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The Development of IRT Based Attitude Scale towards Educational Measurement Course

Year 2016, Volume: 7 Issue: 1, 133 - 144, 30.06.2016
https://doi.org/10.21031/epod.43804

Abstract

In this study, the Scale of Attitude towards Educational Measurement and Evaluation (SAEM) developed by Demirtaşlı (2002) is reconstructed based on polytomous Item Response Theory (IRT) models and its psychometric features are identified. In this context, the best polythomous IRT model was investigated which is fitted SAEM data. IRT models gives invariant person and item parameters, when data-model fit. A version of SAEM  has 41 Likert type items with four points was administered to 519 teacher candidates attending teacher education programs at several universities in Turkey. The data were analyzed according to polythomous IRT models: Samejima’s graded response model (S-GRM), the partial credit model (PCM) and a nominal response model (NRM). The results of the analysis showed that a new version of SAEM, which is based on S-GRM, consists of 33 items, has lower chi-square value than the other models and the classic internal reliability was found to be 0.97. The findings of the study indicate that the validity and reliability features of the scale are fairly good.

References

  • Aktaş, M., & Alıcı, D. (2012). Development of likert type attitude scale towards measurement and evaluation in education course. Journal of Qafqaz University, 33, 66-73.
  • Ajzen, I. (2005). Attitudes, personality and behavior. Milton-Keynes, England: Open University Press/ McGraw- Hill.
  • Baker, F. B. (2001). The basis of item response theory. USA: ERIC Clearing house on Assessment and Evaluation.
  • Baker, J. G., Rounds, J. B. & Zevon, M. A. (2000). A comparison of graded response and rasch partial credit models with subjective well-being. Journal of Educational and Behavioral Statistics, 25, 253–70.
  • Brady, P. & Bowd, A. (2005). Mathematics anxiety, prior experience and confidence to teach mathematics among pre-service education students. Teachers and Teaching: Theory and Practice, 11(1), 37–46.
  • Clauser, B. E.,& Mazor, K. M. (1998). Using statistical procedure to identify differential item functioning test items. Educational Measurement: Issues and Practice, 17, 31-44.
  • de Ayala, R. J. (2009). The theory and practice of item response theory. New York, NY: Guilford Press.
  • DeMars, C. (2010). Item response theory. Oxford: Oxford University Press.
  • Demirtaşlı, Ç. N. (2002). Developing a scale for attitudes toward the measurement and evaluation course. Education and Science, 27, 125, 44-48.
  • Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. LEA publishers: NJ.
  • Greer T. G. (2004). Detection of differential item functioning (DIF) on the SATV: A comparison of four methods: Mantel-Haenszel, logistic regression, simultaneous item bias and likelihood ratio test (Doctoral dissertation, University of Houston, Texas). Retrieved from http://search.proquest.com/pqdtglobal/docview/305196900/EE5E5B40D7A34E5DPQ/1?accountid=8319
  • Gresham, G. (2010). A study exploring exceptional education pre-service teachers’ mathematics anxiety. IUMPST: The Journal, 4, 1-14.
  • Hambleton, R. K., & Swaminathan, H. (1985). Item response theory: Principles and applications. Boston: Kluwer.
  • Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Newbury Park, CA: Sage Publications.
  • Jaggernauth, J. S. (2010). Mathematics anxiety and the primary school teacher: An exploratory study of the relationship between mathematics anxiety, mathematics teacher efficacy, and mathematics avoidance. EDRS6900: Project Report. The University of the West Indies.
  • Kılınç, M. (2011). A perceptual scale for measurement and evaluation of prospective teachers self-efficacy in education. Journal of Kırşehir Education Faculty, 12, 4, 81-93.
  • Kilmen, S., & Çıkrıkçı-Demirtaşlı, N. (2009). The Perceptions of primary school teachers about their application levels of measurement and evaluation principles. Ankara University Journal of Faculty of Educational Sciences, 42(2), 027-054.
  • Kottke, J. L. (2000). Mathematical proficiency, statistics knowledge, attitudes toward statistics, and measurement course performance. The College Student Journal, 34, 334-347.
  • Le, D. T. (2013). Applying item response theory modeling in educational research (Doctoral dissertation, Iowa State University. Retrieved from http://search.proquest.com/pqdtglobal/docview/1450045295/DD5A94235C534A49PQ/1?accountid=8319.
  • Matteucci, M., & Stracqualursi, L. (2006). Student assessment via graded response model. STATISTICA, 4, 435–447.
  • Ozan, C., & Köse, E. (2013). Adaptation of attitudes toward educational measurement inventory (ATEMI) to Turkish. E-İnternational Journal of Educational Research, 4 (2), 29-47.
  • Özbaşı, D., & Demirtaşlı-Çıkrıkçı N. (2013). Primary school teachers’ perceptions of their competencies regarding measurement and evaluation in terms of some variables. Ankara University Journal of Faculty of Educational Sciences, 46(2), 25-46.
  • Thissen, D. (2001). IRTLRDIF v.2.0b: Software for the computation of the statistics involved in item response theory likelihood-ratio tests for differential item functioning. Chapel Hill: L. L. Thurstone Psychometric Laboratory, University of North Carolina at Chapel Hill.
  • Thissen, D., Steinberg, L., & Wainer, H. (1993). Detection of differential item functioning using the parameters of item response models. In P. W. Holland and H. Wainer (Eds.). Differantial item functioning. Lawrence Erlbaum.
  • Ulutaş, S. (2003). Investigating the competency of teachers in high schools in measurement and evaluation and level of application principles of measurement and evaluation (Master thesis, University of Ankara). Retrieved from https://tez.yok.gov.tr/.
  • Yıldırım, H. H. (2006). The differential item functionıng (DIF) analysis of mathematics items in the international assessment programs (Doctoral dissertation, METU). Retrieved from https://tez.yok.gov.tr/.
Year 2016, Volume: 7 Issue: 1, 133 - 144, 30.06.2016
https://doi.org/10.21031/epod.43804

