• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
Master 2020/2021

Quasi-Experimental Research in Education

Category 'Best Course for New Knowledge and Skills'
Area of studies: Psychology
When: 2 year, 1, 2 module
Mode of studies: offline
Open to: students of all HSE University campuses
Master’s programme: Educational and Psychological Measurement
Language: English
ECTS credits: 4

Course Syllabus


This course is based on the following disciplines taking place at the first year of study - “Economics of Social Sector”, and “Methods of Quantitative Data Analysis”. During the course, students acquire knowledge and skills they need for their master thesis preparation. Besides that, this course provides a basis for other disciplines such as “Evidence Based Practice in Management”, “Strategic Management in Education” etc.
Learning Objectives

Learning Objectives

  • This course will introduce experimental and quasi-experimental research designs.
  • Students will get familiar with contemporary studies that investigate what works in education, estimating the effects of different educational resources, technologies or school programs on how much students learn.
Expected Learning Outcomes

Expected Learning Outcomes

  • Students are familiar with the Neyman–Rubin causal model and its assumptions
  • Students can explain why randomized experiment is the "golden standard" of causal analysis. They can design an experiment and prepare research program
  • Students can explain the concept of the instrumental variable and the assumptions of a good instrument
  • Students can define a good instrument, test the instrumental variable assumptions, perform 2SLS analysis, explain it's weakness and strength, and give interpretation to the results
  • Students can explain the discontinuity design and the cases when it is applicable
  • Students can do first difference, difference-in-difference, and regression discontinuity analysis with sharp design. They can give correct interpretations to the results
  • Students can explain the concept of propensity score and are familiar with stratification and matching technics
  • Students can estimate the propensity score, and do causal analysis with different methods - optimal matching, greedy (nearest neighbor, caliper) matching, kernel matching, stratification. Students can test the assumptions and interpret the results correctly
  • Students can estimate a sample size for an experiment and make randomization correctly
Course Contents

Course Contents

  • Introduction to causal analysis
    Causal inference. Internal validity. Endogeneity problem. The Neyman-Rubin causal model. Counterfactuals and potential outcomes. ATE, ATT, ATU. Assumptions: SUTVA, unconfoundedness. General equilibrium effect.
  • Randomized Experiment
    Experimental designs. Steps to implement RCT. Defining the treatment, the outcome, population, and units of observation. Complete simple, cluster, and stratified randomization. Power and minimal detected effect size. Fidelity. Threats to internal validity, attrition, non- complience, spillover effect.
  • Instrumental Variables
    Endogeneity and analysis with an instrumental variable. LATE. 2 SLS. Selecting an in- strument. Testing an instrument strength. Overidentification. Instrumental variable in RCT.
  • First difference. Difference-in-difference. Regression Discontinuity
    Natural experiments and discontinuity design. First difference. Second difference. Differ- ence-in-difference. Regression discontinuity analysis. Choosing a bandwidth. Several cut-off points. Sharp and fuzzy RD designs.
  • Propensity Score Matching
    Selection bias. Strong ignorability and conditional ignorability assumptions. Matching. Multidimensionality problem. Propensity score. Choosing variables for the propensity score es- timation. Estimating the propensity score. Optimal matching, greedy matching (nearest neighbor, caliper), kernel matching, stratification. Common support. Balance test after matching. Estimat- ing ATT.
Assessment Elements

Assessment Elements

  • non-blocking Questions related to the articles assigned to read
    Students write short answers to five questions related to the paper assigned to read.
  • non-blocking Data analysis and description
    Student presents the code and the text with the short description of the research question, data used, analysis, results, and their interpretation.
  • non-blocking Presentations of the articles
  • non-blocking Intermediate Control Project
    Students present their RCT project
  • non-blocking Final Control Project
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.4 * Data analysis and description + 0.6 * Final Control Project


Recommended Core Bibliography

  • Angrist, J. D., & Lavy, V. (1999). Using Maimonides’ Rule to Estimate the Effect of Class Size on Scholastic Achievement. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.8AB10B37
  • Denny, K. (2011). Civic Returns to Education: Its Effect on Homophobia. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.560750D5
  • Patrick J. Wolf, Brian Kisida, Babette Gutmann, Michael Puma, Nada Eissa, & Lou Rizzo. (2013). School Vouchers and Student Outcomes: Experimental Evidence from Washington, DC. Journal of Policy Analysis and Management, (2), 246. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.wly.jpamgt.v32y2013i2p246.270
  • Peter Steiner. (2010). S. Guo & M.W. Fraser (2010). Propensity Score Analysis: Statistical Methods and Applications. Psychometrika, (4), 775. https://doi.org/10.1007/s11336-010-9170-8
  • Roberto Agodini, & Mark Dynarski. (2004). Are Experiments the Only Option? A Look at Dropout Prevention Programs. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.DB76A4C5
  • Thomas S. Dee. (2004). Are there civic returns to education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E97F215F
  • William G. Howell, Patrick J. Wolf, David E. Campbell, & Paul E. Peterson. (2002). School vouchers and academic performance: results from three randomized field trials. Journal of Policy Analysis and Management, (2), 191. https://doi.org/10.1002/pam.10023

Recommended Additional Bibliography

  • Loyola University. (1937). 1937, November 16: Loyola News ; Loyola News & Phoenix. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.A31BC9B1
  • Safe and Drug-Free Schools : [microform] balancing accountability with state and local flexibility : report to Congressional requesters / United States General Accounting Office. (1997). Washington, D.C. : Gaithersburg, MD (P.O. Box 6015, Gaithersburg 20884-6015) : The Office ; The Office, [distributor, 1997. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsgpr&AN=edsgpr.000483840