CHAPTER 8
DATA COLLECTION
By
Niranjan
H
N

What is Data Collection?
• It is the process by which the researcher collects the
information needed to answer the research problem
• The task of data collection begins after a research
problem has been defined and research design
chalked out.

In
collecting
the data,
the
researcher
must
decide
Which
data to
collect?
How to
collect
the Data?
Who will
collect
the Data?
When to
collect
the Data?

Criteria To
Select
Method
Resources available
Credibility
Analysis and reporting
And the skill of the evaluator

Methods of
Data
collection
Primary
Secondary

Methods Of
Primary
Data
Collection
1. Interviews
2. Surveys & Questionnaires
3. Observation
4. Focus Groups
5. Experiments

1. Interviews
Personal Interview
Telephone Interview

Characteristics
It can be Direct Or Indirect,
Structured Or Unstructured,
Focused Or Unfocused.
Includes a notepad or
recording device to record
conversation.
A cell phone, Laptop, Tablet,
or desktop computer with an
internet connection is
required .

Classifications
• Structured Interviews : In this case, a set of predecided questions are there.
• Unstructured Interviews : In this case, we don’t follow a system of pre-
determined questions.
• Focused Interviews : Attention is focused on the given experience of the
respondent and its possible effects.
• Clinical Interviews : Concerned with broad underlying feelings or
motivations or with the course of an individual’s life experience.
• Group Interviews : a group of 6 to 8 individuals is interviewed.
• Qualitative and quantitative Interviews : divided on the basis of subject
matter i.e., whether qualitative or quantitative.
• Individual Interviews : Interviewer meets a single person and interviews
him.
• Selection Interviews : Done for selection of people for certain Jobs.

Steps for an
effective
Interview
Analyze and
interpret
Analyze and interpret data collected from
the interviewer
Conduct Conduct the Interview
Select Select subject/ Key Responded
Prepare Prepare interview Schedule

Advantages
More information at greater depth can be obtained
Resistance may be overcome by a skilled interviewer
Personal information can be obtained
Better communication
Samples can be collected effectively
Questionnaire can be restructured based on the need

Disadvantages
It is an expensive Method
Interviewer bias
Respondent bias
Time consuming

2. Questionnaires
A Questionnaire is sent ( by post or by mail ) to
the persons concerned with a request to answer
the questions and return the Questionnaire.
A Questionnaire consists of a number of
questions printed in a definite order on a form.
Questionnaire – list of questions framed,
relating to the study.
Ex; Business survey, Economics Survey

Types Of
Questions
Open ended questions
Close ended questions

Characteristics
Of Good
Questionnaire
Should be short and simple
Follow a sequence of questions from easy to difficult
one
Technical terms should be avoided
Should provide adequate space for answers in
questionnaire
Directions regarding the filling of questionnaire should
be given Physical Appearance – Quality of paper, Color
Sequence must be clear

Questionnaire
A set of printed or written
questions with a choice of
answers, devised for the purposes
of a survey or statistical study.
Example: question sheet, set of
questions, survey form.

Queries to be decided while designing a
questionnaire
 What type of information is to collected ?
 What types of questions are to be formulated ?
 What should be the wording of each question ?
 What should be their sequence ?
 What should be the layout of the questionnaire ?
 How to undertake pretesting of the questionnaire?
 How to finalize the QUESTIONNAIRE ?

Steps involved in Questionnaire
1. Decide the information required.
2. Define the target respondents.
3. Choose the method(s) of reaching your target respondents.
4. Decide on question content.
5. Develop the question wording.
6. Put questions into a meaningful order and format.
7. Check the length of the questionnaire.
8. Pre-test the questionnaire.
9. Develop the final survey form.

1. Deciding on the information
required
• It should be noted that one does not start by writing questions. The first
step is to decide 'what are the things one needs to know from the
respondent in order to meet the survey's objectives?
• These, as has been indicated in the opening chapter of this textbook,
should appear in the research brief and the research proposal.

