DOCSLIB.ORG

Statistical Techniques for Sampling and Monitoring Natural Resources

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Statistical Techniques for Sampling and Monitoring Natural Resources Statistical Techniques for United States Sampling and Monitoring Department of Agriculture Natural Resources Forest Service Rocky Mountain Research Station General Technical Hans T. Schreuder, Richard Ernst, and Hugo Ramirez-Maldonado Report RMRS-GTR-126 April 2004 CORRECTIONS Page 26: In Equation (31), a should be added to the denominator, i.e., Page 27: The fourth equation from the top and the line following it should be 1 52 52 780 5 25 50 3.9 10 52 52 200 Then 10 8.5 85 with variance estimate 100 3.90 390 Schreuder, Hans T.; Ernst, Richard; Ramirez-Maldonado, Hugo. 2004. Statistical techniques for sampling and monitoring natural resources. Gen. Tech. Rep. RMRS-GTR-126. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 111 p. Abstract We present the statistical theory of inventory and monitoring from a probabilistic point of view. We start with the basics and show the interrelationships between designs and estimators illustrating the methods with a small artificial population as well as with a mapped realistic population. For such applications, useful open source software is given in Appendix 4. Various sources of ancillary information are described and applica- tions of the sampling strategies are discussed. Classical and bootstrap variance estimators are discussed also. Numerous problems with solutions are given, often based on the experiences of the authors. Key additional references are cited as needed or desired. Acknowledgments We owe a considerable measure of gratitude to reviewers for valuable comments. Chistopher Kleinn, Steen Magnusson, Ray Czaplewski, Rudy King, and Keith Rennolls reviewed the entire book, Geoff Wood revised an earlier draft, and Jule Caylor took the time to check the remote sensing section and added some updates to it. Mike Williams reviewed the coarse woody debris section, Jim Baldwin and Paul Geissler the wildlife section, Ron McRoberts and Nick Crookston the modeling section, Frank Roesch and Paul Patterson the section on discrete variable sampling, Charles Scott the section on multilevel sampling, and Gretchen Moisen the section on small area estimation. Gary Boyak reviewed and partially rewrote the sections on GIS and GPS, Henry Lachowski reviewed the section on GIS, and Jeff Goebel reviewed the section on sampling for rare events. Tim Gregoire and Kim Iles made useful suggestions on how to handle boundary trees. Lane Eskew did an excellent job of reviewing the manuscript from an editorial point of view and making sure that it conformed with acceptable standards of publication. We owe a considerable debt of gratitude to Ray Czaplewski for suggesting updating Frank Freese’s book and facilitating the subsequent writing of this book. The Authors Hans T. Schreuder is a Mathematical Statistician (retired) with the USDA Forest Service’s Rocky Mountain Research Station in Fort Collins, CO. Richard Ernst is a Mensurationist with the USDA Forest Service’s Forest Management Service Center (Washington Office) in Fort Collins, CO. Hugo Ramirez-Maldonado is a Director General with the National Institute on Forestry, Agriculture and Animal Husbandry Research in Mexico City, Mexico. You may order additional copies of this publication by sending your mailing information in label form through one of the following media. Please specify the publication title and series number. Fort Collins Service Center Telephone (970) 498-1392 FAX (970) 498-1396 E-mail [email protected] Web site http://www.fs.fed.us/rm Mailing address Publications Distribution Rocky Mountain Research Station 240 West Prospect Road Fort Collins, CO 80526 Rocky Mountain Research Station Natural Resources Research Center 2150 Centre Avenue, Building A Fort Collins, CO 80526 Contents I. Introduction .................................................................................................................. 1 II. Objectives of Sampling and Monitoring ................................................................... 2 Why Sample?.............................................................................................................. 2 Planning Your Survey .................................................................................................. 3 Objectives ............................................................................................................. 3 Information to be collected .................................................................................... 4 Developing the sampling approach....................................................................... 5 III. Sampling Concepts and Methodologies ................................................................. 6 Sampling Frame .......................................................................................................... 6 Purposive and Representative Sampling .................................................................... 6 Populations, Parameters, Estimators, and Estimates ................................................ 7 Bias, Accuracy, and Precision ..................................................................................... 8 Variables: Continuous and Discrete ............................................................................ 9 Distribution Functions ............................................................................................... 10 Tools of the Trade ..................................................................................................... 10 Notation ............................................................................................................... 