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Jaylin Lowe; Charlotte Z. Mann; Jiaying Wang; Adam Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2024
Recent methods have sought to improve precision in randomized controlled trials (RCTs) by utilizing data from large observational datasets for covariate adjustment. For example, consider an RCT aimed at evaluating a new algebra curriculum, in which a few dozen schools are randomly assigned to treatment (new curriculum) or control (standard…
Descriptors: Randomized Controlled Trials, Middle School Mathematics, Middle School Students, Middle Schools
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Hackett, Daniel A.; Davies, Timothy B.; Ibel, Denis; Cobley, Stephen; Sanders, Ross – Measurement in Physical Education and Exercise Science, 2018
This study examined the predictive ability of the medicine ball chest throw and vertical jump for muscular strength and power in adolescents. One hundred and ninety adolescents participated in this study. Participants performed trials of the medicine ball chest throw and vertical jump, with vertical jump peak power calculated via an estimation…
Descriptors: Predictive Measurement, Muscular Strength, Adolescents, Age Differences
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Soltaninejad, Mehraneh – Malaysian Online Journal of Educational Sciences, 2015
The purpose of this study is to examine the relationships between achievement goal orientations and Learning Strategies. The sample of study consists of 350 public high school students (135 males and 215 females, mean age: 17 ± 0.65) from two high schools in Kerman province of Iran selected by random multistage cluster sampling method. In this…
Descriptors: Goal Orientation, Learning Strategies, Structural Equation Models, Investigations
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Weybright, Elizabeth H.; Caldwell, Linda L.; Xie, Hui; Wegner, Lisa; Smith, Edward A. – South African Journal of Education, 2017
Education is one of the strongest predictors of health worldwide. In South Africa, school dropout is a crisis where by Grade 12, only 52% of the age appropriate population remain enrolled. Survival analysis was used to identify the risk of dropping out of secondary school for male and female adolescents and examine the influence of substance use…
Descriptors: Foreign Countries, Predictor Variables, Predictive Measurement, Secondary School Students
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Tanner-Smith, Emily E.; Lipsey, Mark W. – Peabody Journal of Education, 2014
There are many situations where random assignment of participants to treatment and comparison conditions may be unethical or impractical. This article provides an overview of propensity score techniques that can be used for estimating treatment effects in nonrandomized quasi-experimental studies. After reviewing the logic of propensity score…
Descriptors: Probability, Scores, Quasiexperimental Design, High Schools
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Flores, Raymond; Inan, Fethi; Lin, Zhangxi – Journal of Computers in Mathematics and Science Teaching, 2013
In this study, the National Educational Longitudinal Study (ELS:2002) dataset was used and a predictive data mining technique, decision tree analysis, was implemented in order to examine which factors, in conjunction to computer use, can be used to predict high or low probability of success in high school mathematics. Specifically, this study…
Descriptors: Educational Technology, Computer Uses in Education, Longitudinal Studies, Predictor Variables
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Mo, Lun; Yang, Fang; Hu, Xiangen; Calaway, Florance; Nickey, John – Journal of Educational Research, 2011
The authors investigated the extent to which taking specific types of Advanced Placement (AP) courses and the number of courses taken predicts the likelihood of passing subject benchmarks and earning a score of 19 on the composite score on the ACT test, and examined the role gender plays in the projection. They found evidence that taking an AP…
Descriptors: Evidence, Science Tests, College Entrance Examinations, Gender Differences
Proctor, Thomas P.; Kim, YoungKoung Rachel – College Board, 2010
The purpose of this paper is to provide information about how students' scores change when they retake the PSAT/NMSQT as juniors or take the SAT in the spring after they take the PSAT/NMSQT as juniors. Two research questions guided this study and motivated the approach for analysis of the data: How do scores change for students who took the…
Descriptors: Scores, Achievement Gains, Bayesian Statistics, College Entrance Examinations
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Marnewick, Carl – Educational Studies, 2012
First-year students are still failing at an alarming rate. This is an international issue that universities face and there is currently no clear indication of the cause of the problem as universities move from being elite to providing mass education. This article examines the possible correlation between students' high school performance and…
Descriptors: Academic Achievement, Admission Criteria, Correlation, Mathematics Achievement
Gagnon, Daniel A. – Online Submission, 2012
This paper describes the diffusion of Moodle within Cherokee County Schools. The diffusion is evaluated using the Bass Model and the RIPPLES model in order to evaluate relative success or failure. The Bass Model of Diffusion was calculated utilizing forecasting by analogy in order to analyze the adoption rates in a county high school. The adoption…
Descriptors: County School Districts, High Schools, Secondary School Teachers, Adoption (Ideas)
Segel, David – Office of Education, United States Department of the Interior, 1934
The great growth in college enrollments, the changes in the availability of positions for college graduates, and the development in the science of testing go hand-in-hand in making the problem of the prediction of college success of greater importance than ever before. This bulletin brings the attention of educators upon the work which has been …
Descriptors: Academic Achievement, Prediction, Guidance, College Freshmen