Research & Statistics.pptx

RESEARCH AND STATISTICS
Public Safety Officers Candidate Course
Module: I General Subjects- Research and Statistics

WHAT IS RESEARCH ?
Research is the creation of new knowledge
and/or the use of existing knowledge in a new
and creative way so as to generate new
concepts, methodologies and understandings.
This could include synthesis and analysis of
previous research to the extent that it leads to
new and creative outcomes.

-ACCORDING TO WALTZ AND BAUSELL :
Research comprises defining and redefining
problems, formulating hypothesis or suggested
solution.
Research is a systematic, formal, rigorous and
precise process employed to gain solutions to
problems or to discover and interpret new facts
and relationships."

According to Clifford Woody
Research is Collecting, Organizing and
Evaluating data, making deductions and
reaching Conclusions to determine they fit the
formulating hypothesis."
According to John Best
:
Research is a systematic activity directed
towards discovery and the development of an
organized body of knowledge."

NATURE AND SCOPE OF
RESEARCH

1) Systematic Activity
Research follows a systematic procedure to
analyze a research problem in a better way. A
research cannot be conducted in a haphazard
manner. A researcher can come to a step only
when the previous one completed.

2) Logical Process
The basic tenet of research is logic. All the
assumptions and analyses undertaken are
based on certain logic. Research is a scientific,
systematic, and planned investigation to
understand the underlying problem.

3) Iterative Process
Research is an iterative process. Sometimes it
becomes necessary for the researcher to
review the work of earlier stages, which makes
it cyclic in nature. Often it becomes harder for
the researcher to find out the starting and
ending points.

4) Based on Empirical Evidences
Research studies are empirical in nature. Every
step in research is carried-out using various
scientific tools and techniques. Every step in
research is checked for accuracy and is based
on observable experiences or empirical
evidences. Therefore, quantitative research is
easier to validate than qualitative research,
which is more conceptual in nature.

5) Controlled In Nature
The researchers often control the effect of
variables by allowing only some variables to
vary so that their effect can be tested. Due to
this reason, controlling the variables in a
scientific research is much easier than
controlling the factors in a social research.
Hence in research, it is very essential to control
the variables carefully.

REVIEW OF RELATED
LITERATURE AND STUDIES

WHAT IS THE PURPOSE OF A
LITERATURE REVIEW?
 When you write a thesis, dissertation, or
research paper, you will likely have to
conduct a literature review to situate your
research within existing knowledge.

The literature review gives you a chance to:
 Demonstrate your familiarity with the topic and its
scholarly context
 Develop a theoretical framework and methodology
for your research
 Position your work in relation to other researchers
and theorists
 Show how your research addresses a gap or
contributes to a debate
 Evaluate the current state of research and
demonstrate your knowledge of the scholarly
debates around your topic.

EXAMPLES OF LITERATURE
REVIEWS
Module 1 – Breaking Period Phase 2 (Love of Country)

Example literature review #1: “Why Do People Migrate? A
Review of the Theoretical Literature” (Theoretical literature review
about the development of economic migration theory from the
1950s to today.)
Example literature review #2: “Literature review as a research
methodology: An overview and
guidelines” (Methodological literature review about
interdisciplinary knowledge acquisition and production.)
Example literature review #3: “The Use of Technology in English
Language Learning: A Literature Review” (Thematic literature
review about the effects of technology on language acquisition.)
Example literature review #4: “Learners’ Listening
Comprehension Difficulties in English Language Learning: A
Literature Review” (Chronological literature review about how the
concept of listening skills has changed over time.)

WHAT IS A RESEARCH
FRAMEWORK?

-A research framework is a set of concepts,
principles, and guidelines that shape and guide
your research process. It helps you to define
your research problem, objectives, questions,
hypotheses, assumptions, and limitations. It
also helps you to choose your research design,
methods, data collection, analysis, and
interpretation. A research framework can be
derived from a theory, a model, a paradigm, or
a perspective.

WHY IS A RESEARCH
FRAMEWORK IMPORTANT?

 It provides a clear and coherent structure for
your research project.
 It helps you to avoid confusion,
inconsistency, and bias in your research.
 It also helps you to communicate your
research to others, such as your
supervisors, peers, reviewers, and readers.
 A research framework can help you to justify
your research choices, demonstrate your
contribution, and evaluate your results.

TYPES OF RESEARCH

APPLIED RESEARCH
It is a scientific study that seek to solve various
practical problems in the day to day life. It find
answers or solutions to everyday problems,
cure illness, develop innovative technologies
etc.
For example-
1. Improve agricultural crop production
2. Treat or cure specific disease
3. Improve energy efficiency of homes,
offices, modes of transportation.