10 Characterizing a distribution using measures of central tendency and dispersion ................................................................................. 12 Standard errors and confidence limits ................................................................ 14 Expanded variances and standard errors ........................................................... 15 Coefficient of variation ........................................................................................ 16 Covariance, correlation, and regression ............................................................. 16 Independence ..................................................................................................... 18 Variances of products, ratios, and sums ............................................................. 19 Transformation of variables ................................................................................ 21 IV. Sampling Strategies ................................................................................................ 22 Designs With the Horvitz-Thompson Estimator ........................................................ 22 Variance Estimation in General................................................................................. 33 Regression and Ratio Estimators ............................................................................. 34 Some Specific Forestry Sampling Methods .............................................................. 40 Sample Size Determination ...................................................................................... 42 Ground Sampling ...................................................................................................... 44 Edge Effects When Sampling at Stand Boundaries ................................................. 46 Design Issues ........................................................................................................... 47 Instrumentation ......................................................................................................... 48 Sampling for Coarse Woody Debris (CWD) ............................................................. 49 Wildlife Sampling ...................................................................................................... 50 V. Sampling Methods for Discrete Variables.............................................................. 51 Simple Random Sampling (SRS) for Classification Data ......................................... 51 Cluster Sampling for Attributes ................................................................................. 53 Cluster Sampling for Attributes With Unequal-Sized Clusters.................................. 55 Sampling of Count Variables .................................................................................... 57 VI. Remote Sensing and Other Ancillary Information ............................................... 59 Remote Sensing and Photography ........................................................................... 59 Accuracy of Remotely Sensed Information .............................................................. 61 Global Positioning System for Spatial Location Needs............................................. 64 Geographic Information System (GIS) ..................................................................... 64 Small Area Estimation ............................................................................................... 65 i VII. Sampling for Rare Events...................................................................................... 67 VIII. Multiple Level Sampling ....................................................................................... 68 Multistage Sampling.................................................................................................. 68 Multiphase Sampling ................................................................................................
Load more

Recommended publications
  • Hypothesis Testing with Two Categorical Variables 203
    Chapter 10 distribute or Hypothesis Testing With Two Categoricalpost, Variables Chi-Square copy, Learning Objectives • Identify the correct types of variables for use with a chi-square test of independence. • Explainnot the difference between parametric and nonparametric statistics. • Conduct a five-step hypothesis test for a contingency table of any size. • Explain what statistical significance means and how it differs from practical significance. • Identify the correct measure of association for use with a particular chi-square test, Doand interpret those measures. • Use SPSS to produce crosstabs tables, chi-square tests, and measures of association. 202 Part 3 Hypothesis Testing Copyright ©2016 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. he chi-square test of independence is used when the independent variable (IV) and dependent variable (DV) are both categorical (nominal or ordinal). The chi-square test is member of the family of nonparametric statistics, which are statistical Tanalyses used when sampling distributions cannot be assumed to be normally distributed, which is often the result of the DV being categorical rather than continuous (we will talk in detail about this). Chi-square thus sits in contrast to parametric statistics, which are used when DVs are continuous and sampling distributions are safely assumed to be normal. The t test, analysis of variance, and correlation are all parametric. Before going into the theory and math behind the chi-square statistic, read the Research Examples for illustrations of the types of situations in which a criminal justice or criminology researcher would utilize a chi- square test.
