Methods of Research in Computing PDF

 ? - is a systematic and organized process of investigating a specific question, problem, or hypothesis to discover new knowledge, validate existing theories, or develop new insights. It involves the collection, analysis, and interpretation of data, and is typically guided...

 ? - is a systematic and organized process of investigating a specific question, problem, or hypothesis to discover new knowledge, validate existing theories, or develop new insights. It involves the collection, analysis, and interpretation of data, and is typically guided by a well-defined methodology. PURPOSE Discovery Validation Problem-Solving TYPES OF RESEARCH Qualitative Research Quantitative Research Mixed Methods Research DIFFERENCE BETWEEN QUALITATIVE AND QUANTITATIVE RESEARCH 1.Objective QUANTITATIVE RESEARCH QUALITATIVE RESEARCH Seeks to quantify the extent of a Seeks to explore the phenomenon and test underlying reasons, meanings, hypotheses using numerical and motivations behind a data. phenomenon. Focuses on breadth and Focuses on depth and generalizability. understanding context. DIFFERENCE BETWEEN QUALITATIVE AND QUANTITATIVE RESEARCH 2. Methodology QUANTITATIVE RESEARCH QUALITATIVE RESEARCH Structured and follows a fixed Flexible and adaptive. design. Uses interviews, focus groups, Uses surveys, experiments, and observations, and content statistical tools. analysis. DIFFERENCE BETWEEN QUALITATIVE AND QUANTITATIVE RESEARCH 3. Data Collection QUANTITATIVE RESEARCH QUALITATIVE RESEARCH Collects textual or non- Collects numerical data. numerical data. Uses methods like interviews Uses tools like questionnaires with open-ended questions, with closed-ended questions, participant observations, and experiments, and structured analysis of documents or observations. media. DIFFERENCE BETWEEN QUALITATIVE AND QUANTITATIVE RESEARCH 4. Data Analysis QUANTITATIVE RESEARCH QUALITATIVE RESEARCH Statistical analysis, often Thematic, narrative, or content involving graphs, tables, and analysis. charts. Emphasizes interpretation and Emphasizes objectivity and understanding from the replicability. participants' perspective. DIFFERENCE BETWEEN QUALITATIVE AND QUANTITATIVE RESEARCH 5. Outcome QUANTITATIVE RESEARCH QUALITATIVE RESEARCH Produces generalizable findings Produces detailed, contextual with a focus on measuring and findings with a focus on explaining. exploring and understanding. Often answers "how much" or Often answers "why" or "how." "how often." DIFFERENCE BETWEEN QUALITATIVE AND QUANTITATIVE RESEARCH 6. Example QUANTITATIVE RESEARCH QUALITATIVE RESEARCH A study examining the A study exploring individuals' correlation between hours of personal experiences and exercise and weight loss in a challenges with maintaining a large population sample. regular exercise routine. Exploring Teachers' The Effect of Classroom Size on Perceptions of the Challenges Student Academic Performance and Benefits of Remote in High Schools Learning DIFFERENCE BETWEEN QUALITATIVE AND QUANTITATIVE RESEARCH QUANTITATIVE RESEARCH QUALITATIVE RESEARCH Ideal for testing theories and Ideal for exploring new areas hypotheses in a way that can be of inquiry or understanding generalized to larger complex phenomena in depth, populations. It focuses on focusing on words and numbers and statistical meanings. relationships. MIXED METHODS RESEARCH MIXED METHODS RESEARCH Leverages the strengths of both, providing a more comprehensive approach to research by combining the breadth of quantitative research with the depth of qualitative research. CHARACTERISTICS OF A GOOD RESEARCH 1. Originality and Innovation 2. Clarity and Precise 3. Relevance and Impact 4. Ethical Considerations 5. Comprehensive Literature Review 6. Effective Communication 7. Replicability and Transparency 8. Interdisciplinary Integration 9. Sustainability and Future Research RESEARCH IN COMPUTING Thesis - focuses on the theories and concepts of computing in the form of scientific work. Capstone Project - focuses on the IT infrastructure, application or processes involved in implementing a Computing solution to a problem. DIFFERENCE BETWEEN THESIS AND CAPSTONE PROJECTS THESIS CAPSTONE PROJECT The main purpose of a thesis is The purpose of a capstone to contribute new knowledge or project is to demonstrate the insights to a specific academic practical application of the field. It is research-oriented and knowledge and skills acquired involves posing a research during the academic program. question or hypothesis, It is more project-oriented, conducting extensive research, often involving solving a real- and analyzing the findings to world problem, developing a draw conclusions. product, or completing a practical task. DIFFERENCE BETWEEN THESIS AND CAPSTONE PROJECTS THESIS CAPSTONE PROJECT A capstone project focuses on A thesis is typically focused on a applying learned concepts in a theoretical or academic practical setting. It might problem, and the work is often involve creating a business aimed at advancing knowledge plan, designing a system, in a particular field. It involves developing software, or significant original research, implementing a solution to a data collection, and analysis. real-world problem. DIFFERENCE BETWEEN THESIS AND CAPSTONE PROJECTS THESIS CAPSTONE PROJECT The scope of a thesis is usually broader and more in-depth, The scope of a capstone requiring a thorough review of project is generally narrower existing literature, formulation and more focused on practical of a research question, and outcomes. It is typically significant data collection and completed within a single analysis. It often requires a semester or academic year substantial time commitment and may involve collaboration and may span multiple with industry or community semesters. partners. DIFFERENCE BETWEEN THESIS AND CAPSTONE PROJECTS THESIS CAPSTONE PROJECT The output of a thesis is a The output of a capstone lengthy, formal document that project might be a product, a includes a detailed literature report, a presentation, or a review, methodology, data prototype. The final analysis, and discussion of the deliverable often includes a findings. It usually results in a practical solution or publication or defense before an application rather than just a academic committee. written document. DIFFERENCE BETWEEN THESIS AND CAPSTONE PROJECTS THESIS CAPSTONE PROJECT Emphasizes original research, Emphasizes practical theory development, and application, problem-solving, academic inquiry. and real-world relevance. RESEARCH IN COMPUTING Web Application Development - creating a fully functional e-commerce website or a content management system for a small business. Mobile App Development - designing and building a mobile app that addresses a specific need, such as a fitness tracking app or a scheduling tool for students. RESEARCH IN COMPUTING Machine Learning Project - developing a machine learning model to predict customer churn in a business or to classify images for a