2. Steps in Research :
1. objectivity
2. Problem formulation
3. Literature study
4. Research design
5. Formulation of Hypothesis
6. Sampling
7. Data collection
8. Processing and analysis of data
9. Interpretation and recommendation
10. Report writing
3. Survey
A survey is a process by which certain
quantitative/qualitative facts pertaining to certain
field of enquiry are collected to throw light on the
objectives of a research problem.
A descriptive surveys are fact finding surveys
An analytical surveys deal with interrelations
among different variables of interest and their
interaction
4. A survey is a planned observation of objects that are
not controlled by the observer.
These objects are not themselves treated but the
„Nature‟ is assumed to have applied the treatments
and all that analysts can do it to observe the
consequences.
5. A Survey of complete enumeration of population of
interest is called Census.
A Survey based on a subset of the population which
is also called as a sample is termed as sample survey.
6. Sampling or Sampling techniques
A sample as the name implies is smaller
representative of a larger whole.
The method of selecting a portion of the universe for
the study is known as sampling.
It helps to draw conclusions about the said universe
7. The entire group from which a sample is chosen is
known as the population or universe
Census: A complete enumeration of all items in the
population is known as census enquiry
Sampling frame: It is a list of items from which the
sample is to be drawn.
8. Sampling methods or Sampling techniques Sampling
Designs:
Two generic types:
1. Probability or random sampling, and
2. Non-probability or Non-random sampling
9. Probability or random sampling
A. Simple designs
1. Simple random sampling
2. Stratified random sampling
3. Systematic random sampling
B. Complex designs
1. Cluster sampling
2. Area sampling
3. Multi-stage and sub-sampling
10. Non-probability or Non-random sampling
A. Simple designs
Convenience or accidental sampling
Purposive (or Judgement ) sampling
B. Complex designs
1. Quota sampling
2. Snow-ball sampling
11. Reasons for choosing different sampling
designs.
1. Nature of population
2. Simplicity in adoption
3. Availability of frame
4. Representativeness
5. Nature of sampling unit
6. Cost of enumeration
7. Precision criterion
12. Probability or random sampling
A. Simple designs
1. Simple random sampling
Simple random sampling is the simplest of all
sampling designs
Each and every item in the population has an
equal and independent chance of inclusion
This can be done for a homogenous population.
However for heterogeneous population a simple
random sampling may not give the desired
results.
13. 2. Stratified random sampling
This is used for a heterogeneous population.
Here the population is stratified (Grouped) into
a number of overlapping sub-populations or
strata and sample items are selected from each
stratum.
Ex: In survey of business establishments, one
may form large, medium and small
establishments.
Further the sample selection from each strata is
based on simple random selection.
14. 3. Systematic random sampling
Only the first unit is selected randomly and the
remaining units of the sample are selected at fixed
intervals.
Ex: To choose every 10th name or 15th item and so on
In this method the entire list of the universe is given
numbers
It is easier and less expensive
It is spread more evenly over the entire population
The main disadvantage is if there is a hidden periodicity
in the population, this may prove inefficient.
15. B. Complex designs
1. Cluster sampling :
This involves grouping of population and then
selecting the groups or clusters rather than
individual elements for inclusion in the sample.
That is the total population is divided into a
number of relatively small subdivisions which
are themselves clusters of smaller units.
Further some of these clusters are randomly
selected for inclusion in the overall selection
16. 2. Area sampling
Cluster sampling in the form of grids imposed on
maps in certain forms are is termed as Area
sampling.
It will not be grouped by type of establishments
like villages, industries, hospitals etc but based on
areas.
Ex: National population or well defined political or
natural boundaries.
17. Non-probability sampling
This sampling does not provide a chance of
selection to each population
The selection probability is known
A non-probability sample may not be true
representative
Population parameters cannot be estimated from
the sample values
It suffers from sampling bias which suffers from
bias.
Hence generally not advisable
18. When there is no other feasible method for collection
of data or non-availability of population for
collection of data.
When study does not need generalisation of
conditions
When cost is a consideration
When probability sampling needs more time.
19. Non-probability or Non-random sampling
A. Simple designs
1. Convenience or accidental sampling
2. Judgment sampling
B. Complex designs
1. Quota sampling
2. Snow-ball sampling
20. Non-probability or Non-random sampling
A. Simple designs
1. Convenience or accidental sampling:
This method is employed to get information
quickly and inexpensively
Depends on the convenience of the researcher
Keeps in view of the general population
21. 3. Judgment sampling:
Judgment sampling is very appropriate when it is
necessary to reach small and specialized
populations.
