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Showing all 12 results Save | Export
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Fangxing Bai; Benjamin Kelcey; Yanli Xie; Kyle Cox – Society for Research on Educational Effectiveness, 2022
Background: Regression Discontinuous Design (RDD) is widely used in educational studies. Through RDD, researchers can obtain unbiased results when Randomized Experimental Design (RED) is inaccessible. Compared to RED, the RDD only requires a cut score variable (continuous) and a cutoff value to assign students to the treatment or control groups.…
Descriptors: Research Design, Regression (Statistics), Hierarchical Linear Modeling, Mediation Theory
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Lorah, Julie – Practical Assessment, Research & Evaluation, 2022
Applied educational researchers may be interested in exploring random slope effects in multilevel models, such as when examining individual growth trajectories with longitudinal data. Random slopes are effects for which the slope of an individual-level coefficient varies depending on group membership, however these effects can be difficult to…
Descriptors: Effect Size, Hierarchical Linear Modeling, Longitudinal Studies, Maximum Likelihood Statistics
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Rhoads, Christopher H.; Dye, Charles – Journal of Experimental Education, 2016
An important concern when planning research studies is to obtain maximum precision of an estimate of a treatment effect given a budget constraint. When research designs have a "multilevel" or "hierarchical" structure changes in sample size at different levels of the design will impact precision differently. Furthermore, there…
Descriptors: Research Design, Hierarchical Linear Modeling, Regression (Statistics), Sample Size
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Beyene, Kassu Mehari; Yimam, Jemal Ayalew – Journal of Education and Practice, 2016
Education is a process by which man transmits his experiences, new findings, and values accumulated over the years, in his struggle for survival and development, through generations. Accordingly, one of the aims of education is to strengthen the individual's and societies' problem solving capacity, ability and culture starting from basic education…
Descriptors: Foreign Countries, Hierarchical Linear Modeling, Academic Achievement, College Students
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Huang, Francis L. – Journal of Experimental Education, 2016
Multilevel modeling has grown in use over the years as a way to deal with the nonindependent nature of observations found in clustered data. However, other alternatives to multilevel modeling are available that can account for observations nested within clusters, including the use of Taylor series linearization for variance estimation, the design…
Descriptors: Multivariate Analysis, Hierarchical Linear Modeling, Sample Size, Error of Measurement
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Wu, Jiun-Yu; Kwok, Oi-Man; Willson, Victor L. – Journal of Experimental Education, 2014
The authors compared the effects of using the true Multilevel Latent Growth Curve Model (MLGCM) with single-level regular and design-based Latent Growth Curve Models (LGCM) with or without the higher-level predictor on various criterion variables for multilevel longitudinal data. They found that random effect estimates were biased when the…
Descriptors: Longitudinal Studies, Hierarchical Linear Modeling, Prediction, Regression (Statistics)
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Tipton, Elizabeth; Pustejovsky, James E. – Society for Research on Educational Effectiveness, 2015
Randomized experiments are commonly used to evaluate the effectiveness of educational interventions. The goal of the present investigation is to develop small-sample corrections for multiple contrast hypothesis tests (i.e., F-tests) such as the omnibus test of meta-regression fit or a test for equality of three or more levels of a categorical…
Descriptors: Randomized Controlled Trials, Sample Size, Effect Size, Hypothesis Testing
Quesen, Sarah – ProQuest LLC, 2016
When studying differential item functioning (DIF) with students with disabilities (SWD) focal groups typically suffer from small sample size, whereas the reference group population is usually large. This makes it possible for a researcher to select a sample from the reference population to be similar to the focal group on the ability scale. Doing…
Descriptors: Test Items, Academic Accommodations (Disabilities), Testing Accommodations, Disabilities
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Huang, Francis L. – Practical Assessment, Research & Evaluation, 2014
Clustered data (e.g., students within schools) are often analyzed in educational research where data are naturally nested. As a consequence, multilevel modeling (MLM) has commonly been used to study the contextual or group-level (e.g., school) effects on individual outcomes. The current study investigates the use of an alternative procedure to…
Descriptors: Hierarchical Linear Modeling, Regression (Statistics), Educational Research, Sampling
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Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
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Kuriscak, Lisa – Hispania, 2015
This study focuses on variation within a group of learners of Spanish (N = 253) who produced requests and complaints via a written discourse completion task. It examines the effects of learner and situational variables on production--the effect of proficiency and addressee-gender on speech-act choice and the effect of perception of imposition on…
Descriptors: Speech Acts, Spanish, Second Language Learning, Language Variation
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Yen, Wendy M.; Lall, Venessa F.; Monfils, Lora – ETS Research Report Series, 2012
Alternatives to vertical scales are compared for measuring longitudinal academic growth and for producing school-level growth measures. The alternatives examined were empirical cross-grade regression, ordinary least squares and logistic regression, and multilevel models. The student data used for the comparisons were Arabic Grades 4 to 10 in…
Descriptors: Foreign Countries, Scaling, Item Response Theory, Test Interpretation