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IDB Analyzer
Enables creating and exporting scripts for merging data and conducting basic data analyses.
Basic functionalities
Data loading
- Automatic recognition of study-related variables
- Identification of weights and plausible values
- Automatic merging of necessary files
Data exploration
- Basic variable search
- Generating frequency tables
- Creating simple descriptive statistics
- Ability to create cross tabulations
Data management
- Basic subset creation functions
- Simple variable recoding
Descriptive analyses
- Basic analyses considering achievement levels
- Calculation of basic percentiles
- Simple correlation analyses
- Simple analyses of between-group differences
Modeling
- Simple linear models
- Limited multivariable modeling capabilities
Tutorial: Analysis of ILSA data using the IEA IDB Analyzer is available here.
Konferencje i spotkania
Wydarzenia online, hybrydowe i stacjonarne, podczas których przedstawiamy istotne zagadnienia edukacyjne i poznajemy opinie naszych interesariuszy.
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Seminaria i warsztaty
Wydarzenia mające na celu zwiększenie wykorzystania wyników badań przez różne grupy odbiorców.
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TIMSS case study analysis with the IEA IDB Analyzer
In this analysis, we present the process of preparing and analysing a TIMSS 2023 dataset using the IEA IDB Analyzer. This tool was developed for users who do not have advanced programming skills. It enables users to easily generate ready-to-run scripts in SPSS, SAS, or R syntax.
From this tutorial you will learn:
- how to compare students’ achievement in mathematical reasoning and across selected countries, by gender,
- how to analyse the relationship between the school location and students’ achievement in mathematical reasoning,
how to analyse the relationship between students’ socio-economic status and students’ achievement in biology.
SSES case study analysis with Rrepest
In this analysis, we present the process of preparing and analysing data from the SSES study conducted in 2019, which measures social and emotional skills. The analyses are carried out using the R package Rrepest.
From this tutorial you will learn:
- how to compare results on students’ self-control across selected cities participating in the study;
- how to compare students’ results by gender and age group (cohort);
- how to analyse the relationship between socio-economic status and students’ level of empathy.
PISA case study analysis with intsvy
In this analysis, we present the process of preparing and analysing data from PISA 2022 using the intsvy package in R. The analysis was developed for R users who are looking for a tool that automatically supports international educational assessments and enables easy visualisation of analytical results.
From this tutorial you will learn:
- how to compare students’ reading literacy achievement across selected participating countries by gender,
- how to analyse differences in students’ science achievement depending on the student’s school location, as well as by student gender;
- how to analyse the relationship between a student’s family socio-economic status and being absent from school for more than three months due to problem behaviour.