Predicting with sparse data

It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach — based upon expert judgement — adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction

A literature review on the use of expert opinion in probabilistic risk analysis

Risk assessment is part of the decision making process in many fields of discipline, such as engineering, public health, environment, program management, regulatory policy, and finance. There has been considerable debate over the philosophical and methodological treatment of risk in the past few decades, ranging from its definition and classification to methods of its assessment. Probabilistic risk analysis (PRA) specifically deals with events represented by low probabilities of occurring with high levels of unfavorable consequences. Expert judgment is often a critical source of information in PRA, since empirical data on the variables of interest are rarely available. The author reviews the literature on the use of expert opinion in PRA, in particular on the approaches to eliciting and aggregating experts'assessments. The literature suggests that the methods by which expert opinions are collected and combined have a significant effect on the resulting estimates. The author discusses two types of approaches to eliciting and aggregating expert judgments-behavioral and mathematical approaches, with the emphasis on the latter. It is generally agreed that mathematical approaches tend to yield more accurate estimates than behavioral approaches. After a short description of behavioral approaches, the author discusses mathematical approaches in detail, presenting three aggregation models: non-Bayesian axiomatic models, Bayesian models, andpsychological scaling models. She also discusses issues of stochastic dependence.Health Monitoring&Evaluation,ICT Policy and Strategies,Public Health Promotion,Enterprise Development&Reform,Statistical&Mathematical Sciences,ICT Policy and Strategies,Health Monitoring&Evaluation,Statistical&Mathematical Sciences,Science Education,Scientific Research&Science Parks

Adolescents care but don't feel responsible for farm animal welfare

Adolescents are the next generation of consumers with the potential to raise standards of farm animal welfare—to theirsatisfaction—if their preferences and concerns are translated into accurate market drivers and signals. There are no published data about adolescent views of farm animal welfare to allow meaningful design, implementation, and evaluation of educational strategies to improve consideration of—and behavior toward—farm animals. Knowledge of farm animal welfare, as well as beliefs and attitudes about farm animal welfare and behavioral intention relevant to it were determined in a sample of ukadolescents, using a survey incorporating an extended version of the theory of planned behavior and novel assessment tools. Our results indicate that adolescents have only a limited knowledge of welfare problems for farm animals and welfare-relevant product labels. Intentions to identify welfare standards for the animals from whom their food was derived were weak. Although they cared about farm animal welfare and agreed with fundamental principles—for example, the provision of space and the absence of pain and suffering—like adults they held limited belief in the power and responsibility that they possess through their choices as consumers; responsibility was often shifted to others, such as the government and farmers

Personality Assessment, Forced-Choice.

Instead of responding to questionnaire items one at a time, respondents may be forced to make a choice between two or more items measuring the same or different traits. The forced-choice format eliminates uniform response biases, although the research on its effectiveness in reducing the effects of impression management is inconclusive. Until recently, forced-choice questionnaires were scaled in relation to person means (ipsative data), providing information for intra-individual assessments only. Item response modeling enabled proper scaling of forced-choice data, so that inter-individual comparisons may be made. New forced-choice applications in personality assessment and directions for future research are discussed

What image features guide lightness perception?

Lightness constancy is the ability to perceive black and white surface colors under a wide range of lighting conditions. This fundamental visual ability is not well understood, and current theories differ greatly on what image features are important for lightness perception. Here we measured classification images for human observers and four models of lightness perception to determine which image regions influenced lightness judgments. The models were a high-pass-filter model, an oriented difference-of-Gaussians model, an anchoring model, and an atmospheric-link-function model. Human and model observers viewed three variants of the argyle illusion (Adelson, 1993) and judged which of two test patches appeared lighter. Classification images showed that human lightness judgments were based on local, anisotropic stimulus regions that were bounded by regions of uniform lighting. The atmospheric-link-function and anchoring models predicted the lightness illusion perceived by human observers, but the high-pass-filter and oriented-difference-of-Gaussians models did not. Furthermore, all four models produced classification images that were qualitatively different from those of human observers, meaning that the model lightness judgments were guided by different image regions than human lightness judgments. These experiments provide a new test of models of lightness perception, and show that human observers' lightness computations can be highly local, as in low-level models, and nevertheless depend strongly on lighting boundaries, as suggested by midlevel models.York University Librarie

Comparison of the EPIC Physical Activity Questionnaire with combined heart rate and movement sensing in a nationally representative sample of older British adults

Peer reviewedPublisher PD

AN EVALUATION OF CROP FORECAST ACCURACY FOR CORN AND SOYBEANS: USDA AND PRIVATE INFORMATION SERVICES

Using 1971-2000 data, we examine the accuracy of corn and soybean production forecasts provided by the USDA and two private services. All agencies improved their forecasts as the harvest progressed, and forecast errors across the agencies were highly correlated. Relative accuracy varied by crop and month. In corn, USDA 's forecasts ranked as most accurate in all periods except in August during recent times, and improved more markedly as harvest progressed. In soybeans, forecast errors were very similar with the private agencies ranking as most accurate in August and September and making largest relative improvements in August during recent times. The USDA provided the most accurate October and November forecasts.Crop Production/Industries,