Types Of Sampling

Probability sampling:

Probability sampling is a type of sampling method in statistics and research where every
element in the population has a known non-zero chance of being selected to be part of the sample.
In other words, the selection of individuals or items from the population is based on the principles
of probability theory, ensuring that each member of the population has an equal chance of being
included in the sample. This characteristic allows researchers to make valid statistical inferences
and generalize findings from the sample to the entire population with a known level of confidence.

There are several common types of probability sampling methods:

1.Simple Random Sampling:

Simple Random Sampling (SRS) is a probability sampling method used in statistics and
research to select a sample from a larger population. In simple random sampling, each
individual or item in the population has an equal chance of being selected for the sample,
and the selection process is entirely random. This method ensures that every possible
sample of a given size has an equal probability of being chosen, making it one of the most
unbiased and representative sampling techniques.

The process of conducting a simple random sample involves the following steps:

1. Define the Population: The first step is to clearly define the population of interest. The
population is the entire group or set of individuals or items that the researcher wishes to
study and make inferences about.
2. Determine Sample Size: Decide on the desired sample size, which is the number of
individuals or items that will be selected from the population to form the sample. The
sample size should be large enough to provide sufficient statistical power and precision in
the analysis but small enough to be manageable and cost-effective.
3. Assign a Unique Identifier: Each member of the population should have a unique identifier
or label, such as a serial number, name, or ID code. This ensures that each element can be
identified distinctly during the sampling process.
4. Random Selection: To select the sample, the researcher uses a random process, ensuring
that every member of the population has an equal chance of being chosen. There are several
ways to achieve random selection:
a. Random Number Generators: Use a computer-generated random number generator to
select random elements from the population. The numbers should be independent and
uniformly distributed within the population size.
b. Drawing Lots: Write each member's identifier on a piece of paper and draw them from a
hat or container until the desired sample size is achieved.
 Types Of Sampling
c. Random Digit Dialing: In situations where the population consists of individuals with
telephone numbers, a random sample can be obtained by generating random digit
combinations and calling those numbers.
5. Sample Data Collection: Once the random sample is selected, data is collected from the
chosen individuals or items. The data collected in the sample can then be analyzed to draw
conclusions about the entire population.

2. Systematic Sampling:

Systematic sampling is a probability sampling method used in statistics and research to

select a sample from a larger population. In systematic sampling, the researcher chooses
every nth individual or item from a list of the population to form the sample. This method is
relatively easy to implement and is particularly useful when a complete list of the
population is available.

The process of conducting a systematic sample involves the following steps:

1. Define the Population: As with any sampling method, the first step is to clearly define the
population of interest. The population is the entire group or set of individuals or items that
the researcher wishes to study and make inferences about.
2. Create a Sampling Frame: A sampling frame is a list of all the members of the population
from which the sample will be drawn. It is essential to have a complete and accurate
sampling frame to ensure that all individuals or items have an equal chance of being
included in the sample.
3. Determine Sample Size: Decide on the desired sample size, which is the number of
individuals or items that will be selected from the population to form the sample. The
sample size should be determined based on statistical considerations and the research
objectives.
4. Calculate the Sampling Interval: The sampling interval (k) is the number of elements
between each selected individual in the sample. It is calculated by dividing the total
population size (N) by the desired sample size (n): k = N / n. For example, if the population
size is 1,000 and the desired sample size is 100, the sampling interval would be 10 (k =
1,000 / 100).
5. Random Start: To avoid bias, the researcher must start the systematic sampling at a
randomly chosen point in the sampling frame. For example, they can use a random number
generator to select a random number between 1 and k. This randomly selected number
represents the starting point for the sampling process.
6. Select the Sample: Once the random start point is determined, the researcher selects every
nth individual or item from the sampling frame to form the sample. For example, if the
random start point is 7, and the sampling interval is 10, the researcher would select
 Types Of Sampling
individuals with the identifiers 7, 17, 27, 37, and so on until the desired sample size is
reached.

3.Stratified Sampling:
Stratified sampling involves dividing the population into subgroups or strata based on
specific characteristics (e.g., age, gender, income, location). Then, a random sample is
drawn independently from each stratum proportionate to its size or importance in the overall
population. This method ensures representation from each subgroup, and it is particularly
useful when there are significant differences within the population.

The process of conducting a stratified sample involves the following steps:

1. Define the Population: As with any sampling method, the first step is to clearly define the
population of interest. The population is the entire group or set of individuals or items that
the researcher wishes to study and make inferences about.
2. Identify Strata: Based on the research objectives and the characteristics of interest, divide
the population into distinct and non-overlapping subgroups called strata. Each individual or
item in the population should belong to one and only one stratum.
3. Determine Sample Size and Allocation: Decide on the desired sample size for the entire
sample and how it will be allocated among the strata. The sample size for each stratum
should be proportional to the stratum's size or importance in the overall population. Larger
strata may have larger sample sizes, reflecting their greater representation in the population.
4. Random Sampling within Strata: Within each stratum, use a random sampling method (e.g.,
simple random sampling or systematic sampling) to select the required number of
individuals or items. The random selection ensures that each member of the stratum has an
equal chance of being included in the sample.
5. Combine the Samples: Once the required sample size has been obtained from each stratum,
combine the samples to create the final stratified sample. The combined sample will
represent the entire population and provide a more comprehensive picture of the
population's characteristics.

