practical tools for designing and weighting survey samples 51 statistics for social and behavioral sciences

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Practical Tools For Designing And Weighting Survey Samples

Author : Richard Valliant
ISBN : 9783319936321
Genre :
File Size : 71. 42 MB
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Statistical Power Analysis For The Behavioral Sciences

Author : Jacob Cohen
ISBN : 9781134742776
Genre : Psychology
File Size : 55. 10 MB
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Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression/correlation.

Complex Surveys

Author : Thomas Lumley
ISBN : 9781118210932
Genre : Mathematics
File Size : 78. 54 MB
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A complete guide to carrying out complex survey analysis using R As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to the analysis of this kind of data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while also utilizing the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields. The book begins with coverage of basic tools and topics within survey analysis such as simple and stratified sampling, cluster sampling, linear regression, and categorical data regression. Subsequent chapters delve into more technical aspects of complex survey analysis, including post-stratification, two-phase sampling, missing data, and causal inference. Throughout the book, an emphasis is placed on graphics, regression modeling, and two-phase designs. In addition, the author supplies a unique discussion of epidemiological two-phase designs as well as probability-weighting for causal inference. All of the book's examples and figures are generated using R, and a related Web site provides the R code that allows readers to reproduce the presented content. Each chapter concludes with exercises that vary in level of complexity, and detailed appendices outline additional mathematical and computational descriptions to assist readers with comparing results from various software systems. Complex Surveys is an excellent book for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. It is also a practical reference guide for applied statisticians and practitioners in the social and health sciences who use statistics in their everyday work.

Applied Survey Data Analysis

Author : Steven G. Heeringa
ISBN : 1420080679
Genre : Mathematics
File Size : 36. 86 MB
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Taking a practical approach that draws on the authors’ extensive teaching, consulting, and research experiences, Applied Survey Data Analysis provides an intermediate-level statistical overview of the analysis of complex sample survey data. It emphasizes methods and worked examples using available software procedures while reinforcing the principles and theory that underlie those methods. After introducing a step-by-step process for approaching a survey analysis problem, the book presents the fundamental features of complex sample designs and shows how to integrate design characteristics into the statistical methods and software for survey estimation and inference. The authors then focus on the methods and models used in analyzing continuous, categorical, and count-dependent variables; event history; and missing data problems. Some of the techniques discussed include univariate descriptive and simple bivariate analyses, the linear regression model, generalized linear regression modeling methods, the Cox proportional hazards model, discrete time models, and the multiple imputation analysis method. The final chapter covers new developments in survey applications of advanced statistical techniques, including model-based analysis approaches. Designed for readers working in a wide array of disciplines who use survey data in their work, this book also provides a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. A guide to the applied statistical analysis and interpretation of survey data, it contains many examples and practical exercises based on major real-world survey data sets. Although the authors use Stata for most examples in the text, they offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s website: http://www.isr.umich.edu/src/smp/asda/

Nonresponse In Social Science Surveys

Author : Committee on National Statistics
ISBN : 9780309272476
Genre : Social Science
File Size : 28. 75 MB
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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.

Mobile Phone Panel Surveys In Developing Countries

Author : Andrew Dabalen
ISBN : 9781464809057
Genre : Technology & Engineering
File Size : 73. 87 MB
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Household survey data are very useful for monitoring living conditions of citizens of any country. In developing countries, a lot of this data are collected through “traditional†? face-to-face household surveys. Due to the remote and dispersed nature of many populations in developing countries, but also because of the complex nature of many survey questionnaires, collection of timely welfare data has often proved expensive and logistically challenging. Yet, there is a need for faster, cheaper to collect, lighter, more nimble data collection methods to address data gaps between big household surveys. The recent proliferation of mobile phone networks has opened new possibilities. By combining baseline data from a traditional household survey with subsequent interviews of selected respondents using mobile phones, this facilitates welfare monitoring and opinion polling almost real time. The purpose of this handbook is to contribute to the development of the new field of mobile phone data collection in developing countries. The handbook documents how this innovative approach to data collection works, its advantages and challenges. The handbook draws primarily from the authors’ first-hand experiences with mobile phone surveys in Africa and also benefits from experiences elsewhere. It is intended to serve a diverse audience including those involved in collecting (representative) data using mobile phones, and those using data collected through this approach. For those who will be implementing a mobile phone panel survey, the different chapters guide them through every stage of the implementation process. For potential users of the data collected via mobile phone technology, the handbook presents a new approach to data collection which they can use for monitoring programs and facilitate almost real time decision-making. A further purpose of this book is to contribute to the debate regarding the advantages of the method as well as the challenges associated with it.

Continuous Time Modeling In The Behavioral And Related Sciences

Author : Kees Van Montfort
ISBN : 9783319772196
Genre :
File Size : 39. 73 MB
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Panel Surveys

Author : Daniel Kasprzyk
ISBN : UOM:39015015446324
Genre : Mathematics
File Size : 81. 23 MB
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A comprehensive review of the design, problems of implementation, and analytic uses of panel surveys. A multidisciplinary group of contributors presents a comparative evaluation of longitudinal studies, offering both general reviews and detailed discussion of methodological issues. Modes of data collection, statistical design and estimation, database management, sources of nonsampling error, panel conditioning, nonresponse adjustments, estimation of cross-sectional and change parameters, and modeling are covered.

Measuring Health

Author : Ian McDowell
ISBN : 0199725306
Genre : Medical
File Size : 82. 76 MB
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Worldwide economic constraints on health care systems have highlighted the importance of evidence-based medicine and evidence-based health policy. The resulting clinical trials and health services research studies require instruments to monitor the outcomes of care and the output of the health system. However, the over-abundance of competing measurement scales can make choosing a measure difficult at best. Measuring Health provides in-depth reviews of over 100 of the leading health measurement tools and serves as a guide for choosing among them.LNow in its third edition, this book provides a critical overview of the field of health measurement, with a technical introduction and discussion of the history and future directions for the field. This latest edition updates the information on each of the measures previously reviewed, and includes a complete new chapter on anxiety measurement to accompany the one on depression. It has also added new instruments to those previously reviewed in each of the chapters in the book.LChapters cover measurements of physical disability, social health, psychological well-being, anxiety, depression, mental status testing, pain, general health status and quality of life. Each chapter presents a tabular comparison of the quality of the instruments reviewed, followed by a detailed description of each method, covering its purpose and conceptual basis, its reliability and validity and, where possible, shows a copy of the actual scale. To ensure accuracy of the information, each review has been approved by the original author of each instrument or by an acknowledged expert.

Think Stats

Author : Allen B. Downey
ISBN : 9781491907375
Genre : Computers
File Size : 76. 29 MB
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If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data

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