|Course code module||IOB003|
|Study load (hours)||168|
Björn Van Campenhout
|Language of instruction:||English|
|Semester exam information:||exam in the 1st semester|
|Contract restriction information:|
Some units are either compulsory or recommended for students wishing to take certain courses in modules II, III and IV:
- Knowledge of the basic principles of exploratory data analysis and descriptive statistics is required for all units. These topics are dealt with in Research Methods I.
- Knowledge of regression analysis is strongly recommended for students opting for Unit 3 or 4.
- Good English language skills are recommended for Unit 7.
- See also the admission requirements on the IOB website.
- Units 3 and 4 are recommended for students taking Quantitative Development Evaluation in 'Module II-Evaluating Development Effectiveness'.
- Unit 5 is highly recommended for students taking 'Local Institutions and Poverty Reduction' in Module III.
- The first session of Unit 8 (research design, research questions and operationalisation of research questions) is compulsory for the students wishing to conduct fieldwork for their dissertation project in module IV.Unit 8 as a whole is recommended for those students.
- Knowledge of regression analysis is:
- compolsury for students taking Quantitative Development Evaluation in Module III 'Evaluating Development Effectiveness'
- highly recommended for students taking the sub-unit 'Assessing the impact of Trade Policies' in Module III -Local Institutions and Poverty Reduction
2. Objectives (expected learning outcomes)
Students acquire knowledge and practical skills for the application of a range of quantitative and qualitative research approaches and methods with relevance to their work as a development professional.
Unit 1: Working with Data
Students acquire insight into a macro and a micro dataset. They learn how to process real data by means of basic statistical tools within Excel. Furthermore, they become skilled at ‘reading data’ and at making balanced statements when comparing country performances through time or between places. In addition, students are taught how to make pragmatic choices themselves, as it is often unclear what the normative ‘correct’ solution to a problem is. They are also required to assess the effect of different methodologies and interpret associations between several variables within the datasets.
Unit 2: Regression Analysis
Students are familiarized with regression analysis to investigate functional relationships among variables. They are introduced to the various problems that can invalidate standard regression assumptions and to the strengths and weaknesses of graphs complementing regression techniques.
Unit 3: Time Series Data
Students are introduced to best practice in summarizing and graphing time series data. They are able to summarize stationary time series data in univariate and multivariate models and to use this skill for testing hypotheses and prediction. Students are made aware of the dangers associated with using standard analysis on non-stationary data. They are able to detect problems, test for stationarity and suggest solutions.
Unit 4: Cross-section and Panel Data
Students are introduced to best practice in summarizing and graphing grouped data. They are familiarized with the dangers of using standard analysis on grouped data. They are also taught different methods for handling such data as well as some basic tests for differentiating between available methods.
Unit 5: Participatory Research & Development Methods
Students acquire insight into the historical origins and the underlying principles of participatory development approaches as compared to technocratic, top-down approaches. They gain a practical understanding of a number of widely used participatory tools and methods, and are able to evaluate their relative advantages and disadvantages. Finally, students are able to critically evaluate the shortcomings and limitations of participatory development approaches, they understand how some of these may be remedied, and they recognize when participatory approaches are less suitable.
Unit 6: Multi-Actor Processes in Development: Negotiation, Collaboration and Facilitation
The students develop insight into complex negotiations, collaboration and facilitation in the context of development.
Unit 7: Analyzing Text and Discourse in Development
Students understand the role of discourse and framing in development. They acquire knowledge and practical skills for the analysis of discourse.
Unit 8: People as Informants: Organizing, Gathering and Analyzing Qualitative Data
The general objective of this unit is students to acquire a better understanding of qualitative research processes. In a practical way, students learn how to organize qualitative research, how to write a research proposal, how to formulate research and interview questions, how to apply qualitative interview techniques, and how to analyze and report on qualitative data.
3. Course content
Research Methods II consists of eight units each covering a different quantitative or qualitative research method. Each student must combines quantitative and qualitative units, taking into account a number of constraints related to the trajectory of the student. Students must obtain 6 credits by taking between two and four units of the eight units on offer within the module. Their choice of units may be based on previous knowledge, skills, interests and future research plans. The students are obliged to choose at least one quantitative and one qualitative unit.
Working with Data (1.5 ECTS)
Regression Analysis (3 ECTS)
Time Series Data (1.5 ECTS)
Cross-Section and Panel Data (1.5 ECTS)
Participatory Research & Development Methods (3 ECTS)
Multi-Actor Processes in Development: Negotiation, Collaboration and Facilitation (1.5 ECTS)
Analyzing Text and Discourse in Development (1.5 ECTS)
Unit 8 : People as Informants: Organizing, Gathering and Analyzing Qualitative Data (1.5 ECTS)
An information session will be organized on the 28th of September 2011
Content description per (sub)unit :
Unit 1: Working with Data (1.5 ECTS)
This course is practically oriented and teaches students to work with macro and micro-level datasets. In part I, students learn where to find and how to retrieve country information from major datasets covering specific aspects of development. By using a diamond framework, students will combine several variables and make an inter-temporal or inter-country analysis, on which they are required to comment while adding extra non-quantitative information.
