This document defines sampling and key sampling terminology. It discusses the advantages and disadvantages of sampling, including that sampling allows estimates of a larger population while saving resources, but may introduce errors. It outlines principles of sampling such as the relationship between sample size and accuracy, and how variation in a population affects differences between samples and the true population. Random sampling methods like simple random sampling and stratified random sampling are described. The goals of precision and avoiding bias in sampling are also covered.
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.
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.
Probability Sampling and Types by Selbin Babuselbinbabu1
The presentation will cover probability sampling and all the types of probability sampling like Random sampling , systematic random sampling, strtified random sampling, cluster random sampling and multi stage sampling.
Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampli...Alam Nuzhathalam
An overview of Sampling Techniques or Sampling Methods or Sampling Types (Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Random Sampling, Multi Stage Sampling and Non Probability Sampling: Convenience Sampling, Quota Sampling,Judgmental Sampling,Self Selection Sampling,Snow Ball Sampling) Sampling Errors and Non Sampling Errors..
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
Probability Sampling and Types by Selbin Babuselbinbabu1
The presentation will cover probability sampling and all the types of probability sampling like Random sampling , systematic random sampling, strtified random sampling, cluster random sampling and multi stage sampling.
Sampling Techniques and Sampling Methods (Sampling Types - Probability Sampli...Alam Nuzhathalam
An overview of Sampling Techniques or Sampling Methods or Sampling Types (Probability Sampling: Simple Random Sampling, Stratified Random Sampling, Cluster Sampling, Systematic Random Sampling, Multi Stage Sampling and Non Probability Sampling: Convenience Sampling, Quota Sampling,Judgmental Sampling,Self Selection Sampling,Snow Ball Sampling) Sampling Errors and Non Sampling Errors..
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
Research vs research methodology - Research Methodology - Manu Melwin Joymanumelwin
Research methods may be understood as all those methods/ techniques that are used for conduction of research.
Research methods or techniques, thus, refer to the methods the researchers use in performing research operations.
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As mentioned earlier, the mid-term will have conceptual and quantitative multiple-choice questions. You need to read all 4 chapters and you need to be able to solve problems in all 4 chapters in order to do well in this test.
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Suggestion: treat this review set as you would an actual test. Sit down with your one page of notes and your calculator, and give it a try. That way you will know what areas you still need to study.
ADMN 210
Answers to Review for Midterm #1
1) Classify each of the following as nominal, ordinal, interval, or ratio data.
a. The time required to produce each tire on an assembly line – ratio since it is numeric with a valid 0 point meaning “lack of”
b. The number of quarts of milk a family drinks in a month - ratio since it is numeric with a valid 0 point meaning “lack of”
c. The ranking of four machines in your plant after they have been designated as excellent, good, satisfactory, and poor – ordinal since it is ranking data only
d. The telephone area code of clients in the United States – nominal since it is a label
e. The age of each of your employees - ratio since it is numeric with a valid 0 point meaning “lack of”
f. The dollar sales at the local pizza house each month - ratio since it is numeric with a valid 0 point meaning “lack of”
g. An employee’s identification number – nominal since it is a label
h. The response time of an emergency unit - ratio since it is numeric with a valid 0 point meaning “lack of”
2) True or False: The highest level of data measurement is the ratio-level measurement.
True (you can do the most powerful analysis with this kind of data)
3) True or False: Interval- and ratio-level data are also referred to as categorical data.
False (Interval and ratio level data are numeric and therefore quantitative, NOT qualitative….Nominal is qualitative)
4) A small portion or a subset of the population on which data is collected for conducting statistical analysis is called __________.
A sample! A population is the total group, a census IS the population, and a data set can be either a sample or a population.
5) One of the advantages for taking a sample instead of conducting a census is this:
a sample is more accurate than census
a sample is difficult to take
a sample cannot be trusted
a sample can save money when data collection process is destructive
6) Selection of the winning numbers is a lottery is an example of __________.
convenience sampling
random sampling
nonrandom sampling
regulatory sampling
7) A type of random sampling in which the population is divided into non-overlapping subpopulations is called __________.
stratified random sampling
cluster sampling
systematic random sampling
regulatory sampling
8) A ...
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Elementary Statistics Practice Test 1
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Chapter 3: Describing, Exploring, and Comparing Data.
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2. Definition
Sampling: is the process of selecting a few (a sample)
from a bigger group, the sampling population, to
become the basis for estimating or predicting the
prevalence of an unknown piece of information,
situation or outcome regarding the bigger group.
Sample: is a subgroup of population you are interested
in.
