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Doctor of Philosophy (Ph.D.)

Doctor of Philosophy (Ph.D.)

Program Overview

The Doctor of Philosophy (Ph.D.) is a research-intensive doctoral program designed for scholars, educators, and industry leaders seeking to contribute original knowledge to their fields. The program emphasizes advanced theoretical research, critical analysis, and interdisciplinary innovation, preparing graduates for careers in academia, research institutions, corporate leadership, and public policy. With a focus on rigorous scholarship, global perspectives, and real-world impact, the Ph.D. program supports self-directed research in areas such as business, finance, humanities, education, and technology. Candidates engage in academic publications, conferences, and global collaborations, gaining expertise to drive innovation, policy changes, and industry advancements.

Why Pursue a Ph.D. from FLISM?

The Doctor of Philosophy (Ph.D.) program at Florida Institute of Science and Management is designed for scholars, researchers, and professionals who seek to contribute groundbreaking research and thought leadership in their respective fields. With a strong emphasis on academic excellence, interdisciplinary research, and global impact, the program prepares graduates for top roles in academia, research institutions, corporate strategy, and policymaking.

Key Highlights

100% Online Program

100% Online Program

Get the flexibility to complete your education along with your work commitments.
24*7 Access

24*7 Access

State-of-the-art and advanced Learning Management System for enhanced self-learning
Comprehensive Curriculum

Comprehensive Curriculum

Curriculum developed by Industry Experts and world-class faculty members
Networking Opportunities

Networking Opportunities

Gain global networking opportunity

Program Objectives

  • Conduct original, high-impact research that contributes to global academic and industry discussions.
  • Develop expertise in quantitative and qualitative research methodologies.
  • Publish findings in top-tier academic journals, books, and international conferences.
  • Use data-driven insights and evidence-based research to improve decision-making in academia and industry.
  • Foster interdisciplinary research that bridges theory and real-world applications.
  • Explore emerging fields such as AI, digital transformation, global finance, and sustainable business.
  • Encourage research that influences public policy, corporate governance, and economic development.
  • Contribute to cutting-edge discussions in innovation, entrepreneurship, and social impact.

Curriculum

Module 1: Thesis Management

Research

  1. Scope and Significance
  2. Types of Research
  3. Research Process
  4. Characteristics of Good Research
  5. Identifying Research Problem
  6. Meaning of Sampling Design
  7. Steps in sampling
  8. Criteria for good sample design
  9. Types of Sample Design
  10. Probability and non-probability sampling methods
  11. Meaning of Measurement
  12. Types of scales

 

Review of Literature

  1. Data Collection
  2. Types of Data
  3. Sources of Data Collection
  4. Methods of Data Collection
  5. Constructing questionnaire
  6. Establishing, reliability and validity
  7. Data processing
  8. Coding, Editing and tabulation of data
  9. Meaning of Report writing
  10. Types of Report
  11. Steps of report writing
  12. Precautions for writing report
  13. Norms for using Tables
  14. Charts and diagram
  15. Appendix: – Index, Bibliography
  • Meaning and importance of Research
  • Types of Research
  • Selection and formulation of Research Problem
  • Meaning of Research Design
  • Need of Research Design
  • Features of Research Design
  • Inductive, Deductive and Development of models
  • Developing a Research Plan
  • Exploration, Description, Diagnosis, Experimentation
  • Determining Experimental and Sample Designs
  • Analysis of Literature Review
  • Primary and Secondary Sources
  • Web sources
  • Critical Literature Review
  • Hypothesis
  • Different Types of Hypothesis
  • Significance
  • Development of Working Hypothesis
  • Null hypothesis
  • Research Methods: Scientific method vs Arbitrary Method
  • Logical Scientific Methods: Deductive, Inductive, Deductive-Inductive
  • Pattern of Deductive
  • Inductive logical process
  • Different types of inductive logical methods.

Introduction to Quantitative Research

 Part 1:

a. Session Overview
b. RQ Hypothesis Course Context Video
c. What is Quantitative Research?
d. Ethics of Quantitative Research
e. Session Summary

Part 2:

f. Session Overview
g. Introduction to the Scientific Method of Research
h. Comparing Descriptive, Predictive and Prescriptive Research
i. Inductive and Deductive Approaches to Quantitative Research
j. Constructing Models
K. Session Summary

Exploring Quantitative Research Design

 Part 1:

a. Session Overview
b. Fundamentals of Research Design
c. Components of a Research Design
d. Characteristics of a Research Design
e. Session Summary

Part 2:

f. Session Overview
g. Research Design for Experimental Research Studies
h. Research Design for Quasi Experimental Studies
i. Research Design for Non-Experimental Research Studies
j. Evaluating Quantitative Research Design
k. Session Summary

Data Collection for Quantitative Research

 Part 1:

a. Session Overview
b. Defining Surveys
c. Exploring Survey Methods
d. Session Summary
Part 2:

e. Session Overview
f. The Process of Questionnaire Development
g. Designing a Questionnaire
h. Designing Rating Scales
i. The Art of Asking Questions
j. Session Summary

