APPLICATION CLOSES IN:
Green Campus: Capacity Building Program
We are delighted that you have decided to work with us in advancing people’s passion/ career in research. That’s why we need you to share your skills and experience!
In today’s data-driven world, accurate and reliable data is crucial for making informed decisions and planning at all levels, from individual projects to national policies. Recognizing this, our comprehensive research capacity-building program is designed to equip participants with the necessary skills and knowledge to collect, manage, and analyze data effectively.
The program covers a wide range of topics, from the fundamentals of research and data collection techniques to advanced data analysis using R, STATA, Python, and other analytic software. Participants will learn how to draft both technical and financial proposals, develop research tools, manage, and clean data, and report writing.
The program also includes modules on fieldwork and team leadership, as well as monitoring and evaluation. Importantly, participants will have the opportunity to apply their learning in a practical setting, with the chance to work on live projects from GreenStars. Whether you’re new to research or looking to enhance your skills, this program offers valuable insights and hands-on experience to boost your research capabilities.
1. Training Calendar
Duration: This program has a maximum duration of six months.
Training sessions: The program’s training sessions will take place during the weekends (Saturday and Sunday) from 09:00 AM to 02:00 PM.
Note: On the last Saturday of the month / Umuganda Day, the training starts from 2:00 pm to 6:00 pm
2. Training Venue:
GreenStars will operate in two campuses, with one located in Kigali City and the other in Musanze District.
- Kigali – Classic Hotel
- Musanze – Ruhengeri Referral Hospital Teaching Room
To learn more about this program structure, we encourage you to read a detailed document here.
Below you can find the catalogue of the modules that will be delivered througout the program. The modules order are also chronological.
Quantitative Modules
Click on the module title for more details.
Module 1: Introduction to Research
- Overview of research methods
- Understanding research ethics
- Defining research objectives and questions
Module 2: Data Collection Techniques
- Qualitative vs. quantitative data collection
- Ensuring data accuracy and discipline in data collection
- Sampling strategies and understanding the target population
Module 3: Research Tools Development
- Introduction to Excel and Word
- Introduction to SurveyCTO/ODK, KOBO and other softwares for data collection
- Designing surveys and questionnaires
- Questionnaire coding and programming
Module 4: Fieldwork and Team Leadership
- Leading and managing field teams (enumerators)
- Learning effective collaboration and communication skills for interactions with colleagues, clients, and official entities.
- Field or primary data collection Preparation
- Maintaining discipline and reporting structures in the field
Module 5: Understanding of tender or Terms of Reference (ToR)
- Analyzing and understanding TOR
- Drafting technical and financial proposals
Module 6: Comprehensive Data Analysis and Statistical Modeling with R
- Introduction to R: Basics of R syntax, data types, variables, and basic operations.
- Control Structures: Learn about if statements, loops, and conditional statements for controlling program flow.
- Functions: How to define and use functions to encapsulate reusable code.
- Data Structures: Vectors, matrices, arrays, lists, and data frames for organizing and manipulating data.
- Data Import and Cleaning: Techniques for importing data from various sources and cleaning/preprocessing it for analysis.
- Exploratory Data Analysis (EDA): Methods for exploring and summarizing data, including descriptive statistics and data visualization using libraries like ggplot2.
- Statistical Modeling: Introduction to statistical modeling techniques such as linear regression, logistic regression.
- Data Manipulation with dplyr: Using the dplyr package for data manipulation tasks like filtering, selecting, summarizing, and arranging data.
- Machine Learning with caret: Introduction to machine learning concepts and techniques
Module 7: Comprehensive Data Analysis and Statistical Modeling with STATA
Introduction to STATA
- Overview of STATA interface
- Loading data into STATA
- Understanding data types and formats
Data Manipulation
- Sorting and filtering data
- Generating and recoding variables
- Merging and appending datasets
- Dealing with missing data
Descriptive Statistics
- Summarizing data: mean, median, mode, standard deviation, etc.
- Frequency distributions and histograms
- Cross-tabulations and chi-square tests
- Introduction to graphical representation: bar plots, box plots, etc.
Statistical/ Inferential Analysis
- Introduction to regression analysis: simple linear regression
- Multiple regression analysis
- Logistic regression for binary outcomes
- Introduction to hypothesis testing and p-values.
Programming in STATA
- Introduction to do-files and ado-files.
- Automating tasks with loops and macros
Final Project
- Bringing everything together to complete a small analysis project.
- Applying learned techniques to a real dataset
- Presenting results and findings
Module 8: Comprehensive Data Analysis and Statistical Modeling with Python
- Introduction to Python: Basics of Python syntax, data types, variables, and simple operations.
- Control Flow: Learn about if statements, loops, and conditional statements to control the flow of your program.
- Functions: How to define and use functions to encapsulate reusable code
- Project-Based Learning: Encourage hands-on projects where beginners can apply what they’ve learned to real-world datasets or problems.
- Data Structures: Lists, tuples, dictionaries, and sets for organizing and manipulating data.
- NumPy: Introduction to numerical computing with NumPy arrays and basic array operations.
Module 9: Data Management and data Cleaning
- Best practices for data management
- Techniques for data cleaning and preparation
- Data coding and categorization
Module 10: Data Analysis using R, Excel, Python and STATA
- Descriptive and inferential statistics
- Regression analysis
- Hypothesis testing
Module 11: Monitoring and Evaluation (M&E)
- Construction of Logframe matrix and theory of change
- Specifying evaluation questions and outcome variables
- Determine counterfactual: Control group
- Randomized Controlled Trials
- Internal and external validity of findings
- Sampling and power calculations
- Ethical considerations regarding M&E
Module 12: Report Writing and Publication
- Conducting literature review and secondary data analysis.
- Writing inception reports.
- Triangulation of quantitative and qualitative findings.
- Structuring and writing reports.
Qualitative Modules
Click on the module title for more details.
Module 1: Introduction to Qualitative Research
- An exploration of qualitative research principles and methodologies.
Module 2: Ethics and Consent in Qualitative Research
- A focus on ethical considerations and the importance of obtaining consent.
Module 3: Research Design and Planning
- Strategies for designing robust qualitative studies and planning research effectively.
Module 4: Qualitative Data Collection Methods
- Techniques for gathering rich, detailed qualitative data.
Module 5: Data Management and Analysis
- Best practices for organizing and analyzing qualitative data using MAXQDA.
Reporting and Presenting Qualitative Findings
- Guidance on effectively communicating qualitative research results.
GreenStars’ objective is to ensure that the delivery of all modules incorporates 70% practical, hands-on learning experiences.
You may have other questions or need more clarifications. Do not hesitate to contact us at connect@greenstarshub.com to learn more about this program.