NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Practitioners1
Laws, Policies, & Programs
Assessments and Surveys
Graduate Record Examinations1
What Works Clearinghouse Rating
Showing 1 to 15 of 35 results Save | Export
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Autenrieth, Maximilian; Levine, Richard A.; Fan, Juanjuan; Guarcello, Maureen A. – Journal of Educational Data Mining, 2021
Propensity score methods account for selection bias in observational studies. However, the consistency of the propensity score estimators strongly depends on a correct specification of the propensity score model. Logistic regression and, with increasing popularity, machine learning tools are used to estimate propensity scores. We introduce a…
Descriptors: Probability, Artificial Intelligence, Educational Research, Statistical Bias
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Cyrenne, Philippe; Chan, Alan – Canadian Journal of Higher Education, 2022
The ability of universities and colleges to predict the success of admitted students continues to be a key concern of higher education officials. Apart from a desire to see students have successful academic careers, there is also the fiscal reality of greater tuition revenues providing needed support for university budgets. Using administrative…
Descriptors: College Students, Academic Achievement, Predictor Variables, Statistical Analysis
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Engvall, Margareta; Samuelsson, Joakim; Östergren, Rickard – Problems of Education in the 21st Century, 2020
Mastering traditional algorithms has formed mathematics teaching in primary education. Educational reforms have emphasized variation and creativity in teaching and using computational strategies. These changes have recently been criticized for lack of empirical support. This research examines the effect of teaching two differently structured…
Descriptors: Mathematics Skills, Teaching Methods, Elementary School Students, Grade 2
Peer reviewed Peer reviewed
Direct linkDirect link
Muth, Chelsea; Bales, Karen L.; Hinde, Katie; Maninger, Nicole; Mendoza, Sally P.; Ferrer, Emilio – Educational and Psychological Measurement, 2016
Unavoidable sample size issues beset psychological research that involves scarce populations or costly laboratory procedures. When incorporating longitudinal designs these samples are further reduced by traditional modeling techniques, which perform listwise deletion for any instance of missing data. Moreover, these techniques are limited in their…
Descriptors: Sample Size, Psychological Studies, Models, Statistical Analysis
Peer reviewed Peer reviewed
Direct linkDirect link
Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim – Journal of Experimental Education, 2016
The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest…
Descriptors: Hierarchical Linear Modeling, Monte Carlo Methods, Computation, Statistical Bias
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Kim, Sooyeon; Moses, Tim – ETS Research Report Series, 2016
The purpose of this study is to evaluate the extent to which item response theory (IRT) proficiency estimation methods are robust to the presence of aberrant responses under the "GRE"® General Test multistage adaptive testing (MST) design. To that end, a wide range of atypical response behaviors affecting as much as 10% of the test items…
Descriptors: Item Response Theory, Computation, Robustness (Statistics), Response Style (Tests)
Peer reviewed Peer reviewed
Direct linkDirect link
Bai, Haiyan – Journal of Experimental Education, 2013
Propensity score estimation plays a fundamental role in propensity score matching for reducing group selection bias in observational data. To increase the accuracy of propensity score estimation, the author developed a bootstrap propensity score. The commonly used propensity score matching methods: nearest neighbor matching, caliper matching, and…
Descriptors: Statistical Inference, Sampling, Probability, Computation
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Jance, Marsha L.; Thomopoulos, Nick T. – American Journal of Business Education, 2011
The paper shows how to find the min and max extreme interval values for the exponential and triangular distributions from the min and max uniform extreme interval values. Tables are provided to show the min and max extreme interval values for the uniform, exponential, and triangular distributions for different probabilities and observation sizes.
Descriptors: Intervals, Probability, Observation, Statistical Distributions
Bushaw, William J.; Lopez, Shane J. – Phi Delta Kappan, 2011
This is the latest in a series of polls sponsored by Phi Delta Kappa International with the Gallup organization. Some important findings of this year's poll include: About half of us believe teacher unions are hurting public education, but we're more likely to support teacher union leaders than governors in disputes over teacher collective…
Descriptors: Public Education, Public Opinion, Surveys, Educational Quality
Peer reviewed Peer reviewed
Direct linkDirect link
Cloot, A. H. J. J.; Meyer, J. H. – International Journal of Mathematical Education in Science & Technology, 2006
This paper investigates a general identity which expresses an apparently complicated and intriguing sum of fractions as an elegant and straightforward sum of simple terms.
Descriptors: Mathematical Formulas, Mathematics Education, Equations (Mathematics), Mathematical Concepts
Peer reviewed Peer reviewed
Direct linkDirect link
Smith, H. V. – International Journal of Mathematical Education in Science & Technology, 2006
A method for the numerical evaluation of the error term in Gaussian quadrature rules is derived by means of Chebyshev polynomials of the first kind.
Descriptors: Mathematics Education, Problem Solving, Equations (Mathematics), Computation
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Squire, Barry – Australian Mathematics Teacher, 2005
This document shows a different way of adding lists of numbers to find a way of getting general formulae for figurate numbers and use Gauss?s method to check it.
Descriptors: Mathematical Formulas, Computation, Mathematics, Numbers
Peer reviewed Peer reviewed
Direct linkDirect link
Osler, Thomas J. – International Journal of Mathematical Education in Science & Technology, 2006
Euler gave a simple method for showing that [zeta](2)=1/1[superscript 2] + 1/2[superscript 2] + 1/3[superscript 2] + ... = [pi][superscript 2]/6. He generalized his method so as to find [zeta](4), [zeta](6), [zeta](8),.... His computations became increasingly more complex as the arguments increased. In this note we show a different generalization…
Descriptors: Mathematics Education, Mathematical Concepts, College Mathematics, Computation
Peer reviewed Peer reviewed
Direct linkDirect link
Chen, Hongwei; Khalili, Parviz – International Journal of Mathematical Education in Science & Technology, 2005
In this note we give closed forms for a class of logarithmic integrals in terms of Bernoulli polynomials. This provides a method for unifying a large class of definite integrals.
Descriptors: Numbers, Mathematics Education, Computation, Mathematical Formulas
Peer reviewed Peer reviewed
Direct linkDirect link
Knight, D. G. – International Journal of Mathematical Education in Science and Technology, 2004
If the point of suspension of a multiple pendulum is suitably oscillated then the pendulum can remain in motion in an upside-down position. Since such pendulums can model flexible materials, this inverted motion is sometimes referred to as an 'Indian rope trick'. Despite the complexity of the governing differential equations, this rope trick can…
Descriptors: Motion, Algebra, Mathematical Formulas, Computation
Previous Page | Next Page »
Pages: 1  |  2  |  3