Page 41 - DLIS401_METHODOLOGY_OF_RESEARCH_AND_STATISTICAL_TECHNIQUES

Methodology of Research and Statistical Techniques

Notes (d) Theoretical Reasons for Non-Probability Sampling

The previous non-probability sampling designs are related to methodological concerns. In
fact, the issue of representativeness does matter in the background of these designs but is
conceived not feasible or, worse, purported as feasible but not founded on probability theory.
However, more interesting and scientifically valuable are the non-probability sampling designs
based on theoretical insight. In some theoretical models, it is unwise to conceive the world in
terms of probability, sometimes even not as something to be sampled. (this is a kind of
purposive sampling, but now because of theoretical concerns).
First, in field research, the researcher may be interested in acquiring a total, holistic understanding
of a natural setting. As such, there is no real sampling of anything at all. However, since
observations on “everything” or “everybody” can in effect never be achieved, it is best to
study only those elements relevant from a particular research perspective (sometimes called
“theoretical sampling” or “creative sampling”).
Second, when the elements in a natural setting clearly appear in different categories, quota
sampling “in the field” can be used. This is the same as regular quota sampling, but the
decisions on relevant cells and proportions of elements in cells are based on field observations.
Snowball sampling is used when access to the population is impossible (methodological concern)
or theoretically irrelevant. The selection of one element leads to the identification and selection
of others and these in turn to others, and so on. (The principle of saturation, indicating the
point when no more new data are revealed, determines when the snowball stops). Example
(cluster and snowball): in a study of drug-users in the USA, a number of cities (clusters) is
randomly selected, a drug-user is selected in each city (e.g. , through clinics), is interviewed
and asked for friends that use drugs too, and so on. Example (snowball): a researcher is
interested in African-American HIV infected males in Hyde Park, Chicago; the research aims
at in-depth understanding of this setting, and inferences about other HIV infected males are
trivial (apart from being impossible).
Third, the sampling of deviant cases can be interesting to learn more about a general pattern
by selecting those elements that do not conform to the pattern. Example: 99% of the students
at CU voted for Clinton, so I select those that did not, to find out why they are “deviant”.
These samples are purposive samples with a theoretically founded purpose. As long as that
is the case, their use may be perfectly justified and, according to some theories, even the only
applicable ones. The main disadvantage of non-probability sampling designs is the lack of
representativeness for a wider population. But again, based on some theories, these difficulties
can precisely be advantages (as long as the methodological and theoretical positions are clearly
stated, both probability and non-probability sampling designs can be equally “scientific”).

Methods of Observation
A full research design is not just a matter of determining the right methods of observation,
there is always (or there better be) theory first. The following procedure can be suggested:
• First, there should be a theory that states what is to be researched, and how this connects
to the already available body of literature (to ensure, or strive towards, cumulative
knowledge). There is no “naked” or mind-less observation.

• Second, the theory has to be conceptualized, so that the different variables of the theory
are clearly defined and identified. This may also involve acknowledgment of the limitations
of the approach.
• Third, the research topic and methodology is formalized into observable phenomena.
This involves specification of the research topic (where, when) and the methods of observation

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