BASIC RESEARCH
 It is called as Fundamental or Pure
research. It Expands the person's
knowledge.
 This type of research is not going to create
or invent anything new. Instead, it is based
on Basic science investigation.
For example-
1. How did universe begin?
2. What are protons?

CORRELATIONAL RESEARCH
The relationship among 2 or more variables
without necessarily determining the cause and
effect is known as correlational research.
For example-
1. Correlation between job satisfaction and job
performance.
2. Correlation between physical and mental
health and job performance.

Advantages
1. It is easy to collect much information from
many subjects at single time.
2. Wide range of variables and their
interrelations.
3. Study variables are not easily produced in
the laboratory.

Disadvantages
1. Does not indicate causation (cause and
effect)
2. Problems with self reporting method.

DESCRIPTIVE RESEARCH
This type of research provides accurate
portrayal of characteristics of a particular
individual, situation or group. Also known as
statistical research. It deals with everything that
can be counted and studied which have an
impact on the lives of people.

Advantages
1. Less expensive, time consuming
2. Collect a large amount of notes for detailed
studying.
Disadvantages
1. Require more skills
2. Does not identify cause behind the
research.

ETHNOGRAPHIC RESEARCH
 This type of research involves investigation
of a culture through an in-depth study of
members of culture.
 It involves systematic collection, description,
analysis of data for development of theories
of cultural behavior.

ETHNOGRAPHIC RESEARCH
 There are anthropological studies that
studies people, ethnic group, ethnic
formations and social welfare
characteristics.
 It is done on the basis of observations,
interviews, questionnaire and data
collection.

EXPERIMENTAL RESEARCH
 This study involves objective, systematic,
controlled investigation for purpose of
predicting and controlling the phenomena.
 It also includes examining the probability
and causality among variables.

Advantage
 Best in establishing the cause and effect
relationships
Disadvantages
 Artificiality
 Feasibility
 Unethical
Variables
There will be two variables- Dependent and
Independent

EXPLORATORY RESEARCH
 This type of research will be conducted for a
problem that has not been clearly defined.
 It helps to determine the best research design,
data collection method and selection of
subjects.
 It is quite informal relying on the secondary
research.
Example:
Online marketing and exploring through different
sites

GROUND THEORY RESEARCH
 It studies about the problems existing in a
given social environment and how people
involved handles them.
 It operates almost in a reverse fashion from
traditional research and involves 4 stages-
Codes, Concepts, Categories and Theory
For Example
Creating a situation and looking at how people
react to it

HISTORICAL RESEARCH
 Research involving analysis of events that
occurred in the remote or recent past.
 Application- Understanding this can add
perspective on how we can examine the
current situation.

PHENOMENOLOGICAL RESEARCH
It aim to describe an experience that has been
actually lived by a person.
For example-
A person suffering from cancer, quality of life of
the patient at that point of time.

QUANTITATIVE RESEARCH
Involving numbers and quantifying the
results mathematically in numbers.
QUALITATIVE RESEARCH
Difficult or impossible to quantify
mathematically such as beliefs, meanings,
attributes and symbols. It aims to gather an
in-depth understanding of human behavior.

WHAT ARE THE
DIFFERENT METHODS OF
DATA COLLECTION?

The following are seven primary methods of
collecting data in business analytics.
• Surveys
• Transactional Tracking
• Interviews and Focus Groups
• Observation
• Online Tracking
• Forms
• Social Media Monitoring

TWO METHODS OF DATA
COLLECTION

Data collection breaks down into two methods.
As a side note, many terms, such as
techniques, methods, and types, are
interchangeable and depending on who uses
them. One source may call data collection
techniques “methods,” for instance. But
whatever labels we use, the general concepts
and breakdowns apply across the board
whether we’re talking about marketing analysis
or a scientific research project

PRIMARY
As the name implies, this is original, first-hand
data collected by the data researchers. This
process is the initial information gathering step,
performed before anyone carries out any
further or related research. Primary data results
are highly accurate provided the researcher
collects the information. However, there’s a
downside, as first-hand research is potentially
time-consuming and expensive.

SECONDARY
 Secondary data is second-hand data
collected by other parties and already having
undergone statistical analysis.
 This data is either information that the
researcher has tasked other people to
collect or information the researcher has
looked up. Simply put, it’s second-hand
information.

SECONDARY
 Although it’s easier and cheaper to obtain
than primary information, secondary
information raises concerns regarding
accuracy and authenticity.
 Quantitative data makes up a majority of
secondary data.

