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As a dyslexic working in the media can I just advocate that otter. Example: Suppose we have six schools with populations of , , , , , and students respectively total students , and we want to use student population as the basis for a PPS sample of size three. If our random start was , we would select the schools which have been allocated numbers , , and , i. the first, fourth, and sixth schools. The PPS approach can improve accuracy for a given sample size by concentrating sample on large elements that have the greatest impact on population estimates.
PPS sampling is commonly used for surveys of businesses, where element size varies greatly and auxiliary information is often available — for instance, a survey attempting to measure the number of guest-nights spent in hotels might use each hotel's number of rooms as an auxiliary variable. In some cases, an older measurement of the variable of interest can be used as an auxiliary variable when attempting to produce more current estimates.
Sometimes it is more cost-effective to select respondents in groups 'clusters'. Sampling is often clustered by geography, or by time periods. Nearly all samples are in some sense 'clustered' in time — although this is rarely taken into account in the analysis.
For instance, if surveying households within a city, we might choose to select city blocks and then interview every household within the selected blocks. Clustering can reduce travel and administrative costs. In the example above, an interviewer can make a single trip to visit several households in one block, rather than having to drive to a different block for each household.
It also means that one does not need a sampling frame listing all elements in the target population. Instead, clusters can be chosen from a cluster-level frame, with an element-level frame created only for the selected clusters. In the example above, the sample only requires a block-level city map for initial selections, and then a household-level map of the selected blocks, rather than a household-level map of the whole city. Cluster sampling also known as clustered sampling generally increases the variability of sample estimates above that of simple random sampling, depending on how the clusters differ between one another as compared to the within-cluster variation.
For this reason, cluster sampling requires a larger sample than SRS to achieve the same level of accuracy — but cost savings from clustering might still make this a cheaper option. Cluster sampling is commonly implemented as multistage sampling.
This is a complex form of cluster sampling in which two or more levels of units are embedded one in the other. The first stage consists of constructing the clusters that will be used to sample from. In the second stage, a sample of primary units is randomly selected from each cluster rather than using all units contained in all selected clusters.
In following stages, in each of those selected clusters, additional samples of units are selected, and so on.
All ultimate units individuals, for instance selected at the last step of this procedure are then surveyed. This technique, thus, is essentially the process of taking random subsamples of preceding random samples. Multistage sampling can substantially reduce sampling costs, where the complete population list would need to be constructed before other sampling methods could be applied.
By eliminating the work involved in describing clusters that are not selected, multistage sampling can reduce the large costs associated with traditional cluster sampling. In quota sampling , the population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgement is used to select the subjects or units from each segment based on a specified proportion. For example, an interviewer may be told to sample females and males between the age of 45 and It is this second step which makes the technique one of non-probability sampling.
In quota sampling the selection of the sample is non- random. For example, interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for several years.
In imbalanced datasets, where the sampling ratio does not follow the population statistics, one can resample the dataset in a conservative manner called minimax sampling.
The minimax sampling has its origin in Anderson minimax ratio whose value is proved to be 0. This ratio can be proved to be minimax ratio only under the assumption of LDA classifier with Gaussian distributions. The notion of minimax sampling is recently developed for a general class of classification rules, called class-wise smart classifiers. In this case, the sampling ratio of classes is selected so that the worst case classifier error over all the possible population statistics for class prior probabilities, would be the best.
Accidental sampling sometimes known as grab , convenience or opportunity sampling is a type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, a population is selected because it is readily available and convenient.
It may be through meeting the person or including a person in the sample when one meets them or chosen by finding them through technological means such as the internet or through phone. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. For example, if the interviewer were to conduct such a survey at a shopping center early in the morning on a given day, the people that they could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey were to be conducted at different times of day and several times per week.
This type of sampling is most useful for pilot testing. Several important considerations for researchers using convenience samples include:. In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample. Some variants of snowball sampling, such as respondent driven sampling, allow calculation of selection probabilities and are probability sampling methods under certain conditions. The voluntary sampling method is a type of non-probability sampling.
