DOCSLIB.ORG

Sampling and Household Listing Manual [DHSM4]

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Sampling and Household Listing Manual [DHSM4] SAMPLING AND HOUSEHOLD LISTING MANuaL Demographic and Health Surveys Methodology This document is part of the Demographic and Health Survey’s DHS Toolkit of methodology for the MEASURE DHS Phase III project, implemented from 2008-2013. This publication was produced for review by the United States Agency for International Development (USAID). It was prepared by MEASURE DHS/ICF International. [THIS PAGE IS INTENTIONALLY BLANK] Demographic and Health Survey Sampling and Household Listing Manual ICF International Calverton, Maryland USA September 2012 MEASURE DHS is a five-year project to assist institutions in collecting and analyzing data needed to plan, monitor, and evaluate population, health, and nutrition programs. MEASURE DHS is funded by the U.S. Agency for International Development (USAID). The project is implemented by ICF International in Calverton, Maryland, in partnership with the Johns Hopkins Bloomberg School of Public Health/Center for Communication Programs, the Program for Appropriate Technology in Health (PATH), Futures Institute, Camris International, and Blue Raster. The main objectives of the MEASURE DHS program are to: 1) provide improved information through appropriate data collection, analysis, and evaluation; 2) improve coordination and partnerships in data collection at the international and country levels; 3) increase host-country institutionalization of data collection capacity; 4) improve data collection and analysis tools and methodologies; and 5) improve the dissemination and utilization of data. For information about the Demographic and Health Surveys (DHS) program, write to DHS, ICF International, 11785 Beltsville Drive, Suite 300, Calverton, MD 20705, U.S.A. (Telephone: 301-572- 0200; fax: 301-572-0999; e-mail: [email protected]; Internet: http://www.measuredhs.com). Recommended citation: ICF International. 2012. Demographic and Health Survey Sampling and Household Listing Manual. MEASURE DHS, Calverton, Maryland, U.S.A.: ICF International TABLE OF CONTENTS TABLES AND FIGURES ......................................................................................................... vii 1 DEMOGRAPHIC AND HEALTH SURVEYS SAMPLING POLICY ........................................ 1 1.1 General principles ............................................................................................ 1 1.1.1 Existing sampling frame ......................................................................... 1 1.1.2 Full coverage ........................................................................................ 1 1.1.3 Probability sampling .............................................................................. 2 1.1.4 Suitable sample size .............................................................................. 2 1.1.5 Simple design ....................................................................................... 2 1.1.6 Household listing and pre-selection of households ...................................... 2 1.1.7 Good sample documentation ................................................................... 2 1.1.8 Confidentiality ...................................................................................... 3 1.1.9 Exactness of survey implementation ........................................................ 3 1.2 Survey objectives and target population .............................................................. 3 1.3 Survey domain ................................................................................................ 4 1.4 Sampling frame ............................................................................................... 4 1.4.1 Conventional sampling frame .................................................................. 5 1.4.2 Alternative sampling frames ................................................................... 5 1.4.3 Evaluation of the sampling frame ............................................................ 6 1.5 Stratification ................................................................................................... 6 1.6 Sample size .................................................................................................... 7 1.6.1 Sample size and sampling errors ............................................................. 7 1.6.2 Sample size determination ....................................................................10 1.7 Sample allocation ............................................................................................12 1.8 Two-stage cluster sampling procedure ...............................................................15 1.9 Sample “take” per cluster .................................................................................16 1.9.1 Optimum sample take ...........................................................................16 1.9.2 Variable sample take for self-weighting ...................................................17 1.10 Household listing ............................................................................................19 1.11 Household selection in the central office .............................................................20 1.12 Household interviews .......................................................................................21 1.13 Sampling weight calculation ..............................................................................22 1.13.1 Why we need to weight the survey data ..................................................22 1.13.2 Design weights and sampling weights .....................................................22 1.13.3 How to calculate the design weights ........................................................23 iii 1.13.4 Correction of unit non-response and calculation of sampling weights............ 24 1.13.5 Normalization of sampling weights ..........................................................26 1.13.6 Standard weights for HIV testing ............................................................27 1.13.7 De-normalization of standard weights for pooled data................................ 28 1.14 Calibration of sampling weights in case of bias ....................................................29 1.15 Data quality and sampling error reporting ...........................................................30 1.16 Sample documentation ....................................................................................31 1.17 Confidentiality ................................................................................................31 2 HOUSEHOLD LISTING OPERATION ........................................................................... 