Session Information
09 SES 15 A, Unlocking the Potential of National Educational Management Information Systems for Research (Part 4)
Symposium
Contribution
INVALSI database The database contains the results of tests administered to students in grades 2, 5, 8, 10 and 13 of Italian schools. The INVALSI tests are designed to assess students' language (both Italian and English) and mathematics skills using a standardised scale and are administered to all students in both public and private schools. The test results are complemented by data on students' social and economic background, class size and other structural characteristics of the schools. Students are linked to unique identifiers that remain constant over the years, allowing longitudinal tracking and analysis of careers. Since 2018, the measurement scale has been anchored in time, allowing comparisons to be made both between and within cohorts. Integration with other data sources INVALSI data can be linked to administrative data stored in the Student Register (Anagrafe degli Studenti) of the Italian Ministry of Education. This includes data on the marks obtained by students and on their citizenship. It should also be noted that the samples used for international assessments such as PISA or ICILS are subsets of the student population for which INVALSI data are available, allowing for integration between different sources and conjoint analysis of results. Access to INVALSI data Access to elementary (individual) data is granted for research purposes. Requests are managed by the INVALSI Statistical Office, which guarantees data protection procedures. A representative sample of anonymous data will be made available in an open format (cc-by) and can be downloaded from the INVALSI website. Implications for educational research Due to their coverage, temporal consistency and quality, INVALSI data have gradually become one of the main sources for cross-sectional and longitudinal analyses of the Italian school system. This is particularly true for the study of territorial heterogeneity (the data allow us to go down to the level of individual municipalities), socio-economic inequalities and migration background.
References
Bianconcini, S., Mignani, S., Mingozzi, J., Assessing maths learning gaps using Italian longitudinal data. Statistical Methods and Applications, 2023, 32(3), pp. 911-930. Borgonovi, F., Ferrara, A., COVID-19 and inequalities in educational achievement in Italy, Research in Social Stratification and Mobility, 2023, 83, 100760. Pietschnig, J., Oberleiter, S., Toffalini, E., Giofrè, D., Reliability of the g factor over time in Italian INVALSI data (2010-2022): What can achievement-g tell us about the Flynn effect, 2023, Personality and Individual Differences, 2023, 214, 112345. Vegliante, R., Pellecchia, A., Miranda, S., Marzano, A., School Dropout in Italy: A Secondary Analysis on Statistical Sources Starting from Primary School, 2024, Educational Sciences, 14(11), 1222.
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