SPECIFIC DATA
COLLECTION TECHNIQUES

PRIMARY DATA COLLECTION
1. INTERVIEWS
 The researcher asks questions of a large
sampling of people, either by direct
interviews or means of mass communication
such as by phone or mail.
 This method is by far the most common
means of data gathering.

2. PROJECTIVE DATA GATHERING
 Projective data gathering is an indirect
interview, used when potential respondents
know why they're being asked questions and
hesitate to answer.
 For instance, someone may be reluctant to
answer questions about their phone service
if a cell phone carrier representative poses
the questions.

 With projective data gathering, the
interviewees get an incomplete question,
and they must fill in the rest, using their
opinions, feelings, and attitudes.

3. DELPHI TECHNIQUE
 The Oracle at Delphi, according to Greek
mythology, was the high priestess of Apollo’s
temple, who gave advice, prophecies, and
counsel.
 In the realm of data collection, researchers
use the Delphi technique by gathering
information from a panel of experts.
 Each expert answers questions in their field
of specialty, and the replies are consolidated
into a single opinion.

4. FOCUS GROUPS
 Focus groups, like interviews, are a
commonly used technique.
 The group consists of anywhere from a half-
dozen to a dozen people, led by a
moderator, brought together to discuss the
issue.

5. QUESTIONNAIRES
 Questionnaires are a simple, straightforward
data collection method.
 Respondents get a series of questions,
either open or close-ended, related to the
matter at hand.

SECONDARY DATA COLLECTION
Unlike primary data collection, there are no
specific collection methods. Instead, since the
information has already been collected, the
researcher consults various data sources, such
as:
Financial Statements
Sales Reports
Retailer/Distributor/Deal Feedback
Customer Personal Information (e.g., name,
address, age, contact info)

DATA COLLECTION TOOLS

1. WORD ASSOCIATION
The researcher gives the respondent a set of
words and asks them what comes to mind
when they hear each word.
2. SENTENCE COMPLETION
Researchers use sentence completion to
understand what kind of ideas the respondent
has. This tool involves giving an incomplete
sentence and seeing how the interviewee
finishes it.

3. ROLE-PLAYING
Respondents are presented with an imaginary
situation and asked how they would act or react
if it was real.
4. IN-PERSON SURVEYS
The researcher asks questions in person.
5. ONLINE/WEB SURVEYS
These surveys are easy to accomplish, but
some users may be unwilling to answer
truthfully, if at all.

6. MOBILE SURVEYS
These surveys take advantage of the
increasing proliferation of mobile technology.
Mobile collection surveys rely on mobile
devices like tablets or smartphones to conduct
surveys via SMS or mobile apps.
7. PHONE SURVEYS
No researcher can call thousands of people at
once, so they need a third party to handle the
chore. However, many people have call
screening and won’t answer.

8. OBSERVATION
Sometimes, the simplest method is the best.
Researchers who make direct observations
collect data quickly and easily, with little
intrusion or third-party bias. Naturally, it’s only
effective in small-scale situations.

PRELIMINARIES OF A
RESEARCH PAPER

Preliminary pages are, in order, the title page; copyright
page; statement of thesis/dissertation approval;
abstract; dedication (optional); frontispiece (optional);
epigraph (optional); table of contents; lists of tables,
figures, symbols, and abbreviations (necessary only in
certain situations); and acknowledgments (optional).

IMRaD Format
(INTRODUCTION, METHODS, RESULTS and
DISCUSSION)
Page
Title Page i
Approval Sheet ii
Acknowledgment iii
Dedication iv
Table of Contents v
List of Tables vi
List of Figures vii
Abstract viii

Chapter 1
INTRODUCTION
Background of the Study
Review of Related Literature and Studies
Theoretical Framework
Conceptual Framework
Statement of the Problem
Significance of the Study
Scope and Delimitation
Definition of Terms

Chapter 2
METHODS
Research Design
Respondents
Sampling Techniques
Instruments
Construction and Validation of the Instruments
Administration and Retrieval of the Instrument
Statistical Treatment

Chapter 3
RESULTS
Tables, Figures, Text (Presented in the same order)
as the research questions/problems)
Analysis of Data

Chapter 4
DISCUSSION
Summary of Findings
Conclusion
Recommendation
REFERENCES
APPENDICES
CURRICULUM VITAE

CONCEPT PAPER
FORMULATION

It is used to generate feedback and guidance
from a program officer before a full grant
proposal is developed. In most cases, the
program officer will be, or will have been, a
researcher, clinician, or scholar, like yourself.
Before you start writing, think about the Golden
Rule and the kind of project summary you’d like
to read. Avoid forced, “grantsmanship”
language, and communicate your ideas in a
simple, direct manner. Also, read your
sponsor’s mission statement.

