Design, Analysis, and Applications of Failure Amplification Experiments

The main focus of this study is related to the Failure Amplification Method (FAMe) proposed by Joseph and Wu (2004). They suggested the use of an “amplification factor” to increase the information from experiments with a binary response variable. In addition to the amplification factor having a known effect, Joseph and Wu recommended that, for convenience of experimentation, this factor be taken as an easy to change, split unit factor. In such cases, the analysis ought to take into account the possibility of both whole unit and split unit error variation. I present such an analysis here, where the Bayesian approach not only permits proper accounting of the error structure, but also facilitates the subsequent optimization step. FAMe can also be extended to categorical data with more than two categories. I helped design an experiment that was conducted at Huhtamaki Consumer Packaging West Inc., Los Angeles, CA, where the response variable was an ordinal variable characterizing the quality of the Tri Web Taco Bell Disk seal. An amplification factor – speed of the production line - was a whole-unit factor that was hard to change. Therefore an application of FAMe to ordinal data is presented here as well. It is crucial to plan an experiment carefully, particularly with categorical responses. Levels of the split-unit factor can be chosen sequentially or set in advance. In the case of the sequential design, a rule for choosing a split-unit factor level will affect consistency and bias of the parameter estimates. Theory-based sequential rules often are impractical in real life situations. Properties of sequential ad hoc designs are studied and compared to fixed designs using complete enumeration and simulation techniques

Propriedades psicométricas de uma escala para medir o lado escuro da personalidade

The psychometric characteristics of the Dark Triad Scale (Jones & Paulhus, 2014) in an Argentinian context are presented. Two successive studies were carried out. Three hundred sixteen people, with an average age of 34.48 years (SD = 10.57), participated in Study 1. An exploratory factor analysis indicated a three-factor structure with suitable internal consistency (Machiavellianism: α = 0.92; narcissism: α = 0.91, and psychopathy: α = 0.89). Two hundred seventy-five people, with an average age of 32 years (SD = 8.10), participated in Study 2. A confirmatory factor analysis corroborated the three-factor structure. The three factors reached satisfactory composite reliability (CR greater than 0.70) and adequate convergent-discriminant validity (AVE greater than 0.50). The invariance of the scale?s parameters was demonstrated by sex. The results indicate that the Argentinian version of the Dark Triad Scale measures the dark side of personality with appropriate validity and reliability, both in men and women.Este estudo apresenta as características psicométricas da Dark Triad Scale para o contexto da Argentina. Dois estudos sucessivos foram realizados. Participaram do primeiro estudo trezentas e dezesseis pessoas, com uma idade média de 34,48 anos (DP = 10,57). A análise fatorial exploratória indicou uma estrutura de três fatores com adequada consistência interna (Maquiavelismo: α = 0,92; narcisismo: α = 0,91, e psicopatia: α = 0,89). Do segundo estudo participaram duzentas e setenta e cinco pessoas, com uma idade média de 32 anos (DP = 8,10). A análise fatorial confirmatória confirmou a adequação da estrutura trifatorial. Os três fatores resultantes apresentaram Confiabilidade Composta Satisfatória (maior que 0,70) e indicadores adequados de validade convergente-discriminante (AVE maior que 0,50). A invariância dos parâmetros da escala foi demonstrada por meio do sexo. Os resultados indicam que a versão argentina da Dark Triad Scale mede o lado escuro da personalidade com validade e confiabilidade adequadas, tanto em homens quanto em mulheres.Fil: Salessi, Solana Magalí. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Humanidades y Artes. Instituto de Investigaciones; ArgentinaFil: Omar, Alicia Graciela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Humanidades y Artes. Instituto de Investigaciones; Argentin

Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach

User opinions expressed in the form of ratings can influence an individual's view of an item. However, the true quality of an item is often obfuscated by user biases, and it is not obvious from the observed ratings the importance different users place on different aspects of an item. We propose a probabilistic modeling of the observed aspect ratings to infer (i) each user's aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect ratings as ordered discrete data and encode the dependency between different aspects by using a latent Gaussian structure. We handle the Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully Bayesian inference. On two real world datasets, we demonstrate the predictive ability of our model and its effectiveness in learning explainable user biases to provide insights towards a more reliable product quality estimation.Comment: Accepted for publication in IJCAI 201

KernSmoothIRT: An R Package for Kernel Smoothing in Item Response Theory

Item response theory (IRT) models are a class of statistical models used to describe the response behaviors of individuals to a set of items having a certain number of options. They are adopted by researchers in social science, particularly in the analysis of performance or attitudinal data, in psychology, education, medicine, marketing and other fields where the aim is to measure latent constructs. Most IRT analyses use parametric models that rely on assumptions that often are not satisfied. In such cases, a nonparametric approach might be preferable; nevertheless, there are not many software applications allowing to use that. To address this gap, this paper presents the R package KernSmoothIRT. It implements kernel smoothing for the estimation of option characteristic curves, and adds several plotting and analytical tools to evaluate the whole test/questionnaire, the items, and the subjects. In order to show the package's capabilities, two real datasets are used, one employing multiple-choice responses, and the other scaled responses

anchors: Software for Anchoring Vignette Data

When respondents use the ordinal response categories of standard survey questions in different ways, the validity of analyses based on the resulting data can be biased. Anchoring vignettes is a survey design technique intended to correct for some of these problems. The anchors package in R includes methods for evaluating and choosing anchoring vignettes, and for analyzing the resulting data.

L2-determinant class and approximation of L2-Betti numbers

A standing conjecture in L2-cohomology is that every finite CW-complex X is of L2-determinant class. In this paper, we prove this whenever the fundamental group belongs to a large class of groups containing e.g. all extensions of residually finite groups with amenable quotients, all residually amenable groups and free products of these. If, in addition, X is L2-acyclic, we also prove that the L2-determinant is a homotopy invariant. Even in the known cases, our proof of homotopy invariance is much shorter and easier than the previous ones. Under suitable conditions we give new approximation formulas for L2-Betti numbers. Errata are added, rectifying some unproved statements about "amenable extension": throughout, amenable extensions should be extensions with \emph{normal} subgroups.Comment: amsLaTeX2e, 26 pages; v2: Errata are added, rectifying some unproved statements about "amenable extension