    [Show full text]
  • Sampling and Fieldwork Practices in Europe
    www.ssoar.info Sampling and Fieldwork Practices in Europe: Analysis of Methodological Documentation From 1,537 Surveys in Five Cross-National Projects, 1981-2017 Jabkowski, Piotr; Kołczyńska, Marta Veröffentlichungsversion / Published Version Zeitschriftenartikel / journal article Empfohlene Zitierung / Suggested Citation: Jabkowski, P., & Kołczyńska, M. (2020). Sampling and Fieldwork Practices in Europe: Analysis of Methodological Documentation From 1,537 Surveys in Five Cross-National Projects, 1981-2017. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 16(3), 186-207. https://doi.org/10.5964/meth.2795 Nutzungsbedingungen: Terms of use: Dieser Text wird unter einer CC BY Lizenz (Namensnennung) zur This document is made available under a CC BY Licence Verfügung gestellt. Nähere Auskünfte zu den CC-Lizenzen finden (Attribution). For more Information see: Sie hier: https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0/deed.de Diese Version ist zitierbar unter / This version is citable under: https://nbn-resolving.org/urn:nbn:de:0168-ssoar-74317-3 METHODOLOGY Original Article Sampling and Fieldwork Practices in Europe: Analysis of Methodological Documentation From 1,537 Surveys in Five Cross-National Projects, 1981-2017 Piotr Jabkowski a , Marta Kołczyńska b [a] Faculty of Sociology, Adam Mickiewicz University, Poznan, Poland. [b] Department of Socio-Political Systems, Institute of Political Studies of the Polish Academy of Science, Warsaw, Poland. Methodology, 2020, Vol. 16(3), 186–207, https://doi.org/10.5964/meth.2795 Received: 2019-03-23 • Accepted: 2019-11-08 • Published (VoR): 2020-09-30 Corresponding Author: Piotr Jabkowski, Szamarzewskiego 89C, 60-568 Poznań, Poland. +48 504063762, E-mail: [email protected] Abstract This article addresses the comparability of sampling and fieldwork with an analysis of methodological data describing 1,537 national surveys from five major comparative cross-national survey projects in Europe carried out in the period from 1981 to 2017.
    [Show full text]
  • ESOMAR 28 Questions
    Responses to ESOMAR 28 Questions INTRODUCTION Intro to TapResearch TapResearch connects mobile, tablet and pc users interested in completing surveys with market researchers who need their opinions. Through our partnerships with dozens of leading mobile apps, ad networks and websites, we’re able to reach an audience exceeding 100 million people in the United States. We are focused on being a top-quality partner for ad hoc survey sampling, panel recruitment, and router integrations. Our technology platform enables reliable feasibility estimates, highly competitive costs, sophisticated quality enforcement, and quick-turnaround project management. Intro to ESOMAR ESOMAR (European Society for Opinion and Market Research) is the essential organization for encouraging, advancing and elevating market research worldwide. Since 1948, ESOMAR’s aim has been to promote the value of market and opinion research in effective decision-making. The ICC/ESOMAR Code on Market and Social Research, which was developed jointly with the International Chamber of Commerce, sets out global guidelines for self-regulation for researchers and has been undersigned by all ESOMAR members and adopted or endorsed by more than 60 national market research associations worldwide. Responses to ESOMAR 28 Questions COMPANY PROFILE 1) What experience does your company have in providing online samples for market research? TapResearch connects mobile, tablet and pc users interested in completing surveys with market researchers who need their opinions. Through our partnerships with dozens of leading mobile apps, ad networks and websites, we’re able to reach an audience exceeding 100 million people in the United States - we’re currently adding about 30,000 panelists/day and this rate is increasing.
