Testing an Integrative Contextual Career Development Model With Adolescents From High‐Poverty Urban Areas

Variations in the economic recovery rate across the United States have led to even greater chasms that separate the employed, unemployed, and underemployed (DeSilver, 2017 ). Therefore, understanding and supporting the career development of future generations is critical—especially for those who live outside the context of social privilege. The authors examined the applicability of the integrative contextual model of career development (ICM; Lapan, 2004 ) to a sample of 220 adolescents (129 boys, 91 girls) from a high‐poverty urban area. Results indicated that a canonical variate comprising foundational ICM skills (e.g., career exploration, goal setting) predicted a variate composed of ICM outcomes (e.g., self‐efficacy, vocational identity), thus supporting the usefulness of the ICM framework for this population. The skill of setting viable career goals was an especially strong predictor of outcomes. Implications for career counseling with adolescents living in high‐poverty urban areas and directions for future research are discussed.     

State work and the testing concours of citizenship

Anyone trying to be a citizen has to pass through a set of practices trying to be a state. This paper investigates some of the ways testing practices calibrate citizens, and in doing so, perform “the state.” The paper focuses on three forms of citizenship testing, which it considers exemplary forms of “state work,” and which all, in various ways, concern “migration.” First, the constitution of a “border crossing,” which requires an identity test configured by deceptibility. Second, the Dutch asylum process, in which “being gay” can, in certain cases, be reason for being granted asylum, but where “being gay” is also the outcome of an examination organized by suspicion. And third, the Dutch measurement of immigrants’ “integration,” which is comprised of a testing process in which such factishes as “being a member of society” and “being modern” surface. Citizenship is analyzed in this paper as accrued and (re)configured along a migration trajectory that takes shape as a testing concours, meaning that subjects become citizens along a trajectory of testing practices. In contributing both to work on states and citizenship, and to work on testing, this paper thus puts forward the concept of citizenship testing as state work, where “state work is the term for that kind of labor that most knows itself as comparison, equivalency, and exchange in the social realm” (Harney, 2002, pp. 10–11). Throughout the testing practices discussed here, comparison, equivalency, and exchange figure prominently as the practical achievements of crafting states and citizens.     

Robust block bootstrap panel predictability tests*

This article develops two block bootstrap-based panel predictability test procedures that are valid under very general conditions. Some of the allowable features include cross-sectional dependence, heterogeneous predictive slopes, persistent predictors, and complex error dynamics, including cross-unit endogeneity. While the first test procedure tests if there is any predictability at all, the second procedure determines the units for which predictability holds in case of a rejection by the first. A weak unit root framework is adopted to allow persistent predictors, and a novel theory is developed to establish asymptotic validity of the proposed bootstrap. Simulations are used to evaluate the performance of our tests in small samples, and their implementation is illustrated through an empirical application to stock returns.     

Statistical testing strategies for assessing treatment efficacy and marker accuracy in phase III trials

When a candidate predictive marker is available, but evidence on its predictive ability is not sufficiently reliable, all‐comers trials with marker stratification are frequently conducted. We propose a framework for planning and evaluating prospective testing strategies in confirmatory, phase III marker‐stratified clinical trials based on a natural assumption on heterogeneity of treatment effects across marker‐defined subpopulations, where weak rather than strong control is permitted for multiple population tests. For phase III marker‐stratified trials, it is expected that treatment efficacy is established in a particular patient population, possibly in a marker‐defined subpopulation, and that the marker accuracy is assessed when the marker is used to restrict the indication or labelling of the treatment to a marker‐based subpopulation, ie, assessment of the clinical validity of the marker. In this paper, we develop statistical testing strategies based on criteria that are explicitly designated to the marker assessment, including those examining treatment effects in marker‐negative patients. As existing and developed statistical testing strategies can assert treatment efficacy for either the overall patient population or the marker‐positive subpopulation, we also develop criteria for evaluating the operating characteristics of the statistical testing strategies based on the probabilities of asserting treatment efficacy across marker subpopulations. Numerical evaluations to compare the statistical testing strategies based on the developed criteria are provided.     

Assessment of individual bioequivalence using sufficient bootstrap procedure

This paper proposes a sufficient bootstrap method, which uses only the unique observations in the resamples, to assess the individual bioequivalence under 2 × 4 randomized crossover design. The finite sample performance of the proposed method is illustrated by extensive Monte Carlo simulations as well as a real‐experimental data set, and the results are compared with those obtained by the traditional bootstrap technique. Our records reveal that the proposed method is a good competitor or even better than the classical percentile bootstrap confidence limits.     

Testing the feasibility of a value‐added model of school quality in a low‐income country

Value‐added models (VAMS) are commonly used in high‐income countries for measuring the quality of teachers and schools, on the grounds that they are a fairer reflection of true quality than simple average test scores, as they account for differences in student intake. Not accounting for student's prior test scores can give a misleading impression of school quality. In this article, we adapt the current VAM of secondary school quality to the Ugandan context, and test its robustness. Using official test score data from Uganda, we test the robustness of the model to a range of different empirical specifications, including sensitivity to the inclusion of controls for student socioeconomic status. We find that the model is robust to a variety of different specifications and control variables. The VAM is low cost and has the potential to provide a clearer signal to parents, teachers, schools, and policy‐makers about how much learning is actually happening in different schools. This approach could be carried out at low cost in a wide range of low‐income countries that have similar testing regimes.     

Incremental Sampling Methodology: Applications for Background Screening Assessments

This article presents the findings from a numerical simulation study that was conducted to evaluate the performance of alternative statistical analysis methods for background screening assessments when data sets are generated with incremental sampling methods (ISMs). A wide range of background and site conditions are represented in order to test different ISM sampling designs. Both hypothesis tests and upper tolerance limit (UTL) screening methods were implemented following U.S. Environmental Protection Agency (USEPA) guidance for specifying error rates. The simulations show that hypothesis testing using two‐sample t ‐tests can meet standard performance criteria under a wide range of conditions, even with relatively small sample sizes. Key factors that affect the performance include unequal population variances and small absolute differences in population means. UTL methods are generally not recommended due to conceptual limitations in the technique when applied to ISM data sets from single decision units and due to insufficient power given standard statistical sample sizes from ISM.