Study on Parameter Estimation Via Multistage Sampling with Applications
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Survey Sampling
Bayesian SAE using Complex Survey Data Lecture 5A: Survey Sampling Jon Wakefield Departments of Statistics and Biostatistics University of Washington 1 / 119 Outline Overview Design-Based Inference Simple Random Sampling Stratified Simple Random Sampling Cluster Sampling Multistage Sampling Discussion Technical Appendix: Simple Random Sampling Technical Appendix: Stratified SRS Technical Appendix: Cluster Sampling Technical Appendix: Lonely PSUs 2 / 119 Overview 3 / 119 Outline Many national surveys employ stratified cluster sampling, also known as multistage sampling, so that’s where we’d like to get to. In this lecture we will discuss: I Simple Random Sampling (SRS). I Stratified SRS. I Cluster sampling. I Multistage sampling. 4 / 119 Main texts I Lohr, S.L. (2010). Sampling Design and Analysis, Second Edition. Brooks/Cole Cengage Learning. Very well-written and clear mix of theory and practice. I Lumley, T. (2010). Complex Surveys: A Guide to Analysis Using R, Wiley. Written around the R survey package. Great if you already know a lot about survey sampling. I Korn, E.L. and Graubard, B.I. (1999). Analysis of Health Surveys. Wiley. Well written but not as comprehensive as Lohr. I Sarndal,¨ Swensson and Wretman (1992). Model Assisted Survey Sampling. Springer. Excellent on the theory though steep learning curve and hard to dip into if not familiar with the notation. Also, anti- model-based approaches. 5 / 119 The problem We have a question concerning variables in a well-defined finite population (e.g., 18+ population in Washington State). What is required of a sample plan? We want: I Accurate answer to the question (estimate). I Good estimate of the uncertainty of the estimate (e.g., variance). -
IBM SPSS Complex Samples Business Analytics
IBM Software IBM SPSS Complex Samples Business Analytics IBM SPSS Complex Samples Correctly compute complex samples statistics When you conduct sample surveys, use a statistics package dedicated to Highlights producing correct estimates for complex sample data. IBM® SPSS® Complex Samples provides specialized statistics that enable you to • Increase the precision of your sample or correctly and easily compute statistics and their standard errors from ensure a representative sample with stratified sampling. complex sample designs. You can apply it to: • Select groups of sampling units with • Survey research – Obtain descriptive and inferential statistics for clustered sampling. survey data. • Select an initial sample, then create • Market research – Analyze customer satisfaction data. a second-stage sample with multistage • Health research – Analyze large public-use datasets on public health sampling. topics such as health and nutrition or alcohol use and traffic fatalities. • Social science – Conduct secondary research on public survey datasets. • Public opinion research – Characterize attitudes on policy issues. SPSS Complex Samples provides you with everything you need for working with complex samples. It includes: • An intuitive Sampling Wizard that guides you step by step through the process of designing a scheme and drawing a sample. • An easy-to-use Analysis Preparation Wizard to help prepare public-use datasets that have been sampled, such as the National Health Inventory Survey data from the Centers for Disease Control and Prevention -
Sampling and Evaluation
