Understanding and Using the U.S. Census Bureau's American
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
The Effect of Sampling Error on the Time Series Behavior of Consumption Data*
Journal of Econometrics 55 (1993) 235-265. North-Holland The effect of sampling error on the time series behavior of consumption data* William R. Bell U.S.Bureau of the Census, Washington, DC 20233, USA David W. Wilcox Board of Governors of the Federal Reserve System, Washington, DC 20551, USA Much empirical economic research today involves estimation of tightly specified time series models that derive from theoretical optimization problems. Resulting conclusions about underly- ing theoretical parameters may be sensitive to imperfections in the data. We illustrate this fact by considering sampling error in data from the Census Bureau’s Retail Trade Survey. We find that parameter estimates in seasonal time series models for retail sales are sensitive to whether a sampling error component is included in the model. We conclude that sampling error should be taken seriously in attempts to derive economic implications by modeling time series data from repeated surveys. 1. Introduction The rational expectations revolution has transformed the methodology of macroeconometric research. In the new style, the researcher typically begins by specifying a dynamic optimization problem faced by agents in the model economy. Then the researcher derives the solution to the optimization problem, expressed as a stochastic model for some observable economic variable(s). A trademark of research in this tradition is that each of the parameters in the model for the observable variables is endowed with a specific economic interpretation. The last step in the research program is the application of the model to actual economic data to see whether the model *This paper reports the general results of research undertaken by Census Bureau and Federal Reserve Board staff. -
Design and Implementation of N-Of-1 Trials: a User's Guide
Design and Implementation of N-of-1 Trials: A User’s Guide N of 1 The Agency for Healthcare Research and Quality’s (AHRQ) Effective Health Care Program conducts and supports research focused on the outcomes, effectiveness, comparative clinical effectiveness, and appropriateness of pharmaceuticals, devices, and health care services. More information on the Effective Health Care Program and electronic copies of this report can be found at www.effectivehealthcare.ahrq. gov. This report was produced under contract to AHRQ by the Brigham and Women’s Hospital DEcIDE (Developing Evidence to Inform Decisions about Effectiveness) Methods Center under Contract No. 290-2005-0016-I. The AHRQ Task Order Officer for this project was Parivash Nourjah, Ph.D. The findings and conclusions in this document are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views ofAHRQ or the U.S. Department of Health and Human Services. Therefore, no statement in this report should be construed as an official position of AHRQ or the U.S. Department of Health and Human Services. Persons using assistive technology may not be able to fully access information in this report. For assistance contact [email protected]. The following investigators acknowledge financial support: Dr. Naihua Duan was supported in part by NIH grants 1 R01 NR013938 and 7 P30 MH090322, Dr. Heather Kaplan is part of the investigator team that designed and developed the MyIBD platform, and this work was supported in part by Cincinnati Children’s Hospital Medical Center. Dr. Richard Kravitz was supported in part by National Institute of Nursing Research Grant R01 NR01393801. -
13 Collecting Statistical Data
13 Collecting Statistical Data 13.1 The Population 13.2 Sampling 13.3 Random Sampling 1.1 - 1 • Polls, studies, surveys and other data collecting tools collect data from a small part of a larger group so that we can learn something about the larger group. • This is a common and important goal of statistics: Learn about a large group by examining data from some of its members. 1.1 - 2 Data collections of observations (such as measurements, genders, survey responses) 1.1 - 3 Statistics is the science of planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data 1.1 - 4 Population the complete collection of all individuals (scores, people, measurements, and so on) to be studied; the collection is complete in the sense that it includes all of the individuals to be studied 1.1 - 5 Census Collection of data from every member of a population Sample Subcollection of members selected from a population 1.1 - 6 A Survey • The practical alternative to a census is to collect data only from some members of the population and use that data to draw conclusions and make inferences about the entire population. • Statisticians call this approach a survey (or a poll when the data collection is done by asking questions). • The subgroup chosen to provide the data is called the sample, and the act of selecting a sample is called sampling. 1.1 - 7 A Survey • The first important step in a survey is to distinguish the population for which the survey applies (the target population) and the actual subset of the population from which the sample will be drawn, called the sampling frame. -
American Community Survey Accuracy of the Data (2018)
