EPA 304 - Quantitative Methods for Monitoring and Evaluating the Quality of Education

6 May 2019 to 17 May 2019
Application deadline: 
6 February 2019
Training location: 
Paris (France)

Course objectives

To present quantitative/empirical methods to measure the quality of education, in particular those used in the international and sub-regional initiatives, such as PISA, TIMSS, PIRLS, SACMEQ, PASEC, and LLECE. 

At the end of the course, participants should be able to:

  • Review international debates on concepts, terms, and indicators that are associated with the educational policy research in order to monitor and evaluate the quality of education;
  • Explore existing data collection instruments that could be used to collect and prepare data on learning achievement of students and the enabling school conditions;
  • Critically examine techniques that are used to draw a scientific sample for a large-scale national survey to measure the quality of education, as an alternative to a census method;
  • Develop analytical skills that are required to process and interpret data about quality and equality of education in order to translate research results into policy suggestions. 


  1. Steps in educational policy research cycle and the concept of the quality of education: The first component addresses the key steps involved in the educational policy research cycle to monitor and evaluate the quality of education, the meaning and the concept of the “quality of education”, and some key indicators of the “quality of education”. (2 ½ days)
  2. Critical analysis of data collection instruments and data preparation: The techniques of constructing data collection instruments are covered in this component to measure the educational achievement of students as well as the conditions of schooling. (2 ½ days)
  3. Sampling for large-scale surveys of the quality of education: How many students and schools need to be selected in the large-scale studies to have reliable results? How can we ensure that the results from the sample can be generalized to a larger population? This component addresses the sampling techniques in order to constitute the “scientific samples”. (1 day)
  4. Processing, interpreting, and analyzing data to make policy suggestions: The last component covers the computerized data processing (using software such as SPSS) of a set of data, including the construction of new composite variables (for example, indices and test scores). This work aims to respond to the concerns on the quality of education through transforming the graphical and tabular data summaries into meaningful messages that can be applied back to the policy. (4 days)


Basic knowledge of descriptive statistics and good command of computer skills.

Profile of participant:

In addition to the criteria mentioned in the introduction, planners/researchers involved in large-scale surveys aimed at guiding education policies on the quality of education