(HCW) Surveys in Humanitarian Contexts in Lmics
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Nonresponse Bias and Trip Generation Models
64 TRANSPORTATION RESEARCH RECORD 1412 Nonresponse Bias and Trip Generation Models PIYUSHIMITA THAKURIAH, As:H1sH SEN, SnM S66T, AND EDWARD CHRISTOPHER There is serious concern over the fact that travel surveys often On the other hand, if the model does not satisfy these con overrepresent smaller households with higher incomes and better ditions, bias will occur even if there is a 100 percent response education levels and, in general, that nonresponse is nonrandom. rate. These conditions are satisfied by the model if the func However, when the data are used to build linear models, such as trip generation models, and the model is correctly specified, tional form of the model is correct and all important explan estimates of parameters are unbiased regardless of the nature of atory variables are included. the respondents, and the issues of how response rates and non Because categorical models do not have any problems with response bias are ameliorated. The more important task then is their functional form, and weighting and related issues are the complete specification of the model, without leaving out var taken care of, the authors prefer categorical trip generation iables that have some effect on the variable to be predicted. The models. This preference is discussed in a later section. There theoretical basis for this reasoning is given along with an example fore, the issue that remains when assessing bias in estimates of how bias may be assessed in estimates of trip generation model parameters. Some of the methods used are quite standard, but from categorical trip generation models is whether the model the manner in which these and other more nonstandard methods includes all the relevant independent variables or at least all have been systematically put together to assess bias in estimates important predictors. -
Equally Flexible and Optimal Response Bias in Older Compared to Younger Adults
AGING AND RESPONSE BIAS Equally Flexible and Optimal Response Bias in Older Compared to Younger Adults Roderick Garton, Angus Reynolds, Mark R. Hinder, Andrew Heathcote Department of Psychology, University of Tasmania Accepted for publication in Psychology and Aging, 8 February 2019 © 2019, American Psychological Association. This paper is not the copy of record and may not exactly replicate the final, authoritative version of the article. Please do not copy or cite without authors’ permission. The final article will be available, upon publication, via its DOI: 10.1037/pag0000339 Author Note Roderick Garton, Department of Psychology, University of Tasmania, Sandy Bay, Tasmania, Australia; Angus Reynolds, Department of Psychology, University of Tasmania, Sandy Bay, Tasmania, Australia; Mark R. Hinder, Department of Psychology, University of Tasmania, Sandy Bay, Tasmania, Australia; Andrew Heathcote, Department of Psychology, University of Tasmania, Sandy Bay, Tasmania, Australia. Correspondence concerning this article should be addressed to Roderick Garton, University of Tasmania Private Bag 30, Hobart, Tasmania, Australia, 7001. Email: [email protected] This study was supported by Australian Research Council Discovery Project DP160101891 (Andrew Heathcote) and Future Fellowship FT150100406 (Mark R. Hinder), and by Australian Government Research Training Program Scholarships (Angus Reynolds and Roderick Garton). The authors would like to thank Matthew Gretton for help with data acquisition, Luke Strickland and Yi-Shin Lin for help with data analysis, and Claire Byrne for help with study administration. The trial-level data for the experiment reported in this manuscript are available on the Open Science Framework (https://osf.io/9hwu2/). 1 AGING AND RESPONSE BIAS Abstract Base-rate neglect is a failure to sufficiently bias decisions toward a priori more likely options. -
Guidance for Health Care Worker (HCW) Surveys in Humanitarian