32 2.1 Introduction ...................................................................................................32 2.2 Definition of terms ..........................................................................................32 2.3 Responsibilities of the listing staff ......................................................................33 2.4 Locating the cluster .........................................................................................34 2.5 Preparing location and sketch maps ...................................................................35 2.6 Collecting a GPS waypoint for each cluster ..........................................................36 2.7 Listing of households .......................................................................................37 2.8 Segmentation of large clusters ..........................................................................38 2.9 Quality control ................................................................................................39 2.10 Prepare the household listing forms for household selection ..................................39 Appendix 2.1 Example listing forms .............................................................................41 Appendix 2.2 Symbols for mapping and listing ..............................................................46 Appendix 2.3 Examples of completed mapping and listing forms ......................................48 3 SELECTED SAMPLING TECHNIQUES ......................................................................... 52 3.1 Simple random sampling ..................................................................................52 3.2 Equal probability systematic sampling ................................................................53 3.2.1 Sampling theory ..................................................................................53 3.2.2 Excel templates for systematic sampling ..................................................55 3.3 Probability proportional to size sampling .............................................................64 3.3.1 Sampling theory ..................................................................................64 3.3.2 Operational description and examples .....................................................65 3.4 Complex sampling procedures ...........................................................................70 4 SURVEY ERRORS ...................................................................................................... 73 4.1 Errors of coverage and non-response .................................................................73 4.1.1 Coverage errors ...................................................................................73 4.1.2 Deliberate restrictions of coverage ..........................................................74 4.1.3 Non-response ......................................................................................74
Load more

Recommended publications
  • Choosing the Sample
    CHAPTER IV CHOOSING THE SAMPLE This chapter is written for survey coordinators and technical resource persons. It will enable you to: U Understand the basic concepts of sampling. U Calculate the required sample size for national and subnational estimates. U Determine the number of clusters to be used. U Choose a sampling scheme. UNDERSTANDING THE BASIC CONCEPTS OF SAMPLING In the context of multiple-indicator surveys, sampling is a process for selecting respondents from a population. In our case, the respondents will usually be the mothers, or caretakers, of children in each household visited,1 who will answer all of the questions in the Child Health modules. The Water and Sanitation and Salt Iodization modules refer to the whole household and may be answered by any adult. Questions in these modules are asked even where there are no children under the age of 15 years. In principle, our survey could cover all households in the population. If all mothers being interviewed could provide perfect answers, we could measure all indicators with complete accuracy. However, interviewing all mothers would be time-consuming, expensive and wasteful. It is therefore necessary to interview a sample of these women to obtain estimates of the actual indicators. The difference between the estimate and the actual indicator is called sampling error. Sampling errors are caused by the fact that a sample&and not the entire population&is surveyed. Sampling error can be minimized by taking certain precautions: 3 Choose your sample of respondents in an unbiased way. 3 Select a large enough sample for your estimates to be precise.
    [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]
  • Questionnaire Design Guidelines for Establishment Surveys
    Journal of Official Statistics, Vol. 26, No. 1, 2010, pp. 43–85 Questionnaire Design Guidelines for Establishment Surveys Rebecca L. Morrison1, Don A. Dillman2, and Leah M. Christian3 Previous literature has shown the effects of question wording or visual design on the data provided by respondents. However, few articles have been published that link the effects of question wording and visual design to the development of questionnaire design guidelines. This article proposes specific guidelines for the design of establishment surveys within statistical agencies based on theories regarding communication and visual perception, experimental research on question wording and visual design, and findings from cognitive interviews with establishment survey respondents. The guidelines are applicable to both paper and electronic instruments, and cover such topics as the phrasing of questions, the use of space, the placement and wording of instructions, the design of answer spaces, and matrices. Key words: Visual design; question wording; cognitive interviews. 1. Introduction In recent years, considerable effort has been made to develop questionnaire construction guidelines for how questions should appear in establishment surveys. Examples include guidelines developed by the Australian Bureau of Statistics (2006) and Statistics Norway (Nøtnæs 2006). These guidelines have utilized the rapidly emerging research on how the choice of survey mode, question wording, and visual layout influence respondent answers, in order to improve the quality of responses and to encourage similarity of construction when more than one survey data collection mode is used. Redesign efforts for surveys at the Central Bureau of Statistics in the Netherlands (Snijkers 2007), Statistics Denmark (Conrad 2007), and the Office for National Statistics in the United Kingdom (Jones et al.
