다국어인구학사전, 두 번째 통합본, 한국어판

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160

표본 추출 절차¹는 모집단에 대한 정보를 모든 개인 (110-2)이 아닌 일부에서 얻기 위해 이용된다. 조사 대상 모집단의 부분은 표본²이라 부른다. 모집단은 조사 대상인요소³의 집합체이다. 추출 단위⁴는 모집단의 한 요소 또는 요소의 집합일 수 있으며 표본을 선택하기 위해 사용된다. 인구학적 표본에서 요소는 개인 (110-2), 가족 (115-1), 가구 (110-3) 인 경우가 많으며 추출 단위는 개인, 가구, 주택, 지방자치구 또는 지역이다. 표본은표본 설계⁵에 따라 선택된 다수의 추출 단위로 구성된다.

161

A sample whose elements are selected by a chance process is referred to as a random sample¹ or probability sample¹. If a complete list of sampling units is available, this is called a sampling frame³. In simple random sampling⁴ a proportion of sampling units is selected from the frame at random². This proportion is called the sampling fraction⁵ or sampling ratio⁵. Systematic samples⁶ are drawn systematically⁷ from a frame in which the sampling units are consecutively numbered. The sample is selected by taking the n^th, (n + s)^th, (n + 2s)^th, ..., etc. unit, where n is not larger than s and is selected at random. In cluster sampling⁸ population elements are not drawn individually, but in groups which are called clusters⁹ .

• 2. Random, adj. - randomness, n. - randomize, v,

162

In stratified random sampling¹ the population is divided into a number of strata² which are in some sense more homogeneous (134-4) than the population as a whole with respect to the characteristics studied, and a simple random sample (161-4) is drawn in each stratum. Variable sampling fractions (161-5) may be used in the different strata. Multi-stage sampling³ is a method where the selection of the sample is carried out in several stages. A sample of primary units⁴ is first selected and each of these units is then regarded as a population (101-3) from which a sub-sample⁵ of secondary units⁶ is selected, and the process may be repeated. When there is no good sampling frame, a sample of areas delimited on a map may be selected: this procedure is called area sampling⁷.

• 1. Stratify, v,: divide into strata - stratification, n.

163

In probability sampling (161-1), chance methods are used to obtain a representative sample¹ i.e., a sample which is a faithful reflection of the population with respect to all the characteristics under investigation except for random fluctuation. In quota sampling², on the other hand, the sample is purposely selected so as to reflect the population in certain characteristics, and each interviewer (204-2) is given a quota³ of different types of sampling units which are to be included in his sample. Within the limits of the quota the interviewer is free to select the sampling units.

164

A population parameter¹ is a numerical value that characterizes a population. Statistical estimation² is the name given to the procedure by which the values of such parameters are estimated from the sample. Such estimates are subject to sampling errors³ and a measure of the magnitude of the sampling error is generally given by the standard error⁴. Sometimes a confidence interval⁵ is associated with an estimate to show the limits within which the estimated quantity may be expected to lie with a pre-determined probability. A difference between two values is referred to as a significant difference⁶ when the probability that it is due to chance is less than a given value which is called the level of significance⁷. Thus a difference would be significant at the 5 percent level if the probability that it could have arisen by chance is less than 0.05. In addition to sampling errors, observation errors⁸ or response errors⁸ also affect estimates. These errors usually include interviewer biases⁹ which are systematic errors introduced by the interviewers when the basic data are collected.