specific purpose. Cybersecurity - implementing a network security system or creating a tool for detecting and preventing cyber threats. References: Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches(5th ed.). SAGE Publications. Yin, R. K. (2017). Case Study Research and Applications: Design and Methods (6th ed.). SAGE Publications. References: CMO 25 S. 2015  DEFINITION OF SOFTWARE DEVELOPMENT METHODOLOGIES WATERFALL MODEL AGILE METHODOLOGY SCRUM KANBAN  DEVOPS LEAN SOFTWARE DEVELOPMENT EXTREME PROGRAMMING SPIRAL MODEL RAD V-MODEL Are structured approaches to planning, executing, and managing the software development process. These methodologies help teams organize their work, improve collaboration, and ensure that software products are delivered efficiently and effectively. A linear and sequential approach where each phase of development must be completed before the next begins. It includes stages such as requirements analysis, design, implementation, testing, deployment, and maintenance. Advantages Easy to manage due to its rigid structure; well-suited for projects with clear and fixed requirements. Disadvantages Inflexible to changes; not ideal for complex or ongoing projects.   An iterative and incremental approach where software is developed in small, manageable chunks called iterations or sprints. Agile emphasizes flexibility, customer collaboration, and responsiveness to change. Advantages Highly adaptable to changes; promotes continuous feedback and collaboration; faster delivery of functional software. Disadvantages Can be challenging to manage scope; requires close collaboration and communication. 1. Concept/Inception Define Vision - Identify the project’s overall goal and objectives. Stakeholder Engagement - Gather initial requirements from stakeholders. Initial Planning - Outline a high-level plan and prioritize the project’s major features. 2. Iteration Planning Sprint Planning - Break down the project into smaller, manageable iterations (sprints), typically lasting 1-4 weeks. Define specific tasks and goals for each sprint. Backlog Refinement - Create and maintain a prioritized list of features and tasks (Product Backlog) that will be addressed in upcoming sprints. 3. Execution Development - Work on the tasks defined in the sprint plan. This involves coding, designing, and creating the necessary features. Daily Stand-ups - Conduct short daily meetings (stand-ups) where team members discuss progress, plans for the day, and any obstacles encountered. 4. Testing Continuous Testing - Regularly test the developed features throughout the sprint to ensure they meet the required standards and work as intended. Integration - Continuously integrate new code and features with the existing system to ensure compatibility and functionality. 5. Review Sprint Review - At the end of each sprint, present the completed work to stakeholders for feedback. Review what was accomplished and demonstrate new features. Feedback Collection - Gather feedback from stakeholders and users to understand their needs and any changes required. 6. Retrospective Sprint Retrospective - Reflect on the sprint process with the team. Discuss what went well, what did not, and identify areas for improvement. Action Items - Develop actionable items to improve processes and address any issues identified during the retrospective. 7. Release Deployment - Release the product or updated features to users. Ensure that the deployment is smooth and the new features are functioning as expected. Post-Release Feedback - Collect feedback from users to assess the impact of the release and to inform future development. 8. Iteration Repeat - Start the cycle again with the next sprint, incorporating feedback and making adjustments based on the review and retrospective outcomes. Continuous Improvement - Continuously refine and improve the product based on user feedback and team insights. A specific framework within Agile that uses short, time-boxed iterations called sprints, typically lasting 2-4 weeks. Scrum involves roles like Product Owner, Scrum Master, and Development Team. Advantages Promotes transparency and accountability; encourages regular feedback; effective in managing complex projects. Disadvantages Requires a high level of discipline and commitment from the team; can be challenging for large teams. 1. Roles Product Owner Responsibilities - Represents the stakeholders and customers, defines and prioritizes the product backlog, and ensures the team delivers value. Scrum Master Responsibilities - Facilitates Scrum processes, removes impediments, ensures the team follows Scrum practices, and coaches the team and organization. Development Team Responsibilities - Self-organizes to deliver the product increment, collaborates on tasks, and works towards achieving the sprint goals. 2. Artifacts Product Backlog Description - A prioritized list of features, enhancements, bug fixes, and technical tasks needed for the product. Maintenance - Continuously refined and prioritized by the Product Owner. Sprint Backlog Description - A subset of the Product Backlog items selected for the current sprint, along with a plan for delivering the increment. Maintenance - Created and managed by the Development Team during the Sprint Planning meeting. Increment Description - The sum of all completed Product Backlog items during a sprint, plus the value of increments from previous sprints. It must be a usable and potentially shippable product. 3. Events Sprint Duration - Typically 1-4 weeks. Description - A time-boxed period during which a “Done,” usable product increment is created. Sprints are consistent in length throughout the project. Sprint Planning Timing - At the beginning of each sprint. Description - The team defines what will be delivered in the sprint and how the work will be accomplished. The Product Owner presents the top items from the Product Backlog, and the team selects items to include in the Sprint Backlog. Daily Scrum (Stand-Up) Timing - Daily, typically for 15 minutes. Description - A short meeting where the Development Team discusses what they did yesterday, what they will do today, and any obstacles they are facing. Sprint Review Timing - At the end of each sprint. Description - The team demonstrates the completed work to stakeholders. Feedback is gathered to refine the Product Backlog and adjust the project direction if needed. Sprint Retrospective Timing - After the Sprint Review and before the next Sprint Planning. Description - The team reflects on the sprint process, discussing what went well, what could be improved, and how to implement improvements in the next sprint. Backlog Refinement (Grooming) Timing - Ongoing, typically held regularly throughout the project. Description - The Product Owner and team refine and prioritize the Product Backlog items. This involves breaking down larger items into smaller tasks and ensuring the backlog is up-to-date and well-defined. A visual approach to managing tasks and workflows, focusing on continuous delivery without overburdening the team. Tasks are represented on a Kanban board, and work is pulled as capacity permits. Advantages Flexible and easy to implement; promotes continuous improvement; excellent for visualizing workflow. Disadvantages May lack structure; can be less effective for larger, more complex projects without proper management. 