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Rudner, Lawrence – Practical Assessment, Research & Evaluation, 2016
In the machine learning literature, it is commonly accepted as fact that as calibration sample sizes increase, Naïve Bayes classifiers initially outperform Logistic Regression classifiers in terms of classification accuracy. Applied to subtests from an on-line final examination and from a highly regarded certification examination, this study shows…
Descriptors: Accuracy, Bayesian Statistics, Regression (Statistics), Probability
Choi, Kilchan; Kim, Jinok – Journal of Educational and Behavioral Statistics, 2019
This article proposes a latent variable regression four-level hierarchical model (LVR-HM4) that uses a fully Bayesian approach. Using multisite multiple-cohort longitudinal data, for example, annual assessment scores over grades for students who are nested within cohorts within schools, the LVR-HM4 attempts to simultaneously model two types of…
Descriptors: Regression (Statistics), Hierarchical Linear Modeling, Longitudinal Studies, Cohort Analysis
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Hodges, Jaret; McIntosh, Jason; Gentry, Marcia – Journal of Advanced Academics, 2017
High-potential students from low-income families are at an academic disadvantage compared with their more affluent peers. To address this issue, researchers have suggested novel approaches to mitigate gaps in student performance, including out-of-school enrichment programs. Longitudinal mixed effects modeling was used to analyze the growth of…
Descriptors: After School Programs, Enrichment Activities, Academic Achievement, High Achievement
Papay, John P.; Murnane, Richard J.; Willett, John B. – National Bureau of Economic Research, 2011
Students receive abundant information about their educational performance, but how this information affects future educational-investment decisions is not well understood. Increasingly common sources of information are state-mandated standardized tests. On these tests, students receive a score and a label that summarizes their performance. Using a…
Descriptors: Investment, Educational Objectives, Outcomes of Education, Standardized Tests
Tuttle, Christina Clark; Gleason, Philip; Knechtel, Virginia; Nichols-Barrer, Ira; Booker, Kevin; Chojnacki, Gregory; Coen, Thomas; Goble, Lisbeth – Mathematica Policy Research, Inc., 2015
KIPP (Knowledge is Power Program) is a national network of public charter schools whose stated mission is to help underserved students enroll in and graduate from college. Prior studies (see Tuttle et al. 2013) have consistently found that attending a KIPP middle school positively affects student achievement, but few have addressed longer-term…
Descriptors: Program Effectiveness, Program Evaluation, Academic Achievement, Charter Schools
Tuttle, Christina Clark; Gleason, Philip; Knechtel, Virginia; Nichols-Barrer, Ira; Booker, Kevin; Chojnacki, Gregory; Coen, Thomas; Goble, Lisbeth – Mathematica Policy Research, Inc., 2015
KIPP (Knowledge is Power Program) is a national network of public charter schools whose stated mission is to help underserved students enroll in and graduate from college. Prior studies (see Tuttle et al. 2013) have consistently found that attending a KIPP middle school positively affects student achievement, but few have addressed longer-term…
Descriptors: Program Effectiveness, Program Evaluation, Academic Achievement, Charter Schools
Tuttle, Christina Clark; Gleason, Philip; Knechtel, Virginia; Nichols-Barrer, Ira; Booker, Kevin; Chojnacki, Gregory; Coen, Thomas; Goble, Lisbeth – Mathematica Policy Research, Inc., 2015
KIPP (Knowledge is Power Program) is a national network of public charter schools whose stated mission is to help underserved students enroll in and graduate from college. Prior studies (see Tuttle et al. 2013) have consistently found that attending a KIPP middle school positively affects student achievement, but few have addressed longer-term…
Descriptors: Academic Achievement, Charter Schools, Educational Innovation, Institutional Characteristics