Postgraduate Proposal and Thesis development mentorship course
Online Training - We have refined out courses to suit the dynamic world and help achieve your objective

Course date:
17/01/2022 to 28/01/2022
Duration:
10 Days
Course fee: USD 1,440, KES 112,800

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

Who should attend?

·        Post graduate students who have just finished course work and are working on their project and thesis proposals

·        Post graduate students who have are about to collect data

·        Postgraduate students who have collected data and are in the process of analyzing it

·        Postgraduate students who want to publish their research work

Course objective

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. At the end of the training, the participants will be able to;

·        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

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)

Panel data

 

·        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

 

 

TRAINING CUSTOMIZATION

This training can also be customized for your institution upon request. You can also have it delivered your preferred location.

For further inquiries, please contact us through Mobile: +254 732 776 700/+254759285295 or Email: training@fineresultsresearch.org  

REQUIREMENTS

·        Participants should be reasonably proficient in English.  During the trainings, participants should come with their own laptops.

TRAINING FEE

·        The course fee covers the course tuition, training materials, two break refreshments, lunch, and study visits.

ACCOMMODATION

·        Accommodation is arranged upon request. For reservations contact us through

·        Mobile: +254732776700 / +254759285295

or Email: training@fineresultsresearch.org 

Course Date:
17/01/2022 to 28/01/2022
Duration:
10 Days
Course fee: USD 1,440 , KES 112,800
Call us on +254 732 776 700/ +254 759 285 295
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