1. Visualize the Workflow Create a Kanban Board Columns - Set up columns on the board to represent different stages of the workflow, such as “To Do,” “In Progress,” “Testing,” and “Done.” Work Items - Use cards or sticky notes to represent individual tasks or work items, placing them in the appropriate column based on their current status. 2. Define Work Items Break Down Tasks Details - Each card should include details about the task, such as the task description, owner, and any relevant deadlines or priorities. Size and Scope - Ensure tasks are small enough to be completed within a reasonable time frame and can be easily moved through the workflow. 3. Manage Work in Progress (WIP) Set WIP Limits Purpose - Define limits for the number of tasks allowed in each column to prevent bottlenecks and ensure that work is completed efficiently. Adjust Limits - Regularly review and adjust WIP limits based on team capacity and workflow dynamics. 4. Focus on Flow Monitor Progress Track Tasks - Continuously track the movement of tasks through the Kanban board. Ensure tasks are moving smoothly from one stage to the next. Identify Bottlenecks - Look for stages where tasks accumulate or slow down and address the causes of these bottlenecks. 5. Continuous Improvement Review and Reflect Regular Reviews - Hold regular meetings or reviews to discuss workflow performance, bottlenecks, and areas for improvement. Retrospectives - Use these discussions to identify opportunities for process improvements and implement changes. 6. Implement Feedback Make Adjustments Process Changes - Based on feedback and observations, adjust the workflow, WIP limits, or task definitions to improve efficiency and effectiveness. Experiment - Test different approaches and measure their impact to find the most effective solutions for your team. 7. Maintain Flexibility Adapt to Change Update Workflow - Be ready to modify the Kanban board or workflow as project requirements, team dynamics, or priorities change. Iterate - Continuously iterate on processes and practices to better align with team needs and project goals. A methodology that integrates software development (Dev) and IT operations (Ops) to improve collaboration and automate processes, leading to continuous integration, continuous delivery (CI/CD), and faster time to market. Advantages Promotes automation and efficiency; enhances collaboration between development and operations teams; improves deployment frequency and reliability. Disadvantages Requires cultural change and strong communication; initial setup can be complex. 1. Plan Define Objectives Project Goals - Establish clear goals and requirements for the project or release. Backlog Creation - Create and prioritize a backlog of features, enhancements, and bug fixes. Roadmap Development Release Planning - Develop a roadmap for the release cycles and major milestones. 2. Develop Continuous Integration (CI) Source Control - Use version control systems (e.g., Git) to manage code changes. Automated Builds - Set up automated build processes to compile and integrate code changes frequently. Automated Testing - Implement automated tests to verify code quality and functionality. Collaboration Cross-Functional Teams - Foster collaboration between developers, testers, and operations teams to ensure alignment. 3. Build Continuous Deployment (CD) Automated Deployment - Use automated deployment tools to deploy code to staging or production environments. Configuration Management - Manage and automate infrastructure and configuration changes. Artifact Management Store Artifacts - Use artifact repositories to store build artifacts, such as binaries and libraries. 4. Test Automated Testing Test Suites - Run automated test suites, including unit tests, integration tests, and end-to-end tests. Performance Testing - Conduct performance tests to assess the application’s scalability and stability. Feedback Continuous Feedback - Collect feedback from testing and monitoring to identify issues and improve the development process. 5. Release Release Management Deployment Pipelines - Create and manage deployment pipelines to automate the release process. Feature Toggles - Use feature toggles to control the release of new features and mitigate risks. Coordination Collaborate - Work closely with stakeholders to coordinate and plan releases, ensuring minimal disruption. 6. Deploy Production Deployment Automated Rollouts - Use automated deployment tools to release updates to production environments. Rollback Procedures - Implement rollback procedures to quickly revert changes if issues arise. Monitoring Real-Time Monitoring - Set up real-time monitoring and alerting to track application performance and health. 7. Operate Infrastructure Management Infrastructure as Code (IaC) - Use IaC tools to automate the provisioning and management of infrastructure. System Administration - Perform routine maintenance, updates, and configuration changes. Incident Management Incident Response - Establish processes for detecting, responding to, and resolving incidents and outages. 8. Monitor Performance Monitoring Metrics Collection - Collect and analyze metrics related to application performance, user experience, and system health. Logging - Implement centralized logging to capture and analyze log data for troubleshooting. Continuous Improvement Feedback Loop - Use monitoring data and incident reviews to continuously improve processes, performance, and quality. Based on lean manufacturing principles, this methodology focuses on maximizing value by eliminating waste, improving quality, and delivering fast. It emphasizes optimizing efficiency and responsiveness. Advantages Reduces waste and enhances efficiency; focuses on delivering value quickly; encourages continuous improvement. Disadvantages Requires a deep understanding of lean principles; may be challenging to implement in large organizations. 1. Define Value Customer Perspective Identify Value - Determine what is valuable from the customer’s perspective. Understand their needs, preferences, and pain points. Value Proposition - Define clear value propositions and ensure that the product features align with delivering that value. 2. Map the Value Stream Value Stream Mapping Identify Steps - Create a visual map of all the steps involved in the software development process, from concept to delivery. Analyze Flow - Analyze the flow of work and information through the value stream to identify areas where value is added and where waste occurs. 