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Dergiler / International Journal of Assessment Tools in Education / 2019 / Cilt: 6 - Sayı: 5

Coping with Unbalanced Designs of Generalizability Theory: G String V

Gülşen TAŞDELEN TEKER
Sayfa
57–69
Yayın Tarihi
2022-10-18
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Finally, the limitations of the software are shared.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The aim of this paper is to introduce a software that is appropriate for the generalizability theory for not only balanced but also unbalanced data sets.
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Temel Bulgular & Veri:Because it is possible to have unbalanced data sets while conducting a study, the researchers have devised an easy solution, other than deleting data, to balance the design to cope with this situation.
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Sonuç & Katkı:Finally, the limitations of the software are shared.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Generalizability theory · G String V · Unbalanced design

Özet

The aim of this paper is to introduce a software that is appropriate for the generalizability theory for not only balanced but also unbalanced data sets. Because it is possible to have unbalanced data sets while conducting a study, the researchers have devised an easy solution, other than deleting data, to balance the design to cope with this situation. Thus, the software G String V will be introduced. First, the generalizability theory will be reviewed, followed by a description of the unbalanced synthetic data that was used to conduct the analysis using the software. Explanations are provided for installing the software, preparation of the data, and the step-by-step data analysis. Moreover, the interpretation of the data is also explained. Finally, the limitations of the software are shared.

Abstract

The aim of this paper is to introduce a software that is appropriate for the generalizability theory for not only balanced but also unbalanced data sets. Because it is possible to have unbalanced data sets while conducting a study, the researchers have devised an easy solution, other than deleting data, to balance the design to cope with this situation. Thus, the software G String V will be introduced. First, the generalizability theory will be reviewed, followed by a description of the unbalanced synthetic data that was used to conduct the analysis using the software. Explanations are provided for installing the software, preparation of the data, and the step-by-step data analysis. Moreover, the interpretation of the data is also explained. Finally, the limitations of the software are shared.

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Bu Makaleye Atıf Yap

TEKER, G. T. (2022). Coping with Unbalanced Designs of Generalizability Theory: G String V. International Journal of Assessment Tools in Education, Cilt: 6 - Sayı: 5, 57–69. https://ajindex.com/articles/540153

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Kaynakça (7)

  1. Atılgan, H. (2004). Genellenebilirlik kuramı ve cok değişkenlik kaynaklı Rasch modelinin karşılaştırılmasına ilişkin bir araştırma [A research on the comparison of the generalizability theory and many facet Rasch model] (Doctoral Dissertation). Hacettepe University, Ankara.
  2. Bloch, R. & Norman, G. (2018).
  3. G String V User Manual. Hamilton, Ontario, Canada. Bloch, R. & Norman, G. (2015). G String IV (Version 6.1.1) User Manual. Hamilton, Ontario, Canada. Bloch, R. & Norman, G. (2012). Generalizability theory for the perplexed: A practical introduction and guide: AMEE Guide No. 68. Medical Teacher, 34 (11), 960-992. DOI: 10.3109/0142159X.2012.703791 Brennan, R. L. (2001a). Generalizability Theory. New York: Springer. Brennan, R. L. (2001b). Manual for urGENOVA (Version 2.1) (Iowa Testing Programs Occasional Paper Number 49). Iowa City, IA: Iowa Testing Programs, University of Iowa. Brennan, R. L. (2000). Performance Assessments from the Perspective of Generalizability Theory. Applied Psychological Measurement, 24(4), 339-353. Brennan. R. L., & Kane, M. T. (1977). An index of dependability for mastery tests. Journal of Educational Measurement, 14, 277-289.
  4. Cardinet, J., Johnson, S. & Pini, G. (2010). Applying Generalizability Theory using EduG. New York, NY: Routledge – Taylor & Francis Group. Cardinet, J., Tourneur, Y. & Allal, L. (1981). Extension of Generalizability Theory and Its Applications in Educational Measurement. Journal of Educational Measurement, 18 (4), 183-204.
  5. Cardinet, J., Tourneur, Y. & Allal, L. (1976). The Symmetry of Generalizability Theory: Applications to Educational Measurement. Journal of Educational Measurement, 13 (2), 119-135.
  6. Chiu, C. W. T. (2001). Scoring performance assessments based on judgments: Generalizability theory. Boston, MA: Kluwer Academic. Cronbach, L. J., Gleser, G. C., Nanda, H. & Rajaratnam, N. (1972). The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles. New York: Wiley. Furr, R. M. (2011). Scale construction and psychometrics for social and personality psychology. Thousand Oaks, CA: Sage Publications Ltd. Güler, N., Kaya Uyanık, G. & Taşdelen Teker, G. (2012). Genellenebilirlik Kuramı [Generalizability Theory]. Ankara:
  7. PegemA Yayıncılık. Rios, J.A., Li, X., & Faulkner-Bond, M. (2012, October). A review of methodological trends in generalizability theory. Paper presented at the annual conference of the Northeastern Educational Research Association, Rocky Hill, CT. Shavelso