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Hierarchical linear modeling of reading motivation and literacy performance among high-achieving readers in PISA 2018

Cilt: 23 Sayı: 2026 2 Ağustos 2026
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Hierarchical linear modeling of reading motivation and literacy performance among high-achieving readers in PISA 2018

Öz

This study used hierarchical linear modeling to examine the extent to which student-level enjoyment of reading and reading self-efficacy, together with country-level aggregates of student-reported instructional experiences, accounted for variation in reading literacy scores among high-achieving readers in PISA 2018. The analytic sample consisted of 19,932 students from 79 country groups who were consistently located in the highest 10% of the country-specific reading distribution across all ten reading plausible values. Student-level reading motivational resources were operationalized through enjoyment of reading and reading self-efficacy, whereas extrinsic motivational background was operationalized through country-level aggregates of student-reported instructional climate variables. These country-level indicators included adaptive instruction, teacher support, teacher enthusiasm, teacher feedback, stimulation of reading engagement, and teacher-directed instruction. The results showed that enjoyment of reading and reading self-efficacy were positively associated with reading literacy scores, although the magnitudes of these associations were modest. At the country level, adaptive instruction was positively associated with reading literacy scores, whereas teacher support and teacher-directed instruction showed negative conditional associations. Teacher enthusiasm, teacher feedback, and stimulation of reading engagement were not statistically reliable predictors in the full model. The findings indicate that adaptive instructional conditions may be especially important for explaining variation in advanced reading performance among high-achieving readers.

Anahtar Kelimeler

Reading-related motivation, intrinsic motivation, extrinsic motivation, reading literacy scores, PISA 2018

Destekleyen Kurum

Yıldız Tehnical University

Proje Numarası

7366

Etik Beyan

This study is based on the PISA 2018 dataset made publicly available by the OECD. Since the data used in the study were anonymized secondary data, no data were collected directly from human participants by the researchers, and no procedure requiring ethics committee approval was conducted. It is hereby declared that scientific and ethical principles were observed throughout the preparation of this study and that all works utilized have been indicated in the references.

Teşekkür

Not applicable.

Kaynakça

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  2. Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. In E. Parzen, K. Tanabe, & G. Kitagawa (Eds.), Selected Papers of Hirotugu Akaike (pp. 199–213). Springer New York. https://doi.org/10.1007/978-1-4612-1694-0_15
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  4. Alamer, A., Robat, E. S., Shirvan, M. E., & Ryan, R. (2025). Self-determination theory and language learning: A multilevel meta-analysis. Educational Psychology Review, 37(2), 1–35. https://doi.org/10.1007/s10648-025-10038-y
  5. Andrade, C. (2024). Confounding by indication, confounding variables, covariates, and independent variables: Knowing what these terms mean and when to use which term. Indian Journal of Psychological Medicine, 46(1), 78–80. https://doi.org/10.1177/02537176241227586
  6. Aparicio, J., Cordero, J. M., & Ortiz, L. (2022). Plausible values and their use in efficiency analyses with educational data. Applied Economics, 54(29), 3340–3352. https://doi.org/10.1080/00036846.2021.2006136
  7. Arens, A. K., & Möller, J. (2025). The importance of intrinsic reading motivation goes beyond the reading domain: Relations to school performance and motivation in the language domain. Journal of Research in Reading, 48(3), 220–239. https://doi.org/10.1111/1467-9817.70005
  8. Austin, P. C., & van Buuren, S. (2025). Imputation of incomplete ordinal and nominal data by predictive mean matching. Statistical Methods in Medical Research, 34(11), 2163–2182. https://doi.org/10.1177/09622802251362642
  9. Baldwin, A. G., & Nadelson, L. S. (2023). Gaps in college student reader identity: Issues of reading self-determination and reading self-efficacy. Journal of College Reading and Learning, 53(2), 109–130. https://doi.org/10.1080/10790195.2022.2155728
  10. Banerjee, R., & Halder, S. (2021). Amotivation and influence of teacher support dimensions: A self-determination theory approach. Heliyon, 7, 1–11. https://doi.org/10.1016/j.heliyon.2021.e07410

Kaynak Göster

APA
Güzey, S., & Göktentürk, T. (2026). Hierarchical linear modeling of reading motivation and literacy performance among high-achieving readers in PISA 2018. OPUS Journal of Society Research, 23(2026), 1-25. https://doi.org/10.26466/opusjsr.1942642
AMA
1.Güzey S, Göktentürk T. Hierarchical linear modeling of reading motivation and literacy performance among high-achieving readers in PISA 2018. OPUS TAD. 2026;23(2026):1-25. doi:10.26466/opusjsr.1942642
Chicago
Güzey, Simge, ve Talha Göktentürk. 2026. “Hierarchical linear modeling of reading motivation and literacy performance among high-achieving readers in PISA 2018”. OPUS Journal of Society Research 23 (2026): 1-25. https://doi.org/10.26466/opusjsr.1942642.
EndNote
Güzey S, Göktentürk T (01 Ağustos 2026) Hierarchical linear modeling of reading motivation and literacy performance among high-achieving readers in PISA 2018. OPUS Journal of Society Research 23 2026 1–25.
IEEE
[1]S. Güzey ve T. Göktentürk, “Hierarchical linear modeling of reading motivation and literacy performance among high-achieving readers in PISA 2018”, OPUS TAD, c. 23, sy 2026, ss. 1–25, Ağu. 2026, doi: 10.26466/opusjsr.1942642.
ISNAD
Güzey, Simge - Göktentürk, Talha. “Hierarchical linear modeling of reading motivation and literacy performance among high-achieving readers in PISA 2018”. OPUS Journal of Society Research 23/2026 (01 Ağustos 2026): 1-25. https://doi.org/10.26466/opusjsr.1942642.
JAMA
1.Güzey S, Göktentürk T. Hierarchical linear modeling of reading motivation and literacy performance among high-achieving readers in PISA 2018. OPUS TAD. 2026;23:1–25.
MLA
Güzey, Simge, ve Talha Göktentürk. “Hierarchical linear modeling of reading motivation and literacy performance among high-achieving readers in PISA 2018”. OPUS Journal of Society Research, c. 23, sy 2026, Ağustos 2026, ss. 1-25, doi:10.26466/opusjsr.1942642.
Vancouver
1.Simge Güzey, Talha Göktentürk. Hierarchical linear modeling of reading motivation and literacy performance among high-achieving readers in PISA 2018. OPUS TAD. 01 Ağustos 2026;23(2026):1-25. doi:10.26466/opusjsr.1942642