Sampling and Evaluation A Guide to Sampling for Program Impact Evaluation Peter M. Lance Aiko Hattori Suggested citation: Lance, P. and A. Hattori. (2016). Sampling and evaluation: A guide to sampling for program impact evaluation. Chapel Hill, North Carolina: MEASURE Evaluation, University of North Carolina. Sampling and Evaluation A Guide to Sampling for Program Impact Evaluation Peter M. Lance, PhD, MEASURE Evaluation Aiko Hattori, PhD, MEASURE Evaluation ISBN: 978-1-943364-94-7 MEASURE Evaluation This publication was produced with the support of the United States University of North Carolina at Chapel Agency for International Development (USAID) under the terms of Hill MEASURE Evaluation cooperative agreement AID-OAA-L-14-00004. 400 Meadowmont Village Circle, 3rd MEASURE Evaluation is implemented by the Carolina Population Center, University of North Carolina at Chapel Hill in partnership with Floor ICF International; John Snow, Inc.; Management Sciences for Health; Chapel Hill, NC 27517 USA Palladium; and Tulane University. Views expressed are not necessarily Phone: +1 919-445-9350 those of USAID or the United States government. MS-16-112 [email protected] www.measureevaluation.org Dedicated to Anthony G. Turner iii Contents Acknowledgments v 1 Introduction 1 2 Basics of Sample Selection 3 2.1 Basic Selection and Sampling Weights . 5 2.2 Common Sample Selection Extensions and Complications . 58 2.2.1 Multistage Selection . 58 2.2.2 Stratification . 62 2.2.3 The Design Effect, Re-visited . 64 2.2.4 Hard to Find Subpopulations . 64 2.2.5 Large Clusters and Size Sampling . 67 2.3 Complications to Weights . 69 2.3.1 Non-Response Adjustment . -
A Conversation with Richard A. Olshen 3
Statistical Science 2015, Vol. 30, No. 1, 118–132 DOI: 10.1214/14-STS492 c Institute of Mathematical Statistics, 2015 A Conversation with Richard A. Olshen John A. Rice Abstract. Richard Olshen was born in Portland, Oregon, on May 17, 1942. Richard spent his early years in Chevy Chase, Maryland, but has lived most of his life in California. He received an A.B. in Statistics at the University of California, Berkeley, in 1963, and a Ph.D. in Statistics from Yale University in 1966, writing his dissertation under the direction of Jimmie Savage and Frank Anscombe. He served as Research Staff Statistician and Lecturer at Yale in 1966–1967. Richard accepted a faculty appointment at Stanford University in 1967, and has held tenured faculty positions at the University of Michigan (1972–1975), the University of California, San Diego (1975–1989), and Stanford University (since 1989). At Stanford, he is Professor of Health Research and Policy (Biostatistics), Chief of the Division of Biostatistics (since 1998) and Professor (by courtesy) of Electrical Engineering and of Statistics. At various times, he has had visiting faculty positions at Columbia, Harvard, MIT, Stanford and the Hebrew University. Richard’s research interests are in statistics and mathematics and their applications to medicine and biology. Much of his work has concerned binary tree-structured algorithms for classification, regression, survival analysis and clustering. Those for classification and survival analysis have been used with success in computer-aided diagnosis and prognosis, especially in cardiology, oncology and toxicology. He coauthored the 1984 book Classi- fication and Regression Trees (with Leo Brieman, Jerome Friedman and Charles Stone) which gives motivation, algorithms, various examples and mathematical theory for what have come to be known as CART algorithms. -
Target Population” – Do Not Use “Sample Population.”
