TIMSS 2023 Longitudinal G9 International Database_Version 1

DOI

The TIMSS 2023 Longitudinal Study (TIMSS-L) is an optional extension of TIMSS 2023 that explores student learning gains over one year of schooling. This study is designed to measure growth in student achievement by assessing the same sample of students assessed in TIMSS 2023 one year later when the students are in the fifth and ninth grades.

Trends in International Mathematics and Science Study 2023 marks the eighth cycle of the international mathematics and science assessment since the inauguration of the study in 1995. Providing over 20 years of trend data, TIMSS has been a valuable tool for monitoring international trends in mathematics and science achievement at the fourth and eighth grades. Like the previous TIMSS assessments, TIMSS 2023 will collect detailed information about curriculum and curriculum implementation, instructional practices, and school resources.

TIMSS 2023 completes the transition to a fully digital assessment. paperTIMSS was administered in a small number of countries for whom the fully digital TIMSS may not be logistically feasible. Available in digital format only.

The next cycle of TIMSS will be administered in 2027.

Self-administered questionnaires. Target population (students). All students enrolled in the grades representing 9 years of formal schooling respectively, counting from the first year of ISCED Level 1. Group adaptive design. Stratified Two-Stage Cluster Sample Design

Identifier
DOI https://doi.org/10.58150/iea_timss_2023_longitudinal_g9_data_edition_1
Metadata Access https://api.datacite.org/dois/10.58150/iea_timss_2023_longitudinal_g9_data_edition_1
Provenance
Creator IEA; TIMSS and PIRLS International Study Center at Boston College
Publisher International Association for the Evaluation of Educational Achievement
Contributor IEA; Project Manager TIMSS and PIRLS International Study Center at Boston College
Publication Year 2026
Rights After agreement to terms and conditions or signing the license agreement
OpenAccess true
Representation
Language English
Resource Type Dataset
Format SAS;SPSS;R
Version 1
Discipline Social Sciences
Spatial Coverage international