2. Define the target respondents
• in designing the questionnaire, we must take into account factors such as
the age, education, etc. of the target respondents.

3.Choose The Method(s)
Of Reaching Target
Respondents
• Personal Interviews
• Group Or Focus Interviews
• Mailed Questionnaires
• Telephone Interviews.

4. Decide on question content
• Opening questions that are easy to answer and which are not perceived as
being "threatening", and/or are perceived as being interesting, can greatly
assist in gaining the respondent's involvement in the survey and help to
establish a rapport.
• Dummy" questions can disguise the purpose of the survey and/or the
sponsorship of a study

5. Develop the question wording
• Survey questions can be classified into three forms, i.e. closed, open-ended
and open response-option questions. So far only the first of these, i.e.,
closed questions has been discussed. This type of questioning has several
important advantages.

Advantages of close ended
• It provides the respondent with an easy method of indicating his answer -
he does not have to think about how to articulate his answer.· It 'prompts'
the respondent so that the respondent has to rely less on memory in
answering a question.
• Responses can be easily classified, making analysis very straightforward.
• It permits the respondent to specify the answer categories most suitable
for their purposes.

Disadvantages when using such questions
• They do not allow the respondent the opportunity to give a different
response to those suggested.
• They 'suggest' answers that respondents may not have considered before

Open ended Question Advantages
• They allow the respondent to answer in his own words, with no influence
by any specific alternatives suggested by the interviewer.
• They often reveal the issues which are most important to the respondent,
and this may reveal findings which were not originally anticipated when
the survey was initiated.
• Respondents can 'qualify' their answers or emphasize the strength of their
opinions.

Open Ended Question Disadvantages
• Respondents may find it difficult to 'articulate' their responses i.e., to properly
and fully explain their attitudes or motivations.
• Respondents may not give a full answer simply because they may forget to
mention important points. Some respondents need prompting or reminding of
the types of answer they could give.
• Data collected is in the form of verbatim comments - it has to be coded and
reduced to manageable categories. This can be time consuming for analysis and
there are numerous opportunities for error in recording and interpreting the
answers given on the part of interviewers.
• Respondents will tend to answer open questions in different 'dimensions'.

What features of this implement do you
like?
· Performance
· Quality
· Price
· Weight
· Others mentioned:

6. Putting
questions
into a
meaningful
order and
format
Opening questions
Questions flow
Question variety
Closing questions

7. Check the
length of the
questionnaire.
Keep short
as possible
30-
45minutes

8.Piloting the questionnaires
whether the questions as they are worded will achieve the desired
results· whether the questions have been placed in the best order
whether the questions are understood by all classes of respondent
whether additional or specifying questions are needed or whether
some questions should be eliminated
whether the instructions to interviewers are adequate.

9.Develop the final survey form.
All that remains to be done is the mechanical process of laying out
and setting up the questionnaire in its final form.
This will involve grouping and sequencing questions into an
appropriate order, numbering questions, and inserting interviewer
instructions.

Pros of Questionnaire
1. Inexpensive :
• Self administered questionnaire
• Website's questionnaire
• E-mail Questionnaire

2. Questionnaires are practical
• Practical way to gather data
• Targeted Groups
• Pick and choose questions
• For example, KBC Group learned just how practical surveys are.
They were able to spread their quizzes, polls, and questionnaires
during a three-day event. This made collecting real-time feedback
almost effortlessly.

3. Quick Results
• Online and mobile tools
• You don’t need another agency to
deliver you results
• Example:Dajo Associates needed
quality feedback fast. The South
African consulting firm needed a
way to make informed decisions
quickly. An online
questionnaire allowed them to
collect the data they needed in the
shortest time frame possible.

4. Scalability
• Gathers information from a large audience
• Link based Sharing option
• Automated E-mail
• This means that for a relatively low cost, you
can target a city or a country.
• Geography Boundaries are not the limitation
but be aware of culture and language.

