Anchoring Bias in Recall Data
Anchoring Bias in Recall Data

Author: Godlonton, Susan

Publisher: Intl Food Policy Res Inst

Published: 2016-05-20

Total Pages: 36

ISBN-13:

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Understanding the magnitude and source of measurement biases in self-reported data is critical to effective economic policy research. This paper examines the role of anchoring bias in self-reports of objective and subjective outcomes under recall. The research exploits a unique panel survey data set collected over a three-year period from four countries in Central America. It assesses whether respondents use their reported value of specific measures from the most recent survey period as a cognitive heuristic when recalling the value from a previous period, while controlling for the value they reported earlier. We find strong evidence of sizable anchoring bias in self-reported retrospective indicators for both objective measures (household and per capita income, wages, and hours spent on the household’s main activity) and subjective measures (reports of happiness, health, stress, and well-being). In general, we also observe a larger bias in response to negative changes for objective indicators and a larger bias in response to positive changes for subjective indicators.

Can survey design reduce anchoring bias in recall data? Evidence from Malawi
Can survey design reduce anchoring bias in recall data? Evidence from Malawi

Author: Godlonton, Susan

Publisher: Intl Food Policy Res Inst

Published: 2021-11-04

Total Pages: 44

ISBN-13:

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Recall biases in retrospective survey data are widely considered to be pervasive and have important implications for effective agricultural research. In this paper, we leverage the survey design literature and test three strategies to attenuate mental anchoring in retrospective data collection: question order effects, retrieval cues, and aggregate (community) anchoring. We embed a survey design experiment in a longitudinal survey of smallholder farmers in Malawi and focus on anchoring bias in maize production and happiness exploiting differences between recalled and concurrent responses. We find that asking for retrospective data before concurrent data reduces recall bias by approximately 34% for maize production, a meaningful improvement with no increase in survey data collection costs. Retrieval cues are less successful in reducing the bias for maize reports and involve more data collection time, while community anchors can exacerbate the bias. Reversing the order of questions and retrieval cues do not help to ease the bias for happiness reports.

Anchoring Bias in Recall Data
Anchoring Bias in Recall Data

Author: Susan Godlonton

Publisher:

Published: 2020

Total Pages: 0

ISBN-13:

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Self-reported retrospective survey data is widely used in empirical work but may be subject to cognitive biases, even over relatively short recall periods. This paper examines the role of anchoring bias in self-reports of objective and subjective outcomes under recall. We use a unique panel-survey dataset of smallholder farmers from four countries in Central America collected over a period of three years. We exploit differences between recalled and concurrent responses to quantify the degree of mental anchoring in survey recall data. We assess whether respondents use their reported value for the most recent period as a cognitive heuristic when recalling the value from a previous period, while controlling for the value they reported earlier. The results show strong evidence of sizeable anchoring bias in self-reported retrospective indicators for both objective measures (income, wages, and working hours) and subjective measures (reports of happiness, health, stress, and well-being). We also generally observe a larger bias in response to negative changes for objective indicators and a larger bias in response to positive changes for subjective indicators.

Judgment Under Uncertainty
Judgment Under Uncertainty

Author: Daniel Kahneman

Publisher: Cambridge University Press

Published: 1982-04-30

Total Pages: 574

ISBN-13: 9780521284141

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Thirty-five chapters describe various judgmental heuristics and the biases they produce, not only in laboratory experiments, but in important social, medical, and political situations as well. Most review multiple studies or entire subareas rather than describing single experimental studies.

Heuristics and Biases
Heuristics and Biases

Author: Thomas Gilovich

Publisher: Cambridge University Press

Published: 2002-07-08

Total Pages: 884

ISBN-13: 9780521796798

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This book, first published in 2002, compiles psychologists' best attempts to answer important questions about intuitive judgment.