3. Eliminate Waste Identify Waste Types of Waste - Identify and address various types of waste, including overproduction, waiting, defects, extra processing, inventory, motion, and unused talent. Continuous Improvement - Continuously look for ways to streamline processes, reduce delays, and minimize non-value-adding activities. 4. Create Flow Optimize Flow Streamline Processes - Focus on creating a smooth and continuous flow of work through the value stream. Remove bottlenecks and reduce cycle times. Incremental Delivery - Break down work into smaller, manageable chunks and deliver increments regularly to maintain a steady flow of value. 5. Establish Pull Pull System Demand-Based Work - Implement a pull-based approach where work is initiated based on actual demand rather than pushing work through the system. Just-In-Time Delivery - Ensure that work items are pulled into the process only when there is capacity and a need for them, reducing excess work in progress. 6. Seek Perfection Continuous Improvement Kaizen - Apply the principle of Kaizen (continuous improvement) to all aspects of the development process. Regularly review and improve processes, tools, and practices. Feedback Loops - Incorporate feedback from stakeholders, customers, and team members to continuously refine and enhance the software and development practices. 7. Empower Teams Team Involvement Autonomy and Ownership - Empower teams to take ownership of their work and make decisions. Encourage collaboration, self-organizing teams, and shared responsibility. Skill Development - Invest in training and development to enhance team members’ skills and capabilities, enabling them to contribute more effectively. 8. Deliver Fast Speed and Agility Rapid Iteration - Focus on delivering value quickly and frequently. Use iterative and incremental development approaches to release features and improvements in short cycles. Responsiveness - Be responsive to changes in customer needs and market conditions, adapting quickly to deliver relevant and timely solutions. A type of Agile methodology that focuses on technical excellence and customer satisfaction. XP practices include pair programming, test-driven development (TDD), and continuous integration. Advantages Improves code quality through practices like TDD and pair programming; highly responsive to customer needs; promotes regular feedback. Disadvantages Can be resource-intensive; requires a high level of discipline and technical skill. 1. Planning User Stories Collect Requirements - Work with customers to write user stories that describe the features they want. User stories are short, simple descriptions of functionality from the user’s perspective. Estimate Effort - Developers estimate the time and effort required to implement each user story. Iteration Planning Prioritize Stories - The customer prioritizes the user stories based on business value, and the team plans which stories will be implemented in the next iteration (usually 1-2 weeks). Create Task List – Break down the selected user stories into smaller tasks that can be completed d during the iteration. 2. Designing Simple Design Keep It Simple - Focus on creating the simplest design that works. Avoid adding unnecessary features or complexity. Refactor Code - Continuously improve and simplify the design through refactoring—making small improvements to the code without changing its functionality. System Metaphor Define a Metaphor - Use a system metaphor or simple analogy that helps team members understand the overall structure and design of the system. 3. Coding Pair Programming Work in Pairs - Developers work in pairs, with one writing code while the other reviews it. They switch roles frequently, which improves code quality and knowledge sharing. Test-Driven Development (TDD) Write Tests First - Before writing any code, developers write automated tests for the desired functionality. They then write the minimum amount of code needed to pass the test. Continuous Testing - Continuously run tests to ensure that new code doesn’t break existing functionality. Collective Code Ownership Shared Responsibility - The entire team owns the codebase, meaning any developer can work on any part of the code at any time. Coding Standards Consistent Style - Follow a set of coding standards agreed upon by the team to ensure code consistency and readability. 4. Integration Continuous Integration Frequent Integration - Integrate and test code changes frequently (multiple times a day) to catch issues early and ensure that the software remains functional. Automated Build and Test - Use automated tools to build the software and run all tests every time new code is integrated. 5. Testing Unit Testing Automated Unit Tests - Write unit tests for individual components or functions to verify that they work as expected. Acceptance Testing Customer Validation - Work with the customer to define acceptance tests that validate whether the software meets their requirements. Automated Acceptance Tests - Automate acceptance tests to ensure they can be run frequently and reliably. 6. Feedback Customer Feedback Frequent Releases - Deliver working software to the customer frequently (at the end of each iteration) to get feedback and make adjustments. Continuous Communication - Maintain close communication with the customer to ensure their needs are being met. Team Feedback Retrospectives - Hold regular retrospectives to discuss what went well, what didn’t, and how the process can be improved in the next iteration. 7. Deployment Small Releases Frequent Deployment - Release small, functional increments of the software frequently to get it into the hands of users as soon as possible. Early and Often - Encourage early and frequent deployment to reduce the risk of large, complex releases. Combines elements of both iterative and waterfall models, focusing on risk assessment. The development process is divided into several cycles (or spirals), each involving planning, risk analysis, engineering, and evaluation. Advantages Effective for large, complex, and high-risk projects; allows for early detection of risks. Disadvantages Can be expensive and time-consuming; requires careful risk management.  Emphasizes quick development and iteration of prototypes rather than strict planning and testing. It involves user feedback at every stage, leading to faster development cycles. Advantages Fast delivery of software; encourages user feedback and iterative design. Disadvantages May compromise quality for speed; requires strong user involvement and rapid decision-making. An extension of the Waterfall model, where each development stage is associated with a corresponding testing phase. The process is highly structured and emphasizes validation and verification. Advantages Clear and straightforward; ensures high quality through rigorous testing. Disadvantages Inflexible to changes; not suitable for complex or evolving projects.        