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Lojistik Regresyon Analizi ile Pisa Araştırmasında Öğrenci Başarısının Modellenmesi

Author

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  • Recep BİNDAK

    (Gaziantep Üniversitesi Teknik Bilimler Meslek Yüksek Okulu, Gaziantep, Türkiye)

Abstract

Bu çalışmanın amacı Türkiye örneklemindeki öğrencilerin PISA sınavındaki başarılarını binary lojistik regresyon ile modellemektir. Açıklayıcı değişken olarak bazı sosyo-kültürel özellikler kullanımış olup Bağımlı değişken iki kategorilidir ve öğrenci puanının OECD ortalaması üzerinde olup olmamasını belirtmektedir. Veriler PISA-2009 Türkiye örneklemine aittir. PISA 2009 Türkiye örneklemi, okul türlerine göre tabakalı rastgele yöntemle belirlenen toplam 170 okuldan 4996 öğrenciden oluşmaktadır. Katılımcıların “başarı” grubuna girme ihtimali üzerine cinsiyet, evde konuşulan dil, evde kitap sayısı, bölge, ebeveyn eğitim düzeyi, bilgisayara yönelik tutum, okula yönelik tutum ve varlık indeksinin etkilerini saptamak için lojistik regresyon analizi uygulanmıştır. Model istatistiksel olarak anlamlı bulunmuştur. Modelin açıklanabilen değişkenliği %23.8 (Nagelkerke R2), doğru sınıflandırma oranı %67,9’dir. Analiz sonuçlarına göre kızların başarılı grupta yer alması erkeklere göre 1,71 kat daha olasıdır. Evde konuştuğu dil Türkçe olan öğrencinin başarılı grupta yer alması diğerlerine göre 1,65 kat daha olasıdır. Evde bulunan kitap sayısının görece yüksek olması, ebeveyn eğitim düzeyi, bilgisayar tutumu ve varlık indeksi (WEALTH) başarılı grupta yer alma olasılığının yükselmesi ile ilişkili bulunmuştur. Okula yönelik tutum anlamlı bulunamadı.Keywords: Oyunlaştırma, Mobil uygulamalar, Tutum, Kullanım niyeti, Oyunlaştırma kabulü

Suggested Citation

  • Recep BİNDAK, 0. "Lojistik Regresyon Analizi ile Pisa Araştırmasında Öğrenci Başarısının Modellenmesi," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 14(28), pages 57-74.
    • Handle: RePEc:ist:ekoist:v:14:y:2018:i:28:p:57-74
    DOI: 10.26650/ekoist.2018.14.28.0010
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    References listed on IDEAS

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    1. Sevda Gürsakal & Dilek Murat & Necmi Gürsakal, 2016. "Assessment of PISA 2012 Results With Quantile Regression Analysis Within The Context of Inequality In Educational Opportunity," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 4(2), pages 41-54, September.
    2. José Manuel Cordero Ferrera & Eva Crespo Cebada & Francisco Pedraja Chaparro & Daniel Santín González, 2011. "Exploring Educational Efficiency Divergences Across Spanish Regions In Pisa 2006," Revista de Economia Aplicada, Universidad de Zaragoza, Departamento de Estructura Economica y Economia Publica, vol. 19(3), pages 117-145, Winter.
    3. Gijsbert Stoet & David C Geary, 2013. "Sex Differences in Mathematics and Reading Achievement Are Inversely Related: Within- and Across-Nation Assessment of 10 Years of PISA Data," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-10, March.
    4. Gamboa, Luis Fernando & Waltenberg, Fábio D., 2012. "Inequality of opportunity for educational achievement in Latin America: Evidence from PISA 2006–2009," Economics of Education Review, Elsevier, vol. 31(5), pages 694-708.
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    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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