1. 1 Qualitative and Quantitative Sampling Social Research Methods 2117 & 6501 Fall, 2006 November 22~30, 2006

2. 2 Sampling: The process of selecting observations ( 抽樣 : 選擇觀察對象的過程 ) (The purpose: get a representative sample) Think about the following questions: Q: Can we observe every one? Q: Can we generalize our findings? The Key: probability sampling ( 機率抽樣 )

3. 3 A Brief History of Sampling: related to political polling ( 抽樣的發展歷史與政治民意調查有關） The stories of American presidential elections  Literary Digest & Gallop Poll

4. 4 Two Types of Sampling Methods: Nonprobability Sampling vs. Probability Sampling

5. 5 Some Terms First Element & Population  (sampling) element: the unit of analysis or case in population ( 元素 : 母體的構成單元或分析單位 )  Population: the abstract idea of a large group of many cases from which a sample is drawn ( 母體 : 理論上一研究的特定個案元素的集合體 ) A target population (a study population): from which the sample is actually selected, the specific pool of cases studies ( 目標母體或研 究母體 : 實際上從中抽取樣本的元素之集合體 )

6. 6 Nonprobability Sampling Qualitative researchers tend to use nonprobability or nonrandom sample. Qualitative researchers’ concern: relevance

7. 7 Nonprobability Sampling Haphazard, Accidental, or Convenience Sampling ( 就近取得研究對象，便利抽樣 ) Quota Sampling ( 定額抽樣或限額抽樣 ) Purposive or judgmental Sampling ( 立意或判斷抽樣法 : 以研究目的為基礎來抽樣， 通常由專家來判斷，尋找特定或一般較難尋找 的對象 )

8. 8 Nonprobability Sampling Snowball Sampling ( 滾 雪球抽樣法 : 適用於很難 找到特殊的研究對象時， 或研究對象屬於一特定 的團體 )

9. 9 Nonprobability Sampling Deviant Case Sampling: locate unusual, different, or peculiar cases that are not representative of the whole ( 找尋極端個案 ) Sequential Sampling: similar to purposive sampling, but the difference is to gather cases until the amount of new information or diversity of cases is filled ( 與 立意抽樣法不同的是， 繼續蒐集個案直到新資訊 或個案差異、多元性滿足為止 ) Theoretical Sampling: what is sampled comes from grounded theory ( 依紮根理論選取樣本 )

10. 10 Probability Sampling : samples selected accord with probability theory ( 依機率理論抽出的樣本就是機率 抽樣 ) The key: a sample must contain essentially the same variations that exist in the population To control conscious and unconscious sampling bias

11. 11 Probability Theory, Sampling Distribution Element & Population & a target population  (sampling) element: the unit of analysis or case in population ( 元素 : 母體的構成單元或分析單位 )  Population: the abstract idea of a large group of many cases from which a sample is drawn ( 母體 : 理論上一研究的特定個案元素的集合體 )  Sampling ratio ( 抽樣比 )  Sampling frame ( 抽樣架構 ): a list of cases in a population, or the best approximation of it ( 類似母 體元素的列表 )

12. 12 Probability Theory A parameter ( 母數或參數 ) vs. a statistic ( 統計值 )  a parameter: any characteristic of a population ( 母體 既有變數的特徵描述 )  A statistic: information from the sample ( 樣本變數的特 徵描述，用來推估母體 ) A random process: Equal chance of being selected The purpose: to select a representative sample Can calculate the sampling error ， s = √[(P*Q)/n]

13. 13 The logic of sampling The concept of sampling distribution ( 抽樣分布 ) The central limit theorem  Let’s play a game!

14. 14 Sampling distribution

15. 15 Sampling distribution

16. 16 More on Sampling Distribution We can estimate the sampling error Confidence intervals ( 信賴區間 — 估計母體母 數數值的幅度 ) and confidence levels ( 信賴水 準 )  Provide the basis for determining the approximate sample size Be careful:  Theory vs. survey conditions  Tend to overestimate the precision of estimates

17. 17 Types of Probability Samples Simple Random Sampling( 簡單隨機取樣 ): a basic sampling method  Can use random numbers ( 亂數 ) or computer  Seldom used in practice Systematic Sampling ( 系統抽樣 ): every k th element is chosen  Usually apply a random start ( 隨機開始的系統抽樣 )  Sampling interval and sampling ratio ( 抽樣間距與抽樣比率 )  Be careful about any periodicity in the list( 注意元 素排列的週期性 )

18. 18 Simple Random Sampling and Systematic Sampling

19. 19 Types of Probability Sampling Stratified Sampling: to obtain greater representativeness ( 分層抽樣 : 減少誤差 以選取更具代表性的樣本 )  Elements drawn from homogeneous subsets of a population ( 從母群內同質性的次群體中取樣 )  Select variables you want to represent accurately, ex: gender, geographical locations, social class, ethnicity  Implicit stratification in systematic sampling ( 系統抽樣 隱含的分層性） Ex: 對大學生的抽樣  Oversample a specific stratum ( 對母體特定階層抽取超 過比率的樣本 )

20. 20 Types of Probability Sampling (Multistage) Cluster Sampling [( 多階段 ) 集群 抽樣 )]: used when a list of elements of a population not available and/or the cost to reach a sample element very high  A cluster ( 集群 ): a unit that contains final sampling elements but treated temporarily as a sampling element it self  The process: randomly sample clusters, then randomly sample elements  Draw several samples in stages

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22. 22 Cluster Sampling Cluster sampling and sampling error: less expensive but less accurate ( 集群抽樣成本較低，但是樣本較不 正確，因其抽樣誤差較大 )  Each stage introduces sampling errors, so multistage cluster sample has more sampling errors than a one-stage random sample. Cluster design: how to decide the # of clusters and the # of elements within clusters?  The key: more clusters is better ( 多選取集群 ), why? Within-household sampling: to select the individual within the household randomly Stratification in multistage cluster sampling  We can apply stratification techniques at each stage.

23. 23 Cluster Sampling — Probability Proportionate to Size (PPS) ( 隨樣本大小 比例的隨機抽樣 ) Proportionate or unweighted cluster sampling: because the size of each cluster is the same But the more common situation is for clusters to be of different sizes. ( 常見的情況是集群大 小不一致 ) So, we need to use probability proportionate to size (PPS) technique.  The key: each element has an equal chance to be selected into the sample

24. 24 Other Sampling Issues Random-Digit Dialing (RDD): used when the general public is interviewed by telephone  The sampling element in RDD is the phone number Studying hidden populations: apply nonprobability and probability sampling Sample size: how large should a sample be?  A smaller population needs a bigger sampling ratio.  Consider three things: accuracy, variability, the # of different variables examined  Subgroups

25. 25 Other Sampling Issues Drawing Inferences ( 推論 )  Why sampling? Can draw inferences from the sample to the population.  Combining logics of sampling and measurement  Validity and sampling error

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