Here we provide the **South African** TIMSS datasets.

You will need to download the IEA International Database Analyzer (IDB Analyzer), which is a free software tool that can be used to combine and analyse the TIMSS data.

The **HSRC** must be acknowledged in all published and unpublished works based on the HSRC provided TIMSS data (province, poverty index of schools, fee-status of schools) using the following citation: *Human Sciences Research Council. Trends in International Mathematics and Science Study (TIMSS) and specify relevant year of survey.*

Here are a set of videos by Dr Eugenio Gonzalez that will guide you in your analysis:

- Introduction to TIMSS and its components
- Navigating the TIMSS website and databases (International website)
- Installing IDB Analyzer for SPSS/SAS
- Sampling and Assessment design and implications for the analysis
- Calculating and interpreting standard errors in large-scale assessments
- Using the IDB Analyzer to merge files
- Using the IDB Analyzer for analysis

This dataset contains the merged 2019 grade 5 learner and science teacher context data files. The teachers in the SA TIMSS 2019 dataset do not constitute representative samples of teachers but are the teachers of the nationally representative sample of learners. Therefore, analyses with teacher data should be made with learners as the units of analysis and reported in terms of learners who are taught by teachers with a particular attribute. The dataset also includes the set of mathematics and science plausible values to be used when achievement scores are the analysis variable for computing statistics.

This dataset contains the merged 2019 grade 5 learner and school context data files. It includes the set of mathematics and science plausible values to be used when achievement scores are the analysis variable for computing statistics. It is preferable to analyse school context variables as attributes of the learners, therefore, analysing school context data should be done by linking the learners to their schools.

This dataset contains the 2019 grade 5 merged home context data files with the learner context data files. It includes the set of mathematics and science plausible values to be used when achievement scores are the analysis variable for computing statistics. The home context variables are in essence attributes of the learners and must be analysed in the same manner as learner context variables.

This dataset contains the merged 2019 grade 5 learner and mathematics teacher context data files. The teachers in the SA TIMSS 2019 dataset do not constitute representative samples of teachers but are the teachers of the nationally representative sample of learners. Therefore, analyses with teacher data should be made with learners as the units of analysis and reported in terms of learners who are taught by teachers with a particular attribute. The dataset also includes the set of mathematics and science plausible values to be used when achievement scores are the analysis variable for computing statistics.

This dataset includes the merged 2019 grade 9 learner and school context data files. It includes the set of mathematics and science plausible values to be used when achievement scores are the analysis variable for computing statistics. It is preferable to analyse school context variables as attributes of the learners, therefore, analysing school context data should be done by linking the learners to their schools.

This dataset contains the merged 2019 grade 9 learner and science teacher context data files. The teachers in the SA TIMSS 2019 dataset do not constitute representative samples of teachers but are the teachers of the nationally representative sample of learners. Therefore, analyses with teacher data should be made with learners as the units of analysis and reported in terms of learners who are taught by teachers with a particular attribute. The dataset also includes the set of mathematics and science plausible values to be used when achievement scores are the analysis variable for computing statistics.

This dataset contains the merged 2019 grade 9 learner and mathematics teacher context data files. The teachers in the SA TIMSS 2019 dataset do not constitute representative samples of teachers but are the teachers of the nationally representative sample of learners. Therefore, analyses with teacher data should be made with learners as the units of analysis and reported in terms of learners who are taught by teachers with a particular attribute. The dataset also includes the set of mathematics and science plausible values to be used when achievement scores are the analysis variable for computing statistics.

This dataset contains the merged 2015 grade 5 learner and school context data files. It includes the set of mathematics and science plausible values to be used when achievement scores are the analysis variable for computing statistics. It is preferable to analyse school context variables as attributes of the learners, therefore, analysing school context data should be done by linking the learners to their schools.

This dataset contains the merged 2019 grade 5 learner and mathematics teacher context data files. The teachers in the SA TIMSS 2019 dataset do not constitute representative samples of teachers but are the teachers of the nationally representative sample of learners. Therefore, analyses with teacher data should be made with learners as the units of analysis and reported in terms of learners who are taught by teachers with a particular attribute. The dataset also includes the set of mathematics and science plausible values to be used when achievement scores are the analysis variable for computing statistics.

This dataset contains the 2015 grade 5 merged home context data files with the learner context data files. It includes the set of mathematics and science plausible values to be used when achievement scores are the analysis variable for computing statistics. The home context variables are in essence attributes of the learners and must be analysed in the same manner as learner context variables.