NCES Glossary Analysis of Variance: Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to test or analyze the differences among group means in a sample. This technique is operated by modeling the value of the dependent variable Y as a function of an overall mean “µ”, related independent variables X1…Xn, and a residual “e” that is an independent normally distributed random variable. Bias (due to nonresponse): The difference that occurs when respondents differ as a group from non-respondents on a characteristic being studied. Bias (of an estimate): The difference between the expected value of a sample estimate and the corresponding true value for the population. Bootstrap: See Replication techniques. CAPI: Computer Assisted Personal Interviewing enables data collection staff to use portable microcomputers to administer a data collection form while viewing the form on the computer screen. As responses are entered directly into the computer, they are used to guide the interview and are automatically checked for specified range, format, and consistency edits. CATI: Computer Assisted Telephone Interviewing uses a computer system that allows a telephone interviewer to administer a data collection form over the phone while viewing the form on a computer screen. As the interviewer enters responses directly into the computer, the responses are used to guide the interview and are automatically checked for specified range, format, and consistency edits. Cluster: a group of similar people, things or objects positioned or occurring closely together. Cluster Design: Cluster Design refers to a type of sampling method in which the researcher divides the population into separate groups, of similarities - called clusters. -
Sampling Techniques Third Edition
Sampling Techniques third edition WILLIAM G. COCHRAN Professor of Statistics, Emeritus Harvard University JOHN WILEY & SONS New York• Chichester• Brisbane• Toronto• Singapore Copyright © 1977, by John Wiley & Sons, Inc. All nghts reserved. Published simultaneously in Canada. Reproducuon or tran~lation of any pan of this work beyond that permined by Sections 107 or 108 of the 1976 United Slates Copy right Act wuhout the perm1!>sion of the copyright owner 1s unlaw ful. Requests for permission or funher mformat1on should be addressed to the Pcrm1ss1ons Depanmcnt, John Wiley & Sons. Inc Library of Congress Cataloging In Puhllcatlon Data: Cochran, William Gemmell, 1909- Sampling techniques. (Wiley series in probability and mathematical statistic!>) Includes bibliographical references and index. 1. Sampling (Statistics) I. Title. QA276.6.C6 1977 001.4'222 77-728 ISBN 0-471-16240-X Printed in the United States of America 40 39 38 37 36 to Betty Preface As did the previous editions, this textbook presents a comprehensive account of sampling theory as it has been developed for use in sample surveys. It cantains illustrations to show how the theory is applied in practice, and exercises to be worked by the student. The book will be useful both as a text for a course on sample surveys in which the major emphasis is on theory and for individual reading by the student. The minimum mathematical equipment necessary to follow the great bulk of the material is a familiarity with algebra, especially relatively complicated algeb raic expressions, plus a·knowledge of probability for finite sample spaces, includ ing combinatorial probabilities. -
“I Didn't Want to Be a Statistician”
“I didn’t want to be a statistician” Making mathematical statisticians in the Second World War John Aldrich University of Southampton Seminar Durham January 2018 1 The individual before the event “I was interested in mathematics. I wanted to be either an analyst or possibly a mathematical physicist—I didn't want to be a statistician.” David Cox Interview 1994 A generation after the event “There was a large increase in the number of people who knew that statistics was an interesting subject. They had been given an excellent training free of charge.” George Barnard & Robin Plackett (1985) Statistics in the United Kingdom,1939-45 Cox, Barnard and Plackett were among the people who became mathematical statisticians 2 The people, born around 1920 and with a ‘name’ by the 60s : the 20/60s Robin Plackett was typical Born in 1920 Cambridge mathematics undergraduate 1940 Off the conveyor belt from Cambridge mathematics to statistics war-work at SR17 1942 Lecturer in Statistics at Liverpool in 1946 Professor of Statistics King’s College, Durham 1962 3 Some 20/60s (in 1968) 4 “It is interesting to note that a number of these men now hold statistical chairs in this country”* Egon Pearson on SR17 in 1973 In 1939 he was the UK’s only professor of statistics * Including Dennis Lindley Aberystwyth 1960 Peter Armitage School of Hygiene 1961 Robin Plackett Durham/Newcastle 1962 H. J. Godwin Royal Holloway 1968 Maurice Walker Sheffield 1972 5 SR 17 women in statistical chairs? None Few women in SR17: small skills pool—in 30s Cambridge graduated 5 times more men than women Post-war careers—not in statistics or universities Christine Stockman (1923-2015) Maths at Cambridge. -
