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Direct linkRebeka Man – ProQuest LLC, 2024
In today's era of large-scale data, academic institutions, businesses, and government agencies are increasingly faced with heterogeneous datasets. Consequently, there is a growing need to develop effective methods for extracting meaningful insights from this type of data. Quantile, expectile, and expected shortfall regression methods offer useful…
Descriptors: Data, Data Analysis, Data Use, Higher Education
Peterson, Elizabeth Sarah – ProQuest LLC, 2023
Moving Beyond the Ordinal Methodological Controversy: A Systematic Review (Manuscript 1): Ordinal outcome data is a common byproduct of education research. Yet more than seventy-five years after the development of Stevens' original measurement framework, the permissibility of select analytic techniques to ordinal outcome data remains a topic of…
Descriptors: Data, Educational Research, Statistical Analysis, Social Sciences
Download full textWendy Castillo; David Gillborn – Annenberg Institute for School Reform at Brown University, 2023
'QuantCrit' (Quantitative Critical Race Theory) is a rapidly developing approach that seeks to challenge and improve the use of statistical data in social research by applying the insights of Critical Race Theory. As originally formulated, QuantCrit rests on five principles; 1) the centrality of racism; 2) numbers are not neutral; 3) categories…
Descriptors: Educational Research, Data Use, Educational Researchers, Interdisciplinary Approach
Peer reviewedGurcan, Fatih; Cagiltay, Nergiz Ercil – Interactive Learning Environments, 2023
Today's dynamic distance learning environments offer a flexible, comfortable, and lifelong learning experience, independent of space and time. In this way, it also supports and develops existing traditional training programs. The increasing importance of knowledge, skills and learning in today's technological life cycle has led to an increase and…
Descriptors: Educational Research, Trend Analysis, Distance Education, Data Analysis
William Bosshardt; Amanda Jennings; Peter Davies – Journal of Economic Education, 2024
The authors of this article present arguments for why and how qualitative research should be used in economic education. These arguments recognize the nature of economics as a discipline and economics educators' current expertise and preferences. The authors have five goals: (i) clarifying how and why the use of qualitative research in economic…
Descriptors: Economics Education, Educational Research, Qualitative Research, Statistical Analysis
Buckley, Jeffrey; Hyland, Tomás; Seery, Niall – International Journal of Technology and Design Education, 2023
Technology education research is a growing field, with the rate of growth increasing over the last 2 decades. As the field grows, it is paramount that credibility is maintained in published findings. To date there is no evidence to suggest a lack trust is warranted, however in the midst of the replication crisis there is need to ensure continued…
Descriptors: Technology Education, Educational Research, Replication (Evaluation), Credibility
Sandra Jo Wilson; Brian Freeman; E. C. Hedberg – Grantee Submission, 2024
As reporting of effect sizes in evaluation studies has proliferated, researchers and consumers of research need tools for interpreting or benchmarking the magnitude of those effect sizes that are relevant to the intervention, target population, and outcome measure being considered. Similarly, researchers planning education studies with social and…
Descriptors: Benchmarking, Effect Size, Meta Analysis, Statistical Analysis
Matayoshi, Jeffrey; Karumbaiah, Shamya – International Educational Data Mining Society, 2021
Research studies in Educational Data Mining (EDM) often involve several variables related to student learning activities. As such, it may be necessary to run multiple statistical tests simultaneously, thereby leading to the problem of multiple comparisons. The Benjamini-Hochberg (BH) procedure is commonly used in EDM research to address this…
Descriptors: Statistical Analysis, Validity, Classification, Hypothesis Testing
Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2023
Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…
Descriptors: Educational Research, Data Analysis, Error of Measurement, Computation
Larini, Michel; Barthes, Angela – John Wiley & Sons, Inc, 2018
This book presents different data collection and representation techniques: elementary descriptive statistics, confirmatory statistics, multivariate approaches and statistical modeling. It exposes the possibility of giving more robustness to the classical methodologies of education sciences by adding a quantitative approach. The fundamentals of…
Descriptors: Statistical Analysis, Educational Research, Data Collection, Data Processing
Singer, Judith D. – Journal of Research on Educational Effectiveness, 2019
The arc of quantitative educational research should not be etched in stone but should adapt and change over time. In this article, I argue that it is time for a reshaping by offering my personal view of the past, present and future of our field. Educational research--and research in the social and life sciences--is at a crossroads. There are many…
Descriptors: Educational Research, Research Methodology, Longitudinal Studies, Evaluation
Buckley, Jeffrey; Adams, Latif; Aribilola, Ifeoluwapo; Arshad, Iram; Azeem, Muhammad; Bracken, Lauryn; Breheny, Colette; Buckley, Ciara; Chimello, Ismael; Fagan, Alison; Fitzpatrick, Daniel P.; Garza Herrera, Diana; Gomes, Guilherme Daniel; Grassick, Shaun; Halligan, Elaine; Hirway, Amit; Hyland, Tomás; Imtiaz, Muhammad Babar; Khan, Muhammad Bilal; Lanzagorta Garcia, Eduardo; Lennon, Paul; Manaf, Eyman; Meng, Jing; Mohd Sufian, Mohd Sufino Zuhaily; Moraes, Adrielle; Osterwald, Katja Magdalena; Platonava, Anastasia; Reid, Clodagh; Renard, Michèle; Rodriguez-Barroso, Laura G.; Simonassi-Paiva, Bianca; Singh, Maulshree; Szank, Tomasz; Tahir, Mehwish; Vijayakumar, Sowmya; Ward, Cormac; Yan, Xinyu; Zainol, Ismin; Zhang, Lin – International Journal of Technology and Design Education, 2022
A high level of transparency in reported research is critical for several reasons, such as ensuring an acceptable level of trustworthiness and enabling replication. Transparency in qualitative research permits the identification of specific circumstances which are associated with findings and observations. Thus, transparency is important for the…
Descriptors: Technology Education, Educational Research, Research Methodology, Accountability
Daniele Checchi; Alice Bertoletti – European Union, 2024
The European Education Area aims to support Member States' efforts in enhancing the educational attainment of younger generations. In this policy context, there is a need for an objective tool to assess the educational outcomes of EU countries. The present report addresses this need by pursuing two objectives: (1) providing a comprehensive method…
Descriptors: Foreign Countries, Educational Attainment, Educational Policy, Educational Assessment
Brower, Rebecca L.; Bertrand Jones, Tamara; Osborne-Lampkin, La'Tara; Hu, Shouping; Park-Gaghan, Toby J. – Grantee Submission, 2019
Big qualitative data (Big Qual), or research involving large qualitative data sets, has introduced many newly evolving conventions that have begun to change the fundamental nature of some qualitative research. In this methodological essay, we first distinguish big data from big qual. We define big qual as data sets containing either primary or…
Descriptors: Qualitative Research, Data, Change, Barriers
Nissen, Jayson; Donatello, Robin; Van Dusen, Ben – Physical Review Physics Education Research, 2019
Physics education researchers (PER) commonly use complete-case analysis to address missing data. For complete-case analysis, researchers discard all data from any student who is missing any data. Despite its frequent use, no PER article we reviewed that used complete-case analysis provided evidence that the data met the assumption of missing…
Descriptors: Physics, Science Education, Educational Research, Data
