Download or read book Comparing Multiple Imputation and Propensity Score Weighting in Unit Nonresponse Adjustments A Simulation Study written by Ahu Alanya and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The usual approach to unit-nonresponse bias detection and adjustment in social surveys has been post-stratification weights, or more recently, propensity-score weighting (PSW) based on auxiliary information. There exists a third approach, which is far less popular: using multiple imputed values for each missing unit of the survey outcome(s). We suggest multiple imputation (MI) as an alternative to PSW since the latter is known to increase variance substantially without reducing bias when auxiliary variables are not associated with the survey outcome of interest. Given that most social surveys have multiple target variables, creating imputed data sets may address bias in survey outcomes with less variance inflation. We examine the performance of PSW and MI on mean estimates under various conditions using fully simulated data. To evaluate the performance of the methods, we report average bias, root mean squared error, and percent coverage of 95 percent confidence intervals. MI perfor

Download or read book Measuring Media Use and Exposure written by Christina Peter and published by Herbert von Halem Verlag. This book was released on 2019-09-11 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: The precise measurement of media use and exposure to media content posits currently one of the main methodological challenges in communication research. Against this background, new communication technologies have been gaining particular importance because they change existing patterns of media use and create new types of media use. At the same time, these technologies do not only present a challenge for communication research, but they also provide new opportunities for the assessment of media use. The volume regards current developments and trends in the measurement of media use and exposure from various perspectives. Contributions deal with the refinement and advancement of classical approaches, and new methods and measures of assessing media use are introduced and evaluated. They also discuss the advantages and challenges of using online behavioral data as indicators for media exposure. Contributions tackle questions how different methods of measuring media use and exposure can be combined to gain a more accurate picture and what pitfalls can occur.

Download or read book Comparison of Approaches for Handling Missingness in Covariates for Propensity Score Models written by Jiangxiu Zhou and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Causal effect estimation with observational data is subject to bias due to confounding. Although potential confounders could be adjusted for by fitting a multiple regression model, a more effective way to control for confounding is to use propensity score methods. Propensity scores are most commonly estimated from logistic regression with a binary exposure; generalized propensity scores could be estimated instead using linear regression if the exposure is continuous. One unresolved issue in propensity score estimation is handling of missing values in covariates. As covariates are only used for propensity score estimation but not for later outcome analysis, missing values in covariates may need to be handled differently from missing values in outcome analysis. Several approaches have been proposed for handling covariate missingness, including multiple imputation (MI), multiple imputation with missingness pattern (MIMP) and treatment mean imputation. There are other potentially useful approaches that have not been evaluated, including single imputation, single conditional mean imputation and Generalized Boosted Modeling (GBM), which is a nonparametric approach of estimating propensity scores and missing values are automatically accounted for in the estimation.To evaluate the performance of single imputation, single conditional mean imputation and GBM in comparison to the previously proposed approaches including treatment mean imputation, MI and MIMP, a simulation study is conducted with a binary exposure. Results suggest that when all confounders are included for propensity score estimation, single imputation, single conditional mean imputation, MI and MIMP perform almost equally well and better than treatment mean imputation and GBM. To examine whether the finding could be extended to a continuous exposure setting, another simulation study is conducted. Results suggest that single imputation, single conditional imputation, MI, MIMP and GBM with single conditional mean imputation have equally good and better performance than treatment mean imputation and GBM with incomplete data under scenario A (linearity and additivity). None of the approaches perform well under scenario G (nonlinearity and nonadditivity). These approaches are further demonstrated and compared through an empirical analysis with the Adolescent Alcohol Prevention Trial (AAPT). A similar pattern of results is observed as in the simulation study. It is recommended to impute missing covariates using different approaches and similar estimates help provide more confidence in the estimates.

Download or read book Survey Nonresponse written by Robert M. Groves and published by Wiley-Interscience. This book was released on 2002 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume offers coverage of research in the field of survey nonresponse, the primary threat to the statistical integrity of surveys. This book was written in conjunction with the International Conference on Survey Nonresponse, October 1999.

Download or read book Multiple Imputation of Missing Data Using SAS written by Patricia Berglund and published by SAS Institute. This book was released on 2014-07-01 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Find guidance on using SAS for multiple imputation and solving common missing data issues. Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical instruction on the use of SAS for multiple imputation and provides numerous examples that use a variety of public release data sets with applications to survey data. Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. The authors cover the MI and MIANALYZE procedures in detail, along with other procedures used for analysis of complete data sets. They guide analysts through the multiple imputation process, including evaluation of missing data patterns, choice of an imputation method, execution of the process, and interpretation of results. Topics discussed include how to deal with missing data problems in a statistically appropriate manner, how to intelligently select an imputation method, how to incorporate the uncertainty introduced by the imputation process, and how to incorporate the complex sample design (if appropriate) through use of the SAS SURVEY procedures. Discover the theoretical background and see extensive applications of the multiple imputation process in action. This book is part of the SAS Press program.

