Sampling design, sampling errors, sample size determinationVishnupriya T H
This presentation contains census and sample survey, implications of a sample design, steps in sample design, criteria of selecting a sampling procedure
Sampling design, sampling errors, sample size determinationVishnupriya T H
This presentation contains census and sample survey, implications of a sample design, steps in sample design, criteria of selecting a sampling procedure
Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. There are two types of sampling analysis: Simple Random Sampling and Stratified Random Sampling. Sampling is useful in assigning values and predicting outcomes for an entire population, based on a smaller subset or sample of the population.
Explains the different methods of Sampling with diagram. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question.
This is an exclusive presentation on data collection for researchers in National Institutes Labor of Administration & Training (NILAT), Ministry of production, government of Pakistan
Probability Sampling Method- Concept - Types Sundar B N
This ppt contains Probability Sampling Method- Concept - Types which also covers Types of Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Reasons for Sampling
and advantages and disadvantages of each methods
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. There are two types of sampling analysis: Simple Random Sampling and Stratified Random Sampling. Sampling is useful in assigning values and predicting outcomes for an entire population, based on a smaller subset or sample of the population.
Explains the different methods of Sampling with diagram. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question.
This is an exclusive presentation on data collection for researchers in National Institutes Labor of Administration & Training (NILAT), Ministry of production, government of Pakistan
Probability Sampling Method- Concept - Types Sundar B N
This ppt contains Probability Sampling Method- Concept - Types which also covers Types of Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Reasons for Sampling
and advantages and disadvantages of each methods
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
What is Population ?
What is Sample ?
Sampling Techniques
What is Probability sampling ?
What is Non-probability sampling ?
Advantages & Disadvantages sampling
Difference b/w Probability &Non-Probability
Characteristics of sampling
sampling in research, a written report which consists of the following: definitions and terminologies, the sampling types and methods, the sampling process, the sampling storage, and sampling errors.
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We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
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Model Attribute Check Company Auto PropertyCeline George
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Palestine last event orientationfvgnh .pptxRaedMohamed3
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2. What is a sample?
Sample is a small group that you
observe and a population is the larger
group about which your generalization
is made.
3. Definitions
Gay (1976) defines a population as the
group to which a researcher would like
the results to be generalizable.
Ferguson ( 1976) defines a sample as
“any subaggregate drawn from the
population.
Kerlinger ( 1973) defines population as
“all members of any well-defined class
of people, events or objects.”
4. Sampling
Sampling – the process which involves
taking a part of the population, making
observations on this representative
groups, and then generalizing the
findings to the bigger population.
5. Steps in Sampling
1. Identification of the population
2. Determination if the required
sample size
3. Selection of the sample
6. Slovin Formula
Sample Size of the Population
Formula of Slovin
n =
N
1 + Ne
2
Where n = a sample size
N = population size
e = desired margin of error ( percent allowance for non-precision
because of the use of the sample instead of the population.
7. Comparison of Sample Size in
different books
Author Descriptive Correlational Expost facto
or causal
comparative
Experimental
C. A.
Sanchez
(1997)
10 percent of
the
population
30 subjects 15 subjects per
group
15 subject per group
(other consider 30 as
the minimum)
Fraenkel &
Wallen
(2003)
100 minimum At least 50 Minimum of 30
per group
Minimum of 30 per
group
15 per group ( if
critically controlled and
should be replicated)
Sevilla,
Ochave,
Punsalan,
Regala,
Uriarte
(1984)
The size of the sample depends on the resources (cost) of the
person conducting the survey and the degree of accuracy desired
or demanded by the survey
8. Sample size
According to Sevilla, Ochave, Punsalan, Regala,
Uriarte ( 1984) the size of the sample depends on
the following:
Cost
Determine the cost of the entire survey from the
number of manhours to data analysis
Time
The researcher may have a limited amount of
time to complete the survey and to perform the
analyses.
Accuracy
The size of the sample depends on the amount
of difference in the percentages and on how
accurate the analyst wishes to be.
9. Sample size
WHAT CONSTITUTES AN ADEQUATE OR
SUFFICIENT SIZE FOR A SAMPLE?
Fraenkel & Wallen (2003)
There is no clear-cut answer to this
question.
The best answer is that a sample should be
as large as the researcher can obtain with a
reasonable expenditure of time and energy.
10. Difficulties in Sampling
According to Parten (1950)
1. If the sampling plan is not correctly
designed and followed, the results may be
incorrect or misleading
2. If the characteristic to be observed occurs
only rarely in the population ( ex. People
over ninety years of age) special problems
arise in securing statistically reliable
information
11. Difficulties in Sampling
3. When detailed classification of the
sample data is called for precautions
will have to be made in the
breakdown of the data into smaller
number of cases.
4. When sampling requires expert
advice and the area concerned may
be one in which there is a shortage of
competent specialists.
12. Difficulties in Sampling
5. There are characteristic limitations for
each type of sampling
6. Complicated sampling plans may
prove as laborious as a complete
enumeration of the population.
13. Planning a sampling survey
The major problems in planning a sampling survey include:
Statement of the purposes of the
survey
Definition of the population or universe
Selection of the sampling unit and the
unit of tabulation
Location and selection of the source
list
14. Planning a sampling survey
Deciding on the type of sampling to be
used.
Determining the size of the sample of
the sample ratio
Testing of the sample in pilot of
exploratory surveys
Interpretation of the data in the light of
the reliability of the sample
15. Sampling Techniques (Sevilla)
Non probability Sampling
Accidental Sample
Ex. Going to a street corner and interview every
person who goes by
Advantage: Easiest to select Disadvantage: not
representative of the population
When do we use accidental sampling?
ALMOST NEVER
If there are no alternative, it may be better to have poor data than no data at all.
16. Sampling Techniques
Non probability Sampling
Quota Sample
Each interviewer is given a number of person of a specific
type. Ex. Farmers, merchants, soldiers, women or
elders
Advantage: It does not require a
population list or dwelling unit maps
needed for probability sampling
Disadvantage: no assurance of
representativeness; the person
to be interviewed is decided
upon by the interviewer in the
fieldWhen do we use accidental sampling?
When the analyst knows exactly what type
of person he wants to interview and the type
is fairly easily recognized.
17. Sampling Techniques
(Sanchez)
Random Sampling
A method of selecting a sample size from
a universe such that each member of the
population has an equal chance of being
included in the sample and all possible
combinations of size has an equal
chance of being selected as a sample.
(Weirsma, 1975)
18. Random Sampling
1. Prerequisites for Random Sampling:
2. Define your population
3. List all members of your population
4. Select your sample by employing an
adequate procedure where every
member has an equal chance as
samples of investigation
19. Random Sampling
Basic Principles in Random Sampling
EQUI-PROBABILITY
Every member of the population has an equal
chance of being included in the sample
INDEPENDENCE
Refers to the fact that when one member is
selected for the sampling this should not affect
the chances of other members getting chosen.
20. Random Sampling Techniques
Table of random numbers
Lottery sampling
Systematic sampling
Stratified sampling
Cluster sampling
22. References
Methods and Techniques of Research
by C.A. Sanchez PH.D
An Introduction to Research Methods
by C.G. Sevilla, J. A. Ochave,
T. G. Punsalan, B. P. Regala
and G. G. Uriarte
How to Design and Evaluate Research in
Education by J. Fraenkel and N. Wallen