Abstract

References

  • Aktaş, M., & Alıcı, D. (2012). Development of likert type attitude scale towards measurement and evaluation in education course. Journal of Qafqaz University, 33, 66-73.
  • Ajzen, I. (2005). Attitudes, personality and behavior. Milton-Keynes, England: Open University Press/ McGraw- Hill.
  • Baker, F. B. (2001). The basis of item response theory. USA: ERIC Clearing house on Assessment and Evaluation.
  • Baker, J. G., Rounds, J. B. & Zevon, M. A. (2000). A comparison of graded response and rasch partial credit models with subjective well-being. Journal of Educational and Behavioral Statistics, 25, 253–70.
  • Brady, P. & Bowd, A. (2005). Mathematics anxiety, prior experience and confidence to teach mathematics among pre-service education students. Teachers and Teaching: Theory and Practice, 11(1), 37–46.
  • Clauser, B. E.,& Mazor, K. M. (1998). Using statistical procedure to identify differential item functioning test items. Educational Measurement: Issues and Practice, 17, 31-44.
  • de Ayala, R. J. (2009). The theory and practice of item response theory. New York, NY: Guilford Press.
  • DeMars, C. (2010). Item response theory. Oxford: Oxford University Press.
  • Demirtaşlı, Ç. N. (2002). Developing a scale for attitudes toward the measurement and evaluation course. Education and Science, 27, 125, 44-48.
  • Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. LEA publishers: NJ.
  • Greer T. G. (2004). Detection of differential item functioning (DIF) on the SATV: A comparison of four methods: Mantel-Haenszel, logistic regression, simultaneous item bias and likelihood ratio test (Doctoral dissertation, University of Houston, Texas). Retrieved from http://search.proquest.com/pqdtglobal/docview/305196900/EE5E5B40D7A34E5DPQ/1?accountid=8319
  • Gresham, G. (2010). A study exploring exceptional education pre-service teachers’ mathematics anxiety. IUMPST: The Journal, 4, 1-14.
  • Hambleton, R. K., & Swaminathan, H. (1985). Item response theory: Principles and applications. Boston: Kluwer.
  • Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Newbury Park, CA: Sage Publications.
  • Jaggernauth, J. S. (2010). Mathematics anxiety and the primary school teacher: An exploratory study of the relationship between mathematics anxiety, mathematics teacher efficacy, and mathematics avoidance. EDRS6900: Project Report. The University of the West Indies.
  • Kılınç, M. (2011). A perceptual scale for measurement and evaluation of prospective teachers self-efficacy in education. Journal of Kırşehir Education Faculty, 12, 4, 81-93.
  • Kilmen, S., & Çıkrıkçı-Demirtaşlı, N. (2009). The Perceptions of primary school teachers about their application levels of measurement and evaluation principles. Ankara University Journal of Faculty of Educational Sciences, 42(2), 027-054.
  • Kottke, J. L. (2000). Mathematical proficiency, statistics knowledge, attitudes toward statistics, and measurement course performance. The College Student Journal, 34, 334-347.
  • Le, D. T. (2013). Applying item response theory modeling in educational research (Doctoral dissertation, Iowa State University. Retrieved from http://search.proquest.com/pqdtglobal/docview/1450045295/DD5A94235C534A49PQ/1?accountid=8319.
  • Matteucci, M., & Stracqualursi, L. (2006). Student assessment via graded response model. STATISTICA, 4, 435–447.
  • Ozan, C., & Köse, E. (2013). Adaptation of attitudes toward educational measurement inventory (ATEMI) to Turkish. E-İnternational Journal of Educational Research, 4 (2), 29-47.
  • Özbaşı, D., & Demirtaşlı-Çıkrıkçı N. (2013). Primary school teachers’ perceptions of their competencies regarding measurement and evaluation in terms of some variables. Ankara University Journal of Faculty of Educational Sciences, 46(2), 25-46.
  • Thissen, D. (2001). IRTLRDIF v.2.0b: Software for the computation of the statistics involved in item response theory likelihood-ratio tests for differential item functioning. Chapel Hill: L. L. Thurstone Psychometric Laboratory, University of North Carolina at Chapel Hill.
  • Thissen, D., Steinberg, L., & Wainer, H. (1993). Detection of differential item functioning using the parameters of item response models. In P. W. Holland and H. Wainer (Eds.). Differantial item functioning. Lawrence Erlbaum.
  • Ulutaş, S. (2003). Investigating the competency of teachers in high schools in measurement and evaluation and level of application principles of measurement and evaluation (Master thesis, University of Ankara). Retrieved from https://tez.yok.gov.tr/.
  • Yıldırım, H. H. (2006). The differential item functionıng (DIF) analysis of mathematics items in the international assessment programs (Doctoral dissertation, METU). Retrieved from https://tez.yok.gov.tr/.
There are 26 citations in total.

Details

Journal Section Articles
Authors

R. Nükhet Demirtaşlı

Seher Yalçın

Cansu Ayan

Publication Date June 30, 2016
Published in Issue Year 2016 Volume: 7 Issue: 1

Cite

APA Demirtaşlı, R. N., Yalçın, S., & Ayan, C. (2016). The Development of IRT Based Attitude Scale towards Educational Measurement Course. Journal of Measurement and Evaluation in Education and Psychology, 7(1), 133-144. https://doi.org/10.21031/epod.43804