5. Comparability
•Qualified data can be used to compare each
other research.
•Measures changes

6. Easy Analysis and
visualization
• Most survey- and questionnaire providers are quantitative in nature
and allow easy analysis of results.
• With built-in tools, it’s easy to analyze your results without a
background in statistics or scientific research.
• Tools like Survey Anyplace offer easy to interpret reports and
visualizations, meaning that you’ll quickly be turning your data into
results. These results can be put in a wide variety of charts and tables
to present them to your boss, colleagues, clients or customers.

7.Questionnaires Don't have time contraints
When using mail-in, online or email questionnaires, there’s no time
limit and there is no one on the other end waiting for an answer.
Respondents can take their time to complete the questionnaire at their
own leisure.
As a bonus, they will often answer more truthfully, as research has
shown that having a researcher present can lead to less honest and
more social desirable answers.

8. Questionnaires can cover every aspect of a topic
Ask As Many Questions As You Like.
10 Questions For Online Surveys
Since They Are Efficient, Cost-effective In Nature And Have An Easy Mode Of
Delivery, There Is No Harm In Creating Multiple Questionnaires, Each
Covering A Subtopic Of The Main Subject, That Build Upon One Another.

Cons of Questionnaire
1. Dishonest answers
• While there are many positives to questionnaires, dishonesty can be an issue.
• Respondents may not be 100% truthful with their answers.
• This can happen for a variety of reasons, including social desirability bias and
attempting to protect privacy.

2. Unanswered questions
Some Questions Will Be
Ignored Or Left Unanswered.
Mark Questions As Required Make Your Questionnaire
Short And Uncomplicated

3. Differences in understanding
and interpretation
• Without someone to explain the questionnaire fully and ensure
everyone has the same understanding, results can be subjective.
• Trouble grasping the questions sometimes
• This miscommunication can lead to skewed results. The best
way to combat this situation is to create simple questions that
are easy to answer.

4. Hard to convey feelings and
emotions
• A survey or questionnaire cannot fully capture
emotional responses or feelings of respondents.
Without administering the questionnaire face-to-face,
there is no way to observe facial expression, reactions or
body language.
• Solution use Likert scale and rating scale

5. Accessibility issues
For Users With A Visual Or Hearing Impairment, Or Other
Impediments Such As Illiteracy.
Always Choose A Questionnaire Platform That Has
Accessibility Options Built In.

6. Questionnaire
or survey fatigue
Survey Response Fatigue:
Frequent survey forms
Survey Taking Fatigue:
Too long questionnaire or
irrelevant content to
respondent.

BASIS FOR
COMPARISON
QUESTIONNAIRE INTERVIEW
Meaning
Questionnaire implies a form consisting of a series of
written or printed multiple choice questions, to be
marked by the informants.
Interview is a formal conversation between the
interviewer and respondent wherein the two
participates in the question answer session.
Form Written Oral
Nature Objective Subjective
Questions Closed Ended Open Ended
Information provided Factual Analytical
Order of questions
Cannot be changed, as they are written in an
appropriate sequence.
Can be changed as per need and preference.
Cost Economical Expensive
Time Informant's own time Real time
Communication One to many One to one
Non-response High Low
Identity of respondent Unknown Known

Observation
Observation is a method that uses
vision/eyes as its main element for
collecting the data. Observation is
watching behavior of persons who
are under observation as it actually
happens without controlling it. It
includes recording information
without asking any questions.

For example
• A researcher can use the observation method in an
organization and record the behavior of the employee
during working hours with his colleagues as well as
with his clients. Are they comfortable with the working
environment and the available resources, will make a
good study for the researcher.

Advantages of the
Observation Method:
1. Directness: The main advantage of observation is its
directness. We can collect data at the time they occur.
2. Natural environment: Data collected in an observation study
describe the observed phenomena as they occur in their
natural settings.
3. Longitudinal analysis: Since the observation is possible to be
conducted in a natural setting, the observer can conduct his
or her study over a much longer period.
4. Non-verbal behavior: Observation is decidedly superior for
collecting data on nonverbal behavior than survey research,
experimentation, or document study.

