Research Article
Year 2018,
Volume: 38 Issue: 2, 663 - 692, 01.08.2018
Abstract
Bu çalışmada Rosenberg Benlik Saygısı ölçeğinin
madde tepki kuramında farklı çok kategorili modellerde model veri uyumu kontrol
edilip, madde parametreleri kestirimleri arasındaki ilişkilerin seçilen modele
göre farklılaşıp- farklılaşmadığı incelenmiştir. Araştırmada 47974 bireyin
verdiği yanıtlar arasından kayıp veriler temizlendikten sonra rastgele seçilen
Amerika Birleşik Devletli 500, 1000 ve 2000 bireyin tek boyutlu dört kategorili
10 maddelik ölçeğe verdiği yanıtlar kullanılmıştır. Genelleştirilmiş Kısmi
Kredi, 1 parametreli lojistik model gibi sınırlandırılmış Genelleştirilmiş
Kısmi Kredi, Kısmi Kredi ve Aşamalı Tepki modeline 500, 1000 ve 2000 kişilik
örneklemlerden elde edilen verilerden elde edilen -2log-olabilirlik, Akaike bilgi ölçütü ve Bayesian bilgi ölçütü model uyum katsayıları incelendiğinde en fazla uyumun her
koşulda aşamalı tepki modeli ile gerçekleştiği bulunmuştur. Her modelden elde edilen madde parametreleri
arasında manidar yüksek bir ilişkinin olduğu tespit edilmiştir. Farklı modellerden elde edilen bulgulara göre
her üç örneklemden en yüksek ayırt ediciliğe sahip olan maddenin 6. madde, en
az ayırt ediciliğe sahip olan maddenin ise 500 kişilik örneklem için 4. madde,
1000 ve 2000 kişilik örneklemler için ise genelde 8. madde olduğu görülmüştür.
References
- Baker,F. B. (2001). The basics of item response theory. ERIC Clearinghouse on Assessment and Evaluation. Second edition. (ERIC Document Reproduction Service No. ED458219).
- Crocker, L., & Algina, J. (1986). Introduction to modern test theory. California: Thomson Learning.
- Cook K.F., Dodd B.G., Fitzpatrick S.J. (1999). A comparison of three polytomous item response theory models in the context of testlet scoring. Journal of outcome measurement, 3(1):1-20.
- de Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. Methodology in the Social Sciences. New York: Guildford.
- DeMars, C. (2010). Item Response Theory. New York: Oxford University Press.
- Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Erlbaum.
- Hambleton, R. K., & Swaminathan, H. (1985). Item Response Theory: Principles and Applications. Boston: Kluwer Nijhoff.
- Han, K. T., and Hambleton, R. K. (2014). User's Manual for WinGen 3: Windows Software that Generates IRT Model Parameters and Item Responses (Center for Educational Assessment Report No. 642). Amherst, MA: University of Massachusetts.
- Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. London: Sage.
- Hays, R. D., Morales, L. S., & Reise, S. P. (2000). Item response theory and health outcomes measurement in the 21st century. Medical care, 38(9 Suppl), II28. National Institutes of Health Public Access Author Manuscript. Retrieved 17/03/2017 from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1815384/pdf/nihms14476.pdf
- Jiao, H. ve Zhang, Y. (2014). Polytomous multilevel testlet models for testlet-based assessments with complex sampling designs. British Journal of Mathematical and Statistical Psychology, 68(1): 65-83.
- Kang, T., & Chen, T. T. (2008). Performance of the Generalized S‐X2 Item Fit Index for Polytomous IRT Models. Journal of Educational Measurement, 45(4), 391-406.
- Kline, T. J. B. (2005). Psychological testing: A practical approach to design and evaluation, Thousand Oaks, CA: Sage.
- Lord, F. M. (1980). Applications of item response theory to practical testing problems. Hillsdale, NJ Lawrence: Erlbaum.
- Masters, G.N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47 (2), 149–174.
- Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16, 159–176.
- Naumenko, O. (2014). Comparison of various polytomous ıtem response theory modeling approaches for task based simulation cpa exam data. AICPA 2014 Summer Internship Project. The University of North Carolina at Greensboro.
- Ostini, R., & Nering, M. L. (2005). Polytomous item response theory models. Thousand Oaks: Sage. Penfield, R. D. (2014). An NCME ınstructional module on polytomous ıtem response theory models. Educational Measurement: Issues and Practice, 33(1), 36-48.
- Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores (Psychometric Monograph No. 17). Richmond, VA: Psychometric Society. Retrieved from http://www.psychometrika.org/journal/online/MN17.pdf
- Samejima, F. (1972). A general model for free-response data (Psychometric Monograph No. 18). Richmond, VA: Psychometric Society. Retrieved 18.03.2017 from http://www.psychometrika.org/journal/online/MN18.pdf
- Samejima, F. (1995). Acceleration model in the heterogeneous case of the general graded response model. Psychometrika, 60 (4), 549–572.
