Lecture : 2

sampling & data collection

Presented By
Dr ASHESH TIWARI

07/27/23 AN APPROACH FOR RESEARCH METHODOLOGY

DIFFERENT TYPES OF SAMPLE DESIGNS
There are different types of sample designs based on
two factors viz., the representation basis and the
element selection technique. On the representation
basis, the sample may be probability sampling or it
may be non-probability sampling. Probability
sampling is based on the concept of random
selection, whereas non-probability sampling is ‘non-
random’ sampling. On element selection basis, the
sample may be either unrestricted or restricted.

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 CHART SHOWING BASIC SAMPLING DESIGNS

07/27/23 An approach for Research Methodology

Non-probability sampling: Non-probability sampling is that sampling procedure which does not
afford any basis for estimating the probability that each item in the population has of being included
in the sample. Non- probability sampling is also known by different names such as deliberate
sampling, purposive sampling and judgement sampling. In this type of sampling, items for the sample
are selected deliberately by the researcher; his choice concerning the items remains supreme. In
other words, under non-probability sampling the organisers of the inquiry purposively choose the
particular units of the universe for constituting a sample on the basis that the small mass that they so
select out of a huge one will be typical or representative of the whole. For instance, if economic
conditions of people living in a state are to be studied, a few towns and villages may be purposively
selected for intensive study on the principle that they can be representative of the entire state. Thus,
the judgement of the organisers of the study plays an important part in this sampling design.

07/27/23 An approach for Research Methodology

In such a design, personal element has a great chance of entering into the selection of the sample.
The investigator may select a sample which shall yield results favourable to his point of view and if
that happens, the entire inquiry may get vitiated. Thus, there is always the danger of bias entering
into this type of sampling technique. But in the investigators are impartial, work without bias and
have the necessary experience so as to take sound judgment, the results obtained from an analysis
of deliberately selected sample may be tolerably reliable. However, in such a sampling, there is no
assurance that every element has some specifiable chance of being included. Sampling error in this
type of sampling cannot be estimated and the element of bias, great or small, is always there. As
such this sampling design in rarely adopted in large inquires of importance. However, in small
inquiries and researches by individuals, this design may be adopted because of the relative
advantage of time and money inherent in this method of sampling. Quota sampling is also an
example of non-probability sampling. Under quota sampling the interviewers are simply given
quotas to be filled from the different strata, with some restrictions on how they are to be filled. In
other words, the actual selection of the items for the sample is left to the interviewer’s discretion.
This type of sampling is very convenient and is relatively inexpensive. But the samples so selected
certainly do not possess the characteristic of random samples. Quota samples are essentially
judgement samples and inferences drawn on their basis are not amenable to statistical treatment
in a formal way.

07/27/23 An approach for Research Methodology

 PROBABILITY SAMPLING:
Probability sampling is also known as ‘random sampling’ or ‘chance
sampling’. Under this sampling design, every item of the universe has an
equal chance of inclusion in the sample. It is, so to say, a lottery method
in which individual units are picked up from the whole group not
deliberately but by some mechanical process. Here it is blind chance
alone that determines whether one item or the other is selected. The
results obtained from probability or random sampling can be assured in
terms of probability i.e., we can measure the errors of estimation or the
significance of results obtained from a random sample, and this fact
brings out the superiority of random sampling design over the deliberate
sampling design. Random sampling ensures the law of Statistical
Regularity which states that if on an average the sample chosen is a
random one, the sample will have the same composition and
characteristics as the universe. This is the reason why random sampling is
considered as the best technique of selecting a representative sample.
07/27/23 An approach for Research Methodology
In brief, the implications of random
sampling (or simple random sampling) are:
(a) It gives each element in the population
an equal probability of getting into the
sample; and all choices are independent of
one another.
(b) It gives each possible sample
combination an equal probability of being
chosen.