Volunteers choose to complete a survey. Volunteers may be invited through advertisements in social media. The advertisement may include a message about the research and link to a survey. After following the link and completing the survey, the volunteer submits the data to be included in the sample population.
This method can reach a global population but is limited by the campaign budget. Volunteers outside the invited population may also be included in the sample. It is difficult to make generalizations from this sample because it may not represent the total population. Often, volunteers have a strong interest in the main topic of the survey. Line-intercept sampling is a method of sampling elements in a region whereby an element is sampled if a chosen line segment, called a "transect", intersects the element.
Panel sampling is the method of first selecting a group of participants through a random sampling method and then asking that group for potentially the same information several times over a period of time. Therefore, each participant is interviewed at two or more time points; each period of data collection is called a "wave".
The method was developed by sociologist Paul Lazarsfeld in as a means of studying political campaigns. Panel sampling can also be used to inform researchers about within-person health changes due to age or to help explain changes in continuous dependent variables such as spousal interaction.
Snowball sampling involves finding a small group of initial respondents and using them to recruit more respondents. It is particularly useful in cases where the population is hidden or difficult to enumerate. Theoretical sampling  occurs when samples are selected on the basis of the results of the data collected so far with a goal of developing a deeper understanding of the area or develop theories.
Extreme or very specific cases might be selected in order to maximize the likelihood a phenomenon will actually be observable. Sampling schemes may be without replacement 'WOR' — no element can be selected more than once in the same sample or with replacement 'WR' — an element may appear multiple times in the one sample.
For example, if we catch fish, measure them, and immediately return them to the water before continuing with the sample, this is a WR design, because we might end up catching and measuring the same fish more than once. However, if we do not return the fish to the water or tag and release each fish after catching it, this becomes a WOR design.
Sampling enables the selection of right data points from within the larger data set to estimate the characteristics of the whole population.
For example, there are about million tweets produced every day. It is not necessary to look at all of them to determine the topics that are discussed during the day, nor is it necessary to look at all the tweets to determine the sentiment on each of the topics. A theoretical formulation for sampling Twitter data has been developed.
In manufacturing different types of sensory data such as acoustics, vibration, pressure, current, voltage, and controller data are available at short time intervals. To predict down-time it may not be necessary to look at all the data but a sample may be sufficient. Survey results are typically subject to some error. Total errors can be classified into sampling errors and non-sampling errors.
The term "error" here includes systematic biases as well as random errors. Non-sampling errors are other errors which can impact final survey estimates, caused by problems in data collection, processing, or sample design. Such errors may include:.
After sampling, a review should be held [ by whom? A particular problem involves non-response. Two major types of non-response exist:  .
In survey sampling , many of the individuals identified as part of the sample may be unwilling to participate, not have the time to participate opportunity cost ,  or survey administrators may not have been able to contact them. In this case, there is a risk of differences between respondents and nonrespondents, leading to biased estimates of population parameters.
This is often addressed by improving survey design, offering incentives, and conducting follow-up studies which make a repeated attempt to contact the unresponsive and to characterize their similarities and differences with the rest of the frame. Nonresponse is particularly a problem in internet sampling. Reasons for this problem may include improperly designed surveys,  over-surveying or survey fatigue ,   [ need quotation to verify ] and the fact that potential participants may have multiple e-mail addresses, which they don't use anymore or don't check regularly.
In many situations the sample fraction may be varied by stratum and data will have to be weighted to correctly represent the population. Thus for example, a simple random sample of individuals in the United Kingdom might not include some in remote Scottish islands who would be inordinately expensive to sample. A cheaper method would be to use a stratified sample with urban and rural strata.
