Research skills methodology

1. Dr.M.Marsaline Beno
1
1
Enhancing Research Methodology Skills
For Engineering Educators
Dr.M.MARSALINE BENO
Dean- Research
PROFESSOR
DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING
St.XAVIER’S CATHOLIC COLLEGE OF ENGINEERING
Email : beno@sxcce.edu.in Mobile: 9443993659
5/20/2024

2. Presentation Flow
1. Introduction
2. Understanding Research Methodology
3. Research Design
4. Literature Review
5. Data Collection Techniques
6. Data Analysis
7. Interpretation and Reporting
8. Enhancing Teaching Through Research
9. Professional Development Opportunities
10. Conclusion

3. INTRODUCTION

4. Entrepreneurship Ecosystem

5. EDUCATION 4.0

6. What is Innovation
• “Something new or different introduced.”
• Creative Problem Solving

7. SCAMPER

8. SUBSTITUTE

9. COMBINE

10. ADAPT

11. MAGNIFY

12. MINIFY

13. REARRANGE

14. Innovation
•Be Stupid…..

15. Importance of Research Methodology
for Engineering Educators
• Foundation of Knowledge
• Guiding Principles
• Informed Decision Making
• Continuous Improvement
• Enhanced Student Learning
• Professional Development
• Empowerment and Impact

16. Understanding Research Methodology
What is Research ?
Dr.M.Marsaline Beno
16

17. Research
• What is Research ?
• A planned ,Investigation is Research
Dr.M.Marsaline Beno
17

18. Research Flow Diagram

19. The categories of knowledge in research

20. Types of Research :
Basic Research:
• This type of research focuses on fundamental principles and theories, aiming
to expand our understanding of concepts without immediate practical
applications. Basic research often forms the foundation for more applied
research.

21. Applied Research:
• Applied research is conducted with the specific goal of solving real-world
problems or addressing practical issues. Its findings are intended to have
direct implications or applications in various fields.

22. Quantitative Research Design
• Quantitative research involves the collection and analysis of numerical data
to identify patterns, relationships, and trends.
• It often employs statistical techniques to draw conclusions from the data.
• Characteristics:
- Emphasis on objective measurement and statistical analysis.
- Utilizes structured instruments such as surveys, experiments, or tests.
- Seeks to generalize findings to a larger population

23. Qualitative Research Design
• Qualitative research explores the underlying motivations, attitudes, and behaviors
of individuals or groups.
• It relies on non-numerical data, such as interviews, observations, and textual
analysis, to generate insights
• Characteristics:
- Emphasizes naturalistic inquiry and subjective interpretation.
- Uses methods such as interviews, observations, and focus groups.
- Aims to generate rich, descriptive data to uncover underlying meanings and
patterns.

24. Mixed-Methods Research Design
• Mixed-methods research integrates both quantitative and qualitative approaches
within a single study to provide a comprehensive understanding of a research
problem.
• Characteristics:
- Combines quantitative and qualitative data collection and analysis techniques.
- Allows researchers to triangulate findings, validate results, and gain deeper
insights.
- Offers flexibility in addressing research questions that require multiple
perspectives.

25. Selecting the Appropriate Research Design for
Engineering Studies
• Considerations for Selecting Research Design
- Nature of the research question: Determine whether the research aims to
explore relationships (quantitative), understand phenomena (qualitative), or
both (mixed-methods).
- Availability of resources: Assess the feasibility of data collection methods,
sample size requirements, and time constraints.

26. Selecting the Appropriate Research Design for
Engineering Studies
• Tailoring Research Design for Engineering Studies
- Engineering studies often require a combination of quantitative analysis
for empirical data (e.g., performance metrics, numerical simulations) and
qualitative inquiry for understanding human factors, user experiences, and
socio-technical aspects.
- Researchers may employ mixed-methods designs to complement
quantitative data with qualitative insights, providing a holistic understanding
of complex engineering phenomena.

27. Examples of Research Design in Engineering
Education
• Experimental Design
- Example: Assessing the effectiveness of a new teaching method on
student learning outcomes in an engineering course through controlled
experiments with randomized assignment of participants to treatment and
control groups.

