Lesson Objectives
By the end of this lesson, learners should be able to:
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Define qualitative and quantitative data.
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Differentiate between qualitative and quantitative data in RBPM.
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Identify practical examples of each type of data in project monitoring.
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Apply both types of data to improve performance measurement and decision-making.
Introduction
Data is the foundation of monitoring, evaluation, and performance measurement in Result-Based Project Management (RBPM). Collecting and analyzing the right data allows project managers to track progress, make informed decisions, and demonstrate results.
Data comes in two main types: qualitative and quantitative. Both are important and complementary. Using only one type can limit understanding of project performance.
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Quantitative data measures things in numbers.
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Qualitative data describes things in words, observations, or perceptions.
Understanding the differences and knowing when to use each type is essential for accurate monitoring and evaluation.
1. Quantitative Data
Definition:
Quantitative data is numeric data that can be measured and counted. It answers questions like “how many,” “how much,” or “how often.”
Key Features:
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Can be expressed in numbers or percentages.
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Easy to analyze statistically.
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Useful for tracking progress against targets.
Examples in RBPM:
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Number of farmers trained
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Percentage of participants adopting a new technique
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Kilograms of crop harvested per farm
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Number of workshops conducted
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Attendance rates
Uses in Performance Measurement:
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Quantitative data shows whether outputs and outcomes are being achieved.
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Helps set benchmarks, measure progress, and compare results over time.
2. Qualitative Data
Definition:
Qualitative data is non-numeric information that describes experiences, opinions, or observations. It answers questions like “why,” “how,” or “what do people think.”
Key Features:
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Expressed in words, stories, descriptions, or observations.
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Provides context and deeper understanding of project results.
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Complements quantitative data for a full picture of project performance.
Examples in RBPM:
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Farmers’ perceptions of training usefulness
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Challenges faced during adoption of new practices
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Observations of demonstration plot management
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Stakeholder satisfaction with project support
Uses in Performance Measurement:
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Explains why certain results occurred.
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Helps identify barriers, opportunities, and lessons learned.
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Supports decision-making and improvement.
3. Combining Qualitative and Quantitative Data
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Using both types of data provides a comprehensive view of project performance.
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Example: Quantitative data shows 70% of farmers adopted a technique (numeric), while qualitative data explains why some farmers did not adopt it (reasoning).
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This combination allows project managers to adjust strategies, address challenges, and achieve better results.
4. Practical Application in RBPM
Project: Improve maize productivity in Ntchisi District
| Indicator | Type of Data | Example | Use |
|---|---|---|---|
| Number of farmers trained | Quantitative | 200 farmers trained | Measure output achievement |
| % of farmers adopting new techniques | Quantitative | 75% adoption rate | Track outcome progress |
| Farmers’ satisfaction with training | Qualitative | “Training was clear but short” | Identify improvements for future workshops |
| Challenges faced in planting | Qualitative | “Water shortage affected growth” | Inform project adjustments |
Conclusion
Both qualitative and quantitative data are essential for accurate monitoring and performance measurement in RBPM. Quantitative data provides measurable evidence of progress, while qualitative data explains the context, challenges, and perceptions behind the numbers. By collecting, analyzing, and combining both types of data, project managers can make informed decisions, improve project outcomes, and ensure meaningful impact.
Lesson Objectives
By the end of this lesson, learners should be able to:
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Understand the purpose of data collection tools in RBPM.
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Identify different types of data collection tools.
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Select the most appropriate tools for specific project indicators.
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Apply practical criteria to choose effective, reliable, and efficient tools.
Introduction
Data collection is a critical step in monitoring, evaluation, and performance measurement. The quality of your data depends on the tools you use. Choosing the right data collection tools ensures that information is accurate, reliable, and relevant for assessing project progress and results.
In Result-Based Project Management (RBPM), tools are chosen based on the type of data (qualitative or quantitative), the indicators being measured, and the context of the project. Using the wrong tool can lead to poor data, wrong conclusions, and ineffective decisions.
1. What Are Data Collection Tools?
Data collection tools are instruments or methods used to gather information about project activities, outputs, outcomes, or impact.
Key Features:
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They must collect valid and reliable data.