The researcher uses judgment to identify
representative samples
A judgmental sampling is likely to be more reliable
and representative than a probability sample.
However unwelcome bias might creep into results
if not honestly judged.
22. Complex designs
1. Quota sampling:
We observe the responding units non-randomly
according to some fixed quota
It is to assure that the smaller groups are
adequately represented
Bias can exist
23. 2. Snow-ball sampling
First someone is identified who meets the
criteria and further asked to include others.
Useful where representatives are inaccessible
or hard to find
Inherent problem is one who is socially visible
are likely to be selected.
24. Data Collection
Data are facts, figures and other relevant
materials, past and present serving as basis for
study and analysis.
Types of sources of data
1. Primary data
2. Secondary Data
25. 1. Primary data are those which are collected afresh
and for the first time and thus happens to be
original in character
2. Secondary data are those which have already been
collected by someone else and which have salready
been passed through statistical process.
26. Primary data
1. Primary data Primary data are those which are
collected afresh, for the first time and thus
happens to be original in character.
2. First formal appearance of results in the print or
electronic literature.
27. Secondary data
1. Secondary data are those which have already been
collected by someone else and which have already
been passed through statistical process.
2. Secondary sources are works that describe,
interpret, analyse primary data
3. Comments and discussion of the evidence
provided by primary sources
28. Processing of Data
Data processing is an intermediary stage of work
between data collection and data interpretation
The steps involved in processing of data may be
stated as:
1. Identifying data structures
2. Editing the data
3. Coding and classifying the data
4. Transcriptions of data
5. Tabulation of data
29. Editing the data
Data editing at he time of recording the data
Data editing at the time of analysis of data
Completeness
Accuracy
Uniformity
30. Coding and
Numeric coding
Alphabetic coding
Zero coding
Classification
32. Graphs/Charts/Diagrams
Line Graphs
Bar charts
Histograms
Frequency plygon
Ogive
Lorenz curve
Bar charts
Vertical bar charts
Horizontal bar charts
Pie charts
pictograms
33. Line graphs are useful for showing changaes in
data relationships.
The horizontal line is the x-axis and verical line is
the y-axis
34. A bar chart or bar graph is a chart with
rectangular bars with lengths proportional to the
values that they represent. The bars can be plotted
vertically or horizontally.
Bar charts are used for plotting discrete (or
'discontinuous') data i.e. data which has discrete
values and is not continuous.
35. A histogram is a graphical representation,
showing a visual impression of the distribution of
data. It is an estimate of the probability
distribution of a continuous variable and was first
introduced by Karl Pearson.
A histogram consists of tabular frequencies,
shown as adjacent rectangles, erected over discrete
intervals (bins), with an area equal to the
frequency of the observations in the interval.
36. Frequency polygon
In laying out a frequency polygon instead of
drawing a histogram, the frequency of each class is
located at the midpoint of the interval and straight
line to connect the plotted points.
37. An Ogive is a line chart plotted on graph paper
from a cumul;ative ferquency distribution
38. Lorenz Curve is a line chart used to compare the
proportionality in two quantities variables.
39. The circle or pie chart is a component parts bar
chart from the segments of the circle.
It is usually a percentage chart
40. A pictogram uses symbols which may be
appropriate for the type of data.
41. Statistical analysis of data
Purpose
Types of statistical analysis
Descriptive analysis
Inferential analysis
Statitiacl estimation
Testing of hypothesis
42. Types of Statistical analysis
Measures of central tendency
Measures of dispersion
Measures of association/ relations
Analysis of variance
Hypothesis testing
Tests of significance
Time series analysis
43. Methods of collecting Primary data.
In many cases the secondary data are
inappropriate, inadequate or obsolete, primary
data have to be gathered.
Primary data are directly collected by the
researcher from their original source
Method is different from a tool
One or more methods can be chosen
No method is universal but has its own uniqueness
45. Observation:
Observation is defined as a systematic viewing of a
specific phenomenon in its proper setting for the
specific purpose of gathering data for a particular
study.
Observation includes both seeing and hearing.
The main body of knowledge has been developed by
observing the nature
46. Observation
Participant
observation
Researcher’s
Role
Non- participant
observation
Mode of Direct
Observation observation
Indirect
observation
Controlled
System
observation
Adopted
Un-controlled
observation
47. Interviewing
One of the prominent method of data collection
People are generally more willing to talk than to write
It is two way systematic conversation between an
investigator and an informant initiated for obtaining
information relevant to a specific study.
