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In statistics, survey sampling describes the process of selecting a sample of elements from a target population to conduct a survey. The term "survey" may refer to many different types or techniques of observation. In survey sampling it most often involves a questionnaire used to measure the characteristics and/or attitudes of people. Different ways of contacting members of a sample once they have been selected is the subject of survey data collection. The purpose of sampling is to reduce the cost and/or the amount of work that it would take to survey the entire target population. A survey that measures the entire target population is called a census. Survey samples can be broadly divided into two types: probability samples and non-probability samples. Probability-based samples implement a sampling plan with specified probabilities (perhaps adapted probabilities specified by an adaptive procedure). Probability-based sampling allows design-based inference about the target population. The inferences are based on a known objective probability distribution that was specified in the study protocol. Inferences from probability-based surveys may still suffer from many types of bias. Surveys that are not based on probability sampling have greater difficulty measuring their bias or sampling error. Surveys based on non-probability samples often fail to represent the people in the target population.〔Weisberg, Herbert F. (2005), The Total Survey Error Approach, University of Chicago Press: Chicago. p.231.〕 In academic and government survey research, probability sampling is a standard procedure. In the USA, the Office of Management and Budget's "List of Standards for Statistical Surveys" states that federally funded surveys must be performed:
Besides, random sampling and design-based inference are supplemented by other statistical methods, such as model-assisted sampling and model-based sampling.〔Lohr. Brewer. Swedes〕〔Richard Valliant, Alan H. Dorfman, and Richard M. Royall (2000), Finite Population Sampling and Inference: A Prediction Approach, Wiley, New York, p. 19〕 For example, many surveys have substantial amounts of nonresponse. Even though the units are initially chosen with known probabilities, the nonresponse mechanisms are unknown. For surveys with substantial nonresponse, statisticians have proposed statistical models, with which data sets are analyzed. Issues related to survey sampling are discussed in several sources including Salant and Dillman (1994).〔Salant, Priscilla, I. Dillman, and A. Don. How to conduct your own survey. No. 300.723 S3. 1994.〕 ==Probability sampling== In a probability sample (also called "scientific" or "random" sample) each member of the target population has a known and non-zero probability of inclusion in the sample.〔Kish, L. (1965), Survey Sampling, New York: Wiley. p. 20〕 A survey based on a probability sample can in theory produce statistical measurements of the target population that are: * unbiased, the expected value of the sample mean is equal to the population mean E(ȳ)=μ, and〔Kish, L. (1965), Survey Sampling, New York: Wiley. p.59〕 * have a measurable sampling error, which can be expressed as a confidence interval, or margin of error.〔http://www.aapor.org/whysamplingworks〕 A probability-based survey sample is created by constructing a list of the target population, called the sample frame, a randomized process for selecting units from the sample frame, called a selection procedure, and a method of contacting selected units to and enabling them complete the survey, called a data collection method or mode.〔Groves et al., Survey Methodology, Wiley: New York.〕 For some target populations this process may be easy, for example, sampling the employees of a company by using payroll list. However, in large, disorganized populations simply constructing a suitable sample frame is often a complex and expensive task. Common methods of conducting a probability sample of the household population in the United States are Area Probability Sampling, Random Digit Dial telephone sampling, and more recently, Address-Based Sampling.〔Michael W. Link, Michael P. Battaglia, Martin R. Frankel, Larry Osborn, and Ali H. Mokdad, A Comparison of Address-Based Sampling (ABS) Versus Random-Digit Dialing (RDD) for General Population Surveys; Public Opinion Q, Spring 2008; 72: 6 - 27.〕 Within probability sampling, there are specialized techniques such as stratified sampling and cluster sampling that improve the precision or efficiency of the sampling process without altering the fundamental principles of probability sampling. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. The strata should be mutually exclusive: every element in the population must be assigned to only one stratum. The strata should also be collectively exhaustive: no population element can be excluded. Then methods such as simple random sampling or systematic sampling can be applied within each stratum. This often improves the representativeness of the sample by reducing sampling error. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Survey sampling」の詳細全文を読む スポンサード リンク
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