The classification of education in surveys: a generalized framework for ex-post harmonization

Silke L. Schneider ()
Additional contact information
Silke L. Schneider: GESIS - Leibniz Institute for the Social Sciences

Quality & Quantity: International Journal of Methodology, 2022, vol. 56, issue 3, No 44, 1829-1866

Abstract: Abstract All social science (and many other) surveys measure respondents’ educational attainment. However, most of them do it in different ways, resulting in incoherent education variables across surveys. This complicates the cumulation of different datasets and hampers survey data reuse. For cross-national surveys that are designed to be comparative from the outset, methods for ensuring comparability in the measurement of education across countries have improved substantially over the last decades, relying on ex-ante output harmonization. For ex-post harmonization, the situation is more difficult because the data have already been collected, with education measures that only partly overlap in the amount and kind of information they store about respondents’ education. This results in aggregated measures when harmonizing data ex-post. Such aggregated measures may underestimate associations with education in multivariate analyses, leading to biased results. They also do not allow testing hypotheses on the effects of specific types of education, such as vocational programs. This paper presents a new framework for harmonizing education variables ex-post, building on the International Standard Classification of Education (ISCED) and experience from cross-national surveys using ex-ante harmonization. It includes a new coding scheme called ‘generalized ISCED’ or GISCED, and extension variables standardizing aspects of education not covered by ISCED. It proposes solutions for problems that specifically occur in ex-post harmonization, for example source categories spanning ISCED levels. The paper also shows how to apply the GISCED framework to existing data. An empirical illustration shows how detailed harmonized education measures may give insights for research and policy not possible with more aggregate measures.

Keywords: Classification; Education; Survey; Data harmonization; ISCED (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11135-021-01101-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:56:y:2022:i:3:d:10.1007_s11135-021-01101-1

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11135-021-01101-1

Access Statistics for this article

Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi

More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().