Analytics for Operations & COVID-19 Research Roadmap Social Science working groups GUIDANCE BRIEF Guidance for Health Care Worker (HCW) Surveys in humanitarian contexts in LMICs Developed by the Analytics for Operations & COVID-19 Research Roadmap Social Science working groups to support those working with communities and healthcare workers in humanitarian and emergency contexts. This document has been developed for response actors working in humanitarian contexts who seek rapid approaches to gathering evidence about the experience of healthcare workers, and the communities of which they are a part. Understanding healthcare worker experience is critical to inform and guide humanitarian programming and effective strategies to promote IPC, identify psychosocial support needs. This evidence also informs humanitarian programming that interacts with HCWs and facilities such as nutrition, health reinforcement, communication, SGBV and gender. In low- and middle-income countries (LMIC), healthcare workers (HCW) are often faced with limited resources, equipment, performance support and even formal training to provide the life-saving work expected of them. In humanitarian contexts1, where human resources are also scarce, HCWs may comprise formally trained doctors, nurses, pharmacists, dentists, allied health professionals etc. as well as community members who perform formal health worker related duties with little or no trainingi. These HCWs frequently work in contexts of multiple public health crises, including COVID-19. Their work will be affected -
Straight Until Proven Gay: a Systematic Bias Toward Straight Categorizations in Sexual Orientation Judgments
ATTITUDES AND SOCIAL COGNITION Straight Until Proven Gay: A Systematic Bias Toward Straight Categorizations in Sexual Orientation Judgments David J. Lick Kerri L. Johnson New York University University of California, Los Angeles Perceivers achieve above chance accuracy judging others’ sexual orientations, but they also exhibit a notable response bias by categorizing most targets as straight rather than gay. Although a straight categorization bias is evident in many published reports, it has never been the focus of systematic inquiry. The current studies therefore document this bias and test the mechanisms that produce it. Studies 1–3 revealed the straight categorization bias cannot be explained entirely by perceivers’ attempts to match categorizations to the number of gay targets in a stimulus set. Although perceivers were somewhat sensitive to base rate information, their tendency to categorize targets as straight persisted when they believed each target had a 50% chance of being gay (Study 1), received explicit information about the base rate of gay targets in a stimulus set (Study 2), and encountered stimulus sets with varying base rates of gay targets (Study 3). The remaining studies tested an alternate mechanism for the bias based upon perceivers’ use of gender heuristics when judging sexual orientation. Specifically, Study 4 revealed the range of gendered cues compelling gay judgments is smaller than the range of gendered cues compelling straight judgments despite participants’ acknowledgment of equal base rates for gay and straight targets. Study 5 highlighted perceptual experience as a cause of this imbalance: Exposing perceivers to hyper-gendered faces (e.g., masculine men) expanded the range of gendered cues compelling gay categorizations. -
Running Head: EXTRAVERSION PREDICTS REWARD SENSITIVITY
Running head: EXTRAVERSION PREDICTS REWARD SENSITIVITY Extraversion but not Depression Predicts Reward Sensitivity: Revisiting the Measurement of Anhedonic Phenotypes Submitted: 6/xx/2020 EXTRAVERSION PREDICTS REWARD SENSITIVITY 2 Abstract RecentLy, increasing efforts have been made to define and measure dimensionaL phenotypes associated with psychiatric disorders. One example is a probabiListic reward task deveLoped by PizzagaLLi et aL. (2005) to assess anhedonia, by measuring participants’ responses to a differentiaL reinforcement schedule. This task has been used in many studies, which have connected blunted reward response in the task to depressive symptoms, across cLinicaL groups and in the generaL population. The current study attempted to replicate these findings in a large community sample and aLso investigated possible associations with Extraversion, a personaLity trait Linked theoreticaLLy and empiricaLLy to reward sensitivity. Participants (N = 299) completed the probabiListic reward task, as weLL as the Beck Depression Inventory, PersonaLity Inventory for the DSM-5, Big Five Inventory, and Big Five Aspect ScaLes. Our direct replication attempts used bivariate anaLyses of observed variables and ANOVA modeLs. FolLow-up and extension anaLyses used structuraL equation modeLs to assess reLations among Latent reward sensitivity, depression, Extraversion, and Neuroticism. No significant associations were found between reward sensitivity (i.e., response bias) and depression, thus faiLing to replicate previous findings. Reward sensitivity (both modeLed as response bias aggregated across blocks and as response bias controlLing for baseLine) showed positive associations with Extraversion, but not Neuroticism. Findings suggest reward sensitivity as measured by this probabiListic reward task may be reLated primariLy to Extraversion and its pathologicaL manifestations, rather than to depression per se, consistent with existing modeLs that conceptuaLize depressive symptoms as combining features of Neuroticism and low Extraversion. -
