Data Literacy at School: A Scale Development Study

Yıl 2025, Cilt: 18 Sayı: 1, 106 - 130, 29.01.2025

Ayhan Duygulu , Sibel Doğan , Sevgi Yıldız

https://doi.org/10.30831/akukeg.1472451

Öz

The purpose of this study is to develop a valid and reliable scale to determine and evaluate the different dimensions of data literacy at school. The study is a quantitative descriptive survey model. The sampling for exploratory factor analysis was formed of 307 and confirmatory factor analysis 338 teachers and school administrators who are on active duty in 2023-2024 educational year in Kastamonu. Data was collected through a five item likert data collection tool. A three-dimension structure was formed and it was confirmed by CFA. The dimensions of data culture at school are; “data identification”, “data use” and “data management”. Internal reliability and validity was verified through Cronbach Alpha (Cronbach’s α=.882), split half method (r=.837), Spearman-Brown correlation coefficient (R=.911) and Guttman’s lambda (λ=.904). The external reliability and validity was verified by test-retest technique (first application n=44, second application n=39, r=.800, p≤.05, R=.961, p≤.05, and Kendal’s tau-b is τb=.904, p≤.05). The findings confirmed the validity and reliability of the scale.

Anahtar Kelimeler

data literacy, data identification, data culture, data management

Etik Beyan

We confirm the editorial board that ethical princples have been sticked to in all phases and processes of this research.