The rural sample could be under-represented in the sample, but weighted up appropriately in the analysis to compensate. More generally, data should usually be weighted if the sample design does not give each individual an equal chance of being selected. For instance, when households have equal selection probabilities but one person is interviewed from within each household, this gives people from large households a smaller chance of being interviewed. This can be accounted for using survey weights.
Similarly, households with more than one telephone line have a greater chance of being selected in a random digit dialing sample, and weights can adjust for this. Random sampling by using lots is an old idea, mentioned several times in the Bible. In Pierre Simon Laplace estimated the population of France by using a sample, along with ratio estimator. He also computed probabilistic estimates of the error.
His estimates used Bayes' theorem with a uniform prior probability and assumed that his sample was random. Alexander Ivanovich Chuprov introduced sample surveys to Imperial Russia in the s. In the US the Literary Digest prediction of a Republican win in the presidential election went badly awry, due to severe bias .
More than two million people responded to the study with their names obtained through magazine subscription lists and telephone directories. It was not appreciated that these lists were heavily biased towards Republicans and the resulting sample, though very large, was deeply flawed. The textbook by Groves et alia provides an overview of survey methodology, including recent literature on questionnaire development informed by cognitive psychology :.
The other books focus on the statistical theory of survey sampling and require some knowledge of basic statistics, as discussed in the following textbooks:. More mathematical statistics is required for Lohr, for Särndal et alia, and for Cochran classic [ citation needed ] :. The historically important books by Deming and Kish remain valuable for insights for social scientists particularly about the U.
census and the Institute for Social Research at the University of Michigan :. From Wikipedia, the free encyclopedia. Selection of data points in statistics. Main article: Sampling frame. Main article: Nonprobability sampling. Main article: Simple random sampling. Main article: Systematic sampling. Main article: Stratified sampling. Main article: Probability-proportional-to-size sampling. Main article: Cluster sampling. Main article: Quota sampling. Further information: Self-selection bias.
Overall, do you approve or disapprove of the way the US Congress is handling its job? Do you think things in the United States are generally going in the right direction or the wrong direction? How satisfied are you with the way democracy is working in the United States?
Are you very satisfied, somewhat satisfied, not too satisfied, or not at all satisfied? These days, do you feel [rotate]  optimistic [or]  pessimistic that Americans of different political views can still come together and work out their differences? What is your opinion with regard to race relations in the United States today? Would you say things are [rotate 1 and 2]  better ,  worse , or about the same than they were a year ago? When it comes to racial discrimination, which do you think is the bigger problem for the country today—[rotate]  People seeing racial discrimination where it really does NOT exist [or]  People NOT seeing racial discrimination where it really DOES exist?
Next, Next, would you consider yourself to be politically: [read list, rotate order top to bottom]. Generally speaking, how much interest would you say you have in politics—a great deal, a fair amount, only a little, or none? Mark Baldassare is president and CEO of the Public Policy Institute of California, where he holds the Arjay and Frances Fearing Miller Chair in Public Policy.
He is a leading expert on public opinion and survey methodology, and has directed the PPIC Statewide Survey since He is an authority on elections, voter behavior, and political and fiscal reform, and the author of ten books and numerous publications. Before joining PPIC, he was a professor of urban and regional planning in the School of Social Ecology at the University of California, Irvine, where he held the Johnson Chair in Civic Governance. He has conducted surveys for the Los Angeles Times , the San Francisco Chronicle , and the California Business Roundtable.
He holds a PhD in sociology from the University of California, Berkeley. Dean Bonner is associate survey director and research fellow at PPIC, where he coauthors the PPIC Statewide Survey—a large-scale public opinion project designed to develop an in-depth profile of the social, economic, and political attitudes at work in California elections and policymaking.
He has expertise in public opinion and survey research, political attitudes and participation, and voting behavior. Before joining PPIC, he taught political science at Tulane University and was a research associate at the University of New Orleans Survey Research Center. He holds a PhD and MA in political science from the University of New Orleans. Rachel Lawler is a survey analyst at the Public Policy Institute of California, where she works with the statewide survey team.
