Making Sense of the Social World 4th Edition

1. Making Sense of the Social World 4th Edition Chapter 5: Sampling

2. Population: The entire set of individuals or other entities to which study findings are to be generalized. • Example: The United States. • Sample: A subset of a population used to study the population. • Example: 10 states in the US. • Sampling Frame: A list of all elements or other units containing the elements in a population. • A list of all countries • Each country is an element on the list of countries in the population.

3. Sampling methods that allow us to know in advance how likely it is that any element of a population will be selected for the sample are termed probability samplingmethods.

4. Simple Random Sampling • Simple random sampling identifies cases strictly on the basis of chance. • Each sampling unit has a known and equal chance of being included in the sample. • “Coin Flip”

5. Example

6. Systematic Random Sampling The first element is selected randomly from a list or from sequential files, and then every nth element is selected. • In almost all sampling situations, systematic random sampling yields what is essentially a simple random sample. • Beware when the sequence of elements is affected by periodicity—that is, the sequence varies in some regular, periodic pattern. • Example: 100 people in the population. We need 20 people in our sample. So sample every 100 / 20 = 5th person on a list. • 5 is called the “skip interval” or “sampling interval”

7. If the sampling interval is 8 for a study in this neighborhood, every element of the sample will be a house on the northwest corner—and thus the sample will be biased.

8. Cluster Sampling • Cluster sampling is useful when a sampling frame—a definite list—of elements is not available, as often is the case for large populations spread out across a wide geographic area or among many different organizations. • A cluster is a naturally occurring, mixed aggregate of elements of the population, with each element (person, for instance) appearing in one and only one cluster. • Schools could serve as clusters for sampling students, • City blocks could serve as clusters for sampling residents • Counties could serve as clusters for sampling the general population • Restaurants could serve as clusters for sampling waiters.

9. Cluster Sampling • Cluster sampling is at least a two-stage procedure. • First, the researcher draws a random sample of clusters. • Next, the researcher draws a census or random sample of elements within each selected cluster. • Because only a fraction of the total clusters are involved, obtaining the sampling frame at this stage should be much easier.

10. Cluster Sampling

11. Stratified Random Sample • Stratified random sampling ensures that various differinggroups will be included in the sample. • First, all elements in the population (that is, in the sampling frame) are distinguished according to their value on some relevant characteristic (age, rank, ethnicity). That characteristic forms the sampling strata. • Next, elements are sampled randomly from within these strata • Each element must belong to one and only one stratum. • Example: Freshman, Sophomores, Juniors, Seniors

12. Proportionate Stratified Sampling • Imagine that you plan to draw a sample of 500 from an ethnically diverse neighborhood. • The neighborhood population is 15% black, 20% Hispanic, 35% Asian, and 30% white. • If you drew a simple random sample, you might end up with somewhat different percents of each group in your sample. • But if you created sampling strata based on ethnicity proportions in the population, you would randomly select cases from each stratum in exactly the same proportions as in the neighborhood population. • This is termed proportionate stratified sampling and iteliminates any possibility of sampling error in the sample’s distribution of ethnicity.

13. Disproportionate Stratified Sampling In disproportionate stratified sampling, the proportion of each stratum that is included in the sample is intentionally varied from what it is in the population. In the case of the sample stratified by ethnicity, you might select equal numbers of cases from each racial or ethnic group: • 125 blacks (25% of the sample) • 125 Hispanics (25%) • 125 Asians (25%) • 125 whites (25%) • In this type of sample, the probability of selection of every case is known but unequal between strata.

14. Remember that… • One of the main determinants of sample quality is sample size. • Samples will be more representative of the population if they are relatively large and selected through probability sampling methods.

15. Sometimes, a probability sample is not feasible or generalizability is not desired. • Nonprobability sampling methods are often used in qualitative research • They also are used in quantitative studies when researchers are unable to use probability selection methods.

16. Availability Sampling Elements are selected for availability sampling because they’re available or easy to find. Thus this sampling method is also known as a(n) haphazard, accidental, or convenience sample. Examples:

17. Quota Sampling • Quota sampling is intended to overcome the most obvious flaw of availability sampling—that the sample will just consist of whoever or whatever is available, without any concern for its similarity to the population of interest. • The distinguishing feature of a quota sample is that quotas are set to ensure that the sample represents certain characteristics in proportion to their prevalence in the population. • Similar to Proportionate or Disproportionate Stratified Sampling

18. Quota Sampling, Continued The problem is that even when we know that a quota sample is representative of the particular characteristics for which quotas have been set, we have no way of knowing if the sample is representative in terms of any other characteristics. Here quotas have been set for gender only. Under the circumstances, it’s no surprise that the sample is representative of the population only in terms of gender, not in terms of ethnicity. Interviewers are only human; they may avoid potential respondents with menacing dogs in the front yard, or they could seek out respondents who are physically attractive or who look like they’d be easy to interview. That’s why quotas may be needed!!

19. Purposive Sampling • In purposive sampling, each sample element is selected for a certain purpose. • Purposive sampling may involve studying the entire population of some limited group (directors of shelters for homeless adults) or a subset of a population (mid-level managers with a reputation for efficiency). • Or a purposive sample may be a “key informant survey,” which targets individuals who are particularly knowledgeable about the issues under investigation (i.e. “experts”).

20. Snowball Sampling • Snowball sampling is useful for hard-to-reach or hard-to-identify populations for which there is no sampling frame, but the members of which are somewhat interconnected (or at least some members of the population know each other). • It can be used to sample members of such groups as drug dealers, prostitutes, practicing criminals, participants in Alcoholics Anonymous groups, gang leaders, informal organizational leaders, and homeless persons.

21. More on Snowball Sampling More systematic versions of snowball sampling can reduce the potential for bias. For example, “respondent-driven sampling” gives financial incentives to respondents to recruit peers (Heckathorn, 1997).

22. Conclusion • Ultimately, one of the determinants of sample quality is sample size. • Samples will be more representative of the population if they are relatively large and selected through probability sampling methods, but non-probability methods are also an option and are frequently used. • Must disclose procedure used in your research report.