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Chapter 7. The Logic Of Sampling. Chapter Outline. Introduction A Brief History of Sampling Nonprobability Sampling The Theory and Logic of Probability Sampling. Chapter Outline. Populations and Sampling Frames Types of Sampling Designs Multistage Cluster Sampling
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Chapter 7 The Logic Of Sampling
Chapter Outline • Introduction • A Brief History of Sampling • Nonprobability Sampling • The Theory and Logic of Probability Sampling
Chapter Outline • Populations and Sampling Frames • Types of Sampling Designs • Multistage Cluster Sampling • Probability Sampling in Review
Political Polls and Survey Sampling • In the 2004 Presidential election, pollsters generally agreed that the election was “too close to call”. • To gather this information, they interviewed fewer than 2,000 people.
Question • One of the most visible uses of survey sampling lies in _____________. • political polling • probability sampling • core sampling • traditional polling
Answer: A • One of the most visible uses of survey sampling lies in political polling.
Observation and Sampling • Polls and other forms of social research rest on observations. • The task of researchers is to select the key aspects to observe (sample). • Generalizing from a sample to a larger population is called probability sampling and involves random selection.
Nonprobability Sampling • Technique in which samples are selected in a way that is not suggested by probability theory. • Examples include reliance on available subjects as well as purposive (judgmental), quota, and snowball sampling.
Types of Nonprobability Sampling • Reliance on available subjects: • Only justified if less risky sampling methods are not possible. • Researchers must exercise caution in generalizing from their data when this method is used.
Types of Nonprobability Sampling • Purposive or judgmental sampling • Selecting a sample based on knowledge of a population, its elements, and the purpose of the study. • Used when field researchers are interested in studying cases that don’t fit into regular patterns of attitudes and behaviors (非常態的態度與行為)
Types of Nonprobability Sampling • Snowball sampling • Appropriate when members of a population are difficult to locate. • Researcher collects data on members of the target population she can locate, then asks them to help locate other members of that population.
Types of Nonprobability Sampling • Quota sampling • Begin with a matrix of the population. • Data is collected from people with the characteristics of a given cell. • Each group is assigned a weight appropriate to their portion of the population. • Data should represent the total population.
Question • ______________sampling occurs when units are selected on the basis of prespecified characteristics. • snowball • quota • purposive • probability
Answer: B • Quota sampling occurs when units are selected on the basis of prespecified characteristics.
Informant • Someone who is well versed in the social phenomenon that you wish to study and who is willing to tell you what he or she knows about it.
Probability Sampling • Used when researchers want precise, statistical descriptions of large populations.研究結果要能以精確統計描述母體。 • A sample of individuals from a population must contain the same variations that exist in the population. 樣本的內在變異必須與母體相同。
Populations and Sampling Frames • Findings based on a sample represent the aggregation of elements that compose the sampling frame. 研究發現代表抽樣架構的元素集合。 • Sampling frames do not always include all the elements their names imply. 遺漏的可能？ • All elements must have equal representation in the frame. 所有元素在架構內具相等的代表性。
A Population of 100 Folks • Sampling aims to reflect the characteristics and dynamics of large populations. • Let’s assume our total population only has 100 members.
Types of Sampling Designs • Simple random sampling (SRS) • Systematic sampling • Stratified sampling
Representativeness • Representativeness - Quality of a sample having the same distribution of characteristics as the population from which it was selected.諸特質在樣本中與母體具同樣分佈。 • EPSEM - Equal probability of selection method. A sample design in which each member of a population has the same chance of being selected into the sample.
Question • ______________describes a sample whose aggregate characteristics closely approximate the aggregate characteristics of the population. • exclusion • probability sampling • EPSEM • representativeness • none of these choices
Answer: D • Representativeness describes a sample whose aggregate characteristics closely approximate the aggregate characteristics of the population.
Population • The theoretically specified aggregation of study elements. • Study population - Aggregation of elements from which the sample is actually selected. • Element - Unit about which information is collected and that provides the basis of analysis.
Random selection • Each element has an equal chance of selection independent of any other event in the selection process.
Sampling unit • Element or set of elements considered for selection in some stage of sampling.
Parameter • Summary description of a given variable in a population.
The Sampling Distribution of Samples of 1 • In this example, the mean amount of money these people have is $4.50 ($45/10). • If we picked 10 different samples of 1 person each, our “estimates” of the mean would range all across the board.
Range of Possible Sample Study Results • Shifting to a more realistic example, let’s assume that we want to sample student attitudes concerning a proposed conduct code. (如學生行為守則) • Let’s assume 50% of the student body approves and 50% disapproves - though the researcher doesn’t know that.
Results Produced by Three Hypothetical Studies • Assuming a large student body, let’s suppose we selected three different samples, each of substantial size.(例如，三個樣本數都是80位學生) • We would not expect those samples to perfectly reflect attitudes in the whole student body, but they should come close.(平均值應該都相當接近)
Statistic • Summary description of a variable in a sample. • Parameter: Summary description of a given variable in a population.
Sampling Error • The degree of error to be expected of a given sample design. • 樣本統計偏離母群體母數的程度。
Confidence Level • The estimated probability that a population parameter lies within a given confidence interval. • Thus, we might be 95% confident(±1.96se) that between 35 and 45% of all voters favor Candidate A. • Confidence interval - The range of values within which a population parameter is estimated to lie.
Sampling Frame • That list or quasi list of units composing a population from which a sample is selected. • If the sample is to be representative of the population, it is essential that the sampling frame include all (or nearly all) members of the population.
The Sampling Distribution • If we were to select a large number of good samples, we would expect them to cluster around the true value (50%), but given enough such samples, a few would fall far from the mark.
Review of Populations and Sampling Frames: Guidelines • Findings based on a sample represent only the aggregation of elements that compose the sampling frame. • Sampling frames do not include all the elements their names might imply. Omissions are inevitable. • To be generalized, all elements must have equal representation in the frame.
Question • A _______________ is the list or quasi list of elements from which a probability sample is selected. • confidence level • confidence interval • sampling frame • systematic sample • none of these choices
Answer: C • A sampling frame is the list or quasi list of elements from which a probability sample is selected.
Simple Random Sampling • Feasible only with the simplest sampling frame. • Not the most accurate method available.