07/27/23 An approach for Research Methodology

 COMPLEX RANDOM SAMPLING DESIGNS
Probability sampling under restricted sampling
techniques, as stated above, may result in
complex random sampling designs. Such designs
may as well be called ‘mixed sampling designs’
for many of such designs may represent a
combination of probability and non-probability
sampling procedures in selecting a sample.
Some of the popular complex random sampling
designs are as follows:
07/27/23 An approach for Research Methodology
 SYSTEMATIC SAMPLING
In some instances, the most practical way of sampling is to
select every i th item on a list. Sampling of this type is known as
systematic sampling. An element of randomness is introduced
into this kind of sampling by using random numbers to pick up
the unit with which to start. For instance, if a 4 per cent sample
is desired, the first item would be selected randomly from the
first twenty-five and thereafter every 25 th item would
automatically be included in the sample. Thus, in systematic
sampling only the first unit is selected randomly and the
remaining units of the sample are selected at fixed intervals.
Although a systematic sample is not a random sample in the
strict sense of the term, but it is often considered reasonable to
treat systematic sample as if it were a random sample.

07/27/23 An approach for Research Methodology

Systematic sampling has certain plus points. It can be taken as an improvement over
a simple random sample in as much as the systematic sample is spread more evenly
over the entire population. It is an easier and less costlier method of sampling and
can be conveniently used even in case of large populations. But there are certain
dangers too in using this type of sampling. If there is a hidden periodicity in the
population, systematic sampling will prove to be an inefficient method of sampling.
For instance, every 25th item produced by a certain production process is defective.
If we are to select a 4% sample of the items of this process in a systematic manner,
we would either get all defective items or all good items in our sample depending
upon the random starting position. If all elements of the universe are ordered in a
manner representative of the total population, i.e., the population list is in random
order, systematic sampling is considered equivalent to random sampling. But if this
is not so, then the results of such sampling may, at times, not be very reliable. In
practice, systematic sampling is used when lists of population are available and they
are of considerable length.

07/27/23 An approach for Research Methodology

 STRATIFIED SAMPLING:
If a population from which a sample is to be drawn does not
constitute a homogeneous group, stratified sampling
technique is generally applied in order to obtain a
representative sample. Under stratified sampling the
population is divided into several sub-populations that are
individually more homogeneous than the total population (the
different sub-populations are called ‘strata’) and then we
select items from each stratum to constitute a sample. Since
each stratum is more homogeneous than the total population,
we are able to get more precise estimates for each stratum
and by estimating more accurately each of the component
parts, we get a better estimate of the whole. In brief, stratified
sampling results in more reliable and detailed information.
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The following three questions are highly
relevant in the context of stratified
sampling:
(a) How to form strata?
(b) How should items be selected from
each stratum?
(c) How many items be selected from each
stratum or how to allocate the sample
size of each stratum?

07/27/23 An approach for Research Methodology

Regarding the first question, we can say that the strata be
formed on the basis of common characteristic(s) of the items to
be put in each stratum. This means that various strata be formed
in such a way as to ensure elements being most homogeneous
within each stratum and most heterogeneous between the
different strata. Thus, strata are purposively formed and are
usually based on past experience and personal judgement of the
researcher. One should always remember that careful
consideration of the relationship between the characteristics of
the population and the characteristics to be estimated are
normally used to define the strata. At times, pilot study may be
conducted for determining a more appropriate and efficient
stratification plan. We can do so by taking small samples of
equal size from each of the proposed strata and then examining
the variances within and among the possible stratifications, we
can decide an appropriate stratification plan for our inquiry.
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07/27/23 An approach for Research Methodology
In cases where strata differ not only in size but also in variability and it is
considered reasonable to take larger samples from the more variable
strata and smaller samples from the less variable strata, we can then
account for both (differences in stratum size and differences in stratum
variability) by using disproportionate sampling design by requiring:

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07/27/23 An approach for Research Methodology
 PRACTICAL EXAMPLE

07/27/23 An approach for Research Methodology

07/27/23 An approach for Research Methodology