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Julia-Kim Walther; Martin Hecht; Steffen Zitzmann – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Small sample sizes pose a severe threat to convergence and accuracy of between-group level parameter estimates in multilevel structural equation modeling (SEM). However, in certain situations, such as pilot studies or when populations are inherently small, increasing samples sizes is not feasible. As a remedy, we propose a two-stage regularized…
Descriptors: Sample Size, Hierarchical Linear Modeling, Structural Equation Models, Matrices
Sijia Huang; Dubravka Svetina Valdivia – Educational and Psychological Measurement, 2024
Identifying items with differential item functioning (DIF) in an assessment is a crucial step for achieving equitable measurement. One critical issue that has not been fully addressed with existing studies is how DIF items can be detected when data are multilevel. In the present study, we introduced a Lord's Wald X[superscript 2] test-based…
Descriptors: Item Analysis, Item Response Theory, Algorithms, Accuracy
Daniel McNeish; Patrick D. Manapat – Structural Equation Modeling: A Multidisciplinary Journal, 2024
A recent review found that 11% of published factor models are hierarchical models with second-order factors. However, dedicated recommendations for evaluating hierarchical model fit have yet to emerge. Traditional benchmarks like RMSEA <0.06 or CFI >0.95 are often consulted, but they were never intended to generalize to hierarchical models.…
Descriptors: Factor Analysis, Goodness of Fit, Hierarchical Linear Modeling, Benchmarking
Cox, Kyle; Kelcey, Benjamin – Educational and Psychological Measurement, 2023
Multilevel structural equation models (MSEMs) are well suited for educational research because they accommodate complex systems involving latent variables in multilevel settings. Estimation using Croon's bias-corrected factor score (BCFS) path estimation has recently been extended to MSEMs and demonstrated promise with limited sample sizes. This…
Descriptors: Structural Equation Models, Educational Research, Hierarchical Linear Modeling, Sample Size
Mangino, Anthony A.; Finch, W. Holmes – Educational and Psychological Measurement, 2021
Oftentimes in many fields of the social and natural sciences, data are obtained within a nested structure (e.g., students within schools). To effectively analyze data with such a structure, multilevel models are frequently employed. The present study utilizes a Monte Carlo simulation to compare several novel multilevel classification algorithms…
Descriptors: Prediction, Hierarchical Linear Modeling, Classification, Bayesian Statistics
A. N. Varnavsky – IEEE Transactions on Learning Technologies, 2024
The most critical parameter of audio and video information output is the playback speed, which affects many viewing or listening metrics, including when learning using tutoring systems. However, the availability of quantitative models for personalized playback speed control considering the learner's personal traits is still an open question. The…
Descriptors: Hierarchical Linear Modeling, Intelligent Tutoring Systems, Individualized Instruction, Electronic Learning
Sarah E. Robertson; Jon A. Steingrimsson; Issa J. Dahabreh – Evaluation Review, 2024
When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude…
Descriptors: Randomized Controlled Trials, Generalization, Inferences, Hierarchical Linear Modeling
Kyle Cox; Ben Kelcey; Hannah Luce – Journal of Experimental Education, 2024
Comprehensive evaluation of treatment effects is aided by considerations for moderated effects. In educational research, the combination of natural hierarchical structures and prevalence of group-administered or shared facilitator treatments often produces three-level partially nested data structures. Literature details planning strategies for a…
Descriptors: Randomized Controlled Trials, Monte Carlo Methods, Hierarchical Linear Modeling, Educational Research
Yi Feng; Peter M. Steiner – Society for Research on Educational Effectiveness, 2022
Research Context: In educational research, "context effects" are often of inferential interest to researchers as well as of evaluative interest to policymakers. While student education outcomes likely depend on individual-level influences like individual academic achievement, school contexts may also make a difference. Such questions are…
Descriptors: Hierarchical Linear Modeling, Accuracy, Graphs, Educational Research
Avery H. Closser; Adam Sales; Anthony F. Botelho – Grantee Submission, 2024
Emergent technologies present platforms for educational researchers to conduct randomized controlled trials (RCTs) and collect rich data on study students' performance, behavior, learning processes, and outcomes in authentic learning environments. As educational research increasingly uses methods and data collection from such platforms, it is…
Descriptors: Data Analysis, Educational Research, Randomized Controlled Trials, Sampling
Avery H. Closser; Adam Sales; Anthony F. Botelho – Educational Technology Research and Development, 2024
Emergent technologies present platforms for educational researchers to conduct randomized controlled trials (RCTs) and collect rich data to study students' performance, behavior, learning processes, and outcomes in authentic learning environments. As educational research increasingly uses methods and data collection from such platforms, it is…
Descriptors: Data Analysis, Educational Research, Randomized Controlled Trials, Sampling

Dongho Shin – Grantee Submission, 2024
We consider Bayesian estimation of a hierarchical linear model (HLM) from small sample sizes. The continuous response Y and covariates C are partially observed and assumed missing at random. With C having linear effects, the HLM may be efficiently estimated by available methods. When C includes cluster-level covariates having interactive or other…
Descriptors: Bayesian Statistics, Computation, Hierarchical Linear Modeling, Data Analysis
Ben Van Dusen; Heidi Cian; Jayson Nissen; Lucy Arellano; Adrienne D. Woods – Sociology of Education, 2024
This investigation examines the efficacy of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) over fixed-effects models when performing intersectional studies. The research questions are as follows: (1) What are typical strata representation rates and outcomes on physics research-based assessments? (2) To what…
Descriptors: Educational Research, Intersectionality, Critical Race Theory, STEM Education
Hitczenko, Marcin – Sociological Methods & Research, 2022
Researchers interested in studying the frequency of events or behaviors among a population must rely on count data provided by sampled individuals. Often, this involves a decision between live event counting, such as a behavioral diary, and recalled aggregate counts. Diaries are generally more accurate, but their greater cost and respondent burden…
Descriptors: Surveys, Social Science Research, Recall (Psychology), Diaries
Vida, Leonardo J.; Bolsinova, Maria; Brinkhuis, Matthieu J. S. – International Educational Data Mining Society, 2021
The quality of exams drives test-taking behavior of examinees and is a proxy for the quality of teaching. As most university exams have strict time limits, and speededness is an important measure of the cognitive state of examinees, this might be used to assess the connection between exams' quality and examinees' performance. The practice of…
Descriptors: Accuracy, Test Items, Tests, Student Behavior
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