IBM SPSS Complex Samples Business Analytics
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SAMPLING DESIGN & WEIGHTING in the Original
Appendix A 2096 APPENDIX A: SAMPLING DESIGN & WEIGHTING In the original National Science Foundation grant, support was given for a modified probability sample. Samples for the 1972 through 1974 surveys followed this design. This modified probability design, described below, introduces the quota element at the block level. The NSF renewal grant, awarded for the 1975-1977 surveys, provided funds for a full probability sample design, a design which is acknowledged to be superior. Thus, having the wherewithal to shift to a full probability sample with predesignated respondents, the 1975 and 1976 studies were conducted with a transitional sample design, viz., one-half full probability and one-half block quota. The sample was divided into two parts for several reasons: 1) to provide data for possibly interesting methodological comparisons; and 2) on the chance that there are some differences over time, that it would be possible to assign these differences to either shifts in sample designs, or changes in response patterns. For example, if the percentage of respondents who indicated that they were "very happy" increased by 10 percent between 1974 and 1976, it would be possible to determine whether it was due to changes in sample design, or an actual increase in happiness. There is considerable controversy and ambiguity about the merits of these two samples. Text book tests of significance assume full rather than modified probability samples, and simple random rather than clustered random samples. In general, the question of what to do with a mixture of samples is no easier solved than the question of what to do with the "pure" types. -
Stratified Sampling Using Cluster Analysis: a Sample Selection Strategy for Improved Generalizations from Experiments
Article Evaluation Review 1-31 ª The Author(s) 2014 Stratified Sampling Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0193841X13516324 Using Cluster erx.sagepub.com Analysis: A Sample Selection Strategy for Improved Generalizations From Experiments Elizabeth Tipton1 Abstract Background: An important question in the design of experiments is how to ensure that the findings from the experiment are generalizable to a larger population. This concern with generalizability is particularly important when treatment effects are heterogeneous and when selecting units into the experiment using random sampling is not possible—two conditions commonly met in large-scale educational experiments. Method: This article introduces a model-based balanced-sampling framework for improv- ing generalizations, with a focus on developing methods that are robust to model misspecification. Additionally, the article provides a new method for sample selection within this framework: First units in an inference popula- tion are divided into relatively homogenous strata using cluster analysis, and 1 Department of Human Development, Teachers College, Columbia University, NY, USA Corresponding Author: Elizabeth Tipton, Department of Human Development, Teachers College, Columbia Univer- sity, 525 W 120th St, Box 118, NY 10027, USA. Email: [email protected] 2 Evaluation Review then the sample is selected using distance rankings. Result: In order to demonstrate and evaluate the method, a reanalysis of a completed experiment is conducted. This example compares samples selected using the new method with the actual sample used in the experiment. Results indicate that even under high nonresponse, balance is better on most covariates and that fewer coverage errors result. Conclusion: The article concludes with a discussion of additional benefits and limitations of the method. -
Stratified Random Sampling from Streaming and Stored Data
Stratified Random Sampling from Streaming and Stored Data Trong Duc Nguyen Ming-Hung Shih Divesh Srivastava Iowa State University, USA Iowa State University, USA AT&T Labs–Research, USA Srikanta Tirthapura Bojian Xu Iowa State University, USA Eastern Washington University, USA ABSTRACT SRS provides the flexibility to emphasize some strata over Stratified random sampling (SRS) is a widely used sampling tech- others through controlling the allocation of sample sizes; for nique for approximate query processing. We consider SRS on instance, a stratum with a high standard deviation can be given continuously arriving data streams, and make the following con- a larger allocation than another stratum with a smaller standard tributions. We present a lower bound that shows that any stream- deviation. In the above example, if we desire a stratified sample ing algorithm for SRS must have (in the worst case) a variance of size three, it is best to allocate a smaller sample of size one to that is Ω¹rº factor away from the optimal, where r is the number the first stratum and a larger sample size of two to thesecond of strata. We present S-VOILA, a streaming algorithm for SRS stratum, since the standard deviation of the second stratum is that is locally variance-optimal. Results from experiments on real higher. Doing so, the variance of estimate of the population mean 3 and synthetic data show that S-VOILA results in a variance that is further reduces to approximately 1:23 × 10 . The strength of typically close to an optimal offline algorithm, which was given SRS is that a stratified random sample can be used to answer the entire input beforehand. -
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). -
A Primer for Using and Understanding Weights with National Datasets
