Research Design

UNIT 3
Mr.T.SOMASUNDARAM
ASSISTANT PROFESSOR
DEPARTMENT OF MANAGEMENT
KRISTU JAYANTI COLLEGE, BANGALORE

UNIT 3: RESEARCH DESIGN
Meaning, Need, Features, Concept
relating to Research Design, Types of
Research Design, Sampling – Meaning,
Steps in Sampling, Sampling Methods
(Probability and Non-Probability
Methods)

RESEARCH DESIGN
Definition:
“Research Design is the arrangement of conditions for collection
and analysis of data in a manner that aims to combine relevance to the
research purpose with economy in procedure.”
It indicates decisions regarding –
* what, where, when, how much, by what means about a research
study.
(E.g.) What is the study about?
What type of data required?
What periods of time will the study include?
In what style will the report be prepared?
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Research design split into following parts:
a) Sampling design – deals with method of selecting items to observed
for the study.
b) Observational design – related to conditions under which
observations are made.
c) Statistical design – deals with questions of how many items to be
observed.
d) Operational design – deals with techniques by which procedures
specified in the sampling.

Need for Research Design
 Research design is needed because it facilitates the smooth sailing of
the various research operations, thereby making research as efficient
as possible yielding maximal information with minimal expenditure
of effort, time and money.
 Research design has a significant impact on the reliability of the
results obtained.
 A research design acts as a guide to the researcher.
 Without a research design a study may lead towards misleading
conclusions.
 Therefore it is necessary to prepare an effective and appropriate
research design before the actual research operation.

Need for Research Design
 It reduces inaccuracy.
 Helps to get maximum efficiency and reliability.
 Eliminates bias and marginal errors.
 Minimizes wastage of time.
 Helpful for collecting research materials.
 Helpful for testing of hypothesis.
 Gives an idea regarding the type of resources required in terms
of money, manpower, time, and efforts.
 Provides an overview to other experts.
 Guides the research in the right direction.

Features of a Good Research Design
 A good research design is often characterized by adjectives
like flexible, appropriate, efficient and economical and so on.
 It minimizes bias and maximizes the reliability of the data
collected and analyzed is considered a good design.
 The design which gives the smallest experimental error is
supposed to be the best design and yields maximal
information and provides an opportunity for considering
many different aspects of a problem is considered most
appropriate and efficient design in respect of many research
problems.

Features of a Good Research Design
i) It is a plan which describes the sources and kinds of information strongly
related to the research problem.
ii) It is a strategy indicating which method will be employed for collecting
and examining the data.
iii) It also consists of the time and cost budgets because most studies are done
under these two.
A research design appropriate for a particular research problem, usually
involves the following factors -
1) The means of obtaining information
2) The availability and skills of the researcher and his staff, if any
3) The objective of the problem to be studied
4) The nature of the problem to be studied
5) The availability of time and money of the research work.

Limitations:
In a nutshell, research design must, at least, contain -
(a) a clear statement of the research problem.
(b) Processes and methods to be utilized for collecting data.
(c) The population to be researched.
(d) Techniques to be employed in processing and examining data.
Advantages of research design:
1. Consumes less time.
2. Ensures project time schedule.
3. Helps researcher to prepare himself to carry out research in a proper and a
systematic way.
4. Better documentation of the various activities while the project work is going on.
5. Helps in proper planning of the resources and their procurement in right time.
6. Provides satisfaction and confidence, accompanied with a sense of success from
the beginning of the work of the research project.

CONCEPTS RELATING TO RESEARCH DESIGN
1. Variable:
* Variable – a concept which can take on different quantitative values. (E.g.) Weight,
height, income, etc.
It consists of –
a) Dependent variable - If one variable depends upon or is a consequence of the other
variable is termed as a dependent variable. (i.e.) depends on other factors.
(E.g.) Your mid term (or) ESE mark is dependent variable, because it depends on many
factors like how much you studied, how much you books used, how much time you spent,
ho much relevant concept you studied, etc….
b) Independent variable - The variable that is antecedent to the dependent variable is
termed as Independent variable.
it stands alone and doesn’t change or vary or dependent on other variables.
(E.g.) Age doesn’t depend on any factors (i.e.) your age was not identified based on your
colour, physical appearance, etc…..
(E.g.) If we say that height depends upon age, then height is dependent variable and age is
independent variable.