Teşekkür

We wish to thank you for your considerattion to our study

Kaynakça

• Abrams, L. M., Varier, D., & Mehdi, T. (2021). The intersection of school context and teachers’ data use practice: Implications for an integrated approach to capacity building. Studies in Educational Evaluation, 69(2021), 1-13. https://doi.org/10.1016/j.stueduc.2020.100868
• Anderson, C. (2015). Creating a data driven organization: Practical advice from the trenches. O’Reilly Media, Inc.
• Anderson, S., Leithwood, K., & Strauss, T. (2010). Leading data use in schools: Organizational conditions and practices at the school and district levels. Leadership and Policy in Schools, 9(3), 292-327. https://doi.org/10.1080/15700761003731492
• Aydoğdu, F. (2023). AVE ve CR hesaplama. Retrieved from https://www.fuataydogdu.com/avecr
• Bai, J., & Ng, S. (2005). Tests for skewness, kurtosis, and normality for time series data. Journal of Business & Economic Statistics, 23(1), 49-60. http://www.columbia.edu/~sn2294/pub/jbes-05.pdf
• Bartlett, M. S. (1950). Tests of significance in factor analysis. British Journal of Psychology, 5(2), 109-133.
• Bennett, C., Graham, I. D., Kristjansson, E., Kearing, S. A., Clay, K. F., & O’Connor, A. M. (2010). Validation of a preparation for decision making scale. Patient Education and Counseling, 78(1), 130-133. https://doi.org/10.1016/j.pec.2009.05.012
• Breiter, A., & Light, D. (2006). Data for school improvement: Factors for designing effective information systems to support decision-making in schools. Educational Technology & Society, 9(3), 206-217. https://www.ifib-consult.de/publikationsdateien/Breiter_Light-2006.pdf
• Brown, T. A. (2006). Confirmatory factor analysis for applied research. In D. A. Kenny (Edt.). Methodology in the social sciences (pp. 1-100). The Guilford Press. https://epdf.tips/confirmatory-factor-analysis-for-applied-research.html
• Büyüköztürk, Ş., Çokluk, Ö., & Köklü, N. (2013). Sosyal bilimler için istatistik (13th ed.). Pegem Akademi.
• Büyüköztürk, Ş., Çakmak, E. K., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2008). Bilimsel araştırma yöntemleri (2nd ed). Pegem Akademi.
• Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233-255. https://doi.org/10.1207/S15328007SEM0902_5
• Chigwada, J., Chiparausha, B., & Kasiroori, J. (2017). Research data management in research institutions in Zimbabwe. Data Science Journal, 16(31), 1-9. https://doi.org/10.5334/dsj-2017-031
• Coburn, C. E., & Turner, E. O. (2012). The practice of data use: An introduction. American Journal of Education, 118(2), 99-111. https://www.journals.uchicago.edu/doi/abs/10.1086/663272
• Coburn, C. E., & Turner, E. O. (2011). Research on data use: A framework and analysis. Measurement: Interdisciplinary Research & Perspective, 9(4), 173–206. https://doi.org/10.1080/15366367.2011.626729
• Cohen, L., Manion, L. & Morrison, K. (2018). Research methods in education (8th ed.). Routledge.
• Custer, S., King, E. M., Atinc, T. M., Read, L., & Sethi, T. (2018). Toward data-driven education systems: Insights into using information to measure results and manage change. Center for Universal Education at Brookings. https://www.brookings.edu/wp-content/uploads/2018/02/toward-data-driven-education-systems.pdf
• Çokluk, Ö., Şekercioğlu, G. & Büyüköztürk, Ş. (2018). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları (5. bs). Pegem Akademi.
• Datnow, A., & Park, V. (2014). Data-driven leadership. John Wiley & Sons.
• Dogan, E., & Ottekin Demirbolat, A. (2021). Data-driven decision-making in schools scale: A study of validity and reliability. International Journal of Curriculum and Instruction, 13(1), 507-523. https://files.eric.ed.gov/fulltext/EJ1285547.pdf
• Duygulu, A. (2023). Veri yönetimi. Kriter Yayınevi.
• Earl, L., & Fullan, M. (2003). Using data in leadership for learning. Cambridge Journal of Education, 33(3), 383-394. https://doi.org/10.1080/0305764032000122023
• Engmann, S., & Cousineau, D. (2011). Comparing distributions: The two-sample Anderson-Darling test as an alternative to the Kolmogorov-Smirnoff test. Journal of Applied Quantitative Methods, 6(3), 1-38.
• Field, A. (2009). Discovering statistics using SPSS (3rd ed). Sage Publications Ltd.
• Filderman, M. J., Toste, J. R., Didion, L., & Peng, P. (2022). Data literacy training for K–12 teachers: A meta-analysis of the effects on teacher outcomes. Remedial and Special Education, 43(5), 328-343. https://files.eric.ed.gov/fulltext/EJ1350487.pdf
• Fontichiaro, K., & Oehrli, J. A. (2016). Why data literacy matters. Knowledge Quest, 44(5), 21-27. https://files.eric.ed.gov/fulltext/EJ1099487.pdf
• French, B. F., & Finch, W. H. (2006). Confirmatory factor analytic procedures for the determination of measurement invariance. Structural Equation Modeling, 13(3), 378-402.
• Goldring, E., & Berends, M. (2009). Leading with data: Pathways to improve your school. Corwin Press, A Sage Company.
• Gordon, K. (2007). Principles of data management: Facilitating information sharing. Swindon: The British Computer Society Publishing and Information Products.
• Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2014). Multivariate data analysis (7th ed). Pearson Education Limited.
• Harrington, D. (2009). Confirmatory factor analysis. In T. Tripodi (Ed.), Pocket guides to social work reseacrh methods (pp. 3-121). Oxford University Press. https://l24.im/s4Y6den
• Hatchavanich, D. (2014). A comparison of type I error and power of Bartlett’s test, Levene’s test and O’Brien’s test for homogeneity of variance tests. Southeast Asian Journal of Sciences, 3(2), 181-194. https://sajs.ntt.edu.vn/index.php/jst/article/view/106
• Heiman, G. W. (2011). Basic statistics for the behavioral sciences (6th ed.). Wadsworth, Cengage Learning.
• Henderson, J., & Corry, M. (2021). Data literacy training and use for educational professionals. Journal of Research in Innovative Teaching & Learning, 14(2), 232-244. https://doi.org/10.1108/JRIT-11-2019-0074
• Howitt, D., & Cramer, D. (2017). Research methods in psychology (5th ed). Pearson Education Limited.
• Ikemoto, G. S., & Marsh, J. A. (2007). Cutting through the “data-driven” mantra: Different conceptions of data-driven decision making. Teachers College Record, 109(13), 105-131. https://doi.org/10.1177/016146810710901310
