This document defines key terminology related to sampling methods. It discusses population, sample, sampling unit, statistic, parameter, sampling frame, estimator, estimate, bias, and representative sample. It also describes different sampling methods including simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, convenience sampling, quota sampling, and snowball sampling.
1. Adama, Ethiopia
Biniam Zewdie G/Kidan *
•Haramaya Institute of University
P.O.Box:138; Dire Dawa, Ethiopia
•Mobile: +251910408218/+25191582832
•E-mail: nzg2001nzg@gmail.com/zewdienico@gmail.com
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4. Population: Collection of all the sampling units in a given
region at a particular point of time or a particular period is
called the population. Population can be finite or infinite.
Sample: a finite part of a population or a subset of a set
of sampling units selected by some process/sampling
Meth.
Sampling is a device which makes one able to draw
inferences about the whole population simply by
observing or measuring a few of the sampling units
5. o Sampling: It is the process of selecting the sample for
estimating the population characteristics.
o In other words, it is the process of obtaining information
about an entire population by examining only a part of it.
Sampling unit: An element or a group of elements on
which the observations can be taken is called a sampling
unit.
Sampling Unit: Elementary units or group of such units
which besides being clearly defined, identifiable and
observable, are convenient for purpose of sampling are
called sampling units.
Cont.….Sampling Terminology
6. Statistic: Characteristics of the sample. For example,
sample Mean, proportion, etc.
Parameter: Characteristics of the population. For
example, population Mean, proportion, etc.
Sampling frame: The list of all the units of the
population to be surveyed constitutes the sampling
frame.
Cont.….Sampling Terminology
Estimator: An estimator is a rule or method of estimating a
population parameter
7. Sample surveys collect information on a fraction of
the total population.
Sample Survey: An investigation in which elaborate
information is collected on a sample basis is known
as sample survey.
Complete enumeration/Census collects information on
the whole population
The complete count of the population is called a
census.
Cont.….Sampling Terminology
Estimate: A particular value of an estimator
obtained from a set of values of a random sample
is known as estimate
9. o Bias in sampling: Bias in sampling is a systematic
error in sampling procedures that lead to a
distortion in the result of the study
Representative sample: When all the salient
features of the population are present in the
sample, then it is called a representative sample.
Cont.….Sampling Terminology
13. Target Population: A target population is the entire group
about which information is desired and conclusion is made.
Sampled Population: The population, which we actually
sample, is the sampled population. It is also called survey
population.
Cont.….Sampling Terminology
o Sampling With and Without Replacement (SWOR or SWR):
o Sampling without replacement („SWOR' - no element can be
selected more than once in the same sample)
o Sampling with replacement („SWR' - an element may appea
multiple times in the one sample).
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15.
16.
17. The basic purpose of sampling is to provide an
estimate of the population parameter and to test the
hypothesis. Advantages of sampling are ;
Save time and money.
Enable collection of comprehensive data.
Enable more accurate measurement as it conducted
by trained and experienced investigators.
Sampling remains the only way when population
contains infinitely many members.
It provides a valid estimation of sampling error.
18. The sampling process comprises
several stages-
1. Define the population.
2. Specifying the sampling frame.
3. Specifying the sampling unit.
4. Selection of the sampling method.
5. Determination of sample size.
6. Specifying the sampling plan.
7. Selecting the sample.
25. The word random describes the procedure used to
select elements (participants, cars, test items) from a
population.
With simple random sampling, each individual in the
population has an equal chance of being selected for
the sample.
The four steps of simple random sampling are
Step 1. Defining the Population
Step 2. Constructing a List
Step 3. Drawing the Sample
Step 4. Contacting Members of the Sample
26. Applicable when population is small, homogeneous & readily
available
This sampling method is as easy as assigning numbers to the
individuals (sample) and then randomly choosing from those
numbers through an automated process.
27. •The guiding principle behind this technique is that
each element must have an equal and non-zero
chance of being selected.
•This can be achieved by applying a table of
random numbers or lottery system or a
computer generated random numbers to a
numbered sampling frame.
•Another approach involves drawing numbers from a
container.
28. The product of this technique is a sample
determined entirely by chance.
29.
30. The systematic random sampling technique begins
with selecting one element at random in the sampling
frame as the starting point;
however, from this point onward, the rest of the
sample is selected systematically by applying a
predetermined interval.
For example, in this sampling technique, after the
initial element is selected at random, every “kth”
element will be selected (kth refers to the size of the
interval - the ratio of the population to sample size)
and becomes eligible for inclusion in the study.
31.
32. The “kth ” element is selected through the end of
the sampling frame and then from the beginning
until a complete cycle is made back to the starting
point (that is, the place where the initial random
selection was made).
If there is a cyclic repetition in the sampling
frame, systematic sampling is not recommended.
33.
34. Stratified Random Sampling is a form of probability
sampling in which individuals are randomly selected from
specified subgroups (strata) of the population.
This method can be used to increase the representativeness
of the sample and/or to allow comparisons to be made
among individuals in the different strata.
35. To stratify means to classify or to separate people
into groups according to some characteristics, such as
position, rank, income, education, sex, or ethnic
background.
These separate groupings are referred to as subsets
or subgroups (strata).
A random sample is selected from each stratum based
upon the percentage that each subgroup represents in
the population.
36.
37.
38. Stratified random sampling begins with the
identification of some variable, which may be
related indirectly to the research question and
could act as a confounder (such as geography, age,
income, ethnicity, or gender).
Once the sampling frame is arranged by strata, the
sample is selected from each stratum using simple
random sampling or systematic sampling techniques.
Stratified random samples are generally more accurate in
representing the population than are simple random
samples.
39.
40. It may be difficult or impossible to take a simple random
sample of the units of the study population at random,
because a complete sampling frame does not exist.
Logistical difficulties may also discourage random
sampling techniques (e.g., interviewing people who are
scattered over a large area may be too time-
consuming).
However, when a list of groupings of study units is
available (e.g., villages or schools) or can be easily
compiled, a number of these groupings can be randomly
selected.
51. Convenience sampling is quick and inexpensive because it
involves selecting individuals who are readily available at
the time of the study (such as introductory psychology
students)
Sometimes known as grab or opportunity sampling or
accidental or haphazard sampling.
The researcher using such a sample cannot scientifically
make generalizations about the total population from this
sample because it would not be representative enough.
53. Quota sampling involves the selection of a certain
percentage of individuals from specified subgroups of
the population when the population is large and lists
of members are not available.
Many polling organizations use this technique.
