The document discusses the framework for research philosophy, outlining the distinctions between ontological, epistemological, and axiological assumptions in various research paradigms such as positivism, interpretivism, and pragmatism. It highlights the importance of data analysis in decision-making processes, explaining different types of data and analytical approaches, including descriptive, exploratory, diagnostic, predictive, and prescriptive analysis. The text emphasizes the challenges of data collection and analysis, underscoring the need for effective strategies to derive meaningful insights from data.

1. An Introduction
An Introduction
Dr. J D Chandrapal
MBA marketing PGDHRM Ph D CII (Award) London
MBA – marketing , PGDHRM, Ph D, CII (Award) – London
Development Officer - LIC of India – Ahmedabad - 9825070933

2. Framework for Research Philosophy
Framework for Research Philosophy
1 2
Ontological
Assumption
Positivism
Realism
3
Epistemological
A iological
Interpretivism
Pragmatism
Qualitative, Quantitative and Mixed
Axiological
Pragmatism
Questions, Data collection & Analysis, Interpretation, Validation

3. Research Paradigm
Ontological Epistemological Axiological Methodological
g
O to og ca
Questions
p ste o og ca
Questions
Axiological
Questions
et odo og ca
Questions
Wh t i t H d Wh t d H d
What is nature
of Reality or
Knowable?
How do you
know
something?
What do you
value in your
research?
How do you go
about finding
out Knowledge?
Researchers
recognize how
Concerned with
mind's relation
Researcher's
subjective
Assumptions
about what
methods &
certain they can
be about the
nature and
existence of
to reality.
relationship
between
Knower -Known
subjective
values, intuition
, biases.
It is ethics and
methods &
procedures are
allowable within
paradigm.
existence of
objects
Knower -Known
(Knowable)?
aesthetics Research
strategy

4. Fit with the ‘bigger picture’ of the world
M th d d
gg p
Methods and
Techniques
T
Methodology Which precise procedure to use?
Which data can we collect?
M
Epistemology How can we go about acquiring that
knowledge?
E
Ontology
i l di f i Wh ’ h k ? D l
What & how can we know about it?
O
Logical discourse of existence. What’s out there to know? Deals
with the nature of reality. It is the Science or study of being

5. Research Research
approach
Ontology Axiology
strategy
Positivism Deductive Objective Value-free Quantitative
Interpretiv
i
Inductive Subjective Biased Qualitative
ism
j
Q lit ti
Pragmatism
Deductive/
Inductive
Objective or
subjective
Value-
free/biased
Qualitative
and/or
quantitative
quantitative

7. Types of Data
Types of Data
Qualitative Data
(Categorical)
Quantitative Data
(Numerical)
Nominal Ordinal Discrete Continuous
Nominal Ordinal Discrete Continuous
Labelled or
Named
Categories
Categories
with an
implied order
Only
Particular
Number
Any
Numerical
Value

8. Data Constrains the Analysis
Data Constrains the Analysis
Earlier lack of data was the biggest
bl it t i th
problem, it constrains the
analysis. It can lead to inaccurate
analysis poor customer relations
analysis, poor customer relations
and poor business decisions
Having enormous data is biggest
problem in terms of finding
relevant & appropriate information
for making sound decision. it also
constrains the analysis

9. Data Analysis
Data Analysis –
– What?
What?
Decision maker needs to
collect, curate, organize and interpret
the data in order to extract the true
meaning of the useful information;
The process of finding true meaning
• In short data analysis is the process of systematically applying
The process of finding true meaning
of the useful information is called
data analysis.
y p y y pp y g
statistical and/or logical techniques to describe and illustrate,
condense and recap, and evaluate data.
• Data analysis is defined as a process of inspecting, cleaning,
transforming, & modelling data to discover useful information to arrive
at a conclusion for decision making Data analytics allow us to make
at a conclusion for decision-making. Data analytics allow us to make
informed decisions and to stop guessing.

10. Potential of Data analysis
Potential of Data analysis
Let us try to understand the importance and potential of data analysis.
Most crucial part of any
1
Most crucial part of any
research
Uses analytical & logical
1
2
reasoning to gain info.
Gives the insight to uncover
meaning
2
3
meaning.
Interpretation allows ruling
out chance of human bias.
4
Determining patterns, trends
or relationships – Derived
Knowledge
5
• Commonly used alternatives to acquire Information (knowledge)
Knowledge

11. Data Analysis
Data Analysis –
– Why?
Why?
Familiarizing with data Defining objectives
Getting basic overview of Its about knowing what
the data to have 360° vision
of all aspects such as
demographic,
questions the data can
answer; helps in evaluate
info in appropriate or
1 2
demographic,
psychographic aspects
info in appropriate or
efficient manner
Fi di tt d Fi t b d id &
1 2
Finding patterns and
connections in data that
helps to make business
Figure out broad ideas &
assign them labels to
structure the data that
3
4
p
decisions based on facts
and not simple intuition
st uctu e t e data t at
drives success to marketing
strategies
Finding Patterns Making a Plan

12. Data Analysis Categories
Data Analysis Categories
1 Descriptive Analysis - What happened?
2 Exploratory Analysis - How to explore relationships?
3 Diagnostic Analysis - Why it happened?
4 Predictive Analysis What will happen?
4
5
Predictive Analysis - What will happen?
5 Prescriptive Analysis - How will it happen

13. Data Analysis How?
Data Analysis How?
Formulate
Research Goal
Defining Problem
Make Decisions
Written
Formulate new
Research Goals
Defining Problem,
Research
hypothesis, Models
conclusions,
Oral presentations
New hypotheses
New models,
Draw Inferences
Hypotheses
testing,
g,
Model assessment
Collect Data
Survey-
Experimental
t di
Design Study
Sample
size, Variables,
Experimental units
Summarize Data
Data descriptions,
Probability
di t ib ti
studies
Experimental units,
Sampling Plan
distribution

14. Stages in Data analysis
Stages in Data analysis
Editing
Coding
Error
&
Ve
Coding
Data Entry
r
Checking
erification
Data Analysis
Univariate Analysis Bivariate Analysis Multivariate Analysis
O V i bl T V i bl
One Variables Two Variables Two or more Variables
Interpretation