Project Management Goals and Data Collection Methods

Data Collection Methods

• Surveys/Questionnaires: Used to gather responses from many participants. Gathers both quantitative and qualitative data enabling a broad perspective on participant views, opinions, and experiences. Supports data-driven analysis.

• Interviews: One-on-one discussions to explore personal insights, thoughts, and opinions. Reveals nuanced perspectives on specific project topics for deeper understanding. Contributes valuable qualitative data.

• Observations: Records behaviors, actions, and processes as they naturally occur. Allows for accurate insights into actual practices or events in their authentic contexts. Enhances contextual validity for project.

Data Analysis Techniques

• Statistical Analysis: Used for rigorous statistical analysis. Enables researchers to measure, compare, and assess relationships between variables for precise and replicable results.

• Thematic Analysis: Used for qualitative data. Helps uncover complex, subjective meanings and adds contextual richness to research findings.

• Comparative Analysis: Involves comparing and contrasting different variables or data points to identify patterns and relationships.

• SWOT Analysis: Examines a project's strengths, weaknesses, opportunities, and threats to aid strategic decision-making.

• Cost Analysis: Evaluates the costs associated with a project.

• Sensitivity Analysis: Analyzes how changes in specific variables or parameters affect the project.

• Simulation Modeling: Used in projects to simulate a variety of scenarios and potential outcomes before full-scale implementation.

Data Types

• Quantitative Data: Numerical information for statistical analysis. Provides numerical insights.

• Qualitative Data: Non-numerical, descriptive information that aids understanding of experiences, behaviors, and opinions. Provides deeper and context-rich insights.

• Primary Data: Originally gathered for specific research questions through surveys, experiments, or observations. Enables accurate, context-specific data.

• Secondary Data: Previously collected and published by others, like government reports, academic studies, or databases. Contextualizes comparison, validation, or further analysis.

• Textual Data: Information from written sources like articles, books, or reports. Provides background, theoretical insights, or historical context.

• Observational Data: Collected through observing actions, interactions, or behaviors in real-time within authentic contexts. Offers context-rich insights into actual practices.

Project Planning Tools

• Flowchart: Visual tool that depicts the sequential steps and decisions in a project. Illustrates the logical flow of tasks in complex workflows.

• Gantt Chart: Time-based chart that organizes tasks on a timeline. Shows start & end dates, task dependencies, and assists teams to track progress rates, helping adjust schedules to meet deadlines.

• PERT Chart: Tool for project planning that maps task dependencies. Helps estimate task durations. Highlights the critical path to ensure efficient resource allocation for project completion.

Additional Project Components

• Problem Statement: Defines the core issue, gap, or challenge the project addresses. Provides a foundation for research and justificating its relevance.

• Research Questions: Targeted inquiries that guide the project's investigation into scope and depth. Clarifies the purpose, providing structure and enabling systematic exploration.

• Research Assumptions: Statements or hypotheses assumed to be true for the study. Defines the study's parameters, guiding the interpretation of findings in a controlled context.

• Rational Assumptions: Reasonable premises based on established knowledge, theory, and previous research. Ensures the methodology aligns with accepted frameworks, validates the study's approach.

• Project Scope: Outlines project boundaries, detailing objectives, deliverables, and limitations. Helps clarify project extent, manages expectations and focus.