Research – Meaning, Characteristics & Importance Basic Research Process – An overview & steps involved Research Design Components of Research Design Sampling Design

1. REAEARCH METHODOLOGY (504)
Prof. Anita Rathod

2. SYLLABUS
 Introduction to Research
 Sources of Collection of Data
 Methods of Collecting of Data
 Data Processing & Analysis
 Writing Skills for Business Research
Prof. Anita Rathod, BBA Department,
ICCS

3. CHAPTER NO. 1
 Research – Meaning, Characteristics &
Importance
 Basic Research Process – An overview &
steps involved
 Research Design – Meaning,
Characteristics of a good research design
 Components of Research Design
 Sampling Design – Steps involved & Types
of Samplings
Prof. Anita Rathod, BBA Department,
ICCS

4. RESEARCH
Prof. Anita Rathod, BBA Department,
ICCS

5. METHODOLOGY
 Systematic
 Set of methods
 Best practices
 Theoretical analysis of the methods
 Applied to a field of study.
Prof. Anita Rathod, BBA Department,
ICCS

6. DEFINITIONS OF RESEARCH
 According to Kothari, “Research is an
systematic investigation to find solution to a
problem.”
 According to John W. Best, “Research may to
defined as systematic and objective analysis
and recording of controlled observations that
may lead to development of organizations,
principles & possibility ultimate control of
events.”
Prof. Anita Rathod, BBA Department,
ICCS

7. TO BE CONTINUED…..
 According to D. Slesinger and M.
Stephenson, “Research is the manipulation
of things, concepts or symbols for the
purpose of generalizing to extend, correct
or verify knowledge, whether that
knowledge aids in construction of theory or
in the practice of an art.”
Prof. Anita Rathod, BBA Department,
ICCS

8. TO BE CONTINUED…..
 According to Clifford Woody, “Research
comprises defining and redefining
problems, formulating hypothesis or
suggested solutions; collecting, organizing
and evaluating data; making deductions
and reaching conclusions; and at last
carefully testing the conclusions to determine
whether they fit the formulating hypothesis.”
Prof. Anita Rathod, BBA Department,
ICCS

9. CHARACTERISTICS OF RESEARCH:
Evaluate Data
1
2
3 Conclusion
Systematic
investigation
2(A + B) = 2A + 2B
Scientific Method
Prof. Anita Rathod, BBA Department,
ICCS

10. • A1
• B2
• C3
Objective Analysis
Hypothesis
Knowledge
Money
Logical test
Observable evidences
Prof. Anita Rathod, BBA Department,
ICCS

11. Solution to Problem
Generalisation
Principles
Theories
Development
Test of criticism
Prof. Anita Rathod, BBA Department,
ICCS

12. IMPORTANCE
Improve
Knowledge
Contribution
for national
development
Investigate
facts
Invention
Contributes to
the theories
Prediction of
events
Inter
relationship
between
variables
Finding
solutions to
problem
Study
unknown
Phenomenon
Prof. Anita Rathod, BBA Department,
ICCS

13. RESEARCH PROCESS
Definr the
research
Problem
Literature
Review
Develop
Hypothesis
Data
Collection
Sampling
design
Research
design
Data
analysis
Hypothesis
testing
Preparation
of research
report
Prof. Anita Rathod, BBA Department,
ICCS

14. EXAMPLE (JOB SATISFACTION OF EMPLOYEES)
 What exactly you want to find out (problem)?
 Go through all available information available to
you.
 Create assumptions (hypothesis)
 Step by step procedure to follow research
 Universe, population, samples
 Sources of data collection
 Interprete the collected data
 Use tools such as chi square, z test, ANOVA
etc..
 Prepare research report.
Prof. Anita Rathod, BBA Department,
ICCS

15. DEFINTION’S OF RESEARCH DESIGN:
 According to Miller,“Research Design is the
planned sequence of the entire process involved
in conducting a research study”.
 According to P.V. Young, “Research design is
the logical and systematic planning and directing
of a piece of research.”
Prof. Anita Rathod, BBA Department,
ICCS

16.  According to E.A. Suchman ,“A research
design is not a highly specific plan to be followed
without deviation, but rather a series of guide
posts to keep one headed in the right direction.”
 According to Ackoff ,“Research Design is the
process of making decisions before the situation
arises in which the decision has to be carried out.
It is a process of deliberate anticipation directed
towards bringing an unexpected situation under
control.”
Prof. Anita Rathod, BBA Department,
ICCS

17.  According to Jahoda, Deutsch and Cook, “A
research design is the arrangement of conditions
for collection and analysis of data in a manner
that alms to combine relevance to the research
purpose with economy in procedure.”
Prof. Anita Rathod, BBA Department,
ICCS

