Araştırma Makalesi

Yıl 2019,
Cilt: 6 Sayı: 5, 57 - 69, 30.12.2019
### Öz

### Anahtar Kelimeler

### Kaynakça

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.

- 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.
- Bloch, R. & Norman, G. (2018). 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.
- 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.
- Cardinet, J., Tourneur, Y. & Allal, L. (1976). The Symmetry of Generalizability Theory: Applications to Educational Measurement. Journal of Educational Measurement, 13 (2), 119-135.
- 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: 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.
- Shavelson, J. R. & Webb, N. M. (2006). Generalizability theory. In: Green, J.L., Camill, G., Elmore, P.B., editors. Handbook of complementary methods in education research. Mahwah: Lawrence Erlbaum Associates Publishers, p. 309–322.
- Shavelson, J. R. & Webb, N. M. (1991). Generalizability Theory: A Primer. Newbury Park. CA: Sage Publications.
- Shavelson, R.J., Webb, N.M., & Rowley, G.L. (1989). Generalizability theory. American Psychologist, 44(6), 922-932.
- Shavelson, R. J., & Webb, N. M. (1981). Generalizability theory: 1973–1980. British Journal of Mathematical and Statistical Psychology, 34, 133–166.
- Suen, H. K. & Lei, P.W. (2007). Classical Versus Generalizability Theory of Measurement. Educational Measurement, 4, 1-13.
- Taşdelen Teker, G. & Güler, N. (2019). Thematic Content Analysis of Studies Using Generalizability Theory. International Journal of Assessment Tools in Education, 6(2), 279–299. https://dx.doi.org/10.21449/ijate.569996
- Webb, N. M., Shavelson, R. J. & Haertel, E. H. (2006). Reliability Coefficients and Generalizability Theory. Handbook of Statistics, 26, 81-124. DOI: 10.1016/S0169-7161(06)26004

Yıl 2019,
Cilt: 6 Sayı: 5, 57 - 69, 30.12.2019
### Öz

### Anahtar Kelimeler

### Kaynakça

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.

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.

- 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.
- Bloch, R. & Norman, G. (2018). 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.
- 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.
- Cardinet, J., Tourneur, Y. & Allal, L. (1976). The Symmetry of Generalizability Theory: Applications to Educational Measurement. Journal of Educational Measurement, 13 (2), 119-135.
- 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: 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.
- Shavelson, J. R. & Webb, N. M. (2006). Generalizability theory. In: Green, J.L., Camill, G., Elmore, P.B., editors. Handbook of complementary methods in education research. Mahwah: Lawrence Erlbaum Associates Publishers, p. 309–322.
- Shavelson, J. R. & Webb, N. M. (1991). Generalizability Theory: A Primer. Newbury Park. CA: Sage Publications.
- Shavelson, R.J., Webb, N.M., & Rowley, G.L. (1989). Generalizability theory. American Psychologist, 44(6), 922-932.
- Shavelson, R. J., & Webb, N. M. (1981). Generalizability theory: 1973–1980. British Journal of Mathematical and Statistical Psychology, 34, 133–166.
- Suen, H. K. & Lei, P.W. (2007). Classical Versus Generalizability Theory of Measurement. Educational Measurement, 4, 1-13.
- Taşdelen Teker, G. & Güler, N. (2019). Thematic Content Analysis of Studies Using Generalizability Theory. International Journal of Assessment Tools in Education, 6(2), 279–299. https://dx.doi.org/10.21449/ijate.569996
- Webb, N. M., Shavelson, R. J. & Haertel, E. H. (2006). Reliability Coefficients and Generalizability Theory. Handbook of Statistics, 26, 81-124. DOI: 10.1016/S0169-7161(06)26004

Birincil Dil | İngilizce |
---|---|

Konular | Eğitim Üzerine Çalışmalar |

Bölüm | Special Issue |

Yazarlar | |

Yayımlanma Tarihi | 30 Aralık 2019 |

Gönderilme Tarihi | 28 Ekim 2019 |

Yayımlandığı Sayı | Yıl 2019 Cilt: 6 Sayı: 5 |