This document provides information about selecting samples for research. It discusses key concepts like population, sample, sampling unit and frame. It describes different types of sampling designs including random, non-random, and mixed methods. Random sampling designs discussed include simple random sampling, stratified random sampling, and cluster sampling. Non-random designs include convenience, quota, judgmental, and snowball sampling. Systematic sampling is classified as a mixed method. Factors that influence sample size calculations are level of confidence, accuracy, and population variation. Sample size should allow for precise estimates while avoiding bias in selection.

1. StudentDevelopment Institute
Faculty of Arts, Humanities and Languages
Selecting the Sample
Submitted to : Prean Sopheak
Students Kum visal
Khem socheat
Keom chanroza
Li meymey
Loun Voleak
Lem seha
Ko rina
Batch II, Group II , Year III, Semester II
Academic year 2016-2017

2. CONTENTs
I. The concept of sampling
II. Sampling terminology
III. Principle of sampling
IV. Factors affecting the interference drawn from a
sample
V. Aim in selecting a sampling
VI. Type of sampling
Random /probability designs
Non –random/non-probability sampling designs
Mixed sampling designs
The calculation of sampling size

3. I. The Concept of Sampling
Nature and quality of the frame
Availability of auxiliary information about
units on the frame
Accuracy requirements, and the need to
measure accuracy
Whether detailed analysis of the sample is
expected
• Cost/operational concerns

4. The concept of sampling in qualitative
Research
What is qualitative research?
Qualitative research is a type of scientific research.
In general terms, scientific research consists of an
investigation that:
• seeks answers to a question
• systematically uses a predefined set of procedures
to answer the question
• collects evidence
• produces findings that were not determined in
advance
• produces findings that are applicable beyond the
immediate boundaries of the study

5. Con-
 Qualitative research shares these
characteristics. Additionally, it seeks to
understand a given research problem or topic
from the perspectives of the local population
it involves. It is especially effective in
obtaining culturally specific information
about the values, opinions, behaviors, and
social contexts of particular populations

6. II. Sampling terminology
Sampling terminology is let us, again, consider
the examples used above. Our main aims are to find our
the average age of the class, the average income of the
families living in the city, and the likely election
outcome for a particular state or country.
In this process there are a number of
aspects:
 population
 Sample
 Sample size
 Sampling Design

7. Con-
 Sampling unit
 Sampling frame
 Sample statistics
 Population mean
 Saturation point

8. III. Principle of sampling
• Principle one: In a majority of cases of sampling
there will be a difference between the same statistic
and the true population mean, which is attributable to
the selection of the units in the sample.
• Principle two: The greater the sample size, the
more accurate will be the estimate of the true
population mean.
• Principle three: The greater the difference in the
variable under study in a population for a given size,
the greater will the difference between the sample
statistics and the true population mean.

9. IV. Factor affecting the inference drawn
from the sample
The two factors have influence:
Sample size used in a study is determined
based on the expense of data collection, and
the need to have sufficient statistical power.
Extent to change in sampling population
• The greater extent of is the process of taking a
subset of subjects that is representative of the
entire population.

10. V. Aim to selecting a sample
• Achieving the maximum precision in estimates
within given sample size
• Avoid bias in the selection of your sample
• Bias can occur if:
• Sampling in done by a non-random method
• The sampling frame
• A selection of a sampling population is
impossible to find or refuse to operate.

11. VI. Type of Sampling
A sample design is made up of two elements:
 Sampling method refers to the rules and
procedures by which some elements of the
population are included in the sample.
 Estimator is a estimated process for
calculating sample statistics. Different
sampling methods may use different
estimators.

12. Random/probability sampling designs

13. Methods of Drawing a Random
Sampling
 The fishbowl draw: the numbers identity and stand
for specific elements in the populations and presumably
the entire population of elements has been numbered and
is presented in the bowl.
Computer program:
 A table of random numbers: A random number
table is a list of numbers, composed of the digits 0,
1, 2, 3, 4, 5, 6, 7, 8, and 9. Numbers in the list are
arranged so that each digit has no predictable
relationship to the digits that preceded it or to the
digits that followed it. In short, the digits are
arranged randomly

14. Drawing a Random Sample
 Consisting of 2 methods for selecting a random
sample:
1. Sampling without replacement: Sampling is called
without replacement when a unit is selected at
random from the population and it is not returned to
the main lot.
2. Sampling with replacement : Sampling without
replacement is used to find probability with
replacement. In other words, you want to find the
probability of some event where there’s a number of
balls, cards or other objects, and you replace the item
each time you choose one.

15. The specific random /probability sampling
design
Consisting of 3 types of random sampling
designs:
1. Sample random sampling (SRS)
The most commonly used method of selecting a
probability sample.
2. Stratified random sampling
Depending on the extend of variability or
heterogeneity of study population.
3. Cluster sampling
The ability of classifying the sampling population in
to groups.

16. Non-random/non-probability
 Non-probability sampling is a sampling technique
where the samples are gathered in a process that does not
give all the individuals in the population equal chances of
being selected. Non-random/non- probability sampling
designs are used when the number of element in a
population is either unknown or it isn’t specific. Those
types of sampling can be used when demonstrating that a
particular trait exists in the population.
 There are four types of Non-random/non-probability
sampling designs
 Convenience Sampling or Accidental Sampling
 Quota Sampling
 Judgmental Sampling or Purposive Sampling
• Snowball sampling

17. Con-
 Convenience sampling is probably the most common of all
sampling techniques. With convenience sampling, the samples
are selected because they are accessible to the researcher.
Subjects are chosen simply because they are easy to recruit.
This technique is considered easiest, cheapest and least time
consuming

18. Con-
• Quota sampling is a non-probability sampling
technique wherein the researcher ensures equal or
proportionate representation of subjects depending on
which trait is considered as basis of the quota.

19. Con-
• Judgmental sampling is more commonly known as purposive sampling.
In this type of sampling, subjects are chosen to be part of the sample with a
specific purpose in mind. With judgmental sampling, the researcher
believes that some subjects are fit for the research compared to other
individuals. This is the reason why they are purposively chosen as subjects

20. Con-
• Snowball sampling is usually done when there is a
very small population size. In this type of sampling,
the researcher asks the initial subject to identify
another potential subject who also meets the criteria
of the research. The downside of using a snowball
sample is that it is hardly representative of the
population.

21. Mixed sampling designs:
Systematic sampling designs have been classified under
the “Mixed” sampling category because it has the characteristic of
both random and non-random sampling designs.
The procedure for selecting a systematic sample:
+ Step 1: Prepare a list of all the elements in the study population
(N).
+ Step 2: Decide on the sample size (n).
+ Step 3: Determine the width of the interval (k) = total
population /Sample size
+ Step 4: Using the SRS, select an element from the first interval
(nth order).
+ Step 5: Select the same order element from each subsequent
interval

22. The calculation of simple size:
 Calculation of exact sample size is an important
part of research design. It is very important to
understand that different study design need
different method of sample size calculation and
one formula cannot be used in all designs.
 The size of the sample is important for testing a
hypothesis or establishing an association, but for
other studies the general rule is the large the
sample size, the more accurate will be your
estimates.

23. Con-
 In determining the size of your simple for
quantitative studies and in particular for cause
and effect studies, you need to consider the
following
1. At what level of confidence do you want to
test your results, finding or hypotheses?
2. With what degree of accuracy do you wish 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?

24. Thanks