Sampling

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

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

 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.

 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.

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

 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.

Sampling methods or Sampling techniques Sampling
Designs:

 Two generic types:
1. Probability or random sampling, and
2. Non-probability or Non-random sampling

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

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

 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

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.

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.

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.

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

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.

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

 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.


A. Simple designs
1. Convenience or accidental sampling
2. Judgment sampling
B. Complex designs
1. Quota 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

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.

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

 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.

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

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.

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.

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

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

 Editing the data
 Data editing at he time of recording the data

 Data editing at the time of analysis of data

 Completeness
 Accuracy
 Uniformity

 Coding and
 Numeric coding

 Alphabetic coding

 Zero coding

 Classification

 Tabulation
 Manual tabulation

Graphs/Charts/Diagrams
 Line Graphs
 Bar charts
 Histograms
 Frequency plygon
 Ogive
 Lorenz curve
 Bar charts
 Vertical bar charts

 Horizontal bar charts

 Pie charts
 pictograms

 Line graphs are useful for showing changaes in
data relationships.
 The horizontal line is the x-axis and verical line is
the y-axis

 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.

 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.

 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.

 An Ogive is a line chart plotted on graph paper
from a cumul;ative ferquency distribution

 Lorenz Curve is a line chart used to compare the
proportionality in two quantities variables.

 The circle or pie chart is a component parts bar
chart from the segments of the circle.
 It is usually a percentage chart

 A pictogram uses symbols which may be
appropriate for the type of data.

Statistical analysis of data

 Purpose
 Types of statistical analysis
 Descriptive analysis

 Inferential analysis
 Statitiacl estimation
 Testing of hypothesis

 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

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

1. Observation
2. Interviewing
3. Mail survey
4. Experimentation
5. Simulation
6. Projective technique

 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

Observation

Participant
observation
Researcher’s
Role
Non- participant
observation

Mode of Direct
Observation observation

Indirect
observation

Controlled
System
observation
Adopted

Un-controlled
observation

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

 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.

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.

 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

 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

 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

 Research Design
 - Data collection
 Observational research
 Ethnographic group Research
 Focus group Research
 Survey research
 Behavioral data
 Experimental research( cause & effect
relationships)

 - Research instrument
 Questionnaires:
Close-end
Open-end
 Mechanical instruments: like,
 Galvanometers-emotions
 Tachistoscopes flashes
 Eye cameras
 Audiometer-TV
 - Sampling plan

 Field work
 - Planning and supervision
 Data Analysis
 - Classifying raw data
 - Summarising data
 - Analytical methods to analyse and then make an
inference

 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.

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

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

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

 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.

 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

 Application of research :
 - Sales and market analysis
 - Product research
 - Corporate research
 - Advertising research

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.

 Forecasting and
Demand
measurement

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

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.

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.

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

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

Sales Budget

Sales department
Sales volume budget Selling expense budget
Administrative budget

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

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

Review Situation
Sales Budget
Process
Communication

Subordinate budgets

Approval of budget

Other departments

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.

Interview Guides and Schedules

 Interview Guides
 Schedules
 Types of Interviews
 Structured directive Interviews
 Unstructured or Non-directive Interview
 Focused Interview
 Clinical interview
 Depth Interview

Interviewing process
1. Preparation
2. Introduction
3. Developing rapport
4. Carrying the interview forward
5. Recording the interview
6. Closing the Interview

Interview problems
Inadequate response
Interviewer‟s bias
Non-response
Non-availability
Refusal
Inaccebility
Telephonic interview

Sampling

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