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This article examines the antecedents and consequences of residential choice and school transfers within one of the eight largest urban school districts in Texas. This study is based on survey data from a representative sample of parents of K-12 students enrolled in this district. In addition to demographic characteristics of the family, the parent decision-making model of Schneider, Teske, & Marschall (2000) was examined to determine if aspects of this model were useful in understanding the school choices made at the beginning of the school year and the parents' motivation to move to another school at the end. The results provide some support for the view that residential choice is related to enhanced achievement and satisfaction; while, within-district transfers were used more by better-educated White parents who did not qualify as low income. Parents' motivation to move their children to another school was greater when they perceived the school as less receptive to their involvement and their children as less successful in school. (Contains 5 tables.)

We develop a Bayesian hierarchical model for the analysis of ordinal data from multirater ranking studies. The model for a rater's score includes four latent factors: one is a latent item trait determining the true order of items and the other three are the rater's performance characteristics, including bias, discrimination, and measurement error in the ratings. The proposed approach aims at three goals. First, three Bayesian estimators are introduced to estimate the ranks of items. They all show a substantial improvement over the widely used score sums by using the information on the variable skill of the raters. Second, rater performance can be compared based on rater bias, discrimination, and measurement error. Third, a simulation-based decision-theoretic approach is described to determine the number of raters to employ. A simulation study and an analysis based on a grant review data set are presented. (Contains 2 tables and 7 figures.)

According to the recent Nation's Report Card, 12th-graders failed to produce gains on the 2005 National Assessment of Educational Progress (NAEP) despite earning better grades on average. One possible explanation is that 12th-graders were not motivated taking the NAEP, which is a low-stakes test. We develop three Bayesian IRT mixture models to describe the results from a group of examinees including both nonguessers and partial guessers. The first assumes that the guesser answers questions based on his or her knowledge up to a certain test item, and guesses thereafter. The second model assumes that the guesser answers relatively easy questions based on his or her knowledge and guesses randomly on the remaining items. The third is constructed to describe more general low-motivation behavior. It assumes that the guesser gives less and less effort as he or she proceeds through the test. The models can provide not only consistent estimates of IRT parameters but also estimates of each examinee's nonguesser/guesser status and degree of guessing behavior. We show results of a simulation study comparing the performance of the three guessing models to the 2PL-IRT model. Finally, an analysis of real data from a low-stakes test administered to university students is presented.

This study provides insight on how sample recruitment methods may affect reported rates of sexual victimization on college campuses. The study compares sexual victimization rates among students who complete surveys after initial requests with those who complete them only after multiple reminders. Using probability sampling methods, undergraduate students from 12 universities were invited to complete a survey on campus violence; initial invitations were followed with up to five reminders. Women (n = 1,008) and men (n = 344) who completed surveys were categorized as early, middle, or late responders based on the number of reminders required to convert them from non-responders to responders. About 24.2% of women and 15.6% of men reported sexual victimization in the previous two months. In initial analyses, female early and late responders did not differ on sexual victimization, but males did. Male late responders reported higher rates of sexual victimization than early responders. In sensitivity analyses that re-defined early and late responders, women who were early responders reported more sexual victimization than women who were late responders, while men who were early responders reported less sexual victimization than men who were late responders. These findings suggest that researchers may underestimate sexual victimization rates for male college students unless multiple attempts are made to solicit their participation. Researchers are encouraged to utilize multiple reminders to increase research participation. [ABSTRACT FROM AUTHOR]

The National Marine Fisheries Service is the agency charged with estimating the number of fish removed from US oceans by recreational anglers. Two surveys produce data for these estimates; one measures number of angler trips and the other fish caught per trip by species, geography, and time period. Both surveys collect data from people and have the usual sources of nonsampling error afflicting demographic surveys. Due to lack of accessibility to fishing sites, they also have additional sources such as undercoverage. The rare incidence of saltwater fishing among the general population makes the current method costly and time-consuming. Consequently, new ways of obtaining information to supplement or replace the current method are of interest. One is the electronic logbook (ELB); this approach allows anglers to self-report their catch using cellphones or other communication devices. Estimation of catch from these data are possible with estimators using capture-recapture methods, but new sources of nonsampling error are rising. In this article, we examine three sources of nonsampling error in estimators of catch and approximate their biasing effect. We illustrate the method by comparing the effects using data from an ELB study in the Gulf of Mexico. [ABSTRACT FROM AUTHOR]

The article discusses the Bayesian hierarchical model for the analysis of ordinal data from multirater ranking studies. It states that the multirater ordinal models and ordinal item response theory (IRT) models are connected to each other. It uses the analysis provided by a faculty review panel of 10 raters for 39 grant proposals. It mentions that the Bayesian ranking model is constructed under the assumption that the true ranks of items are agreed upon the by item latent traits.

For the problem of dual system estimation, we propose a Bayesian treed capture-recapture model to account for heterogeneity of capture probabilities where individual auxiliary information is available. The model uses a binary tree to partition the covariate space into ‘homogeneous’ regions, within each of which the capture response can be described adequately by a simple model that assumes equal catchability. The attractive features of the proposed model include reduction of correlation bias, robustness and practical flexibility as well as simplicity and interpretability. In addition, it provides a systematic and effective way of forming post-strata for the Sekar–Deming estimator of population size. We compare the performance of estimators based on this model to those of alternative estimators in three scenarios. [ABSTRACT FROM PUBLISHER]