Read the background materials for this module and, after doing so, address the following
questions in a four-page paper:
- The sampling frame is arguably the most critical element of a study’s sampling plan.
Why is this so?
- How might a poorly specified sampling frame forestall the research process?
- Are studies that employ convenience sampling invalid? Please explain.
- of the sampling methods presented in this module, which optimize external validity (if
this term is unfamiliar, revisit the Module 2 home page)? Please explain.
The sampling frame as an essential facet of a sampling plan
Sampling frame is defined as a list, record or register of the entire eligible members or
constituents of a populace where the sample will be strained. It is not the sample in itself but it
forms a boundary within which the sample will be picked. The sampling frame should be
representative of the whole population (Morse, 2010).
Sampling frame is a very important tool in a study especially when planning for a sample. The
sample frame chosen will determine if the sample picked is relevant or not. For instance, some
individuals may be picked in a population and fail to respond to the questionnaire or observation.
Others may even not be traced at all for the response. Every element of the population should be
evident in the sampling frame and only once not more than that (Morse, 2010).
In our case study, a population is taken in which epidemiology is to be done. Therefore, the
sampling plan should posses as enough information as possible about the population to which an
inference is being sought. Some of this information is used to track the responses from various
individuals in a population. For instance, those who will fail to respond can be investigated in
terms of their age, may be they moved to a new residence. Also their level of education may be
checked to see if they are illiterate enough to respond. Also their habit in smoking or alcohol
may be observed to see if they failed due to fear of exposure and many others (European Journal
of Epidemiology, 2005).
The sampling frames used in this case study were Population Register (PR), Health Register
(HR) and Electoral Register (ER) all of which contained information about the population to be
studied. Population register was seen to be more reliable in providing all information required for
the study. It contained more contact numbers for the population and more information on chosen
sample. The frame will restrict the population under study to a handy figure through which
unbiased and precise conclusion is drawn (European Journal of Epidemiology, 2005).
A scantily specified sampling frame can preclude the research process
Setting up an apparent sampling frame is significant to the accomplishment of any investigation
or study because a flawed sampling frame will lead to incoherent or erroneous results, findings
or conclusions. A sampling frame should have a specific boundary within which the sample will
be picked. It should not include those outside the specifications, nor should it leave out anyone
eligible for responses. The sampling frame should not have clusters inside but just individuals. It
is erroneous to survey a member in the frame more than once also. Such problems as mentioned
can hinder the research process (Morse, 2010).
Response rate (rr) of the sampled survey is majorly dependent on the quality of sampling frame
and the recruitment strategy. The quality of sampling frame is mostly measured by use of the
contact rate (cr). Enrolment rate (er) will also depend on the convincing power of the researcher,
recruitment strategy used as well as sampling frame to some extent. For instance PR showed a
lot of people who could be contacted because it was regularly updated while for the other
sampling frames, there was a low correlation between recruitment strategy and contact rate hence
the major problem might have arose from poor quality sampling frame (European Journal of
Also a certain age of people who showed less response were 35-44 years which is mainly young
people at the start of career with many job prospects hence always on the move relocating. Those
who were non-respondents were asked questions on their age, schooling level and smoking
status. Low cr would be as a result of people having relocated hence could not be contacted. It
could also be as a result of the sampling frame being out of date hence some people might also
have died hence could not be contacted (European Journal of Epidemiology, 2005).
Therefore proper selection of a sampling frame is vital to getting good results in a sample to be
examined and extrapolate it as representative of the whole population. Failure to do so, the
research process can easily be forestalled and data collected will be inappropriate and misleading
Studies that make use of convenience sampling are not automatically invalid. Convenience
sampling is also referred to as accidental or opportunistic sampling. It is a non probabilistic
sampling method that entails the sample being taken from that fraction of the population which is
in close proximity to the researcher. Such a sample is generally picked because it is convenient
and readily available for study. However, a researcher cannot use convenient sampling to be a
scientifically generalize his sample as a representative sample of the whole population. Thus, he
can only use such a sample to study the characteristics of that sample alone (Lohr, 2010).
For instance, a researcher may decide to study a sample of the people who go to a bank in
automated teller for transactions. The behavior exhibited by such customers may not necessarily
represent what also happens with a human teller. Therefore such a sample can be studied for that
particular instance only and not as a representative of the whole population (Lohr, 2010).
In our case study, a convenient sampling would be picked from the population register showing
people who are near and can easily be contacted. The researcher could just call the available
contacts and just study that. Scientifically, this sample would not be a representative of the total
population but just a small fraction (European Journal of Epidemiology, 2005).To be a
representative sample, it should be picked systematically and using probability methods.
Convenience sampling is advantageous in that it saves time and at the same time it is cost
effective hence it can only be used where a researcher just want to save time and money but not
necessarily representing the total population (Lohr, 2010).
Sampling method which optimize external validity
The eventual goal of a sampling design is come up with a set of elements or parameters of a
population in which their description precisely portrays the distinctive features of the population
from which it was singled out. Another goal for doing a sample design is to ensure maximum
precision by minimizing variance in results (Dattalo, 2010).
From the sampling methods given in this module, the use of population register is gives optimal
externally validity since the sample picked 21 regions out of the total 37 regions which are
approximately 57% of the total. The PR is also regularly updated hence giving up to date
information on the population (European Journal of Epidemiology, 2005).
To maximize external validity, an appropriate population of a proper sample size in composition
with an appropriate formulated sampling strategy is necessary. External validity is the degree to
which the outcome of a study can be generalized to represent the total population. It is the
validity of scientific generalized inferences (Dattalo, 2010).
Mostly, the loss of external validity especially when dealing with human population is evident
when the size picked is too small as compared to the total population and basically when the
sample is picked from one or a few small geographical areas. This sample will not represent the
characteristic of the whole population because other geographical areas may portray different
characteristic from ones observed (Dattalo, 2010).
Dattalo, P (2010). Ethical dilemmas in sampling. A journal of Social Work Values and Ethics,
European Journal of Epidemiology (2005) 20: 293–299 Ó Springer 2005 DOI 10.1007/s10654-
Lohr, S. L. (2010). Sampling: design and analysis. Cengage Learning.
Morse, J. (2010). Sampling in grounded theory. The Sage handbook of grounded theory,