28. Examples of Research Design in Engineering
Education
• Survey Research Design
- Example: Conducting a survey to investigate engineering students'
attitudes, perceptions, and experiences related to online learning tools and
technologies

29. Examples of Research Design in Engineering
Education
• Case Study Design
- Example: Examining the implementation of a project-based learning
approach in an engineering curriculum by conducting in-depth case studies
of multiple courses across different institutions

30. Examples of Research Design in Engineering
Education
• Mixed-Methods Design
- Example: Integrating quantitative analysis of assessment scores with
qualitative interviews to explore factors influencing student engagement and
learning experiences in a robotics engineering program

31. Key steps in the research process
• Research is a dynamic process that follows a structured methodology to ensure rigor,
accuracy, and reliability of results.
• Key steps in the research process include defining research questions or hypotheses,
designing a research plan, collecting data, analyzing data using appropriate methods,
drawing conclusions, and communicating findings through publications, presentations,
or reports.
• In both academia and industry, research contributes to innovation, problem-solving,
policy formulation, and informed decision-making. It drives progress in various fields
and fosters a deeper understanding of the complex world we live in.
•

32. Literature Review:
• New knowledge in research can only be interpreted within the context of what is
already known, and cannot exist without the foundation of existing knowledge.
• A literature review involves gathering and analyzing existing research and
publications on a specific topic. It helps researchers understand the current state
of knowledge, identify gaps, and build on existing research.
• Often, but not always, the textbooks contain the older established knowledge and
the research papers the newer work. Reading the textbooks on one’s topic
provide the established knowledge and the background to be able to read the
newer work usually recorded in the research papers

33. A good literature survey is typically a two-step
process
• Generally, a good literature survey is the first expectation of a supervisor
from the research student, and when done well can create a good impression
that the state of art in the chosen field is well understood
• (i) Identify the major topics or subtopics or concepts relevant to the subject
under consideration
• (ii) Place the citation of the relevant source (article/patent/website/data,
etc.) in the correct category of the concept/topic/subtopic

34. literature survey
• The goal of literature survey is to bring out something new to work on through
the identification of unsolved issues, determine the problems in the existing
models or experimental designs, and present a novel idea and recommendations
• Technical Reading
• Conceptualizing Research
• Taking Notes While Reading
• Attributions and Citations: Giving Credit Wherever Due

35. The literature survey grid

36. Overview of Data Collection Methods
• Surveys
- Surveys involve gathering information from a sample of participants through structured questionnaires
or online forms.
• Interviews
- Interviews entail direct communication between the researcher and participants to gather in-depth
insights and perspectives.
• Observations
- Observations involve systematically watching and recording behaviors, interactions, or phenomena in
natural or controlled settings.
• Experiments
- Experiments involve manipulating variables and measuring their effects on outcomes in controlled
settings.

37. Considerations for Choosing Data Collection
Techniques
• Research Objectives
- Align data collection methods with the specific research questions and objectives.
- Consider whether quantitative, qualitative, or mixed-methods approaches are most appropriate.
• Participant Characteristics
- Take into account the characteristics of the target population, including demographics,
preferences, and accessibility.
• Resource Availability
- Assess the availability of time, budget, and expertise required for implementing different data
collection methods.
• Ethical Considerations
- Ensure that data collection methods adhere to ethical guidelines, including informed consent,
privacy, and confidentiality

38. Best Practices for Ensuring Data Validity and
Reliability
• Pilot Testing
- Conduct pilot tests to refine data collection instruments and procedures before full-scale implementation.
• Standardization
- Standardize data collection protocols to minimize variations and ensure consistency across data collection
sessions.
• Triangulation
- Use multiple data sources or methods (triangulation) to corroborate findings and enhance the validity of results.
• Data Verification
- Verify collected data for accuracy and completeness through regular checks and validation procedures.
• Documentation
- Maintain detailed records of data collection procedures, including any deviations or modifications made during
the process.
• Peer Review
- Seek feedback from peers or experts in the field to validate data collection methods and interpretations.

