The application of bias to discriminant analysis

When classification rules are constructed using sample estimatest it is known that the probability of misclassification is not minimized. This article introduces a biased minimum X² rule to classify items from a multivariate normal population. Using the principle of variance reduction, the probability of misclassification is reduced when the biased procedure is employed. Results of sampling experiments over a broad range of conditions are provided to demonstrate this improvement.     

Exploring Networking of Third Sector Organizations: A Case Study Based on the Quartieri Spagnoli Neighborhood in Naples (Italy)

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - In the last decades, European Third Sector Organizations have been increasingly affected by marketization and the...     

A randomized response procedure for multiple-sensitive questions

The Randomized Response (RR) technique is a well-established interview procedure which guarantees privacy protection in social surveys dealing with sensitive items. The RR method assumes a stochastic mechanism to create uncertainty about the true status of the respondents in order to ensure privacy protection and to avoid tendencies to dissimulate or respond in a socially desirable direction. A very general model for the RR method was introduced by Franklin (Commun Stat Theory Methods 18:489?C505, 1989) when a single-sensitive question is under study. However, since social surveys are often based on questionnaires containing more than a single-sensitive question, the analysis of multivariate RR data is of considerable interest. This paper focuses on the generalization of the Franklin model in a multiple-sensitive question setting and on related inferential issues.     

Identity and Identification in Industrial Districts

The aim of this article is to extend theconcept of identity and identification to thestudy of industrial districts. The theoreticalmodel presented explains how identity andidentification occur within localizedproduction networks and examines how theprocess of identification affects theindustrial districts' performance andcompetitiveness. The study adopts a multi-disciplinary approachwhich integrates the traditional frameworkrooted in the fields of regional studies,governance structures, and management theorywith some theoretical contributions developedin the field of social psychology.The concept of district identity is proposed asthe missing link between the traditionalmacro-perspective based on shared culture,history, and values and a micro-foundedapproach to the study of industrial districts.It is stated that, through the process ofidentification, district identity explains atthe actor level the adoption of distinctivebehaviors, such as commitment, trust, andcooperation, which represent the ultimatesource of competitive advantage of thedistrict model.     

Identifying Guttman Structures in Incomplete Rasch Datasets

In applications of IRT, it often happens that many examinees omit a substantial proportion of item responses. This can occur for various reasons, though it may well be due to no more than the simple fact of design incompleteness. In such circumstances, literature not infrequently refers to various types of estimation problem, often in terms of generic “convergence problems” in the software used to estimate model parameters. With reference to the Partial Credit Model and the instance of data missing at random, this article demonstrates that as their number increases, so does that of anomalous datasets, intended as those not corresponding to a finite estimate of (the vector parameter that identifies) the model. Moreover, the necessary and sufficient conditions for the existence and uniqueness of the maximum likelihood estimation of the Partial Credit Model (and hence, in particular, the Rasch model) in the case of incomplete data are given – with reference to the model in its more general form, the number of response categories varying according to item. A taxonomy of possible cases of anomaly is then presented, together with an algorithm useful in diagnostics.     

Building evaluation capacity: definitional and practical implications from an Australian case study

Internationally, evaluation capacity-building activities have mushroomed as demands have increased for government-funded programs to demonstrate that they are effective and efficient. Despite this, there is a lack of clarity in the way in which evaluation capacity-building is defined and conceptualized. The current paper presents a case study of a national evaluation capacity building exercise that we are in the midst of conducting in Australia, and discusses the findings in relation to definitional, conceptual and practical issues. Specifically, we describe an evaluation capacity building exercise involving over 100 mental health projects, detailing the methods that we employ, some of the challenges that we have faced, and the benefits we feel we are achieving. Our key message is that definitions of evaluation capacity-building should not only make reference to equipping organizations to routinely conduct evaluations, but should also stress the varied uses to which evaluation findings can be put. In addition, such definitions should acknowledge some of the valuable by-products of evaluation capacity building activities, such as the development of shared understandings of the program or project being evaluated.     

The effect of natural disasters on nuptiality: evidence from L’Aquila earthquake (Italy)

Population and Environment - Drawing on socio-psychology and economics literature contributions, this paper provides an original conceptual framework examining whether and how natural disasters...     

A Semi‐empirical Bayesian Chart to Monitor Weibull Percentiles

This paper develops a Bayesian control chart for the percentiles of the Weibull distribution, when both its in‐control and out‐of‐control parameters are unknown. The Bayesian approach enhances parameter estimates for small sample sizes that occur when monitoring rare events such as in high‐reliability applications. The chart monitors the parameters of the Weibull distribution directly, instead of transforming the data as most Weibull‐based charts do in order to meet normality assumption. The chart uses accumulated knowledge resulting from the likelihood of the current sample combined with the information given by both the initial prior knowledge and all the past samples. The chart is adapting because its control limits change (e.g. narrow) during Phase I. An example is presented and good average run length properties are demonstrated.     

Corruption,lack of Transparency and the Misuse of Public Funds in Times of Crisis: An introduction

Public Organization Review -