    [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]
  • Interactive Voice Response for Data Collection in Low and Middle-Income Countries
    Interactive Voice Response for Data Collection in Low and Middle-Income Countries Viamo Brief April 2018 Suggested Citation Greenleaf, A.R. Vogel, L. 2018. Interactive Voice Response for Data Collection in Low and Middle- Income Countries. Toronto, Canada: Viamo. 1 0 - EXECUTIVE SUMMARY Expanding mobile network coverage, decreasing cost of cellphones and airtime, and a more literate population have made mobile phone surveys an increasingly viable option for data collection in low- and middle-income countries (LMICs). Interactive voice response (IVR) is a fast and cost-effective option for survey data collection. The benefits of trying to reach respondents in low and middle-income countries (LMICs) via cell phone have been described by The World Bank,[1] academics[2,3], and practitioners[4] alike. IVR, a faster and less expensive option than face-to-face surveys, can collect data in areas that are difficult for human interviewers to reach. This brief explains applications of IVR for data collection in LMICs. Sections 1- 4 provide background information about IVR and detail the advantages of “robo-calls”. The next three sections explain the three main target groups for IVR. Beginning with Section 5 we outline the four approaches to sampling a general population and address IVR data quality. Known respondents, who are often enrolled for monitoring and evaluation, are covered in Section 6, along with best practices for maximizing participant engagement. Finally, in Section 7 we explain how professionals use IVR for surveillance and reporting. Woven throughout Sections 5-7, four case studies illustrate how four organizations have successfully used IVR to for data collection.
    [Show full text]
  • When Should We Ask, When Should We Measure?
    Page 1 – CONGRESS 2015 Copyright © ESOMAR 2015 WHEN SHOULD WE ASK, WHEN SHOULD WE MEASURE? COMPARING INFORMATION FROM PASSIVE AND ACTIVE DATA COLLECTION Melanie Revilla • Carlos Ochoa • Roos Voorend • Germán Loewe INTRODUCTION Different sources of data Questionnaires have been a fundamental tool for market research for decades. With the arrival of internet, the questionnaire, a tool invented more than 100 years ago, was simply adapted to online data collection. The arrival of online panels in the 2000s meant an important boost for online questionnaires, and as a consequence, the tipping point for their online migration. We have come this far making all kind of market research projects using almost always the same tool. But this does not mean there is no room or need for improvement. If researchers massively used questionnaires during all these years, it is not because this is the most suited data collection tool for all the problems they faced; but because there were no better alternatives available. In addition, nowadays, things are changing really fast. This affects survey-based market research, particularly when the data collection process relies on respondent’s memory. A tool that was working reasonably in the past may not be sufficient anymore. Indeed, in the last decades, with the development of new technologies, an increased part of the consumer activity takes place on the internet. Also, we have witnessed an explosion of relevant events for marketing: increased consumer ad exposure through multiple channels, increased availability of products and brands, complex and quick decision making processes, etc. Larger memory issues may be expected in this new situation.
    [Show full text]
  • 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,..
    [Show full text]
  • Survey Data Collection Using Complex Automated Questionnaires
    From: AAAI Technical Report SS-03-04. Compilation copyright © 2003, AAAI (www.aaai.org). All rights reserved. Survey Data Collection Using Complex Automated Questionnaires William P. Mockovak, Ph.D. Bureau of Labor Statistics Office of Survey Methods Research 2 Massachusetts Ave. N.E. Washington, DC 20212 [email protected] Abstract Since the early 1980s, researchers in the federal statistical com- The CPS is conducted by a workforce of about 1,500 inter- munity have been moving complex surveys from paper question- viewers scattered across the U.S. who conduct interviews naires to computer-assisted interviewing (CAI). The data col- in respondents’ homes and, occasionally, on doorsteps, lected through such surveys cover a variety of needs, from critical porches, lawns, etc., or over the telephone from either an information on the health of the economy to social issues as interviewer’s home or from a centralized telephone inter- measured by statistics on health, crime, expenditures, and educa- viewing facility. Although the composition of the inter- tion. This paper covers some of the key issues involved in devel- viewer workforce has undergone some changes in recent oping applications used primarily by a middle-age, part-time years with the increased hiring of retirees, historically, the workforce, which uses the software in a variety of situations, majority of interviewers have been women between the while interacting with an occasionally uncooperative public. ages of 45 and 55, who work part-time, and who lack many of the computer skills taken for granted in younger popula- Introduction tions. Many interviewers tend to work on a single, primary survey, but are available to work on others (some continu- Government surveys provide critical information on a vari- ous, some one-time).
