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dc.contributor.advisorKelecioğlu, Hülya
dc.contributor.authorZor, Yaşar Mehmet
dc.date.accessioned2023-10-23T08:00:59Z
dc.date.issued2023
dc.date.submitted2023-07-13
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Test equating: What, why, who?. Research Quarterly for Exercise and Sport, 69(1), 11-23.tr_TR
dc.identifier.govdoc10563718
dc.identifier.urihttps://hdl.handle.net/11655/34068
dc.description.abstractIn this study, multidimensional and unidimensional scale transformation procedures were performed on multidimensional test forms under various conditions, and it was aimed to compare the equating errors obtained from item and ability parameters. Equating error (RMSE) was used as an evaluation criterion. Two-dimensional and simple structured test forms were produced in R software. IRTPRO software was used for estimation of item and ability parameters, Linkmirt software was used for multidimensional scale transformation, and IRTEQ software was used for unidimensional scale transformation. Sample size (1000 and 2000), correlation between dimensions (0.1; 0.5 and 0.9), common item ratio (20% and 40%), difference in ability distribution between groups (0.05 and 0.5) and parameter estimation model (2PLM and 3PLM) were taken as study conditions. Multidimensional and unidimensional scale transformation procedures were performed in the non-equivalent groups anchor test design and the RMSE values obtained were compared. The statistical significance of the difference between the RMSE values according to the conditions and their interactions was examined by means of multi-way analysis of variance and t-test. Lower RMSE values were obtained when the sample size was 2000 and the common item ratio was 40%. In multidimensional and unidimensional scale transformation, lower RMSE values were estimated when the correlation between dimensions was 0.1 and 0.9, respectively. RMSE values are lower when the difference in ability distribution between groups is low. Lower RMSE values were obtained when the mean-mean method was used in multidimensional scale transformation and Stocking-Lord method was used in unidimensional scale transformation.tr_TR
dc.language.isoturtr_TR
dc.publisherEğitim Bilimleri Enstitüsütr_TR
dc.rightsinfo:eu-repo/semantics/openAccesstr_TR
dc.subjectMadde tepki kuramı
dc.subjectÇok boyutlu madde tepki kuramı
dc.subjectTest eşitleme
dc.subjectÖlçek dönüştürme
dc.subjectÇok boyutlu ölçek dönüştürme
dc.titleÇok Boyutlu Testlerin Tek Boyutlu ve Çok Boyutlu Yöntemlere Göre Eşitlenmesitr_TR
dc.typeinfo:eu-repo/semantics/doctoralThesistr_TR
dc.description.ozetBu çalışmada çok boyutlu test formlarına çeşitli koşullar altında çok boyutlu ve tek boyutlu ölçek dönüştürme işlemleri yapılıp, madde ve yetenek parametrelerinden elde edilen eşitleme hatalarının karşılaştırılması amaçlanmıştır. Değerlendirme ölçütü olarak eşitleme hatası (RMSE) kullanılmıştır. R yazılımında iki boyutlu ve basit yapılı test formları üretilmiştir. Madde ve yetenek parametrelerinin kestiriminde IRTPRO, çok boyutlu ölçek dönüştürmede Linkmirt, tek boyutlu ölçek dönüştürmede IRTEQ yazılımı kullanılmıştır. Çalışma koşulları olarak örneklem büyüklüğü (1000 ve 2000), boyutlar arası korelasyon (0.1; 0.5 ve 0.9), ortak madde oranı (%20 ve %40), gruplar arası yetenek dağılımı farkı (0.05 ve 0.5) ve parametre kestirim modeli (2PLM ve 3PLM) alınmıştır. Eşdeğer olmayan gruplar ortak test deseninde çok boyutlu ve tek boyutlu ölçek dönüştürme işlemleri yapılmış ve elde edilen RMSE değerleri karşılaştırılmıştır. Çok yönlü varyans analizi ve t testi ile ele alınan koşullara ve bunların etkileşimlerine göre RMSE değerleri arasındaki farkın istatistiksel olarak anlamlılığı incelenmiştir. Örneklem büyüklüğü 2000 ve ortak madde oranı %40 olduğunda daha düşük RMSE değerleri elde edilmiştir. Çok boyutlu ve tek boyutlu ölçek dönüştürmede boyutlar arası korelasyonun sırasıyla 0.1 ve 0.9 olduğu koşullarda daha düşük RMSE değerleri kestirilmiştir. Gruplar arası yetenek dağılımı farkı düşük olduğu koşullarda elde edilen RMSE değerleri daha düşüktür. Çok boyutlu ölçek dönüştürmede ortalama-ortalama yöntemi, tek boyutlu ölçek dönüştürmede Stocking-Lord yöntemi kullanıldığında daha düşük RMSE değerleri elde edilmiştir.tr_TR
dc.contributor.departmentEğitim Bilimleritr_TR
dc.embargo.termsAcik erisimtr_TR
dc.embargo.lift2023-10-23T08:00:59Z
dc.fundingYoktr_TR


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