A presentation on probability sampling. students from university of education, Winneba

1. Probability Sampling
GROUP THREE MEMBERS ( unit EID)

2. By Aniruddha Deshmukh - M. Sc. Statistics, MCM 2
NAMES INDEX NUMBERS
JAMES OWUSU 5170150057
RICHARD MENSAH 5170150012
ENOCK ASIAKO 5170150076
EBENEZER AKONNOR 5170150126
BOATENG ISAAC 5170150022
AGBAGBA AMOS 4180150025
BEATRICE YAA TAKYIWA 5170150028
DANSO KYEI EMMANUEL 5170150046
ABOAGY ADGEI LORD 5170150137
ANKU SAMUEL DELALI 4180150003

3. BY GROUP THREE MEMBERS (Unit EID)
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SAMPLING
Sampling is the method of selecting a representative subset of the population called sample.
Sampling makes research more accurate and economical.
Sampling in educational research is generally conducted in order to permit the detailed study
of part, rather than the whole, of a population.
Sampling has received varied definitions by major authors on social research methods. It has
been defined as “the process of selecting a smaller group of participants to tell us essentially
what a larger population might tell us if we asked every member of the larger population the
same questions
The information derived from the resulting sample is customarily employed to develop useful
generalizations about the population.

4. Probability Sampling
• Probability sampling the sampling method in which all the
members of the population has a pre-specified and an equal
chance to be a part of the sample.
• This technique is based on the randomization principle,
wherein the procedure is so designed, which guarantees that
each and every individual of the population has an equal
selection opportunity.
• This helps to reduce the possibility of bias.
• The methods of probability sampling:
– Simple Random Sampling
– Stratified Sampling
– Cluster Sampling
– Systematic Sampling
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Simple Random Sampling
Simple random sampling is a type of probability sampling in which the
units composing a population are assigned numbers. Random selection
is the process of choosing the components of a sample that
ensures each member of a population stands the same chance of
selection. Is a completely random method of selecting a sample in
which each element and each combination of elements in the population
have an equal probability. This method is a fair way to select a sample.
As each member of the population has an equal probability of being
selected, simple random sampling is the best-known probability sample.
Even though it may not be considered an ideal method of choosing the
sample, still result obtained through this method has high external
validity or generalizability as compared to some other method of sample
selection.

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Systematic Random Sampling
Systematic sampling is an improvement over the simple random sampling. This
method requires the complete information about the population. In this sampling
method, we select one unit from the sampling frame and then calculations to draw
following units are done on the basis of the interval size. Systematic sampling being
a very easy method to do, you actually choose every “nth” participant from a
complete list. Even though each element has an equal probability of selection, but
unlike as in simple random sampling, a combination of elements has different
probabilities in systematic random sampling.

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Stratified Sampling
Stratified sampling involves a method where a larger population can be divided into smaller
groups that usually don’t overlap but represent the entire population together. Stratified Random
Sampling is an improvement over systematic sampling. To stratify means to classify or to
separate people into groups according to some characteristics, such as position, rank, income,
education, sex, or ethnic background. These separate groupings are referred to as subsets or
subgroups. In this method, the population elements are divided into strata on the basis of some
characteristics. Stratified random sampling can be of two types (1) proportionate stratified
sampling and (2) disproportionate stratified random sampling. When the size of the sample is
proportionate to the size of the unit, it is called proportionate stratified sampling. When it is not
proportionate to the size of the unit, it is called disproportionate stratified sampling.
Disproportionate stratified sampling depends upon considerations involving personal judgments
and convenience.

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Cluster Sampling
Cluster Sampling is a way to randomly select participants when they are geographically
spread out. Cluster sampling usually analyzes a particular population in which the
sample consists of more than a few elements, for example, city, family, university,
school etc. The clusters are then selected by dividing the greater population into
various smaller sections. Cluster sampling is one of the efficient methods of random
sampling in which the population is first divided into clusters, and then a sample is selected
from the clusters randomly. In contrary to stratified sampling, there should be heterogeneity
within the clusters and homogeneity between the clusters. The more homogeneity among the
clusters, lesser will be the margin of error or vice-versa. The method is mostly feasible in case
of diverse population spread over different areas. Not possible in case of simple random
sampling, the participants with different demographics are selected randomly from these areas.
This is an economical method and saves the time of researcher.