Report on the First Workshop on Bias in Automatic Knowledge Graph Construction at AKBC 2020
EVENT REPORT Report on the First Workshop on Bias in Automatic Knowledge Graph Construction at AKBC 2020 Tara Safavi Edgar Meij Fatma Ozcan¨ University of Michigan Bloomberg Google [email protected] [email protected] [email protected] Miriam Redi Gianluca Demartini Wikimedia Foundation University of Queensland [email protected] [email protected] Chenyan Xiong Microsoft Research [email protected] Abstract We report on the First Workshop on Bias in Automatic Knowledge Graph Construction (KG- BIAS), which was co-located with the Automated Knowledge Base Construction (AKBC) 2020 conference. Identifying and possibly remediating any sort of bias in knowledge graphs, or in the methods used to construct or query them, has clear implications for downstream systems accessing and using the information in such graphs. However, this topic remains relatively unstudied, so our main aim for organizing this workshop was to bring together a group of people from a variety of backgrounds with an interest in the topic, in order to arrive at a shared definition and roadmap for the future. Through a program that included two keynotes, an invited paper, three peer-reviewed full papers, and a plenary discussion, we have made initial inroads towards a common understanding and shared research agenda for this timely and important topic. 1 Introduction Knowledge graphs (KGs) store human knowledge about the world in structured relational form. Because KGs serve as important sources of machine-readable relational knowledge, extensive re- search efforts have gone into constructing and utilizing knowledge graphs in various areas of artificial intelligence over the past decade [Nickel et al., 2015; Xiong et al., 2017; Dietz et al., 2018; Voskarides et al., 2018; Shinavier et al., 2019; Safavi and Koutra, 2020]. -
1 Encoding Information Bias in Causal Diagrams Eyal Shahar, MD, MPH
* Manuscript Encoding information bias in causal diagrams Eyal Shahar, MD, MPH Address: Eyal Shahar, MD, MPH Professor Division of Epidemiology and Biostatistics Mel and Enid Zuckerman College of Public Health The University of Arizona 1295 N. Martin Ave. Tucson, AZ 85724 Email: [email protected] Phone: 520-626-8025 Fax: 520-626-2767 1 Abstract Background: Epidemiologists usually classify bias into three main categories: confounding, selection bias, and information bias. Previous authors have described the first two categories in the logic and notation of causal diagrams, formally known as directed acyclic graphs (DAG). Methods: I examine common types of information bias—disease related and exposure related—from the perspective of causal diagrams. Results: Disease or exposure information bias always involves the use of an effect of the variable of interest –specifically, an effect of true disease status or an effect of true exposure status. The bias typically arises from a causal or an associational path of no interest to the researchers. In certain situations, it may be possible to prevent or remove some of the bias analytically. Conclusions: Common types of information bias, just like confounding and selection bias, have a clear and helpful representation within the framework of causal diagrams. Key words: Information bias, Surrogate variables, Causal diagram 2 Introduction Epidemiologists usually classify bias into three main categories: confounding bias, selection bias, and information (measurement) bias. Previous authors have described the first two categories in the logic and notation of directed acyclic graphs (DAG).1, 2 Briefly, confounding bias arises from a common cause of the exposure and the disease, whereas selection bias arises from conditioning on a common effect (a collider) by unnecessary restriction, stratification, or “statistical adjustment”. -