    [Show full text]
  • Esomar/Grbn Guideline for Online Sample Quality
    ESOMAR/GRBN GUIDELINE FOR ONLINE SAMPLE QUALITY ESOMAR GRBN ONLINE SAMPLE QUALITY GUIDELINE ESOMAR, the World Association for Social, Opinion and Market Research, is the essential organisation for encouraging, advancing and elevating market research: www.esomar.org. GRBN, the Global Research Business Network, connects 38 research associations and over 3500 research businesses on five continents: www.grbn.org. © 2015 ESOMAR and GRBN. Issued February 2015. This Guideline is drafted in English and the English text is the definitive version. The text may be copied, distributed and transmitted under the condition that appropriate attribution is made and the following notice is included “© 2015 ESOMAR and GRBN”. 2 ESOMAR GRBN ONLINE SAMPLE QUALITY GUIDELINE CONTENTS 1 INTRODUCTION AND SCOPE ................................................................................................... 4 2 DEFINITIONS .............................................................................................................................. 4 3 KEY REQUIREMENTS ................................................................................................................ 6 3.1 The claimed identity of each research participant should be validated. .................................................. 6 3.2 Providers must ensure that no research participant completes the same survey more than once ......... 8 3.3 Research participant engagement should be measured and reported on ............................................... 9 3.4 The identity and personal
    [Show full text]
  • Lecture 1: Why Do We Use Statistics, Populations, Samples, Variables, Why Do We Use Statistics?
    1pops_samples.pdf Michael Hallstone, Ph.D. [email protected] Lecture 1: Why do we use statistics, populations, samples, variables, why do we use statistics? • interested in understanding the social world • we want to study a portion of it and say something about it • ex: drug users, homeless, voters, UH students Populations and Samples Populations, Sampling Elements, Frames, and Units A researcher defines a group, “list,” or pool of cases that she wishes to study. This is a population. Another definition: population = complete collection of measurements, objects or individuals under study. 1 of 11 sample = a portion or subset taken from population funny circle diagram so we take a sample and infer to population Why? feasibility – all MD’s in world , cost, time, and stay tuned for the central limits theorem...the most important lecture of this course. Visualizing Samples (taken from) Populations Population Group you wish to study (Mostly made up of “people” in the Then we infer from sample back social sciences) to population (ALWAYS SOME ERROR! “sampling error” Sample (a portion or subset of the population) 4 This population is made up of the things she wishes to actually study called sampling elements. Sampling elements can be people, organizations, schools, whales, molecules, and articles in the popular press, etc. The sampling element is your exact unit of analysis. For crime researchers studying car thieves, the sampling element would probably be individual car thieves – or theft incidents reported to the police. For drug researchers the sampling elements would be most likely be individual drug users. Inferential statistics is truly the basis of much of our scientific evidence.
    [Show full text]
  • 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).
    [Show full text]
  • Assessment of Socio-Demographic Sample Composition in ESS Round 61
    Assessment of socio-demographic sample composition in ESS Round 61 Achim Koch GESIS – Leibniz Institute for the Social Sciences, Mannheim/Germany, June 2016 Contents 1. Introduction 2 2. Assessing socio-demographic sample composition with external benchmark data 3 3. The European Union Labour Force Survey 3 4. Data and variables 6 5. Description of ESS-LFS differences 8 6. A summary measure of ESS-LFS differences 17 7. Comparison of results for ESS 6 with results for ESS 5 19 8. Correlates of ESS-LFS differences 23 9. Summary and conclusions 27 References 1 The CST of the ESS requests that the following citation for this document should be used: Koch, A. (2016). Assessment of socio-demographic sample composition in ESS Round 6. Mannheim: European Social Survey, GESIS. 1. Introduction The European Social Survey (ESS) is an academically driven cross-national survey that has been conducted every two years across Europe since 2002. The ESS aims to produce high- quality data on social structure, attitudes, values and behaviour patterns in Europe. Much emphasis is placed on the standardisation of survey methods and procedures across countries and over time. Each country implementing the ESS has to follow detailed requirements that are laid down in the “Specifications for participating countries”. These standards cover the whole survey life cycle. They refer to sampling, questionnaire translation, data collection and data preparation and delivery. As regards sampling, for instance, the ESS requires that only strict probability samples should be used; quota sampling and substitution are not allowed. Each country is required to achieve an effective sample size of 1,500 completed interviews, taking into account potential design effects due to the clustering of the sample and/or the variation in inclusion probabilities.