American Community Survey Accuracy of the Data (2018) INTRODUCTION This document describes the accuracy of the 2018 American Community Survey (ACS) 1-year estimates. The data contained in these data products are based on the sample interviewed from January 1, 2018 through December 31, 2018. The ACS sample is selected from all counties and county-equivalents in the United States. In 2006, the ACS began collecting data from sampled persons in group quarters (GQs) – for example, military barracks, college dormitories, nursing homes, and correctional facilities. Persons in sample in (GQs) and persons in sample in housing units (HUs) are included in all 2018 ACS estimates that are based on the total population. All ACS population estimates from years prior to 2006 include only persons in housing units. The ACS, like any other sample survey, is subject to error. The purpose of this document is to provide data users with a basic understanding of the ACS sample design, estimation methodology, and the accuracy of the ACS data. The ACS is sponsored by the U.S. Census Bureau, and is part of the Decennial Census Program. For additional information on the design and methodology of the ACS, including data collection and processing, visit: https://www.census.gov/programs-surveys/acs/methodology.html. To access other accuracy of the data documents, including the 2018 PRCS Accuracy of the Data document and the 2014-2018 ACS Accuracy of the Data document1, visit: https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html. 1 The 2014-2018 Accuracy of the Data document will be available after the release of the 5-year products in December 2019. -
Generalized Linear Models for Aggregated Data
Generalized Linear Models for Aggregated Data Avradeep Bhowmik Joydeep Ghosh Oluwasanmi Koyejo University of Texas at Austin University of Texas at Austin Stanford University [email protected] [email protected] [email protected] Abstract 1 Introduction Modern life is highly data driven. Datasets with Databases in domains such as healthcare are records at the individual level are generated every routinely released to the public in aggregated day in large volumes. This creates an opportunity form. Unfortunately, na¨ıve modeling with for researchers and policy-makers to analyze the data aggregated data may significantly diminish and examine individual level inferences. However, in the accuracy of inferences at the individ- many domains, individual records are difficult to ob- ual level. This paper addresses the scenario tain. This particularly true in the healthcare industry where features are provided at the individual where protecting the privacy of patients restricts pub- level, but the target variables are only avail- lic access to much of the sensitive data. Therefore, able as histogram aggregates or order statis- in many cases, multiple Statistical Disclosure Limita- tics. We consider a limiting case of gener- tion (SDL) techniques are applied [9]. Of these, data alized linear modeling when the target vari- aggregation is the most widely used technique [5]. ables are only known up to permutation, and explore how this relates to permutation test- It is common for agencies to report both individual ing; a standard technique for assessing statis- level information for non-sensitive attributes together tical dependency. Based on this relationship, with the aggregated information in the form of sample we propose a simple algorithm to estimate statistics. -
Meta-Analysis Using Individual Participant Data: One-Stage and Two-Stage Approaches, and Why They May Differ Danielle L
CORE Metadata, citation and similar papers at core.ac.uk Provided by Keele Research Repository Tutorial in Biostatistics Received: 11 March 2016, Accepted: 13 September 2016 Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/sim.7141 Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ Danielle L. Burke*† Joie Ensor and Richard D. Riley Meta-analysis using individual participant data (IPD) obtains and synthesises the raw, participant-level data from a set of relevant studies. The IPD approach is becoming an increasingly popular tool as an alternative to traditional aggregate data meta-analysis, especially as it avoids reliance on published results and provides an opportunity to investigate individual-level interactions, such as treatment-effect modifiers. There are two statistical approaches for conducting an IPD meta-analysis: one-stage and two-stage. The one-stage approach analyses the IPD from all studies simultaneously, for example, in a hierarchical regression model with random effects. The two-stage approach derives aggregate data (such as effect estimates) in each study separately and then combines these in a traditional meta-analysis model. There have been numerous comparisons of the one-stage and two-stage approaches via theoretical consideration, simulation and empirical examples, yet there remains confusion regarding when each approach should be adopted, and indeed why they may differ. In this tutorial paper, we outline the key statistical methods for one-stage and two-stage IPD meta-analyses, and provide 10 key reasons why they may produce different summary results. We explain that most differences arise because of different modelling assumptions, rather than the choice of one-stage or two-stage itself. -
Observational Studies and Bias in Epidemiology
