{"title":"Test-based measurement of skill mismatch: a validation of five different measurement approaches using the NEPS","authors":"Stephan Bischof","doi":"10.1186/s12651-024-00370-1","DOIUrl":null,"url":null,"abstract":"<p>Skill mismatch is a key indicator of labour market research that has received significant attention. To date, various approaches of test-based measurement of skill mismatch have been used in research, generating differing results. However, it remains unclear which method is the most valid for measuring skill mismatch. This study provides a comparative validation of five commonly used approaches to test-based measurement of skill mismatches in reading and mathematics to detect the most valid method. Drawing on the 2016 wave of the German National Educational Panel Study (NEPS) Adult Cohort, I find significantly varying distributions for the different measurement approaches, and highly valid skill mismatch measures for the statistical and the mixed approach. Overall, the mixed approach emerges as the most valid method. The findings highlight the critical importance of measurement approaches in skill mismatch research.</p>","PeriodicalId":45469,"journal":{"name":"Journal for Labour Market Research","volume":"1 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal for Labour Market Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s12651-024-00370-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INDUSTRIAL RELATIONS & LABOR","Score":null,"Total":0}
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Abstract
Skill mismatch is a key indicator of labour market research that has received significant attention. To date, various approaches of test-based measurement of skill mismatch have been used in research, generating differing results. However, it remains unclear which method is the most valid for measuring skill mismatch. This study provides a comparative validation of five commonly used approaches to test-based measurement of skill mismatches in reading and mathematics to detect the most valid method. Drawing on the 2016 wave of the German National Educational Panel Study (NEPS) Adult Cohort, I find significantly varying distributions for the different measurement approaches, and highly valid skill mismatch measures for the statistical and the mixed approach. Overall, the mixed approach emerges as the most valid method. The findings highlight the critical importance of measurement approaches in skill mismatch research.
期刊介绍：
The Journal for Labour Market Research is a journal in the interdisciplinary field of labour market research. As of 2016 the Journal publishes Open Access. The journal follows international research standards and strives for international visibility. With its empirical and multidisciplinary orientation, the journal publishes papers in English language concerning the labour market, employment, education / training and careers. Papers dealing with country-specific labour market aspects are suitable if they adopt an innovative approach and address a topic of interest to a wider international audience. The journal is distinct from most others in the field, as it provides a platform for contributions from a broad range of academic disciplines. The editors encourage replication studies, as well as studies based on international comparisons. Accordingly, authors are expected to make their empirical data available to readers who might wish to replicate a published work on request. Submitted papers, who have passed a prescreening process by the editors, are generally reviewed by two peer reviewers, who remain anonymous for the author. In addition to the regular issues, special issues covering selected topics are published at least once a year. As of April 2015 the Journal for Labour Market Research has a "No Revisions" option for submissions (see ‘Instructions for Authors’).
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