Factfinder for the Nation, History and Organization
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DATA COLLECTION METHODS Section III 5
AN OVERVIEW OF QUANTITATIVE AND QUALITATIVE DATA COLLECTION METHODS Section III 5. DATA COLLECTION METHODS: SOME TIPS AND COMPARISONS In the previous chapter, we identified two broad types of evaluation methodologies: quantitative and qualitative. In this section, we talk more about the debate over the relative virtues of these approaches and discuss some of the advantages and disadvantages of different types of instruments. In such a debate, two types of issues are considered: theoretical and practical. Theoretical Issues Most often these center on one of three topics: · The value of the types of data · The relative scientific rigor of the data · Basic, underlying philosophies of evaluation Value of the Data Quantitative and qualitative techniques provide a tradeoff between breadth and depth, and between generalizability and targeting to specific (sometimes very limited) populations. For example, a quantitative data collection methodology such as a sample survey of high school students who participated in a special science enrichment program can yield representative and broadly generalizable information about the proportion of participants who plan to major in science when they get to college and how this proportion differs by gender. But at best, the survey can elicit only a few, often superficial reasons for this gender difference. On the other hand, separate focus groups (a qualitative technique related to a group interview) conducted with small groups of men and women students will provide many more clues about gender differences in the choice of science majors, and the extent to which the special science program changed or reinforced attitudes. The focus group technique is, however, limited in the extent to which findings apply beyond the specific individuals included in the groups. -
811D Ecollomic Statistics Adrllillistra!Tioll
811d Ecollomic Statistics Adrllillistra!tioll BUREAU THE CENSUS • I n i • I Charles G. Langham Issued 1973 U.S. D OF COM ERCE Frederick B. Dent. Secretary Social Economic Statistics Edward D. Administrator BU OF THE CENSUS Vincent P. Barabba, Acting Director Vincent Director Associate Director for Economic Associate Director for Statistical Standards and 11/1",1"\"/1,, DATA USER SERVICES OFFICE Robert B. Chief ACKNOWLEDGMENTS This report was in the Data User Services Office Charles G. direction of Chief, Review and many persons the Bureau. Library of Congress Card No.: 13-600143 SUGGESTED CiTATION U.S. Bureau of the Census. The Economic Censuses of the United by Charles G. longham. Working Paper D.C., U.S. Government Printing Office, 1B13 For sale by Publication Oistribution Section. Social and Economic Statistics Administration, Washington, D.C. 20233. Price 50 cents. N Page Economic Censuses in the 19th Century . 1 The First "Economic Censuses" . 1 Economic Censuses Discontinued, Resumed, and Augmented . 1 Improvements in the 1850 Census . 2 The "Kennedy Report" and the Civil War . • . 3 Economic Censuses and the Industrial Revolution. 4 Economic Censuses Adjust to the Times: The Censuses of 1880, 1890, and 1900 .........................•.. , . 4 Economic Censuses in the 20th Century . 8 Enumerations on Specialized Economic Topics, 1902 to 1937 . 8 Censuses of Manufacturing and Mineral Industries, 1905 to 1920. 8 Wartime Data Needs and Biennial Censuses of Manufactures. 9 Economic Censuses and the Great Depression. 10 The War and Postwar Developments: Economic Censuses Discontinued, Resumed, and Rescheduled. 13 The 1954 Budget Crisis. 15 Postwar Developments in Economic Census Taking: The Computer, and" Administrative Records" . -
From Master Plan to Mediocrity: Higher Education Performance & Policy in California
~ Perin,],'J ,!,., ,,, ,, I INSTITUTE for RESEARCH on HIGHER EDUCATION From Master Plan to Mediocrity: Higher Education Performance & Policy in California Joni E. Finney, Christina Riso, Kata Orosz, and William Casey Boland Graduate School of Education, University of Pennsylvania April 2014 0 INSTITUTE FOR RESEARCH ON HIGHER EDUCATION Contents Preface 2 Introduction 4 The Golden State: Its People, Economy, & Politics 5 The People of California 5 The California Economy 5 The Political Environment of California 6 The State’s Higher Education Structure 6 University of California 7 California State University 7 California Community Colleges 7 Gubernatorial and Legislative Powers over California’s Higher Education System 8 California Higher Education Performance 9 Preparation 9 Participation 9 Affordability 10 Completion 10 Research 11 Gaps in Performance 12 What Policies Explain Higher Education Performance Over Time? 13 The Complex Political Environment and Political Indifference 14 The Indirect Costs of a “Direct Democracy” 14 The Limitations of Term Limits 15 The Cost of Political Indifference and Short-Term Fixes 15 Absence of Statewide Higher Education Goals 16 Absence of Statewide Finance Strategy for Higher Education 18 State Appropriations Are Not Targeted to Performance 18 Tuition Setting Is Not Tied to Finance Policy 20 Financial Aid Is Increasingly Not Meeting Student Needs 21 Easing Student Transitions 23 Alignment Concerns between K-12 and Higher Education 23 College Transfer: The Unfulfilled Mission of the Master Plan 25 Inadequate Incentives for Improving the Career-Technical Education Pathway 27 Conclusion 27 Notes 29 References 36 About the Authors 48 1 FROM MASTER PLAN TO MEDIOCRITY: HIGHER EDUCATION PERFORMANCE AND POLICY IN CALIFORNIA Preface From Master Plan to Mediocrity: Higher Education Performance and Policy in California is the result of the hard work and persistence of nine graduate students enrolled in my Advanced Public Policy Seminar at the University of Pennsylvania in the spring semester of 2013. -