Download or read book Application of Multiple Imputation in Propensity Score Methods with Partially Observed Covariates written by Albee Ling and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Since its introduction in the 1980s, propensity score (PS) methods have been widely used. They have been particularly prevalent in the field of causal inference in order to draw valid inference from observational data. Specifically, PS matching and weighting strategies have been applied to a variety of studies. PS weighting has also been adapted to many other contexts, including the recent methodological developments in generalizing and transporting randomized clinical trial (RCT) results to target populations of interest, referred to as inverse probability of sampling weighting (IPSW). Application of the aforementioned approaches is not straightforward in the presence of missing data, which threatens their statistical validity. Unfortunately, missing data is inevitable in almost all biomedical studies, multiple imputation (MI) is a flexible solution for handling missing data with good statistical properties that is easily accessible in many mainstream computational software. However, one has to make a number of key choices to implement MI in applying PS-based methods that greatly impact the statistical properties of resulting estimators. The choices include which imputation model to use (variables or subpopulations), how to impute PS, how to integrate PS into analysis, and how to estimate the uncertainty of the estimated relationship of interest. We built upon previous work to evaluate novel MI strategies for two key contexts through extensive simulation studies. Specifically, we have designed and implemented Monte Carlo simulations to illustrate the heterogeneity of findings and to develop guidelines for applied methodologists. We additionally illustrated principles using two studies, the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) and the Frequent Hemodialysis Network (FHN) Daily Network Trial. For both PS matching in the context of classic causal inference and PS weighting in the context of transporting RCT results to target populations of interest, we recommend 1) adopting MI-derPassive and deriving the PS after applying MI 2) implementing INT-within and conducting PS matching or weighting within each imputed dataset before averaging the treatment effects into one summarized quantity 3) estimating the uncertainty of the relationship of interest through a bootstrapped variance estimator for PS matching and a robust variance estimator for IPSW and 4) including key auxiliary variables in the imputation model when possible.

Download or read book Flexible Imputation of Missing Data Second Edition written by Stef van Buuren and published by CRC Press. This book was released on 2018-07-17 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.

Download or read book A Simulation Study Comparing Weighted Estimation Equations with Multiple Imputation Based Estimating Equations for Longitudinal Binary Data written by Naresh Khatiwada and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Statistical Modeling for the Social and Behavioral Sciences written by G. Arminger and published by Springer Science & Business Media. This book was released on 1995 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contributors thoroughly survey the most important statistical models used in empirical reserch in the social and behavioral sciences. Following a common format, each chapter introduces a model, illustrates the types of problems and data for which the model is best used, provides numerous examples that draw upon familiar models or procedures, and includes material on software that can be used to estimate the models studied. This handbook will aid researchers, methodologists, graduate students, and statisticians to understand and resolve common modeling problems.

Download or read book Statistical Analysis with Missing Data written by Roderick J. A. Little and published by John Wiley & Sons. This book was released on 2019-03-21 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, comprehensive treatment of a classic text on missing data in statistics The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subject Features over 150 exercises (including many new ones) Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods Revises previous topics based on past student feedback and class experience Contains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work "has been no less than defining and transforming." (ISI) Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.

Download or read book Semiparametric Theory and Missing Data written by Anastasios Tsiatis and published by Springer Science & Business Media. This book was released on 2007-01-15 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.

Download or read book Handbook of Missing Data Methodology written by Geert Molenberghs and published by CRC Press. This book was released on 2014-11-06 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research. Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods. The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative, routinely non-verifiable assumptions about the missing data process. The final part discusses special topics, such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part, an introduction provides useful background material and an overview to set the stage for subsequent chapters. Covering both established and emerging methodologies for missing data, this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.

Download or read book Nonresponse in Social Science Surveys written by National Research Council and published by National Academies Press. This book was released on 2013-10-26 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many household surveys in the United States, responses rates have been steadily declining for at least the past two decades. A similar decline in survey response can be observed in all wealthy countries. Efforts to raise response rates have used such strategies as monetary incentives or repeated attempts to contact sample members and obtain completed interviews, but these strategies increase the costs of surveys. This review addresses the core issues regarding survey nonresponse. It considers why response rates are declining and what that means for the accuracy of survey results. These trends are of particular concern for the social science community, which is heavily invested in obtaining information from household surveys. The evidence to date makes it apparent that current trends in nonresponse, if not arrested, threaten to undermine the potential of household surveys to elicit information that assists in understanding social and economic issues. The trends also threaten to weaken the validity of inferences drawn from estimates based on those surveys. High nonresponse rates create the potential or risk for bias in estimates and affect survey design, data collection, estimation, and analysis. The survey community is painfully aware of these trends and has responded aggressively to these threats. The interview modes employed by surveys in the public and private sectors have proliferated as new technologies and methods have emerged and matured. To the traditional trio of mail, telephone, and face-to-face surveys have been added interactive voice response (IVR), audio computer-assisted self-interviewing (ACASI), web surveys, and a number of hybrid methods. Similarly, a growing research agenda has emerged in the past decade or so focused on seeking solutions to various aspects of the problem of survey nonresponse; the potential solutions that have been considered range from better training and deployment of interviewers to more use of incentives, better use of the information collected in the data collection, and increased use of auxiliary information from other sources in survey design and data collection. Nonresponse in Social Science Surveys: A Research Agenda also documents the increased use of information collected in the survey process in nonresponse adjustment.

