Postgraduate Proposal And Thesis Development Mentorship Course

Introduction

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.

Duration

10 days

Modules to be covered

Module 1: Introduction to research methods

·        Understanding the academic  research process

·        Developing an academic research idea

·        Identification and writing a problem statement

·        Formulation of good research questions and hypothesis

Module 2: Literature Review

·        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

Module 3: Data and Methodology

Cross-sectional data

·        Conceptual, analytical and theoretical frameworks

·        Difference between qualitative and quantitative research designs

·        Empirical framework and econometric model specification

·        Data types and sources

o   Qualitative and quantitative data

o   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)

Module 4: Data and Methodology (continued)

Time Series

·        Conceptual, analytical and theoretical frameworks

·        Research design

·        Empirical framework and econometric model specification

·        Data types and sources

o   Qualitative and quantitative data

o   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

Module 5: Data and Methodology (continued)

P 

·        Conceptual, analytical and theoretical frameworks

·        Research design

·        Empirical framework and econometric model specification

·         Data types and sources

o   Qualitative and quantitative data

o   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

Module 6: Introduction to Software skills and practical applications

·        General overview of statistical software (SPSS, Stata, R studio, Eviews, Stata, SPSS, Nvivo, Atlas ti)

Module 7: Model Estimation Techniques, Interpretation and Discussion of Results

Cross section

·        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

Basic software skills and practice

·        Overview of relevant software (SPSS, Stata, R studio, Nvivo, Atlas ti etc)

·        Practical estimation of cross sectional models using relevant software

Module 8: Time series

·        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

Basic software skills and practice

·        Overview of relevant software (Eviews, Stata etc)

·        Practical estimation of time series models using relevant software

Module 9: Panel data 

·        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

Basic software skills and practice

·        Overview of relevant software (Eviews, Stata etc)

·        Practical estimation of panel models using relevant software

Module 10: writing the research output thesis or journal article

·        Content and scope of a research proposal

·        Content and scope of a thesis and journal article