Bias in Science and Communication
Bias in Science and Communication

Author: Matthew Brian Welsh

Publisher: IOP Publishing Limited

Published: 2018

Total Pages: 0

ISBN-13: 9780750313124

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This book is intended as an introduction to a wide variety of biases affecting human cognition, with a specific focus on how they affect scientists and the communication of science. The role of this book is to lay out how these common biases affect the specific types of judgements, decisions and communications made by scientists.

Learning from Imbalanced Data Sets
Learning from Imbalanced Data Sets

Author: Alberto Fernández

Publisher: Springer

Published: 2018-10-22

Total Pages: 385

ISBN-13: 3319980742

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This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.

Eliciting and Analyzing Expert Judgment
Eliciting and Analyzing Expert Judgment

Author: Mary A. Meyer

Publisher: SIAM

Published: 2001-01-01

Total Pages: 471

ISBN-13: 0898714745

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Expert judgment is invaluable for assessing products, systems, and situations for which measurements or test results are sparse or nonexistent. Eliciting and Analyzing Expert Judgment: A Practical Guide takes the reader step by step through the techniques of eliciting and analyzing expert judgment, with special attention given to helping the reader develop elicitation methods and tools adaptable to a variety of unique situations and work areas. The analysis procedures presented in the book may require a basic understanding of statistics and probabilities, but the authors have provided detailed explanations of the techniques used and have taken special care to define all statistical jargon. Originally published in 1991, this book is designed so that those familiar with the use of expert judgment can quickly find the material appropriate for their advanced background.

Making Data Talk
Making Data Talk

Author: David E. Nelson (M.D.)

Publisher:

Published: 2009

Total Pages: 340

ISBN-13: 019538153X

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The demand for health information continues to increase, but the ability of health professionals to provide it clearly remains variable. The aim of this book is (1) to summarize and synthesize research on the selection and presentation of data pertinent to public health, and (2) to provide practical suggestions, based on this research summary and synthesis, on how scientists and other public health practitioners can better communicate data to the public, policy makers, and the press in typical real-world situations. Because communication is complex and no one approach works for all audiences, the authors emphasize how to communicate data "better" (and in some instances, contrast this with how to communicate data "worse"), rather than attempting a cookbook approach. The book contains a wealth of case studies and other examples to illustrate major points, and actual situations whenever possible. Key principles and recommendations are summarized at the end of each chapter. This book will stimulate interest among public health practitioners, scholars, and students to more seriously consider ways they can understand and improve communication about data and other types of scientific information with the public, policy makers, and the press. Improved data communication will increase the chances that evidence-based scientific findings can play a greater role in improving the public's health.

Cognitive Biases in Visualizations
Cognitive Biases in Visualizations

Author: Geoffrey Ellis

Publisher: Springer

Published: 2018-09-27

Total Pages: 185

ISBN-13: 3319958313

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This book brings together the latest research in this new and exciting area of visualization, looking at classifying and modelling cognitive biases, together with user studies which reveal their undesirable impact on human judgement, and demonstrating how visual analytic techniques can provide effective support for mitigating key biases. A comprehensive coverage of this very relevant topic is provided though this collection of extended papers from the successful DECISIVe workshop at IEEE VIS, together with an introduction to cognitive biases and an invited chapter from a leading expert in intelligence analysis. Cognitive Biases in Visualizations will be of interest to a wide audience from those studying cognitive biases to visualization designers and practitioners. It offers a choice of research frameworks, help with the design of user studies, and proposals for the effective measurement of biases. The impact of human visualization literacy, competence and human cognition on cognitive biases are also examined, as well as the notion of system-induced biases. The well referenced chapters provide an excellent starting point for gaining an awareness of the detrimental effect that some cognitive biases can have on users’ decision-making. Human behavior is complex and we are only just starting to unravel the processes involved and investigate ways in which the computer can assist, however the final section supports the prospect that visual analytics, in particular, can counter some of the more common cognitive errors, which have been proven to be so costly.