In that role, she led and contributed to a variety of quantitative and qualitative studies for both government and corporate clients. She holds an MA in American politics and foreign policy from the University College Dublin and a BA in political science from Chapman University. Deja Thomas is a survey analyst at the Public Policy Institute of California, where she works with the statewide survey team. Prior to joining PPIC, she was a research assistant with the social and demographic trends team at the Pew Research Center.
In that role, she contributed to a variety of national quantitative and qualitative survey studies. She holds a BA in psychology from the University of Hawaiʻi at Mānoa. This survey was supported with funding from the Arjay and Frances F. Ruben Barrales Senior Vice President, External Relations Wells Fargo. Mollyann Brodie Executive Vice President and Chief Operating Officer Henry J.
Kaiser Family Foundation. Bruce E. Cain Director Bill Lane Center for the American West Stanford University. Jon Cohen Chief Research Officer and Senior Vice President, Strategic Partnerships and Business Development Momentive-AI. Joshua J. Dyck Co-Director Center for Public Opinion University of Massachusetts, Lowell. Lisa García Bedolla Vice Provost for Graduate Studies and Dean of the Graduate Division University of California, Berkeley.
Russell Hancock President and CEO Joint Venture Silicon Valley. Sherry Bebitch Jeffe Professor Sol Price School of Public Policy University of Southern California. Carol S. Larson President Emeritus The David and Lucile Packard Foundation. Lisa Pitney Vice President of Government Relations The Walt Disney Company. Robert K.
Ross, MD President and CEO The California Endowment. Most Reverend Jaime Soto Bishop of Sacramento Roman Catholic Diocese of Sacramento. Helen Iris Torres CEO Hispanas Organized for Political Equality. David C. Wilson, PhD Dean and Professor Richard and Rhoda Goldman School of Public Policy University of California, Berkeley.
Chet Hewitt, Chair President and CEO Sierra Health Foundation. Mark Baldassare President and CEO Public Policy Institute of California. Ophelia Basgal Affiliate Terner Center for Housing Innovation University of California, Berkeley.
Louise Henry Bryson Chair Emerita, Board of Trustees J. Paul Getty Trust. Sandra Celedon President and CEO Fresno Building Healthy Communities. Marisa Chun Judge, Superior Court of California, County of San Francisco. Steven A. Leon E. Panetta Chairman The Panetta Institute for Public Policy. Cassandra Walker Pye President Lucas Public Affairs. Gaddi H. Vasquez Retired Senior Vice President, Government Affairs Edison International Southern California Edison.
The Public Policy Institute of California is dedicated to informing and improving public policy in California through independent, objective, nonpartisan research. PPIC is a public charity. It does not take or support positions on any ballot measures or on any local, state, or federal legislation, nor does it endorse, support, or oppose any political parties or candidates for public office. Short sections of text, not to exceed three paragraphs, may be quoted without written permission provided that full attribution is given to the source.
Research publications reflect the views of the authors and do not necessarily reflect the views of our funders or of the staff, officers, advisory councils, or board of directors of the Public Policy Institute of California.
PPIC Higher Education Center. People Our Team Board of Directors Statewide Leadership Council Adjunct Fellows. Support Ways to Give Our Contributors. Table of Contents Key Findings Overall Mood Gubernatorial Election State Propositions 26, 27, and 30 Congressional Elections Democracy and the Political Divide Approval Ratings Regional Map Methodology Questions and Responses Authors and Acknowledgments PPIC Statewide Advisory Committee PPIC Board of Directors Copyright.
Key Findings Overall Mood Gubernatorial Election State Propositions 26, 27, and 30 Congressional Elections Democracy and the Political Divide Approval Ratings Regional Map Methodology Questions and Responses Authors and Acknowledgments PPIC Statewide Advisory Committee PPIC Board of Directors Copyright. Key Findings California voters have now received their mail ballots, and the November 8 general election has entered its final stage.
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