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Peter Z. Schochet – Journal of Educational and Behavioral Statistics, 2025
Random encouragement designs evaluate treatments that aim to increase participation in a program or activity. These randomized controlled trials (RCTs) can also assess the mediated effects of participation itself on longer term outcomes using a complier average causal effect (CACE) estimation framework. This article considers power analysis…
Descriptors: Statistical Analysis, Computation, Causal Models, Research Design
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
Braun, Henry – Journal of Educational and Behavioral Statistics, 2023
It is a much-lamented fact that research with the potential to inform or influence education policy instead remains policy inert. There are many reasons for this frustrating state of affairs, including a lack of strategic thinking on the part of researchers on how to successfully accomplish outreach--as opposed to communication with peers…
Descriptors: Educational Policy, Educational Research, Educational Researchers, Persuasive Discourse
Lu, Jiannan; Ding, Peng; Dasgupta, Tirthankar – Journal of Educational and Behavioral Statistics, 2018
Assessing the causal effects of interventions on ordinal outcomes is an important objective of many educational and behavioral studies. Under the potential outcomes framework, we can define causal effects as comparisons between the potential outcomes under treatment and control. However, unfortunately, the average causal effect, often the…
Descriptors: Outcomes of Treatment, Mathematical Applications, Probability, Behavioral Science Research
Drechsler, Jörg – Journal of Educational and Behavioral Statistics, 2015
Multiple imputation is widely accepted as the method of choice to address item-nonresponse in surveys. However, research on imputation strategies for the hierarchical structures that are typically found in the data in educational contexts is still limited. While a multilevel imputation model should be preferred from a theoretical point of view if…
Descriptors: Hierarchical Linear Modeling, Statistical Analysis, Educational Research, Statistical Bias
Sweet, Tracy M.; Thomas, Andrew C.; Junker, Brian W. – Journal of Educational and Behavioral Statistics, 2013
Intervention studies in school systems are sometimes aimed not at changing curriculum or classroom technique, but rather at changing the way that teachers, teaching coaches, and administrators in schools work with one another--in short, changing the professional social networks of educators. Current methods of social network analysis are…
Descriptors: Educational Research, Models, Social Networks, Network Analysis
Cepeda-Cuervo, Edilberto; Núñez-Antón, Vicente – Journal of Educational and Behavioral Statistics, 2013
In this article, a proposed Bayesian extension of the generalized beta spatial regression models is applied to the analysis of the quality of education in Colombia. We briefly revise the beta distribution and describe the joint modeling approach for the mean and dispersion parameters in the spatial regression models' setting. Finally, we motivate…
Descriptors: Regression (Statistics), Foreign Countries, Educational Quality, Educational Research
Schochet, Peter Z. – Journal of Educational and Behavioral Statistics, 2013
In education randomized control trials (RCTs), the misreporting of student outcome data could lead to biased estimates of average treatment effects (ATEs) and their standard errors. This article discusses a statistical model that adjusts for misreported binary outcomes for two-level, school-based RCTs, where it is assumed that misreporting could…
Descriptors: Control Groups, Experimental Groups, Educational Research, Data Analysis

Kaplan, David – Journal of Educational and Behavioral Statistics, 2002
Considers the problem of modeling sustained educational change through the use of dynamic multipliers applied to panel data and attempts to develop and advocate dynamic multiplier analysis for educational research. Presents three examples to illustrate the approach and closes with a discussion of the implications of dynamic multiplier analysis for…
Descriptors: Educational Change, Educational Research, Models, Policy Formation

Hill, Peter W.; Goldstein, Harvey – Journal of Educational and Behavioral Statistics, 1998
Presents a method for handling educational data in which students belong to more than one unit at a given level, but there is missing information on the identification of the units to which students belong. The method, which involves setting up a cross-classified model, is illustrated with longitudinal data on students' progress in English. (SLD)
Descriptors: Classification, Educational Research, Group Membership, Longitudinal Studies
Robinson, Dan – Journal of Educational and Behavioral Statistics, 2005
Robinson interviews Juliet Popper Shaffer, a scientist, who graduated from Swarthmore College in 1953 and Stanford in 1957 with degrees in psychology and concentrations in math, philosophy, and statistics. In 2004 she received the second Florence Nightingale David award given biannually by the Committee of Presidents of Statistical Societies to a…
Descriptors: Interviews, Biographies, Career Development, Scientists
Bradlow, Eric T. – Journal of Educational and Behavioral Statistics, 2003
In this article, the author comments on an article by Dunn, Kadane, and Garrow, "Comparing Harm Done by Mobility and Class Absence: Missing Students and Missing Data." He believes the research reported in that article should serve as a model for future applications of Bayesian methods in important educational research problems. The author lauds…
Descriptors: Research Problems, Educational Research, Bayesian Statistics, Researchers

de Leeuw, Jan; Kreft, Ita G. G. – Journal of Educational and Behavioral Statistics, 1995
Practical problems with multilevel techniques are discussed. These problems relate to terminology, computer programs employing different algorithms, and interpretations of the coefficients in either one or two steps. The usefulness of hierarchical linear models (HLMs) in common situations in educational research is explored. While elegant, HLMs…
Descriptors: Algorithms, Computer Software, Definitions, Educational Research
McCaffrey, Daniel F.; Lockwood, J. R.; Koretz, Daniel; Louis, Thomas A.; Hamilton, Laura – Journal of Educational and Behavioral Statistics, 2004
The insightful discussions by Raudenbush, Rubin, Stuart and Zanutto (RSZ) and Reckase identify important challenges for interpreting the output of VAM and for its use with test-based accountability. As these authors note, VAM are statistical models for the correlations among scores from students who share common teachers or schools during the…
Descriptors: Educational Testing, Accountability, Mathematical Models, Teacher Influence