Page 40 - DLIS401_METHODOLOGY_OF_RESEARCH_AND_STATISTICAL_TECHNIQUES

Unit 2: Research Design

so on, up until elements within clusters. While cluster sampling is more efficient, the disadvantage Notes
is that there are sampling errors (of representativeness) involved at each stage of sampling, a
problem which is not only repeated at each stage, but also intensified since sample size grows
smaller at each stage. However, since elements in clusters are often found to be homogeneous,
this problem can be overcome by selecting relatively more clusters and less elements in each
cluster (at the expense of administrative efficiency).
When information is available on the size of clusters (the number of elements it contains), we
can decide to give each cluster a different chance of selection proportionate to its size (then
selecting a fixed number within each cluster). This method has the advantage of being more
efficient: since elements in clusters are typically more homogeneous, only a limited number of
elements for each cluster has to be selected. Finally, disproportionate sampling can be useful
to focus on any one sample separately, or for the comparison of several samples. In this case,
generalizability of sample findings to the entire population should not and cannot be considered.
Example (multi-stage cluster, proportionate to size, stratified): for research on political attitudes
of students in the USA, no list of all students are available, but we have a list of all US states;
we select a number of states (clusters); they are given a chance of selection proportionate to
the “size” of (number of universities in) each state, because, for instance, there are more
universities in the north-eastern states (probability proportionate to size); out of the selected
states, we select cities (again proportionate to size, since metropolitan areas have more universities),
select universities out of each selected city, take the student lists of each selected university,
and select a relatively small number of students (assuming homogeneity among them since we
know all students in Harvard are conservative and everybody at CU-Boulder is a liberal).
2. Non-Probability Sampling
The choice between probability or non-probability design is dependent on theoretical premises
and choice of method. While probability sampling can avoid biases in the selection of elements
and increase generalizability of findings (these are the two big advantages), it is methodologically
sometimes not feasible or theoretically inappropriate to undertake them. Then non-probability
samplings can be used.
(a) Quota Sampling
In quota sampling, a matrix is created consisting of cells of the same attributes of different
variables known to be distributed in the population in a particular way. Elements are selected
having all attributes in a cell relative to their proportion in the population (e.g., take 90% white
and 10% black because based on census data that is the racial composition of the entire
population). Although the information on which the proportionate distribution of elements is
based can be inaccurate, quota sampling does strive for representativeness (but it is not based
on probability theory).

(b) Purposive Sampling
Purposive or judgmental sampling can be useful in explorative studies or as a test of research
instruments. In explorative studies, elements can purposively be selected to disclose data on
an unknown issue, which can later be studied in a probability sample. Questionnaires and
other research instruments can be tested (on their applicability) by purposively selecting “extreme”
elements (after which a probability sample is selected for the actual research).
(c) Sampling by Availability
When samples are being selected simply by the availability of elements, issues of representativeness
about the population cannot justifiably be addressed. A researcher may decide to just pick any
element that she/he bumps in to. As such, there is nothing wrong with this method, as long
as it is remembered that the selection of samples may be influenced by dozens of biases and
cannot be assumed to represent anything more than the selected elements.

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