Course Research Methodology – MBO528

Topic Selecting Samples and Questionnaire Design

Faculty Dr. Anamika Kumari

 Recap

Research Designs
Meaning of Research Designs, Nature and Classification of Research Designs,
Exploratory Research Designs, Secondary Resource analysis, Case study Method,
Expert opinion survey, Focus group discussions, Descriptive Research Designs,
Cross-sectional studies and Longitudinal studies, Experimental Designs, Errors
affecting Research Design

Primary and Secondary Data

Classification of Data, Secondary Data, Uses, Advantages, Disadvantages, Types
and sources, Primary Data Collection, Observation method, Focus Group
Discussion, Personal Interview method
 Important Questions
1. Types of research design
2. Difference qualitative and quantitative
3. Types of data
4. Primary and Secondary data
5. Difference between FGD and Interview
 Units to be covered
Attitude Measurement and Scaling
Types of Measurement Scales, Attitude, Classification of Scales, Single item vs
Multiple Item scale, Comparative vs Non-Comparative scales, Measurement
Error, Criteria for Good Measurement

Sampling
Sampling concepts- Sample vs Census, Sampling vs Non-Sampling error, Sampling
Design, Probability and Non-Probability Sampling design, Determination of
Sample size, Sample size for estimating population mean, Determination of
sample size for estimating the population proportion
By the end of the chapter, you should:

•Understand the need for sampling in business and management

•Be able to select appropriate sampling techniques
•Be able to assess the representativeness of respondents
•Be able to select appropriate scale
What is the need to sample
1. It would be impractical to survey
the entire population
2. Your budget constraints prevent
you from surveying the entire
population
3. Your time constraints prevent you
from surveying the entire
population
4. You have collected all the data but
need the results quickly
 SAMPLING
✓ All items in any field of inquiry constitute a ‘Universe’ or ‘Population.’

✓ A complete enumeration of all items in the ‘population’ is known as a

census inquiry.

✓Practically it will not be possible to include all the item of the population
for the study, hence sampling technique is used to draw samples from the
population.

✓The selected respondents are technically called a ‘sample’ and the

selection process is called ‘sampling technique.’ The survey so conducted is
known as ‘sample survey.’

✓The sampling technique must be in such a way that it is unbiased and

represents the population from where it is selected.
SAMPLING
Sampling, therefore, is the process of selecting a few (a sample) from a bigger
group (the sampling population) to become the basis for estimating or
predicting the prevalence of an unknown piece of information, situation or
outcome regarding the bigger group.
A sample is a subgroup of the population you are interested in.
SAMPLING TERMINOLOGY

Population: The collection of all the sampling units in a given region at a particular
point of time or a particular period is called population, and are usually denoted by
the letter N.

Sampling Unit: An element or a group of elements on which observations can be

taken is called a sampling unit.

Sample: A subset of the population, which represents the entire population, is called
a sample. One or more sampling units are selected from the population according to
some specified procedure. A sample consists only of a portion of the population units.

Sampling Frame: List of all the units of the population to be surveyed constitutes the
sampling frame. All the sampling units in the sampling frame have identification
particulars.
SAMPLING TERMINOLOGY
Suppose you want to estimate the average age of the students in your class. Suppose you want to find out
the average income of families living in a city.
The class or families living in the city from which you select your sample are called the population or
study population, and are usually denoted by the letter N.
The number of students or families from whom you obtain the required information is called the sample
size and is usually denoted by the letter n.
The way you select students or families is called the sampling design or sampling strategy.
Each student or family that becomes the basis for selecting your sample is called the sampling unit or
sampling element.
Your findings based on the information obtained from your respondents (sample) are called sample
statistics. Your sample statistics become the basis of estimating the prevalence of the above
characteristics in the study population.
CHARACTERISTICS OF A GOOD SAMPLE DESIGN
Sample design must result in a truly representative sample
Sample design must be such which results in a small sampling error
Sample design must be viable in the context of funds available for the research study
Sample design must be such so that systematic bias can be controlled in a better way
Sample should be such that the results of the sample study can be applied, in general, for
the universe with a reasonable level of confidence.
Advantages of Sampling Surveys

Reduced cost and enlarged scope

Organization of work
Greater accuracy and Urgent information required
Feasibility
TYPES OF SAMPLING
Probability - Probability sampling is the scientific method of selecting
samples according to some laws of probability in which each unit in the
population has some definite pre-assigned probability of being selected in
the sample.

