Influences on the Development of Economic Knowledge over the First Academic Year

Results of a Germany-Wide Longitudinal Study
  • J. SchlaxEmail author
  • Olga Zlatkin-Troitschanskaia
  • C. Kühling-Thees
  • S. Brückner


Despite significant research, it remains unclear whether the goal of developing domain-specific knowledge in higher education is actually being achieved. This is also true for the internationally most popular study domain of business and economics. In Germany, a test for measuring economic knowledge was validated, enabling the analysis of change in knowledge over the course of studies. Business and economics students from across Germany were surveyed over the course of one study year: 7,111 beginning students in the winter term of 2016/2017, and 1,705 third semester students in the winter term of 2017/2018. Investigating the longitudinal matched sample of 734 students who took part at both measurement points, we found that economic knowledge developed slightly positively in the first year of study in economics. Prior economic knowledge, general intellectual ability and the courses attended are among the most important influencing factors of the knowledge test performance and grades after one academic year.


Higher education knowledge development prior knowledge general intellectual ability economic knowledge test longitudinal study large-scale assessment 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. American Educational Research Association (AERA), American Psychological Association (APA) & National Council on Measurement in Education (NCME). (2014). The standards for educational and psychological testing. Washington DC: American Psychological Association.Google Scholar
  2. Anderson, G., Benjamin, D., & Fuss, M. A. (1994). The determinants of success in university introductory economics courses. Journal of Economic Education, 25(2), 99–119.Google Scholar
  3. Aprea, C., & Wuttke, E. (2016). Financial Literacy of Adolescents and Young Adults: Setting the Course for a Competence-Oriented Assessment Instrument. In C. Aprea, E. Wuttke, K. Breuer, N. Keng Koh, P. Davies, B. Greimel-Fuhrmann & J. Lopus (Eds.), International Handbook of Financial Literacy (pp. 397–414). Singapore: Springer.Google Scholar
  4. Baker, F.B., & Kim, S.H. (Eds.). (2004). Item Response Theory: Parameter Estimation Techniques. New York: Dekker.Google Scholar
  5. Beck, K. (1989). “Ökonomische Bildung”-Zur Anatomie eines wirtschaftspädagogischen Begriffs [“Economic Education”-On the Anatomy of an Economic Education Concept]. Zeitschrift für Berufs- und Wirtschaftspädagogik [Journal for Business and Vocational Education], 85, 579–596.Google Scholar
  6. Biasi, V., De Vincenzo, C., & Patrizi, N. (2018). Cognitive Strategies, Motivation to Learning, Levels of Wellbeing and Risk of Drop-out: An Empirical Longitudinal Study for Qualifying Ongoing Universty Guidance Services. Journal of Educational and Social Research, 8(2), 79–91.Google Scholar
  7. Biewen, M., Happ, R., Schmidt, S., & Zlatkin-Troitschanskaia, O. (2018). Knowledge Growth, Academic Beliefs and Motivation of Students in Business and Economics – A longitudinal German Case Study. Higher Education Studies 8, 9–28.Google Scholar
  8. Blömeke, S., Gustafsson, J., & Shavelson, R. J. (2015). Beyond dichotomies: Competence viewed as a continuum. Zeitschrift für Psychologie [Journal for psychology], 223, 3–13.Google Scholar
  9. Blömeke, S., Zlatkin-Troitschanskaia, O., Kuhn, C., & Fege, J. (2013). Modeling and Measuring Competencies in Higher Education. In S. Blömeke, O. Zlatkin-Troitschanskaia, C. Kuhn, & J. Fege (Eds.), Modeling and Measuring Competencies in Higher Education: Tasks and Challenges (pp. 1–12). Rotterdam: Sense.Google Scholar
  10. Boyatzis, R. E. (1982). The Competent Manager. A Model For Effective Performance. New York: John Wiley & Sons, Inc.Google Scholar
  11. Bromme, R. (2001). Teacher Expertise. In N. J. Smelser, P. B. Baltes & F. E. Weinert (Eds.), International Encyclopedia of the Behavioral Sciences: Education (pp. 15459–15465). London: Pergamon.Google Scholar
  12. Brückner, S., Förster, M., Zlatkin-Troitschanskaia, O., & Walstad, W. B. (2015). Effects of prior economic education, native language, and gender on economic knowledge of firstyear students in higher education. A comparative study between Germany and the USA. Studies in Higher Education, 40(3), (437–453).Google Scholar
  13. Brückner, S., & Pellegrino, J. W. (2016). Integrating the Analysis of Mental Operations into Multilevel Models to Validate an Assessment of Higher Education Students’ Competency in Business and Economics. Journal of Educational Measurement, 53(3), 293–312.Google Scholar
  14. Bucher-Koenen, T., Lusardi, A., Alessie, R., & Van Rooij, M. (2017). How financially literate are women? An overview and new insights. Journal of Consumer Affairs, 51(2), 255–283.Google Scholar
  15. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York: Routledge.Google Scholar
  16. Eberle, F., Schumann, S., Ackermann, N., Jüttler, A., & Kaufmann, E. (2016). Modellierung und Messung wirtschaftsbürgerlicher Kompetenz (WBK), Valorisierungsbericht z.H. des SBFI [Modelling and measurement of economic civic competence (WBK), valorisation report for the attention of the SBFI]. Accessed from (17th June 2019).
