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V. IMPLICATIONS This Article's conclusion--that the immigration appeals process fails to promote uniformity because it rarely reviews removal orders issued by harsh judges to immigrants who lack lawyers at the [...]

Within the same immigration court, some immigration judges are up to three times more likely than their colleagues to order immigrants deported. Theories of appeal and of administrative adjudication imply [...]

Prior research considers the extent to which public assistance recipients' charitable activity differs from the habits of the general population. Although receiving public assistance is negatively associated with donating money, the relationship to volunteering is unclear. In response to challenges overcoming selection bias, we conducted a multivariate cluster-based subgroup analysis to reduce bias in our claims about the ways in which public assistance receipt affects charitable activity. This innovative approach to dealing with the problem of selection bias has implications and applications across the social sciences. KEY WORDS: public assistance, welfare, charity, selection bias, cluster analysis, Much scholarship is dedicated to understanding the factors that influence one to donate time or money to charitable causes; but only a small portion of that scholarship considers those within [...]

Dealing with spam is very costly, and many organizations have tried to reduce spam-related costs by installing spam filters. Relying on modern econometric methods to reduce the selection bias of [...]

A novel modeling approach was used to investigate the residence times of Oyster Cove, an artificial canal system connected to adjacent water bodies by unidirectional and bidirectional flow structures. A field program was carried out to evaluate and quantify the exchange of water through the system of flow structures and to gain an understanding of the mixing dynamics within the artificial canal. Results from the field program were also used to validate a three-dimensional circulation model and a flushing model used to quantify the existing residence time of the canal system. Finally, the model was used to compare several hypothetical design alternatives, to identify the effect on the canal's residence time, by changing the positions of the flow structures and using different combinations of structures. The comparison showed the significant improvements in residence times that could be achieved. DOI: 10.1061/(ASCE)WW.1943-5460.0000029 CE Database subject headings: Hydrodynamics; Hydraulic models; Flushing; Canals; Hydraulic structures; Case studies. Author keywords: Hydrodynamic modeling; Flushing; Canal system; Hydraulic structure; Flow structure; Residence time.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.forsciint.2009.07.022 Byline: Marjan Sjerps (a), Ronald Meester (b) Keywords: Selection effects; Database search; Evaluation of evidence; Interpretation; Post-hoc correction; Selection bias Abstract: We argue that it is, in principle, not difficult to deal with selection effects in forensic science. If a suspect is selected through a process that is related to the forensic evidence, then the strength of the evidence will be compensated by very small prior odds. No further correction is necessary. The same is true for so-called data-dependent hypotheses. These are allowed, since if the hypothesis is really 'tailored around' the evidence, the evidential value will be high but the prior odds will compensate for that. The assessment of the prior odds is outside the scope of the forensic scientist, but he should make lawmakers, judges and juries aware of the phenomenon. This discussion applies to many situations -- we discuss four concrete examples. Author Affiliation: (a) Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB DEN HAAG, Postbus 24044, 2490 AA DEN HAAG, The Netherlands (b) Vrije Universiteit Amsterdam, Department of mathematics, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands Article History: Received 21 November 2008; Revised 2 July 2009; Accepted 28 July 2009

Objective: This paper uses data provided by the Police Service for Northern Ireland (PSNI) to compare the characteristics and outcomes of reported sexual offences involving child and adult victims and explore the factors associated with case outcomes. Method: PSNI provided data on 8,789 sexual offences recorded between April 2001 and March 2006. Case outcomes were based on whether a case was recorded by police as having sufficient evidence to summons, charge, or caution an offender (detected). Where an offender was summonsed, charged, or cautioned, this is classified as detection with a formal sanction. A case can also be classified as 'detected' without a formal sanction. The analysis focused on two key categories of detection without formal sanction: cases in which the police deem there to be sufficient evidence to charge an offender but took no further action because the victim did not wish to prosecute, or because the police or the Public Prosecution Service (PPS) decided that no useful purpose would be served by proceeding. Results: The analysis confirmed that the characteristics of recorded sexual offences involving adult and child victims vary significantly according to gender, offence type, the timing of report and victim-offender relationship. Almost half of child sex abuse cases are not detected by police and a quarter do not proceed through the criminal justice system because either the victim declines to prosecute or the Police/PPS decide not to proceed. Only one in five child cases involved detection with a formal sanction. Child groups with lower detection with formal sanction rates included children under 5, teenagers, those who do not report when the abuse occurs but disclose at a later date; and those who experience abuse at the hands of peers and adults known to them but not related. The analysis also highlighted variation in formal sanction rates depending on where the offence was reported. Conclusions: Consideration needs to be given to improving the criminal justice response to specific child groups as well as monitoring detection rates in different police areas in order to address potential practice variation. Practice implications: Consideration needs to be given to improving the professional response in relation to with particularly lower detection with formal sanction rates. There is also a need to monitor case outcomes to ensure that child victims in different areas receive a similar service. Keywords: Criminal justice system Child sexual abuse Attrition Conviction rate Sexual offences Child maltreatment

Corrections for dynamic selection bias on forecasting accuracy in a multi-period stay/leave model do not necessarily produce more accurate forecasts than uncorrected techniques. Differences in relative accuracy vary directly with selection bias and inversely with retention percentage.