Non-probability - Non-probability sampling is a sampling technique in

which not all members of the population have a chance of being selected.
This approach is often used when it is not feasible or practical to conduct
probability sampling due to constraints such as time, budget, or the nature
of the research. Non-probability sampling relies on the judgment of the
researcher or on the accessibility and availability of subjects.
TYPES OF PROBABILITY SAMPLING

Here are the main types of probability

sampling:
1. Simple Random Sampling
2. Stratified Random Sampling
3. Cluster Sampling
4. Systematic Sampling
 TYPES OF
PROBABILITY
SAMPLING

Simple Random sampling:

Simple random sampling
(SRS) is a method of
selection of a sample
comprising of n number of
sampling units from the
population having N number
of units such that every
sampling unit has an equal
chance of being chosen.
.
Procedure of selection of a random sample
Suppose there are N units in the population out of which n units are to
be selected.
1. Identify the N units in the population with the numbers 1 to N.
2. Choose any random number arbitrarily from the random numbers
table and start reading numbers.
3. Choose the sampling unit whose serial number corresponds to the
random number drawn from the table of random numbers.
4. In case of SRSWR, all the random numbers are accepted even if
repeated more than once.
5. In case of SRSWOR, if any random number is repeated, then it is
ignored, and more numbers are drawn.
 TYPES OF
STRATIFIED
SAMPLE
Stratified simple random sampling: The
basic idea behind the stratified sampling is to
• divide the whole heterogeneous population
into smaller groups or subpopulations, such that
the sampling units are homogeneous with
respect to the characteristic under study within
the subpopulation and heterogeneous with
respect to the characteristic under study
between/among the subpopulations. Such
subpopulations are termed as strata.
• Treat each subpopulation as separate
population and draw a sample by SRS from
each stratum.
In order to find the average height of students in a school of class 1 to class 12, the
height varies a lot as the students in class 1 are of age around 6 years and students in
class 10 are of age around 16 years.

So one can divide all the students into different subpopulations or strata such as
● Students of class 1, 2 and 3: Stratum 1
● Students of class 4, 5 and 6: Stratum 2
● Students of class 7, 8 and 9: Stratum 3
● Students of class 10, 11 and 12: Stratum 4

Now draw the samples by SRS from each of the strata 1, 2, 3 and 4. All the drawn
samples combined together will constitute the final stratified sample for further
analysis.
 TYPES OF
PROBABILITY
SAMPLING
Systematic sampling: In this
method of sampling, the first unit is
selected with the help of random
numbers and the remaining units
are selected automatically
according to a predetermined
pattern. This method is known as
systematic sampling.
Suppose the units in the population are numbered
1 to N in some order. Suppose further that N is
expressible as a product of two integers n and k,
so that N = nk.
To draw a sample of size n
● select a random number between 1 and k.
● Suppose it is i.
● Select the first unit whose serial number is i.
● Select every kth unit after ith unit.
● Sample will contain serial number units.
So first unit is selected at random and other units
are selected systematically. This is also known as
systematic sampling.
 TYPES OF
PROBABILITY
SAMPLING
Cluster sampling: Cluster sampling
occurs when a random sample is drawn
from certain aggregation geographical
groups. In many practical situations and
many types of populations, a list of
elements is not available and so the use
of an element as a sampling unit is not
feasible. The method of cluster
sampling or area sampling can be used
in such situations.
In cluster sampling
● divide the whole population into
clusters according to some well-defined
rule.
● Treat the clusters as sampling units.
● Choose a sample of clusters according
to some procedure.
● Carry out a complete enumeration of
the selected clusters, i.e., collect
information on all the sampling units
available in selected clusters.
Advantages of Probability Sampling

Generalizability: Results can be generalized to the broader population

because each member has a known chance of being included.
Reduced Bias: Random selection helps minimize bias, making the sample
more representative of the population.
Statistical Validity: Allows for the use of inferential statistics to make
conclusions about the population.
Disadvantages of Probability Sampling

Complexity: Some methods (e.g., stratified or cluster sampling) can be

complex to design and implement.
Resource-Intensive: Requires more time, effort, and resources,
especially for large populations.
Need for a Sampling Frame: Often requires a complete list of the
population, which may not always be available.
TYPES OF NON-PROBABILITY SAMPLING

Here are the main types of probability

sampling:
1. Convenience Sampling
2. Judgmental sampling
3. Quota sampling
4. Snowball sampling
 Convenience Sampling:
Convenience sampling
involves selecting
individuals who are
easiest to reach or most
readily available to
participate in the study.

Characteristics:
Quick and easy to implement.
Cost-effective as it minimizes

TYPESOFNONPROBABILITY travel and other logistical

expenses.

SAMPLING
 Judgmental sampling:
Judgmental sampling
involves selecting
individuals based on
the researcher’s
judgment about who will
provide the most useful
or representative data.