It is not only conversation, but also learning from the
respondent's gestures, expressions, pauses and
environment
It is carried out in a structured schedule
It calls for interviewing skills
48. Interviewing can be used as a main method or a
supplementary method
It is the only method for gathering information from
illiterate and uneducated method.
It can be used for collecting personal and intimate
information relating to a person‟s opinions,
attitudes, values, future intentions etc.
49. Questionnaire
A questionnaire is a series of questions asked to individuals
to obtain statistically useful information about a given
topic.
When properly constructed and responsibly administered,
questionnaires become a vital instrument
Questionnaires are frequently used in quantitative research.
They are a valuable method of collecting a wide range of
information from a large number of individuals, often
referred to as respondents. Good questionnaire
construction is critical to the success of a survey.
50. Types of questions
1. Contingency questions - A question that is answered
only if the respondent gives a particular response to a
previous question. This avoids asking questions of people
that do not apply to them
2. Matrix questions - Identical response categories are
assigned to multiple questions.
3. Closed ended questions - Respondents‟ answers are
limited to a fixed set of responses. Most scales are closed
ended. Other types of closed ended questions include:
1. Yes/no questions - The respondent answers with a “yes” or a
“no”.
2. Multiple choice - The respondent has several option from which
to choose.
3. Scaled questions - Responses are graded on a continuum
(example : rate the appearance of the product on a scale from 1 to
10, with 10 being the most preferred appearance). Examples of
types of scales include the Likert scale, semantic differential scale,
etc
51. Open ended questions - No options or predefined
categories are suggested. The respondent supplies their own
answer without being constrained by a fixed set of possible
responses. Examples of types of open ended questions include:
1. Completely unstructured - For example, “What is your
opinion of questionnaires?”
2. Word association - Words are presented and the
respondent mentions the first word that comes to mind.
3. Sentence completion - Respondents complete an
incomplete sentence. For example, “The most important
consideration in my decision to buy a new house is . . .”
4. Story completion - Respondents complete an incomplete
story.
5. Picture completion - Respondents fill in an empty
conversation.
6. Thematic apperception test - Respondents explain a
picture or make up a story about what they think is happening
in the picture
52. Question sequence
1. Questions should flow logically from one to the next.
2. The researcher must ensure that the answer to a
question is not influenced by previous questions.
3. Questions should flow from the more general to the
more specific.
4. Questions should flow from the least sensitive to the
most sensitive.
5. Questions should flow from factual and behavioral
questions to attitudinal and opinion questions.
6. Questions should flow from unaided to aided questions.
7. The sandwich theory - three stage theory : Initial
questions should be screening and rapport questions.
Then in the second stage you ask all the product specific
questions. In the last stage you ask demographic
questions
53. Research Design
- Data collection
Observational research
Ethnographic group Research
Focus group Research
Survey research
Behavioral data
Experimental research( cause & effect
relationships)
55. Field work
- Planning and supervision
Data Analysis
- Classifying raw data
- Summarising data
- Analytical methods to analyse and then make an
inference
56. Iceberg principle
Observation that in many (if not most) cases only a very small
amount (the 'tip') of information is available or visible about a
situation or phenomenon, whereas the 'real' information or bulk
of data is either unavailable or hidden. The principle gets its
name from the fact that only about 1/10th of an iceberg's mass is
seen outside while about 9/10th of it is unseen, deep down in
water.
57. Formulation of Hypothesis
Hypotheses is an imaginary, verifiable statement
which is a possible answer to the research question.
It is a tentative proposition formulated for empirical
testing.