CHAPTER 1-3  How to set focus on best Capstone Project? Identify your interests and strengths Assess the scope and feasibility Literature review Seek guidance and feedback Consider practical impact Reflect on ethical and social implications Final Decision  Examples of Capstone Projects Topics Developing a Mobile Application for Real-Time Traffic Monitoring Using AI Implementing a Blockchain-Based Voting System for Local Elections Designing a Cybersecurity Awareness Program for Small Businesses Automated System for IoT Device Management in Smart Homes Creating a Predictive Analytics Model for E-Commerce Customer Behavior  Examples of Capstone Projects Topics Developing a Predictive Model for Stock Market Trends Using Machine Learning Analyzing Customer Churn in Subscription-Based Services Using Big Data Implementing a Sentiment Analysis Tool for Social Media Monitoring Creating a Data-Driven Strategy for Optimizing Supply Chain Management Exploring the Use of Artificial Intelligence in Personalizing Online Shopping Experiences How do proponents prepare the problem statement for the Capstone Project? Understand the Concept - Background Research - Identify the Gaps Define the Problem Clearly - Specificity - Root Cause - Impact Craft the Problem Statement - State the Problem - Contextual Information - Evidence How do proponents prepare the problem statement for the Capstone Project? Justify the Need - Relevance - Consequences of Inactions Align with Objectives - Link with Objectives - Scope and Feasibility Review and Refine - Feedback - Clarity and Precision  Example of Problem Statement Topic: Developing a Mobile Application for Real-Time Traffic Monitoring Problem Statement Urban commuters in [City Name] face significant delays due to unpredictable traffic congestion, resulting in lost productivity, increased fuel consumption, and higher stress levels. Current traffic monitoring systems are either outdated or do not provide real-time, accurate information tailored to individual routes. This project aims to address this problem by developing a mobile application that leverages AI and real-time data to provide personalized traffic updates and route recommendations for urban commuters in [City Name].  Example of Problem Statement Context The traffic congestion problem has been exacerbated by rapid urbanization, with the number of vehicles on the road increasing by 20% annually over the past five years. Existing traffic monitoring solutions, such as radio updates or static online maps, fail to account for real-time changes, leading to inefficient route planning for commuters. Relevance By developing a more accurate and responsive traffic monitoring application, this project aims to reduce commute times by up to 15% for users, contributing to improved quality of life and economic productivity in [City Name].  What arguments make a good Chapter 1 for a Capstone Project 1. Introduction to the Topic Contextual Background - Start by introducing the broader context of your topic. Explain the general field or industry your project is related to and why it’s important. Relevance of the Topic - Highlight the significance of the topic in current times. Explain how it fits into current trends, issues, or innovations in the field. This establishes why the topic is worth exploring.  What arguments make a good Chapter 1 for a Capstone Project 2. Problem Statement Clearly Defined Problem - Present a concise problem statement that articulates the specific issue or challenge your project addresses. A good problem statement is clear, specific, and outlines the gap or need that your project aims to fill. Impact of the Problem - Explain the implications of the problem. Why is it important to solve? What are the consequences of not addressing this issue? This justifies the need for your project.  What arguments make a good Chapter 1 for a Capstone Project 3. Purpose of the Study/Project Objective or Aim - Clearly state the main objective or aim of your capstone project. This should directly relate to the problem you’ve identified. Research Questions or Hypotheses - If applicable, present the research questions or hypotheses that guide your investigation. These should be closely aligned with your problem statement and objectives.  What arguments make a good Chapter 1 for a Capstone Project 4. Significance of the Study Contribution to the Field - Discuss how your project contributes to the existing body of knowledge or practice. What new insights, solutions, or innovations does it offer? Practical Applications - Explain the practical implications of your project. How will the findings or outcomes be useful in real-world scenarios? Who will benefit from this project, and in what ways? Societal or Academic Impact - Highlight the broader impact of your work. Does it address a societal need, improve a process, or contribute to academic discourse?  What arguments make a good Chapter 1 for a Capstone Project 5. Scope and Delimitations Project Scope - Define the scope of your project. What are the boundaries of your study? What aspects will you focus on, and what will be excluded? Delimitations - Discuss any limitations or constraints that may affect your project, such as time, resources, or data availability. This helps set realistic expectations for what your project can achieve.  What arguments make a good Chapter 1 for a Capstone Project 6. Assumptions Underlying Assumptions - State any assumptions you are making in your project. These might include assumptions about the data, the environment, or the behavior of participants. Be clear about what you are assuming to be true for your project to proceed.  What arguments make a good Chapter 1 for a Capstone Project 7. Theoretical or Conceptual Framework (if applicable) Theoretical Foundation - If your project is research-based, include a brief discussion of the theoretical or conceptual framework that underpins your study. This provides a foundation for understanding the variables and relationships you are exploring. Key Theories or Models - Introduce any key theories, models, or concepts that are central to your project. Explain how these frameworks will guide your analysis or development efforts.  What arguments make a good Chapter 1 for a Capstone Project 8. Overview of the Methodology Research Design or Development Approach - Provide a brief overview of the methodology you will use to achieve your objectives. Are you conducting a qualitative study, a quantitative analysis, a software development project? This section gives the reader a sense of how you plan to tackle the problem. Data Collection and Analysis - Mention the type of data you will collect and how you plan to analyze it. If your project involves creating a product or system, describe the development process you will follow.  What arguments make a good Chapter 1 for a Capstone Project 9. Organization of the Study Chapter Outline - Provide a brief outline of the structure of the rest of your capstone project. Summarize what each chapter will cover, giving the reader a roadmap of the document.  