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They should be suitable for the type of indicator.
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They need to be practical and feasible given resources, time, and skills.
Examples:
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Surveys and questionnaires
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Interviews (individual or group)
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Focus group discussions (FGDs)
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Observation checklists
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Records, reports, and registers
2. Criteria for Choosing Data Collection Tools
When selecting tools, consider:
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Type of Data:
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Quantitative data → structured tools like surveys, checklists, or forms.
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Qualitative data → unstructured or semi-structured tools like interviews, FGDs, or observation guides.
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Relevance to Indicators:
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The tool must measure the specific indicator defined in the M&E plan.
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Example: To measure farmer satisfaction, an interview or FGD is better than just counting attendance.
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Accuracy and Reliability:
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Choose tools that minimize errors and biases.
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Ensure consistency in how data is collected across respondents or locations.
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Feasibility:
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Consider resources, time, and staff capacity.
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Example: Digital surveys may be efficient but may not work in areas without internet.
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Cost-effectiveness:
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The tool should deliver quality data without excessive cost.
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Ethical Considerations:
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Ensure privacy, confidentiality, and informed consent when collecting data, especially qualitative data.
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3. Practical Steps to Choose Data Collection Tools
Step 1: Review the Indicator
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Understand what you need to measure: output, outcome, or impact.
Step 2: Identify Possible Tools
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List all available methods for collecting the required data.
Step 3: Evaluate Each Tool Against Criteria
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Consider relevance, accuracy, feasibility, cost, and ethics.
Step 4: Select the Best Tool or Combination
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Some indicators may require multiple tools (e.g., surveys + observation).
Step 5: Pilot the Tool
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Test the tool on a small scale to identify challenges and improve it before full use.
4. Practical Example – Malawi Context
Project: Improve maize productivity in Ntchisi District
| Indicator | Type of Data | Recommended Tool | Notes |
|---|---|---|---|
| Number of farmers trained | Quantitative | Attendance registers | Easy and accurate tracking of output |
| % of farmers adopting new techniques | Quantitative | Surveys / checklists | Collect measurable adoption data |
| Farmers’ perception of training usefulness | Qualitative | Focus group discussions | Gather opinions and experiences |
| Challenges in implementing new techniques | Qualitative | Key informant interviews | Understand barriers and solutions |
By carefully selecting tools, the project ensures reliable and meaningful data for decision-making.
Conclusion
Choosing the right data collection tools is critical for accurate monitoring and evaluation in RBPM. Tools should match the type of data, indicator, context, and resources. By applying criteria such as relevance, reliability, feasibility, cost, and ethics, project managers can collect high-quality data that supports informed decisions, accountability, and results-based learning.
Lesson Objectives
By the end of this lesson, learners should be able to:
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Define a Performance Measurement Framework (PMF) in RBPM.
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Understand the importance of PMFs for monitoring project results.
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Identify the key components of a PMF.
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Develop a simple, practical PMF for a project.
Introduction
A Performance Measurement Framework (PMF) is a structured tool that links project activities, outputs, outcomes, and impacts to measurable indicators. In Result-Based Project Management (RBPM), a PMF is essential for tracking progress, measuring performance, and reporting results.
Without a PMF, project managers may struggle to determine whether activities are achieving intended results. PMFs help answer questions like:
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Are we doing the right activities?
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Are our outputs contributing to outcomes?
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Are we making a meaningful impact?
A PMF provides clarity, accountability, and focus by clearly showing what to measure, how to measure it, and how results contribute to goals.
1. What is a Performance Measurement Framework (PMF)?
A PMF is a visual or tabular representation of a project’s results chain that links:
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Inputs and activities → Outputs → Outcomes → Impact
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Each level has indicators, targets, means of verification, and responsible persons
Purpose of a PMF:
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Guides data collection and monitoring
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Supports evaluation and reporting
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Helps track performance against targets
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Facilitates evidence-based decision-making
2. Key Components of a PMF
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Project Goal/Impact:
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The long-term change the project aims to achieve.
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Outcomes:
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Medium-term changes resulting from project outputs.
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Outputs:
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Tangible results produced by project activities.