Data Extraction for Complex Meta-Analysis (Decimal) Guide
Pedder, H. , Sarri, G., Keeney, E., Nunes, V., & Dias, S. (2016). Data extraction for complex meta-analysis (DECiMAL) guide. Systematic Reviews, 5, [212]. https://doi.org/10.1186/s13643-016-0368-4 Publisher's PDF, also known as Version of record License (if available): CC BY Link to published version (if available): 10.1186/s13643-016-0368-4 Link to publication record in Explore Bristol Research PDF-document This is the final published version of the article (version of record). It first appeared online via BioMed Central at http://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-016-0368-4. Please refer to any applicable terms of use of the publisher. University of Bristol - Explore Bristol Research General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/red/research-policy/pure/user-guides/ebr-terms/ Pedder et al. Systematic Reviews (2016) 5:212 DOI 10.1186/s13643-016-0368-4 RESEARCH Open Access Data extraction for complex meta-analysis (DECiMAL) guide Hugo Pedder1*, Grammati Sarri2, Edna Keeney3, Vanessa Nunes1 and Sofia Dias3 Abstract As more complex meta-analytical techniques such as network and multivariate meta-analyses become increasingly common, further pressures are placed on reviewers to extract data in a systematic and consistent manner. Failing to do this appropriately wastes time, resources and jeopardises accuracy. This guide (data extraction for complex meta-analysis (DECiMAL)) suggests a number of points to consider when collecting data, primarily aimed at systematic reviewers preparing data for meta-analysis. -
2019 TIGER/Line Shapefiles Technical Documentation
TIGER/Line® Shapefiles 2019 Technical Documentation ™ Issued September 2019220192018 SUGGESTED CITATION FILES: 2019 TIGER/Line Shapefiles (machine- readable data files) / prepared by the U.S. Census Bureau, 2019 U.S. Department of Commerce Economic and Statistics Administration Wilbur Ross, Secretary TECHNICAL DOCUMENTATION: Karen Dunn Kelley, 2019 TIGER/Line Shapefiles Technical Under Secretary for Economic Affairs Documentation / prepared by the U.S. Census Bureau, 2019 U.S. Census Bureau Dr. Steven Dillingham, Albert Fontenot, Director Associate Director for Decennial Census Programs Dr. Ron Jarmin, Deputy Director and Chief Operating Officer GEOGRAPHY DIVISION Deirdre Dalpiaz Bishop, Chief Andrea G. Johnson, Michael R. Ratcliffe, Assistant Division Chief for Assistant Division Chief for Address and Spatial Data Updates Geographic Standards, Criteria, Research, and Quality Monique Eleby, Assistant Division Chief for Gregory F. Hanks, Jr., Geographic Program Management Deputy Division Chief and External Engagement Laura Waggoner, Assistant Division Chief for Geographic Data Collection and Products 1-0 Table of Contents 1. Introduction ...................................................................................................................... 1-1 1. Introduction 1.1 What is a Shapefile? A shapefile is a geospatial data format for use in geographic information system (GIS) software. Shapefiles spatially describe vector data such as points, lines, and polygons, representing, for instance, landmarks, roads, and lakes. The Environmental Systems Research Institute (Esri) created the format for use in their software, but the shapefile format works in additional Geographic Information System (GIS) software as well. 1.2 What are TIGER/Line Shapefiles? The TIGER/Line Shapefiles are the fully supported, core geographic product from the U.S. Census Bureau. They are extracts of selected geographic and cartographic information from the U.S. -
2020 Census Barriers, Attitudes, and Motivators Study Survey Report
2020 Census Barriers, Attitudes, and Motivators Study Survey Report A New Design for the 21st Century January 24, 2019 Version 2.0 Prepared by Kyley McGeeney, Brian Kriz, Shawnna Mullenax, Laura Kail, Gina Walejko, Monica Vines, Nancy Bates, and Yazmín García Trejo 2020 Census Research | 2020 CBAMS Survey Report Page intentionally left blank. ii 2020 Census Research | 2020 CBAMS Survey Report Table of Contents List of Tables ................................................................................................................................... iv List of Figures .................................................................................................................................. iv Executive Summary ......................................................................................................................... 1 Introduction ............................................................................................................................. 3 Background .............................................................................................................................. 5 CBAMS I ......................................................................................................................................... 5 CBAMS II ........................................................................................................................................ 6 2020 CBAMS Survey Climate ........................................................................................................ -
