. It is a modification to the residual maximum likelihood neighbour analysis of Gleeson & Cullis (1987) using the two neighbouring treatment effects to estimate interplot competition. A real example is used to illustrate this methodology. The results indicate that the extended model gives no appreciable difference in standard error of mean differences compared with the model taking into account the trend effect only. However, the rankings of estimated treatment means do differ. More research using both real and simulated data is required before such models that incorporate trend and competition effects can be confidently recommended.     

The Association of Survey Setting and Mode with Self-Reported Health Risk Behaviors among High School Students

This study examined whether the prevalence of self-reportedhealth risk behaviors among high school students varied by surveysetting (school versus home) and mode of administration (paperand pencil versus computer). Students in grades 9 and 11 wereassigned randomly to one of four conditions—school paper-and-pencilinstrument (PAPI), school computer-assisted self-interview (CASI),home PAPI, and home CASI. During the spring of 2004, 4,506 studentscompleted identically worded questionnaires based on the YouthRisk Behavior Survey questionnaire. Logistic regression analysescontrolling for sex, grade, and race/ethnicity revealed thatsetting was associated significantly with the reporting of 30of the 55 risk behaviors examined, and mode was associated significantlywith the reporting of 7 of the 55 behaviors. For all behaviorswith a significant setting main effect, the odds of reportingthe behavior were greater among students who completed questionnairesat school than among students who completed questionnaires athome. For all behaviors with a significant mode main effect,PAPI mode students had lower odds of reporting the behaviorthan CASI mode students. Because social measurement researchassumes that higher prevalence estimates are more valid thanlower estimates, methodological factors shown to increase estimates,such as setting and mode, should be considered when planningsurveys.     

Status Seeking and Social Welfare: Is There Virtue in Vanity?*

Objectives. This article examines how social status rewards can affect social welfare outcomes in the presence of positive and negative externalities. Method. A rational choice approach is used to characterize how individuals respond to incentives to seek high status (and avoid stigma) given existing preferences, technology, and the sources of status defined within a given reference group. Results. In the presence of market failures, the analysis identifies several kinds of status‐related interventions that encourage acts with positive externalities or discourage those with negative externalities, with results that contribute to social well‐being generally. Conclusions. The analysis characterizes ways that formal and informal social mechanisms can make these incentives more effective, and finds abundant evidence that interventions of this kind are widely observed in society today.     

Employment motivations of farm spouses

In the study reported here, reasons for working off the farm and reasons for not working off the farm are examined as a function of gender and socioeconomic characteristics. Women are significantly less likely than men to work off the farm to supplement farm income, and significantly more likely to work off the farm for personal reasons. A logistic regression analysis finds that men, those employed fulltime off the farm, and those with lower household incomes are most likely to cite economic reasons for working off the farm. Men are nearly twice as likely as women to cite time constraints as the primary reason for not working off the farm, while only women cite family responsibilities. Logistic regression reveals that men, those with higher household incomes, and those with livestock operations are most likely to cite lack of time as the primary reason for not working off the farm.Patricia J. Wozniak is Associate Professor in the Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA 70803. Her research interests include family life and family economics, as well as the application of statistics to the social sciences. She received her Ph.D. in Psychology from the University of Wisconsin—Milwaukee.Kathleen K. Scholl is Senior Coordinator of the Economics Team of the Public Policy Institute at the American Association of Retired Persons, 1909 K Street NW, Washington, DC 20049. She received her Ph.D. from Purdue University.     

Robust methods for generalized linear models with nonignorable missing covariates

The EM algorithm is often used for finding the maximum likelihood estimates in generalized linear models with incomplete data. In this article, the author presents a robust method in the framework of the maximum likelihood estimation for fitting generalized linear models when nonignorable covariates are missing. His robust approach is useful for downweighting any influential observations when estimating the model parameters. To avoid computational problems involving irreducibly high‐dimensional integrals, he adopts a Metropolis‐Hastings algorithm based on a Markov chain sampling method. He carries out simulations to investigate the behaviour of the robust estimates in the presence of outliers and missing covariates; furthermore, he compares these estimates to the classical maximum likelihood estimates. Finally, he illustrates his approach using data on the occurrence of delirium in patients operated on for abdominal aortic aneurysm.     

Patient violence. Report of 200 incidents

Studies of assaultive behavior in hospital settings are increasing. Results are often contradictory. These variances are dependent upon the type of hospital, the specific setting, and the disciplines involved in the reporting process. Open recognition that violent incidents occur, and the substitution of fact finding for finger pointing, are the first steps toward prevention and management.