It is tentative because its veracity can be tested only
after it has been tested empirically
They are useful and they guide the research process
in the particular direction
In exploratory and Descriptive studies hypothese
may not be required but it is essential in all
analytical and experimental studies
58. Types of Hypotheses
With reference to their function:
Discreptive
and Relational hypotheses,
Casual Hypotheses
With ref. to working
Null hypotheses, working hypotheses and
Statistical hypotheses
Level of abstraction:
Common sense Hypotheses, Complex
Hypotheses and analytical Hypotheses
59. Types of Hypotheses
With reference to their function:
Dicretiveand Relational hypotheses,
Casual Hypotheses
With ref. to working
Null hypotheses, working hypotheses and
Statistical hypotheses
Level of abstraction:
Common sense Hypotheses, Complex
Hypotheses and analytical Hypotheses
60. Types of Hypotheses
With reference to their function:
Dicretiveand Relational hypotheses,
Casual Hypotheses
With ref. to working
Null hypotheses, working hypotheses and
Statistical hypotheses
Level of abstraction:
Common sense Hypotheses, Complex
Hypotheses and analytical Hypotheses
61. Types of Hypotheses
With reference to their function:
Dicretiveand Relational hypotheses,
Casual Hypotheses
With ref. to working
Null hypotheses, working hypotheses and
Statistical hypotheses
Level of abstraction:
Common sense Hypotheses, Complex
Hypotheses and analytical Hypotheses
62. Six Thinking Hats
The de Bono Hats system (also known as "Six Hats" or "Six
Thinking Hats") is a thinking tool for group discussion and
individual thinking. Combined with the idea of parallel
thinking which is associated with it, it provides a means for
groups to think together more effectively, and a means to plan
thinking processes in a detailed and cohesive way. The
method is attributed to Dr. Edward de Bono and is the subject
of his book, Six Thinking Hats.
The paternity of this method is disputed by the School of
Thinking.
The method is finding some use in the UK innovation sector,
is offered by some facilitation companies and has been
trialled within the UK civil service.
63. Six distinct states are identified and assigned a
color:
Information: (White) - considering purely what
information is available, what are the facts?
Emotions (Red) - instinctive gut reaction or statements
of emotional feeling (but not any justification)
Bad points judgment (Black) - logic applied to
identifying flaws or barriers, seeking mismatch
Good points judgment (Yellow) - logic applied to
identifying benefits, seeking harmony
Creativity (Green) - statements of provocation and
investigation, seeing where a thought goes
Thinking (Blue) - thinking about thinking
64.
65.
66. Data Collection
Data are facts, figures and other relevant
materials, past and present serving as basis for
study and analysis.
Types of sources of data
1. Primary data
2. Secondary Data
67. 1. Primary data are those which are collected afresh
and for the first time and thus happens to be
original in character
2. Secondary data are those which have already been
collected by someone else and which have salready
been passed through statistical process.
68. Primary data
1. Primary data Primary data are those which are
collected afresh, for the first time and thus
happens to be original in character.
2. First formal appearance of results in the print or
electronic literature.
69. Secondary data
1. Secondary data are those which have already been
collected by someone else and which have already
been passed through statistical process.
2. Secondary sources are works that describe,
interpret, analyse primary data
3. Comments and discussion of the evidence
provided by primary sources
70. Methods of collecting Primary data.
In many cases the secondary data are
inappropriate, inadequate or obsolete, primary
data have to be gathered.
Primary data are directly collected by the
researcher from their original source
Method is different from a tool
One or more methods can be chosen
No method is universal but has its own uniqueness
72. Observation:
Observation is defined as a systematic viewing of a
specific phenomenon in its proper setting for the
specific purpose of gathering data for a particular
study.
Observation includes both seeing and hearing.
The main body of knowledge has been developed by
observing the nature
73. Observation
Participant
observation
Researcher’s
Role
Non- participant
observation
Mode of Direct
Observation observation
Indirect
observation
Controlled
System
observation
Adopted
Un-controlled
observation
74. Interviewing
One of the prominent method of data collection
People are generally more willing to talk than to write
It is two way systematic conversation between an
investigator and an informant initiated for obtaining
information relevant to a specific study.
It is not only conversation, but also learning from the
respondent's gestures, expressions, pauses and
environment
It is carried out in a structured schedule
It calls for interviewing skills
75. Interviewing can be used as a main method or a
supplementary method
It is the only method for gathering information from
illiterate and uneducated method.
It can be used for collecting personal and intimate
information relating to a person‟s opinions,
attitudes, values, future intentions etc.
76. Questionnaire
A questionnaire is a series of questions asked to individuals
to obtain statistically useful information about a given
topic.
When properly constructed and responsibly administered,
questionnaires become a vital instrument
Questionnaires are frequently used in quantitative research.
They are a valuable method of collecting a wide range of
information from a large number of individuals, often
referred to as respondents. Good questionnaire
construction is critical to the success of a survey.
77. Types of questions
1. Contingency questions - A question that is answered
only if the respondent gives a particular response to a
previous question. This avoids asking questions of people
that do not apply to them
2. Matrix questions - Identical response categories are
assigned to multiple questions.
3. Closed ended questions - Respondents‟ answers are
limited to a fixed set of responses. Most scales are closed
ended. Other types of closed ended questions include:
1. Yes/no questions - The respondent answers with a “yes” or a
“no”.
