School of Environment, Resources and Development, (SERD)

ED52.9006 : Selected Topic: Quantitative Research Methods I  2(1-2)
Course Objectives:
With increasing need for evidence-based policies, understanding of fundamental statistics has become a requirement for those inspiring to work in the field of international development. This course introduces descriptive and inferential statistics applied to analysis of data from development studies. It will allow students to familiarize themselves with key statistical concepts and acquire practical skills for understanding and conducting quantitative research in areas related to sustainable development.
Learning Outcomes:
After completing this course, students will be able to:
-        Understand basic statistics and their importance for development and sustainability
-        Apply relevant statistical analysis techniques for quantitative studies in international development
-        Use SPSS for basic statistical analysis
-       Have a strong foundation in quantitative analysis, which will allow them to pursue more advanced courses in statistics


Course Outline:
I.          Introduction to statistics for development studies
1.     What are statistics?
2.     Use of statistics in analyzing development issues
3.     Types and measurement scales of variables
II.        Descriptive statistics
1.     Graphic descriptions of data
2.     Numerical descriptions of data
3.     Frequencies and cross-tabulations
III.       Probability and sampling
1.     Introduction to probability
2.     Probability distributions
3.     Sampling approaches
IV.       Cluster analysis
1.     Introduction to cluster analysis
2.     Hierarchical Cluster Analysis
V.        Inferential Statistics
1.     Basics of hypothesis testing
2.     T-test
3.     Chi-square tests
4.     Correlation
5.     Analysis of Variance (ANOVA)
6.     Introduction to linear regression models
Laboratory Sessions:

SPSS Practicals


No one specific textbook is used for this course. Students should be familiar with the material presented during the lectures and practical sessions and selected chapters in the reference books. Specific readings/reading references will be provided in advance of each class.

Reference Books:
1.     Triola, M. F. (2014). Elementary Statistics, 12th Edition, Cambridge: Pearson Educational Limited.
2.     Diamond, I. and Jefferies, J. (2001). Beginning Statistics. London: Sage Publications.
3.     Weinberg, S.L. and Abramowitz, S.K. (2008). Statistics Using SPSS. An Integrative Approach. Cambridge: Cambridge University Press.
4.     Gorard, S. (2003). Quantitative Methods in Social Science Research. The role of numbers made easy. London: Continuum.
Journals and Magazines:
1.        International Journal of Social Research Methodology (Taylor & Francis)
Time Distribution and Study Load:

Lecture: 15 hours, exercises: 30 hours, self-study: 45 hours

Teaching and Learning Methods:

Statistical methods and techniques will be illustrated by using SPSS. Students are expected to do self-study before and after class.

Evaluation Scheme:
Mid-term exam (closed books) (40%); participation (10%), final exam (closed books) (50%).
An “A” would be awarded if a student can elaborate the knowledge learned in class by giving his/her own analysis in national and project case studies conducted in this course and from journal articles and including assigned readings. A “B” would be awarded if a student shows an overall understanding of all given topics, a “C” would be given if a student meets below average expectation on both knowledge acquired and analysis. A “D” would be given if a student does not meet basis expectations in understanding and analyzing the topics and issues presented in the course.
A Prof. Rajendra Prasad Shrestha