CONCEPTS RELATING TO RESEARCH DESIGN
c) Continuous variable - Phenomena which can take on quantitatively different values
even in decimal points are called continuous variable. (i.e.) it is applicable in experiments,
testing, etc..
(E.g.) time to complete the 100 meter track could be 10.42 seconds but it can be
10.4241254723……….
d) Non Continuous variable / Discrete variable - All variables are not continuous. If they
can only be expressed in integer values, they are non continuous variables or in statistical
language discrete variables. (i.e.) it indicates the exact number.
(E.g.) No. of children or students in class room or schools or college (i.e) exactly 44 students, not
44.5 students.
2. Extraneous Variable:
“Independent variables that are not related to the purpose of study, but may affect the
dependent variable are termed as extraneous variable.”
(E.g.) Children’s gain in social studies – dependent variable.
Self -concepts – independent variable.
Intelligence – Extraneous variable.

3. Control:
“The term ‘Control’ is used when we design the study minimizing the effects of
extraneous independent variables.”
- it is used to refer to restrain experimental conditions.
4. Confounded relationship:
“When the dependent variable is not free from the influence of extraneous variable,
the relationship between the dependent and independent variables is said to be
‘confounded’ by an extraneous variable.”
5. Research Hypothesis:
‘When a prediction or a hypothesized relationship is to be tested by scientific
methods, it is termed as research hypothesis.”
6. Experimental and Non-experimental hypothesis-testing research:
“The purpose of research is to test a research hypothesis, is termed as hypothesis-
testing research.”
- independent variable (IDV) is manipulated is ‘experimental hypothesis testing
research.
- IDV is not manipulated is ‘non-experimental hypothesis-testing research.

7. Experimental and Control groups:
“When a group is exposed to usual conditions, is termed as ‘control group’ in
an experimental hypothesis testing research, but when the group is exposed to
special condition is termed as experimental group.”
8. Treatment:
“The different conditions under which experimental and control groups are
put are usually referred to as ‘treatments’.”
9. Experiment:
“The process of examining the truth of a statistical hypothesis, relating to
some research problems, is known as an experiment.”
* Absolute experiment (E.g.) impact of fertilizer on yield of crop.
* Comparative experiment (E.g.) one fertilizer compared with other fertilizer.
10. Experimental Unit(s):
‘The pre-determined plots or blocks, where different treatments are used, are
known as experimental units.”

Elements of a Research Design
Elements of a Research Design:
1) Introduction: - The introduction of a research plan or proposal should place the
research problem in its historical perspective, state the need for studying it and the
researcher‘s precise interests in the study of the problem.
2) Statement of the problem: - The research problem should be well defined,
pointing out its core nature and its importance. The issues relating to the problem
may also be stated. This statement gives direction to the research process.
3) Review of the previous studies: - Under this head the researcher presents what is
so far known about the problem under consideration. A review of available
literature will bring out information on them
4) Scope of the study: - A complete study of any problem is well nigh
unmanageable. It would entail such an over-whelming volume of data that it would
require more than a student‘s life time to comprehend and complete the study.

5) Objective of the study: - The specific objective of the study should be stated
clearly. These refer to the questions to which the researcher proposes to seek
answers through the study. The objectives mentioned should be well within the
scope of the study.
6) Hypothesis to be tested: - Hypothesis is a proposition, condition or principle
which is assumed, perhaps without belief in order to draw logical conclusions. They
should be conceptually clear, specific and simple.
7) Operational definition of Concepts: - All terms that might be ambiguous should
be clarified. A clear understanding of the terms used in the study is important. It is
necessary to identify and label the variables. The variables can be labeled as
independent variable and dependent variable
8) Geographical area to be covered: - The area to be chosen depends on the
purpose of the study and time and other resources available.

9) Reference period: - This period may be one year or two or more years depending on
the nature of the study and availability of data. The period should be longer, say 5 or 10
years, if the study aims at making a trend analysis of an activity like production or sale or
profitability.
10) Methodology: - In this section, over all typology of the design-experimental,
descriptive, survey, case study – is specified. Further the methods or methods to be
adopted for collection of data, Observation, Interviewing or Mailing are specified
11) Sampling: - It involves taking a portion of population, making observation on this
smaller group and then generalizing the findings to be applied to a large population.
12) Tools for gathering data: - In this section, the tools to be used for gathering data –
interview schedule / questionnaire or check list, etc. are listed and each of them is
described. The tools chosen should be appropriate to the methods to be adopted for
gathering data.