39. Introduction to Data Analysis Techniques
• Descriptive Statistics
- Descriptive statistics involve summarizing and describing the characteristics of a dataset
through measures such as mean, median, mode, standard deviation, and frequency
distributions.
• Inferential Statistics
- Inferential statistics are used to make inferences and predictions about a population
based on sample data. It includes techniques such as hypothesis testing, correlation analysis,
regression analysis, and analysis of variance (ANOVA).
• Qualitative Analysis
- Qualitative analysis involves interpreting and making sense of non-numerical data, such
as text, images, or observations. It includes methods such as thematic analysis, content analysis,
and grounded theory.

40. Application of Statistical Software Tools in
Engineering Research
• Examples of Statistical Software
- Statistical software tools such as SPSS (Statistical Package for the Social Sciences), SAS
(Statistical Analysis System), R, and MATLAB are commonly used in engineering research
for data analysis.
• Functions and Capabilities
- These software tools offer a wide range of functions and capabilities for conducting
various statistical analyses, data visualization, and reporting results.
• Advantages
- Statistical software enhances efficiency, accuracy, and reproducibility in data analysis,
allowing researchers to handle large datasets and complex statistical models effectively

41. Examples of Data Analysis in Engineering
Education Studies
• Assessment of Student Performance
- Using descriptive statistics to analyze exam scores, grade distributions, and student
performance trends to assess the effectiveness of teaching methods and curriculum.
• Evaluation of Teaching Interventions
- Applying inferential statistics to compare the learning outcomes of students exposed
to different teaching interventions or instructional strategies.
• Qualitative Research on Pedagogical Practices
- Conducting qualitative analysis of student feedback, classroom observations, or
reflective journals to explore perceptions, attitudes, and experiences related to engineering
education.

42. Importance of Interpretation
• Interpretation is a critical component of research methodology that involves
making sense of the collected data and deriving meaningful insights.
• It enables researchers to draw conclusions, identify patterns or trends, and
evaluate the implications of their findings in the context of existing
knowledge and theories.

43. Key Aspects of Interpretation
• Contextualization
- Understanding the broader context in which the research was conducted and
how the findings contribute to advancing knowledge in the field.
• Critical Analysis
- Evaluating the strengths and limitations of the research methodology, data
collection techniques, and analytical methods used in the study.
• Synthesis
- Integrating and synthesizing the findings to develop coherent explanations or
theories that explain observed phenomena or relationships

44. Strategies for Interpreting Research Findings
Effectively
• Compare and Contrast
- Compare findings with existing literature and contrasting perspectives to
identify similarities, discrepancies, or areas for further investigation.
• Seek Patterns and Trends
- Look for recurring patterns, trends, or outliers in the data that may provide
insights into underlying relationships or phenomena.
• Consider Alternative Explanations
- Explore alternative explanations or interpretations for the findings and assess
their plausibility based on available evidence.

45. Guidelines for Reporting Research Results in
Engineering Education Journals
• Clarity and Precision
- Present findings clearly and concisely, using appropriate language and
terminology to ensure comprehension by readers.
• Transparency
- Provide sufficient detail on the research methodology, data collection
procedures, and analytical techniques to allow for replication and verification by
other researchers.
• Discussion of Implications
- Discuss the practical implications of the findings for engineering education
practice, policy, or future research directions.

46. Enhancing Teaching Through Research
Integration
• Student –led projects
• Incorporating case studies
• Collaborate Research with Industry
• Project Based Learning
• Research Groups
• Mentorship

47. Professional Development Opportunities
• Professional Organizations
• Workshops, Seminars, Conferences
• Collaboration & Networking

48. Research and Development vs.
Product Development
• Research and Development is the conception phase
in the product life cycle,
• while Product Development is the entire process of
designing, creating, and marketing new products or
existing products with new features.

49. Design Thinking

57. Understanding Technology Readiness
Level (TRL), Manufacturing Readiness
Level (MRL) and Investment Readiness
Level (IRL)

59. Innovation Skills- 7 Habits
1. Think out of the box
2. Always be eager in doing things(interest)
3. Sleep with problems
4. Suspend judgments
5. Use analogy
6. Decide fast
7. Bank ideas