Course description
This course is designed for non-statisticians and has relevance to data users especially in the project management domain. It is intended to equip project managers, research users, policymakers, monitoring and evaluation professionals with the ability to understand, interpret and question the validity of the reports done by consultants. It is important for end users to understand key assumptions that underpin statistical methods before deciding which approach to endorsing.
The course is a precursor data analysis using software (SPSS & Stata). Research Hub strongly encourages participants to enroll in this course before applying for data analysis. A discount of Rwf 50,000 applies to applicants who enroll for both courses.
Learning objectives
Participants who have gone through this course are able to;
• Articulate key relevant concepts as used in qualitative research
• Determine sample size for a descriptive research designs
• Identify the appropriate sample statistical designs for a study
• Understand what it takes to generalize findings
• Interpret statistical reports
• Have a relevant understanding of counterfactual evaluation designs
• Tell when to use what statistical test
Who should attend
• Monitoring evaluation experts, project managers and all professional who supervise work done by researchers
• Report writers and communication experts tasked to communicate research findings
• Clinical Project Leaders who will be redesigning and evaluating studies
Course content.
Day One
• Introduction to statistical concepts
• Statistical symbols
• Descriptive versus inferential studies
• Definition and types of survey bias
• Definition and types of sampling techniques (practical)
• Extrapolation and generalization of research findings to the wider population
• Types of data (categorical, continuous ordinal scale)
• Statistical tests
Day 2
Evaluation design
• Evaluation design concepts (treatment, control dependent &independent variable etc.)
• Randomized control trial (RCT)
• Quasi-experiments
• Propensity score matching
• Cross section studies
Analysis
• Analysis of variance (ANOVA)
• T-tests
• Frequency
• Correlation coefficient
• Regression analysis
• Assumptions of underpinning inferential analyses
• Data interpretation
• Limitation of quantitative data
Day two
• Analysis of variance (ANOVA)
• T-tests
• Frequency
• Correlation coefficient
• Regression analysis
• Assumptions of underpinning inferential analyses
• Data interpretation
• Limitation of quantitative data
Duration (2-day programme and 3 days evening)
Application process
Interested applicants are encouraged to send filled application form (see attachment) and their latest CVs to info@researchub.co.rw.
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