13) Plan of analysis: - Once the data have been collected, they must be reduced to
meaningful results by statistical analysis so that the conclusion for generalizations
can be drawn from them.
14) Chapter Scheme: - The chapter scheme of the report to be prepared for
communicating the findings of the study to the academic community and the
purpose of each chapter should be stated.
15) Time budget: - The time period required for each stage of work and the total
time duration of the study are specified.
16) Financial budget: - This should include as estimate of the expected costs of the
project under various major categories like salary (if any), printing and stationary,
postage, travel expenses, computation, secretarial and typing, etc.

Types of Research Design
Research design are classified on the basis of the fundamental objective of
the research. It is classified into -
1. Exploratory Research Design (or Formulative):
“Exploratory research is most commonly unstructured, “informal”
research that is undertaken to gain background information about the
general nature of the research problem.
* It is usually conducted when the researcher does not know much about the
problem and needs additional information or desires new or more recent
information.
* The main purpose of this design is that of formulating a problem for more
precise investigation of developing the working hypothesis from an
operational definition.
* It provide opportunity for considering different aspects of problem under
study.

* Exploratory research is used in a number of situations:
 To gain background information.
 To define terms.
 To clarify problems and hypotheses.
 To establish research priorities.
Methods:
a) Survey of concerning literature (Or) Search of Secondary data – this is simple
method of formulating the research problem or developing hypothesis from the
available literature.
- it used to consider whether already stated hypothesis suggest new hypothesis.
b) Experience survey (Or) Survey of Knowledgeable persons – it indicate the use
of reservoir of knowledge and experience and familiar with specific subject and
it is obtain insight into the relationships between variables and new ideas relating
to the research problem.

- for such a survey respondents should be selected carefully selected and flexible
in responding the questions.
- this experience survey may enable researcher to define the problem more and
help in formulation of research hypothesis.
c) Case Study / Analysis - it involves intensive study of selected cases of the
phenomenon.
- it includes the examination of existing records, by observing the occurrence of
phenomenon.
- it includes the examination of existing records by observing the occurrence of the
phenomenon
e) Focus groups – it is useful for gathering ideas and insights.
- a small no. of individuals are brought together in a room to sit and talk about
some topic and to follow a rough outline of issues.
- exposed to the ideas of the others and submits his ideas to the group for
consideration.

f) Survey of experts:
 Discussion with the experts and decision maker helps researcher in
identifying problem.
 Usually expert information is obtained by unstructured personal
interview, without administering formal questionnaire.
g) Pilot Surveys:
 A pilot study is a small scale replica of the main study.

2. Conclusive Research Design:
a) Descriptive and Diagnostic research design:
“Descriptive research studies are those studies which are concerned
with describing the characteristics of a particular individual or group,
whereas diagnostic research studies determine the frequency with
which something occurs or association with something else.”
- it define clearly, what he wants to measure, adequate methods for
measuring it.
This studies must focus on –
* Formulating the objective of study.
* Designing the methods of data collection.
* Selecting the sample.
* Collecting the data, analyzing the data.
* Reporting findings.

There are two types of descriptive research design –
i) Longitudinal (Panel) Method:
 A panel is simple with fixed sample of individuals or some other
entities form whom repeated measurements are taken.
It is of two types –
a) True panel – it relies on repeated measurements of the same
variables.
(E.g.) Consumer panel may be selected where by each family members
may be examined at different points of time or their behavior.
- it is conducted to get periodical information regarding market,
frequency of purchase, impact of new brand, etc.
b) Omnibus panel – a sample of members is selected and maintained
but the information collected from members varies.
- sample of members of panel may be representative to the population.

ii) Cross – Sectional method:
 It is termed as field study since the repetitive nature of inquiry is
not present in this case.
 It seeks a comparison between various group and differing
characteristics of those groups.
It is of two types –
a) Field Study – it emphasis on the interrelationships of number of
factors.
- it lies in realism, strength of variables and quality.
b) Field Survey – it relies on the survey function and it attempts to
represent some known universe.
- It is generation of summary statistics like average and percentage.

b) Experimental research design:
“An experiment is defined as manipulating
(changing values/situations) one or more independent
variables to see how the dependent variable(s) is/are
affected, while also controlling the affects of additional
extraneous variables.”
“Experimental research design are those where the
researcher tests the hypotheses of casual relationships
between variables.”
- it need procedure to reduce bias & increase reliability.

