Define and distinguish probability and non-probability sampling. What is the relationship between the techniques and sample generalizability?

Question

What will be an ideal response?

Answer 1

The most important distinction made about samples is whether they are based on a probability or a nonprobability sampling method. 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 probability sampling methods. Sampling methods that do not reveal the likelihood of selection in advance are nonprobability sampling methods.
Probability sampling methods rely on a random selection procedure. In principle, this is the same as flipping a coin to decide which person wins and which one loses. Heads and tails are equally likely to turn up in a coin toss, so both persons have an equal chance to win. That chance, or the probability of selection, is 1 out of 2, or.5.
Flipping a coin is a fair way to select 1 of 2 people because the selection process harbors no systematic bias. You might win or lose the coin toss, but you know that the outcome was due simply to chance, not to bias (unless your opponent tossed a two-headed coin!). For the same reason, rolling a six-sided die is a fair way to choose 1 of 6 possible outcomes (the odds of selection are 1 out of 6, or.17). Similarly, state lotteries use a random process to select winning numbers. Thus, the odds of winning a lottery—the probability of selection—are known even though they are very small (perhaps 1 out of 1 million) compared with the odds of winning a coin toss. As you can see, the fundamental strategy in probability sampling is the random selection of elements into the sample. When a sample is randomly selected from the population, every element has a known and independent chance of being selected into the sample.
There is a natural tendency to confuse the concept of probability, in which cases are selected only on the basis of chance, with a haphazard method of sampling. On first impression, leaving things up to chance seems to imply the absence of control over the sampling method. But to ensure that nothing but chance influences the selection of cases, the researcher must actually proceed very methodically and leave nothing to chance except the selection of the cases themselves. The researcher must carefully follow controlled procedures if a purely random process is to occur. In fact, when reading about sampling methods, do not assume that a random sample was obtained just because the researcher used a random selection method at some point in the sampling process. Look for these two particular problems: selecting elements from an incomplete list of the total population and failing to obtain an adequate response rate (say, only 45% of the people who were asked to participate actually agreed).
If the sampling frame, or list from which the elements of the population are selected, is incomplete, a sample selected randomly from the list will not be random. How can it be when the sampling frame fails to include every element in the population? Even for a simple population, such as a university’s student body, the registrar’s list is likely to be at least a bit out of date at any given time. For example, some students will have dropped out, but their status will not yet be officially recorded. Although you may judge the amount of error introduced in this particular situation to be negligible, the problems are greatly compounded for a larger population. The sampling frame for a city, state, or nation is always likely to be incomplete because of constant migration into and out of the area. Even unavoidable omissions from the sampling frame can bias a sample against particular groups within the population.
A very inclusive sampling frame may still yield systematic bias if many sample members cannot be contacted or refuse to participate. Nonresponse is a major hazard in survey research because individuals who do not respond to a survey are likely to differ systematically from those who take the time to participate. You should not assume that findings from a randomly selected sample will be generalizable to the population from which the sample was selected if the rate of nonresponse is considerable (certainly if it is much above 30%).