Disadvantages of the Observation Method:
1. Lack of control: Despite the advantage as achieved from the natural
environment, the observation study, however, has little control over extraneous
variables that may affect the data.
2. Difficulties in quantification: Measurement in observational studies generally
takes the form of observer’s un-quantified perceptions rather than the
quantitative measures often used in the survey and experimental studies.
3. Smallness in sample size: Because observational studies are generally
conducted in-depth, with data that are often subjective and difficult to quantify,
the sample size is usually kept at a minimum. This feature tends to limit the
size of the sample.
4. No opportunity to learn past: In an observational study, there is no way to
know the past. It is also difficult to gather information on such topics as
intentions, opinions, attitudes, or preferences.

Focus Groups
A focus group is a group interview of approximately six to twelve
people who share similar characteristics or common interests. Focus
groups are useful for gathering in-depth information on perceptions,
insights, attitudes, experiences, or beliefs. Focus groups are a
qualitative data collection method, meaning that the data is descriptive
and cannot be measured numerically.
The main methods of data collection during a focus group discussion
include audio and tape recording, note-taking and participant
observation

Advantages of
focus groups
Quick and relatively easy to set
up
The group dynamic can provide
useful information that
individual data collection does
not provide.
Is useful in gaining insight into
a topic that may be more
difficult to gather through other
data collection methods.

Disadvantages of
focus groups
Susceptible to facilitator bias.
The discussion can be dominated or
sidetracked by a few individuals.
Data analysis is time consuming and
needs to be well planned in advance.
Does not provide valid information at the
individual level.
The information is not representative of
other groups.

Experiments
An experiment is a data collection method where a
researcher change some variables and observe
their effect on other variables. The variables that
manipulate are referred to as independent while
the variables that change as a result of
manipulation are dependent variables.
Experimental research can be adapted to different
fields like medical research, agriculture, sociology,
and psychology.

Advantages of Experimental Research
1. It gives researchers a high level of control.
2. It allows researchers to utilize many variations.
3. It can lead to excellent results.
4. It can be used in different fields.

Disadvantages of Experimental Research
1. It can lead to artificial situations
2. It can take a lot of time and money
3. It can be affected by errors
4. It might not be feasible in some situations

SECONDARY DATA
Data gathered and recorded by someone else prior to and for a purpose
other than the current project
Secondary data is data that has been collected for another purpose. It
involves less cost, time and effort
Secondary data is data that is being reused. Usually in a different context.
For example: data from a book.

SOURCES
INTERNAL SOURCES
• Internal sources of secondary data are usually for marketing application-
• Sales Records
• Marketing Activity
• Cost Information
• Distributor reports and feedback
• Customer feedback

EXTERNAL SOURCES
• External sources of secondary data are usually for Financial application-
• Journals
• Books
• Magazines
• Newspaper
• Libraries
• The Internet SOURCES

Advantages of Secondary Data
Ease Of
Access
Low Cost To
Acquire
Clarification
Of Research
Question
May Answer
Research
Question

Disadvantages
of Secondary
Data
Quality of
Research
Not Specific
to
Researcher’s
Needs
Incomplete
Information
Not Timely

Basis For Comparison Primary Data Secondary Data
Meaning
Primary Data Refers To The
Firsthand Data Gathered By The
Researcher Himself.
Secondary Data Means Data
Collected By Someone Else Earlier.
Data Real Time Data Past Data
Process Very Involved Quick And Easy
Source
Survey, Observations,
Expérimentes, Questionnaire,
Personale Interview, Etc.
Government Publications, Websites,
Books, Journal Articles, Internal
Records Etc.
Cost Effectiveness Expensive Economical
Collection Time Long Short
Specific
Always Specific To The
Researcher's Needs.
May Or May Not Be Specific To The
Researcher's Need.
Available In Crude Form Refined Form
Accuracy And
Reliability
More Relatively Less