Multilevel Modeling of Complex Survey Data
Multilevel Modeling of Complex Survey Data Sophia Rabe-Hesketh, University of California, Berkeley and Institute of Education, University of London Joint work with Anders Skrondal, London School of Economics 2007 West Coast Stata Users Group Meeting Marina del Rey, October 2007 GLLAMM – p.1 Outline Model-based and design based inference Multilevel models and pseudolikelihood Pseudo maximum likelihood estimation for U.S. PISA 2000 data Scaling of level-1 weights Simulation study Conclusions GLLAMM – p.2 Multistage sampling: U.S. PISA 2000 data Program for International Student Assessment (PISA): Assess and compare 15 year old students’ reading, math, etc. Three-stage survey with different probabilities of selection Stage 1: Geographic areas k sampled Stage 2: Schools j =1, . , n(2) sampled with different probabilities πj (taking into account school non-response) (1) Stage 3: Students i=1, . , nj sampled from school j, with conditional probabilities πi|j Probability that student i from school j is sampled: πij = πi|jπj GLLAMM – p.3 Model-based and design-based inference Model-based inference: Target of inference is parameter β in infinite population (parameter of data generating mechanism or statistical model) called superpopulation parameter Consistent estimator (assuming simple random sampling) such as maximum likelihood estimator (MLE) yields estimate β Design-based inference: Target of inference is statistic in finiteb population (FP), e.g., mean score yFP of all 15-year olds in LA Student who had a πij =1/5 chance of being sampled represents wij =1/πij =5 similar students in finite population Estimate of finite population mean (Horvitz-Thompson): FP 1 y = w y w ij ij ij ij Xij b P Similar for proportions, totals, etc. -
CHAPTER 5 Choosing the Type of Probability Sampling
CHAPTER 5 CHOOSING THE TYPE OF PROBABILITY SAMPLING What you will learn in this chapter: •• The types of probability sampling and how they differ from each other •• Steps in carrying out the major probability sample designs •• The strengths and weaknesses of the various types of probability sampling •• Differences between stratified sampling and quota sampling •• Differences between stratified sampling and cluster sampling •• Differences between multistage cluster sampling and multiphase sampling INTRODUCTION Once a choice is made to use a probability sample design, one must choose the type of probability sampling to use. This chapter includes descriptions of the major types of probability sampling. It covers steps involved in their adminis- tration, their subtypes, their weaknesses and strengths, and guidelines for choosing among them. There are four major types of probability sample designs: simple random sampling, stratified sampling, systematic sampling, and cluster sampling (see Figure 5.1). Simple random sampling is the most recognized probability sam- pling procedure. Stratified sampling offers significant improvement to simple random sampling. Systematic sampling is probably the easiest one to use, and cluster sampling is most practical for large national surveys. These sampling procedures are described below. 125 126 Sampling Essentials Figure 5.1 Major Types of Probability Sampling Probability Sample Designs Simple Stratified Systematic Cluster Random Sampling Sampling Sampling Sampling SIMPLE RANDOM SAMPLING What Is Simple Random Sampling? Simple random sampling is a probability sampling procedure that gives every element in the target population, and each possible sample of a given size, an equal chance of being selected. As such, it is an equal probability selection method (EPSEM). -
An Overview of Primary Sampling Units (Psus) in Multi-Stage Samples for Demographic Surveys1
Section on Government Statistics – JSM 2008 An Overview of Primary Sampling Units (PSUs) in Multi-Stage Samples 1 for Demographic Surveys Padraic Murphy U.S. Census Bureau, Washington, DC 20233-9200 Abstract This paper will present an overview of historical and current issues in the definition, stratification, and selection of Primary Sampling Units (PSUs) in large demographic surveys. By PSUs, we mean the large clusters (usually geographic areas such as counties) that are the sampling frame in the first stage of a multi-stage sample design. While simple in concept, the details of defining, stratifying, and selecting PSUs can prove to be surprisingly complex. We look at developments pertaining to PSUs (as used by the U.S. Census