The Journal of Experimental Education, 2005, 73(3), 221–248 A Primer for Using and Understanding Weights With National Datasets DEBBIE L. HAHS-VAUGHN University of Central Florida ABSTRACT. Using data from the National Study of Postsecondary Faculty and the Early Childhood Longitudinal Study—Kindergarten Class of 1998–99, the author provides guidelines for incorporating weights and design effects in single-level analy- sis using Windows-based SPSS and AM software. Examples of analyses that do and do not employ weights and design effects are also provided to illuminate the differ- ential results of key parameter estimates and standard errors using varying degrees of using or not using the weighting and design effect continuum. The author gives recommendations on the most appropriate weighting options, with specific reference to employing a strategy to accommodate both oversampled groups and cluster sam- pling (i.e., using weights and design effects) that leads to the most accurate parame- ter estimates and the decreased potential of committing a Type I error. However, using a design effect adjusted weight in SPSS may produce underestimated standard errors when compared with accurate estimates produced by specialized software such as AM. Key words: AM, design effects, design effects adjusted weights, normalized weights, raw weights, unadjusted weights, SPSS LARGE COMPLEX DATASETS are available to researchers through federal agencies such as the National Center for Education Statistics (NCES) and the National Science Foundation (NSF), to -
Design Effects in School Surveys
Joint Statistical Meetings - Section on Survey Research Methods DESIGN EFFECTS IN SCHOOL SURVEYS William H. Robb and Ronaldo Iachan ORC Macro, Burlington, Vermont 05401 KEY WORDS: design effects, generalized variance nutrition, school violence, personal safety and physical functions, school surveys, weighting effects activity. The YRBS is designed to provide estimates for the population of ninth through twelfth graders in public and School surveys typically involve complex private schools, by gender, by age or grade, and by grade and multistage stratified sampling designs. In addition, survey gender for all youths, for African-American youths, and for objectives often require over-sampling of minority groups, Hispanic youths. The YRBS is conducted by ORC Macro and the reporting of hundreds of estimates computed for under contract to CDC-DASH on a two year cycle. many different subgroups of interest. Another feature of For the cycle of the YRBS examined in this paper, 57 these surveys that limits the ability to control the precision primary sampling units (PSUs) were selected from within 16 for the overall population and key subgroups simultaneously strata. Within PSUs, at least three schools per cluster is that intact classes are selected as ultimate clusters; thus, spanning grades 9 through 12 were drawn. Fifteen additional the designer cannot determine the ethnicity of individual schools were selected in a subsample of PSUs to represent students nor the ethnic composition of classes. very small schools. In addition, schools that did not span the ORC Macro statisticians have developed the design entire grade range 9 through 12 were combined prior to and weighting procedures for numerous school surveys at the sampling so that every sampling unit spanned the grade national and state levels. -
Computing Effect Sizes for Clustered Randomized Trials
Computing Effect Sizes for Clustered Randomized Trials Terri Pigott, C2 Methods Editor & Co-Chair Professor, Loyola University Chicago [email protected] The Campbell Collaboration www.campbellcollaboration.org Computing effect sizes in clustered trials • In an experimental study, we are interested in the difference in performance between the treatment and control group • In this case, we use the standardized mean difference, given by YYTC− d = gg Control group sp mean Treatment group mean Pooled sample standard deviation Campbell Collaboration Colloquium – August 2011 www.campbellcollaboration.org 1 Variance of the standardized mean difference NNTC+ d2 Sd2 ()=+ NNTC2( N T+ N C ) where NT is the sample size for the treatment group, and NC is the sample size for the control group Campbell Collaboration Colloquium – August 2011 www.campbellcollaboration.org TREATMENT GROUP CONTROL GROUP TT T CC C YY,,..., Y YY12,,..., YC 12 NT N Overall Trt Mean Overall Cntl Mean T Y C Yg g S 2 2 Trt SCntl 2 S pooled Campbell Collaboration Colloquium – August 2011 www.campbellcollaboration.org 2 In cluster randomized trials, SMD more complex • In cluster randomized trials, we have clusters such as schools or clinics randomized to treatment and control • We have at least two means: mean performance for each cluster, and the overall group mean • We also have several components of variance – the within- cluster variance, the variance between cluster means, and the total variance • Next slide is an illustration Campbell Collaboration Colloquium – August 2011 www.campbellcollaboration.org TREATMENT GROUP CONTROL GROUP Cntl Cluster mC Trt Cluster 1 Trt Cluster mT Cntl Cluster 1 TT T T CC C C YY,...ggg Y ,..., Y YY11,.. -
Using Sampling Matching Methods to Remove Selectivity in Survey Analysis with Categorical Data
Using Sampling Matching Methods to Remove Selectivity in Survey Analysis with Categorical Data Han Zheng (s1950142) Supervisor: Dr. Ton de Waal (CBS) Second Supervisor: Prof. Willem Jan Heiser (Leiden University) master thesis Defended on Month Day, 2019 Specialization: Data Science STATISTICAL SCIENCE FOR THE LIFE AND BEHAVIOURAL SCIENCES Abstract A problem for survey datasets is that the data may cone from a selective group of the pop- ulation. This is hard to produce unbiased and accurate estimates for the entire population. One way to overcome this problem is to use sample matching. In sample matching, one draws a sample from the population using a well-defined sampling mechanism. Next, units in the survey dataset are matched to units in the drawn sample using some background information. Usually the background information is insufficiently detaild to enable exact matching, where a unit in the survey dataset is matched to the same unit in the drawn sample. Instead one usually needs to rely on synthetic methods on matching where a unit in the survey dataset is matched to a similar unit in the drawn sample. This study developed several methods in sample matching for categorical data. A selective panel represents the available completed but biased dataset which used to estimate the target variable distribution of the population. The result shows that the exact matching is unex- pectedly performs best among all matching methods, and using a weighted sampling instead of random sampling has not contributes to increase the accuracy of matching. Although the predictive mean matching lost the competition against exact matching, with proper adjust- ment of transforming categorical variables into numerical values would substantial increase the accuracy of matching. -
