Responsive Survey Design,Demographic Data Collection,and Models of Demographic Behavior

To address declining response rates and rising data-collection costs, survey methodologists have devised new techniques for using process data (“paradata”) to address nonresponse by altering the survey design dynamically during data collection. We investigate the substantive consequences of responsive survey design—tools that use paradata to improve the representative qualities of surveys and control costs. By improving representation of reluctant respondents, responsive design can change our understanding of the topic being studied. Using the National Survey of Family Growth Cycle 6, we illustrate how responsive survey design can shape both demographic estimates and models of demographic behaviors based on survey data. By juxtaposing measures from regular and responsive data collection phases, we document how special efforts to interview reluctant respondents may affect demographic estimates. Results demonstrate the potential of responsive survey design to change the quality of demographic research based on survey data.     

The joint paediatric–forensic examination in child abuse

The investigation of alleged child abuse should be multi-professional and coordinated to minimize distress to the child and family. Much attention has focused on the joint interview process, with less prominence being given to the medical component of the initial investigation. No standard practice exists in the United Kingdom regarding the conduct of the medical examination or in the training requirements of the doctors who perform the examination. We present a model of practice which has evolved in Edinburgh and the Lothians, drawing on the combined expertise and experience of paediatricians and police surgeons, which has operated for over 2 years; and propose a formal accreditation process for doctors who wish to specialize in the field of child abuse. © 1998 John Wiley & Sons, Ltd.     

Replanning the Master Production Schedule for a Capacity-Constrained Job Shop

This research examines the use of both frozen and replanning intervals for planning the master production schedule (MPS) for a capacity-constrained job shop. The results show that forecast error, demand lumpiness, setup time, planned lead time, and order size have a greater impact on the mean total backlog, total inventory, and number of setups than the frozen and replanning intervals. The study also shows that a repetitive lot dispatching rule reduces the importance of lot sizing, and a combination of repetitive lot dispatching rule and single-period order size consistently produces the lowest mean total backlog and total inventory. The results also indicate that rescheduling the open orders every period produces a lower mean total backlog and total inventory when the forecast errors are large relative to the order sizes. This result suggests that the due date of an open order should be updated only when a significant portion of the order is actually needed on the new due date.     

Designing Lateral Organizations: An Analysis of the Benefits,Costs, and Enablers of Nonhierarchical Organizational Forms*

Twenty-first century organizations will require designs that enable them to cope with turbulent environments. Organizations have experimented with lateral organizational designs for this purpose, but research evidence concerning these forms is sparse. We analyzed data obtained from 512 employees within eight diverse organizations implementing flexible lateral organizations. Using a sequence comparison methodology, we were able to identify and categorize the major costs, benefits, and enablers associated with implementing these forms of complex organizations. Propositions for effectively managing lateral relations were tested and managerial implications were explored.     

Generalized phase I-II designs to increase long term therapeutic success rate

Designs for early phase dose finding clinical trials typically are either phase I based on toxicity, or phase I-II based on toxicity and efficacy. These designs rely on the implicit assumption that the dose of an experimental agent chosen using these short-term outcomes will maximize the agent's long-term therapeutic success rate. In many clinical settings, this assumption is not true. A dose selected in an early phase oncology trial may give suboptimal progression-free survival or overall survival time, often due to a high rate of relapse following response. To address this problem, a new family of Bayesian generalized phase I-II designs is proposed. First, a conventional phase I-II design based on short-term outcomes is used to identify a set of candidate doses, rather than selecting one dose. Additional patients then are randomized among the candidates, patients are followed for a predefined longer time period, and a final dose is selected to maximize the long-term therapeutic success rate, defined in terms of duration of response. Dose-specific sample sizes in the randomization are determined adaptively to obtain a desired level of selection reliability. The design was motivated by a phase I-II trial to find an optimal dose of natural killer cells as targeted immunotherapy for recurrent or treatment-resistant B-cell hematologic malignancies. A simulation study shows that, under a range of scenarios in the context of this trial, the proposed design has much better performance than two conventional phase I-II designs.     

Organizational Learning in Global Purchasing: A Model and Test of Internal Users and Corporate Buyers*

This research examines a model centered on organizational learning in purchasing. Two different studies are conducted to test the hypotheses among purchasing users (Study 1) and buyers (Study 2). The user sample consists of users representing 355 strategic business units of a Fortune 500 multinational corporation. The buyer sample consists of corporate buyers of 200 multinational corporations drawn from the membership directory of the National Association of Purchasing Management (NAPM). In each study, the focus is on the learning relationships between corporate buyers and internal users in the purchasing organization. Based on the two studies, the results suggest that organizational learning in the purchasing process is influenced by the organizational culture factors of localness, transformational leadership, and openness. Organizational learning has a positive effect on information processing in the purchasing system, which, in turn, has a positive influence on the cycle time of the purchasing process.     

Masking methods that preserve positivity constraints in microdata

Statistical agencies have conflicting obligations to protect confidential information provided by respondents to surveys or censuses and to make data available for research and planning activities. When the microdata themselves are to be released, in order to achieve these conflicting objectives, statistical agencies apply statistical disclosure limitation (SDL) methods to the data, such as noise addition, swapping or microaggregation. Some of these methods do not preserve important structure and constraints in the data, such as positivity of some attributes or inequality constraints between attributes. Failure to preserve constraints is not only problematic in terms of data utility, but also may increase disclosure risk.In this paper, we describe a method for SDL that preserves both positivity of attributes and the mean vector and covariance matrix of the original data. The basis of the method is to apply multiplicative noise with the proper, data-dependent covariance structure.     

A computational framework for empirical Bayes inference

In empirical Bayes inference one is typically interested in sampling from the posterior distribution of a parameter with a hyper-parameter set to its maximum likelihood estimate. This is often problematic particularly when the likelihood function of the hyper-parameter is not available in closed form and the posterior distribution is intractable. Previous works have dealt with this problem using a multi-step approach based on the EM algorithm and Markov Chain Monte Carlo (MCMC). We propose a framework based on recent developments in adaptive MCMC, where this problem is addressed more efficiently using a single Monte Carlo run. We discuss the convergence of the algorithm and its connection with the EM algorithm. We apply our algorithm to the Bayesian Lasso of Park and Casella (J. Am. Stat. Assoc. 103:681–686, 2008) and on the empirical Bayes variable selection of George and Foster (J. Am. Stat. Assoc. 87:731–747, 2000).     

Separate Spheres or Increasing Equality? Changing Gender Beliefs in Postwar Japan

This research investigates change in gender beliefs in Japan during a period of economic hard times in the late 1990s. Using data from the International Social Survey Programme on the Japanese population from 1994 (n = 1,054) and 2002 (n = 872), we examined how cohort replacement and intracohort change contributed to changes in gender beliefs. We found important differences from the patterns of change reported for many Western countries, namely, a decoupling between societal trends in the female labor force participation rate and beliefs about gender. Such differences may be attributable to factors such as the high societal valuation of the housewife role compared to that in other postindustrial countries and sanctions against full‐time employment for women in Japan.