ISS-3209 Techniques for Understanding Quantitative Secondary
Data
Code
Weight of the course
Period
Course Leader
Lecturer
Teaching Methods
Modes of Assessment
Contact
ISS-3209
4 ECTS
TERM 2
Mahmood Messkoub
Mahmood Messkoub, TRA and other staff
Participatory Lecture, Tutorial, Computer Exercises
Assignment(s): 35%, Group assignment: 15%, Written Exam: 50%
Nalini Harnam
Learning Objectives
The broad objectives of the course are to:
- improve skills in working with numbers;
- improve skills to use quantitative data for research and policy analysis;
- improve skills to order, present, analyse and interpret quantitative data;
- improve the ability to construct valid evidence-based arguments as well as to assess the validity
of arguments made by others;
- enhance quantitative skills required for writing the research paper.
Course Description
This course is meant for MA participants with a broad interest in policy-oriented research. Although
the course aims to be interesting and challenging for participants with serious research ambitions, it is
also meant for participants who, in their work with either governmental agencies or non-governmental
organizations, will not undertake elaborate research projects themselves, but who are expected to be
able to gather and analyse information, and commission and assess research work undertaken by
others.
The course starts with some mathematical techniques to introduce participants how handle numbers
from three different perspectives: (1) to deal with order of magnitude (absolute or relative), (2) to deal
with structure of composition, and (3) to deal with change. The second part of the course deals with
the collection of quantitative secondary data (from national sources such as census and international
sources such as the World Bank), its analysis and presentation, and aims to enhance intellectual
insights in the research process, as well as improve concrete research skills. A prerequisite to the
course is some basic knowledge of spreadsheet packages like Excel, for example a pass grade of at
least 70% in the ECDL test and a willingness to engage with quantitative data.
Indicative Reading
Miles, M.B., A.M. Huberman and J. Saldaña (2013) Qualitative Data Analysis: A Methods
Sourcebook. (3rd edn). LA, London: Sage.
Swift, L.and S. Piff (2010) Quantitative Methods for Business, Management and Finance.
Basingstoke: Palgrave MacMillan.
Thomas, A. and G. Mohan (eds) (2007) Research Skills for Policy and Development. How to Find Out
Fast. London: Sage Publications in association with the Open University.
Wuyts, Marc et al. (2004) Exploring Data on Inequality and Poverty. Tanzania Diploma in Poverty
Analysis. Dar Es Salaam and The Hague: ESRF/REPOA/ISS.