The Young Epidemiology Scholars Program (YES) is supported by The Robert Wood Johnson Foundation and administered by the College Board. Observational Studies and Bias in Epidemiology Manuel Bayona Department of Epidemiology School of Public Health University of North Texas Fort Worth, Texas and Chris Olsen Mathematics Department George Washington High School Cedar Rapids, Iowa Observational Studies and Bias in Epidemiology Contents Lesson Plan . 3 The Logic of Inference in Science . 8 The Logic of Observational Studies and the Problem of Bias . 15 Characteristics of the Relative Risk When Random Sampling . and Not . 19 Types of Bias . 20 Selection Bias . 21 Information Bias . 23 Conclusion . 24 Take-Home, Open-Book Quiz (Student Version) . 25 Take-Home, Open-Book Quiz (Teacher’s Answer Key) . 27 In-Class Exercise (Student Version) . 30 In-Class Exercise (Teacher’s Answer Key) . 32 Bias in Epidemiologic Research (Examination) (Student Version) . 33 Bias in Epidemiologic Research (Examination with Answers) (Teacher’s Answer Key) . 35 Copyright © 2004 by College Entrance Examination Board. All rights reserved. College Board, SAT and the acorn logo are registered trademarks of the College Entrance Examination Board. Other products and services may be trademarks of their respective owners. Visit College Board on the Web: www.collegeboard.com. Copyright © 2004. All rights reserved. 2 Observational Studies and Bias in Epidemiology Lesson Plan TITLE: Observational Studies and Bias in Epidemiology SUBJECT AREA: Biology, mathematics, statistics, environmental and health sciences GOAL: To identify and appreciate the effects of bias in epidemiologic research OBJECTIVES: 1. Introduce students to the principles and methods for interpreting the results of epidemio- logic research and bias 2. -
Learning to Personalize Medicine from Aggregate Data
medRxiv preprint doi: https://doi.org/10.1101/2020.07.07.20148205; this version posted July 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license . Learning to Personalize Medicine from Aggregate Data Rich Colbaugh Kristin Glass Volv Global Lausanne, Switzerland Abstract There is great interest in personalized medicine, in which treatment is tailored to the individual characteristics of pa- tients. Achieving the objectives of precision healthcare will require clinically-grounded, evidence-based approaches, which in turn demands rigorous, scalable predictive analytics. Standard strategies for deriving prediction models for medicine involve acquiring ‘training’ data for large numbers of patients, labeling each patient according to the out- come of interest, and then using the labeled examples to learn to predict the outcome for new patients. Unfortunate- ly, labeling individuals is time-consuming and expertise-intensive in medical applications and thus represents a ma- jor impediment to practical personalized medicine. We overcome this obstacle with a novel machine learning algo- rithm that enables individual-level prediction models to be induced from aggregate-level labeled data, which is read- ily-available in many health domains. The utility of the proposed learning methodology is demonstrated by: i.) lev- eraging US county-level mental health statistics to create a screening tool which detects individuals suffering from depression based upon their Twitter activity; ii.) designing a decision-support system that exploits aggregate clinical trials data on multiple sclerosis (MS) treatment to predict which therapy would work best for the presenting patient; iii.) employing group-level clinical trials data to induce a model able to find those MS patients likely to be helped by an experimental therapy. -
Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy
Review Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy Yuxi Zhao and Lifeng Lin * Department of Statistics, Florida State University, Tallahassee, FL 32306, USA; [email protected] * Correspondence: [email protected] Abstract: Systematic reviews and meta-analyses have been increasingly used to pool research find- ings from multiple studies in medical sciences. The reliability of the synthesized evidence depends highly on the methodological quality of a systematic review and meta-analysis. In recent years, several tools have been developed to guide the reporting and evidence appraisal of systematic reviews and meta-analyses, and much statistical effort has been paid to improve their methodological quality. Nevertheless, many contemporary meta-analyses continue to employ conventional statis- tical methods, which may be suboptimal compared with several alternative methods available in the evidence synthesis literature. Based on a recent systematic review on COVID-19 in pregnancy, this article provides an overview of select good practices for performing meta-analyses from sta- tistical perspectives. Specifically, we suggest meta-analysts (1) providing sufficient information of included studies, (2) providing information for reproducibility of meta-analyses, (3) using appro- priate terminologies, (4) double-checking presented results, (5) considering alternative estimators of between-study variance, (6) considering alternative confidence intervals, (7) reporting predic- Citation: Zhao, Y.; Lin, L. Good tion intervals, (8) assessing small-study effects whenever possible, and (9) considering one-stage Statistical Practices for Contemporary methods. We use worked examples to illustrate these good practices. Relevant statistical code Meta-Analysis: Examples Based on a is also provided. -
