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Lounek, Vítezslav; Ryška, Radim – Research in Comparative and International Education, 2023
Ensuring comparability of Likert-style items across different countries is a widespread challenge for authors of large-scale international surveys. Using data from the EUROGRADUATE Pilot Survey, this study employs a series of latent class analyses to explore which response patterns emerge from self-assessment of acquired and required skills of…
Descriptors: Self Evaluation (Individuals), Surveys, College Graduates, Multivariate Analysis
Steven Glazerman; Larissa Campuzano; Nancy Murray – Evaluation Review, 2025
Randomized experiments involving education interventions are typically implemented as cluster randomized trials, with schools serving as clusters. To design such a study, it is critical to understand the degree to which learning outcomes vary between versus within clusters (schools), specifically the intraclass correlation coefficient. It is also…
Descriptors: Educational Experiments, Foreign Countries, Educational Assessment, Research Design
Ann A. O'Connell; Nivedita Bhaktha; Jing Zhang – Society for Research on Educational Effectiveness, 2021
Background: Counts are familiar outcomes in education research settings, including those involving tests of interventions. Clustered data commonly occur in education research studies, given that data are often collected from students within classrooms or schools. There is a wide array of distributions and models that can be used for clustered…
Descriptors: Hierarchical Linear Modeling, Educational Research, Statistical Distributions, Multivariate Analysis
Park, Sunyoung; Natasha Beretvas, S. – Journal of Experimental Education, 2021
When selecting a multilevel model to fit to a dataset, it is important to choose both a model that best matches characteristics of the data's structure, but also to include the appropriate fixed and random effects parameters. For example, when researchers analyze clustered data (e.g., students nested within schools), the multilevel model can be…
Descriptors: Hierarchical Linear Modeling, Statistical Significance, Multivariate Analysis, Monte Carlo Methods