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Магистратура 2019/2020

Квазиэкспериментальные исследования в образовании

Направление: 37.04.01. Психология
Когда читается: 2-й курс, 1, 2 модуль
Формат изучения: Full time
Прогр. обучения: Измерения в психологии и образовании
Язык: английский
Кредиты: 5

Course Syllabus

Abstract

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

Learning Objectives

  • This course has two major goals. First, it introduces experimental and quasi-experimental research designs. Second, during this course students will read about contemporary studies that investigate important educational issues such as the effects of class size, different educational resources and school programs on students’ outcomes.
Expected Learning Outcomes

Expected Learning Outcomes

  • students are aware of what causal analysis is and how to do a randomized experiment
  • students understand how to do a randomized experiment
  • students understand contemporary methods of quasi-experimental design, their weaknesses and strengths
  • students experience in application of these methods in practice
  • students get some experience in application of these methods in practice
  • students understand contemporary methods of quasi-experimental design, their weaknesses and strength
Course Contents

Course Contents

  • Introduction to causal analysis
    Causal inference. Internal validity. Endogeneity problem. Neuman-Rubin causal mod- el. Counterfactuals and potential outcomes. ATE, ATT, ATU. Assumptions: SUTVA, uncon- foundedness. 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 Tasks on papers
  • non-blocking Analysis in Stata
  • non-blocking Presentations of the articles
  • non-blocking Intermediate Control Project
  • non-blocking Final Control Project
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.2 * Analysis in Stata + 0.3 * Final Control Project + 0.2 * Intermediate Control Project + 0.15 * Presentations of the articles + 0.15 * Tasks on papers
Bibliography

Bibliography

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