Measurement Error Estimation Methods in Survey Methodology
Available at Applications and http://pvamu.edu/aam Applied Mathematics: Appl. Appl. Math. ISSN: 1932-9466 An International Journal (AAM) Vol. 11, Issue 1 (June 2016), pp. 97 - 114 ___________________________________________________________________________________________ Measurement Error Estimation Methods in Survey Methodology Alireza Zahedian1 and Roshanak Aliakbari Saba2 1Statistical Centre of Iran Dr. Fatemi Ave. Post code: 14155 – 6133 Tehran, Iran 2Statistical Research and Training Center Dr. Fatemi Ave., BabaTaher St., No. 145 Postal code: 14137 – 17911 Tehran, Iran [email protected] Received: May 7, 2014; Accepted: January 13, 2016 Abstract One of the most important topics that are discussed in survey methodology is the accuracy of statistics or survey errors that may occur in the parameters estimation process. In statistical literature, these errors are grouped into two main categories: sampling errors and non-sampling errors. Measurement error is one of the most important non-sampling errors. Since estimating of measurement error is more complex than other types of survey errors, much more research has been done on ways of preventing or dealing with this error. The main problem associated with measurement error is the difficulty to measure or estimate this error in surveys. Various methods can be used for estimating measurement error in surveys, but the most appropriate method in each survey should be adopted according to the method of calculating statistics and the survey conditions. This paper considering some practical experiences in calculating and evaluating surveys results, intends to help statisticians to adopt an appropriate method for estimating measurement error. So to achieve this aim, after reviewing the concept and sources of measurement error, some methods of estimating the error are revised in this paper. -
Reconciling Household Surveys and National Accounts Data Using a Cross Entropy Estimation Method
TMD DISCUSSION PAPER NO. 50 RECONCILING HOUSEHOLD SURVEYS AND NATIONAL ACCOUNTS DATA USING A CROSS ENTROPY ESTIMATION METHOD Anne-Sophie Robilliard Sherman Robinson International Food Policy Research Institute Trade and Macroeconomics Division International Food Policy Research Institute 2033 K Street, N.W. Washington, D.C. 20006, U.S.A. November 1999 (Revised January 2001) TMD Discussion Papers contain preliminary material and research results, and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers will eventually be published in some other form, and that their content may also be revised. This paper is available at http://www.cgiar.org/ifpri/divs/tmd/dp/papers/dp50.pdf Abstract This paper presents an approach to reconciling household surveys and national accounts data that starts from the assumption that the macro data represent control totals to which the household data must be reconciled, but the macro aggregates may be measured with error. The economic data gathered in the household survey are assumed to be accurate, or have been adjusted to be accurate. Given these assumptions, the problem is how to use the additional information provided by the national accounts data to re-estimate the household weights used in the survey so that the survey results are consistent with the aggregate data, while simultaneously estimating the errors in the aggregates. The estimation approach represents an efficient “information processing rule” using an estimation criterion based on an entropy measure of information. The survey household weights are treated as a prior. New weights are estimated that are close to the prior using a cross-entropy metric and that are also consistent with the additional information. -
STANDARDS and GUIDELINES for STATISTICAL SURVEYS September 2006
OFFICE OF MANAGEMENT AND BUDGET STANDARDS AND GUIDELINES FOR STATISTICAL SURVEYS September 2006 Table of Contents LIST OF STANDARDS FOR STATISTICAL SURVEYS ....................................................... i INTRODUCTION......................................................................................................................... 1 SECTION 1 DEVELOPMENT OF CONCEPTS, METHODS, AND DESIGN .................. 5 Section 1.1 Survey Planning..................................................................................................... 5 Section 1.2 Survey Design........................................................................................................ 7 Section 1.3 Survey Response Rates.......................................................................................... 8 Section 1.4 Pretesting Survey Systems..................................................................................... 9 SECTION 2 COLLECTION OF DATA................................................................................... 9 Section 2.1 Developing Sampling Frames................................................................................ 9 Section 2.2 Required Notifications to Potential Survey Respondents.................................... 10 Section 2.3 Data Collection Methodology.............................................................................. 11 SECTION 3 PROCESSING AND EDITING OF DATA...................................................... 13 Section 3.1 Data Editing ........................................................................................................