What arguments make a good Chapter 1 for a Capstone Project 10. Conclusion Summarize the Key Points - Conclude Chapter 1 with a summary of the main points discussed. Reinforce the importance of the problem, the purpose of the project, and its potential impact. Transition to Next Chapter - Offer a transition that leads into the next chapter, whether it’s a literature review, methodology, or another section. This helps maintain the flow of the document. Sample Chapter 1.docx Chapter 2. Review of Related Literature How to demonstrate a good grasp of the systems and published works that are related to the topic? Conduct Thorough Literature Review Critically Analyze Key Concepts Compare and Contrast with Other Works Reference Authoritative Works Demonstrate Applications Use Proper Citations and References Stay Current How to identify gaps? Conduct a Comprehensive Literature Review Look for Inconsistencies or Contradictions Examine Emerging Trends and Technologies Identify Understudied Areas Analyze the Depth of the Existing Studies Pay Attention to Authors Recommendation How to identify gaps? Consider Societal and Technological Changes Synthesize Ideas Across Papers Evaluate Unexplored Implications Engage with Experts How to avoid plagiarism during research process? Understand What Constitutes Plagiarism Start With Proper Note-Taking Cite Sources Properly Paraphrase Carefully Use Direct Quotes Sparingly How to avoid plagiarism during research process? Summarize Accurately Check Your Work for Unintentional Plagiarism Maintain a List of Sources Acknowledge Common Knowledge Develop Your Own Ideas What arguments make a good Chapter 2 for a Capstone Project? Technical Background Related Literature Related Studies/Systems Project Synthesis Technical Background 1. Theoretical Foundations - Discussion of theories or models relevant to the research topic. For instance, in computer science, this could include algorithms, architectures, protocols, etc. 2. Existing Research - Summary of previous studies and findings related to your topic. This section identifies gaps in current knowledge that your research will address. Technical Background 3. Technology Overview - Explanation of the technologies used in your study. For example, in IoT research, the technical background might cover wireless communication standards, sensors, or cloud platforms. 4. Current Trends - Highlight any recent developments or emerging trends that are relevant to your research area. 5. Challenges - Discussion of current technical challenges or limitations in the field.  Related Literature Definition: This refers to existing theories, concepts, models, books, reports, reviews, and academic papers that provide a conceptual or theoretical foundation for your research. It typically includes publications like textbooks, journal articles, or papers that explain ideas, frameworks, and general knowledge related to your field. Focus: The focus of related literature is often on broad concepts, ideas, and academic discourse in the field. It provides the theoretical underpinnings or background necessary to understand the subject of your research.  Related Literature Sources: Books, review articles, theoretical papers, and reference materials that discuss the subject matter conceptually. Example: A literature review discussing various theories of remote sensing technology or Internet of Things (IoT) frameworks without testing them through a study.  Related Studies/Systems Definition: This refers to empirical research, experiments, case studies, or surveys conducted by other researchers that are directly related to your study. These studies are usually primary research or investigations with data collection and analysis relevant to your research question. Focus: The focus of related studies is on specific research findings, experiments, or surveys conducted by others. These are often used to compare your methodology, results, and conclusions with similar works. Sources: Empirical research papers, dissertations, theses, case studies, and experimental reports where new data was collected and analyzed. Example: A study where researchers measure air quality using NB-IoT technology or assess the effectiveness of a pesticide-monitoring tool. Related Studies/Systems Aspect Related Literature Related Studies Nature Conceptual or theoretical Empirical or Experimental Books, articles, reports Research papers, dissertations, Sources (theoretical) case studies Broader theories, concepts, or Specific findings, experiments, or Focus models data To provide a theoretical To provide examples of similar Purpose framework research References Lavina, C. G., Manabo, D. R., Hernandez, D. G., Hablanida, D. F., Lacorte, D. A., & Ebron, J. G. (2016). Writing the review of literature and studies. In Outcomes-based practical guide to thesis and capstone project writing in computing (pp. 45-61). Manila: Mindshapers Crewell, J.W.et.al. Research Design: Qualitative, Quantitative and Mixed Methods Approaches, 3rd Edition. SAGE 2009 Booth, W.C.et. al. The Craft of Research, 5th Ed. University of Chicago Press, 2024 Ridley, D. The Literature Review: A Step-by-Step Guide for Students, SAGE 2012 Chapter 3 - Research Methodology What is research design? Research design refers to the overall strategy or blueprint that guides the researcher in the process of collecting, measuring, and analyzing data to address specific research questions or hypotheses. It is the framework that outlines how the research will be conducted, ensuring that the study is structured in a way that enables the researcher to draw valid, reliable, and accurate conclusions. What are the key components of research design? 1. Purpose of the Study - Identifies whether the research is exploratory, descriptive, explanatory, or evaluative. Each purpose dictates different design strategies. 2. Research Questions or Hypotheses - Clear formulation of what the study aims to answer or investigate, guiding the choice of methods and analysis techniques. What are the key components of research design? 3. Research Methods - The specific techniques used to collect data (e.g., surveys, interviews, experiments, case studies, etc.). - The method chosen depends on the research problem, objectives, and the nature of the data required (qualitative or quantitative). 4. Sampling Design - Defines how participants or data sources will be selected. This includes choosing between probability sampling (random selection) or non-probability sampling (purposive or convenience selection). What are the key components of research design? 5. Data Collection Procedures - Specifies how data will be gathered, whether through direct observation, surveys, experiments, interviews, or other means. 6. Data Analysis Strategy - Outlines how the collected data will be processed and analyzed to draw meaningful insights. This might involve statistical analysis, thematic analysis, or other techniques. What are the key components of research design? 7. Time Frame - A study can be cross-sectional (data collected at one point in time) or longitudinal (data collected over an extended period). 