4.Cluster Sampling:

Cluster sampling is a probability sampling method used in statistics and research to

select a sample from a larger population. In cluster sampling, the population is divided into
clusters or groups, and a random sample of clusters is selected. Then, data is collected from
all individuals or items within the selected clusters to form the final sample. This method is
particularly useful when it is difficult or impractical to obtain a complete list of the
population, and it can be more cost-effective and convenient than other probability
sampling methods.

The process of conducting a cluster sample involves the following steps:

 Types Of Sampling
1. Define the Population: As with any sampling method, the first step is to clearly define the
population of interest. The population is the entire group or set of individuals or items that
the researcher wishes to study and make inferences about.
2. Create Clusters: Divide the population into clusters or groups. Clusters should ideally be
heterogeneous and representative of the overall population. Each cluster should be mutually
exclusive, meaning that individuals or items should belong to only one cluster.
3. Randomly Select Clusters: Use a random sampling method (e.g., simple random sampling
or systematic sampling) to select a random sample of clusters from the population. It is
essential to ensure that each cluster has an equal chance of being selected to avoid bias.
4. Collect Data from Selected Clusters: Once the clusters are selected, data is collected from
all individuals or items within each selected cluster. This can be done using various data
collection methods, such as surveys, interviews, or observations.
5. Combine the Cluster Samples: The data collected from the selected clusters are combined to
form the final cluster sample. The combined sample represents the entire population, and
statistical analysis can be performed on this sample to draw inferences about the population.

5. Multi Stage Sampling:

Multi-stage Sampling: Multi-stage sampling is a combination of different
sampling methods. It involves selecting samples at multiple stages, such as using
stratified sampling to select clusters and then using simple random sampling to
choose individuals within those clusters. This approach is useful for large and
diverse populations.

Probability sampling methods are preferred in most research studies because they provide stronger
statistical foundations and allow researchers to make more reliable generalizations about the
population. By using probability sampling, researchers can calculate sampling errors, confidence
intervals, and margins of error, which are essential for assessing the precision of the study's
findings. However, probability sampling might be more complex and time-consuming to
implement than non-probability sampling methods. Despite this, it ensures a higher level of
confidence in the representativeness of the sample and the validity of the study's conclusions.

Non-probability sampling:
Non-probability sampling is a method of selecting a sample from a larger population in which not
all individuals, items, or units in the population have a known and equal chance of being included
in the sample. In non-probability sampling, the selection of elements is based on the researcher's
judgment, convenience, or accessibility, rather than randomization or a defined probabilistic
 Types Of Sampling
method. This type of sampling is often used when probability sampling methods are not feasible,
practical, or too costly. However, non-probability sampling can introduce bias and may not
accurately represent the entire population, making it less suitable for making statistical inferences
or generalizations to the broader population.

There are several types of non-probability sampling methods, each with its own advantages and
disadvantages:
1. Convenience Sampling: This method involves selecting subjects who are most readily
available and accessible to the researcher. It is one of the most common non-probability
sampling techniques due to its simplicity and convenience. However, it often leads to a
biased sample because it may not represent the entire population adequately.
2. Judgmental Sampling: In judgmental sampling, the researcher uses their judgment to
handpick specific individuals or items that they believe are representative of the population.
This method is highly subjective and can introduce bias if the researcher's judgment is
flawed.
3. Quota Sampling: Quota sampling involves dividing the population into subgroups or strata
and then setting a predetermined quota for each subgroup. The researcher then selects
participants conveniently until the quotas are filled. While this method allows the researcher
to control the sample composition, it can lead to selection bias if the quotas are not well-
defined or accurately represent the population's characteristics.
4. Snowball Sampling: This method is used when the target population is hard to reach or
identify. The researcher starts with a small number of participants and then asks them to
refer other relevant participants, who, in turn, refer more participants, creating a "snowball"
effect. It is often used in studies involving hidden or marginalized populations. However, it
can introduce biases since the initial participants may share similar characteristics or
perspectives.
5. Purposive Sampling: In purposive sampling, the researcher deliberately selects individuals
or items based on specific criteria relevant to the research objectives. This method allows
the researcher to focus on specific subgroups or cases of interest. However, it can lead to
bias if the criteria for selection are not well-defined or if the researcher's subjectivity
influences the choices.

Non-probability sampling is commonly used in exploratory research or situations where probability

sampling is impractical or too expensive. However, when generalizing findings to the entire
population is essential, researchers often prefer probability sampling methods. It is essential for
researchers to acknowledge the limitations of non-probability sampling and interpret the results
cautiously, taking into account the potential biases inherent in the sampling method used.