Weighting Differences

Irrespective of the level of analysis, identity remains a vague concept, slippery, and insufficiently elaborated and defined. Be it individual or collective, ethnical or social, local or general, regional (e.g. the EU) or global, identity is a recurrent subject in political debates. Situated on the edge of history, anthropology, sociology, political science, and psychology it increasingly becomes a leading paradigm in the area of social sciences. Starting from the broader European perspective, this volume has a multidisciplinary approach and gathers relevant works of internationally renowned scholars who tackle questions related to the Romanian identity: Who are the Romanians? What is the essence of their identity and how has it evolved along history? What are their primary qualities and flaws? How do Romanians perceive their Europeanness and how do they assume their European condition? With no claim to unique answers, the book provides a multi-layered view of what Romanian identity means in contemporary period and how it develops in the broader European context. By challenging the common sense understanding of identity, we raise even more questions to be anasered by further research.

An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.

The fifth round of the Myanmar Household Welfare Survey (MHWS)–a nationwide phone panel consisting of 12,953 households–was implemented between March, 2023 and June, 2023. Of those households, 3,981 households were interviewed in Round 1, Round 2, Round 3, Round 4, and Round 5, all the previous survey rounds. Another 1,673 households were interviewed in Round 2, Round 3, Round 4, and Round 5 only. The remaining 5,732 households participated in at least one other round of MHWS. Finally, 3,256 new households that had never participated in any other rounds were added in Round 5 to replace the households that dropped out of the sample after the fourth round. The objective of the survey was to collect data on a wide range of household and individual welfare indicators–including wealth, livelihoods, unemployment, food insecurity, diet quality, health shocks, and coping strategies–in a country exceptionally hard hit by conflict, severe economic collapse, and several damaging waves of COVID-19. The respondents interviewed in the MHWS were purposely selected from a large phone database aimed at being representative at the region/state level and urban/rural level in Myanmar. A novel sampling strategy in combination with the development of household and population weights allows for estimates that are nationally, regionally, and urban/rural representative.

The goal of this book is to put an array of tools at the fingertips of students, practitioners, and researchers by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This volume serves at least three audiences: (1) students of applied sampling techniques; 2) practicing survey statisticians applying concepts learned in theoretical or applied sampling courses; and (3) social scientists and other survey practitioners who design, select, and weight survey samples. The text thoroughly covers fundamental aspects of survey sampling, such as sample size calculation (with examples for both single- and multi-stage sample design) and weight computation, accompanied by software examples to facilitate implementation. Features include step-by-step instructions for calculating survey weights, extensive real-world examples and applications, and representative programming code in R, SAS, and other packages. Since the publication of the first edition in 2013, there have been important developments in making inferences from nonprobability samples, in address-based sampling (ABS), and in the application of machine learning techniques for survey estimation. New to this revised and expanded edition: • Details on new functions in the PracTools package • Additional machine learning methods to form weighting classes • New coverage of nonlinear optimization algorithms for sample allocation • Reflecting effects of multiple weighting steps (nonresponse and calibration) on standard errors • A new chapter on nonprobability sampling • Additional examples, exercises, and updated references throughout Richard Valliant, PhD, is Research Professor Emeritus at the Institute for Social Research at the University of Michigan and at the Joint Program in Survey Methodology at the University of Maryland. He is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and has been an Associate Editor of the Journal of the American Statistical Association, Journal of Official Statistics, and Survey Methodology. Jill A. Dever, PhD, is Senior Research Statistician at RTI International in Washington, DC. She is a Fellow of the American Statistical Association, Associate Editor for Survey Methodology and the Journal of Official Statistics, and an Assistant Research Professor in the Joint Program in Survey Methodology at the University of Maryland. She has served on several panels for the National Academy of Sciences and as a task force member for the American Association of Public Opinion Research’s report on nonprobability sampling. Frauke Kreuter, PhD, is Professor and Director of the Joint Program in Survey Methodology at the University of Maryland, Professor of Statistics and Methodology at the University of Mannheim, and Head of the Statistical Methods Research Department at the Institute for Employment Research (IAB) in Nürnberg, Germany. She is a Fellow of the American Statistical Association and has been Associate Editor of the Journal of the Royal Statistical Society, Journal of Official Statistics, Sociological Methods and Research, Survey Research Methods, Public Opinion Quarterly, American Sociological Review, and the Stata Journal. She is founder of the International Program for Survey and Data Science and co-founder of the Coleridge Initiative.

Measuring Stress is the definitive resource for health and social scientists interested in assessing stress in humans. With contributions from leading experts, this work provides for the first time a unified conceptual overview of the intricate relationship between stress and a variety of disorders. Its interdisciplinary approach to the selection of appropriate environmental, psychological, and biological measures includes comprehensive evaluations and practical advice regarding a wide range of measurement approaches. For environmental stress, techniques such as checklists and interviews that measure life event, daily event, and chronic stress are discussed. An analysis of psychological measurements includes methods for assessing stress appraisal and affective response. Neuroendocrine, cardiovascular, and immune measures are examined as important biological stress assessments. Contributors also uncover the conceptual underpinnings of each approach as well as the various costs and benefits of available assessment techniques. Reflecting the diversity of theoretical conceptions of stress, Measuring Stress masterfully provides integrative, incisive guidelines that will prove invaluable to students, clinicians, and researchers in health and social psychology, medicine, nursing, epidemiology, sociology, and psychiatry.