18. ELEMENTS OF RESEARCH DESIGN:
5W
H
Research
Design
Basic framework specifying
methods and procedures of
collecting and analyzing data.
Prof. Anita Rathod, BBA Department,
ICCS

19. CHARACTERISTICS OF RESEARCH DESIGN
Specifies
Data Collection
Cost
Analysis
method
Time
Responsibility
Probable
Outcome
Actions
Objectives
Prof. Anita Rathod, BBA Department,
ICCS

20. SAMPLE DESIGN
Prof. Anita Rathod, BBA Department,
ICCS

21. CONTD…
 It is definite plan for obtaining a sample from a given
population.
(EX: population – ICCS; Sample – Student)
 It refers to technique or the procedure the researcher would
adopt in selecting items for the sample.
 It lay down number of items to be included in the sample
i.e. size of sample.
 It is determined before data are collected.
Prof. Anita Rathod, BBA Department,
ICCS

22. CONTD..
 There are many sample designs from which a researcher
can choose.
 Researcher must prepare a sample design which should be
reliable and appropriate for his research study.
 Two keys
1. Selecting the right people
 Have to be selected scientifically so that they are representative of the
population
2. Selecting the right number of the right people
 To minimize sampling errors I.e. choosing the wrong people by
chance
Prof. Anita Rathod, BBA Department,
ICCS

23. POPULATION VS. SAMPLE
Population of Interest
Sample
Population Sample
Parameter Statistic
We measure the sample using statistics in order to draw
inferences about the population and its parameters.
Prof. Anita Rathod, BBA Department,
ICCS

24. TERMINOLOGY -
 Population
 The entire group of people of interest from whom the researcher needs to
obtain information.
 Element (sampling unit)
 one unit from a population
 Sampling
 The selection of a subset of the population
 Sampling Frame
 Listing of population from which a sample is chosen
Census
 A polling of the entire population
Survey
 A polling of the sample
Prof. Anita Rathod, BBA Department,
ICCS

25. CONTD..
 Parameter
 The variable of interest
 Statistic
 The information obtained from the sample about the parameter
 Goal
 To be able to make inferences about the population parameter from
knowledge of the relevant statistic - to draw general conclusions about the
entire body of units
 Critical Assumption
 The sample chosen is representative of the population
Prof. Anita Rathod, BBA Department,
ICCS

26. STEPS IN SAMPLE DESIGN
Type of
Universe
Sampling
Unit
Source List
Size of
sample
Parameters
of interest
Budgetary
Constraint
Sampling
procedure
Prof. Anita Rathod, BBA Department,
ICCS

27. EXAMPLE : Students approach towards online
teaching process
World
Population
Student
Institution
Admission
department
10% or 30%
of Population
Response
from samples
Investment
for complete
research
How to chose
sample
(types)
Prof. Anita Rathod, BBA Department,
ICCS

28. TYPE OF SAMPLE DESIGN -
 Probability sampling - equal chance of being
included in the sample (random)
 simple random sampling
 systematic sampling
 stratified sampling
 cluster sampling
 Non-probability sampling - - unequal chance of
being included in the sample (non-random)
 convenience sampling
 judgement sampling
 snowball sampling
 quota sampling
Prof. Anita Rathod, BBA Department,
ICCS

29. PROBABILITY SAMPLING
 An objective procedure in which the probability of selection is non zero
and is known in advance for each population unit.
 It is also called random sampling.
 Ensures information is obtained from a representative sample of the
population
 Sampling error can be computed
 Survey results can be projected to the population
 More expensive than non-probability samples
Prof. Anita Rathod, BBA Department,
ICCS

30. SIMPLE RANDOM SAMPLING (SRS)
 Population members are selected directly from the sampling frame
 Equal probability of selection for every member (sample size/population
size)
 400/10,000 = .04
 Use random number table or random number generator
 Example:
List of Manufacturing industry
List of MBA institutes
Prof. Anita Rathod, BBA Department,
ICCS

31. CONTD…
 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
 Objective: To select n units out of N such that each NCn has an equal
chance of being selected
 Procedure: Use a table of random numbers, a computer random number
generator, or a mechanical device to select the sample
Prof. Anita Rathod, BBA Department,
ICCS

32. SYSTEMATIC SAMPLING
 Order all units in the sampling frame based on some variable and
number them from 1 to N
 Choose a random starting place from 1 to N and then sample every k
units after that
 Example: Odd roll No. or Roll no. in the multiplication of 3, etc..
Prof. Anita Rathod, BBA Department,
ICCS

33. CONTD…
 number the units in the population from 1 to N
 decide on the n (sample size) that you want or need
 k = N/n = the interval size
 randomly select an integer between
 1 to k
 then take
 every kth unit
Prof. Anita Rathod, BBA Department,
ICCS

