3. Definition
Sampling is a word that refers to your method or
process of selecting respondents or people to answer
questions meant to yield data for a research study.
The chosen ones constitute the sample through which you
will derive facts and evidence to support the claims or
conclusions propounded by your research problem.
The bigger group from where you choose the sample is the
population.
Sampling frame is the term used to mean the list of the
members of such population from where you will get the
sample.
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5. History
The beginning of sampling could be traced back to the
early political activities of the Americans in 1920 when
Literary Digest did a pioneering survey about the
American citizens’ favorite among the 1920 presidential
candidates.
This was the very first survey that served as the impetus
for the discovery by academic researchers of other
sampling strategies that they categorized into two classes:
probability sampling or unbiased sampling and non-
probability sampling. (Babbie 2013)
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7. Probability
Sampling or
Unbiased
Sampling
Probability sampling involves all members listed in the
sampling frame representing a certain population focused
on by your study. An equal chance of participation in the
sampling or selection process is given to every member
listed in the sampling frame. By means of this unbiased
sampling, you are able to obtain a sample that is capable of
representing the population under study or of showing
strong similarities in characteristics with the members of
the population.
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8. Probability
Sampling or
Unbiased
Sampling
A sampling error crops up if the selection does not take
place in the way it is planned. Such sampling error is
manifested by strong dissimilarity between the sample and
the ones listed in the sampling frame. (P) How numerous
the sampling errors are depends on the side of the sample.
The smaller the sample is, the bigger the number sampling
errors. Thus, choose to have a big sample of respondents
to avoid sampling errors. However, deciding to increase
the size of your sample is not so easy. There are these
things you have to mull over in finalizing about this such
as expenses for questionnaires and interview trips,
interview schedules, and time for reading respondents'
answers.
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9. Probability
Sampling or
Unbiased
Sampling
The right sample size also depends on whether or not the
group is heterogeneous or homogeneous. The first group
requires a bigger size; the second, a smaller one. For a
study of the field of social sciences requiring an in-depth
investigation of something such as one involving the
national government, the right sample size ranges from
1,000 to 1,500 or up to 2,500. On the other hand,
hundreds, not thousands, of respondents suffice for a study
about any local government unit. (Suter 2012; Emmel
2013)
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11. SimpleRandom
Sampling
Systematic
Sampling
Stratified
Sampling
Cluster
Sampling
1. SIMPLE RANDOM SAMPLING
Simple random sampling is the best type of probability sampling
through which you can choose sample from a population. Using a pure-chance
selection, you assure every member the same opportunity to be in the sample.
Here, the only basis of including or excluding a member is by chance or
opportunity, not by any occurrence accounted for by cause-effect relationships.
Simple random sampling happens through any of these two methods: (Burns 2012)
1) Have a list of all members of the population; write each name on a
card through a pure-chance selection.
2) Have a list of all members; give a number to member and then use
randomized or unordered numbers in selecting names from the list.
16. Non-
Probability
Sampling
Non-probability sampling disregards random selection of
subjects. The subjects are chosen based on their
availability or the purpose of the study, and in some cases,
on the sole discretion of the researcher. This is not a
scientific way of selecting respondents. Neither does it
offer a valid or an objective way of detecting sampling
errors. (Edmond 2013)
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18. Quota
Sampling
Voluntary
Sampling
Purposeor
Judgmental
Sampling
Availability
Sampling
Snowball
Sampling
1. QUOTA SAMPLING
You resort to quota sampling when you think you know
the characteristics of the target population very well. In this case, you
tend to choose sample members possessing or indicating the
characteristics of the target population. Using a quota or specific set of
persons whom you believed to have the characteristics of the target
population involved in the study is your way of showing that the
sample you have chosen closely represents the target population as
regard such characteristics.
21. Quota
Sampling
Voluntary
Sampling
Purposeor
Judgmental
Sampling
Availability
Sampling
Snowball
Sampling
4. AVAILABILITY SAMPLING
The willingness of a person as your subject to interact
with you counts a lot in this non-probability sampling method. If
during the data-collection time, you and counter people walking on a
school campus, along corridors, and along the park or employees
lining up at an office, and these people show willingness to respond to
your questions, then you automatically consider them as your
respondents.
22. Quota
Sampling
Voluntary
Sampling
Purposeor
Judgmental
Sampling
Availability
Sampling
Snowball
Sampling
5. SNOWBALL SAMPLING
Similar to snow expanding wildly or rolling rapidly,
this sampling method does not give a specific set of examples. This is
true for a study involving unspecified group of people. Dealing with
varied groups of people such as street children, mendicants, drug
dependents call center workers, informal settlers, street vendors, and
the like is possible in this kind of non-probability sampling. Free to
obtain data from any group just like snow really expanding and
accumulating at a certain place, you tend to increase the number of
people you want to form the sample of your study. (Harding 2013)