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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.
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.
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.
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.
This book, first published in 2002, compiles psychologists' best attempts to answer important questions about intuitive judgment.
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.
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.
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.
This paper reviews, from the perspective of developing countries, the recent agreement reached at the 10th WTO Ministerial at Nairobi related to export competition, including exports subsidies, food aid, export credits and guarantees, and state trading enterprises (STEs). The legal and economic aspects of the agreement are examined, and the relevance of banning agricultural export subsidies are noted. This eliminates some of the worst-case scenarios, if agricultural world prices continue to soften and the important margin of export subsidies still allowed under the WTO framework was to be used. But given the relatively longer transition period for some relevant products before export subsidies are completely banned, the paper argues for continued monitoring of the potential use of this instrument. The paper also discusses the other components of export competition, looking into the legal and economic aspects. Some suggestions about continuous work on transparency and monitoring of current practices, and further disciplines are also presented.
This edition is the most updated since its inception, is the essential text for students and professionals working in and around epidemiology or using its methods. It covers subject areas - genetics, clinical epidemiology, public health practice/policy, preventive medicine, health promotion, social sciences and methods for clinical research.