Sampling Methods
Sampling:
It is the procedure by which some members are selected as representative of the entire population.
Some
sampling terms:
Population:
Collection of units sharing common characteristics
Ex:
Finite: Possibility of counting all units such as students in a school.
Infinite: such as RBCs of an individual.
Collection of units sharing common characteristics
Ex:
Finite: Possibility of counting all units such as students in a school.
Infinite: such as RBCs of an individual.
Sample:
A subset of a population obtained to investigate
properties of the parent population.
Target population:
Population upon which the results of the study will be generalized.
Sampling population:
Population from which the sample was taken.
Sampling
unit:
Subject under observation on which information is collected
such as: Children events. years, hospital discharges health.
Study population:
The population selected to be sampled it is a subset of the target population
Sampling frame:
Any list of all the sampling units in the population: List
of households, health care units.
Sampling scheme:
Method of selecting sampling units from sampling
frame Randomly, convenience sample.
Let's give example to understand:
I want to make a research on medical students in Portsaid university especially those from Sharkia and their age range is 20-22, so my target population is medical students, my study population is medical student from Sharkia whose age is from 20 to 22 from which I am able to take my sample.
The sampling frame is all medical students in Portsaid university from which sampling units(from Sharkia) are taken. The sampling scheme is the way that I used to determine my sampling units.
Why we need sampling:
Data on large population at relatively
low cost and at greater speed.
2)
Provided measure of the amount of error introduced
in the sampling process.
3)
More accurate data.
4)
Method to acquire data that might
otherwise be impossible to gain.
Good sample saves time,
money, and effort and
assures data collection and valid estimates of population characteristics.
Basic
types of sampling:
Probability (Random) sampling:
Simple Random
sampling:
Each element in population has equal and independent
chance of selection in sample.
Advantages:
a)
Represent the total sampling population. And can be generalized.
b)
Some statistical tests are based on theory of probability
can be applied only to data collected from random samples.
Methods:
Conditions:
1) Applied when population is
small, homogeneous and readily available.
2) All subsets of the frame are
given an equal probability and also each element.
3) Used in lotteries.
Procedures:
1) Number each element using
separate slips of paper for each element.
2) Put all slips into box and
pick them out one by one without looking.
Systematic random sampling:
Properties:
1)
Relies on
arranging the target population according to some ordering scheme
and then selecting elements at regular intervals through that ordered
list.
2)
Systematic
sampling involves a random start and then proceeds with the selection of
every kth element from then onwards. In this case, k= (population
size/sample size).
3) There is a gap, interval between each
selected unit in sample.
As you see in figure.
Stratified Random Sampling:
1)
Where population
embraces a number of distinct categories, the frame can be organized into
separate "strata." Each stratum is then sampled as an
independent sub-population, out of which individual elements can be randomly
selected.
2)
Every unit in
a stratum has same chance of being selected.
5)
Each stratum
is homogenous inside, heterogeneous to outside.
Cluster sampling:
1)
The researcher
divides the population into separate groups, called clusters. Then, a
simple random sample of clusters is selected from the population.
2)
Each cluster
is heterogeneous inside, homogeneous to outside.
Multistage sampling:
Large clusters
of population are divided into smaller clusters in several stages in order to
make primary data collection more manageable.
For example:
Your research objective is to evaluate online spending
patterns of households in the US through online questionnaires. You can form
your sample group comprising 120 households in the following manner:Choose 6
states in the USA using simple random sampling (or any other probability sampling).
Choose 4 districts within each state using systematic sampling method (or any other probability sampling).
Choose 4 districts within each state using systematic sampling method (or any other probability sampling).
Choose 5
households from each district using simple random or systematic sampling
methods. This will result in 120 households to be included in your sample
group.
Non-probability
sampling:
Non-probability
sampling:
Probability of being included in the
sample is not known and not equal for the sampling units.
Such as:
a) Convenient.
b) Quota.
c) Snow ball.
d) Accidental sampling.
e) Purpose / judgmental
sampling.
f) Expert sampling.
Quota sampling:
The population is first segmented
into mutually exclusive sub-groups.
Then judgment used to select
subjects or units from each segment based on a specified proportion
For example:
if you want 100 girl and 100 boy for job interview and their age ranges are 25-50. first, you will divide them into subgroups with age range (25-30) (30-35) (35-40) (40-45) (45-50). second, you will determine proportion for example half of people in group (25-30) and third from (40-45) and so on
Purpose sampling:
You go only to those who are in your opinion
are likely to have required information.
Convenience sampling:
For example:It depends on convenience and guided by some visible characteristics.If you are teacher in class and you want to make average for age of 20 males in the class, so as a teacher, you will stand at the entrance of class (convenience) and each one enters the class, ask him for age (characteristic).
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