by Dr. Sultan Muhammad Khan
Keywords ; What is a sample? Types of sampling, Probability and Non-probability sampling, Simple Random Sample, Systematic Random Sample, Stratified sample, cluster sample, Convenience Sampling, Quota Sampling, purposive sample, snowball sample,
Sampling
A sample is a finite part of a
statistical population whose properties are studied to
gain information about the whole(Webster, 1985).
Keywords ; What is a sample? Types of sampling, Probability and Non-probability sampling, Simple Random Sample, Systematic Random Sample, Stratified sample, cluster sample, Convenience Sampling, Quota Sampling, purposive sample, snowball sample,
Sampling
What is a
sample?
When dealing with people, it can
be defined as a set of respondents (people) selected from a larger population
for the purpose of a survey.
A population is a group of individuals’ persons, objects, or
items from which samples are taken for measurement for example a population of
presidents or professors, books etc
Sampling
Rationale
•
Explain
the reasoning behind proposed sample locations, number of samples, types of
analyses,
•
Sampling
is the process of selecting units (e.g., people, organizations) from a population
of interest so that by studying the sample we may fairly generalize our results
back to the population from which they were chosen.
•
When
an organizations require data they either use data collected by somebody else
(secondary data), or collect it themselves (primary data).This is usually done
by SAMPLING that is collecting data from a representative SAMPLE of the
population they are interested in.
Types
of sampling
Probability
and Non-probability sampling
Probability
The term probability samplings used when
the selection of the sample is purely based on
chance. The human mind has no control on
the selection or non- selection of the units for
the sample. Every unit of the population has known nonzero probability of
being selected for the sample.
•
The
probability of selection may be equal or unequal but it should be
non-zero and should be known. The probability samplings also called
the random sampling (not simple random sampling).
Probability
Sampling Methods
Simple Random Sample
Every subset of a specified size
n from the population has an equal chance of being selected
•
Simple
Random sample is obtained by choosing elementary units in such a way that each
unit in the population has an equal chance of being selected from a very large
Population. Random sample is free from sampling bias. However, using a random
number table to choose the elementary units can be cumbersome. If the sample is
to be collected by a person untrained in statistics, then instructions may be
misinterpreted and selections may be made improperly. Instead of using a list
of random numbers, data collection can be simplified by selecting say every
10th or 100th unit after the first unit has been chosen randomly as discussed
below. Such a procedure is called systematic random sampling.
2. Systematic Random Sample
•
Systematic Random sample is obtained by
choosing elementary units in such a way that each unit in the population has an
equal chance of being selected.Every kth member ( for example: every 10th
person) is selected from a list of all population members.
3) A stratified sample:-The population is divided into
two or more groups called strata, according to some criterion, such as
geographic location, grade level, age, or income, and subsamples are randomly
selected from each strata.
A stratified sample is obtained by
independently selecting a separate simple random sample from each population
stratum.A population can be divided into different groups may be based on some
characteristic or variable like income of education. Like any body with ten
years of education will be in group A, between 10 and 20 group B and between 20
and 30 group C. These groups are
referred to as strata. You can then randomly select from each stratum a given
number of units which may be based on proportion like if group A has 100 persons
while group B has 50, and C has 30 you may decide you will take 10% of each. So
you end up with 10 from group A, 5 from group B and 3 from group C.
4) A cluster sample:-The population is divided into
subgroups (clusters) like families. A
simple random sample is taken of the subgroups and then all members of the
cluster selected are surveyed.
A cluster sample is obtained by
selecting clusters from the population on the basis of simple random
sampling.The sample comprises a census of each random cluster selected. For
example, a cluster may be something like a village or a school, a state.So you
decide all the elementary schools in Peshawar are clusters. You want 20 schools
selected. You can use simple or systematic random sampling to select the
schools, and then every school selected becomes a cluster.
Non
Probability Sampling Methods
In non-probability sampling,
the sample is not based on chance. It is rather determined by some
person. We cannot assign an element of population the probability of its being
selected in the sample.
1)
Convenience Sampling:-
Where the researcher questions anyone who is available. This method is quick
and cheap. However we do not know how representative the sample is and how
reliable the result.A convenience sample is a matter of taking what you can
get. It is an accidental sample. Although selection may be unguided, it
probably is not random, using the correct definition of everyone in the
population having an equal chance of being selected. Volunteers would
constitute a convenience sample.
•
Convenience
Sample
•
Selection
of whichever individuals are easiest to reach
•
It
is done at the “convenience” of the researcher
2)
Quota Sampling:-
Using this method the sample audience is made up of potential purchasers of
your product. For example if you feel that your typical customers will be male
between 18-23, female between 26-30, then some of the respondents you interview
should be made up of this group, i.e. a quota is given.
•
3) The
judgment sample:-
A judgment sample is obtained according to the discretion of someone who is
familiar with the relevant characteristics of the population.
4. purposive
sample
•
A purposive sample is a non-representative subset of
some larger population, and is constructed to serve a very specific need or
purpose. A researcher may have a specific group in mind, such as high level
business executives. It may not be possible to specify the population - they
would not all be known, and access will be difficult. The researcher will
attempt to zero in on the target group, interviewing whomever is available.
5) snowball
sample
•
A
subset of a purposive sample is a snowball
sample -- so named because one picks up the sample along the way, A
snowball sample is achieved by asking a participant to suggest someone else who
might be willing or appropriate for the study. Snowball samples are
particularly useful in hard-to-track populations, such as truants, drug users,
etc.
In
statistics, sampling error is incurred when statistical characteristics of a
population are estimated from a subset, or sample, of the population. Since the
sample does not include all members of the population, statistics on the
sample, such as means, generally differ from statistics on the entire
population. For example, if
one measures the height of a thousand individuals from a country of one
million, the average height of the thousand is typically not the same as the
average height of all one million people in the country.
•
Since
sampling is typically done to determine the characteristics of a whole
population, the difference between the sample and population values is
considered a sampling error
•
Exact
measurement of sampling error is generally infeasible since the true population
values are unknown; however, sampling error can often be estimated by
probabilistic modeling of the sample
•
Errors
in Sampling
•
Non-Observation Errors
–
Sampling
error: naturally occurs
–
Coverage
error: people sampled do not match the population of interest
–
Underrepresentation
–
Non-response: won’t or can’t participate
•
Errors
of Observation
•
Interview
error- interaction between interviewer and person being surveyed
•
Respondent
error: respondents have difficult time answering the question
•
Measurement
error: inaccurate responses when person doesn’t understand question or poorly
worded question
•
Errors
in data collection