In part II, the focus shifts to the micro level by making available personalized household and expenditure datasets. Successively, students learn how to manage and explore these datasets (importing and exporting data, using univariate descriptive statistics to check for errors, summarizing data, weighting data to account for sampling design, etc). Furthermore, students are expected to uncover associations in the data using simple cross-tabulations and averages by groups. Next, they learn how to construct wealth indicators and calculate poverty lines. These are subsequently combined to arrive at poverty estimates. The survey data are also used to calculate various measures of inequality. The importance of conceptual issues is illustrated by comparing results obtained through different methods. Finally, the students are required to decompose poverty measures and to construct a poverty profile.
This course is organized in six sessions covering three main analytical sections: Analyzing country information from major datasets (section I), Creating wealth indicators from micro data (section II), Calculating poverty and inequality measures and profiles (section III).
Unit 2: Regression Analysis(3 ECTS)
The course examines the concept of simple linear regression and correlation. It explains the concepts of inference, the normal distribution and other continuous and discrete distributions. Aspects of statistical inference for properly estimating parameters, predicting outcomes and testing hypothesis, given the characteristics of the data, are introduced. The course deals with partial regression, the interpretation of multiple regression coefficients and the detection of model violations through regression diagnostic techniques. It also introduces students to nonlinear relationships and the notion of heteroscedasticity. The course emphasizes the simultaneous use of regression and graphs to provide compact numerical summaries, to check and enhance results by using visual displays of the data.
Unit 3: Time Series Data (1.5 ECTS)
This unit begins with a survey of best practice in summarizing and graphing time series data. Subsequently, it discusses trends and seasonality in time series data and how to deal with these aspects. Next, it considers univariate stationary time series modeling in the Box-Jenkins tradition (ARMA). Stationary AR models are compared with non-stationary models and ways are presented to test for the order of integration. Another important topic in development studies is testing for (and the identification of) structural breaks.
Subsequently, the focus shifts to more interesting multivariate time series models. When regressing stationary time series variables on one another, an often-encountered problem is autocorrelation in the residuals. Students are taught how to test (Durbin-Watson) and account for this (Cochraine-Orcutt and the related Prais-Winsten models). Then, the course deals briefly with autoregressive distributed lag models (ADL) and structural models (VAR), which are often used in impact analysis on a macro level. Finally, an illustration is provided of the dangers of regressing non-stationary time series on one another and solutions to this problem are discussed (first-difference regressions, cointegration and related error-correction models).
Unit 3 is recommended for students taking Quantitative Development Evaluation in 'Module II - Evaluating Development Effectiveness'.
Unit 4: Cross-section and Panel Data (1.5 ECTS)
Students are first introduced to graphical techniques for detecting patterns in grouped data (parallel box plots, strip charts, bar plots and dot charts). Subsequently, the consequences are illustrated of not accounting for groupings in the data. Students then learn how to deal with groupings in the data using different methods. The first method adjusts for heteroscedasticity in the error terms caused by the grouping using Generalized Least Squares. The course also explains how heteroscedasticity can be accounted for using the Least Squares Dummy Variables approach. Next, it considers interacting explanatory variables and discusses how such a fully interacting model is related to regression by group. Here, students are also familiarized with the Chow test. The foregoing is also compared to the within-group transformation.
Next, the concept of error components is introduced and the standard random effects model is presented. The reasons for using random effects are discussed and the Hausman test is introduced. Next; the course deals with simple multilevel models in the context of linear mixed-effects models. It also briefly considers the topic of dynamic panel data.
Unit 4 is recommended for students taking Quantitative Development Evaluation in 'Module II - Evaluating Development Effectiveness'.
Unit 5: Participatory Research & Development Methods (3 ECTS)
This unit provides an introduction to the origins and principles of the participatory approach to development (in particular Participatory Rural Appraisal). It also introduces practical tools and instruments of participatory research and development planning. It further presents a joint analysis of criticism of the participatory approach from different perspectives and proposals for improvements in participatory methods.
Unit 5 is highly recommended for students taking Local Institutions and Poverty Reduction in Module III.
Unit 6: Multi-Actor Processes in Development: Negotiation, Collaboration and Facilitation (1.5 ECTS)
Students experience multi-actor processes in a simulated development context. The class is intended to develop insight into complex negotiations, collaboration and facilitation in the context of development.
Unit 7: Analyzing Text and Discourse in Development (1.5 ECTS)
Introduction to text and discourse analysis. Revision of aspects related to data collection (interviews, recordings, transcriptions) and data analysis methods. Practical application of development issues to frame analysis.
Unit 8: People As Informants: Organizing, Gathering and Analyzing Qualitative Data (1.5 ECTS)
Qualitative research is an umbrella term for a wide range of research approaches and research methods. This unit is very practically oriented and provides insight into the organization of qualitative research processes and into methods for collecting and interpreting qualitative data. The focus is on qualitative research methods such as interviewing, focus groups, life histories and participant observation.
Unit 8 is strongly recommended for students wishing to conduct qualitative fieldwork for their dissertation project in module IV.
The first session of Unit 8 (research proposal, research questions and operationalisation of research questions) is compulsory for the students wishing to conduct fieldwork.
4. Teaching method
Direct contact: LecturesExercise sessionsSkills training
Personal work: ExercisesAssignments - individualAssignments - in groupPaper - individualSupervised self-study
5. Assessment method
Exam: Written, without oral presentationOpen bookOpen questionsPractical exam
Continuous assessment: ExercisesAssignments
Written assignment: Without oral presentation
6. Compulsory reading – study material
7. Recommended reading - study material
For more information on the different units, see the section on assessment methods (section 5).
You can contact tutors for content-related issues or if experiencing difficulties in selecting appropriate subunits. There are two tutors: