This document discusses population and sampling methods used in research. It defines a population as the entire group being studied, while a sample is a subset of the population that is representative of it. There are two main types of sampling: probability sampling, which allows researchers to calculate sampling error, and nonprobability sampling, which does not. Some examples of nonprobability sampling methods provided are convenience sampling, volunteer sampling, purposive sampling, and snowball sampling. Probability sampling methods discussed include simple random sampling, systematic random sampling, stratified sampling, and cluster sampling.

1. Population & Sample
Rashid Ali Khuhro

2. • Population
A group or class of subjects, variables, concepts, or
phenomena.
The process of examining every member in a
population is called a census.
• Sample
Sample is a subset of the population that is
representative of the entire population. An important
word in this definition is representative.
If a sample is chosen according to proper guidelines
and is representative of the population, then the
results from a study using the sample can be
generalized to the population.

3. The sample selection process is illustrated using a
Venn diagram (Figure 4.1); the population is
represented by the larger of the two circles. A
census would test or measure every element in the
population (A), whereas a sample would measure
or test a segment of the population (A1).

4. • Research Error
All research is riddled with error. Much of the source of
error in the behavioral sciences is that research is
conducted with human subjects/respondents who
constantly change.
There are two broad types of error present in all research:
(1) sampling error, or error related to selecting a sample
from a population; and
(2) nonsampling error, which is error created by every
other aspect of a research study, such as measurement
errors, data analysis errors, the influence of the
research situation itself, or even error from an
unknown source that can never be identified and
control or eliminated. One form of nonsampling error,
measurement error, is further divided into two
categories: random error and systematic error.

5. TYPES OF SAMPLING PROCEDURES
1. Probability Sampling
Probability sampling uses mathematical guidelines whereby each unit’s chance for
selection is known. Probability sampling allows researchers to calculate the amount of
sampling error present in a research study
2. Nonprobability Sampling
Nonprobability sampling does not follow the guidelines of mathematical probability.
Non-probability sampling does not allow researchers to calculate the amount of
sampling error present in a research study.

6. • Types of Nonprobability Sampling
• Available Sample
An available sample (also known as a convenience sample) is a collection of
readily accessible subjects, elements, or events for study, such as a group of
students enrolled in an introductory mass media course or shoppers in a mall.
• Unqualified Volunteer Sample
Unqualified volunteer sample, where respondents or subjects voluntarily agree to
participate in a research project and are not selected according to any
mathematical guidelines.
• Purposive Sample
Purposive sample, which includes respondents, subjects, or elements selected
for specific characteristics or qualities and eliminates those who fail to meet
these criteria (as demonstrated by the example of the radio station including only
men between the ages of 25 and 44 in its research). In other words, the sample is
deliberately selected nonrandomly.
The last nonprobability sampling method in this discussion is a method known as
snowball sampling. (The term snowball sampling is used most often in academic
research. In private sector research, this approach is known as referrals.) In either
case, the method is the same. A researcher (or research company or field service)
randomly contacts a few qualified respondents and then asks these people for
the names of friends, relatives, or

7. • Types of Probability Samples
Simple random sample, where each subject, element, event, or unit in
the population has an equal chance of being selected.
Systematic random sampling, in which every nth subject, unit, or
element is selected from a population.
A stratified sample is the approach used to get adequate representation
of a subsample. The characteristics of the subsample (strata or
segment) may include almost any variable: age, gender, religion, income
level, or even individuals who listen to specific radio stations or read
certain magazines. Stratified sampling ensures that a sample is drawn
from a homogeneous subset of the population—that is, from a
population that has similar characteristics. Homogeneity helps
researchers to reduce sampling error.
Cluster sampling for example, analyzing magazine readership habits of
people in Wisconsin would be time-consuming and complicated if
individual subjects were randomly selected. With cluster sampling, the
state can be divided into districts, counties, or zip code areas, and
groups of people can be selected from each area.