NMST 570 Selected topics in psychometrics
(Vybraná témata z psychometrie)
Syllabus (last update: September 23, 2020)Course description in English and in Czech.
Pages of a related course Seminar in Psychometrics.
Course schedule
| Lecture: | Tuesday, 3:40-4:30pm, K4, Sokolovská 83, Praha 2. |
| Lab session: | Tuesday, 4:30-5:10pm, K4, Sokolovská 83, Praha 2. (webpage) |
| Online: | Link for ZOOM sessions will be sent to registered students via e-mail. |
News
| (Aug 31, 2020) | Course starts on October 6, 2020. |
| (Sep 23, 2020) | Due to COVID-19 restrictions, class will take place remotely. |
Course description
Psychometrics uses statistical models for analysis of educational, psychological, or patient-reported measurements. This course covers 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, other psychometric software is also introduced.
Tentative course plan
| Wellcome! (29.9.2020) | Welcome message |
| R setup Perusall | |
| Lesson 1 (6.10.2020) | Introduction, measurement data |
| Lesson 2 (13.10.2020) | Reliability and measurement error |
| Lesson 3 (20.10.2020) | Validity |
| Lesson 4 (27.10.2020) | Traditional item analysis |
| Lesson 5 (3.11.2020) | Regression models for item description |
| Lesson 6 (10.11.2020) | Regression models for item description |
| Lesson 7 (24.11.2019) | Item response theory models |
| Lesson 8 (1.12.2020) | Item response theory models |
| Lesson 9 (8.12.2020) | Differential item functioning |
| Lesson 10 (15.12.2020) | Differential item functioning |
| Lesson 11 (22.12.2020) | Computerized adaptive testing |
| Lesson 12 (5.1.2021) | Further topics in psychometrics. Final project assignment |
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 homework assignments which will involve calculations, software implementation, and reading. Part of the assignments will ask students to annotate readings using perusall.com.
Course credit requirements
The credit for the exercise class will be awarded to the student who hands in satisfactory solutions to homework assignments (8 assignments in total, requiring 60% of total points) by the prescribed deadline. It is possible to skip up to 4 assignments and to provide satisfactory feedback (at least 10 relevant annotations) to readings instead. Homework will be assigned during lab sessions and will be due one week later.
Exam and grade
Final project will be assigned during the last lecture. Students are welcome and encouraged to use their data for the project in lieu of the project assigned to the class. In such a case, student is expected to prepare written project proposal and submit it to the lecturer at least one week before the last lecture. Final grade will be assigned during oral examination, which will consist of follow-up questions on final project (50%) and on homework (50%). Project report needs to be submitted to the lecturer at least 2 days before oral exam.
Course texts
Introduction to psychometric methods in education, psychology, and health: With examples in R. (Book in preparation)
Rao, C. R. & Sinharay S. (2006). Handbook of statistics. Volume 26: Psychometrics. Amsterdam, NL: Elsevier.
van der Linden, W. J. (2016). Handbook of item response theory: Models, statistical tools, and applications (Vols.1-3). Boca Raton, FL: Chapman & Hall/CRC.