Characteristics:
Focuses on specific
JUDGMENTALSAMPLING characteristics or criteria defined
by the researcher.
Often used in qualitative
research.
 Quota sampling: Quota
sampling involves
selecting a sample that
reflects the
characteristics of the
whole population
according to specific
quotas.
Characteristics:
Divides the population into mutually
QUOTA SAMPLING: exclusive subgroups.
Samples are taken from each
subgroup to meet predefined quotas.
 Snowball sampling: Snowball
sampling is where research
participants (sample unit) recruit
other participants (sample unit)
for a test or study. It is used where
potential participants are hard to
find.

Characteristics:
Particularly useful for studying hard-to-
SNOWBALLSAMPLING reach or hidden populations.
Relies on social networks to identify
additional participants.
 Practical and Feasible: Useful
when probability sampling is not
Advantages Advantages
possible due to time,
logistical constraints.
budget, or

Quick Data Collection: Faster and

easier to implement, making it
suitable for exploratory research.

Flexibility: Allows researchers to

use their judgment to select cases
that best meet the research
objectives.
Disadvantages

Sampling Bias: Higher risk of bias as not all population members

have a chance of being included.
Limited Generalizability: Results may not be representative of the
broader population, limiting the ability to generalize findings.
Subjectivity: Relies on the researcher’s judgment, which can
introduce bias and affect the reliability of the results.
 Sampling error: This error arises when a sample is
not representative of the population. Sampling errors
occur due to the selection of a sample rather than
conducting a census of the entire population. These
errors are inherent in any sampling process and can
be quantified and controlled to some extent.

Sampling error is the variation between the sample

statistic and the actual population parameter due to
chance alone.

SAMPLINGVSNON- Can be minimized by increasing the sample size and

using appropriate sampling techniques to ensure
SAMPLINGERROR randomness.
 NON-SAMPLING ERROR

This error arises not because a sample is not

representative of the population but because of other
reasons. Some of the reasons listed below:
The respondents when asked for information on a
particular variable may not give the correct answers.
The error can arise while transferring the data from the
questionnaire to the spreadsheet on the computer.
If the population of the study is not properly defined, it
could lead to errors.
There can be errors at the time of coding, tabulation,
and computation.
A political pollster wants to gather opinions from voters in
a particular state. They divide the state into regions
(urban, suburban, and rural) and randomly select a fixed
number of registered voters from each region. Which
sampling technique is being used?
A researcher wants to understand the experiences of
individuals who have successfully quit smoking. They recruit
participants by reaching out to online support groups and
forums focused on smoking cessation. Which sampling
technique is being used?
A company wants to gather feedback from customers
about a new product. They select participants from a
specific demographic group by using advertisements
targeted at that group and inviting interested individuals to
participate. Which sampling technique is being used?
A researcher wants to study the reading habits
of university students. They have a list of all
students enrolled in the university and
randomly select every 10th student from the
list to participate in the study. Which sampling
technique is being used?
TYPES OF SCALES

Here are the main types of scales:

1. Nominal
2. Ordinal
3. Ratio
4. Interval
 evel of measurement
Nominal Data

• Categorical Variable
• Numbers are assigned for identification
• Mutually Exclusive and Collectively Exhaustiv
e

•Gender: Categories are male and female. There’s no inherent order, and one category isn’t "higher" or
"lower" than another.
•Hair Color: Categories could be black, brown, blonde, red, etc. These categories are distinct and cannot be
ordered.
•Types of Cuisine: Italian, Mexican, Chinese, Indian, etc. These categories are labels without any ranking or
numeric value.
 evel of measurement
Ordinal Data

• Categorical Variable
• Numbers are assigned for identification & as a
ranking v
• Tells whether an object has more or less of e
characteristics than some other objects

•Customer Satisfaction Ratings: Levels like very satisfied, satisfied, neutral, dissatisfied, very dissatisfied.
These can be ranked, but the intervals between them are not necessarily equal.
•Education Levels: High school, Bachelor’s, Master’s, Doctorate. This represents an order of
achievement, but the difference between levels isn’t equal (e.g., the gap between Bachelor’s and Master’s
is not equivalent to the gap between Master’s and Doctorate).
•Socioeconomic Status: Categories like low, middle, and high. These can be ranked, but the differences
between levels aren’t quantifiable.
 evel of measurement
Interval Data

• Continuous Variable
• Defined as a numerical scale where the order
of the variables is known as well as the
difference between these variables.
• the differences of the score on the scale has
meaningful interpretation

•Temperature in Celsius or Fahrenheit: Temperatures can be ordered and have equal intervals (e.g., the
difference between 10°C and 20°C is the same as between 20°C and 30°C), but there is no true zero (0°C
does not mean "no temperature").
•IQ Scores: An IQ of 100 versus 120 represents a 20-point difference, but there’s no true zero point that
would represent an absence of intelligence.
•Calendar Years: The difference between years (like 2000 and 2020) is consistent, but there’s no absolute
zero point, as 0 AD doesn’t represent the "beginning of time."
 evel of measurement
Ratio Data

• Continuous Variable
• Ratio scale allows any researcher to compare
the intervals or differences.
• Possesses a zero point or character of origin.
This is a unique feature of ratio scale.