3. Adv. & Disad. Of Sampling Process
Advantages
Saves time
Saves financial and human resources
Disadvantages
Unable to find out the information about the
population’s characteristics of interest to you but you
only estimate or predict them
The possibility of an error in your estimation exists
4. Sampling Terminology
Term Definition
Population/stud The large general group of many cases from which a researcher draw a
y population sample and are usually denoted by the letter (N)
Sample A smaller set of cases a researcher selects from a larger group and
generalizes to the population
Sample size The number of selected cases from larger population from who you obtain
the required information and is usually denoted by the letter (n)
Sampling The method you use to select your sample
design/strategy
Sampling unit/ The name for a case or single unit to be selected
sampling
element
Sampling frame The list of units composing a population from which a sample is selected
Sample statistics Information obtained from your respondents
Population A characteristic of the entire population that is estimated from a sample
parameters/pop
ulation mean
5. Principles of Sampling
Average age of four people: A, B, C
Principle One: & D.
In a majority of cases of A is 18 yrs, B is 20, C is 23 & D is 25
sampling there will be a Average age is : 21.5 (18+20+23+25
difference between the
sample statistics and the true = 86 divided by 4)
population mean, which is By selecting a sample of two we
attributable to the selection can estimate their average age.
of the units in the sample And we can have six possible
combinations of two:
1. A & B
2. A & C
3. A & D
4. B & C
5. B & D
6. C & D
6. Difference between Sample average &
population Average (2 cases)
Sample Sample Population Difference bet 1
average mean &2
1. A & B
2. A & C 1 19.0 21.5 -2.5
3. A & D
4. B & C 2 20.5 21.5 -1.5
5. B & D
6. C & D
3 21.5 21.5 0.0
4 21.5 21.5 0.0
5 22.5 21.5 +1.0
6 24.0 21.5 +2.5
7. Average age of four people: A, B, C
Principle Two: & D.
The greater the A is 18 yrs, B is 20, C is 23 & D is 25
Average age is : 21.5 (18+20+23+25 =
sample size, the more 86 divided by 4)
accurate will be the By selecting a sample of three we
estimate of the true can estimate their average age.
And we can have four possible
population mean combinations of three:
1. A + B+C
2. A + B+D
3. A + C+D
4. B + C+D
8. Difference between Sample
& Population Average (3 cases)
Sample Sample average Population Difference bet 1 &
mean 2
1. A +
1 20.33 21.5 --1.17
B+C
2. A +
B+D
3. A + 2 21.00 21.5 -0.5
C+D
4. B +
C+D
3 22.00 21.5 +0.5
4 22.67 21.5 +1.17
9. Principle Three:
The greater the difference A is 18 yrs, B is 26, C is 32
in the variable under study & D is 40
in a population for a given Average age is: 29
sample size, the greater (18+26+32+40 = 116
will be the difference divided by 4)
between the sample
statistics and the true
population mean
10. Difference between Sample Statistics &
Population Mean (2 cases)
Sample Sample Population Difference bet
1. A & B average mean 1&2
2. A & C
3. A & D 1 22 29.00 -7.00
4. B & C
5. B & D
2 25 29.00 -4.00
6. C & D
3 29 29.00 0.00
4 29 29.00 0.00
5 33 29.00 +4.00
6 36 29.00 +7.00
11. Difference between Sample and
Population Average (3 cases)
Sample Sample Population Difference bet 1 &
average mean 2
1 25.33 29.00 --3.67
2 28.00 29.00 -1.00
3 30.00 29.00 +1.00
4 32.66 29.00 +3.66
1. A + B+C
2. A + B+D
3. A + C+D
4. B + C+D
12. Factors affecting the inferences of sample
The size of the sample
The extent of variation in the sampling population
13. Aims in selecting a sample
To achieve maximum precision in your estimates
within a given sample size
To avoid bias in the selection of your sample
Bias in the selection of a sample can occur if:
Sampling is done by a non-random method
The sampling frame does not cover the sampling
population accurately and completely
A section of a sampling population is impossible
to find or refuses to cooperate
14.
15. Random/probability sampling Designs
Each element in the population has an equal and independent chance
of selection in the sample.
Equal : means the probability of selection of each element in the
population is the same.
That is, the choice of an element in the sample is not influenced by
other considerations such as personal preference.
Independent : means that the choice of one element is not dependent
upon the choice of another element in the sampling
That is, the selection or rejection of one element does not affect the
inclusion or exclusion of another.
A sample can only be considered a random/probability sample and
representative of the population under study if these conditions are
met. If not, bias can be introduced into the study.
16. Advantages of Random/Probability Samples
As they represent the total sampling population, the
inferences drawn from such samples can be
generalized to the total sampling population.
Some statistical tests based upon the theory of
probability can be applied only to data collected from
random samples. Some of these tests are important
for establishing conclusive correlations.
18. Procedure for using a table of random
numbers
Identify the total number of elements in the study population.
The total number of elements in a study population may run up to
four or more digits.
Number each element starting from 1.
If the table for random numbers is on more than one page, choose the
starting page by a random procedure.
Again select a column or row that will be your starting point with a
random procedure and proceed from there in a predetermined
direction
Corresponding to the number of digits to which the total population
runs, select the same number, randomly, of columns or rows of digits
from the table
Decided on your sample size
Select the required number of elements for your sample from the
table
If you happen to select the same number twice, discard it and go to
19.
