Download Sampling METHODS

INTRODUCTION
 In all spheres of life the need for statistical investigation and
data analysis is rising day by day. There are two methods of
collection of data: (i) CENSUS METHOD and (ii)
SAMPLE METHOD . Under census method information
relating to entire field of investigation or units of population is
collected , where as under sample method, rather than
collecting information about all the units of population,
information relating to only selected units is collected.
Sampling Concepts
 Population/Target population: This is any complete, or the
theoretically specified aggregation of study elements. It is usually
the ideal population or universe to which research results are to
be generalized. For example, all adult population of the U.S.
 Sample: In statistics, a sample is a subset of a population.
Typically, the population is very large, making a census or a
complete enumeration of all the values in the population
impractical or impossible. The sample represents a subset of
manageable size. Samples are collected and statistics are
calculated from the samples so that one can
make inferences or extrapolations from the sample to the
population. This process of collecting information from a sample
is referred to as sampling.
What exactly IS a “sample”?
CENSUS METHOD
 A census is the procedure of systematically
acquiring and recording information about the
members of a given population. It is a regularly
occurring and official count of a particular
population. The term is used mostly in connection
with national population and housing censuses;
other common censuses include agriculture,
business, and traffic censuses. In the latter cases the
elements of the 'population' are farms, businesses,
and so forth, rather than people. This method is also
known as Complete Enumeration Method
SAMPLING METHOD
 In statistics , sampling is concerned with the selection of a
subset of individuals from within a statistical population to
estimate characteristics of the whole population. Two
advantages of sampling are that the cost is lower and data
collection is faster.
 Each observation measures one or more properties (such as
weight, location, color) of observable bodies distinguished as
independent objects or individuals. In survey sampling,
weights can be applied to the data to adjust for
the sample design, particularly stratified sampling (blocking).
Results from probability theory and statistical theory are
employed to guide practice. In business and medical research,
sampling is widely used for gathering information about a
population.
Types of samples
Simple Random Sample

Get a list or “sampling frame”
a.

This is the hard part! It must not systematically exclude
anyone.
Generate random numbers
Select one person per random
numbers

Systematic Random Sample

Select a random number, which will be known as k

Get a list of people, or observe a flow of people (e.g.,
pedestrians on a corner)

Select every kth person


Careful that there is no systematic rhythm to the flow or list of
people.
If every 4th person on the list is, say, “rich” or “senior” or some
other consistent pattern, avoid this method
Stratified Random Sample
1.
Separate your population into
groups or “strata”
2.
Do either a simple random
sample or systematic random
sample from there
a.
Note you must know easily
what the “strata” are before
attempting this
b.
If your sampling frame is
sorted by, say, school
district, then you’re able to
use this method
Multi-stage Cluster Sample

Get a list of “clusters,” e.g., branches of a company

Randomly sample clusters from that list

Have a list of, say, 10 branches

Randomly sample people within those branches

This method is complex and expensive
The Convenience Sample
Find some people that are easy to find
The Snowball Sample

Find a few people that are relevant to your topic.

Ask them to refer you to more of them.
The Quota Sample

Determine what the population looks like in terms of
specific qualities.

Create “quotas” based on those qualities.

Select people for each quota.
Accidental sampling
 A type of nonprobability sampling which involves the
sample being drawn from that part of the population which
is close to hand
 The researcher using such a sample cannot scientifically
make generalizations about the total population
 In social science research, snowball sampling is a similar
technique
Panel sampling
 The method of first selecting a group of participants
through a random sampling
 Period of data collection is called a "wave“
 Panel sampling can also be used to inform researchers
about within-person health changes due to age
Sampling Errors

These are the errors which occur due to the nature
of sampling. The sample selected from the population is
one of all possible samples. Any value calculated from the
sample is based on the sample data and is called sample
statistic. The sample statistic may or may not be close to
the population parameter. If the statistic is and the true
value of the population parameter is, then the
difference is called sampling error. It is important to note
that a statistic is a random variable and it may take any
value. A particular example of sampling error is the
difference between the sample mean and the population
mean. Thus sampling error is also a random term.
Reducing the Sampling Errors:
 By increasing the size of the sample.
 By Stratification.
Non sampling errors
A statistical error caused by human error to which a specific statistical analysis is
exposed. These errors can include, but are not limited to, data entry errors, biased
questions in a questionnaire, biased processing/decision making, inappropriate
analysis conclusions and false information provided by respondents.
Some are following:
1.
2.
3.
4.
Faulty plaining
Faulty selection of sample units
Errors in compilation
Framing of wrong questionnaire