8. Ethical Considerations - Ensures that the research adheres to ethical standards, such as informed consent, privacy, and confidentiality of participants. 9. Validity and Reliability - Validity - The degree to which the research accurately reflects the concept or relationship it is intended to measure. - Reliability - The consistency or repeatability of the research results. Type of Research Design 1. Exploratory Research Design - Used when the problem is not well understood, and the researcher seeks to explore it further. - Typically involves flexible methods such as literature reviews, interviews, or focus groups. 2. Descriptive Research Design - Aims to describe the characteristics or behaviors of a particular population or phenomenon. - Surveys and observational studies are common methods. Type of Research Design 3. Explanatory (Causal) Research Design - Focuses on understanding cause-and-effect relationships. - Typically involves experiments where variables are manipulated to observe their effects on other variables. 4. Experimental and Quasi-Experimental Design - Experimental Involves random assignment of participants to treatment and control groups. - Quasi-Experimental Similar to experimental, but lacks random assignment, used in situations where true experiments are not feasible. Type of Research Design 5. Correlational Research Design - Aims to determine whether and how two or more variables are related, without establishing causal relationships Importance of Research Design Guides the Study - Ensures that the research question is addressed systematically. Ensures Validity - Proper research design helps avoid bias and errors, enhancing the accuracy of conclusions. Efficient Use of Resources - Helps in planning resources, time, and methods in a way that avoids unnecessary effort. Framework for Analysis - Provides a structured approach for collecting and analyzing data, enabling meaningful interpretation of results. What is Sampling? Sampling is the process of selecting a subset of individuals, units, or data points from a larger population to represent the whole. The goal is to make inferences about the entire population based on observations from this smaller group (the sample), as it is often impractical or impossible to study the entire population. Importance of Sampling Feasibility - Collecting data from the entire population can be time- consuming, costly, and impractical, especially for large populations. Efficiency - A well-chosen sample can provide accurate and reliable insights, allowing researchers to make valid generalizations about the population. Key Terms in Sampling Population - The entire group of individuals, events, or objects that a researcher is interested in studying. Sample - A subset of the population selected for the study. Sampling Frame - A list or representation of all the elements in the population from which the sample will be drawn. Sampling Bias - Occurs when the sample is not representative of the population, leading to skewed or inaccurate results. Types of Sampling Methods 1. Probability Sampling In probability sampling, every member of the population has a known, non-zero chance of being selected. This method tends to produce more representative samples, reducing bias and allowing for stronger generalizations. 2. Non-Probability Sampling In non-probability sampling, the probability of each individual being chosen is unknown, and not every individual has a chance of being selected. This method is often used in exploratory research where generalizability is less important. Probability Sampling Simple Random Sampling - Every individual in the population has an equal chance of being selected. - Selection is typically done using random numbers or drawing names from a hat. - Example Randomly selecting 100 students from a university’s student body. Systematic Sampling - Selection is made at regular intervals from a list of the population after randomly choosing a starting point. - Example If you want to sample every 10th person on a list of 1,000 names, you first choose a random starting point, say the 5th person, and then select every 10th person (15th, 25th, 35th, etc.). Probability Sampling Stratified Sampling - The population is divided into subgroups or strata based on a specific characteristic (e.g., gender, age, income), and then a random sample is taken from each stratum. - This ensures representation from each subgroup. - Example Dividing a population into male and female groups and randomly selecting individuals from both groups. Cluster Sampling - The population is divided into naturally occurring clusters (e.g., schools, neighborhoods), and a random sample of clusters is selected. Then, all individuals within the selected clusters are studied. - Example Randomly selecting a few schools from a district and studying all the students in those schools. Probability Sampling Multistage Sampling - A complex form of cluster sampling where random sampling is conducted in multiple stages. - Example Randomly selecting regions, then towns within those regions, and then households within those towns. Non-Probability Sampling Convenience Sampling - Samples are taken from a group that is conveniently available to the researcher. - Example Interviewing people at a shopping mall because they are easily accessible. Purposive (Judgmental) Sampling - The researcher uses their judgment to select participants who are most relevant or have the most information for the study. - Example Selecting experts in a field to participate in a study on a specialized topic. Non-Probability Sampling Snowball Sampling - Participants recruit other participants, creating a "snowball" effect, often used when studying hard-to-reach populations. - Example In a study of homeless individuals, one participant refers others in their network to the researcher. Quota Sampling - The population is divided into subgroups (similar to stratified sampling), but participants are chosen non-randomly based on a set quota. - Example Sampling 50 males and 50 females from a population based on specific characteristics, without random selection. Non-Probability Sampling Voluntary Sampling - Participants volunteer to take part in the study, often responding to advertisements or requests. - Example People responding to an online survey about their shopping habits. Factors to Consider in Sampling 1. Population Size - Larger populations typically require larger samples to ensure representativeness. 2. Sample Size - The size of the sample should be adequate to provide accurate estimates and minimize sampling error. 3. Diversity of Population - More diverse populations require larger or more stratified samples to ensure all groups are adequately represented. 4. Sampling Bias - Efforts should be made to reduce bias, particularly in non-probability sampling methods. Sampling Errors Sampling Error - The difference between the characteristics of the sample and those of the population, arising because the sample is only part of the population. Non-Sampling Error - Errors not related to the sampling process, such as data collection mistakes, non-response, or inaccurate measurement. How to Collect Data from the Target Respondents? 