20.
21. Difference Systems of Drawing a Random
Sample
Sampling without replacement
Sampling with replacement
22. Type of Specific Random/Probability
Sampling Designs
Simple random sampling (SRS)
Stratified random sampling
Cluster sampling
23. Procedure for Selecting Simple Random
Sampling
1. Identify by a number all elements or sampling units
in the population
2. Decide on the sample size (n)
3. Select (n) using either the fishbowl draw, the table
of random numbers or a computer program
24. Stratified Random Sampling
In this sampling the researcher attempts to stratify the
population in such a way that population within a stratum
is homogeneous with respect to the characteristic on the
basis of which it is being stratified.
It is important that the characteristics chosen as the basis
of stratification are clearly identifiable in the study
population
For example, it is much easier to stratify a population on
the basis of gender than on the basis of age, income or
attitude.
Once the sampling population has been separated into
non-overlapping groups you select the required number of
elements from each stratum, using the simple random
sampling technique.
25. Types of stratified Random Sampling
Proportionate stratified sampling : the number of
elements from each stratum in relation to its proportion
in the total population is selected.
Disproportionate stratified sampling: consideration is not
given to the size of the stratum.
26. Cluster Sampling
Based on the ability of the researcher to divide
the sampling population into groups, called
cluster, and then to select elements within each
cluster, using the SRS technique.
Depending on the level of clustering, sometimes
sampling may be done at different levels. These
levels constitute the different stages (single,
double or multi-stage cluster sampling).
27. Non-random/non-probability Sampling
Designs
These are used when the number of elements in a
population is either unknown or cannot be
individually identified.
In such situations the selection of elements is
dependent upon other considerations.
29. Quota Sampling
The researcher is guided by some visible
characteristic, such as gender or race, of the study
population
The sample is selected from a location convenient
to the researcher, and whenever a person with
this visible relevant characteristic is seen that
person is asked to participate in the study.
The process continues until the researcher has
been able to contact the required number of
respondents (quota).
30. Quota Sampling
Advantages:
It is the least expensive way of selecting a sample
You do not need any information, such as a sampling frame,
the total number of elements, their location, or other
information about the sampling population
It guarantees the inclusion of the type of people you need
Disadvantages:
The resulting sample is not a probability one, the findings
cannot be generalized to the total sampling population
The most accessible individuals might have characteristics
that are unique to them and hence might not be truly
representative of the total sampling population
31. Accidental sampling
Whereas quota sampling attempts to include
people possessing an obvious/visible
characteristic, accidental sampling makes no such
attempt.
The method of sampling is common among
market research and newspaper reporters.
It has same advantages and disadvantages as
quota sampling.
As you are guided by any obvious characteristics,
some people contact may not have the required
information
32. Judgmental or purpose sampling
Is the judgment of the researcher as to who can
provide the best information to achieve the
objectives of the study.
The researcher only goes to those people who in
his/her opinion are likely to have the required
information and be willing to share it.
This type of sampling is extremely useful when
you want to construct a historical reality, describe
phenomenon or develop something about which
only a little is known.
33. Snowball sampling
Is the process of selecting a sample using networks.
To start with, a few individuals in a group or
organization are selected and the required
information is collected from them.
They are then asked to identify other people in the
group or organization, and the people selected by
them become a part of the sample.
This process continued until the required number
or a saturation point bas been researched.
This method is useful for studying communication
patterns, decision making or diffusion of knowledge
within a group.
34.
35. Mixed Sampling Design :
Systematic Sampling Design
Systematic Sampling has the characteristics of both
random and non-random sampling designs
In systematic sampling the sampling frame is first
divided into a number of segments called intervals.
If the first interval is the fifth element, the fifth
element of each subsequent interval will be chosen
36. Procedure for Selecting a Systematic
Sample
Prepare a list of all the elements in the study
population (N)
Decide on the sample size (n)
Determine the width of the interval (k)
= total population
sample size
Using the SRS, select an element from the first
interval (nth order)
Select the same order element from each
subsequent interval
37.
38. Calculation of sample Size
Depends on what you want to do with the findings
and what type of relationships you want to establish.
In qualitative research the question of sample size is
less important as the main focus is to explore or
describe a situation, issue, process or phenomenon.
39. Calculation of sample Size
In qantative research and particularly for cuase-
and-effect studies, you need to consider the
following:
1. At what level of confidence do you want to test your
results, findings or hypotheses?
2. With what degree of accuracy do you wich to
estimate the population parameters?
3. What is the estimated level of variation (standard
deviation, with respect to the main variable you are
studying, in the study population?
40. Calculation of Sample Size
The size of the sample is important for testing a
hypothesis or establishing an association, but for
other studies the general rule is the larger the sample
size, the more accurate will be your estimates.
In practice, your budget determines the size of your
sample.
Your skills in selecting a sample, within the
constraints of your budget, lie in the way you select
your elements so that they effectively and adequately
represent your sampling population.