Statistical methods in psychometrics / Selected topics of psychometrics

Pages of a related course Seminar in Psychometrics.
Pages of last years' course: Winter 2022

Course schedule

Lecture: Tuesday, 3:40-4:25pm, K4, Sokolovská 83, Praha 8 - Karlín.
Lab session: Tuesday, 4:25-5:10pm, K4, Sokolovská 83, Praha 8 - Karlín.


(Oct 2, 2023) Course starts on October 10, 2023.
(Oct 2, 2023) Registrations are open for related EduTest workshop (in Czech).
(Dec 19, 2023) Exam dates are now in SIS, exam questions were added (see below).

Course description

Psychometrics uses statistical models for analysis of educational, psychological, or patient-reported measurements. This course covers computational aspects of main topics in psychometrics including reliability and validity of measurement, traditional item analysis, use of regression models for item description, item response theory (IRT) models, differential item functioning (DIF), computerized adaptive testing (CAT), and an overview of further topics. Methods are demonstrated using data of behavioral measurements from different areas. Exercises are prepared in freely available statistical software R, and in interactive ShinyItemAnalysis application and its modules.

Tentative course plan

Welcome! (3.10.2023) (No meeting) Welcome message via e-mail
PerusallR code on GitHubProject
Lesson 1 (10.10.2023) Introduction to measurement data analysis
Lesson 2 (17.10.2023) Validity of measurement
Lesson 3 (24.10.2023) Internal structure and factor analysis
Lesson 4 (31.10.2023) Reliability
Lesson 5 (7.11.2023) Traditional Item Analysis
Lesson 6 (14.11.2023) Item analysis with regression models
Lesson 7 (21.11.2023) Item response theory (IRT) models
Lesson 8 (28.11.2023) More complex IRT models
Lesson 9 (5.12.2023) Differential item functioning (DIF)
Lesson 10 (12.12.2023) Computerized adaptive testing (CAT), further topics.
Lesson 11 (19.12.2023) Projects
Lesson 12 (9.1.2023) Course closing.

Grading policy

Each week, students are expected to be actively present in lecture (45 minutes), and lab session (45 minutes). Lecture may take form of a Zoom meeting and/or video presentation and/or individual work on assignment. Lab session provides hints and solutions for assignments which will also involve calculations and software implementation.

Course credit requirements

The credit for the exercise class will be awarded to the student who is actively present at lectures and exercise sessions, or hands in satisfactory solutions to assignments in case of absence.

Exam and grade

Final project will be assigned during the course. Students can work in teams of size 2 or 3, multidisciplinary teams are preferred. Teams are welcome and encouraged to use their own data for the project in lieu of the data assigned to the class. In such a case, teams are expected to prepare written project proposal and submit it to the lecturer during the first month of the course. Final grade will be assigned during oral examination, which will take into account project (40%), assignments (40%), and answers to follow-up questions (20%). Project report needs to be submitted at least 2 days before oral exam, one feedback is provided to projects sent to the instructor at least two weeks before the oral exam.

Exam questions for NMST570 (Statistical methods in psychometrics)
Exam questions for OIDQ1P107/OPDQ1P119B (Selected topics in psychometrics)

Course texts

Martinková, P. & Hladká, A. (2023) Computational Aspects of Psychometric Methods: With R (1st ed.). Chapman and Hall/CRC.

© 2023 Patrícia Martinková / Template design adapted from Andreas Viklund