Grievance 19 State of New California (Pdf)
State of New California NOTICE TO ALL CALIFORNIANS! Grievance 19 May 29, 2018 New California is a new state in development exercising its Constitutional Right to form from the State of California. The process to form New California is authorized and codified in Article IV Sections 3 & 4 of the United States Constitution. Article IV Section 3 United States Constitution states: New States may be admitted by the Congress into this Union; but no new States shall be formed or erected within the Jurisdiction of any other State; nor any State be formed by the Junction of two or more States, or parts of States, without the Consent of the Legislatures of the States concerned as well as of the Congress. Statement of Intent The Citizens of New California have decided to remedy the abuse of power by the government of California by exercising their right to form a new state provided in United States Constitution Article IV Sections 3 and Section 4. “We are determined to live under a State Government in the United States of America and under the Constitution of the United States”. New California Declaration of Independence of January 15, 2018 states: “Whenever any Form of Government becomes destructive it is the Right of the People to alter or to abolish it, and to institute new Government. When a long train of abuses and acts to seize and hold the people’s power without legal authority and pursuing invariably the same Object that clearly demonstrates a design to reduce them under absolute Despotism, it is their right, it is their duty, to throw off such Government, and to provide new Guards for their future security. -
THE CENSUS in U.S. HISTORY Library of Congress of Library
Bill of Rights Constitutional Rights in Action Foundation FALL 2019 Volume 35 No1 THE CENSUS IN U.S. HISTORY Library of Congress of Library A census taker talks to a group of women, men, and children in 1870. The Constitution requires that a census be taken every ten After the 1910 census, the House set the total num- years. This means counting all persons, citizens and ber of House seats at 435. Since then, when Congress noncitizens alike, in the United States. In addition to reapportions itself after each census, those states gain- conducting a population count, the census has evolved to collect massive amounts of information on the growth and ing population may pick up more seats in the House at development of the nation. the expense of states declining in population that have to lose seats. Why Do We Have a Census? Who is counted in apportioning seats in the House? The original purpose of the census was to determine The Constitution originally included “the whole Number the number of representatives each state is entitled to in of free persons” plus indentured servants but excluded the U.S. House of Representatives. The apportionment “Indians not taxed.” What about slaves? The North and (distribution) of seats in the House depends on the pop- South argued about this at the Constitutional Conven- ulation of each state. Every state is guaranteed at least tion, finally agreeing to the three-fifths compromise. one seat. Slaves would be counted in each census, but only three- After the first census in 1790, the House decided a fifths of the count would be included in a state’s popu- state was allowed one representative for each approxi- lation for the purpose of House apportionment. -
Survey Nonresponse Bias and the Coronavirus Pandemic∗
Coronavirus Infects Surveys, Too: Survey Nonresponse Bias and the Coronavirus Pandemic∗ Jonathan Rothbaum U.S. Census Bureau† Adam Bee U.S. Census Bureau‡ May 3, 2021 Abstract Nonresponse rates have been increasing in household surveys over time, increasing the potential of nonresponse bias. We make two contributions to the literature on nonresponse bias. First, we expand the set of data sources used. We use information returns filings (such as W-2's and 1099 forms) to identify individuals in respondent and nonrespondent households in the Current Population Survey Annual Social and Eco- nomic Supplement (CPS ASEC). We link those individuals to income, demographic, and socioeconomic information available in administrative data and prior surveys and the decennial census. We show that survey nonresponse was unique during the pan- demic | nonresponse increased substantially and was more strongly associated with income than in prior years. Response patterns changed by education, Hispanic origin, and citizenship and nativity. Second, We adjust for nonrandom nonresponse using entropy balance weights { a computationally efficient method of adjusting weights to match to a high-dimensional vector of moment constraints. In the 2020 CPS ASEC, nonresponse biased income estimates up substantially, whereas in other years, we do not find evidence of nonresponse bias in income or poverty statistics. With the sur- vey weights, real median household income was $68,700 in 2019, up 6.8 percent from 2018. After adjusting for nonresponse bias during the pandemic, we estimate that real median household income in 2019 was 2.8 percent lower than the survey estimate at $66,790. ∗This report is released to inform interested parties of ongoing research and to encourage discussion. -