Prior Belief Innuences on Reasoning and Judgment: a Multivariate Investigation of Individual Differences in Belief Bias
Prior Belief Innuences on Reasoning and Judgment: A Multivariate Investigation of Individual Differences in Belief Bias Walter Cabral Sa A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Education University of Toronto O Copyright by Walter C.Si5 1999 National Library Bibliothèque nationaIe of Canada du Canada Acquisitions and Acquisitions et Bibliographic Services services bibliographiques 395 Wellington Street 395, rue WePington Ottawa ON K1A ON4 Ottawa ON KtA ON4 Canada Canada Your file Votre rëfërence Our fi& Notre réterence The author has granted a non- L'auteur a accordé une licence non exclusive Licence allowing the exclusive permettant à la National Libraq of Canada to Bibliothèque nationale du Canada de reproduce, loan, distribute or sell reproduire, prêter, distribuer ou copies of this thesis in microforni, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfiche/film, de reproduction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor subçtantial extracts fi-om it Ni la thèse ni des extraits substantiels may be printed or otherwise de celle-ci ne doivent être imprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation. Prior Belief Influences on Reasoning and Judgment: A Multivariate Investigation of Individual Differences in Belief Bias Doctor of Philosophy, 1999 Walter Cabral Sa Graduate Department of Education University of Toronto Belief bias occurs when reasoning or judgments are found to be overly infiuenced by prior belief at the expense of a normatively prescribed accommodation of dl the relevant data. -
Building Representative Corpora from Illiterate Communities: a Review of Challenges and Mitigation Strategies for Developing Countries
Building Representative Corpora from Illiterate Communities: A Review of Challenges and Mitigation Strategies for Developing Countries Stephanie Hirmer1, Alycia Leonard1, Josephine Tumwesige2, Costanza Conforti2;3 1Energy and Power Group, University of Oxford 2Rural Senses Ltd. 3Language Technology Lab, University of Cambridge [email protected] Abstract areas where the bulk of the population lives (Roser Most well-established data collection methods and Ortiz-Ospina(2016), Figure1). As a conse- currently adopted in NLP depend on the as- quence, common data collection techniques – de- sumption of speaker literacy. Consequently, signed for use in HICs – fail to capture data from a the collected corpora largely fail to repre- vast portion of the population when applied to LICs. sent swathes of the global population, which Such techniques include, for example, crowdsourc- tend to be some of the most vulnerable and ing (Packham, 2016), scraping social media (Le marginalised people in society, and often live et al., 2016) or other websites (Roy et al., 2020), in rural developing areas. Such underrepre- sented groups are thus not only ignored when collecting articles from local newspapers (Mari- making modeling and system design decisions, vate et al., 2020), or interviewing experts from in- but also prevented from benefiting from de- ternational organizations (Friedman et al., 2017). velopment outcomes achieved through data- While these techniques are important to easily build driven NLP. This paper aims to address the large corpora, they implicitly rely on the above- under-representation of illiterate communities mentioned assumptions (i.e. internet access and in NLP corpora: we identify potential biases literacy), and might result in demographic misrep- and ethical issues that might arise when col- resentation (Hovy and Spruit, 2016). -
Self-Serving Assessments of Fairness and Pretrial Bargaining
SELF-SERVING ASSESSMENTS OF FAIRNESS AND PRETRIAL BARGAINING GEORGE LOEWENSTEIN, SAMUEL ISSACHAROFF, COLIN CAMERER, and LINDA BABCOCK* In life it is hard enough to see another person's view of things; in a law suit it is impossible. [JANET MALCOM, The Journalist and the Murderer, 1990] I. INTRODUCTION A PERSISTENTLY troubling question in the legal-economic literature is why cases proceed to trial. Litigation is a negative-sum proposition for the litigants-the longer the process continues, the lower their aggregate wealth. Although civil litigation is resolved by settlement in an estimated 95 percent of all disputes, what accounts for the failure of the remaining 5 percent to settle prior to trial? The standard economic model of legal disputes posits that