1. Define Your Research Objectives and Questions - Clearly identify what you want to learn from your respondents. Your objectives and research questions will determine the type of data you need (qualitative or quantitative) and guide your choice of data collection method. 2. Identify and Profile Your Target Respondents - Determine who your respondents are (e.g., students, customers, employees, etc.). Make sure your sample reflects the population you are studying in terms of relevant characteristics (age, gender, location, occupation, etc.). How to Collect Data from the Target Respondents? 3. Choose the Data Collection Method The choice of method depends on factors like time, cost, accessibility of respondents, and the nature of the data (subjective opinions, behaviors, facts, etc.). Data Collection Methods Surveys/Questionnaires - Description Standardized questions presented to respondents in written or digital form. - When to Use When you need to collect data from a large number of respondents quickly. - Advantages Easy to administer, cost-effective, and allows for quantitative analysis. - Tools Google Forms, SurveyMonkey, Microsoft Forms, Typeform. - Steps - Develop a list of questions aligned with your research objectives. - Use clear and concise language. - Distribute the survey digitally (email, social media, online platforms) or in person. - Example A satisfaction survey asking customers to rate their experience with a product or service. Data Collection Methods Interviews - Description One-on-one or group conversations where the researcher asks open-ended or structured questions. - When to Use When in-depth qualitative data is needed or when understanding respondents' perspectives is crucial. - Advantages Allows for probing and clarifying responses, yielding detailed and rich data. - Steps - Create an interview guide with relevant questions. - Conduct interviews face-to-face, via phone, or through video conferencing. - Record and transcribe interviews for analysis. - Example Interviewing employees about their experiences with new workplace technology. Data Collection Methods Focus Groups - Description Group discussions moderated by the researcher, where participants interact and share opinions on a specific topic. - When to Use When you need to explore group dynamics or generate collective insights. - Advantages Encourages interaction, providing a deeper understanding of shared perspectives. - Steps - Prepare guiding questions or topics for discussion. - Recruit a diverse group of respondents. - Facilitate the discussion and record the interactions. - Example A focus group on consumer preferences for a new product. Data Collection Methods Observation - Description Collecting data by observing respondents' behavior in natural or controlled settings. - When to Use When understanding actual behavior (rather than self-reported data) is important. - Advantages Provides unbiased, real-time data on how respondents act in certain situations. - Steps - Develop a structured observation checklist. - Observe respondents in the relevant environment (e.g., workplace, public setting). - Record observations systematically. - Example Observing customer behavior in a retail store to assess the effectiveness of store layout. Data Collection Methods Experiments - Description Controlled studies where respondents are subjected to specific conditions to observe their responses. - When to Use When testing cause-and-effect relationships between variables. - Advantages Allows researchers to establish causal links between variables. - Steps - Design an experiment with control and experimental groups. - Manipulate the independent variable and observe changes in the dependent variable. - Analyze the results. - Example Testing how different website layouts affect user engagement. Data Collection Methods Online Analytics Tools - Description Digital platforms that track user interactions, behaviors, or feedback online. - When to Use When studying user behavior on digital platforms (websites, apps, social media). - Advantages Provides real-time data on user engagement and activities. - Tools Google Analytics, social media insights, website heatmaps. - Example Using Google Analytics to monitor how users navigate through a website. How to Collect Data from the Target Respondents? 4. Design Your Data Collection Instrument - Create the actual tool (survey, interview guide, observation sheet) based on your research goals. - Ensure questions are relevant, concise, and understandable by your target respondents. - If using a survey, ensure the layout is clear, and include different types of questions (e.g., multiple-choice, Likert scales, open-ended). 5. Pilot Test Your Instrument - Conduct a small-scale test of your data collection tool with a subset of respondents. - Use the feedback to refine your questions or approach, ensuring it works effectively for the main data collection process. How to Collect Data from the Target Respondents? 6. Administer the Data Collection - In-Person Conduct interviews, surveys, or observations directly with respondents. - Online Use digital platforms (email, social media, websites) to distribute surveys or administer online interviews. - Telephone Conduct interviews or surveys over the phone when face-to-face interactions are not feasible. 7. Follow-Up (if necessary) - For non-responses, send reminders to increase participation rates, especially in surveys. - Offer incentives or rewards to motivate participation (optional, depending on the study).  How to Collect Data from the Target Respondents? 8. Ensure Ethical Considerations - Informed Consent Explain the purpose of the research to respondents and ensure they agree to participate voluntarily. - Confidentiality Protect respondents' identities and personal information. - Data Privacy Ensure that the collected data is securely stored and used only for the intended purposes. 9. Data Storage and Organization - Safely store the collected data, whether digital or physical. - Use proper labeling and organization methods to ensure easy access for analysis. - For digital data collection, download and back up the responses to avoid data loss. 10. Analyze and Interpret Data - Once you have collected the data, proceed with the analysis, using appropriate tools and techniques depending on whether you collected quantitative or qualitative data.  References Lavina, C. G., Manabo, D. R., Hernandez, D. G., Hablanida, D. F., Lacorte, D. A., & Ebron, J. G. (2016). Writing the review of literature and studies. In Outcomes-based practical guide to thesis and capstone project writing in computing (pp. 45-61). Manila: Mindshapers. Purdue Writing Lab. (n.d.). OWL // Purdue Writing Lab. Retrieved June 28, 2020, from https://owl.purdue.edu/