【正文】 families, social clubs, or corporations might be the elements of a study. (Note: Elements and units of analysis are often the same in a given study, though the former refers to sample selection while the latter refers to data analysis.) 2. Sampling Concepts and Terminology (2) ? (總體 ). A population is the theoretically specified aggregation of study elements. – For example, specifying the term ―college students‖ would include a consideration of fulltime and parttime students, degree candidates and nondegree candidates, undergraduate and graduate students, and similar issues. ? 2. Study Population（ 研究總體 ） . A study population is that aggregation of elements from which the sample is actually selected. – As a practical matter, you are seldom in a position to guarantee that every element meeting the theoretical definitions laid down actually has a chance of being selected in the sample. Even where lists of elements exist for sampling purposes, the lists are usually somewhat inplete. Some students are always omitted, inadvertently, from student roster. Some telephone subscribers request that their names and numbers be unlisted. The study population, then, is the aggregation of elements from which the sample is selected. 2. Sampling Concepts and Terminology (3) ? 3. Sampling Unit（ 抽樣單位 ） . A sampling unit is that element or set of elements considered for selection in some stage of sampling. – In a simple, singlestage sample, the sampling units are the same as the elements. In more plex samples, however, different levels of sampling units may be employed. For example, you might select a sample of census blocks in a city, then select a sample of households on the selected blocks, and finally select a sample of adults from selected households. ? 4. Sampling Frame（ 抽樣框 ） . A sampling frame is the actual list of sampling units from which the sample, or some stage of the sample, is selected. ? 5. Observation Unit（ 觀察單位 ） . An observation unit, or unit of data collection, is an elements from which information is collected. – Again, the unit of analysis and unit of observation are often the same—the individual person—but that need not be the case. Thus the researcher may interview heads of households (the observation unit) to collect information about all family members of the households ( the units of analysis). 2. Sampling Concepts and Terminology (4) ? 6. Variable（ 變量 ） . A variable is a set of mutually exclusive attributes: sex, age, employment status, and so forth. ? 7. Parameter（ 參數(shù)值 ） . A parameter is the summary description of a given variable in a population. ? 8. Statistic（ 統(tǒng)計(jì)值 ） . A statistic is the summary description of a given variable in a sample. Sample statistics are used to make estimates of population parameters. ? Error（抽樣誤差） . Probability sampling methods seldom, if ever, provide statistics exactly equal to the parameters that they are used to estimate. Probability theory, however, permits us to estimate the degree of error to be expected for a given sample design. 2. Sampling Concepts and Terminology (5) ? 10. Confidence Levels and Confidence Intervals（ 顯著性水平與置信區(qū)間 ） . – We express the accuracy of our sample statistics in terms of a level of confidence that the statistics fall within a specified interval from the parameter. – For example, we may say we are 95 percent confident that our sample statistics are within plus or minus 5 percentage points of the population parameter. Probability Sampling (1) ? Simple Random Sampling (簡(jiǎn)單隨機(jī)抽樣 ). A type of probability sample in which the units posing a population are assigned numbers, a set of random numbers is then generated, and the units having those numbers are included in the sample. Although probability theory and the calculations it provides assume this basic sampling method, it is seldom used for practical reasons. Probability Sampling (2) ? Systematic Sampling (系統(tǒng)抽樣 ). A type of probability sample in which every kth unit in a list is selected for inclusion in the sample: ., every 25th student in the college directory of students. K is puted by dividing the size of the population by the desired sample size and is called the sampling interval. Within certain constraints, systematic sampling is a functional equivalent of simple random sampling and usually easier to do. ? Sampling interval = population size / sample size ? sampling ratio = sample size / population size Probability Sampling (3) – Stratified sampling (分層抽樣 ): to anize the population into homogeneous subsets (with heterogeneity between subsets.) and to select the appropriate number of elements from each. Probability Sampling (4) ? Cluster Sampling (整群抽樣 ). A multistage sample in which natural groups (clusters) are sampled initially, with the members of each selected group being subsampled afterward . – For example, you might select a sample of . colleges and universities from a directory, get lists of the students at all the selected schools, then draw samples of students from each. NonProbability Sampling (1) ? Purposive or judgmental sampling(立意抽樣 ). A type of nonprobability sampling in which you select the units to be observed on the basis of your own judgment about which ones will be the most useful or reprsentative. NonProbability Sampling (2) ? Quota sampling (配額抽樣 ). A type of nonprobability sampling in which units are selected into the sample on the basis of prespecified characteristics, so that the total sample will have the same distribution of characteristics as are assumed to exist in the population being studied. NonProbability Sampling (3) ? Snowball sampling (滾雪球抽樣 ). A nonprobability sampling method often employed in filed research. Each person interviewed ma