- Samejima, F. (1996). The graded response model. In: van der Linden, WJ.; Hambleton, R., editors. Handbook of modern item response theory. New York, NY: Springer; p. 85-100.
- Schermelleh-Engel, K., Moosbrugger, H. & Müler, H. (2003). Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit. Measures Of Psychological Research Online, (8), 2, 23-74.
- Thissen, D., Pommerich, M., Billeaud, K., & Williams, V. S. (1995). Item response theory for scores on tests including polytomous items with ordered responses. Applied Psychological Measurement, 19(1), 39-49. DOI: 10.1177/014662169501900105
- Yurekli, H. (2010). The relationship between parameters from some polytomous item response theory models. Unpublished Master’s thesis. The Florıda State Unıversıty College Of Educatıon.
Year 2018,
Volume: 38 Issue: 2, 663 - 692, 01.08.2018
Abstract
References
- Baker,F. B. (2001). The basics of item response theory. ERIC Clearinghouse on Assessment and Evaluation. Second edition. (ERIC Document Reproduction Service No. ED458219).
- Crocker, L., & Algina, J. (1986). Introduction to modern test theory. California: Thomson Learning.
- Cook K.F., Dodd B.G., Fitzpatrick S.J. (1999). A comparison of three polytomous item response theory models in the context of testlet scoring. Journal of outcome measurement, 3(1):1-20.
- de Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. Methodology in the Social Sciences. New York: Guildford.
- DeMars, C. (2010). Item Response Theory. New York: Oxford University Press.
- Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Erlbaum.
- Hambleton, R. K., & Swaminathan, H. (1985). Item Response Theory: Principles and Applications. Boston: Kluwer Nijhoff.
- Han, K. T., and Hambleton, R. K. (2014). User's Manual for WinGen 3: Windows Software that Generates IRT Model Parameters and Item Responses (Center for Educational Assessment Report No. 642). Amherst, MA: University of Massachusetts.
- Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. London: Sage.
- Hays, R. D., Morales, L. S., & Reise, S. P. (2000). Item response theory and health outcomes measurement in the 21st century. Medical care, 38(9 Suppl), II28. National Institutes of Health Public Access Author Manuscript. Retrieved 17/03/2017 from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1815384/pdf/nihms14476.pdf
- Jiao, H. ve Zhang, Y. (2014). Polytomous multilevel testlet models for testlet-based assessments with complex sampling designs. British Journal of Mathematical and Statistical Psychology, 68(1): 65-83.
- Kang, T., & Chen, T. T. (2008). Performance of the Generalized S‐X2 Item Fit Index for Polytomous IRT Models. Journal of Educational Measurement, 45(4), 391-406.
- Kline, T. J. B. (2005). Psychological testing: A practical approach to design and evaluation, Thousand Oaks, CA: Sage.
- Lord, F. M. (1980). Applications of item response theory to practical testing problems. Hillsdale, NJ Lawrence: Erlbaum.
- Masters, G.N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47 (2), 149–174.
- Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16, 159–176.
- Naumenko, O. (2014). Comparison of various polytomous ıtem response theory modeling approaches for task based simulation cpa exam data. AICPA 2014 Summer Internship Project. The University of North Carolina at Greensboro.
- Ostini, R., & Nering, M. L. (2005). Polytomous item response theory models. Thousand Oaks: Sage. Penfield, R. D. (2014). An NCME ınstructional module on polytomous ıtem response theory models. Educational Measurement: Issues and Practice, 33(1), 36-48.
- Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores (Psychometric Monograph No. 17). Richmond, VA: Psychometric Society. Retrieved from http://www.psychometrika.org/journal/online/MN17.pdf
- Samejima, F. (1972). A general model for free-response data (Psychometric Monograph No. 18). Richmond, VA: Psychometric Society. Retrieved 18.03.2017 from http://www.psychometrika.org/journal/online/MN18.pdf
- Samejima, F. (1995). Acceleration model in the heterogeneous case of the general graded response model. Psychometrika, 60 (4), 549–572.
- Samejima, F. (1996). The graded response model. In: van der Linden, WJ.; Hambleton, R., editors. Handbook of modern item response theory. New York, NY: Springer; p. 85-100.
- Schermelleh-Engel, K., Moosbrugger, H. & Müler, H. (2003). Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit. Measures Of Psychological Research Online, (8), 2, 23-74.
- Thissen, D., Pommerich, M., Billeaud, K., & Williams, V. S. (1995). Item response theory for scores on tests including polytomous items with ordered responses. Applied Psychological Measurement, 19(1), 39-49. DOI: 10.1177/014662169501900105
- Yurekli, H. (2010). The relationship between parameters from some polytomous item response theory models. Unpublished Master’s thesis. The Florıda State Unıversıty College Of Educatıon.
There are 25 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | August 1, 2018 |
| Published in Issue | Year 2018 Volume: 38 Issue: 2 |