Classification of data
• Classification is the process of arranging things(either normally or notionally) in
groups or classes according to their resemblances and affinities and give
expressions of the unity attributes that may subsist amongst a diversity
individuals”. – Conner

Functions of Data:
•Bulk of the data
•Simplifies of the data
•Facilitates comparison of characteristics
•Renders the data for statistical analysis

Characteristics of
classification
Unambiguous
Stable
Flexible
Exhaustiveness
Mutually
exclusive

Objectives of
classification
To condense the mass of data
To prepare the data for
tabulation
To study the relationships
To facilitate comparison

Types of
Classification
Geographical (or spatial) classification
Chronological classification
Qualitative classification
Quantitative classification
Alphabetical classification

Geographical (or spatial)
classification
• When the data classified according to
geographical location or region (like
states, cities, regions, zones , areas
etc.) It is called a geographical
classification. For example, the
production of food grains in INDIA
may be presented state- wise in
following manner.

Chronological classification
• When data are observed over a period of time the type of classification is
known as chronological classification ( on the basis of its time of occurrence ).
Various the serious such as National income figures , annual output of wheat
monthly expenditure of a house hold , daily consumptions of milk, etc. Are
some examples of chronological classification . For examples we may present
the figures of population (or production , sales,etc.) as follows……

Qualitative classification
• We may first divide the population in to males and females on the basis of the
attribute ‘sex’, each of this class may be further subdivide into ‘literate’ and
‘illiterate’ on the basis of attribute ‘literacy’ further classification can be made on
the basis of same other attribute ,say , employment.

Quantitative classification
• Quantitative classification is refers to the
classification of data according to some
characteristics that can be measured, such as
height, weight ,income, sales profit,
production,etc. For example, the student of a
college may be classified according to weight as
follows:

Alphabetical classification
•When the data are arranged
according to alphabetical order, it is
called alphabetical classification.
For example state-wise density of
population in India is depicted in
an alphabetical order below;

Scale of measurement
• Nominal, Ordinal, Interval, and Ratio scales can be defined as the 4
measurement scales used to capture and analyze data from survey,
questionnaire, and similar research instruments.
• All of the scales use multiple-choice questions.
• Psychologist Stanley Smith Stevens created these 4 levels of measurement
in 1946.
• Data
• Nominal & Ordinal – Qualitative/Categorical
• Interval & Ratio – Quantitative/Numerical

Scale of
measurement

Nominal Scale
• A nominal scale is the 1st level of measurement scale in which the numbers
serve as “tags” or “labels” to classify or identify the objects. A nominal scale
usually deals with the non-numeric variables or the numbers that
do not have any value

Example:
• An example of a nominal scale measurement is given below:
• What is your gender?
• M- Male
• F- Female
• Here, the variables are used as tags, and the answer to this question should be
either M or F.
• Male may be assigned a number 1, female may be assigned a number 2. The
assignment of number is only for the purpose of identification.

Ordinal Scale
• The ordinal scale is the 2nd level of measurement that reports the
ordering and ranking of data without establishing the degree of
variation between them. Ordinal represents the “order.” Ordinal data is
known as qualitative data or categorical data. It can be grouped, named
and also ranked.

Example:
• Ratings in restaurants
• Rank the following
attributes, while
choosing a restaurant
for dinner. The most
important attribute
may be ranked one,
the next important
may be assigned a
rank of 2 and so on.
Attribute Rank
Food quality
Price
Menu variety
Ambience

Scale
• Volume of production
• Interval Scale
• The interval scale is the 3rd level of measurement
scale. It is defined as a quantitative measurement
scale in which the difference between the two
variables is meaningful. In other words, the
variables are measured in an exact manner,
not as in a relative way in which the presence of zero
is arbitrary.

