Missing Data Management Practices in L2 Research: The Good, The Bad and The Ugly
Öz
Missing data are one of the frequently encountered problems in quantitative research. When
neglected or handled improperly, this problem can have adverse impact on research results.
However, the issue of missing data in quantitative second language (L2) research has largely been
ignored when compared to the other sister disciplines such as education and psychology. The
purpose of this methodological synthesis was, therefore, to investigate the issue of missing data
in L2 research, with a particular focus on L2 researchers’ current missing data management
practices. A total of 143 studies published in six leading L2 journals were reviewed in this
synthesis. The results indicated that missing data were indeed quite common in L2 research in
that 41% of the studies indicated evidence of missing data, but L2 researchers’ management and
reporting of missing data was often less than optimal. In light of the results, several directed
suggestions were made to improve the rigor and quality of L2 research.
Anahtar Kelimeler
Kaynakça
- Author (2016)
- Author (2018)
- Author & co-author (2015).
- Author & co-authors (2017).
- Author & co-authors (forthcoming).
- Bennett, D. A. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25(5), 464-469.
- Cheema, J. R. (2014). A review of missing data handling methods in education research. Review of Educational Research, 84(4), 487-508.
- Cohen, J., & Cohen, P. (1983). Applied multiple regression/ correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
Ayrıntılar
Birincil Dil
İngilizce
Konular
Alan Eğitimleri
Bölüm
Araştırma Makalesi
Yazarlar
Talip Gönülal
*
0000-0001-6441-4278
Türkiye
Yayımlanma Tarihi
29 Nisan 2019
Gönderilme Tarihi
27 Temmuz 2018
Kabul Tarihi
26 Şubat 2019
Yayımlandığı Sayı
Yıl 2019 Cilt: 21 Sayı: 1
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