Katma-değerli değerlendirme modellerinde eşitleme önemli mi?

Year 2018, Volume: 7 Issue: 4, 186 - 195, 31.10.2018

Sedat Şen , Ragıp Terzi , İbrahim Yıldırım , Allan Cohen

https://doi.org/10.19128/turje.456656

Abstract

Bu çalışmanın amacı, eşitlenmiş ve eşitlenmemiş verilerin katma-değerli değerlendirme analizlerine etkisini incelemektir. Katma-değerli değerlendirme yaklaşımını uygulayabilmek için literatürde birçok model önerilmiştir. Bu çalışma, iki farklı katma değerli değerlendirme modeli karşılaştırmıştır: düzeltilmemiş hiyerarşik doğrusal model (UHLMM) ve genelleştirilmiş süreklilik (GP) modeli. Birinci model eşitlenmiş testler için kullanılırken, ikincisi bu varsayımı esnetir. Her iki modelde iki farklı veri seti (eşitlenmiş ve eşitlenmemiş) analiz edildi. Her iki model için eyalet çapında yapılan bir sınav (eşitlenmiş) ve ülke çapında yapılan bir sınav (eşitlenmemiş) verilerine dayanan katma-değer kestirimleri genellikle tutarlı bulundu. Okul sıralamalarında iki model arasında bazı farklılıklar gözlendi. Bu çalışmanın pratik çıkarımı, okul sıralamasında küçük farklılıklar olmasına rağmen, test formları arasında eşitlemenin mümkün olmadığı durumlarda eşitlenmemiş bir veri seti gerektiren bir modelin uygulanabileceğidir.

Keywords

Katma-değerli değerlendirme, Hiyerarşik doğrusal model, Genelleştirilmiş süreklilik modeli, Test eşitleme

Does equating matter in value-added models?

Abstract

The purpose of this study was to examine the effect of equated and non-equated data on value-added assessment analyses. Several models have been proposed in the literature to apply the value-added assessment approach. This study compared two different value-added models: the unadjusted hierarchical linear model and the generalized persistence model. The former model assumes equated tests while the latter one relaxes this assumption. Two different data sets (equated and non-equated) were analyzed with both models. Value-added estimates for both models based on a statewide examination (equated) and a countrywide examination (non-equated) data were generally consistent. School rankings showed differences between the two models. The practical implication of this study is that although there were small differences in school rankings, a model requiring an equating assumption can be applied to a non-equated data set in a case when equating between test forms is not possible.

Keywords

Value-added assessment, Hierarchical linear model, Generalized persistence model, Test equating

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