It has two groups –
 Test Group – in this group, the experiment is done (i.e.) variable
are allowed to change, according to the result of the cause.
 Control Group – in this group, variables are not changed and no
experiment or cause is incurred. The extraneous variables are
held constant in the control group.
Advantages of Experimental Design:
1) Experimental design has the advantages of internal and external
validity.
2) This design involves caring to minimize human judgment and
whenever separate groups are used for different treatments or controls,
they are identical in all pertinent characteristics.

Experimental research design has two types –
i) Laboratory Experiment:
“A laboratory experiment may be defined as one in which the
investigator creates a situation with the exact conditions he wants to have and
in which he controls some and manipulates other variables.”
 It is able to observe and measure the effects of the manipulation of
independent variables.
(E.g.) Recording the purchase volume at different prices to find out the
impacts of price changes due to influence of various advertisements.
ii) Field Experiment:
“A field experiment is a research study in a realistic situation in which
one or more independent variables are manipulated by the experimenter under
as carefully controlled conditions as the solution will permit.”
 The experimental variables factors are taken to the fields (i.e.) at different
places.

Difference between Exploratory Vs Descriptive Research Design:

c) Causal research design (or Causal studies):
Causality may be thought of as understanding a phenomenon in terms of
conditional statements of the form “If x, then y.”
Causal relationships are typically determined by the use of experiments,
but other methods are also used.
Causal Research Design (Causation) is the ideal standard that one variable
always causes another and no other variable has the same causal effect.
Causal Research is -
- undertaken with the aim of identifying cause and effect relationships
amongst variables.
- are normally preceeded by exploratory and descriptive research studies.
(E.g.) Higher ice-cream consumption causes more people to drown
(indicative of a causal relationship).

Causal relationships:
“It is how one variable affects, or is ‘relationship for’, changes in another
variable.”
Types of Causal relationships:
a) Symmetrical relationship – two variables fluctuate together but we
assume the changes in neither variable are due to changes in the other.
b) Reciprocal relationships – this exists when two variables mutually
influence or reinforce each other.
c) Asymmetrical relationships – the changes in one variable (IDV or DV) are
responsible for changes in another variable (DV or IDV).
* The identification of DV or IDV is obvious, so in that cases we depend on
following basis -
i) degree to which each variable may be altered. (unaltered variable will be
IDV) (e.g.) age, social status.
ii) time order between the variables. (IDV precedes DV).

Probability Sampling
 Probability Sampling – It is the method of sampling that utilizes some
form of random selection.
 Every element in the population under study has a non-zero probability of
selection to a sample, and every member of the population has an equal
probability of being selected.
 Some basic terms are:
• N = the number of cases in the sampling frame
• n = the number of cases in the sample.
•
NCn = the number of combinations (subsets) of n from N
• f = n/N = the sampling fraction.

Simple Random Sampling – This is a technique which ensures that each
element in the population has an equal chance of being selected for the
sample.
 This is used in population sampling situations when reviewing historical or
batch data.
How to select simple random sample?
• Let’s assume that the research to be done with small service agency to
assess client’s view of quality of service over the past year.
• First, we have to get sampling frame organized.
• To accomplish this, we can go through agency records and draw the
sample.
1. Simple Random Sampling

(E.g.) Let’s say we want to select 100 clients and there were 1000 clients
over the past year. Then, the sampling fraction is f = n/N = 100/1000 = .10
or 10%.
 To draw 100 samples, we can drop 1000 clients list separately in box and
we can pick out one by one randomly.
(E.g.) Choosing raffle tickets from a drum, computer-generated selections,
random-digit telephone dialing.
Advantages of simple random sampling:
 It is simple to accomplish and easy to explain.
 it is reasonable to generalize the results from sample.
Disadvantages:
* It is not most statistically efficient method, just because of the luck of
draw.

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 Systematic Sampling – This is a technique which in which an initial starting point
is selected by a random process, after which every nth number on the list is
selected to constitute part of the sample.
 It involves taking sample according to systematic rule like every fourth unit, the
first five units every hour in process operation, etc.
Note: Systematic rule may match some underlying structure and bias the sample.
Steps:
1. no. of units in the population from 1 to N.
2. Decide on the n (sample size) that you want or need.
3. K = N/n = the interval size.
4. Randomly select an integer between 1 to k.
2. Systematic Sampling

(E.g.) From a list of 1500 name entries, a name on the list is randomly
selected and then (say) every 25th name thereafter. The sampling interval
in this case would equal 25.
 Manager of billing center use systematic sampling to monitor processing
rates like random times around each hour, every five consecutive bills.
 For systematic sampling to work best, the list should be random in nature
and not have some underlying systematic pattern.

