Chapter 7 The Logic of Sampling

1. Chapter 7The Logic of Sampling

2. Probability sampling is the primary method for selecting large, representative samples for social science research.

3. Two types of Sampling • Nonprobability sample • Probability sample

4. Nonprobability Sampling • does not represent the population • qualitative based • study specific characteristics • poor reliability • good validity

5. Purposive or judgmental sampling • select sample on the basis of your own knowledge of the population and the purpose of the study • selecting deviant cases for study is another example of purposive sampling

6. Snowball sampling • form of accidental sampling • convenient sampling • appropriate when the members of a special population are difficult to locate • E.g. : prostitutes, steroid users, rape victims, abusers, homeless

7. Snowball sampling • researcher collect data on the few members of the target population they can locate, and then ask those individuals to provide the information needed to locate other members of that population who they happen to know • exploratory purposes

8. Quota sampling • address the issue of representativeness • begins with a matrix or table describing the characteristics of the target population -> you need to know what proportion of the population is for example male and female as well as what proportions of each gender fall into various age categories, education levels, ethnic groups, etc.

9. Quota sampling • then you collect the data from people having all the characteristics of a given cell • then assign weight to all the people in a given cell that is appropriate to their portion of the total population

10. Topic: Single Parents

11. Informants • is a member of the group who can talk directly about the group • select informants some what typical of the groups you’re studying • informants will sometimes be marginal or atypical within their group

12. The Theory and Logic of Probability Sampling • generalize • representative • quantitative

13. bias • those selected are not typical or representative of the larger population they have been chosen from – can be unintended

14. representativeness and probability of selection • representative -> sample will be representative of the population from which it is selected if the aggregate characteristic of the sample closely approximates those same aggregates characterized in the population

15. basic principle • all members of the population have an equal chance of being selected in the sample -> EPSEM (Equal Probability of Selection Method)

16. 2 Advantages • more representative than other types- biases are avoided • estimate the accuracy or representative of samples • based on random selection procedure

17. Element • unit about which information is collected and that provides the basis of analysis -> people, certain types of people • element used for sample selection • unit of analysis used for data analysis

18. Population • theoretically specified aggregation of the elements in a study

19. Study population • aggregation of elements from which the sample is actually selected

20. Random selection • purpose of sampling -> to select a set of elements from a population in such a way that descriptions of those elements (stats) accurately portray the parameters of the total population from which the elements are selected

21. Key: Random Selection • each element has an equal chance of selection independent of any other event in the selection process

22. A sampling unit • element or set of elements considered for selection in some stage of sampling • serves as a check on bias

23. Parameter • probability theory -> basis for estimating the parameters of a population • parameters -> summary description of a given variable in a population

24. Normal curve • probability theory enables us to estimate the sampling error – the degree of error to be expected for a given sample design -> standard deviation

25. Confidence level and interval • - statistics fall within a specified interval from the parameter

26. Sampling frame • the list or quasi list of elements from which a probability sample is selected • Ex. : if a sample of students is selected from a student roster, the roster is the frame

27. Types of Sampling Design

28. Simple random sampling Basic sampling method assumed Use sampling frame ↓ Researcher gives each element a number ↓ A table of random numbers used to select elements (Appendix B)

29. Systematic sampling • every kth element in the total list is chosen (systematically) for inclusion in the sample • should select the first element at random • ex. Select a random number between 1 and 10 • the element having that number is included in the sample, plus every tenth element following it • systematic sample with a random start • sampling interval is the standard distance between elements selected in the sample

30. Stratified sampling • obtains greater degree of representativeness-decreasing the probable sampling error • rather than selecting a sample from the total population at large, the researcher ensures that appropriate numbers of elements are drawn from homogeneous subsets of that population • can stratify, for example, by class, gender

31. Stratified sampling • the ultimate function of stratification is to organize the population into homogeneous subsets (with heterogeneity between subsets), and to select the appropriate number of elements from each • the choice of stratification variables typically depends on what variables are available – you should be concerned primarily with those that are related to variables you want to represent accurately

32. Multistage cluster sampling • cluster sampling may be used when it’s either impossible or impractical to compile an exhaustive list of the elements composing the target population • involves repeating two basic steps: listing and sampling

33. Multistage cluster sampling • the list of primary sampling units (churches, blocks) is compiled and stratified for sampling • then a sample of those units is selected • the selected primary sampling units are then listed and perhaps stratified • the list of secondary sampling units is then sampled

34. Multistage cluster sampling • the general guideline for cluster design is to maximize the number of elements within each cluster, this scientific guideline must be balanced against an administrative constraint • the efficiency of cluster sampling is based on the ability to minimize the listing of population elements • by initially selecting clusters, you need only list the elements composing the selected clusters