    [Show full text]
  • Statistical Theory and Methodology for the Analysis of Microbial Compositions, with Applications
    Statistical Theory and Methodology for the Analysis of Microbial Compositions, with Applications by Huang Lin BS, Xiamen University, China, 2015 Submitted to the Graduate Faculty of the Graduate School of Public Health in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2020 UNIVERSITY OF PITTSBURGH GRADUATE SCHOOL OF PUBLIC HEALTH This dissertation was presented by Huang Lin It was defended on April 2nd 2020 and approved by Shyamal Das Peddada, PhD, Professor and Chair, Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh Jeanine Buchanich, PhD, Research Associate Professor, Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh Ying Ding, PhD, Associate Professor, Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh Matthew Rogers, PhD, Research Assistant Professor, Department of Surgery, UPMC Children's Hospital of Pittsburgh Hong Wang, PhD, Research Assistant Professor, Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh Dissertation Director: Shyamal Das Peddada, PhD, Professor and Chair, Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh ii Copyright c by Huang Lin 2020 iii Statistical Theory and Methodology for the Analysis of Microbial Compositions, with Applications Huang Lin, PhD University of Pittsburgh, 2020 Abstract Increasingly researchers are finding associations between the microbiome and human diseases such as obesity, inflammatory bowel diseases, HIV, and so on. Determining what microbes are significantly different between conditions, known as differential abundance (DA) analysis, and depicting the dependence structure among them, are two of the most challeng- ing and critical problems that have received considerable interest.
    [Show full text]
  • Sampling Methods It’S Impractical to Poll an Entire Population—Say, All 145 Million Registered Voters in the United States
    Sampling Methods It’s impractical to poll an entire population—say, all 145 million registered voters in the United States. That is why pollsters select a sample of individuals that represents the whole population. Understanding how respondents come to be selected to be in a poll is a big step toward determining how well their views and opinions mirror those of the voting population. To sample individuals, polling organizations can choose from a wide variety of options. Pollsters generally divide them into two types: those that are based on probability sampling methods and those based on non-probability sampling techniques. For more than five decades probability sampling was the standard method for polls. But in recent years, as fewer people respond to polls and the costs of polls have gone up, researchers have turned to non-probability based sampling methods. For example, they may collect data on-line from volunteers who have joined an Internet panel. In a number of instances, these non-probability samples have produced results that were comparable or, in some cases, more accurate in predicting election outcomes than probability-based surveys. Now, more than ever, journalists and the public need to understand the strengths and weaknesses of both sampling techniques to effectively evaluate the quality of a survey, particularly election polls. Probability and Non-probability Samples In a probability sample, all persons in the target population have a change of being selected for the survey sample and we know what that chance is. For example, in a telephone survey based on random digit dialing (RDD) sampling, researchers know the chance or probability that a particular telephone number will be selected.