Example:
Likert
Scale
• How do you rate the work environment of your organization
VERY
GOOD
GOOD NEUTRAL BAD
VERY
BAD
5 4 3 2 1

Ratio Scale
• The ratio scale is the 4th level of measurement
scale, which is quantitative.
• It is a type of variable measurement scale.
• It allows researchers to compare the
differences or intervals.
• The ratio scale has a unique feature. It
possesses the character of the origin or zero
points.

Example:
• What is your weight in Kgs?
• Less than 55 kgs
• 55 – 75 kgs
• 76 – 85 kgs
• 86 – 95 kgs
• More than 95 kgs

SCALE CHARACTERISTICS
EXAMPLE
PERMISSIBLE STATISTICS
Nominal
Numbers are used to
label and classify
objects
Players of Team, Caste,
Religion, Gender, Martial
Status, Brands, Types, etc.,
Percentages', Mode, Chi-Square
test, Contingency coefficient,
Binominal test
Ordinal
Numbers indicate the
relative position of the
objects
Preference ranking, Image
ranking, Social class etc.,
Percentile, Quartiles, Median,
Rank order correlation,
Friedman, ANOVA
Interval
Numbers indicate the
relative position of the
objects
Attitude, opinion, index
number
Product moment, correlation
coefficient test, Z-test, ANOVA,
Regression analysis, Factor
analysis
Ratio
Numbers indicate the
relative position of the
objects
Age, Income, market share,
Sales, cost etc.,
Geometric Mean, Harmonic
Mean and coefficient of
variation.

Preparing the data for analysis
• Data Preparation
• The data collected from the respondents is generally not in the form to be
analyzed directly. After the responses are recorded or received, the next stage
is that of preparation of data i.e. to make the data amenable for appropriate
analysis.
• Data preparation includes editing, coding, and data entry and is the activity
that ensures the accuracy of the data and their conversion from raw form to
reduced and classified forms that are more appropriate for analysis. Preparing
a descriptive statistical summary is another preliminary step leading to an
understanding of the collected data

Data Preparation:
Editing
Coding
Validation of data
Data entry
Classification
Tabulation

EDITING
• The customary first step in analysis is to edit the raw data. Editing
detects errors and omissions, corrects them when possible, and certifies
that maximum data quality standards are achieved. The editor's
purpose is to guarantee that data are:
• 1. Accurate.
• 2. Consistent with the intent of the question and other information in
the survey.
• 3. Uniformly entered.
• 4. Complete.
• 5. Arranged to simplify coding and tabulation

Editing
• Field Editing
• Central Editing
• In large projects, field editing review is a responsibility of the field
supervisor.
• It, should be done soon after the data have been gathered. During
the stress of data collection in a personal interview and paper-
and-pencil recording in an observation, the researcher often uses
ad hoc abbreviations special symbols.
• Soon after the interview, experiment, or observation, the
investigator should review the reporting forms

Central Editing
• It should take place when all forms or schedules have been completed and
returned to the office.
• This type of editing implies that all forms should get a thorough editing by a
single editor in a small study and by a team of editors in case of a large inquiry.
• Editor(s) may correct the obvious errors such as an entry in the wrong place,
entry recorded in months when it should have been recorded in weeks, and the
like. In case of inappropriate on missing replies, the editor can sometimes
determine the proper answer by reviewing the other information in the
schedule. At times, the respondent can be contacted for clarification.

Be familiar with instructions given to
interviewers and coders.
• Do not destroy, erase, or make illegible the original entry by the interviewer;
• Original entries should remain legible.
• Make all editing entries on an instrument in some distinctive color and in a
standardized form.
• Initial all answers changed or supplied.
• Place initials and date of editing on each instrument completed.

