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Murrah, William M. – Educational and Psychological Measurement, 2020
Multiple regression is often used to compare the importance of two or more predictors. When the predictors being compared are measured with error, the estimated coefficients can be biased and Type I error rates can be inflated. This study explores the impact of measurement error on comparing predictors when one is measured with error, followed by…
Descriptors: Error of Measurement, Statistical Bias, Multiple Regression Analysis, Predictor Variables
Lu, Rui; Keller, Bryan Sean – AERA Online Paper Repository, 2019
When estimating an average treatment effect with observational data, it's possible to get an unbiased estimate of the causal effect if all confounding variables are observed and reliably measured. In education, confounding variables are often latent constructs. Covariate selection methods used in causal inference applications assume that all…
Descriptors: Factor Analysis, Predictor Variables, Monte Carlo Methods, Comparative Analysis
Cain, Meghan K.; Zhang, Zhiyong; Bergeman, C. S. – Educational and Psychological Measurement, 2018
This article serves as a practical guide to mediation design and analysis by evaluating the ability of mediation models to detect a significant mediation effect using limited data. The cross-sectional mediation model, which has been shown to be biased when the mediation is happening over time, is compared with longitudinal mediation models:…
Descriptors: Mediation Theory, Case Studies, Longitudinal Studies, Measurement Techniques
Cain, Meghan K.; Zhang, Zhiyong; Bergeman, C.S. – Grantee Submission, 2018
This paper serves as a practical guide to mediation design and analysis by evaluating the ability of mediation models to detect a significant mediation effect using limited data. The cross-sectional mediation model, which has been shown to be biased when the mediation is happening over time, is compared to longitudinal mediation models:…
Descriptors: Mediation Theory, Case Studies, Longitudinal Studies, Measurement Techniques
Aydin, Burak; Leite, Walter L.; Algina, James – Educational and Psychological Measurement, 2016
We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…
Descriptors: Error of Measurement, Predictor Variables, Randomized Controlled Trials, Experimental Groups
Dong, Nianbo – American Journal of Evaluation, 2015
Researchers have become increasingly interested in programs' main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. A challenge for estimating main and interaction effects is to eliminate selection bias across A-by-B groups. I introduce Rubin's causal model to…
Descriptors: Probability, Statistical Analysis, Research Design, Causal Models
Shaw, Stacy; Radwin, David – National Center for Education Statistics, 2014
The web tables in this report provide original and revised estimates of statistics previously published in 2007-08 National Postsecondary Student Aid Study (NPSAS:08): Student Financial Aid Estimates for 2007-08 (NCES 2009-166). The revised estimates were generated using revised weights that were updated in August 2013. NPSAS:08 data were…
Descriptors: Student Financial Aid, Tables (Data), Comparative Analysis, Statistical Data
Bradford, George; Wyatt, Shelly – Internet and Higher Education, 2010
A study by Mullen and Tallent-Runnels (2006) found significance in the differences between online and traditional students' reports of instructors' academic support, instructors' demands, and students' satisfaction. They also recognized that the limitation to their study was their demographic data. In an original report funded by the Alfred P.…
Descriptors: Electronic Learning, Ethnicity, Student Attitudes, Satisfaction
Rice, Jennifer King – National Education Policy Center, 2012
Schools and school systems throughout the nation are increasingly experimenting with using various instructional technologies to improve productivity and decrease costs, but evidence on both the effectiveness and the costs of education technology is limited. A recent report published by the Thomas B. Fordham Institute sets out to describe "the…
Descriptors: Evidence, Electronic Learning, Distance Education, Online Courses

Tisak, John – Multivariate Behavioral Research, 1994
The regression coefficients and the associated standard errors in hierarchical regression, when a theoretical basis for the analysis exists, are determined for four regression models. Each reflects different controlling or partialling of the variates. An illustration is presented using data from the Berkeley Growth Study. (SLD)
Descriptors: Comparative Analysis, Error of Measurement, Estimation (Mathematics), Predictor Variables
Vehrs, Pat R.; George, James D.; Fellingham, Gilbert W.; Plowman, Sharon A.; Dustman-Allen, Kymberli – Measurement in Physical Education and Exercise Science, 2007
This study was designed to develop a single-stage submaximal treadmill jogging (TMJ) test to predict VO[subscript 2]max in fit adults. Participants (N = 400; men = 250 and women = 150), ages 18 to 40 years, successfully completed a maximal graded exercise test (GXT) at 1 of 3 laboratories to determine VO[subscript 2]max. The TMJ test was completed…
Descriptors: Metabolism, Body Composition, Physical Activities, Physical Fitness

Anderson, Lance E.; And Others – Multivariate Behavioral Research, 1996
Simulations were used to compare the moderator variable detection capabilities of moderated multiple regression (MMR) and errors-in-variables regression (EIVR). Findings show that EIVR estimates are superior for large samples, but that MMR is better when reliabilities or sample sizes are low. (SLD)
Descriptors: Comparative Analysis, Error of Measurement, Estimation (Mathematics), Interaction
Rodgers, Willard L.; Bachman, Jerald G. – 1986
This paper explores various procedures of panel data in the estimation of causal models. The reported analyses are from the Monitoring the Future study, a nationwide questionnaire survey of 16,000 to 17,000 high school seniors conducted annually since 1975. First, the parameters of causal models are estimated in which the dependent variables are…
Descriptors: Attitude Measures, Attribution Theory, Comparative Analysis, Drug Use