Bureau) over the past half-century, and describe some current problems. The issues discussed include (1) constraints on PSU size and boundaries, (2) choosing "building blocks" for PSUs, (3) methodology for and objectives of PSU stratification, (4) coordination among multiple surveys, and (5) coordination with preceding designs (maximizing or minimizing overlap.) Key Words: Sample Design, Stratified Sampling, History of Surveys, Multistage Sampling 1. Introduction In 1802, the French mathematician Laplace persuaded the French government to use a sample survey to estimate the population of France as of September 22 of that year. The method used was one he had demonstrated earlier in estimating the 1782 population of France; they first took a sample of small administrative districts known as communes, counted the total population y of each sample commune, and then used the known number of births in both the communes and the nation – x and X, respectively – to calculate the ratio estimator Y = X(y/x). -
A Conversation with John Hartigan Daniel Barry
Statistical Science 2005, Vol. 20, No. 4, 418–430 DOI 10.1214/088342304000000242 © Institute of Mathematical Statistics, 2005 A Conversation with John Hartigan Daniel Barry Abstract. John Anthony Hartigan was born on July 2, 1937 in Sydney, Aus- tralia. He attended the University of Sydney, earning a B.Sc. degree in math- ematics in 1959 and an M.Sc. degree in mathematics the following year. In 1960 John moved to Princeton where he studied for his Ph.D. in statistics under the guidance of John Tukey and Frank Anscombe. He completed his Ph.D. in 1962, and worked as an Instructor at Princeton in 1962–1963, and as a visiting lecturer at the Cambridge Statistical Laboratory in 1963–1964. In 1964 he joined the faculty at Princeton. He moved to Yale as Associate Professor with tenure in 1969, became a Professor in 1972 and, in 1983, be- came Eugene Higgins Professor of Statistics at Yale—a position previously held by Jimmie Savage. He served as Chairman of the Statistics Department at Yale from 1973 to 1975 and again from 1988 to 1994. John was instrumen- tal in the establishment of the Social Sciences Statistical Laboratory at Yale and served as its Director from 1985 to 1989 and again from 1991 to 1993. He served as Chairman of the National Research Council Committee on the General Aptitude Test Battery from 1987 to 1990. John’s research interests cover the foundations of probability and statistics, classification, clustering, Bayes methods and statistical computing. He has published over 80 journal papers and two books: Clustering Algorithms in 1975 and Bayes Theory in 1983. -
Comparing Four Bootstrap Methods for Stratified Three-Stage Sampling
Journal of Official Statistics, Vol. 26, No. 1, 2010, pp. 193–207 Comparing Four Bootstrap Methods for Stratified Three-Stage Sampling Hiroshi Saigo1 For a stratified three-stage sampling design with simple random sampling without replacement at each stage, only the Bernoulli bootstrap is currently available as a bootstrap for design-based inference under arbitrary sampling fractions. This article extends three other methods (the mirror-match bootstrap, the rescaling bootstrap, and the without-replacement bootstrap) to the design and conducts simulation study that estimates variances and constructs coverage intervals for a population total and selected quantiles. The without-replacement bootstrap proves the least biased of the four methods when estimating the variances of quantiles. Otherwise, the methods are comparable. Key words: Multistage sampling; high sampling fractions; resampling methods; quantile estimation. 1. Introduction For most stratified multi-stage sampling designs, unbiased variance estimators of statistics expressed by linear functions of the observations are available. However, for nonlinear statistics and functionals, closed-form variance formulas are often unavailable. Consequently, bootstrap methods can serve for variance estimation of such statistics under stratified multi-stage sampling designs. When the sampling fractions at the first stage are small, bootstrap methods for stratified multi-stage sampling designs are simplified because without-replacement sampling can be approximated by with-replacement sampling (Shao and Tu 1995, p. 235). But when the sampling fractions at the first stage are not negligible, bootstrap methods for consistent variance estimation become complicated and few have been developed. For instance, for a stratified three-stage with simple random sampling without replacement at each stage (ST–SI3) with arbitrary sampling fractions, no bootstrap procedure is available except the Bernoulli Bootstrap (BBE) proposed by Funaoka, Saigo, Sitter, and Toida (2006) for the 1997 Japanese National Survey of Prices (NSP).