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van der Linden, Wim J.; Ren, Hao – Journal of Educational and Behavioral Statistics, 2020
The Bayesian way of accounting for the effects of error in the ability and item parameters in adaptive testing is through the joint posterior distribution of all parameters. An optimized Markov chain Monte Carlo algorithm for adaptive testing is presented, which samples this distribution in real time to score the examinee's ability and optimally…
Descriptors: Bayesian Statistics, Adaptive Testing, Error of Measurement, Markov Processes
Gagnon-Bartsch, J. A.; Sales, A. C.; Wu, E.; Botelho, A. F.; Erickson, J. A.; Miratrix, L. W.; Heffernan, N. T. – Grantee Submission, 2019
Randomized controlled trials (RCTs) admit unconfounded design-based inference--randomization largely justifies the assumptions underlying statistical effect estimates--but often have limited sample sizes. However, researchers may have access to big observational data on covariates and outcomes from RCT non-participants. For example, data from A/B…
Descriptors: Randomized Controlled Trials, Educational Research, Prediction, Algorithms
Finch, William Holmes; Hernandez Finch, Maria E. – AERA Online Paper Repository, 2017
High dimensional multivariate data, where the number of variables approaches or exceeds the sample size, is an increasingly common occurrence for social scientists. Several tools exist for dealing with such data in the context of univariate regression, including regularization methods such as Lasso, Elastic net, Ridge Regression, as well as the…
Descriptors: Multivariate Analysis, Regression (Statistics), Sampling, Sample Size
McNeish, Daniel – Review of Educational Research, 2017
In education research, small samples are common because of financial limitations, logistical challenges, or exploratory studies. With small samples, statistical principles on which researchers rely do not hold, leading to trust issues with model estimates and possible replication issues when scaling up. Researchers are generally aware of such…
Descriptors: Models, Statistical Analysis, Sampling, Sample Size
Jo, See-Heyon – 1995
The question of how to analyze unbalanced hierarchical data generated from structural equation models has been a common problem for researchers and analysts. Among difficulties plaguing statistical modeling are estimation bias due to measurement error and the estimation of the effects of the individual's hierarchical social milieu. This paper…
Descriptors: Algorithms, Bayesian Statistics, Equations (Mathematics), Error of Measurement
Coffman, William E.; Shigemasu, Kazuo – 1978
Appraisal of a school's relative effectiveness is complicated by: (1) the need to control for input differences; (2) measurement error in input measures; and (3) small sample size within schools. This study compares the performance of two successive cohorts in 19 schools in a small midwestern city on the five Iowa Tests of Basic Skills using both…
Descriptors: Academic Achievement, Accountability, Achievement Gains, Analysis of Covariance
Penfield, Douglas A. – 1972
Thirty-four papers on educational statistics which were presented at the 1971 AERA Conference are summarized. Six major interest areas are covered: (a) general information; (b) non-parametric methods; (c) errors of measurement and correlation techniques; (d) regression theory; (e) univariate and multivariate analysis; (f) factor analysis. (MS)
Descriptors: Analysis of Variance, Bayesian Statistics, Behavioral Science Research, Computers