Reasoning with Qualitative Data: Using Retroduction with Transcript Data
Reasoning With Qualitative Data: Using Retroduction With Transcript Data © 2019 SAGE Publications, Ltd. All Rights Reserved. This PDF has been generated from SAGE Research Methods Datasets. SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. All Rights Reserved. 2 Reasoning With Qualitative Data: Using Retroduction With Transcript Data Student Guide Introduction This example illustrates how different forms of reasoning can be used to analyse a given set of qualitative data. In this case, I look at transcripts from semi-structured interviews to illustrate how three common approaches to reasoning can be used. The first type (deductive approaches) applies pre-existing analytical concepts to data, the second type (inductive reasoning) draws analytical concepts from the data, and the third type (retroductive reasoning) uses the data to develop new concepts and understandings about the issues. The data source – a set of recorded interviews of teachers and students from the field of Higher Education – was collated by Dr. Christian Beighton as part of a research project designed to inform teacher educators about pedagogies of academic writing. Interview Transcripts Interviews, and their transcripts, are arguably the most common data collection tool used in qualitative research. This is because, while they have many drawbacks, they offer many advantages. Relatively easy to plan and prepare, interviews can be flexible: They are usually one-to one but do not have to be so; they can follow pre-arranged questions, but again this is not essential; and unlike more impersonal ways of collecting data (e.g., surveys or observation), they Page 2 of 13 Reasoning With Qualitative Data: Using Retroduction With Transcript Data SAGE SAGE Research Methods Datasets Part 2019 SAGE Publications, Ltd. -
Chapter 5 Statistical Inference
Chapter 5 Statistical Inference CHAPTER OUTLINE Section 1 Why Do We Need Statistics? Section 2 Inference Using a Probability Model Section 3 Statistical Models Section 4 Data Collection Section 5 Some Basic Inferences In this chapter, we begin our discussion of statistical inference. Probability theory is primarily concerned with calculating various quantities associated with a probability model. This requires that we know what the correct probability model is. In applica- tions, this is often not the case, and the best we can say is that the correct probability measure to use is in a set of possible probability measures. We refer to this collection as the statistical model. So, in a sense, our uncertainty has increased; not only do we have the uncertainty associated with an outcome or response as described by a probability measure, but now we are also uncertain about what the probability measure is. Statistical inference is concerned with making statements or inferences about char- acteristics of the true underlying probability measure. Of course, these inferences must be based on some kind of information; the statistical model makes up part of it. Another important part of the information will be given by an observed outcome or response, which we refer to as the data. Inferences then take the form of various statements about the true underlying probability measure from which the data were obtained. These take a variety of forms, which we refer to as types of inferences. The role of this chapter is to introduce the basic concepts and ideas of statistical inference. The most prominent approaches to inference are discussed in Chapters 6, 7, and 8. -
2017 National Population Projections: Methodology and Assumptions
Methodology, Assumptions, and Inputs for the 2017 National Population Projections September 2018 Erratum Note: The 2017 National Population Projections were revised after their original release date to correct an error in infant mortality rates. The files were removed from the website on August 1, 2018 and an erratum note posted. The error incorrectly calculated infant mortality rates, which erroneously caused an increase in the number of deaths projected in the total population. Correcting the error in infant mortality results in a decrease in the number of deaths and a slight increase in the total projected population in the revised series. The error did not affect the other two components of population change in the projections series (fertility and migration). Major demographic trends, such as an aging population and an increase in racial and ethnic diversity, remain unchanged. Table of Contents Introduction .......................................................................................................................................................................2 Methods...............................................................................................................................................................................2 Base Population ...........................................................................................................................................................2 Fertility and Mortality Denominators...................................................................................................................3