Treatment selection bias leads to an inaccurate estimation of treatment effects as applied to specific sites or problem locations. Treatment selection bias is a major source of inconsistency in the results obtained from conventional before and after and cross-sectional models. One of the major expressions of treatment selection bias concerns the use of collision occurrence data in justifying intervention. For example, in highway safety field, a treatment is often introduced at a given site based on its high collision experience. Under normal conditions we would expect these collision numbers to return to a lower long term expected value, regardless of intervention. For treated sites, conventional observational models ascribe this reduction in collisions to the given treatment. This results in an overestimation of treatment effect. In this paper, a propensity score model is introduced that deals explicitly with treatment selection bias. The model is applied to Canadian highway--railway grade crossings data to estimate reductions in collision subject to upgrades in warning devices. The results of the propensity score model are compared for similar types of treatments to a number of before and after and cross-sectional models for both U.S. and Canadian data. The propensity score method is shown to reduce treatment selection bias and has probable merit that need to be further examined. DOI: 10.1061/(ASCE)0733-947X(2007)133:2(112) CE Database subject headings: Safety; Railroad grade crossings; Evaluation; Statistics.

Suppose we want to know whether the ethics of persons with one characteristic differ from the ethics of persons having another characteristic. Self-selection bias occurs if people have control over that characteristic. When there is self-selection bias, we cannot be sure observed differences in ethics are correlated with the characteristic or are the result of individual self-selection. Self-selection bias is germane to many important business ethics questions. In this paper I explain what self-selection bias is, how it relates to business ethics research, and how to correct for it. I also illustrate the correction process in an empirical analysis of the effect of organizational rank on worker ethics. Using data from the European Values Survey, I find that being a supervisor is positively correlated with worker ethics. However, I also find a negative self-selection effect. Workers with relatively lower ethics are selected into supervisory roles.

Byline: Eric Bradlow (1) Keywords: Instrumental variables; randomized designs; self-selection bias Abstract: When subjects choose their own treatment (are self-selected into treatments based on their varying compliance with assignment to treatment states), many of the well-developed techniques of randomization-based experimental design and analysis are no longer applicable. With random assignment to treatment and control conditions, we can reasonable assume that over many replications, the two groups will be similar in all respects (observed and not observed) but that of receiving the treatment. With non-random assignment, it may be true that the groups differ on the variable of interest prior to receiving the treatment, and that a measured post-treatment difference (or lack thereof) will be erroneously attributed to the treatment. Our approach involves making all subjects aware of the availability of the treatment, but then offering extra encouragement (an encouragement design) to participate in the treatment to a randomly selected half of the population. If the encouragement is successful, we show how this leads to an estimable treatment effect (with associated asymptotic standard error). An illustrative example using fictitious data is presented in which we measure the effects of coupon books on store purchase behavior. A randomly selected half of the customers at a given store are assigned to receive coupon books as part of a package of store materials (the treatment condition) whereas the other half are not. We overlay on this initial randomized design an encouragement condition in which a second randomly chosen half of the store's customers receive their materials from an employee (encouraging participation with the treatment), and for the other half the materials were simply placed on a table. The market share of persons who chose to take the coupon book was 44% and 20% from those who did not. A naive estimate of the effect of the treatment is 24% however this estimate ignores the self-selection bias (those persons assigned to receive the coupon book but refused to take it, and those not assigned who picked one up anyway). Using the encouragement design estimator derived leads to a lower estimated impact of the treatment at 5%. The difference in these results may have significant impact on the choice by store management to utilize employees to hand out promotional materials. Author Affiliation: (1) Wharton School of Business of the University of Pennsylvania, 3620 Locust Walk, Suite 1400 SH-DH, Philadelphia, PA, 19104 Article History: Registration Date: 02/10/2004 Article note: Eric T. Bradlow is Assistant Professor of Marketing and Statistics

This descriptive study examined factors associated with adolescent girls continuing in a randomized controlled trial of an HIV-prevention intervention after enrollment based on an analysis of locator form and demographic data. When the authors piloted the study, 48% (62 of 129) of the participants (aged 15-19 years) continued in the intervention after enrollment. Once a formal recruitment and retention protocol was initiated, 80% (264 of 334 girls 15-19 years old) returned after enrollment. Chi-square analyses found statistically significant differences (p < .001 to .05) between the girls who continued (n = 264) and those who did not (n = 68), associated with recruiter's experience, recruitment method, contact status, and parental awareness of study participation. Living situation was not relevant, nor was being able to receive email or cell phone messages. These findings suggest factors to consider when developing and implementing recruitment protocols to enhance retention in clinical trials involving adolescent girls.

A study was conducted to examine the impact, detection, and amelioration of selection effects within benefit transfer. These effects may arise from sociopolitical influences, researcher choices, and meta-analyst choices. Findings indicate that metaregression analysis may be the best tool for detecting corrective measures for selection biases.