Pretest-posttest-posttest multilevel IRT modeling of competence growth of students in higher education in Germany

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • Susanne Schmidt
  • Olga Zlatkin-Troitschanskaia
  • Jean-Paul Fox
Sammlungen
  • metadata
URN
doi:10.1111/jedm.12115
ISSN
1745-3984
Ausgabe der Veröffentlichung
3
Zeitschrift
Journal of educational measurement
Schlüsselwörter
  • 370 Erziehung
  • 370 Education
Sprache
eng
Paginierung
Seiten: 332 - 351
Datum der Veröffentlichung
2016
Herausgeber
Wiley- Blackwell
Datum der Datenerfassung
2020
Datum, an dem der Datensatz öffentlich gemacht wurde
2020
Zugang
Public
Titel
Pretest-posttest-posttest multilevel IRT modeling of competence growth of students in higher education in Germany
Ausgabe der Zeitschrift
53

Datenquelle: METADATA.UB

Andere Metadatenquellen:
Autoren
  • Susanne Schmidt
  • Olga Zlatkin-Troitschanskaia
  • Jean-Paul Fox
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000388940700005&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1111/jedm.12115
eISSN
1745-3984
Externe Identifier
  • Clarivate Analytics Document Solution ID: ED6BY
ISSN
0022-0655
Ausgabe der Veröffentlichung
3
Zeitschrift
JOURNAL OF EDUCATIONAL MEASUREMENT
Paginierung
332 - 351
Datum der Veröffentlichung
2016
Status
Published
Titel
Pretest-Posttest-Posttest Multilevel IRT Modeling of Competence Growth of Students in Higher Education in Germany
Sub types
  • Article
Ausgabe der Zeitschrift
53

Datenquelle: Web of Science (Lite)

Abstract
<jats:p>Longitudinal research in higher education faces several challenges. Appropriate methods of analyzing competence growth of students are needed to deal with those challenges and thereby obtain valid results. In this article, a pretest‐posttest‐posttest multivariate multilevel IRT model for repeated measures is introduced which is designed to address educational research questions according to a German research project. In this model, dependencies between repeated observations of the same students are considered not, as usual, by clustering observations within participants but rather by clustering observations within semesters. Estimation of the model is conducted within a Bayesian framework. Results indicate that competences grew over time. Gender, intelligence, motivation, and prior education could explain differences in the level of competence among business and economics students.</jats:p>
Autoren
  • Susanne Schmidt
  • Olga Zlatkin‐Troitschanskaia
  • Jean‐Paul Fox
DOI
10.1111/jedm.12115
eISSN
1745-3984
ISSN
0022-0655
Ausgabe der Veröffentlichung
3
Zeitschrift
Journal of Educational Measurement
Sprache
en
Online publication date
2016
Paginierung
332 - 351
Datum der Veröffentlichung
2016
Status
Published
Herausgeber
Wiley
Herausgeber URL
http://dx.doi.org/10.1111/jedm.12115
Datum der Datenerfassung
2023
Titel
Pretest‐Posttest‐Posttest Multilevel IRT Modeling of Competence Growth of Students in Higher Education in Germany
Ausgabe der Zeitschrift
53

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