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Activities:
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Tasks and interventions carried out to produce outputs.
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Indicators:
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Quantitative or qualitative measures used to track progress at each level.
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Targets:
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The specific value or achievement expected for each indicator.
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Means of Verification:
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Data sources and tools used to measure indicators.
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Responsible Persons:
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Staff or stakeholders accountable for collecting and reporting data.
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3. Steps to Build a PMF
Step 1: Review the Project LogFrame
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Identify the goal, outcomes, outputs, and activities.
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The PMF is often built directly from the LogFrame.
Step 2: Define Indicators
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Use SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound).
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Ensure each result has at least one indicator to measure progress.
Step 3: Set Targets
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Define expected achievements for each indicator.
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Example: Train 200 farmers within six months.
Step 4: Identify Means of Verification
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Decide how data will be collected and verified.
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Example: Attendance registers, surveys, observation reports.
Step 5: Assign Responsibilities
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Specify who collects, analyzes, and reports each indicator.
Step 6: Review and Validate
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Ensure the PMF is realistic, relevant, and achievable.
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Consult stakeholders to ensure alignment with project priorities.
4. Practical Example – Malawi Context
Project: Improve maize productivity in Ntchisi District
| Results Level | Indicator | Target | Means of Verification | Responsible |
|---|---|---|---|---|
| Impact | % increase in maize yield | 25% | Field survey, yield reports | Project Manager |
| Outcome | % of farmers applying new techniques | 75% | Farmer surveys | M&E Officer |
| Output | Number of farmers trained | 200 | Attendance registers | Field Officer |
| Activity | Conduct 10 training workshops | 10 workshops | Training schedule, reports | Training Coordinator |
This PMF ensures that every activity and output is linked to measurable outcomes and impact, making project monitoring and evaluation effective.
Conclusion
A Performance Measurement Framework (PMF) is a core tool in RBPM for linking activities to results. By defining indicators, targets, means of verification, and responsibilities, project managers can track performance, improve accountability, and make evidence-based decisions. A well-built PMF transforms monitoring and evaluation from a routine task into a strategic tool for achieving results.
Lesson Objectives
By the end of this lesson, learners should be able to:
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Define baselines and targets in the context of RBPM.
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Understand the importance of establishing baselines before project implementation.
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Set realistic, measurable, and time-bound targets.
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Apply baselines and targets to monitor project progress and measure performance.
Introduction
In Result-Based Project Management (RBPM), measuring progress and achieving results requires clear starting points and specific goals. Baselines and targets provide this foundation.
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A baseline is the initial value or condition of a particular indicator before project interventions begin.
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A target is the desired level of achievement for that indicator within a specific timeframe.
By establishing baselines and targets, project managers can track improvements, measure success, and demonstrate impact effectively. Without them, it is difficult to know whether project activities are making a difference.
1. Understanding Baselines
Definition:
A baseline is the existing situation, level, or condition of a variable before the project starts.
Purpose of Baselines:
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Provide a starting point for measuring change.
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Help set realistic targets.
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Enable comparison between the initial and current situation to determine progress.
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Identify gaps and priority areas for intervention.
Example:
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Indicator: % of farmers using climate-smart maize techniques
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Baseline: 20% of farmers are currently using the techniques
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This baseline shows where the project starts and what progress needs to be achieved.
2. Understanding Targets
Definition:
A target is the specific, measurable level of achievement expected for an indicator by a certain time.
Purpose of Targets:
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Guide project implementation toward results.
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Provide clear performance expectations for project staff.
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Help measure success and accountability to stakeholders.
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Support planning, resource allocation, and monitoring.
Characteristics of Good Targets:
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Specific: Clearly defined and unambiguous.
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Measurable: Can be quantified or assessed.
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Achievable: Realistic given resources and constraints.
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Relevant: Linked to project outcomes and impact.
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Time-bound: Has a clear deadline.
Example:
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Baseline: 20% of farmers using climate-smart techniques
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Target: 75% of farmers using climate-smart techniques within 12 months
3. Steps to Set Baselines and Targets
Step 1: Review Project Indicators
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Identify which outputs, outcomes, or impacts require measurement.
