Capacity Building – Green Stars

APPLICATION CLOSES IN:

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.

Venues:

Kigali – Classic Hotel
Musanze – Ruhengeri Referral Hospital Teaching Room

3. Who Is This For?

GreenStars Research Capacity Building welcomes all research enthusiasts. We would like to host as many applicants as possible but due to limited space, the applicants will go through a selection process.

4. Training Fees

The comprehensive fee for the six-month training is 300,000 RWF. Participants have the option to make monthly payments of 50,000 RWF. Additionally, there is a pre-training registration charge of 20,000 RWF, which includes 10,000 RWF for the registration itself and 10,000 RWF that will be credited towards the training fee, reducing the total amount to 290,000 RWF.

Note: Whether you opt for all modules or only a few, the participation fee remains the same.

5. Benefits of Participants.

Comprehensive Research Training: Participants gain access to a wide range of research methodologies, enhancing their skills and employability.

Practical Application: The opportunity to practice skills in live GreenStars projects provides valuable real-world experience.

Certification: Upon successful completion of the six-month program, participants will receive a GreenStars certificate, acknowledging their expertise and dedication to learning.

Access to Pre-Paid Software: The STATA software will be made available to participants at no additional cost, as GreenStars has already obtained the required licenses for the tool as well as access to SurveyCTO platform, while R and Python are open-source programming languages.

Networking oppotunity: Upon program completion, GreenStars commits to connecting graduates with various organizations/ Stakeholders seeking researchers, thereby facilitating their entry into the workforce.

Scholarship Opportunities: Depending on partnerships and sponsorships from different stakeholders, GreenStars would offer scholarships to participants, making the program accessible to a diverse group of learners.

To learn more about this program structure and fees, 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.