IRT Scales for Self-reported Test-Taking Motivation of Swedish Students in International Surveys

  • Denise Reis CostaEmail author
  • Hanna Eklöf
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 265)


This study aims at modeling the self-reported test-taking motivation items in PISA and TIMSS Advanced studies for Swedish students using an IRT approach. In the last two cycles of the assessments, six test-specific items were included in the Swedish student questionnaires to evaluate pupil’s effort, motivation and how they perceived the importance of the tests. Using a Multiple-Group Generalized Partial Credit model (MG-GPCM), we created an IRT motivation scale for each assessment. We also investigated measurement invariance for the two cycles of PISA (i.e., 2012 and 2015) and of TIMSS Advanced (i.e., 2008 and 2015). Results indicated that the proposed scales refer to unidimensional constructs and measure reliably students’ motivation (Cronbach’s alpha above 0.78). Differential item functioning across assessment cycles was restricted to two criteria (RMSD and DSF) and had more impact on the latent motivation scale for PISA than for TIMSS Advanced. Overall, the test-taking motivation items fit well the purpose of a diagnostic of test-taking motivation in these two surveys and the proposed scales highlighted the slight increase of pupils’ motivation across the assessment cycles.


Test-taking motivation PISA TIMSS IRT 


  1. Brown, T. A. (2014). Confirmatory factor analysis for applied research. New York: Guilford.Google Scholar
  2. Buuren, S. V., & Groothuis-Oudshoorn, K. (2010). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–68.Google Scholar
  3. Chalmers, R. P. (2012). Mirt: A multidimensional item response theory package for the R environment. Journal of Statistical Software, 48(6), 1–29.CrossRefGoogle Scholar
  4. Eklöf, H., & Nyroos, M. (2013). Pupil perceptions of national tests in science: Perceived importance, invested effort, and test anxiety. European Journal of Psychology of Education, 28(2), 497–510.CrossRefGoogle Scholar
  5. Kiefer, T., Robitzsch, A., & Wu, M. (2015). Tam: Test analysis modules. R Package.Google Scholar
  6. Muraki, E. (1999). Stepwise analysis of differential item functioning based on multiple-group partial credit model. Journal of Educational Measurement, 36(3), 217–232.MathSciNetCrossRefGoogle Scholar
  7. OECD. (2017). PISA 2015 Technical Report. Paris: OECD Publishing. Scholar
  8. Penfield, R. D. (2005). Difas: Differential item functioning analysis system. Applied Psychological Measurement, 29(2), 150–151.MathSciNetCrossRefGoogle Scholar
  9. Penfield, R. D. (2008). Three classes of nonparametric differential step functioning effect estimators. Applied Psychological Measurement, 32(6), 480–501.MathSciNetCrossRefGoogle Scholar
  10. Revelle, W. (2014). Psych: Procedures for psychological, psychometric, and personality research. R Package.Google Scholar
  11. Wagemaker, H. (2013). International large-scale assessments: From research to policy. In L. Rutkowski, M. von Davier, & D. Rutkowski D (Eds.), Handbook of international large-scale assessment: Background, technical issues, and methods of data analysis (pp. 11–35). New York: Chapman Hall/CRC.Google Scholar
  12. Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54(3), 427–450.MathSciNetCrossRefGoogle Scholar
  13. Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Educational MeasurementUniversity of OsloGaustadalleen, OsloNorway
  2. 2.Department of Applied Educational ScienceUmeå UniversityUmeåSweden

Personalised recommendations