(E.g.) Let’s assume that we have a
population N = 100 people and you
want to take a sample of n = 20.
 To use systematic sampling, the
population must be listed in a random
order.
(i.e.) sampling fraction f = 20/100 = 20%, then
interval size k = N/n = 100/20 = 5.
 Now, select a random integer from 1 to 5.
In our example, imagine that you are choosing sample 4.
* Now , to select the sample, start with 4th unit in the list and take every kth unit (k = 5), that is
your sampling will be 4,9,14,19 and so on upto 20 sample that you take.

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3. Stratified Random Sampling
 Stratified Random Sampling – This is a technique which in which simple random sub
samples are drawn from within different strata that share some common characteristic.
 It involves dividing your
population into homogeneous
subgroups (strata) and then
taking a simple random sample
in each subgroup.
 This is used in population
sampling situations when
reviewing historical or batch
data, but when the population
has different groups (strata)
and this groups should be represented in the sample.

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(E.g.) The Student body of CIIT is
Divided into two groups (management
science & engineering) and from each
group, students are selected for a
sample using simple random sampling
in each of the two groups, whereby the
size of the sample for each group is
determined by that group’s overall
strength.
 It has the advantage of giving
more representative samples and
less random sampling error.
 It has the disadvantage that, it
is more complex and information
on strata may be difficult to obtain.

4. Cluster Sampling
Cluster Sampling (Area Sampling):
Heterogeneous elements are taken and on the basis of their homogeneous
characteristics, a group is formed and information is gathered from such groups. It may
be single stage or multistage.
 It is the process of randomly selecting intact groups, not individuals, within the defined
population sharing similar characteristics.
 Clusters are locations within which an intact group of members of the population can be
found.
(E.g.) Neighborhoods, Schools, Classrooms, etc.
Reason for existence of Cluster Sampling:
* When we have to sample a population that’s disbursed across a wide geographic region
is that you will have to cover a lot of geographically in order to get to each of units you
sampled.

Steps:
1. Divide the population into clusters (usually along geographic boundaries).
2. Randomly sample clusters.
3. Measure all units within sampled clusters.
Area Sampling :
Population within identifiable geographical areas are taken and information is
gathered.
Double Sampling :
This plan is resorted to when further information is needed from a subset of
the group from which some information has already been collected for the
same study.

Stratified Vs Cluster Sampling
Stratified Cluster
Population divided into
few subgroups
Population divided into
many subgroups
Homogeneity within
subgroups
Heterogeneity within
subgroups
Heterogeneity between
subgroups
Homogeneity between
subgroups
Choice of elements from
within each subgroup.
Random choice of
subgroups.

5. Multi-stage sampling
● Combination of simple four methods like simple random sampling,
stratified, systematic and Cluster sampling is known as multi stage
sampling.
(E.g.) Consider the idea of sampling Tamilnadu state residents for face to
face interviews. First, we would use cluster sampling for meeting
respondents, but it is not possible to meet everyone, so we might use
stratified sampling methods to divide the exact sampling within the
clusters, Thus, we would have a two-stage sampling process and more, that
is called multi – stage sampling.

Non-Probability Sampling
● Non-Probability Sampling – An arbitrary means
of selecting sampling units based on subjective
considerations, such as personal judgment or
convenience. It is less preferred to probability
sampling.

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1. Convenience Sampling
Convenience Sampling – This is a sampling technique which selects those
sampling units most conveniently available at a certain point in, or over a period,
of time.
 Picking up the first and most accessible respondents like approaching your
friends, relatives or interviewing the 1st 100 person that you meet in the entrance
of a super market.
 When you don’t control the composition of your sample, you don’t know
whether the opinion you are gathering is representative of the population.
(i.e.) It is like throwing a net in the water and ‘fish’ until you have reached the
number of people you decide to interview.
 It is important to understand that this is not a random sample, but in order to
produce reliable research, this technique should not be used.

The first 5 people you meet are selected
(E.g.) Suppose, the respondents wants to select 100 car owners. Then he may collect the
list of car owners from RTO’s office and make a selection
of 100 from that to form the sample.
Advantage:
 It is that is quick, convenient and
Economical.
Disadvantage:
○ Sample may not be representative.
 Convenience sampling is best used for the purpose of exploratory research and
supplemented subsequently with probability sampling.

