Nonprobability Sampling
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. 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.
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.
Accidental, Haphazard or Convenience Sampling
One of
the most common methods of sampling goes under the various titles listed here.
I would include in this category the traditional "man on the street"
(of course, now it's probably the "person on the street") interviews
conducted frequently by television news programs to get a quick (although
nonrepresentative) reading of public opinion. I would also argue that the
typical use of college students in much psychological research is primarily a
matter of convenience. (You don't really believe that psychologists use college
students because they believe they're representative of the population at
large, do you?). In clinical practice,we might use clients who are available to
us as our sample. In many research contexts, we sample simply by asking for
volunteers. Clearly, the problem with all of these types of samples is that we
have no evidence that they are representative of the populations we're
interested in generalizing to -- and in many cases we would clearly suspect
that they are not.
Purposive Sampling
In
purposive sampling, we sample with a purpose in mind. We usually would
have one or more specific predefined groups we are seeking. For instance, have
you ever run into people in a mall or on the street who are carrying a
clipboard and who are stopping various people and asking if they could
interview them? Most likely they are conducting a purposive sample (and most
likely they are engaged in market research). They might be looking for Caucasian
females between 30-40 years old. They size up the people passing by and anyone
who looks to be in that category they stop to ask if they will participate. One
of the first things they're likely to do is verify that the respondent does in
fact meet the criteria for being in the sample. Purposive sampling can be very
useful for situations where you need to reach a targeted sample quickly and
where sampling for proportionality is not the primary concern. With a purposive
sample, you are likely to get the opinions of your target population, but you
are also likely to overweight subgroups in your population that are more
readily accessible.
All of
the methods that follow can be considered subcategories of purposive sampling
methods. We might sample for specific groups or types of people as in modal
instance, expert, or quota sampling. We might sample for diversity as in
heterogeneity sampling. Or, we might capitalize on informal social networks to
identify specific respondents who are hard to locate otherwise, as in snowball
sampling. In all of these methods we know what we want -- we are sampling with
a purpose.
Modal Instance Sampling
In
statistics, the mode is the most frequently occurring value in a distribution.
In sampling, when we do a modal instance sample, we are sampling the most
frequent case, or the "typical" case. In a lot of informal public
opinion polls, for instance, they interview a "typical" voter. There
are a number of problems with this sampling approach. First, how do we know
what the "typical" or "modal" case is? We could say that
the modal voter is a person who is of average age, educational level, and
income in the population. But, it's not clear that using the averages of these
is the fairest (consider the skewed distribution of income, for instance). And,
how do you know that those three variables -- age, education, income -- are the
only or even the most relevant for classifying the typical voter? What if
religion or ethnicity is an important discriminator? Clearly, modal instance
sampling is only sensible for informal sampling contexts.
Expert Sampling
Expert
sampling involves the assembling of a sample of persons with known or
demonstrable experience and expertise in some area. Often, we convene such a
sample under the auspices of a "panel of experts." There are actually
two reasons you might do expert sampling. First, because it would be the best
way to elicit the views of persons who have specific expertise. In this case,
expert sampling is essentially just a specific subcase of purposive sampling.
But the other reason you might use expert sampling is to provide evidence for
the validity of another sampling approach you've chosen. For instance, let's
say you do modal instance sampling and are concerned that the criteria you used
for defining the modal instance are subject to criticism. You might convene an
expert panel consisting of persons with acknowledged experience and insight
into that field or topic and ask them to examine your modal definitions and
comment on their appropriateness and validity. The advantage of doing this is
that you aren't out on your own trying to defend your decisions -- you have
some acknowledged experts to back you. The disadvantage is that even the experts
can be, and often are, wrong.
Quota Sampling
In quota
sampling, you select people nonrandomly according to some fixed quota. There
are two types of quota sampling: proportional and non proportional.
In proportional quota sampling you want to represent the major
characteristics of the population by sampling a proportional amount of each.
For instance, if you know the population has 40% women and 60% men, and that
you want a total sample size of 100, you will continue sampling until you get
those percentages and then you will stop. So, if you've already got the 40
women for your sample, but not the sixty men, you will continue to sample men
but even if legitimate women respondents come along, you will not sample them
because you have already "met your quota." The problem here (as in
much purposive sampling) is that you have to decide the specific
characteristics on which you will base the quota. Will it be by gender, age,
education race, religion, etc.?
Nonproportional
quota sampling is a bit
less restrictive. In this method, you specify the minimum number of sampled
units you want in each category. here, you're not concerned with having numbers
that match the proportions in the population. Instead, you simply want to have
enough to assure that you will be able to talk about even small groups in the
population. This method is the nonprobabilistic analogue of stratified random
sampling in that it is typically used to assure that smaller groups are
adequately represented in your sample.
Heterogeneity Sampling
We sample
for heterogeneity when we want to include all opinions or views, and we aren't
concerned about representing these views proportionately. Another term for this
is sampling for diversity. In many brainstorming or nominal group
processes (including concept mapping), we would use some form of heterogeneity
sampling because our primary interest is in getting broad spectrum of ideas,
not identifying the "average" or "modal instance" ones. In
effect, what we would like to be sampling is not people, but ideas. We imagine
that there is a universe of all possible ideas relevant to some topic and that
we want to sample this population, not the population of people who have the
ideas. Clearly, in order to get all of the ideas, and especially the
"outlier" or unusual ones, we have to include a broad and diverse
range of participants. Heterogeneity sampling is, in this sense, almost the
opposite of modal instance sampling.
Snowball Sampling
In
snowball sampling, you begin by identifying someone who meets the criteria for
inclusion in your study. You then ask them to recommend others who they may
know who also meet the criteria. Although this method would hardly lead to
representative samples, there are times when it may be the best method
available. Snowball sampling is especially useful when you are trying to reach
populations that are inaccessible or hard to find. For instance, if you are
studying the homeless, you are not likely to be able to find good lists of
homeless people within a specific geographical area. However, if you go to that
area and identify one or two, you may find that they know very well who the
other homeless people in their vicinity are and how you can find them
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