    [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]
  • Evaluating Probability Sampling Strategies for Estimating Redd Counts: an Example with Chinook Salmon (Oncorhynchus Tshawytscha)
    1814 Evaluating probability sampling strategies for estimating redd counts: an example with Chinook salmon (Oncorhynchus tshawytscha) Jean-Yves Courbois, Stephen L. Katz, Daniel J. Isaak, E. Ashley Steel, Russell F. Thurow, A. Michelle Wargo Rub, Tony Olsen, and Chris E. Jordan Abstract: Precise, unbiased estimates of population size are an essential tool for fisheries management. For a wide variety of salmonid fishes, redd counts from a sample of reaches are commonly used to monitor annual trends in abundance. Using a 9-year time series of georeferenced censuses of Chinook salmon (Oncorhynchus tshawytscha) redds from central Idaho, USA, we evaluated a wide range of common sampling strategies for estimating the total abundance of redds. We evaluated two sampling-unit sizes (200 and 1000 m reaches), three sample proportions (0.05, 0.10, and 0.29), and six sampling strat- egies (index sampling, simple random sampling, systematic sampling, stratified sampling, adaptive cluster sampling, and a spatially balanced design). We evaluated the strategies based on their accuracy (confidence interval coverage), precision (relative standard error), and cost (based on travel time). Accuracy increased with increasing number of redds, increasing sample size, and smaller sampling units. The total number of redds in the watershed and budgetary constraints influenced which strategies were most precise and effective. For years with very few redds (<0.15 reddsÁkm–1), a stratified sampling strategy and inexpensive strategies were most efficient, whereas for years with more redds (0.15–2.9 reddsÁkm–1), either of two more expensive systematic strategies were most precise. Re´sume´ : La gestion des peˆches requiert comme outils essentiels des estimations pre´cises et non fausse´es de la taille des populations.
    [Show full text]
  • Cluster Sampling
    Day 5 sampling - clustering SAMPLE POPULATION SAMPLING: IS ESTIMATING THE CHARACTERISTICS OF THE WHOLE POPULATION USING INFORMATION COLLECTED FROM A SAMPLE GROUP. The sampling process comprises several stages: •Defining the population of concern •Specifying a sampling frame, a set of items or events possible to measure •Specifying a sampling method for selecting items or events from the frame •Determining the sample size •Implementing the sampling plan •Sampling and data collecting 2 Simple random sampling 3 In a simple random sample (SRS) of a given size, all such subsets of the frame are given an equal probability. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results. SRS can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn't reflect the makeup of the population. Systematic sampling 4 Systematic sampling (also known as interval sampling) relies on arranging the study population according to some ordering scheme and then selecting elements at regular intervals through that ordered list Systematic sampling involves a random start and then proceeds with the selection of every kth element from then onwards. In this case, k=(population size/sample size). It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the kth element in the list. STRATIFIED SAMPLING 5 WHEN THE POPULATION EMBRACES A NUMBER OF DISTINCT CATEGORIES, THE FRAME CAN BE ORGANIZED BY THESE CATEGORIES INTO SEPARATE "STRATA." EACH STRATUM IS THEN SAMPLED AS AN INDEPENDENT SUB-POPULATION, OUT OF WHICH INDIVIDUAL ELEMENTS CAN BE RANDOMLY SELECTED Cluster sampling Sometimes it is more cost-effective to select respondents in groups ('clusters') Quota sampling Minimax sampling Accidental sampling Voluntary Sampling ….
    [Show full text]