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Advancing Measurement and Quantitative Methods in Education Research

Join Dr. Spyros Konstantopoulos for a focused faculty visit on Measurement and Quantitative Methods. This session will include individual meetings where you will discuss your progress on assignments, including annotations, meeting notes, and annotated glossaries. Participants will critique articles as a group and explore key themes in educational research, such as teacher effects and causal inference. Engage in enriching discussions on advanced topics like psychometrics, multi-level modeling, and the latest statistical applications in education and social sciences.

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Advancing Measurement and Quantitative Methods in Education Research

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  1. cep900 11.09.11 • Faculty visit. Dr. Spyros Konstantopoulos, Measurement and Quantitative Methods • Assignments • Discussion • Public Intellectual 4

  2. assignments RDP: Individual meetings with David • Appointment Schedule Google Docs, RDP meeting #2 • Bring a one page summary of your progress on the following: 15+ annotations 3+ meetings & notes Annotated glossary (20+ terms) Historical view of field Annotated publications, conferences, organizations The progress you made toward the goals we set in our first meeting. • During the meeting, we'll also talk about what's been interesting to you and what you might do to complete your RDP.

  3. assignments Article Critique: Bring a copy of your article critique and any notes you made. We will critique the article as a group. • Find focus • On writing…

  4. faculty visit Dr. Spyros Konstantopoulos, Measurement and Quantitative Methods

  5. public intellectual 3 30 second “sound bite” about the article you read…

  6. MQM: Measurement (Psychometrics) & QM Measurement • Ebel • Reckase (item construction, IRT, Multi-dimensional IRT) • large scale assessment, instrument development, surveys, • Reykov (structural equation modeling, • Roeber (assessment issues, item development)

  7. MQM Quantitative Research Methods • application of statistics in education and social science • Raudenbush (HLM, multi-level modeling) • Becker (meta-analysis) • Ken Frank (social network analysis, causal inferences) • Kim Maier (Bayesian methods, applied statistics) • Barbara Schneider (interventions w/ at-risk Ss) • Teacher effects research, value-added, • Floden • Konstantopoulos (school & teacher effects, nesting problem, research design, meta-analysis) • Schmidt (TIMSS, international studies, curricula)

  8. How can teacher effects not be causal • Bayesian – very trendy • How can evidence on class size be mixed? What do you tell policy makers? Is there any school/teacher characteristic that has a consistent effect on student achievement? • Do teachers affect student achievement (how did that become an important question?) • Rise of experimental design and school interventions • How does an economist think of school issues differently than an education person? (school level, prove causality, macro-level analysis, informs policymakers)

  9. Discussion

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