Part 3:

k. Session Overview
l. Tips to Conduct Effective Surveys
m. Ethics of Using Technology in Surveys
n. Session Summary

Measurement and Sampling

Part 1:

a. Session Overview
b. What is measurement?
c. True Score Theory, Estimating Measurement Errors
d. Evaluating Validity of Measures
e. Evaluating Reliability of Measures
f. Session Summary

Part 2:

g. Session Overview
i. Basic Concepts of Sampling
j. Problems and Blases in Sampling
k. Probability Sampling
l. Non-Probability Sampling
m. Session Summary

Part 3:

n. Session Overview
o. Determining the Sample Size
p. Sampling Distribution and Statistical Inference
q. Demonstrations on Sampling
r. Session Summary

Constructing Statistical Models   
Part 1:

a. Session Overview
b. Significance of Comparing Means for Analysis
c. What is ANOVA?
d. Types of ANOVA
e. Calculating and Interpreting One-Way ANOVA
f. Session Summary

Part 2:

g. Session Overview
h. Building a Statistical Model
i. Effect of Moderating and Mediating Variables
j. Demonstration on Mediation and Moderation
k. Session Summary

Enhancing Statistical Models
Part 1:

a. Session Overview
b. What is Factor Analysis?
c. Conducting Factor Analysis
d. Demonstration on R: Factor Analysis
e. Interpreting Factor Scores
f. Session Summary

Part 2:

g. Session Overview
h. What is Factorial ANOVA?
i. Dealing with Interaction Effects in Factorial ANOVA
j. Calculating and Interpreting Factorial ANOVA
k. Session Summary

Multivariate Analyses
Part 1:

a. Session Overview
b. Multivariate regression
c. MANOVA
d. Logistic Regression
e. Structural Equation Modeling
f. Tree Structured Methods
g. Conjoint Analysis
h. Session Summary

Part 2:

i. Session Overview
j. Time Series
k. Cluster Analysis
l. Session Summary

Writing a Quantitative Research Paper
Part 1:

a. Session Overview
b. Introduction to Formatting the Research Project for Quantitative Research
c. Components of a Quantitative Research Paper
d. Writing the Summary, Background and Purpose of Quantitative Research
e. Writing the Literature Review
f. Detailing your Research Design/Methodology
g. Curating your Results, Analysis and Supplementary Findings
h. Outlining your Conclusions and Recommendations
i. Making Appendices
j. Session Summary

Part 2:

k. Session Overview
l. Writing Different Types of Quant Papers
m. Guidelines for Fine-Tuning your Research Presentation
n. Session Summary

Introduction to Qualitative Research

a .Key Elements of Qualitative Research
b. Writing Qualitative Research Question
c. Qualitative Research: Framework
d. Steps to Write a Qualitative Research Paper
e. Ethics for Qualitative Research and IRB
f. Introduction to Design Strategies
g. Data Collection and Analysis Strategies
h. Introduction to research design
i. Major aspects of research design

Data Collection in Qualitative Research

  1. Sources of Evidence: A Comparative
    b. Assessment (Forms-Strengths-Weaknesses)
    c. Principles of Data Collection
    d. Sampling
    e. Reliability and Validity
  • Interviews and Focus Groups
  • Introduction to Data Analysis
  •   An Introduction to Data Analysis
    b. First Cycle Coding (Description Demo)
    c. Second Cycle Coding (Description Demo)
    d. Jottings and Analytic Memoing (Description Demo)
    e. Assertions and Propositions (Description Demo)
    f. Within Case and Cross-Case Analysis (Description Demo)

Data Display and Exploration

  1. Matrix and Networks
    b. Timing, formatting
    c. Extracting Inferences and Conclusions
    d. Exploring Fieldwork in Progress
    e. Exploring Variables
    f. Exploring Reports in Progress

Data Analysis Process – Next Steps

  1. Describing Participants
    b. Describing Variability
    c. Describing Action
    d. Ordering by time
    e. Ordering by process
    f. Explaining Interrelationship-Change
    g. Explaining Causation
    h. Making Predictions

Verifying Conclusions

  1. Tactics to achieve integration among diverse pieces of data
    b. Tactics to sharpen understanding by differentiation
    c. Tactics of seeing relationships in data abstractly
    d. Tactics to assemble a coherent understanding of data
    e. Tactics for testing or confirming findings
    f. Standards for quality of conclusions

Writing a Report and New Technologies

  1. Other methods in Qualitative Research
    b. Audiences and Effects
    c. Different aspects / apa
    d. An Introduction to Mixed Methods Research

Frequently Asked Questions

As a Ph.D. student, you will develop advanced skills in research methodologies, critical thinking, analytical reasoning, academic writing, and problem-solving within your chosen field.

The Ph.D. program typically takes 3 to 5 years to complete, depending on the research area, pace of study, and dissertation progress.

Students are assigned a dedicated research supervisor based on their research topic. The supervisor provides mentorship, guidance, and academic support throughout the dissertation process.

The topics vary depending on the discipline but generally include advanced coursework in your field, research methodology, ethics, and the completion of an original dissertation.