Statistical methods in psychometrics / Selected topics of psychometrics
Pages of a related course Seminar in Psychometrics.Pages of last years' course: Winter 2023
Course schedule
| Lecture: | Thu or Mon, 3:40-4:25pm, 318, Pod vodárenskou věží 2, Praha 8. |
| Lab session: | Thu or Mon, 4:25-5:10pm, 318, Pod vodárenskou věží 2, Praha 8. |
News
| (Oct 1, 2024) | Course starts on October 10, 2023. |
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! (3.10.2024) | (No meeting) Welcome message via e-mail |
| Book R code on GitHub Project | |
| Lesson 1 (Thu 10.10.2024) | Introduction to measurement data analysis |
| Slides | |
| Lesson 2 (Mon 14.10.2024) | Validity of measurement Internal structure and factor analysis |
| Lesson 3 (21.10.2024) | Reliability |
| Lab session (24.10.2024) | Project preparation |
|
Rmd
PDF
R Data item Metadata SIAmain SIAgroup |
|
| Lesson 4 (28./31.10.2024) | (No meeting) Project part I |
| Lesson 5 (4./7.11.2024) | (No meeting) Project - Part I due |
| Lesson 6 (14.11.2024) | (Online) Traditional Item Analysis. Towards regression models |
| Lesson 7 (18.11.2024) | (Online) Item analysis with regression models |
| Lesson 8 (25.11.2024) | (Online) Item response theory (IRT) models |
| Lesson 9 (2.12.2024) | More complex IRT models |
| Lesson 10 (9.12.2024) | Differential item functioning (DIF) |
| Lesson 11 (16.12.2024) | Computerized adaptive testing (CAT). Further topics. |
| Lesson 12 (6.1.2025) | Project presentations. 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 take into account project (40%), project presentation (40%), and answers to follow-up questions (20%). Project report needs to be submitted at least one week before the project presentation, one feedback is provided to projects sent to the instructor and TAs at least two weeks before the project presentation.
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. https://doi.org/10.1201/9781003054313