Download or read book The SAGE Handbook of Online Research Methods written by Nigel G Fielding and published by SAGE. This book was released on 2016-09-30 with total page 685 pages. Available in PDF, EPUB and Kindle. Book excerpt: Online research methods are popular, dynamic and fast-changing. Following on from the great success of the first edition, published in 2008, The SAGE Handbook of Online Research Methods, Second Edition offers both updates of existing subject areas and new chapters covering more recent developments, such as social media, big data, data visualization and CAQDAS. Bringing together the leading names in both qualitative and quantitative online research, this new edition is organised into nine sections: 1. Online Research Methods 2. Designing Online Research 3. Online Data Capture and Data Collection 4. The Online Survey 5. Digital Quantitative Analysis 6. Digital Text Analysis 7. Virtual Ethnography 8. Online Secondary Analysis: Resources and Methods 9. The Future of Online Social Research The SAGE Handbook of Online Research Methods, Second Edition is an essential resource for anyone interested in the contemporary practice of computer-mediated research and scholarship.

Download or read book Target Estimation and Adjustment Weighting for Survey Nonresponse and Sampling Bias written by Devin Caughey and published by Cambridge University Press. This book was released on 2020-10-22 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: We elaborate a general workflow of weighting-based survey inference, decomposing it into two main tasks. The first is the estimation of population targets from one or more sources of auxiliary information. The second is the construction of weights that calibrate the survey sample to the population targets. We emphasize that these tasks are predicated on models of the measurement, sampling, and nonresponse process whose assumptions cannot be fully tested. After describing this workflow in abstract terms, we then describe in detail how it can be applied to the analysis of historical and contemporary opinion polls. We also discuss extensions of the basic workflow, particularly inference for causal quantities and multilevel regression and poststratification.

Download or read book Multiple Imputation and its Application written by James Carpenter and published by John Wiley & Sons. This book was released on 2012-12-21 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to analysing partially observeddata. Collecting, analysing and drawing inferences from data iscentral to research in the medical and social sciences.Unfortunately, it is rarely possible to collect all the intendeddata. The literature on inference from the resultingincomplete data is now huge, and continues to grow both asmethods are developed for large and complex data structures, and asincreasing computer power and suitable software enable researchersto apply these methods. This book focuses on a particular statistical method foranalysing and drawing inferences from incomplete data, calledMultiple Imputation (MI). MI is attractive because it is bothpractical and widely applicable. The authors aim is to clarify theissues raised by missing data, describing the rationale for MI, therelationship between the various imputation models and associatedalgorithms and its application to increasingly complex datastructures. Multiple Imputation and its Application: Discusses the issues raised by the analysis of partiallyobserved data, and the assumptions on which analyses rest. Presents a practical guide to the issues to consider whenanalysing incomplete data from both observational studies andrandomized trials. Provides a detailed discussion of the practical use of MI withreal-world examples drawn from medical and social statistics. Explores handling non-linear relationships and interactionswith multiple imputation, survival analysis, multilevel multipleimputation, sensitivity analysis via multiple imputation, usingnon-response weights with multiple imputation and doubly robustmultiple imputation. Multiple Imputation and its Application is aimed atquantitative researchers and students in the medical and socialsciences with the aim of clarifying the issues raised by theanalysis of incomplete data data, outlining the rationale for MIand describing how to consider and address the issues that arise inits application.

Download or read book Analysis of Incomplete Multivariate Data written by J.L. Schafer and published by CRC Press. This book was released on 1997-08-01 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis. Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools accessible to a broad audience. It presents a unified, Bayesian approach to the analysis of incomplete multivariate data, covering datasets in which the variables are continuous, categorical, or both. The focus is applied, where necessary, to help readers thoroughly understand the statistical properties of those methods, and the behavior of the accompanying algorithms. All techniques are illustrated with real data examples, with extended discussion and practical advice. All of the algorithms described in this book have been implemented by the author for general use in the statistical languages S and S Plus. The software is available free of charge on the Internet.