2025/2026
Углубленные методы в психометрике и анализе данных
Статус:
Маго-лего
Кто читает:
Департамент образовательных программ
Где читается:
Институт образования
Когда читается:
2, 3 модуль
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
6
Контактные часы:
64
Course Syllabus
Abstract
This course aims to introduce advanced statistical methods and statistical models which are used in psychometrics and data analysis of psychological, sociological and educational data. Students will learn different approaches to latent variables analysis, such as Confirmatory Factor Analysis (CFA) and some models within Item Response Theory (IRT) framework such as bifactor IRT models etc. Topics of structural equation modelling, analysis of mediated and moderated relations between latent variables will be also introduced. Course continues with discussion of generalized linear models and extension of this models – generalized linear mixed effects models (GLMM). Different types of GLMM, their assumptions and application in social data analysis will be reviewed. At the end of the course some models with discrete latent variables (Latent Class Analysis, Cognitive Diagnostic Models) will be discussed. During the course students learn to select, apply and discuss the results of statistical models appropriate for addressing a given research problem.Prerequisites:1) Basic knowledge of statistics (especially regression analysis and factor analysis)2) Basic knowledge of Item Response Theory (recommended, but now required)3) Experience of working with base R.
Learning Objectives
- To familiarize students with Generalized Linear Mixed Models (GLMM) when the individuals are clustered (e.g., students belonging to different schools), introduce the idea of fixed-effects and random-effects terms in models.
- To familiarize students with Confirmatory Factor Analysis (CFA) Structural Equation Modeling (SEM) paradigm, to demonstrate the concepts of moderation and mediation in SEM.
- To familiarize students with special chapters of individual differences modeling in social sciences: Measurement Invariance (MI) and Differential Item Functioning (DIF) analysis, Latent Class Analysis, Latent Profile Analysis, Cognitive Diagnostic Modeling.
- To illustrate students how they can use R software to perform GLMM, CFA and SEM analysis.
Expected Learning Outcomes
- Capable of GLM application and interpretation; correctly selects the link function; realizes the main fallacies for GLM.
- Capable of calibrating IRT Rasch models and explanatory IRT models in the GLMM framework; capable of utilizing the full potential of GLMM in applied research
- Capable of calibrating, selecting, improving model quality, and interpreting CFA models
- Capable of calibrating, re-norming and interpreting parameters of IRT models in CFA parametrization
- Capable of conducting path analysis, including mediation and moderation
- Capable of conducting latent class and latent profile analysis, cognitive diagnostic modeling, and mixture IRT analyses
- Capable of estimation, selection and interpretation of LMM and various GLMM: random intercept, random slope models and their special cases.
- Capable of calibrating, selecting and interpreting unidimensional, multidimensional, second-order and bifactor CFA models.
- Capable of performing measurement invariance analyses for different types of data and compare the fit of nested models
- Calibrates, selects, improves model quality, and interprets CFA models
- Calibrates, re-norms and interprets parameters of IRT models in CFA parametrization
- Performs measurement invariance analyses for different types of data and compares the fit of nested models
- Conducts path analysis, including mediation and moderation
- Calibrates and interprets alternative CFA specifications (e.g., bifactor, second-order, multidimensional)
- Conducts latent class and latent profile analysis for identifying unobserved subgroups
- Conducts cognitive diagnostic modeling (CDM)
- Specifies and interprets fixed and random effects in multilevel models
- Calibrates Rasch models and explanatory IRT models within the multilevel framework
Course Contents
- Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM)
- Alternative CFA specifications
- Measurement invariance (MI)
- Path analysis and SEM
- Relations between IRT and CFA
- Categorical and ordinal latent variables
- Introduction into GLM
- Multilevel Modeling: Fixed and Random Effects
- Explains IRT as a special case of multilevel modeling. Explanatory IRT models.
Interim Assessment
- 2025/2026 3rd module0.12 * R Exercises + 0.24 * Final Test + 0.24 * Written homework + 0.4 * Exam
Bibliography
Recommended Core Bibliography
- Applied latent class analysis, , 2002
- Applied latent class analysis, , 2009
- Applied regression analysis and generalized linear models, Fox, J., 2008
- Confirmatory factor analysis for applied research, Brown, T. A., 2006
- 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
- Handbook of structural equation modeling, , 2012
- Hierarchical linear models : applications and data analysis methods, Raudenbush, S. W., 2002
- Latent class analysis of survey error, Biemer, P. P., 2011
- Latent class analysis, McCutcheon, A. L., 1987
- Multilevel analysis : techniques and applications, Hox, J., 2002
- New developments and techniques in structural equation modeling, Marcoulides, G. A., 2001
- Principles and practice of structural equation modeling, Kline, R. B., 2011
- Structural equation modeling : applications using Mplus, Wang, J., 2012
- Structural equation modeling : foundations and extensions, Kaplan, D., 2000
- Structural equation modeling : foundations and extensions, Kaplan, D., 2009