  17. Erdel, B. (2010). Welche Determinanten beeinflussen den Studienerfolg? Eine empirische Analyse zum Studienerfolg der ersten Kohorte der Bachelorstudenten in der Assessmentphase am Fachbereich Wirtschaftswissenschaften der Friedrich-Alexander-Universität Erlangen-Nürnberg [Which determinants influence study success? An empirical analysis of the academic success of the first cohort of bachelor students in the assessment phase at the Department of Economics of the Friedrich-Alexander-University Erlangen-Nuremberg]. Accessed: (25th September 2015).
  18. Ericsson, K. A., & Smith, J. (1991). Toward a general theory of expertise: Prospects and limits. New York: Cambridge University Press.Google Scholar
  19. European Commission (EC) (2010). Europe 2020: A strategy for smart, sustainable and inclusive growth. Brussels: European Commission. Accessed: (17th June 2019).
  20. Federal Statistical Office (Destatis) [Statistisches Bundesamt] (2017). Bildung und Kultur: Studierende an Hochschulen Fachserie 11 Reihe 4.1 [Education and Culture – Students at Universities – Preliminary Report Winter Term 2016/17 (subject series 11, series 4.1]). Wiesbaden: Destatis Statistisches Bundesamt.Google Scholar
  21. Förster, M., Zlatkin-Troitschanskaia, O. & Happ, R. (2015). Adapting and Validating the Test of Economic Literacy to Assess the Prior Economic Knowledge of First-Year Students in Business and Economic Studies in Germany (Discussion Paper; Annual Meeting of the American Economic Association). Boston: AEA.Google Scholar
  22. Giese, S., Otte, F., Stoetzer, M. W. & Berger, C. (2013). Einflussfaktoren des Studienerfolges im betriebswirtschaftlichen Studium: Eine empirische Untersuchung [Influencing factors of study success in business studies: an empirical study]. Jena Contributions to Economic Research, 1.Google Scholar
  23. Glug, I. (2009). Entwicklung und Validierung eines Multiple-Choice-Tests zur Erfassung prozessbezogener naturwissenschaftlicher Grundbildung [Development and validation of a multiple-choice test for the assessment of process-related scientific basic education]. Dissertation, Christian-Albrechts-Universität zu Kiel. Accessed: (21st June 2019).
  24. Gruber H. & Mandl H. (1996). Expertise und Erfahrung [Expertise and experience]. In H. Gruber & A. Ziegler, (Eds.), Expertiseforschung [Expertise research] (pp. 18–34). Wiesbaden: VS Verlag für Sozialwissenschaften.Google Scholar
  25. Gruber, H. (2001). Acquisition of expertise. In N. Smelser & P. Baltes (Eds.), International encyclopedia of the social & behavioral sciences. Amsterdam: Elsevier.Google Scholar
  26. Happ, R., Zlatkin-Troitschanskaia, O., & Schmidt, S. (2016). An Analysis of Economic Learning among Undergraduates in Introductory Economics Courses in Germany. The Journal of Economic Education, 47(4), 300–310.Google Scholar
  27. Happ, R., Zlatkin-Troitschanskaia, O., & Förster, M. (2018). How Prior Economic Education Influences Beginning University Students’ Knowledge of Economics. Empirical Research in Vocational Education and Training, 10(5), 1–20.Google Scholar
  28. Happ, R., Nagel, M., Zlatkin-Troitschanskaia, O., & Schmidt, S. (2019). How migration background affects master degree students’ knowledge of business and economics. Studies in Higher Education, 1–16.
