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.     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

The Story of Belle, Minnie, Louise and the Sovjets: throwing light on the dark side of an institution

Our post-modernist story is composed as a narrative analysis of the lived experiences of Belle and Louise--two women with 'learning difficulties'--and our ethnographic field notes while doing narrative inquiry. The narratives mirror a shared construction of meaning and broaden our understanding--throwing light on the dark side of an institution. The narrative analysis points out a clear illustration of power dynamics and discourses in their lives, and shows how the women boast of resilience and offer (hidden) resistance. This paper particularly illuminates the individual, personal and even private celebration of activism and self-empowerment of Belle and Louise. Their vivid stories take us on an enthralling journey, getting to know their world through their eyes.     

Empirical Non-Parametric Control Charts: Estimation Effects and Corrections

Owing to the extreme quantiles involved, standard control charts are very sensitive to the effects of parameter estimation and non-normality. More general parametric charts have been devised to deal with the latter complication and corrections have been derived to compensate for the estimation step, both under normal and parametric models. The resulting procedures offer a satisfactory solution over a broad range of underlying distributions. However, situations do occur where even such a large model is inadequate and nothing remains but to consider non- parametric charts. In principle, these form ideal solutions, but the problem is that huge sample sizes are required for the estimation step. Otherwise the resulting stochastic error is so large that the chart is very unstable, a disadvantage that seems to outweigh the advantage of avoiding the model error from the parametric case. Here we analyse under what conditions non-parametric charts actually become feasible alternatives for their parametric counterparts. In particular, corrected versions are suggested for which a possible change point is reached at sample sizes that are markedly less huge (but still larger than the customary range). These corrections serve to control the behaviour during in-control (markedly wrong outcomes of the estimates only occur sufficiently rarely). The price for this protection will clearly be some loss of detection power during out-of-control. A change point comes in view as soon as this loss can be made sufficiently small.     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

Comment on “compromised birth outcomes and infant mortality among racial and ethnic groups”

Frisbie, Forbes, and Pullum (1996) show that it is meaningful to account for low birth weight, preterm delivery, and intrauterine growth-retardation when analyzing differences in compromised birth outcomes and infant mortality among racial and ethnic groups. I compare their findings for the 1987 U.S. birth cohort with findings for the 1988 U.S. birth cohort, using linked birth and infant death vital statistics from the National Center for Health Statistics. I focus on their calculation of fetal growth curves, which are highly at odds with the curves commonly used in the obstetric and pediatric literature. I compare birth outcome distributions and infant death probabilities using Frisbie et al. 's method and other standards. I conclude that Frisbie et al. 's method is not suited for the study of intrauterine growth-retardation at the population level because of the major flaws in gestational age measurement that exist in the type of data they use. An appropriate alternative is to apply a standard of normal intrauterine growth derived from antenatal estimation of fetal weight-for-gestational-age to the vital statistics data.     

Predictors of Antisocial and Prosocial Behavior in an Adolescent Sports Context

This study examined antisocial and prosocial behavior of N = 439 adolescent athletes between 14 and 17 years of age (67 teams). Multi‐level analyses showed that team membership explained 20 and 13 percent of the variance in antisocial and prosocial behavior in the sports context, respectively. The team effects suggest that aggregating antisocial or prosocial adolescents within teams may partially explain differences in antisocial and prosocial behavior among athletes in the sports context. A trend was found toward a relation between higher levels of moral reasoning within teams, and less antisocial behavior in the sports context. Favorable moral atmosphere was positively associated with more prosocial behavior in the sports context. Finally, supportive coach–athlete relationships were associated with both less antisocial and more prosocial behavior in the sports context.     

Local influence diagnostics for incomplete overdispersed longitudinal counts

We develop local influence diagnostics to detect influential subjects when generalized linear mixed models are fitted to incomplete longitudinal overdispersed count data. The focus is on the influence stemming from the dropout model specification. In particular, the effect of small perturbations around an MAR specification are examined. The method is applied to data from a longitudinal clinical trial in epileptic patients. The effect on models allowing for overdispersion is contrasted with that on models that do not.