Research Class Report: Sampling Types/Procedures

PROBABILITY (RANDOM SAMPLING)

These samples are based on probability theory. Every unit of the population of interest must be identified, and all units must have a known, non-zero chance of being selected into the sample

· Convenience sample

· Purposive sample

· Quota sample

NON-PROBABILITY (NON- RANDOM SAMPLING)

· Convenience sample

· Purposive sample

· Quota sample

These samples focus on volunteers, easily available units, or those that just happen to be present when the research is done. Non-probability samples are useful for quick and cheap studies, for case studies, for qualitative research, for pilot studies, and for developing hypotheses for future research.

The differences between Probability (Random) Sampling and Non-Probability (Non-Random) Sampling are summarized below.

Probability (Random) Sampling	Non-Probability (Non-Random) Sampling
Allows use of statistics, tests hypotheses	Exploratory research, generates hypotheses
Can estimate population parameters	Population parameters are not of interest
Eliminates bias	Adequacy of the sample can't be known
Must have random selection of units	Cheaper, easier, quicker to carry out

Parameter - a parameter is a characteristic of a population.

Statistic - a statistic is a characteristic of a sample

Probability (random) samples:

Simple random sample: Each unit in the population is identified, and each unit has an equal chance of being in the sample. The selection of each unit is independent of the selection of every other unit. Selection of one unit does not affect the chances of any other unit. Example: lottery method and table of random numbers method

Systematic random sampling is the process of selecting every nth member of the population arranged in a list. For example you could take every 10th member of a list of people (the population) arranged alphabetically.

Stratified random sampling is obtained by dividing the population into subgroups and then randomly selecting from each of the subgroups. The number of units selected from each subgroup can be proportional to the groups number in the population or can be equal-sized among the subgroups.

Cluster sampling: groups are selected rather than individuals. For example select 5 elementary schools from among the 25 elementary schools in the district.

Non-Probability (Non-Random) Samples:

Convenience Sampling also called an "accidental" sample or "man-in-the-street" samples. The researcher selects units that are convenient, close at hand, easy to reach, etc. Taking an intact group (e.g. your own forth grade class of pupils) and using this group to represent the population (e.g. all fourth grade students in your state, province, or country). This is not really sampling at all and there are severe problems in generalizing the results from your sample to the population in incidental or convenience sampling.

Purposive sample: the researcher selects the units with some purpose in mind, for example, students who live in dorms on campus, or experts on urban development.

Quota sample: the researcher constructs quotas for different types of units. For example, to interview a fixed number of shoppers at a mall, half of whom are male and half of whom are female.

OTHERS

Snowball Sampling: In social science research, snowball sampling is a technique for developing a research sample where existing study subjects recruit future subjects from among their acquaintances. Thus the sample group appears to grow like a rolling snowball. As the sample builds up you gain enough data to use for your data. This sampling technique is often used in hidden populations which are difficult for researchers to access; example populations would be drug users or commercial prostitutes.