  29. Hartig, J. (2007). Skalierung und Definition von Kompetenzniveaus [Scaling and definition of competence levels]. In: B. Beck & E. Klieme (Eds.), Sprachliche Kompetenzen. Konzepte und Messung [Language skills. Concepts and measurement] (pp. 83–99). Weinheim: Beltz Verlag.Google Scholar
  30. Hasler, A., & Lusardi, A. (2017). The gender gap in financial literacy: A global perspective. Global Financial Literacy Excellence Center. Accessed: (19th June 2019).
  31. Hell, B., Linsner, M. & Kurz, G. (2008). Prognose des Studienerfolgs [Prognosis of study success]. In M. Rentschler & H.-P. Voss (Eds.), Studieneignung und Studierendenauswahl – Untersuchungen und Erfahrungsberichte [Aptitude for studies and student selection – Studies and experience reports] (pp. 132–177). Aachen: Shaker.Google Scholar
  32. Helmke, A., & Schrader, F.-W. (2013). Angebots-Nutzungs-Modell [offer-utilization-model]. In M. A. Wirtz (Eds.), Dorsch – Lexikon der Psychologie [Lexicon of Psychology] (p. 147–148). Bern: Huber.Google Scholar
  33. Hunsley, J., & Meyer, G. J. (2003). The Incremental Validity of Psychological Testing and Assessment: Conceptual, Methodological, and Statistical Issues. Psychological Assessment, 15(4), 446–455.Google Scholar
  34. Jirjahn, U. (2007). Welche Faktoren beeinflussen den Erfolg im wirtschaftswissenschaftlichen Studium [Which factors influence the success of economics studies]? Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung [Schmalenbachs jornal for economic research], 59(3), 286–313.CrossRefGoogle Scholar
  35. Kim, H., & Lalancette, D. (2013). Literature Review on the Value-added Measurement in Higher Education. OECD. Accessed: (19th June 2019).
  36. Klieme, E., & Leutner, D. (2006). Kompetenzmodelle zur Erfassung individueller Lernergebnisse und zur Bilanzierung von Bildungsprozessen. Beschreibung eines neu eingerichteten Schwerpunktprogramms der DFG [Competence models for assessing individual learning outcomes and for balancing educational processes. Description of a newly established priority programme of the DFG]. Zeitschrift für Pädagogik [Journal for Pedagogy], 52(6), 876–903.Google Scholar
  37. Krapp, A. (1999). Intrinsische Lernmotivation und Interesse. Forschungsansätze und konzeptuelle Überlegungen [Intrinsische Lernmotivation und Interesse. Forschungsansätze und konzeptuelle Überlegungen]. Zeitschrift für Pädagogik [Journal for Pedagogy], 45(3), 387–406.Google Scholar
  38. Lammers, W. J., Onweugbuzie, A. J., & Slate, J. R. (2001). Academic success as a function of gender, class, age, study habits, and employment of college students. Research in the Schools, 8(2), 71–81.Google Scholar
  39. Macha, K., & Schuhen, M. (2011). Modellierung ökonomischer Kompetenz in der Pilotstudie zu ECOS [Modelling economic competence in the ECOS pilot study]. In H. J. Schlösser & M. Schuhen, (Eds.), Siegener Beiträge zur ökonomischen Bildung [Siegens Contributions to Economic Education]. Siegen: ZöBiS.Google Scholar
  40. National Research Council; Committee on Defining Deeper Learning and 21st Century Skills, Pellegrino, J. W. & Hilton, M. L. (Eds.). Board on Testing and Assessment and Board on Science Education, Division of Behavioral and Social Sciences and Education (2012). Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century. Washington, DC: The National Academies Press.Google Scholar