ERROR DETECTION
• First step in error detection is to determine whether the software used for data entry and
tabulation will allow the researcher to perform “error edit routines” which identifies the
wrong type of data.
• Example – Say that for a particular field on a given data record, only the codes of 1 or
2 should appear. An error edit routine can display an error message on the data
output if any number other than 1 or 2 has been entered
• Another approach to error detection is for the researcher to review a printed
representation of entered data
• The final approach to error detection is to produce a data/column list for the entered
data. Quick view of this data/column list procedure can indicate to the analyst whether
inappropriate codes were entered into data fields

Data tabulation
• INTRODUCTION
• The classification of data leads to the problem of presentation of data. The
presentation of data means exhibition of the data in such a clear and attractive
manner that these are easily understood and analyzed.
• There are many forms of presentation of data of which the following three are well
known:
• (i) Textual Presentation,
• (ii) Tabular Presentation,
• (iii) Diagrammatic Presentation. Here, we discuses in detail Tabular method
of data presentation.

What is a Table
• A table is a symmetric arrangement of statistical data in rows and columns.
• DEFINITIONS
• “Table involves the orderly and systematic presentation of numerical data in a
form designed to elucidate the problem under consideration.” ---According
Prof. L.R.Connor,”

Features of a good Table
• Title as compatible with the objective of the study
• To facilitate comparison.
• Ideal Size
• Stubs
• Use of Zero
• Heading
• Abbreviation
• Footnote
• Total
• Source of data
• Size of Columns
• Simple, Economical and Attractive

Objectives of Tabulation
• To carry out investigation
• To do comparison
• To locate omissions and errors in the data.
• To use space economically
• To simplify data
• To use it as future reference

Parts of a Table
•Table number
•Title of the table
•Caption and stubs
•Body
•Prefatory or head note
•Footnotes

Types of Tables
•There are three basis of classifying tables.
•I. Purpose of a table
•II. Originality of a table
•III. Construction of a table

I. According to Purpose
• General Purpose Table: General purpose table is that
table which is of general use. It is does not serve any specific
purpose or specific problem under consideration.
• Special Purpose Table: Special Purpose table is that
table which is prepared with some specific purpose in mind.

II. According to Originality
• Original Table: An original table is that in which data
are presented in the same form and way they are
collected.
• Derived Table: A derived table is that in which data
are not presented in the form or way these are collected.
Instead, the data are first converted into ratios or
percentage and then presented.

III. According to Construction
•Simple Table
•Complex Tables
•a. Double or Two-Way Table
•b. Three-Way Table
•c. Manifold (or Higher Order) Table

Simple Table
• In a simple table (also
known as one-way table),
data are presented based
on only one characteristic

Complex Tables
• In a complex table (also known as a manifold table) data are
presented according to two or more characteristics
simultaneously. The complex tables are two-way or three-
way tables according to whether two or three characteristics
are presented simultaneously.
• a. Double or Two-Way Table
• b. Three-Way Table
• c. Manifold (or Higher Order) Table

Double or Two-Way Table
• In such a table, the variable under study is further subdivided into two groups
according to two inter-related characteristics. The two-way table is shown in
Table 1.2.

Three-Way Table
• In such a table, the variable under
study is divided according to three
interrelated characteristics. The
Three- Way Table is shown in
Table 1.3.

Manifold (or Higher Order) Table
• Such tables provide information about a large no of interrelated characteristics in
the data set. Manifold (or Higher Order) Table is shown in Table 1.4.
• CONCLUSION
• With the help of above discussion we can say that table are help us to
represent the data in the form of rows and columns and make it useful for the
purposes.

Reliability and validity test
• Reliability (Cronbach’s Alpha)
• Reliability refers to whether your data collection techniques and analytic
procedures would reproduce consistent findings if they were repeated on
another occasion or if they were replicated by another researcher.
• Don't mix Positive and Negative question
• LIKERT scale variables (only)

Analysis using SPSS
A Commonly accepted rule

Reliability (Cronbach’s Alpha) – Interpretation
Reliability and Scale Statistics
S.No Benefits No. of items Cronbach’s Alpha
1 Monetary 5 Value from spss
2 Non Monetary 5 Value from spss
The Cronbach’s Alpha Value of the monetary benefit is **** which is more
than ***. Hence the reliability of the question is proved. The Cronbach’s
Alpha Value of the non monetary benefit is *** which is more than the value
of ***. Hence the reliability of the question is proved ie., the questionnaire is
reliable for the purpose of data collection.

