NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 6 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Shen, Ting; Konstantopoulos, Spyros – Practical Assessment, Research & Evaluation, 2022
Large-scale assessment survey (LSAS) data are collected via complex sampling designs with special features (e.g., clustering and unequal probability of selection). Multilevel models have been utilized to account for clustering effects whereas the probability weighting approach (PWA) has been used to deal with design informativeness derived from…
Descriptors: Sampling, Weighted Scores, Hierarchical Linear Modeling, Educational Research
Peer reviewed Peer reviewed
Direct linkDirect link
Shen, Ting; Konstantopoulos, Spyros – Journal of Experimental Education, 2022
Large-scale education data are collected via complex sampling designs that incorporate clustering and unequal probability of selection. Multilevel models are often utilized to account for clustering effects. The probability weighted approach (PWA) has been frequently used to deal with the unequal probability of selection. In this study, we examine…
Descriptors: Data Collection, Educational Research, Hierarchical Linear Modeling, Bayesian Statistics
Peer reviewed Peer reviewed
Direct linkDirect link
Wang, Weimeng; Liao, Manqian; Stapleton, Laura – Educational Psychology Review, 2019
Many national and international educational data collection programs offer researchers opportunities to investigate contextual effects related to student performance. In those programs, schools are often used in the first-stage sampling process and students are randomly drawn from selected schools. However, the "incidental" dependence of…
Descriptors: Educational Research, Context Effect, Sampling, Children
Peer reviewed Peer reviewed
Direct linkDirect link
Smith, Lindsey J. Wolff; Beretvas, S. Natasha – Journal of Experimental Education, 2017
Conventional multilevel modeling works well with purely hierarchical data; however, pure hierarchies rarely exist in real datasets. Applied researchers employ ad hoc procedures to create purely hierarchical data. For example, applied educational researchers either delete mobile participants' data from the analysis or identify the student only with…
Descriptors: Student Mobility, Academic Achievement, Simulation, Influences
Peer reviewed Peer reviewed
Direct linkDirect link
Sun, Shuyan; Pan, Wei – International Journal of Research & Method in Education, 2014
As applications of multilevel modelling in educational research increase, researchers realize that multilevel data collected in many educational settings are often not purely nested. The most common multilevel non-nested data structure is one that involves student mobility in longitudinal studies. This article provides a methodological review of…
Descriptors: Statistical Analysis, Hierarchical Linear Modeling, Longitudinal Studies, Educational Research