  41. OECD (2018). Education at a Glance 2017: OECD Indicators. OECD Publishing. Accessed: (19th June 2019).
  42. Prins, F. J., Veenman, M. V. J., & Elshout, J. J. (2006). The impact of intellectual ability and metacognition on learning: New support for the thresholds of problematicity theory. Learning and Instruction, 16(4), 374–387.Google Scholar
  43. Ramm, G., Prenzel, M., Baumert, J., Blum, W., Lehmann, R., Leutner, D., & Schiefele, U. (2006). PISA 2003: Dokumentation der Erhebungsinstrumente [PISA 2003: Documentation of the survey instruments]. Münster: Waxmann.Google Scholar
  44. R Core Team (2018). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
  45. Schipolowski, S., Wilhelm, O., & Schroeders, U. (2017). Berliner Test zur Erfassung fluider und kristalliner Intelligenz ab der 11. Jahrgangsstufe (BEFKI 11+) [Berlin test of fluid and crystallized intelligence for grades 11 and above]. Göttingen: Hogrefe.Google Scholar
  46. Schlax, J., Zlatkin-Troitschanskaia, O., Schmidt, S., Kühling-Thees, C., Jitomirski, J. & Happ, R. (2019, April). Analyzing Learning Processes and Distinct Learning Patterns in Higher Education Economics. Paper presented at Annual Meeting of the American Educational Research Association, Toronto, Canada.Google Scholar
  47. Schlax, J., Zlatkin-Troitschanskaia, O., Happ, R., Pant, H. A., Jitomirski, J., Kühling-Thees, C., Förster, M., & Brückner, S. (in review). Validity and Fairness of a New Entry Diagnostics Test in Higher Education Economics. Manuscript submitted for publication.Google Scholar
  48. Schmidt, S., Brückner, S., Zlatkin-Troitschanskaia, O., & Förster, M. (2015). Das wirtschaftswissenschaftliche Wissen in der Hochschulbildung – eine Analyse der messinvarianten Erfassung finanzwirtschaftlichen Fachwissens bei Studierenden [The economic knowledge in higher education – an analysis of the measurement invariant assessment of financial economic expertise among students]. Empirische Pädagogik [Empirical Pedagogy], 29(1), 106–124.Google Scholar
  49. Schmidt, S., Zlatkin-Troitschanskaia, O., & Fox, J.-P. (2016). Pretest-Posttest-Posttest Multilevel IRT Modeling of Competence Growth of Students in Higher Education in Germany. Journal of Educational Measurement, 53(3), 332–351.Google Scholar
  50. Schmidt, S., Zlatkin-Troitschanskaia, O. & Walstad, W. W. (2020). IRT Modeling of Decomposed Student Learning Patterns as Positive and Negative Learning in Higher Education Economics. In Frontiers and Advances in Positive Learning in the Age of InformaTiOn (PLATO).Google Scholar
  51. Schumann, S., Eberle, F., & Oepke, M. (2013). Ökonomisches Wissen und Können am Ende der Sekundarstufe II: Effekte der Bildungsgang-, Klassen- und Geschlechtszugehörigkeit [Economic knowledge and skills at the end of upper secondary level II: effects of educational pathways, class and gender affiliation]. In U. Faßhauer, B. Fürstenau, & E. Wuttke (Eds.), Jahrbuch der berufs- und wirtschaftspädagogischen Forschung 2013 [Yearbook of Vocational and Business Education Research 2013] (pp. 35–46). Leverkusen: Budrich.Google Scholar
  52. Shavelson, R. J., Zlatkin-Troitschanskaia, O., & Marino, J. (2018). Performance Indicators of Learning in Higher-Education Institutions: Overview of the Field. In E. Hazelkorn, H. Coates & A. Cormick (Eds.), Research Handbook on Quality, Performance and Accountability in Higher Education (pp. 249–263). Cheltenham: Edward Elgar.Google Scholar
  53. Smith, B. O., & Wagner, J. (2018). Adjusting for guessing and applying a statistical test to the disaggregation of value-added learning scores. The Journal of Economic Education, 49(4), 307–323.Google Scholar
  54. Snijders, T. A. B., & Bosker, R. J. (2011). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). London: SAGE Publications.Google Scholar
  55. Spencer, L. M. & Spencer, S. M. (1993). Competence at Work: Models for Superior Performance. New York: Wiley.Google Scholar
  56. Spiel, C., Litzenberger, M., & Haiden, D. (2006). Bildungswissenschaftliche und psychologische Aspekte von Auswahlverfahren [Educational and psychological aspects of selection procedures]. University Wien. Retrieved from
