QUAT6221 LU7
QUAT6221 LU7 – Sampling and
Sampling Methods
Chapter 6 – Sampling and Sampling Distributions
6.1 Introduction
Inferential statistics are statistical methods that use sample findings to either estimate or test
values of population parameters of random variables under study.
4 pillars that support inferential stats:
- Descriptive stats (sample means, proportions and standard deviation)
- Probabilities, especially the normal probability distribution
- Sampling methods (and their influence on the sampling error)
- The concept of the sampling distribution
6.2 Sampling and Sampling Methods
A sample is a subset of a population, a sample must be a representative of its target
population if it is to produce valid and reliable estimates of the population.
2 basic methods of sampling:
Non-probability (Non-random) Sampling
= Any sampling method where the sample members are not selected randomly
Criteria other than random selection are used to choose the sample members from the
population.
4 types of non-probability sampling methods:
1. Convenience sampling
When a sample is drawn to suit the convenience of the researcher
It is more convenient to conduct interviews on textile industry labour practises with
employees from only 1 company
2. Judgement sampling
When researchers use their judgement alone to select the best sampling units to include in
the sample
Only professional soccer players are selected and interviewed on the need for rule
changes in the sport
3. Quota sampling
Involves selecting a set number of participants from specific subgroups within a population.
Once a quota for a subgroup is met, no more participants from that group are included,
which can lead to selection bias. This method is non-random, making it similar to stratified
sampling but without random selection.
1
, QUAT6221 LU7
A researcher may set a quota to interview 40 males and 70 females from the 25- to
40-year age group on their savings practices. When the quota of interviews for any
one subgroup is reached, no further eligible sampling units from that subgroup are
selected for interview purposes.
4. Snowball sampling
Used when it is easy to id the members of the target population for reasons of sensitivity or
confidentiality. If one members can be identified, this person is asked to id other members of
the same target population.
Studies related to AIDS/HIV, gangs, drug use etc
2 major disadvantages of non-probability sampling:
1. Samples are likely to be unrepresentative of their target population = bias
2. Not possible to measure the sampling error from data based on a non-probability
sample. Sampling error is the difference between the actual population parameter
value and its sample statistic. As a result, it is not valid to draw statistical inferences
from non-probability sample data.
Non-probability samples can be useful in exploratory research situations or in less-scientific
surveys to provide initial insights into and profiles of random variables under study
Probability (random) Sampling
Any selection method where the sample members (sampling units) are selected from the
target population on a purely random (chance) basis.
Under random sampling, every member of the target population has a chance of being
selected for the sample.
4 probability-based sampling methods:
1. Simple Random Sampling
Each member of the target population has an equal chance of being selected.
Population is homogeneous w respect to the random variable under study.
One way to draw a simple random sample is to assign a number to every element of the
population and then electively draw numbers from a hat. If a database of names exist, a
random number generator can be used to draw a simple random sample.
2. Systematic Random Sampling
Used to sample items from a continuous production process or when a sampling frame
exists. Sampling begins by randomly selecting the first sampling unit. Thereafter subsequent
sampling units are selected at a uniform interval relative to the first sampling unit. Since only
the first sampling unit is randomly selected, some randomness is sacrificed.
To draw a systematic random sample, first divide the sampling frame by the sample size to
determine the size of a sampling block. Randomly choose the first sample member from
within the first sampling block. Then choose subsequent sample members by selecting one
member from each sampling block at a constant interval from the previously sampled
member.
2
QUAT6221 LU7 – Sampling and
Sampling Methods
Chapter 6 – Sampling and Sampling Distributions
6.1 Introduction
Inferential statistics are statistical methods that use sample findings to either estimate or test
values of population parameters of random variables under study.
4 pillars that support inferential stats:
- Descriptive stats (sample means, proportions and standard deviation)
- Probabilities, especially the normal probability distribution
- Sampling methods (and their influence on the sampling error)
- The concept of the sampling distribution
6.2 Sampling and Sampling Methods
A sample is a subset of a population, a sample must be a representative of its target
population if it is to produce valid and reliable estimates of the population.
2 basic methods of sampling:
Non-probability (Non-random) Sampling
= Any sampling method where the sample members are not selected randomly
Criteria other than random selection are used to choose the sample members from the
population.
4 types of non-probability sampling methods:
1. Convenience sampling
When a sample is drawn to suit the convenience of the researcher
It is more convenient to conduct interviews on textile industry labour practises with
employees from only 1 company
2. Judgement sampling
When researchers use their judgement alone to select the best sampling units to include in
the sample
Only professional soccer players are selected and interviewed on the need for rule
changes in the sport
3. Quota sampling
Involves selecting a set number of participants from specific subgroups within a population.
Once a quota for a subgroup is met, no more participants from that group are included,
which can lead to selection bias. This method is non-random, making it similar to stratified
sampling but without random selection.
1
, QUAT6221 LU7
A researcher may set a quota to interview 40 males and 70 females from the 25- to
40-year age group on their savings practices. When the quota of interviews for any
one subgroup is reached, no further eligible sampling units from that subgroup are
selected for interview purposes.
4. Snowball sampling
Used when it is easy to id the members of the target population for reasons of sensitivity or
confidentiality. If one members can be identified, this person is asked to id other members of
the same target population.
Studies related to AIDS/HIV, gangs, drug use etc
2 major disadvantages of non-probability sampling:
1. Samples are likely to be unrepresentative of their target population = bias
2. Not possible to measure the sampling error from data based on a non-probability
sample. Sampling error is the difference between the actual population parameter
value and its sample statistic. As a result, it is not valid to draw statistical inferences
from non-probability sample data.
Non-probability samples can be useful in exploratory research situations or in less-scientific
surveys to provide initial insights into and profiles of random variables under study
Probability (random) Sampling
Any selection method where the sample members (sampling units) are selected from the
target population on a purely random (chance) basis.
Under random sampling, every member of the target population has a chance of being
selected for the sample.
4 probability-based sampling methods:
1. Simple Random Sampling
Each member of the target population has an equal chance of being selected.
Population is homogeneous w respect to the random variable under study.
One way to draw a simple random sample is to assign a number to every element of the
population and then electively draw numbers from a hat. If a database of names exist, a
random number generator can be used to draw a simple random sample.
2. Systematic Random Sampling
Used to sample items from a continuous production process or when a sampling frame
exists. Sampling begins by randomly selecting the first sampling unit. Thereafter subsequent
sampling units are selected at a uniform interval relative to the first sampling unit. Since only
the first sampling unit is randomly selected, some randomness is sacrificed.
To draw a systematic random sample, first divide the sampling frame by the sample size to
determine the size of a sampling block. Randomly choose the first sample member from
within the first sampling block. Then choose subsequent sample members by selecting one
member from each sampling block at a constant interval from the previously sampled
member.
2
