New imperfect rankings models for ranked set sampling

Ranked set sampling is a sampling approach that leads to improved statistical inference in situations where the units to be sampled can be ranked relative to each other prior to formal measurement. This ranking may be done either by subjective judgment or according to an auxiliary variable, and it need not be completely accurate. In fact, results in the literature have shown that no matter how poor the quality of the ranking, procedures based on ranked set sampling tend to be at least as efficient as procedures based on simple random sampling. However, efforts to quantify the gains in efficiency for ranked set sampling procedures have been hampered by a shortage of available models for imperfect rankings. In this paper, we introduce a new class of models for imperfect rankings, and we provide a rigorous proof that essentially any reasonable model for imperfect rankings is a limit of models in this class. We then describe a specific, easily applied method for selecting an appropriate imperfect rankings model from the class.     

Scissors, Surveys, And Psycho-Prophylactics: Prenatal Health Care Campaigns And State Building In China, 1949–1954

This paper examines how the Chinese state's prenatal health care campaigns of the early 1950s attempted to redefine women's social and political roles. The replacement of local midwifing practices with a uniform birthing method in order to radically reduce infant and mother mortality entailed complex ramifications regarding the relationship of women vis-a-vis the state. Campaigns involved demonizing "traditional" midwifing, promoting a statistical vision of female reproductivity and children as national resources, and the isolation of individual mothers as directly responsible to the state for managing reproduction for the national interest. In sum, a physiological definition of gender was used to open women's bodies to state management. Utopic visions of painless childbirth and of the socialist nation as a giant new family were used to promote participation in grassroots campaigns, but the sources also point to forms of local resistance to the micro-level reorganization of power these campaigns intended.     

Brief Emotion Focused Family Therapy: An Intervention for Parents of Children and Adolescents with Mental Health Issues

This study evaluated the 2‐day intensive modality of Emotion Focused Family Therapy (EFFT). The intervention attempts to prepare parents to take a primary role in their child's recovery from a range of mental health issues. One hundred and twenty‐four parents completed the intervention and provided data a week prior to intervention, post‐intervention and at 4‐month follow‐up. Results include significantly reduced parent blocks and increased parental self‐efficacy in relation to involvement in their child's recovery, as well as significant improvement in child symptomatology. The findings confirm positive results from an earlier pilot study involving eating disorders and demonstrate the potential for EFFT as an intervention for a range of clinical problems in children and youth.     

The limitations of using school league tables to inform school choice

Summary. In England, so-called 'league tables' based on examination results and test scores are published annually, ostensibly to inform parental choice of secondary schools. A crucial limitation of these tables is that the most recent published information is based on the current performance of a cohort of pupils who entered secondary schools several years earlier, whereas for choosing a school it is the future performance of the current cohort that is of interest. We show that there is substantial uncertainty in predicting such future performance and that incorporating this uncertainty leads to a situation where only a handful of schools' future performances can be separated from both the overall mean and from one another with an acceptable degree of precision. This suggests that school league tables, including value-added tables, have very little to offer as guides to school choice.     

Efficacy of Statistical Outlier Analysis for Monitoring Quality of Care

Researchers have proposed that hospitals with excessive statistically unexplained mortality rates are more likely to have quality-of-care problems. The U.S. Health Care Financing Administration currently uses this statistical “outlier” approach to screen for poor quality in hospitals. Little is known, however, about the validity of this technique, since direct measures of quality are difficult to obtain. We use Monte Carlo methods to evaluate the performance of the outlier technique as parameters of the true mortality process are varied. Results indicate that the screening ability of the technique may be very sensitive to how widespread quality-related mortality is among hospitals but insensitive to other factors generally thought to be important.     

Bayes linear analysis for graphical models: The geometric approach to local computation and interpretive graphics

This paper concerns the geometric treatment of graphical models using Bayes linear methods. We introduce Bayes linear separation as a second order generalised conditional independence relation, and Bayes linear graphical models are constructed using this property. A system of interpretive and diagnostic shadings are given, which summarise the analysis over the associated moral graph. Principles of local computation are outlined for the graphical models, and an algorithm for implementing such computation over the junction tree is described. The approach is illustrated with two examples. The first concerns sales forecasting using a multivariate dynamic linear model. The second concerns inference for the error variance matrices of the model for sales, and illustrates the generality of our geometric approach by treating the matrices directly as random objects. The examples are implemented using a freely available set of object-oriented programming tools for Bayes linear local computation and graphical diagnostic display.     

An omnibus two-sample test for ranked-set sampling data

We develop an omnibus two-sample test for ranked-set sampling (RSS) data. The test statistic is the conditional probability of seeing the observed sequence of ranks in the combined sample, given the observed sequences within the separate samples. We compare the test to existing tests under perfect rankings, finding that it can outperform existing tests in terms of power, particularly when the set size is large. The test does not maintain its level under imperfect rankings. However, one can create a permutation version of the test that is comparable in power to the basic test under perfect rankings and also maintains its level under imperfect rankings. Both tests extend naturally to judgment post-stratification, unbalanced RSS, and even RSS with multiple set sizes. Interestingly, the tests have no simple random sampling analog.