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 . -
3 Stratified Simple Random Sampling
3 STRATIFIED SIMPLE RANDOM SAMPLING • Suppose the population is partitioned into disjoint sets of sampling units called strata. If a sample is selected within each stratum, then this sampling procedure is known as stratified sampling. • If we can assume the strata are sampled independently across strata, then (i) the estimator of t or yU can be found by combining stratum sample sums or means using appropriate weights (ii) the variances of estimators associated with the individual strata can be summed to obtain the variance an estimator associated with the whole population. (Given independence, the variance of a sum equals the sum of the individual variances.) • (ii) implies that only within-stratum variances contribute to the variance of an estimator. Thus, the basic motivating principle behind using stratification to produce an estimator with small variance is to partition the population so that units within each stratum are as similar as possible. This is known as the stratification principle. • In ecological studies, it is common to stratify a geographical region into subregions that are similar with respect to a known variable such as elevation, animal habitat type, vegetation types, etc. because it is suspected that the y-values may vary greatly across strata while they will tend to be similar within each stratum. Analogously, when sampling people, it is common to stratify on variables such as gender, age groups, income levels, education levels, marital status, etc. • Sometimes strata are formed based on sampling convenience. For example, suppose a large study region appears to be homogeneous (that is, there are no spatial patterns) and is stratified based on the geographical proximity of sampling units. -
Overview of Propensity Score Analysis
1 Overview of Propensity Score Analysis Learning Objectives zz Describe the advantages of propensity score methods for reducing bias in treatment effect estimates from observational studies zz Present Rubin’s causal model and its assumptions zz Enumerate and overview the steps of propensity score analysis zz Describe the characteristics of data from complex surveys and their relevance to propensity score analysis zz Enumerate resources for learning the R programming language and software zz Identify major resources available in the R software for propensity score analysis 1.1. Introduction The objective of this chapter is to provide the common theoretical foundation for all propensity score methods and provide a brief description of each method. It will also introduce the R software, point the readers toward resources for learning the R language, and briefly introduce packages available in R relevant to propensity score analysis. Draft ProofPropensity score- Doanalysis methodsnot aim copy, to reduce bias inpost, treatment effect or estimates distribute obtained from observational studies, which are studies estimating treatment effects with research designs that do not have random assignment of participants to condi- tions. The term observational studies as used here includes both studies where there is 1 Copyright ©2017 by SAGE Publications, Inc. This work may not be reproduced or distributed in any form or by any means without express written permission of the publisher. 2 Practical Propensity Score Methods Using R no random assignment but there is manipulation of conditions and studies that lack both random assignment and manipulation of conditions. Research designs to estimate treatment effects that do not have random assignment to conditions are also referred as quasi-experimental or nonexperimental designs. -
The Design Effect: Do We Know All About It?
Proceedings of the Annual Meeting of the American Statistical Association, August 5-9, 2001 THE DESIGN EFFECT: DO WE KNOW ALL ABOUT IT? Inho Park and Hyunshik Lee, Westat Inho Park, Westat, 1650 Research Boulevard, Rockville, Maryland 20850 Key Words: Complex sample, Simple random where y is an estimate of the population mean (Y ) sample, pps sampling, Point and variance under a complex design with the sample size of n, f is a estimation, Ratio mean, Total estimator 2 = ()− −1∑N − 2 sampling fraction, and S y N 1 i=1(yi Y ) is 1. Introduction the population element variance of the y-variable. In general, the design effect can be defined for any The design effect is widely used in survey meaningful statistic computed from the sample selected sampling for planning a sample design and to report the from the complex sample design. effect of the sample design in estimation and analysis. It The Deff is a population quantity that depends on is defined as the ratio of the variance of an estimator the sample design and the statistic. The same parameter under a sample design to that of the estimator under can be estimated by different estimators and their simple random sampling. An estimated design effect is Deff’s are different even under the same design. routinely produced by sample survey software such as Therefore, the Deff includes not only the efficiency of WesVar and SUDAAN. the design but also the efficiency of the estimator. This paper examines the relationship between the Särndal et al. (1992, p. 54) made this point clear by design effects of the total estimator and the ratio mean defining it as a function of the design (p) and the estimator, under unequal probability sampling.