34. STRATIFIED SAMPLING
 The chosen sample is forced to contain units from each of the
segments, or strata, of the population
 equalizing "important" variables
 year in school, geographic area, product use, etc.
 Steps:
 Population is divided into mutually exclusive and exhaustive
strata based on an appropriate population characteristic. (e.g.
race, age, gender etc.)
 Simple random samples are then drawn from each stratum.
Prof. Anita Rathod, BBA Department,
ICCS

35. CONTD..
Prof. Anita Rathod, BBA Department,
ICCS

36. CONTD…
 Population is divided on the basis of characteristic of interest
in the population e.g. male and female may have different
consumption patterns
 Has a smaller sampling error than simple random sample
since a source of variation is eliminated
 Ensures representativeness when proportional sampling used
 Direct Proportional Stratified Sampling
 The sample size in each stratum is proportional to the stratum
size in the population
 Disproportional Stratified Sampling
Prof. Anita Rathod, BBA Department,
ICCS

37. CONTD…
 The sample size in each stratum is NOT proportional to the
stratum size in the population
 Used if
1) some strata are too small
2) some strata are more important than others
3) some strata are more diversified than others
4) If primary research objective is to compare groups
5) Using stratified sampling may reduce sampling errors
Prof. Anita Rathod, BBA Department,
ICCS

38. CLUSTER SAMPLING
 Clusters of population units are selected at random and
then all or some randomly chosen units in the selected
clusters are studied.
 Steps:
 Population is divided into mutually exclusive and
exhaustive subgroups, or clusters. Ideally, each cluster
adequately represents the population.
 A simple random sample of a few clusters is selected.
 All or some randomly chosen units in the selected
clusters are studied.
Prof. Anita Rathod, BBA Department,
ICCS

39.  divide population
into clusters (usually
along geographic
boundaries)
 randomly sample
clusters
 measure units within
sampled clusters
Prof. Anita Rathod, BBA Department,
ICCS

40. When to use cluster sampling
 If there are substantial fixed costs associated with
each data collection location
 When there is a list of clusters but not of individual
population members
Prof. Anita Rathod, BBA Department,
ICCS

41. NON-PROBABILITY SAMPLING
 Subjective procedure in which the probability of
selection for some population units are zero or
unknown before drawing the sample.
 information is obtained from a non-representative
sample of the population
 Sampling error can not be computed
 Survey results cannot be projected to the population
 Cheaper and faster than probability
 Reasonably representative if collected in a thorough
manner
Prof. Anita Rathod, BBA Department,
ICCS

42. CONVENIENCE SAMPLING
 A researcher's convenience forms the basis for
selecting a sample.
 people in my classes
 Mall intercepts
 People with some specific characteristic
Prof. Anita Rathod, BBA Department,
ICCS

43. JUDGEMENT SAMPLING
 A researcher experts some effort in selecting a sample
that seems to be most appropriate for the study.
 Regarding any gadget information sample techsavy
person
 If you want to know some political related issues, sample
will be considered as political experienced leaders
Prof. Anita Rathod, BBA Department,
ICCS

44. SNOWBALL SAMPLING
 Selection of additional respondents is based on
referrals from the initial respondents.
friends of friends
 Used to sample from low incidence or rare
populations.
 Customers behavior towards any product
 For availing any service from bank
Prof. Anita Rathod, BBA Department,
ICCS

45. QUOTA SAMPLING
 The population is divided into cells on the basis of
relevant control characteristics.
 A quota of sample units is established for each cell.
50 women, 50 men
 A convenience sample is drawn for each cell until the
quota is met.
(similar to stratified sampling)
Prof. Anita Rathod, BBA Department,
ICCS

46. NON-SAMPLING ERRORS (I)
– systematic Error
– the level of it is NOT controlled by sample size
 The basic types of non-sampling error
 Non-response error
 Response or data error
 A non-response error occurs when units selected as part of
the sampling procedure do not respond in whole or in part
 If non-respondents are not different from those that did
respond, there is no non-response error
Prof. Anita Rathod, BBA Department,
ICCS

47. CONTD….
 A response or data error is any systematic bias that occurs
during data collection, analysis or interpretation
 Respondent error (e.g., lying, forgetting, etc.)
 Interviewer bias
 Recording errors
 Poorly designed questionnaires
Prof. Anita Rathod, BBA Department,
ICCS

48. COMPONENT OF RESEARCH DESIGN
 Variable: Quantitative values (weight, height,
income)
 1. Dependent Variables
 2. Independent variables
Example:
Different types of Ice cubes placed at different
surfaces. Then,
DV = Ice cube melting time
IV = Types of ice cube and type of surface
Prof. Anita Rathod, BBA Department,
ICCS