2. Judgment Sampling
Judgment (purposive) Sampling – This is a sampling technique in which the
business researcher selects the sample based on judgment about some
appropriate characteristic of the sample members
 It is a form of convenience sampling in which the population elements are
selected based on the judgment of the researcher.
 Example 1: Test markets selected to determine the potential of new product.
 Example 2: Selection of certain voting districts which serve as indicators for
the national voting trend.
 Example 3: Purchase engineers selected in industrial marketing research.

Advantages:
○ useful in exploratory research.
○ makes certain that the widest variety of elements is chosen in the sample.
Disadvantages:
○ the researcher should be fully aware
of purpose and objective of research.
○ the bias of researcher may affect
representativeness of sample.

3. Quota Sampling
Quota Sampling – This is a sampling technique in which the business researcher
ensures that certain characteristics of a population are represented in the sample
to an extent which is he or she desires.
 Selecting participant in numbers proportionate to their numbers in the larger
population, no randomization.
 There is a specific list of relevant control categories or quota such as age,
gender, income or education.
(E.g.) The no. of students from each group that we would include in the sample
would be based on the proportion of male and female students amongst the
10,000 university students. (Proportion; 50 male & 50 female or 40 female &
60 male).

(E.g.) In a study, wherein the researcher likes to compare the academic
performance of the different high school class levels, its relationships with
gender and socioeconomic status, the researcher first identifies the subgroups.
Steps for Quota Sampling:
1. Quota sampling is to divide the population into exclusive sub groups.
2. Identify the proportions of subgroups in the population.
3. Select the subjects form various subgroups while taking into consideration the
proportions noted in the previous step.
4. It make ensure that the sample is representative of the entire population.
Advantages - include the speed of data collection, less cost, the element of
convenience, and representativeness (if the subgroups in the sample are selected
properly).
Disadvantages - include the element of subjectivity (convenience sampling rather
than probability-based which leads to improper selection of sampling units).

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4. Snowball Sampling
Snowball Sampling – This is a sampling technique in which individuals or
organizations are selected first by probability methods, and then additional
respondents are identified based on information provided by the first group of
respondents
 Example: Through a sample of 500 individuals, 20 scuba-diving enthusiasts
are identified which, in turn, identify a number of other scuba-divers
 The advantage of snowball sampling is that smaller sample sizes and costs
are necessary; a major disadvantage is that the second group of respondents
suggested by the first group may be very similar and not representative of the
population with that characteristics.

COMPARISON OF PROBABILITY SAMPLE
Description Cost and Degree of
use
Advantages Disadvantages
Simple Random Sample:
Researcher assigns each member of the
sampling frame a number, then selects
sample units by a random method
High cost
Most likely used
Only minimal advance
knowledge of population
needed; easy to analyse data and
compute error
Requires sampling frame to work
from; Does not use knowledge of
population; larger errors for same
sample size than with stratified
sampling.
Stratified Random sample:
Researcher divides the population into
groups and randomly selects sub-
samples from each group
High cost
Moderately used
Assures representation of all
groups in sample;
Reduces variability for same
sample size
Requires accurate information
on proportion in each stratum;
If stratified lists are not already
available, they can be costly to
prepare.
Systematic:
Researcher uses natural ordering or
order of sampling frame, selects an
arbitrary staring point, then selects items
at a preselected intervals.
Moderate cost
Moderately used
Simple to draw sample; easy to
check
If sampling interval is related
to a periodic ordering of the
population, may introduce
increased variability.
Cluster sampling:
Researcher selects sampling units at
random, then does complete
observations of all units in the groups
Low cost
Frequently used
If clusters geographically defined,
yields lowest field cost; requires
listing of all clusters but of
individuals only within clusters
Larger error for comparable
size than other probability
samples.

COMPARISON OF NON – PROBABILITY SAMPLE
Description Cost and Degree of use Advantages Disadvantages
Convenience:
Researcher uses most convenient sample
or most economical sample
Very low cost
Extensively Used
No need to list of
population
Variability and bias of
estimates cannot be
measured or controlled
Judgement:
An export or experienced researcher
selects the sample to fulfill a purpose
Moderate cost
Average use
Useful for certain
types of forecasting
Bias due to experts’
beliefs
Quota:
Researcher classifies population by
pertinent properties, determines desired
proportion of sample from each class
Moderate cost
Very extensively used
Introduces some
stratification of
population; requires
no list of population
Bias in researcher’s
classification of
subjects.
Snowball:
Initial respondents are selected by
probability samples; additional
respondents are obtained by referral from
initial respondents.
Low cost
Used in special situations
Useful in locating
members of rare
populations
High bias because
sample units are not
independent.

Research Design

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