Sampling Terminology

Sampling

References: Neuman, ch. 8, pp. 219-244 Sampling terminology http://www.socialresearchmethods.net/kb/sampterm.htm Sampling & Statistics http://www.socialresearchmethods.net/kb/sampstat.htm Probability sampling http://www.socialresearchmethods.net/kb/sampprob.htm Nonprobability sampling http://www.socialresearchmethods.net/kb/sampnon.htm

Usually, sampling is the process by which we check out a small portion of a thing and then generalize to the entire thing. The preference for studying samples instead of entire populations mostly comes from practical concerns. Usually, lack of money and time dictate why we use samples of populations instead of entire populations. However, we may also use sampling to get at really specific information from a small group--- we may not have any desire to generalize.

We all use sampling in everyday life. When you had that first sip of coffee this morning, was it representative of every sip after that? If you take enough samples, can you predict a pattern? What about getting a pulse on “the will of the people” for legislation?

Why do we sample? - could be to get estimates and predictability and generalization, or it could be to get very specific information about a particular group. Regardless, a key reason we do sampling is to engage in hypothesis testing or hypothesis constructing.

Definition of terms:

Population: (your “who”) the entire set of people, things, events or groups that are of interest to the researcher. Ex. Residents on the Nisqually reservation.

(N) Sampling frame: List of all accessible members in the population. Ex. A contact list of names of those residents.

(n) Sample: (a portion of your “who”) a subset of the population. Ex. residents over the age of 18 with postal addresses.

Element: a number that describes some attribute of the population. Ex. Average income. Usually, this is the number we do not know and it is why we conduct a sample in order to estimate that value.

Statistic: a number that describes some attribute of the sample. Ex. The average income of the residents. (You can then use this to get to an estimate of the population parameter.)

How do we pick the Sample approach? Depends on what you want to get out of the sample for the purposes of your research. Three general steps in sampling: identify the target population, put together a population list, select the sample.

1 What are the two basic types of sampling?

1) Probability sampling: researcher decides which segment of the population will be used in order to accurately portray the larger population. Every subject in the sample has the same chance of getting selected. Therefore, the sample group possesses the same characteristics of the larger population. The assumption is that this random selection will result in a normal distribution and we can then generalize to the larger population.

Cluster: naturally occurring elements of population- city blocks and then subgroups are sampled within- ex. “cancer clusters.”

Simple random sample: (random selection) procedure that generates numbers or cases strictly on the basis of chance. Ex. random digit dialing (RDD)

Stratified random sampling: (random assignment) uses information already known about the population before sampling. First breaks the population down into strata (male students, female students) and then randomly selects sample from stratum. Allows for oversampling and weighting: Proportionate stratified sample - (equal unit) each stratum is represented exactly in proportion to the population (50 men, 50 women). Disproportionate stratified sample - (unequal unit) varies intentionally in proportion to the population (20 men, 80 women).

2) Nonprobability sampling: used when probability sampling is too expensive, or when exact representation of population is not important to study, or when the population cannot be defined. What purpose does nonprobability sampling serve if we cannot generalize? We do not measure the sampling error with nonprobability samples because we do not assume a normal distribution nor have the goal of generalizability.

*Judgment/purposive: group of women w/ HIV- they can tell their story best. *Convenience: results from hanging out, use whoever is around. Stand on street corner and survey people. *Snowball sample (telephone game): one member of the sample is identified and then they identify another person who could take part in the study and so on. Ex. gang members, wine drinkers.

* Most of you will conduct your research using one of the three sampling techniques above. Therefore, your findings will state: While we cannot generalize to the larger population, according to the respondents within this research study……*

Quota: weighted to end up with same percent as larger population. Systematic sampling with a random start: your sample is a city's phone book, you randomly select what letter to start with, you then pick every tenth name on a list.

2 How do we pick the Sample size? Depends on what you are doing. If you want to interview female nuclear physicists who grew up in San Diego, your sample is likely going to be very small.

Further, it depends on if you want to generalize to the larger population. According to the laws of numbers, the more we sample, the more accurate we can be. The idea being that the larger the sample the more we can capture the diversity or similarities of the population (homogeneous or heterogeneous).

This assumes a normal sampling distribution. (common problem with a lot of public opinion polls)

For some researchers, size is not as important as randomization when you want to decrease your sampling error. Why? Because randomization results in a natural distribution of variance. It tells you that things did not just happen by chance. Alternatively, you may keep increasing your numbers, but be less and less accurate dependent upon the split in the sample….you may not have a normal sampling distribution.

This is a major debate between social scientists, size vs. substance.