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HU Credits:
3
Degree/Cycle:
2nd degree (Master)
Responsible Department:
Glocal International Development
Semester:
2nd Semester
Teaching Languages:
English
Campus:
Mt. Scopus
Course/Module Coordinator:
Gil Maymon
Coordinator Office Hours:
Monday, 11:15-12:15
Teaching Staff:
Ms. gil maymon
Ms. Daniela Tolchinsky
Course/Module description:
The purpose of this course is to provide students with basic tools for orientation in quantitative methods. Social sciences - including International Development Studies - are becoming more evidence-based, and big database analysis is one of the main tools for empirical exploration. While qualitative methods are extremely useful in this field of study, statistical techniques are required in order to complete, extend, and enrich research abilities.
Course/Module aims:
As a first step, we will learn how to understand empirical data, read and interpret the meaning of numbers and statistical models. Next, we will learn how to design our own research – from data collection, to analysis, and finally to a conclusion of relevant implications. This course combines theory and practice. In class we will learn the ground rules of quantitative methods, while in tutorials we will practice their usage.
Learning outcomes - On successful completion of this module, students should be able to:
Design an empirical research study, with commonly used quantitative tools, including:
Formulating a research question & hypotheses, according to quantitative approaches’ ground rules; Conceptualize & operationalize variables; Collect data; Use statistical models for univariate, bivariate and multivariate analyses; Report findings and convert them into practical implications; Apply Mixed Methods Approaches.
Attendance requirements(%):
80%
Teaching arrangement and method of instruction:
Lectures, Lab sessions
Course/Module Content:
Introduction: How can we answer interesting questions using statistical tools?
Exploring phenomena with quantitative tools: Quantitative research questions & hypotheses, research design, and introducing large N studies
Descriptive statistics: Central & dispersion measures; Normal distribution
Defining and operationalizing variables in quantitative methods
Measurement: Validity & reliability; Scaling & indices
Sampling & Data collection: Methods
Data analysis (1): Association measures & statistical significance
Data analysis (2): Differences between groups
Handling competing explanations: Statistical control
Regression (1): Regression equation and simple linear regression
Regression (2): Multivariate linear regression
Mixed Methods Approaches
Required Reading:
Bernard, H. Russell. 2006. Research Methods in Anthropology: Qualitative and Quantitative Approaches (3rd edition).
Diamond, Ian & Julie Jefferies. 2001. Beginning statistics: An introduction for social scientists. London: Sage.
Punch, F. Keith. 2013. Introduction to social research: Quantitative and qualitative approaches. Sage.
Hilbert, Martin. 2016. “Big data for development: A review of promises and challenges,” Development Policy Review, 34(1): 135-174.
White, Marilyn Domas, and Marsh, Emily E. 2006. “Content analysis: A flexible methodology,” Library trends, 55(1): 22-45.
Treisman, Daniel. 2000. “The Causes of Corruption: A Cross-National Study,” Journal of Public Economics, 76(3): 399-457.
Acemoglu, Daron, Johnson, Simon, and Robinson, James A. 2001. “The colonial origins of comparative development: An empirical investigation,” American Economic Review, 91(5): 1369-1401.
Creswell, W. John & Clark, Vicki L. Plano. 2011 (2nd edition). Designing and conducting mixed methods research. Sage publications, pp. 1-17 (chapter 1).
Bamberger, Michael, Rao, Vijayendra, and Woolcock, Michael. 2010. Using mixed methods in monitoring and evaluation: experiences from international development.
Burstein, Alon. 2018. “Armies of God, Armies of Men: A Global Comparison of Secular and Religious Terror Organizations,” Terrorism and Political Violence, 30(1): 1-21
Additional Reading Material:
Kshetri, Nir. 2014. "The emerging role of Big Data in key development issues: Opportunities, challenges, and concerns", Big Data & Society, 1(2): 1-20.
Thomas, Alan. 2000. “Poverty and the ‘End of Development’,” in: Thomas A., and T. Allen (eds.), Poverty and Development into the 21st Century. Oxford University Press, pp. 10-19 (1.2 – “Conceptions of poverty”).
Miller, Bernard. 2011. “Making Measures Capture Concepts: Tools for Securing Correspondence between Theoretical Ideas and Observations,” in: Gschwend, T., and F. Schimmelfennig (eds.), Research Design in Political Science. Springer, pp. 83-102.
Cheibub, Jos’e Antonio, Gandhi, Jennifer, and Vreeland, James Raymond. 2010. “Democracy and dictatorship revisited,” Public choice, 143(1-2): 67-101.
Falgari, Matteo, Golini, Ruggero, Kalchschmidt, Matteo, and Landoni, Paolo. 2013. “Managing international development projects: evidences from an international survey,” Atti della XXIV Riunione scientifica annuale Associazione Italiana di Ingegneria Gestionale (RSA AiIG 2013), 17(18): 1-12.
Eshbaugh-Soha, Matthew. 2010. “The tone of local presidential news coverage,” Political Communication, 27(2): 121-140.
Beath, Andrew, Christia, Fotini, and Enikolopov, Ruben. 2013. “Empowering women through development aid: Evidence from a field experiment in Afghanistan,” American Political Science Review, 107(3): 540-557.
Course/Module evaluation:
End of year written/oral examination 0 %
Presentation 0 %
Participation in Tutorials 10 %
Project work 50 %
Assignments 40 %
Reports 0 %
Research project 0 %
Quizzes 0 %
Other 0 %
Additional information:
See Moodle for most current version of the syllabus
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