Step 2: Collect Baseline Data
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Conduct surveys, interviews, field observations, or review existing records.
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Ensure data is accurate, reliable, and representative of the project population.
Step 3: Analyze Baseline Data
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Determine the current status of each indicator.
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Identify gaps and potential areas for improvement.
Step 4: Set Realistic Targets
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Consider resources, time, and context.
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Targets should be ambitious yet achievable.
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Align targets with project goals and donor expectations.
Step 5: Document Baselines and Targets
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Include them in the M&E plan or PMF for reference during project implementation.
4. Practical Example – Malawi Context
Project: Improve maize productivity in Ntchisi District
| Indicator | Baseline | Target | Timeframe | Notes |
|---|---|---|---|---|
| % of farmers adopting climate-smart techniques | 20% | 75% | 12 months | Achievable with 10 training workshops |
| Number of demonstration plots established | 0 | 5 | 6 months | Supports hands-on learning |
| Average maize yield per farmer | 1.5 tons | 2.5 tons | 12 months | Measured through field surveys |
By setting baselines and targets, the project can track changes, evaluate success, and adjust activities if progress is slower than expected.
Conclusion
Baselines and targets are cornerstones of performance measurement in RBPM. Baselines provide a starting point, while targets define desired achievements. Together, they enable project managers to measure progress, make informed decisions, and demonstrate meaningful results. Establishing clear baselines and realistic targets ensures that projects remain results-focused and accountable throughout their lifecycle.
Tools: Google Forms or KoboToolbox
Time Required: 30–60 minutes
Objective of the Activity
By the end of this activity, learners should be able to:
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Create a simple baseline survey using a mobile phone.
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Collect data from at least 5 people.
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View and interpret the baseline data results.
Step-by-Step Instructions
Step 1: Identify the Purpose of Your Baseline
Choose a small project example such as:
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“Improving savings habits of youth.”
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“Increasing vegetable sales for small farmers.”
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“Improving study habits of students.”
Write down 3–5 key questions you want to measure before your project starts.
Example questions:
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How much do you save per week?
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What vegetables do you sell most?
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How many hours do you spend studying per day?
Step 2: Choose Your Tool (Google Forms or KoboToolbox)
Option A: Google Forms (Very Easy)
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Open your browser and search Google Forms.
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Tap Blank Form.
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Enter your form title: "Baseline Survey"
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Add question types:
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Multiple Choice
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Short Answer
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Checkbox
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Tap Send → Link → Copy to share your survey.
Option B: KoboToolbox (More Professional)
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Go to www.kobotoolbox.org
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Create a free account.
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Tap New Project → Create Project.
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Add questions one by one.
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Deploy and copy the data collection link.
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Share it with respondents.
Step 3: Build Your Baseline Questions
Use simple, measurable questions.
Example baseline survey for a youth saving project:
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Age – (Short answer)
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Do you save money every week? – (Yes/No)
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How much do you save weekly? – (Multiple choice: MK0–500, MK500–1,000, MK1,000+)
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Where do you keep your savings? – (Mobile money, Bank, Village group, Cash at home)
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What challenges do you face when saving? – (Short answer)
Make sure your questions collect baseline values, not future values.
Step 4: Collect Data From at Least 5 People
Share the link in:
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WhatsApp
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Messenger
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SMS
Ask them to fill it in.
You can also interview people using your phone while you fill in the form on their behalf.
Step 5: View and Interpret Your Baseline Data
Google Forms
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Tap Responses
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View summary charts and graphs
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Export to Excel or Sheets if needed
KoboToolbox
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Go to Data → Reports
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View graphs and all responses
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Download as Excel or CSV
Expected Output
Learners should produce:
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A functional baseline survey (Google Forms or Kobo).
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At least 5 completed responses.
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A short summary such as:
Example Summary:
“From 5 respondents, 3 save through mobile money, 1 uses a bank, and 1 keeps cash at home. The average weekly saving is MK500–1,000. The main challenge is inconsistent income.”
Reflection Questions for Students
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What was easy or difficult in creating the baseline tool?
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Which question gave you the most useful information?
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How would you improve this baseline survey for a larger project?
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