Shortlisting by Incomplete Descriptions: The Power of Combination.

In this paper we make use of a particular technique of data analysis to empirically study the effect of joint attributes presentation in a multi-step process of choice, in which consumers first use a simple method for shortlisting, then proceed with a closer inspection of a restrict number of alternatives. Shortlisting is based on an incomplete description of the attributes of an alternative. We focus, in particular, on the presentation couples of attributes. The mathematical framework we used is the generalized spectral analysis. We tested this method on data collected through an ad hoc survey. Thanks to this powerful machinery we were able to identify the attraction single attributes have, from the effect of their combination. The use of generalized spectral analysis to decompose data on preferences is totally new. The decomposition allows us to underline two effects: the first and second order effect. The first order effect measures the average attraction that a single feature has when it is coupled with a second one. The second order effect detects the positive (or negative) power of combination of two coupled attributes. We present here a particular case, the choice of a car, among the ones we studied, to show how the method can be used, and its power. A particular emphasis will be given to gender differences in the evaluation of car attributes in the choice process.

Shortlisting by Incomplete Descriptions: The Power of Combination.

In this paper we make use of a particular technique of data analysis to empirically study the effect of joint attributes presentation in a multi-step process of choice, in which consumers first use a simple method for shortlisting, then proceed with a closer inspection of a restrict number of alternatives. Shortlisting is based on an incomplete description of the attributes of an alternative. We focus, in particular, on the presentation couples of attributes.x10The mathematical framework we used is the generalized spectral analysis. We tested this method on data collected through an ad hoc survey. Thanks to this powerful machinery we were able to identify the attraction single attributes have, from the effect of their combination.x10The use of generalized spectral analysis to decompose data on preferences is totally new. The decomposition allows us to underline two effects: the first and second order effect.x10The first order effect measures the average attraction that a single feature has when it is coupled with a second one. The second order effect detects the positive (or negative) power of combination of two coupled attributes. We present here a particular case, the choice of a car, among the ones we studied, to show how the method can be used, and its power. A particular emphasis will be given to gender differences in the evaluation of car attributes in the choice process.

Preventing Incomplete/Hidden Requirements: Reflections on Survey Data from Austria and Brazil

Many software projects fail due to problems in requirements engineering (RE). The goal of this paper is analyzing a specific and relevant RE problem in detail: incomplete/hidden requirements. We replicated a global family of RE surveys with representatives of software organizations in Austria and Brazil. We used the data to (a) characterize the criticality of the selected RE problem, and to (b) analyze the reported main causes and mitigation actions. Based on the analysis, we discuss how to prevent the problem. The survey includes 14 different organizations in Austria and 74 in Brazil, including small, medium and large sized companies, conducting both, plan-driven and agile development processes. Respondents from both countries cited the incomplete/hidden requirements problem as one of the most critical RE problems. We identified and graphically represented the main causes and documented solution options to address these causes. Further, we compiled a list of reported mitigation actions. From a practical point of view, this paper provides further insights into common causes of incomplete/hidden requirements and on how to prevent this problem.Comment: in Proceedings of the Software Quality Days, 201

Counting What Is Measured or Measuring What Counts? League Tables and Their Impact On Higher Education Institutions in England

This report investigates league tables and their impact on higher education institutions (HEIs) in England. It presents findings from two strands of research: – an analysis of five league tables selected for the study, their methodologies and the underlying data employed, and – an investigation of how higher education institutions respond to league tables generally and the extent to which they influence institutional decision-making and actions. The purpose of the research is to stimulate informed debate about the approaches and limitations of the various league tables, and greater understanding among the users and stakeholders of the implications of making decisions based on these sources of information

A method of classification for multisource data in remote sensing based on interval-valued probabilities

An axiomatic approach to intervalued (IV) probabilities is presented, where the IV probability is defined by a pair of set-theoretic functions which satisfy some pre-specified axioms. On the basis of this approach representation of statistical evidence and combination of multiple bodies of evidence are emphasized. Although IV probabilities provide an innovative means for the representation and combination of evidential information, they make the decision process rather complicated. It entails more intelligent strategies for making decisions. The development of decision rules over IV probabilities is discussed from the viewpoint of statistical pattern recognition. The proposed method, so called evidential reasoning method, is applied to the ground-cover classification of a multisource data set consisting of Multispectral Scanner (MSS) data, Synthetic Aperture Radar (SAR) data, and digital terrain data such as elevation, slope, and aspect. By treating the data sources separately, the method is able to capture both parametric and nonparametric information and to combine them. Then the method is applied to two separate cases of classifying multiband data obtained by a single sensor. In each case a set of multiple sources is obtained by dividing the dimensionally huge data into smaller and more manageable pieces based on the global statistical correlation information. By a divide-and-combine process, the method is able to utilize more features than the conventional maximum likelihood method

Information architecture for effective Workload Control: an insight from a successful implementation

The implementation of Workload Control (WLC), a Production Planning and Control concept uniquely designed for Make-To-Order companies, has been a constant challenge. Scholars argued that WLC is largely developed through simulations of well-defined environments while much more complex circumstances (e.g. information availability) have emerged in field research. A recent trend of WLC research is to improve the practical applicability of the concept, where empirical evidence is essential. However, success in WLC implementation remains impeded. The availability of data has been a significant area that frustrates the implementation process. While there is a tendency to simplify data requirements in recent WLC theory development, it is important to understand and maintain the information that is essential for the concept to be effective. For the first time in the field, this paper details the information architecture for WLC. Key informational entities of relevance to the input/output control functions in WLC as well as performance measurement are discussed based on evidence from a successful implementation. The paper not only sheds light for practitioners on how to construct an information system that facilitates successful WLC implementation but also has implications for future development of WLC mechanisms coping with information uncertainties in practice

Complement Activation in Patients With Probable Systemic Lupus Erythematosus and Ability to Predict Progression to American College of Rheumatology-Classified Systemic Lupus Erythematosus.

ObjectiveTo evaluate the frequency of cell-bound complement activation products (CB-CAPs) as a marker of complement activation in patients with suspected systemic lupus erythematosus (SLE) and the usefulness of this biomarker as a predictor of the evolution of probable SLE into SLE as classified by the American College of Rheumatology (ACR) criteria.MethodsPatients in whom SLE was suspected by lupus experts and who fulfilled 3 ACR classification criteria for SLE (probable SLE) were enrolled, along with patients with established SLE as classified by both the ACR and the Systemic Lupus International Collaborating Clinics (SLICC) criteria, patients with primary Sjögren's syndrome (SS), and patients with other rheumatic diseases. Individual CB-CAPs were measured by flow cytometry, and positivity rates were compared to those of commonly assessed biomarkers, including serum complement proteins (C3 and C4) and autoantibodies. The frequency of a positive multianalyte assay panel (MAP), which includes CB-CAPs, was also evaluated. Probable SLE cases were followed up prospectively.ResultsThe 92 patients with probable SLE were diagnosed more recently than the 53 patients with established SLE, and their use of antirheumatic medications was lower. At the enrollment visit, more patients with probable SLE were positive for CB-CAPs (28%) or MAP (40%) than had low complement levels (9%) (P = 0.0001 for each). In probable SLE, MAP scores of >0.8 at enrollment predicted fulfillment of a fourth ACR criterion within 18 months (hazard ratio 3.11, P < 0.01).ConclusionComplement activation occurs in some patients with probable SLE and can be detected with higher frequency by evaluating CB-CAPs and MAP than by assessing traditional serum complement protein levels. A MAP score above 0.8 predicts transition to classifiable SLE according to ACR criteria