Post graduate students taking either a Master’s degree or a Doctor of Philosophy degree are mostly faced with challenges in developing an academic proposal and thesis/dissertation. Some of the challenges are experienced on choosing the topic of the study, literature review, coming up with problem statement, data analysis method and the appropriate software for quantitative and qualitative data. This research mentorship course aim at improving research knowledge and skills, proposal and thesis/dissertation quality as well as quantity and quality of journal articles publishable in refereed journals emerging from postgraduate student’s research work.
Understanding the academic research process
Developing an academic research idea
Identification and writing a problem statement
Formulation of good research questions and hypothesis
Identifying different sources of literature to review
Theoretical versus empirical literature
Purpose of literature review
Ingredients of a good literature review
Assessing value of literature and critical review of literature
Citation of literature review (why, what, when)
Avoiding plagiarism
How to document literature review?
Conceptual, analytical and theoretical frameworks
Difference between qualitative and quantitative research designs
Empirical framework and econometric model specification
Data types and sources
Qualitative and quantitative data
Primary versus secondary data and sources
Sampling techniques (probability and non-probability sampling) and sample size determination
Variable description, selection and definition
Data management (database design, data entry, data cleaning, data processing)
Data collection methods (qualitative and quantitative data)
Conceptual, analytical and theoretical frameworks
Research design
Empirical framework and econometric model specification
Data types and sources
Qualitative and quantitative data
Primary vs. Secondary data and sources
Sampling and sample size determination
Data management (database design, data entry, data cleaning, data processing)
Variable creation, selection and definition
Conceptual, analytical and theoretical frameworks
Research design
Empirical framework and econometric model specification
Data types and sources
Qualitative and quantitative data
Primary vs. Secondary data and sources
Sampling and sample size determination
Data management (database design, data entry, data cleaning, data processing)
Variable creation, selection and definition
General overview of statistical software (SPSS, Stata, R studio, Eviews, Stata, SPSS, Nvivo, Atlas ti)
Descriptive statistics and interpretation
Diagnostic testing, econometric problems and how to solve them(correlation, endogeneity, heterogeneity, sample selection bias etc)
Estimation techniques (logit, probit, tobit, OLS, LPM etc)
Impact evaluation techniques (Randomized control trials (experiments), propensity score matching, difference-in-difference estimation, regression discontinuity, doubly robust estimation)
Presentation and Interpretation of results (coefficients, signs, significance)
Discussion of results
Descriptive statistics and interpretation
Diagnostic testing, econometric problems and how to solve them (unit roots, cointegration, granger-causality, autocorrelation, heteroskedasticity, multi-collinearity etc)
Estimation techniques (OLS, GLS, GMM etc)
Presentation and Interpretation of results (coefficients, signs, significance)
Discussion of results
Overview of relevant software (SPSS, Stata, R studio, Nvivo, Atlas ti etc)
Practical estimation of cross sectional models using relevant software
Descriptive statistics and interpretation
Diagnostic testing
Econometric problems and how to solve them (e.g. heterogeneity, granger-causality
Estimation techniques (pooled, fixed effects, random effects)
Presentation and Interpretation of results (coefficients, signs, significance)
Discussion of results
Content and scope of a research proposal
Content and scope of a thesis and journal article
The objective of this course is to guide participant on a step by step process of developing an academic proposal, thesis or dissertation or a scientific paper for publishing in a referred journal.
Learn how to choose a research topic,
Know how to do literature review without plagiarism
Understand useful tips on how to write a problem statement
Know how to develop specific, measurable, achievable and realistic research objectives
Understand both quantitative, qualitative and mixed methods research designs
Learn different sampling techniques and sample size determination
Learn different data collection methods
Learn data analysis methods (Descriptive statistics and inferential statics)
Identify fundamental style for developing a journal article for publication in a refereed journal
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