Research Methods

Research Methods

Non-Probability

The difference between nonprobability and probability sampling is that nonprobability sampling does not involve random selection and probability sampling does. Does that mean that nonprobability samples aren't representative of the population? Not necessarily. (Hacking 1990) But it does mean that nonprobability samples cannot depend upon the rationale of probability theory. At least with a probabilistic sample, we know the odds or probability that we have represented the population well. We are able to estimate confidence intervals for the statistic.

With nonprobability samples, we may or may not represent the population well, and it will often be hard for us to know how well we've done so. In general, researchers prefer probabilistic or random sampling methods over nonprobabilistic ones, and consider them to be more accurate and rigorous. However, in applied social research there may be circumstances where it is not feasible, practical or theoretically sensible to do random sampling. Here, we consider a wide range of nonprobabilistic alternatives. (Desrosières 2004)

We can divide nonprobability sampling methods into two broad types: accidental or purposive. Most sampling methods are purposive in nature because we usually approach the sampling problem with a specific plan in mind. The most important distinctions among these types of sampling methods are the ones between the different types of purposive sampling approaches.

With nonprobability sampling, in contrast, population elements are selected on the basis of their availability (e.g., because they volunteered) or because of the researcher's personal judgment that they are representative. (Best 2001)

The consequence is that an unknown portion of the population is excluded (e.g., those who did not volunteer). One of the most common types of nonprobability sample is called a convenience sample - not because such samples are necessarily easy to recruit, but because the researcher uses whatever individuals are available rather than selecting from the entire population.

Because some members of the population have no chance of being sampled, the extent to which a convenience sample - regardless of its size - actually represents the entire population cannot be known. (Desrosières 2004)

Recruiting a probability sample is not always a priority for researchers. A scientist can demonstrate that a particular trait occurs in a population by documenting a single instance. For example, the assertion that all lesbians are mentally ill can be refuted by documenting the existence of even one lesbian who is free from psychopathology.

Another situation in which a probability sample is not necessary is when a researcher wishes to describe a particular group in an exploratory way. For example, interviewing 25 people with AIDS (PWAs) about their experiences with HIV could provide valuable insights about stress and coping, even though it would not yield data about the proportion of PWAs in the general population who share those experiences. (Hacking 1990)

Random Selection

The simplest form of random sampling is called simple random sampling. Pretty tricky, huh? Here's the quick description of simple random sampling:

Objective: To select n units out of N such that each NCn has an equal chance of being ...