DESIGN YOUR PAPER TO ANSWER THESE
QUESTIONS:
1. What’s the problem?
 State the problem/gap in a manner that’s
concise and demonstrates your grasp of the
literature.
 If you’re proposing a research project, state
the research objective(s).

2. Who cares? (This question has both
figurative and literal meanings.)
 What is the significance of the project in
terms of both advancing knowledge and
benefiting the public?
 Who/which group(s) will benefit from the
research?

3. What exactly are you going to do? (How will
you address the problem?)
 Provide goals/specific aims.
 What is the study design? For problems
involving data samples, how do you know
that the sample size is adequate?
 Provide a statement about your access to
the population, if applicable.
 Communicate why you are ideally suited to
tackle this specific problem and/or what is
unique about your approach.

4. What will the deliverables be?
 The deliverables can include intangibles as
well as tangibles.
 Examples: outreach events, grant
applications, trained individuals/groups, new
or improved products, patents, partnerships,
paradigms, process improvement,
dissemination products, etc.

5. How will you know you’ve achieved your
goals?
 The evaluation section is often a critical part
of an application.
 While you may not need to discuss this
aspect of the project to generate a positive
response from a program officer, doing so
may lead to valuable feedback.

6. Who are your team members? (May not be
applicable.)

STATISTICAL TOOLS

Statistical methods and analytical tools help
collect and analyze samples of data to identify
patterns and trends. These insights help make
predictions that can be useful in making
strategic business decisions. Statistical analysis
tools are also effective at analyzing, describing,
summarizing, and comparing data of different
organizations in the same industry.

1. Mean
Mean is an important method for performing
statistical analysis and presents the average data
to provide a common measure of central tendency.
The formula for mean is as follows:
Mean = (sum of all values) / (total number of
values in the data set) :

For example, to find the mean for the values 2,
4, 6, 8, and 10, follow these steps:
Summation: Add all the different values in the
data set (2+4+6+8+10) to find 30.
Division: Divide the total value (30) by the
total number of values in the data set (5) to
yield 6. The mean of the above data set is 6.

2. Standard deviation
Standard deviation is the measure of the spread of a
data set. The degree of variance of the data set is the
average square of the difference between the mean
value and each data value. Standard deviation is the
spread of data values around the mean or average
data. The formula for standard deviation is:
σ = √(∑x−¯x)2 /n)
Where "σ," = standard deviation is where
"Σ" = sum of the data
"x" = the value of the data set.
“n” = number of data points in the population.

3. Hypothesis testing
A hypothesis is a claim or assumption about a data
set. Hypothesis testing is a standard process to
draw conclusions about the property of a population
parameter or a population probability distribution. It's
also called T Testing and is helpful while testing the
two sets of random variables within the data set.
Hypotheses testing compares the data against
various hypotheses and assumptions and assists in
forecasting and decision-making.

The formula for hypothesis testing is:
H0: P = 0.5
H1: P ≠ 0.5

4. Regression
Regression is the relationship between a
dependent variable and an independent variable.
Researchers and statisticians use it to explain how
one variable influences another or how the
changes in a variable trigger change in another.
Regression analysis graphs and charts can help
show the relationships between the variables and
trends over a specific amount of time.

4. Regression
The formula for regression is:
Y = a + b(x) + Є
Where:
"Y" = dependent variable
"x" = independent variable
"a“ = stands for the intercept
"b“ = slope
"Є" = denotes the regression residual.

5. Sample size determination
In statistics, it is critical to determine the right size
of the sample to get accurate results and
predictions. In most cases, businesses have a
large amount of data to process and analyze and
may study only a part of it in greater detail.
Statisticians determine the correct sample size by
considering factors like cost, time, or
convenience.

6. Variance
Variance in statistics refers to the expected
deviation between values in a specific data set.
Businesses use this to measure the average
value and volatility of the market and the
stability of a specific investment return within a
period. It's helpful in the mathematical sense to
analyze data, but to use the insights gathered
from variance, you may have to take the
square root of the sample variance. It simply
measures the variability of data from the
average
.

To calculate it, take the differences between each
number in the data set and the mean. Then
square the difference in the number to make
them positive. Finally, divide the sum of the
squares by the number of values.

The formula for variance is:
σ² = ∑(x−xˉ)2 / (n−1)
Where:
"σ²" = illustrates variance,
"σ" = standard deviation,
"x“ = represents the ith data point
"xˉ" = mean value of all data points
"n" = the number of data points.

WHAT IS DATA ANALYSIS?