Validity (Pearson Correlation)
• Validity is about the accuracy of a measure
• Validity is a judgement based on various type of evidence

Go to SPSS
• Transform – Compute Variable – m1 + m2+ m3 + m4+ m5
• Analyse – Corrolate – Bivariate (Move needed variable from left to
right – m1, m2, m3, m4, m5, mtotal) – click ok
• Mtotal values must be > 0.159 is acceptable – Valid
• And also do for nm

DATA ANALYSIS
• Once the data have been collected and prepared for analysis, there
are some basic statistical analysis procedures that researcher will
want to perform
• An obvious need for these statistics comes from the fact that almost all
data sets are disaggregated
• Graphics should be used whenever practical availing information user
to quickly grasp the essence of the information developed in research
project
• Charts also can be an effective visual aid to enhance the
communication process and add clarity and impact to research reports
• i.e Bar Charts,
• Line charts,
• pie or round chart

• Data must be accurately scored and systematically organized to facilitate
data analysis vide
• descriptive analysis,
• univariate ,
• bivariate analysis and
• multivariate analysis
• Descriptive statistics : permit the researcher to describe many pieces of
data with a few indices
• Statistics : indices calculated by the researcher for a sample drawn from a
population
• Parameter : indices calculated by the researcher for an entire population
• (Adults in bangalore city - % that are married, average age)

Types of descriptive statistics
• 1) Graphs
• 2) Measures of Central Tendency
• 3) Measures of central variability

Graphs :
• . Representations of data enabling the researcher to see what
the distribution of scores look like Bar graph, line graph and Pie
or Round chart

Measure of Central Tendency
• Indices enabling the researcher to determine the typical or
average score of a group of scores.
• They are :
• a)Mean –
• The arithmetic average of the sample
• All values of a distribution of responses are summed and divided by the number
of valid responses
• b) Median –
• The middle value of rank ordered distribution
• Exactly half of the responses are above and half are below the median value
• C) Mode –
• The most common value in the set of responses to a question i.e the response most
often given to a question

Measure of Variability
• Indices enabling the researcher to indicate how spread out a group of
scores are They are :
• a)Range
• b) Variance
• c)Standard Deviation

a) Range –
The difference between the highest and lowest score in a distribution
b) Variance –
A summary statistic indicating the degree of variability among participants for
a given variable
The average squared deviation about the mean of distribution of values
c) Standard deviation –
The square root of variance providing an index of variability in the distribution
of scores. It describes the average distance of distribution values from the
mean

How to determine the sample size?
• Sample Size is determined in two steps:
• 1. Calculate the sample size for infinite populations.
• S = Z2 * p * (1-p) /M2
• S = Sample Size for infinite population
• Z = Z score
• P = Population proportion (assumed to be 50% = 0.5)
• M = margin of error
• Z score is determined based on confidence level
• Confidence level: The probability that the value of a parameter falls within a
specified range of values
Confidence Level Z- Value
90% 1.645
95% 1.960
99% 2.576

• If we consider 95% confidence level then Z – score value is 1.96
• Margin of error is a small amount that is allowed for in case of miscalculation or
change of circumstance
• Generally we take margin of error as 5 % i.e m = 0.05
• Z – score = 1.96
• P = 0.5
• M = 0.05
• S = Z2*P*(1-p)/m2
• S = (1.96)2*0.5*(1-0.5)/(0.05)2
• S = 384.16
• So, sample size for the infinite population is ***

• 2. Adjust the sample size to required Population.
• For example, If we want to adjust the sample size to 1,00,000 population
• Then use the following formula for adjusted sample size
• Adjusted sample size = (S) /1 + ((S-1)/Population)
• Adjusted S = 384.16/1+((384.16-1)/100000)
• Adjusted S = 382.69
• Adjusted S = 382.69 =>383
• Finally, we have determined the sample size for 1,00,000 population as 383