"Who should be in the sample?" and "How many do you need?"

([] knowledge ) ([])

1. Define the population of study carefully.
2. Choose an appropriate sampling method.

A [] is a special subset of a population observed for purposes of making inferences about the nature of the total population itself.

A census is the collection of information from [] members of a population.

To conduct a census, you need to identify all of the people that make up the population. The units making up a population are defined as [ ].

A listing of all the sampling elements is defined as a sampling [].

1. Make an assessment of the completeness and currency of any sampling frame.
2. Is the public defined by the sampling frame really the one that you want to study?

1. Simple
2. [] (not estimates!)

1. Rarely successful
2. Sources of bias
3. [ ]

[]

The reliability of the knowledge claims about population dependents on the type of [] used.

[]sampling: a. useful when the research is exploratory

[] sampling allow us to use probability theory to make precise knowledge claims about an entire population, based on a sample drawn from that population.

A nonprobability sample is one in which the probability of selecting any sampling element is [ ] known.

1. []
2. Inexpensive
3. Timeliness

1. []
2. Low quality of knowledge claims about populations

Appropriate uses of nonprobability sampling are….

1. Useful in scanning and detecting [].
2. [] to more rigorous sampling and research.

[] research techniques often involve nonprobability sampling

1. [] sampling (accidental sampling) involves selecting any convenient person as a sample element.
2. [] sampling: Some key characteristic of the target public is specified; then sample elements are selected to insure that the sample reflects the distribution of that characteristic in the population.
3. [] sampling is an extended form of quota sampling, where many quota criteria or strata are involved. Generally, an effort is made to insure that at least one individual is included in the sample for every possible combination of the criteria.
4. [] sampling is a nonprobability technique useful when you can only identify a few members of a target public directly, but members of the target public are likely to know others in that public.
5. Purposive sampling ([] sampling): Purposive samples are drawn in a manner that meets the special needs of the research effort.

Probability samples are drawn in such a way that the probability of selecting any particular sampling element is known.

[]:

Facts about probability sampling:

1. The average value for all the samples taken together tends to converge on the actual value in the target public
2. The percentages provided by many samples of the same size drawn from the same population will form a [ ] curve around the true population parameter

[]: the actual value in the population

The decision will weight the time and cost of collecting the information against the margin of error required for answering your research question about a population.

1. [] Samples: Probability samples in which every element in the target public (population) has an equal chance of selection in the sample.
2. [] sampling involves the selection of every kth member from a sampling frame.
3. [] samples are used when the target public studied is made up of nonoverlapping sub-publics of strata.

Stratification is the process of grouping the members of a population into relatively homogeneous strata before sampling.

4. [] Samples are useful when a comprehensive list of the target public, a comprehensive sample frame is not available. This sampling technique involves the initial sampling of groups of elements - clusters - followed by the selection of elements within each of the selected clusters.