settlement occurs when there exists a postively valued settlement zone-a range of transfer amounts from defendant to plaintiff that leave both parties better off than they would be if they went to trial. The location of the settlement zone depends on three factors: the parties' probability distributions of award amounts, the litigation costs they face, and their risk preferences. * Loewenstein is Professor of Economics, Carnegie Mellon University; Issacharoff is Assistant Professor of Law, University of Texas at Austin School of Law; Camerer is Professor of Organizational Behavior and Strategy, University of Chicago Graduate School of Business; Babcock is Assistant Professor of Economics, the Heinz School, Carnegie Mellon University. We thank Douglas Laycock for his helpful comments. Jodi Gillis, Na- than Johnson, Ruth Silverman, and Arlene Simon provided valuable research assistance. Loewenstein's research was supported by the Russell Sage Foundation. -
Thinking and Reasoning
Thinking and Reasoning Thinking and Reasoning ■ An introduction to the psychology of reason, judgment and decision making Ken Manktelow First published 2012 British Library Cataloguing in Publication by Psychology Press Data 27 Church Road, Hove, East Sussex BN3 2FA A catalogue record for this book is available from the British Library Simultaneously published in the USA and Canada Library of Congress Cataloging in Publication by Psychology Press Data 711 Third Avenue, New York, NY 10017 Manktelow, K. I., 1952– Thinking and reasoning : an introduction [www.psypress.com] to the psychology of reason, Psychology Press is an imprint of the Taylor & judgment and decision making / Ken Francis Group, an informa business Manktelow. p. cm. © 2012 Psychology Press Includes bibliographical references and Typeset in Century Old Style and Futura by index. Refi neCatch Ltd, Bungay, Suffolk 1. Reasoning (Psychology) Cover design by Andrew Ward 2. Thought and thinking. 3. Cognition. 4. Decision making. All rights reserved. No part of this book may I. Title. be reprinted or reproduced or utilised in any BF442.M354 2012 form or by any electronic, mechanical, or 153.4'2--dc23 other means, now known or hereafter invented, including photocopying and 2011031284 recording, or in any information storage or retrieval system, without permission in writing ISBN: 978-1-84169-740-6 (hbk) from the publishers. ISBN: 978-1-84169-741-3 (pbk) Trademark notice : Product or corporate ISBN: 978-0-203-11546-6 (ebk) names may be trademarks or registered trademarks, and are used -
Recall Bias: a Proposal for Assessment and Control
International Journal of Epidemiology Vol. 16, No. 2 ©International Epidemiological Association 1987 Printed in Great Britain Recall Bias: A Proposal for Assessment and Control KAREN RAPHAEL Probably every major text on epidemiology offers at Whether the source of the bias is underreporting of least some discussion of recall bias in retrospective true exposures in controls or overreporting of true research. A potential for recall bias exists whenever exposures in cases, the net effect of the bias is to historical self-report information is elicited from exaggerate the magnitude of the difference between respondents. Thus-, the potential for its occurrence is cases and controls in reported rates of exposure to risk greatest in case-control studies or cross-sectional factors under investigation. Consequently, recall bias studies which include retrospective components. leads to an inflation of the odds ratio. It leads to the Downloaded from Recall bias is said to occur when accuracy of recall likelihood thai-significant research findings based upon regarding prior exposures is different for cases versus retrospective data can be interpreted in terms of a controls. methodological artefact rather than substantive The current paper will describe the process and con- theory. sequences of recall bias. Most critically, it will suggest methods for assessment and adjustment of its effects. It is important to note that recall bias is not equiva- http://ije.oxfordjournals.org/ lent to memory failure itself. If memory failure regard- ing prior events is equal in case and control groups, WHAT IS RECALL BIAS? recall bias will not occur. Rather, memory failure itself Generally, recall bias has been described in terms of will lead to measurement error which, in turn, will 'embroidery' of personal history by those respondents usually lead to a loss of statistical power.