2. Multiple choice - The respondent has several option from which
to choose.
3. Scaled questions - Responses are graded on a continuum
(example : rate the appearance of the product on a scale from 1 to
10, with 10 being the most preferred appearance). Examples of
types of scales include the Likert scale, semantic differential scale,
etc
78. Open ended questions - No options or predefined
categories are suggested. The respondent supplies their own
answer without being constrained by a fixed set of possible
responses. Examples of types of open ended questions include:
1. Completely unstructured - For example, “What is your
opinion of questionnaires?”
2. Word association - Words are presented and the
respondent mentions the first word that comes to mind.
3. Sentence completion - Respondents complete an
incomplete sentence. For example, “The most important
consideration in my decision to buy a new house is . . .”
4. Story completion - Respondents complete an incomplete
story.
5. Picture completion - Respondents fill in an empty
conversation.
6. Thematic apperception test - Respondents explain a
picture or make up a story about what they think is happening
in the picture
79. Question sequence
1. Questions should flow logically from one to the next.
2. The researcher must ensure that the answer to a
question is not influenced by previous questions.
3. Questions should flow from the more general to the
more specific.
4. Questions should flow from the least sensitive to the
most sensitive.
5. Questions should flow from factual and behavioral
questions to attitudinal and opinion questions.
6. Questions should flow from unaided to aided questions.
7. The sandwich theory - three stage theory : Initial
questions should be screening and rapport questions.
Then in the second stage you ask all the product specific
questions. In the last stage you ask demographic
questions
80. Research Design
- Data collection
Observational research
Ethnographic group Research
Focus group Research
Survey research
Behavioral data
Experimental research( cause & effect
relationships)
82. Field work
- Planning and supervision
Data Analysis
- Classifying raw data
- Summarising data
- Analytical methods to analyse and then make an
inference
83. Application of research :
- Sales and market analysis
- Product research
- Corporate research
- Advertising research
84. Barriers to the use of MR
A narrow conception of Marketing Research
Uneven caliber of Marketing researchers
Poor framing of the problem
Late and erroneous findings by marketing research
Personality and presentational differences.
86. 3. Decomposition method:
The company‟s previous periods sales data is broken
into four major components Trend, cycle, seasonal
and erratic
4. Naive/Ratio method: Time series
Sales forecast for next year=
Actual sales of this year x Actual sales of this year
Actual sales of last year
87. 6. Regression analysis: Company sale is
dependent on many factors such as price,
promotional expenditure, population etc.
Statistical forecasting - SPSS used- Multiple
regression analysis is used
7. Econometric analysis : Many regression
equations are built to forecast industry sales. A
forecast is prepared by solving these equations
on computer software.
88. To improve forecasting accuracy:
1. Use multiple forecasting methods
2. Identify suitable method
3. Obtain a range of forecasts
4. Use computer hardware and software.
89. Steps in sales forecasting
As per the conference board of America report 1978, 10 steps are listed.
1. Determine the Purpose for which Forecasts are used
2. Divide the company products into homogenous groups
3. Determine the factors affecting the sales of each product
and their relative importance
4. Choose the forecasting methods
5. Gather the available data
6. Analyse the data
7. Check and recheck the deductions
8. Make assumptions regarding other factors
9. Convert deductions and assumptions into forecasts
10. Apply the forecast to company operations
90. Sales Budget
A sales budget consists of
estimates of expected volume
of sales and selling expenses.
Sales budget is generally fixed slightly lower than
the sales forecast to avoid risk
Selling expense budget consists of the selling
expense budget and sales department
administrative budget
The sales budget is the key factor for the
successful performance of the sales department
92. Purposes of
the sales budget
1. Planning: From total
corporate plan
marketing and sales
budgets are developed
considering sales
goals, sales strategy,
action plan, expense,
etc.
2. Coordination:
Coordinating among
various functions
3. Control : Evaluation
of performance
93. Methods used for deciding sales
expenditure budget
Sales managers are
required to decide
expenditure levels for
each item of selling
expenses.
1. Percentage of sales method
2. Executive judgment method
3. Objective and task method
95. STATISTICS
The word Statistics means an „organised political
state‟ in German
Organised numerical data
It is a numerical statement of facts in any
department of enquiry placed in relation to each
other.
97. Interviewing process
1. Preparation
2. Introduction
3. Developing rapport
4. Carrying the interview forward
5. Recording the interview
6. Closing the Interview