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When to use probability sampling
 When the sampling bias has to be reduced: This sampling method
is used when the bias has to be minimum. The selection of the sample
largely determines the quality of the research’s inference
 When the population is usually diverse: When your population size
is large and diverse this sampling method is usually used extensively
as probability sampling helps researchers create samples that fully
represent the population.
 To create an accurate sample: researchers create an accurate
sample of their population. Researchers can use proven statistical
methods to draw accurate sample size to obtained well-defined data.

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THE STEPS INVOLVED IN PROBABILITY SAMPLING
 Choose your population of interest carefully: Carefully think and choose from
the population, people you think whose opinions should be collected and then
include them in the sample.
 Determine a suitable sample frame: Your frame should include a sample from
your population of interest and no one from outside in order to collect accurate
data.
 Select your sample and start your survey: It can sometimes be challenging to
find the right sample and determine a suitable sample frame.

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Advantages of Probability Sampling
 It is Cost-effective: This process is both cost and time effective and a larger
sample can also be chosen based on numbers assigned to the samples and then
choosing random numbers from the bigger sample. Work here is done.
 It’s simple and easy: Probability sampling is an easy way of sampling as it does
not involve a complicated process. Its quick and saves time. The time saved can
thus be used to analyze the data and draw conclusions.
 It non-technical: This method of sampling doesn’t require any technical knowledge
because of the simplicity with which this can be done. This method doesn’t require
complex knowledge and its not at all lengthy.

12. • Probability Sampling can be more expensive and time-
consuming
• Cluster sampling: might not work well if unit members are
not homogeneous (i.e. if they are different from each other).
• Simple random sampling: tedious and time consuming,
especially when creating larger samples.
• Stratified random sampling: tedious and time consuming,
especially when creating larger samples.
• Systematic sampling: not as random as simple random
sampling,
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DISADVANTAGES

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CONCLUSION
Probability sampling is a sampling technique wherein the samples are gathered in a
process that gives all the individuals in the population equal chances of being
selected. Probability sampling specifies to the researcher that each segment of a
known population will be represented in the sample. Probability samples lend itself to
rigorous analysis to determine the likelihood and possibility of bias and error (2).

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REFERENCES
1. Andale. (June 26, 2015). Probability Sampling: Definition,Types, Advantages and
Disadvantages. Statistics How To. Retrieved from http://www.statisticshowto.com/probability-
sampling/.
2. Babbie, E. R. (n.d.). The Logic of Sampling. The Basics of Social Research. Wadsworth
Cengage Learning, pp 208.
3. Barreiro, P. L., &Albandoz, J. P. (2001). Population and sample. Sampling techniques.
MaMaEuSch† (Management Mathematics for European School). Retrieved from
http://optimierung.mathematik.unikl.de/mamaeusch/veroeffentlichungen/
ver_texte/sampling_en.pdf.
4. Chaudhuri, A., &Stenger, H. (2005). Survey Sampling: Theory and Methods - 2nd ed.
Chapman & Hall/CRC. 5. Daniel, J. (2012). Sampling Essentials: Practical Guidelines for
Making Sampling Choices. Sage Publications, pp 103.
5. Doherty, M. (1994) Probability versus Non-Probability Sampling in Sample Surveys, The
New Zealand Statistics Review March 1994 issue, pp 21-28. 7. Fink, A. (2003) How to Sample
in Surveys. 2nd Edition. Thousand Oaks: Sage.

15. REFERENCES:
6. King, R. M. Types of sampling. Advanced Research Methods.
Retreived from http://www.psyking.net/HTMLobj-
3829/Types_of_Sampling.pdf.
7.Lynchi, G. (n.d.). Sampling. Retreived from
https://www.kent.ac.uk/religionmethods/documents/Sampling.pdf.
8. Wiid, J., &Diggines, C. (n.d.). Marketing Research. Juta, pp 200.
9. Zikmund, W., &Babin, B. (n.d.). Sampling Designs and Sampling
Procedures. Exploring Marketing Research. Thomson South –Western,
pp 411. View