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Магистратура 2021/2022

Мощность и размер выборки для многоуровневых и лонгитюдных исследований

Направление: 38.04.04. Государственное и муниципальное управление
Когда читается: 2-й курс, 2 модуль
Формат изучения: с онлайн-курсом
Охват аудитории: для всех кампусов НИУ ВШЭ
Прогр. обучения: Доказательное развитие образования
Язык: английский
Кредиты: 3
Контактные часы: 4

Course Syllabus

Abstract

Power and Sample Size for Longitudinal and Multilevel Study Designs, a five-week, fully online course covers innovative, research-based power and sample size methods, and software for multilevel and longitudinal studies. The power and sample size methods and software taught in this course can be used for any health-related, or more generally, social science-related (e.g., educational research) application. All examples in the course videos are from real-world studies on behavioral and social science employing multilevel and longitudinal designs. The course philosophy is to focus on the conceptual knowledge to conduct power and sample size methods.
Learning Objectives

Learning Objectives

  • The goal of the course is to teach and disseminate methods for accurate sample size choice, and ultimately, the creation of a power/sample size analysis for a relevant research study in your professional context. Power and sample size selection is one of the most important ethical questions researchers face. Interventional studies that are too large expose human volunteer research participants to possible, and needless, harm from research. Interventional studies that are too small will fail to reach their scientific objective, again bringing possible harm to research participants, without the possibility of concomitant gain from the increase in knowledge. For observational studies in which there are no possible harms to the participants, such as observational studies, proper power ensures good stewardship of both time and money. 
Expected Learning Outcomes

Expected Learning Outcomes

  • Demonstrate the feasibility of recruitment
  • Describe a longitudinal and multilevel study design
  • Describe expected missing data and dropout
  • Plan a sampling design for subgroups
  • Use a framework and strategy for study planning
  • Write a power and sample size analysis that is aligned with the planned statistical analysis 
  • Write a statistical analysis plan
  • Write study aims as testable hypotheses
Course Contents

Course Contents

  • Introduction to Multilevel and Longitudinal Designs
  • Foundations of Complex Multilevel and Longitudinal Designs
  • Model Assumptions, Alignment, Missing Data, and Dropout
  • Inputs to Analysis, Recruitment Feasibility, and Multiple Aims
Assessment Elements

Assessment Elements

  • blocking Финальный экзамен
Interim Assessment

Interim Assessment

  • 2021/2022 2nd module
    Баллы за сертификат переводятся в десятибальную систему согласно следующей шкале: N = 5 + [(S - 60)/8], N – число вышкинских баллов S – число баллов, набранных на курсе, S ≥ 60 [ ] – целая часть, с округлением к большему целому числу, если дробная часть ≥ 0,5; с округлением к меньшему числу, если дробная часть < 0,5 (т. е. 60 баллов = 5 "вышкинских", далее по 1 "вышкинскому" баллу за каждые 8 баллов от онлайн-курса)
Bibliography

Bibliography

Recommended Core Bibliography

  • Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education (Vol. Eighth edition). New York: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1614634
  • Galbraith, S., Bowden, J., & Mander, A. (2017). Accelerated longitudinal designs: An overview of modelling, power, costs and handling missing data. https://doi.org/10.17863/CAM.21873

Recommended Additional Bibliography

  • Bynner, J. M., & Stribley, K. M. (2017). Research Design : The Logic of Social Inquiry. London: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1608766
  • Zhang, W., & Sethuraman, V. (2011). On Power and Sample Size Calculation in Ethnic Sensitivity Studies. Journal of Biopharmaceutical Statistics, 21(1), 18–23. https://doi.org/10.1080/10543406.2010.494266