Sampling and sampling methods for data collection

1. COURSE: RESEARCH METHODOLOGY
LEVEL: B-TECH
ASSIGNMENT: SAMPLING AMD SAMPLING METHODS
- Sampling (Definition, Needs and Importance).
- Types of sampling (Probability and Non-Probability sampling).
-Probability sampling techniques (simple random, stratified and systematic).
GROUP FOR COMPUTER ENGINEERING
S/N NAMES OPTION
1 MICHAEL NGWENA MBAH SOFTWARE
2 SAKE RANDY BROWN SOFTWARE
3 CLEMENT KENAN SOFTWARE
4 SIEWE GODWIN ENOW SOFTWARE
5 ELANGWE YAMNICK ELANGWE NETWORK
6 EMILE KENT NETWORK

2. ASSIGNMENT: SAMPLING AND SAMPLING METHODS
SAMPLING
a) Definition
Sampling refers to the process of picking a subset or a sub-population from a chosen population to represent
the entire population of interest under study. The sub-population is being studied to understand the behavior
of the entire population. Therefore the sub-population represents the entire population.
To fully understand the meaning of sampling it is also important to understand the following;
 The sub-population is called a sample.
 The total number for the sub-population is called the sample size.
 The method of obtaining resources to make up the sub-population is called the sample frame.
 The total number of people or things of interest is called the population or population size.
b) Why Do Researchers Need Sampling?
Sampling is normally recommended when it is impossible to study the entire population either due to time,
cost or other irregularities. In such a case, a smaller size of the population called a sample can be used to get
a clear picture of the entire population.
c) Importance Of Sampling
Sampling reduces research time and also minimizes cost. Sampling also helps to eliminate impossibilities.
Sampling allows large-scale research to be carried out with a more realistic cost and time-frame. That’s
because it uses a smaller number of members in the population with representative characteristics to stand in
for the entire population.
TYPES OF SAMPLING
There exist two major types of research sampling methods which are probability sampling and non-
probability sampling.
a) Probability sampling:
In this case, each member of the target population has a known chance to be selected and the probability of
selecting any member is different from zero. Selections are done randomly base on probability. These
selections are usually done by assigning numbers to each member and then use a random number generator
to choose a number at random. For instance in a class of 8 boys and two girls, a number from 1 to 10 can be
assigned to each member and a random generator can be used to select at random. The probability of
selecting a boy at first instance is 0.8 while that of a girl is 0.2.

3. b) None-probability sampling:
This also called convenient sampling. In this case, the researcher deliberately picks particular members of
study without any randomness. This is being done based on the research goal or purpose of convenience.
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TYPES OF PROBABILITY SAMPLING TECHNIQUE
a) Simple random sampling:
In this technique of sampling, every member of the population is giving an equal chance to be selected.
All members are simply giving numbers and a random number generator is use to pick out the sample
population. Even though this method avoids individual bias, it might lead to unrepresentativeness. For
example giving that in a class of 6 boys and 4 girls where a sample of size 6 is needed. Choosing sample
using simple random sampling might end up producing s sample containing 6 boys which has missed to
represent girls’ opinion. However, the degree of unrepresentativeness can be minimized by increasing
the sample size.
b) Stratified Sampling:
In this technique, the population is broken down into smaller groups called strata before random
selection is being done separately. For example, in a class of 7 boys and 3 girls where a sample of 7
members is needed. The research can decide to regroup the population in terms of boys and girls and
then use simple random sampling on each sub group to obtain 5 boys and 2 girls. Stratified sampling
helps to minimize the problem of unrepresentativeness.
c) Systematic Sampling:
With systematic sampling, the researcher applies random selection only to get the first member of the
sample. After that an interval is chosen for which an element is picked repeatedly. Let us consider a
class of 50 students of which a sample of 10 students is needed for a study. The researcher can number
all members in the population and group them in tens. For the first group of 1 to 10 a random selection is
done to choose the first member and after that a member is chosen at an interval of ten till the population
is exhausted.
Conclusion
Even though sampling is great for making your research quicker and less costly to do, how you choose
individuals will affect how well your sample represents the entire population of interest.
Before you can decide how to choose individuals for your sample, it is important to first identify who is in
your target population and who is not. Your sample size may also vary depending on the size of the total
population.