2022/2023
Психометрические теории и анализ тестовых заданий
Лучший по критерию «Новизна полученных знаний»
Статус:
Маго-лего
Кто читает:
Институт образования
Когда читается:
3, 4 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Язык:
английский
Кредиты:
6
Контактные часы:
60
Course Syllabus
Abstract
Prerequisite: basic knowledge in statistics. This course introduces the fundamental concepts of modern measurement theory, including the Rasch measurement approach and item response theory (IRT). It begins with a brief review of classical test theory, its relationship to IRT, and the basic assumptions underlying the latter.
The course then transitions smoothly to Rasch measurement, covering its basic principles and assumptions. Through Rasch models for dichotomous and polytomous data, students will learn how Rasch analysis transforms scored observations into linear measures. Both theoretical and practical aspects of data analysis, output interpretation, and reporting are addressed throughout.
Beyond the Rasch approach, the course covers core methods for parameter estimation, fit and dimensionality assessment, and differential item functioning (DIF), along with an introduction to linking and equating. It further examines a range of dichotomous and polytomous IRT models — including 1PL, 2PL, 3PL, GRM, and GPCM — as well as extensions to multidimensional and multi-facet modeling.
Designed to be accessible to students without an advanced background in psychometrics or statistics, the course also offers depth for those seeking a more thorough understanding of measurement theory. Using R software and AI tools, students will learn to apply core psychometric analysis methods to real-world examples in education, psychology, and human resources.
Learning Objectives
- The objectives of mastering the discipline "Psychometric Theories and Analysis of Test Items" is to master the basic theoretical principles and practical skills of analyzing test items, building and analyzing tools and scales within the framework of IRT and modern measurement approaches.
Expected Learning Outcomes
- Assess the quality of measurement tools in psychology and education based on reliability and validity of measurements;
- Possess practical skills in using standard IRT methods and models for building and analyzing specific assessment tools in education, building and analyzing scales and methods in psychology, sociology and other social sciences;
- Navigate the flow of scientific information to search for the necessary mathematical models and methods of analysis to solve psychometric problems.
- Be able to choose and apply IRT models that correspond to the goals of the analysis (dichotomous and polytomic, one-dimensional and multidimensional, multifaceted, etc).
- Analyze test items and tests in the framework of IRT using appropriate software products, interpret and present the results of the analysis
Course Contents
- Classical Test Theory (CTT)
- Principles for measuring latent variables within the framework of IRT.
- Rasch models
- Basic dichotomous models.
- Polytomous Rasch models
- Rating Scale Analysis
- IRT modeling for the development of psychological scales
- Linking
- Psychometrics and AI
- Dichotomous and Polytomous models of IRT
- Estimating the parameters of IRT models
- Analysis of measurement properties of items in IRT
- DIF analysis
- Explanatory Item Response Theory Models. LLTM.
- Standard Setting
- Multifaceted models
- Multidimensional IRT models
Assessment Elements
- IWW - individual and group written work
- Group Presentations on Written Assignment (GP WPI)
- Group Article Presentations (GPA)
- Exam. Theory
- Exam. Practice
Interim Assessment
- 2022/2023 4th module0.2 * Exam. Practice + 0.15 * Group Article Presentations (GPA) + 0.2 * Exam. Theory + 0.3 * IWW - individual and group written work + 0.15 * Group Presentations on Written Assignment (GP WPI)
Bibliography
Recommended Core Bibliography
- Andrich, D., & Hagquist, C. (2014). Real and Artificial Differential Item Functioning in Polytomous Items. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E4A6BD97
- Applications of item response theory to practical testing problems, Lord, F. M., 2008
- Applying the rasch model : fundamental measurement in the human sciences, Bond, T. G., 2007
- Baker, F. B., & Kim, S.-H. (2017). The Basics of Item Response Theory Using R. Springer.
- Bond, T. G., & Fox, C. M. (2015). Applying the Rasch Model : Fundamental Measurement in the Human Sciences, Third Edition (Vol. Third edition). New York: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1002030
- Explanatory item response models: a generalized linear and nonlinear approach. (2005). Journal of Educational Measurement, 42(3), 303–307. https://doi.org/10.1111/j.1745-3984.2005.00016.x
- Fundamentals of item response theory, Hambleton, R. K., 1991
- Introduction to classical and modern test theory, Crocker, L., 2008
- Item response theory for psychologists, Embretson, S. E., 2009
- James, A., Algina, J., & Swaminathan, H. (2015). Psychometrics: Classical Test Theory. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.7E3F0482
- Ludlow, L. H., Matz-Costa, C., Johnson, C., Brown, M., Besen, E., & James, J. B. (2014). Measuring Engagement in Later Life Activities: Rasch-Based Scenario Scales for Work, Caregiving, Informal Helping, and Volunteering. Measurement & Evaluation in Counseling & Development, 47(2), 127–149. https://doi.org/10.1177/0748175614522273
- Measuring the mind : conceptual issues in contemporary psychometrics, Borsboom, D., 2009
- Mescheder, L., Nowozin, S., & Geiger, A. (2017). Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsarx&AN=edsarx.1701.04722
- Morera, O. F., & Stokes, S. M. (2016). Coefficient α as a Measure of Test Score Reliability: Review of 3 Popular Misconceptions. American Journal of Public Health, 106(3), 458. https://doi.org/10.2105/AJPH.2015.302993
- Purwo Susongko, Mobinta Kusuma, & Heru Widiatmo. (2019). Using Rasch Model to Detect Differential Person Functioning and Cheating Behavior in Natural Sciences Learning Achievement Test. Jurnal Penelitian Dan Pembelajaran IPA, 5(2), 94–111. https://doi.org/10.30870/jppi.v5i2.5945
- Rasch models for measurement, Andrich, D., 1988
- Scale construction and psychometrics for social and personality psychology, Furr, R. M., 2011
- The intrinsic and extrinsic motivation subscales of the Motivated Strategies for Learning Questionnaire:A Rasch-based construct validity study. (2018). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.322BDC71
- Tutz, G. (2020). A Taxonomy of Polytomous Item Response Models.
- Wanichtanom, R. (2001). Methods of Detecting Differential Item Functioning: A Comparison of Item Response Theory and Confirmatory Factor Analysis. Psychology Theses & Dissertations.
Recommended Additional Bibliography
- Ronald K. Hambleton, & Russell W. Jones. (n.d.). ========================= = ITEMS. Instructional Topics in Educational Measurement ========================= An NCME Instructional Module on Comparison of Classical Test Theory and Item Response Theory and Their Applications to Test Development. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.46F2C9F0