Data analysis is described “as the process of
bringing order, structure, and meaning” to the
collected data. The data analysis aims to
unearth patterns or regularities by observing,
exploring, organizing, transforming, and
modelling the collected data.

There are several data analysis tools available. Some of
those are:
• Python
• R
• SAS
• Apache Spark
• Tableau
• Power BI
• QlikView
• Microsoft Excel
• KNIME
• RapidMiner
• Solver
• OpenRefine
• NodeXL
• io

FIVE TYPES OF DATA
ANALYSIS

1. Descriptive Analysis
 The foundation step simply looks at the past
data and tells what has happened in the past. It
captures and summarizes the past using
measures of central tendency, measures of
dispersion, visualizing using dashboards.
 This analysis helps understand how the data is
present and does not make any predictions or
answers why something has happened.
 It is useful for generating reports, tracking Key
Performance Indicators (KPIs), sales leads, and
revenue reports.

2. Diagnostic Analysis
 Helps dig further by creating detailed,
informative, dynamic and interactive
dashboards
 It separates the root cause of the problem
and identifies the source of the patterns. It is
also useful in anomaly detection.
 It can be applied to determine which factors
led to improvement in sales

3. Predictive Analysis
 Predicts the likelihood of an event, forecasting
any measurable amount, risk assessment, and
segmenting customers into groups.
 Since it forecasts the occurrence of an event,
it employs probability.
 Along with the previous summarized and root
cause analysis, the models use statistics and
machine learning algorithms for predicting
future outcomes.

4. Prescriptive Analysis
 Prescribes the best course of action,
strategies. The prescriptive analysis is not
predicting one individual standalone event
but a collection of future events using
simulation and optimization.
 It is heavily applied in the financial, social
media, marketing, and transportation
domains. Its uses are varied from
recommending products or movies to
suggesting which strategies to use to reap
maximum returns and minimize risk.

5. Cognitive Analysis
 It combines technologies such as artificial
intelligence, semantics, machine learning, and
deep learning algorithms.
 It learns and even generates data using the
already available data and retrieves features
and hidden patterns.
 Real-time data cognitive analysis is heavily
employed in image classification and
segmentation, detection of objects, machine
translations, virtual assistants, and chatbots

WHAT IS DATA
INTERPRETATION?

It is the process of assigning meaning to the
processed and analyzed data. It enables us to
make informed and meaningful conclusions,
implications, infer the significance between the
relationships of variables and explain the
patterns in the data.

THERE ARE TWO
PRIMARY TECHNIQUES
AVAILABLE TO
UNDERSTAND AND
INTERPRET THE DATA

Quantitative Methods
The quantitative data interpretation technique is
applicable for the measurable or numerical
type of data. The numerical data is of two
types:
Discrete: countable, finite quantities. Eg: the
number of ice-creams
Continuous: not countable. Eg: height,
weight, time, speed, humidity, temperature

Qualitative Methods
Qualitative methods are implemented to analyze
the textual and the descriptive data called the
categorical data. Text data is usually unstructured.
The qualitative data is subdivided further based on
their characteristics:
Nominal: The attributes have no ranking or order
Ex: Region, Gender, Classes in school
Ordinal: The attributes are ranked or ordered in a
sequence. Ex: Grades
Binary: It has only two categories. Either yes or
no, Class 1 or 0.

WHAT IS THE DIFFERENCE
BETWEEN DATA ANALYSIS
AND INTERPRETATION?

Data Analysis Data Interpretation
Meaning
Data analysis is the process of
uncovering patterns and
trends in the data.
Data interpretation is the process of
assigning meaning to the data. It
involves explaining those discovered
patterns and trends in the data.
Chronology
Data analysis comes first,
followed by data
interpretation.
Data interpretation is the next
proceeding step after data analysis.
Types/
Methods
The five types of data analysis
are Descriptive Analysis,
Diagnostic Analysis, Predictive
Analysis, Prescriptive
Analysis, and Cognitive
Analysis.
The data interpretation methods are
Quantitative Methods and Qualitative
Methods.

Why is it
needed?
To summarize,
condense data in a
comprehensible and
usable form for further
advanced analytics and
prediction.
Data interpretation is
required because the
numbers can not speak for
themselves. It needs
manual human intervention
to understand what the
numbers are saying.
Example
For example, the top 5
teams in terms of the
winning Percentages are
Real Madrid, Barcelona,
Atletico Madrid,
Valencia, and Athletic
Bilbao.
An example of interpretation
is what does it imply 95% of
the population lies within the
range of 136.54 to 143.45.

END OF PRESENTATION

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