• Kippers, W. B., Poortman, C. L., Schildkamp, K., & Visscher, A. J. (2018). Data literacy: What do educators learn and struggle with during a data use intervention? Studies in Educational Evaluation, 56(2018), 21-31. https://doi.org/10.1016/j.stueduc.2017.11.001
• Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed). Guilford Press.
• Knapp, M. S., Copland, M A., & Swinnerton, J. A. (2007). Understanding the promise and dynamics of data-informed leadership. Yearbook of the National Society for the Study of Education, 106(1), 74-104. https://doi.org/10.1111/j.1744-7984.2007.00098.x.
• Li, L., & Bentler, P. M. (2011). Quantified choice of root-mean-square errors of approximation for evaluation and power analysis of small differences between structural equation models. Psychological Methods, 16(2), 116–126. https://doi.org.10.1037/a0022657
• Little, J. W. (2012). Understanding data use practice among teachers: The contribution of micro-process studies. American Journal of Education, 118(2), 143-166. https://www.jstor.org/stable/10.1086/663271?origin=JSTOR-pdf
• Mandinach, E. B., & Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30-37. https://doi.org/10.3102/0013189x12459803
• Martin, W. E., & Bridgmon, K. D. (2012). Quantitative and statistical research methods: From hypothesis to results. Jossey-Bass.
• Matthews, P. (2016). Data literacy conceptions, community capabilities. The Journal of Community Informatics, 12(3), 47-56. https://openjournals.uwaterloo.ca/index.php/JoCI/article/download/3277/4300/ McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods 23(3), 412-434. http://dx.doi.org/10.1037/met0000144
• Naillioğlu Kaymak, M., & Doğan, E. (2023). Veri Okuryazarliği Ölçeği’nin Türk kültürüne uyarlanmasi. Trakya Journal of Education, 13(2). https://dergipark.org.tr/tr/download/article-file/2521835
• Nordstokke, D. W., & Colp, S. M. (2014). Investigating the robustness of the nonparametric Levene test with more than two groups. Psicológica, 35(2), 361-383. https://www.redalyc.org/pdf/169/16931314010.pdf
• Obst, P. L., & White, K. M. (2004). Revisiting the sense of community index: A confirmatory factor analysis. Journal of Community Psychology, 32(6), 691-705. https://eprints.qut.edu.au/604/2/604.pdf
• Pangrazio, L., & Sefton-Green, J. (2020). The social utility of ‘data literacy’. Learning, Media and Technology, 45(2), 208-220. https://sci-hub.se/10.1080/17439884.2020.1707223
• Pangrazio, L., & Selwyn, N. (2019). ‘Personal data literacies’: A critical literacies approach to enhancing understandings of personal digital data. New Media & Society, 21(2), 419-437. https://doi.org/10.1177/1461444818799523
• Prudon, P. (2015). Confirmatory factor analysis as a tool in research using questionnaires: A critique. Comprehensive Psychology, 4(10), 1-17. https://journals.sagepub.com/doi/pdf/10.2466/03.CP.4.10
• Qin, J., & D'Ignazio, J. (2010). Lessons learned from a two-year experience in science data literacy education. Proceedings of the International Association of Scientific and Technological University Libraries, 31st Annual Conference, 5(2010), 1-11. http://docs.lib.purdue.edu/iatul2010/conf/day2/5
• Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., Kelley, D., Matwin, S., & Wuetherick, B. (2015). Strategies and best practices for data literacy education: Knowledge synthesis report. Dalhousie University.
• Schildkamp, K., & Poortman, C. (2015), Factors influencing the functioning of data teams. Teachers College Record, 117(4), 1-42. https://doi.org/10.1177/016146811511700403
• Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323-338. https://doi.org/10.3200/JOER.99.6.323-338
• Shek, D. T., & Yu, L. (2014). Confirmatory factor analysis using AMOS: A demonstration. International Journal on Disability and Human Development, 13(2), 191-204. https://www.academia.edu/download/54409775/cfa-amos.pdf
• Shields, M. (2005). Information literacy, statistical literacy, data literacy. IASSIST Quarterly, 28(2-3), 6-11. https://iassistquarterly.com/index.php/iassist/article/view/790/782
• Singh, K. (2007). Quantitative social research methods. Sage Publications Inc.
• Sivo, S. A., Fan, X., Witta, E. L., & Willse, J. T. (2006). The search for “optimal" cutoff properties: Fit index criteria in structural equation modeling. The Journal of Experimental Education, 74(3), 267-288. https://doi.org/10.3200/JEXE.74.3.267-288
• Stephenson, E., & Schifter Caravello, P. (2007). Incorporating data literacy into undergraduate information literacy programs in the social sciences: A pilot project. Reference Services Review, 35(4), 525-540. https://doi.org/10.1108/00907320710838354
• Stockemer, D. (2019). Quantitative methods for the social sciences: A practical introduction with examples in SPSS and stata. Springer International Publishing.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed). Pearson Education, Inc.
• Thompson, B. L., Green, S. B., & Yang, Y. (2010). Assessment of the maximal split-half coefficient to estimate reliability. Educational and Psychological Measurement, 70(2), 232-251.
• Van Audenhove, L., Van den Broeck, W., & Mariën, I. (2020). Data literacy and education: Introduction and the challenges for our field. Journal of Media Literacy Education, 12(3), 1-5. https://doi.org/10.23860/JMLE-2020-12-3-1
• van Geel, M., Keuning, T., Visscher, A., & Fox, J. P. (2017). Changes in educators' data literacy during a data-based decision making intervention. Teaching and Teacher Education, 64(2017), 187-198. http://dx.doi.org/10.1016/j.tate.2017.02.015
• Vanhoof, J., Verhaeghe, G., Petegem, P., & Valcke, M. (2013). Improving data literacy in schools: Lessons from the school feedback project. In, K. Schildkamp, M. Lai, & L. Earl (Eds.), Data-based decision making in education: Studies in educational leadership, volume 17 (pp. 113-134). Springer. https://doi.org/10.1007/978-94-007-4816-3_7
• Wolff, A., Gooch, D., Montaner, J. J. C., Rashid, U., & Kortuem, G. (2016). Creating an understanding of data literacy for a data-driven society. The Journal of Community Informatics, 12(3), 1-26. https://openjournals.uwaterloo.ca/index.php/JoCI/article/view/3275
• Yılmaz, E., & Jafarova, G. (2022). Development of data driven decision making scale: a validity and reliability study. Research on Education and Psychology, 6(Special Issue), 69-91. https://dergipark.org.tr/en/download/article-file/2376933