  57. StataCorp (2017). Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC.Google Scholar
  58. Trapmann, S., Hell, B., Hirn, J.-O. W. & Schuler, H. (2007a). Meta-Analysis of the Relationship Between the Big Five and Academic Success at University. Zeitschrift für Psychologie, 215(2), 132–151.Google Scholar
  59. Trapmann, S., Hell, B., Weigand, S., & Schuler, H. (2007b). Die Validität von Schulnoten zur Vorhersage des Studienerfolgs – eine Metaanalyse [The validity of school grades for predicting study success – a meta-analysis]. Zeitschrift für pädagogische Psychologie [Journal for Educational Psychology], 21(1), 11–27.Google Scholar
  60. Walstad, W. B., Watts, M. & Rebeck, K. (2007). Test of Understanding in College Economics: Examiner`s manual (4th ed.). New York: National Council on Economic Education.Google Scholar
  61. Walstad, W. B. & Rebeck, K. (2008). The Test of Understanding of College Economics. American Economic Review, 98, 547–551.Google Scholar
  62. Walstad, W. B., Rebeck, K. &, Butters, R. B. (2013). Test of economic literacy: Examiner’s manual (4th ed.). New York: Council for Economic Education.Google Scholar
  63. Walstad, W. B., Schmidt, S., Zlatkin-Troitschanskaia, O. & Happ, R. (2018). Pretest-posttest measurement of economic knowledge of undergraduates – Estimating guessing effects. Paper presented at the AEA Annual Meeting, Philadelphia, USA.Google Scholar
  64. Weinert, F. E. (2001). Concept of competence: A conceptual clarification. In D. S. Rychen & L. H. Salganik (Eds.), Defining and selecting key competencies (pp. 45–65). Ashland, OH, US: Hogrefe & Huber Publishers.Google Scholar
  65. Wuttke, E., & Beck, K. (2002). Eingangsbedingungen von Studienanfängern – Die Prognostische Validität wirtschaftskundlichen Wissens für das Vordiplom bei Studierenden der Wirtschaftswissenschaften [Entrance conditions for first-year students – Prognostic validity of economic knowledge for the pre-diploma of students of economic sciences]. In K. Beck & K. Breuer, Arbeitspapiere WP [Working Papers Business Education], JGU Mainz. Accessed: (19th June 2019).
  66. Zlatkin-Troitschanskaia, O., Förster, M., & Kuhn, C. (2013). Modeling and measurement of university students’ subject-specific competencies in the domain of business and economics – The ILLEV project. In S. Blömeke, O. Zlatkin-Troitschanskaia, C. Kuhn, & J. Fege (Eds.), Modeling and measuring competencies in higher education (pp. 159–170). Rotterdam: Sense.Google Scholar
  67. Zlatkin-Troitschanskaia, O., Förster, M., Brückner, S., & Happ, R. (2014). Insights from a German assessment of business and economics competence. In H. Coates (Ed.), Higher Education Learning Outcomes AssessmentInternational perspectives (pp. 175–197). Frankfurt am Main: Lang.Google Scholar
  68. Zlatkin-Troitschanskaia, O., Pant, H. A., Lautenbach, C., Molerov, D., Toepper, M., & Brückner, S. (2017). Modeling and measuring competencies in higher education – Approaches to challenges in higher education policy and practice. Wiesbaden: Springer.Google Scholar
  69. Zlatkin-Troitschanskaia, O., Jitomirski, J., Happ, R., Molerov, D., Schlax, J., Kühling-Thees, C., Förster, M., &, Brückner, S. (2019a). Validating a Test for Measuring Knowledge and Understanding of Economics Among University Students. Zeitschrift für Pädagogische Psychologie, 32 (2), 119-133.Google Scholar
  70. Zlatkin-Troitschanskaia, O., Schlax, J., Jitomirski, J., Happ, R., Kühling-Thees, C. & Pant, H. A. (2019b). Ethics and Fairness in Assessing Learning Outcomes in Higher Education. Journal Higher Education Policy, 32 (4), 537-556.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020

Authors and Affiliations

  • J. Schlax
    • 1
    Email author
  • Olga Zlatkin-Troitschanskaia
    • 3
  • C. Kühling-Thees
    • 1
  • S. Brückner
    • 2
  1. 1.Johannes Gutenberg-UniversitätMainzGermany
  2. 2.Johannes Gutenberg-Universität MainzMainzGermany
  3. 3.Johannes Gutenberg University MainzMainzGermany

Personalised recommendations