•Weight: A weight of 0 kg represents an absence of weight, and ratios are meaningful (e.g., 20 kg is
twice as much as 10 kg).
•Height: A height of 0 cm indicates no height, and it’s possible to say that someone who is 180 cm tall is
twice as tall as someone who is 90 cm.
•Income: Income can start at zero, meaning no income, and comparisons are meaningful (e.g., earning
$60,000 is twice as much as earning $30,000).
Summary of Scale Characteristics

Scale Type Order Equal Intervals True Zero Examples

Gender, colors,
Nominal No No No
vehicle type

Satisfaction
Ordinal Yes No No ratings, class
rank

Temperature
Interval Yes Yes No (Celsius), IQ
score

Weight, time,
Ratio Yes Yes Yes
income
In the employee feedback survey, which scale of
measurement is used to collect data on the
employees' gender? a) Nominal scale b) Ratio scale
c) Interval scale d) Ordinal scale
A market research study asks participants to rank different features of a
smartphone according to their importance. Which scale of measurement
is appropriate for analyzing the rankings?
A taste test asks participants to
rank various brands of
chocolate from best to worst.
Which scale of measurement is
appropriate
In an experiment, researchers measure the number
of errors made by participants. Which scale of
measurement is being used for this variable
A retail store wants to analyze the sales revenue generated by
each of its product categories. Which scale of measurement is
appropriate for measuring the sales revenue?
A financial analyst is examining the return
on investment (ROI) for different
investment portfolios. Which scale of
measurement is applicable for measuring
ROI?
Scaling
Important Scaling Techniques
Likert Scale
The Likert scale is a popular tool used to measure people’s attitudes, opinions, or
feelings on a specific topic. It presents a statement and asks respondents to rate
how much they agree or disagree with it on a scale. Each response option is
assigned a numerical value, making it easy to analyze the responses.

Here’s a common 5-point Likert scale:

•1 - Strongly Disagree
•2 - Disagree
•3 - Neutral
•4 - Agree
•5 - Strongly Agree
A restaurant owner wants to assess customer satisfaction to identify areas for
improvement and enhance the overall dining experience. They decide to design
a questionnaire to gather feedback from their customers.
Define research objectives: The restaurant owner's objective is to understand customer satisfaction and identify
areas for improvement in their restaurant.
Determine the target population: The target population for this study is the restaurant's customers, both regular
patrons and new visitors.
Choose appropriate question types: The restaurant owner selects a combination of multiple-choice, Likert scale, and
open-ended questions to capture different aspects of customer satisfaction. For example:
Multiple-choice: How often do you visit our restaurant? (a) Once a week (b) Once a month (c) Lessthan once a
month
Likert scale: Rate the quality of our food on a scale of 1-5, with 1 being "Poor" and 5 being "Excellent."
Open-ended: What suggestions do you have for improving our service?
Draft clear and concise questions: Therestaurant owner drafts questions that are clear, concise,and relevant to
customer satisfaction. They avoid biased or leading questions. Example: "How would you rate the friendliness of
our staff?"
Sequence questions logically: The questionnaire starts with an introductory message, followed by questions about
overall satisfaction, specific aspects like food quality, service, ambiance, and concludes with demographic
questions.
Provide comprehensive response options: For closed-ended questions, the owner provides response options that
cover the range of possible answers. For example, for the question "How likely are you to recommend our
restaurant to others?" the response options can be a scale from 1 to 10. Open-ended questions allow customers to
provide detailed feedback.
Pilot test the questionnaire: The restaurant owner selects a small group of customers and asks them
to complete the questionnaire. They gather feedback on question clarity, length, and any potential
improvements.
Review and revise: Based on the pilot test feedback, the owner revises any questions that were
unclear or confusing. Theyalso streamline the questionnaire by removing any redundant or
unnecessary questions.
Format and aesthetics: The owner pays attention to the questionnaire's formatting, ensuring it is
visually appealing and easy to read. They use clear fonts, appropriate spacing, and consistent
design elements.
Pretest the final version: The revised questionnaire is pretested with another group of customers to
ensure its effectiveness and identify any remaining issues.
Finalize the questionnaire: After addressing any final revisions or adjustments, the restaurant owner
finalizes the questionnaire for distribution to all customers. They plan the data collection process
and analyze the results to gain insightsfor improving customer satisfaction and the overall dining
experience.
IMPORTANTQUESTIONS
Types of Sampling
Difference between Probability and Non Probability sampling
Different types of Scales
How to develop a questionnaire