    [Show full text]
  • Summary of Human Subjects Protection Issues Related to Large Sample Surveys
    Summary of Human Subjects Protection Issues Related to Large Sample Surveys U.S. Department of Justice Bureau of Justice Statistics Joan E. Sieber June 2001, NCJ 187692 U.S. Department of Justice Office of Justice Programs John Ashcroft Attorney General Bureau of Justice Statistics Lawrence A. Greenfeld Acting Director Report of work performed under a BJS purchase order to Joan E. Sieber, Department of Psychology, California State University at Hayward, Hayward, California 94542, (510) 538-5424, e-mail [email protected]. The author acknowledges the assistance of Caroline Wolf Harlow, BJS Statistician and project monitor. Ellen Goldberg edited the document. Contents of this report do not necessarily reflect the views or policies of the Bureau of Justice Statistics or the Department of Justice. This report and others from the Bureau of Justice Statistics are available through the Internet — http://www.ojp.usdoj.gov/bjs Table of Contents 1. Introduction 2 Limitations of the Common Rule with respect to survey research 2 2. Risks and benefits of participation in sample surveys 5 Standard risk issues, researcher responses, and IRB requirements 5 Long-term consequences 6 Background issues 6 3. Procedures to protect privacy and maintain confidentiality 9 Standard issues and problems 9 Confidentiality assurances and their consequences 21 Emerging issues of privacy and confidentiality 22 4. Other procedures for minimizing risks and promoting benefits 23 Identifying and minimizing risks 23 Identifying and maximizing possible benefits 26 5. Procedures for responding to requests for help or assistance 28 Standard procedures 28 Background considerations 28 A specific recommendation: An experiment within the survey 32 6.
    [Show full text]
  • MRS Guidance on How to Read Opinion Polls
    What are opinion polls? MRS guidance on how to read opinion polls June 2016 1 June 2016 www.mrs.org.uk MRS Guidance Note: How to read opinion polls MRS has produced this Guidance Note to help individuals evaluate, understand and interpret Opinion Polls. This guidance is primarily for non-researchers who commission and/or use opinion polls. Researchers can use this guidance to support their understanding of the reporting rules contained within the MRS Code of Conduct. Opinion Polls – The Essential Points What is an Opinion Poll? An opinion poll is a survey of public opinion obtained by questioning a representative sample of individuals selected from a clearly defined target audience or population. For example, it may be a survey of c. 1,000 UK adults aged 16 years and over. When conducted appropriately, opinion polls can add value to the national debate on topics of interest, including voting intentions. Typically, individuals or organisations commission a research organisation to undertake an opinion poll. The results to an opinion poll are either carried out for private use or for publication. What is sampling? Opinion polls are carried out among a sub-set of a given target audience or population and this sub-set is called a sample. Whilst the number included in a sample may differ, opinion poll samples are typically between c. 1,000 and 2,000 participants. When a sample is selected from a given target audience or population, the possibility of a sampling error is introduced. This is because the demographic profile of the sub-sample selected may not be identical to the profile of the target audience / population.
    [Show full text]
  • Intro Surveys for Honors Theses, 2009
    PROGRAM ON SURVEY RESEARCH HARVARD UNIVERSITY ` Introduction to Survey Research ` The Survey Process ` Survey Resources at Harvard ` Sampling, Coverage, and Nonresponse ` Thinking About Modes April 14, 2009 ` Question Wording `Representation ◦The research design make inference to a larger population ◦Various population characteristics are represented in research data the same way there are present in population ◦Example: General population survey to estimate population characteristics Reporting `Realism Research Survey ◦A full picture of subjects emerges And ◦Relationships between multiple variables and multiple ways of looking at Methods the same variables can be studied Theories Analysis ◦Example: A qualitative case study to evaluate the nature of democracy in a small town with community meetings `Randomization ◦Variables which are not important in model are completely randomized ◦Effects of non-randomized variables can be tested ◦Example: Randomized clinical trial to test effectiveness of new cancer drug Integrated Example: Surveys and the Research Process Theories About Things `Good Measures: ◦Questions or measures impact your ability to study concepts ◦Think carefully about the underlying concepts a survey is trying to measure. Do the survey questions do a good job of capturing this? ◦The PSR Tip Sheet on Questionnaire Design contains good ideas on Concepts Population Validity External how to write survey questions. Specification Error ` Measures Sample Good Samples: ◦Samples give you the ability to generalize your findings Survey Errors ◦Think carefully about the population you are trying to generalize Internal Validity Internal your findings to. Does your sample design do a good job of Data Respondents representing these people? ◦The PSR Tip Sheet on Survey Sampling, Coverage, and Nonresponse contains thinks to think about in designing or evaluating a sample.
    [Show full text]