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Showing 1 to 15 of 27 results Save | Export
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Ting Dai; Yang Du; Jennifer Cromley; Tia Fechter; Frank Nelson – Journal of Experimental Education, 2024
Simple matrix sampling planned missing (SMS PD) design, introduce missing data patterns that lead to covariances between variables that are not jointly observed, and create difficulties for analyses other than mean and variance estimations. Based on prior research, we adopted a new multigroup confirmatory factor analysis (CFA) approach to handle…
Descriptors: Research Problems, Research Design, Data, Matrices
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Duane Knudson – Measurement in Physical Education and Exercise Science, 2025
Small sample sizes contribute to several problems in research and knowledge advancement. This conceptual replication study confirmed and extended the inflation of type II errors and confidence intervals in correlation analyses of small sample sizes common in kinesiology/exercise science. Current population data (N = 18, 230, & 464) on four…
Descriptors: Kinesiology, Exercise, Biomechanics, Movement Education
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Cartwright, Nancy – Educational Research and Evaluation, 2019
Across the evidence-based policy and practice (EBPP) community, including education, randomised controlled trials (RCTS) rank as the most "rigorous" evidence for causal conclusions. This paper argues that that is misleading. Only narrow conclusions about study populations can be warranted with the kind of "rigour" that RCTs…
Descriptors: Evidence Based Practice, Educational Policy, Randomized Controlled Trials, Error of Measurement
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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
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Deke, John; Wei, Thomas; Kautz, Tim – National Center for Education Evaluation and Regional Assistance, 2017
Evaluators of education interventions are increasingly designing studies to detect impacts much smaller than the 0.20 standard deviations that Cohen (1988) characterized as "small." While the need to detect smaller impacts is based on compelling arguments that such impacts are substantively meaningful, the drive to detect smaller impacts…
Descriptors: Intervention, Educational Research, Research Problems, Statistical Bias
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Tang, Yang; Cook, Thomas D.; Kisbu-Sakarya, Yasemin – Society for Research on Educational Effectiveness, 2015
Regression discontinuity design (RD) has been widely used to produce reliable causal estimates. Researchers have validated the accuracy of RD design using within study comparisons (Cook, Shadish & Wong, 2008; Cook & Steiner, 2010; Shadish et al, 2011). Within study comparisons examines the validity of a quasi-experiment by comparing its…
Descriptors: Pretests Posttests, Statistical Bias, Accuracy, Regression (Statistics)
Gelman, Andrew; Imbens, Guido – National Bureau of Economic Research, 2014
It is common in regression discontinuity analysis to control for high order (third, fourth, or higher) polynomials of the forcing variable. We argue that estimators for causal effects based on such methods can be misleading, and we recommend researchers do not use them, and instead use estimators based on local linear or quadratic polynomials or…
Descriptors: Regression (Statistics), Mathematical Models, Causal Models, Research Methodology
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Citkowicz, Martyna; Polanin, Joshua R. – Society for Research on Educational Effectiveness, 2014
Meta-analyses are syntheses of effect-size estimates obtained from a collection of studies to summarize a particular field or topic (Hedges, 1992; Lipsey & Wilson, 2001). These reviews are used to integrate knowledge that can inform both scientific inquiry and public policy, therefore it is important to ensure that the estimates of the effect…
Descriptors: Meta Analysis, Accountability, Cluster Grouping, Effect Size
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Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
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Bernard, Robert M.; Borokhovski, Eugene; Schmid, Richard F.; Tamim, Rana M. – Journal of Computing in Higher Education, 2014
This article contains a second-order meta-analysis and an exploration of bias in the technology integration literature in higher education. Thirteen meta-analyses, dated from 2000 to 2014 were selected to be included based on the questions asked and the presence of adequate statistical information to conduct a quantitative synthesis. The weighted…
Descriptors: Meta Analysis, Bias, Technology Integration, Higher Education
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Wing, Coady; Cook, Thomas D. – Journal of Policy Analysis and Management, 2013
The sharp regression discontinuity design (RDD) has three key weaknesses compared to the randomized clinical trial (RCT). It has lower statistical power, it is more dependent on statistical modeling assumptions, and its treatment effect estimates are limited to the narrow subpopulation of cases immediately around the cutoff, which is rarely of…
Descriptors: Regression (Statistics), Research Design, Statistical Analysis, Research Problems
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Volkwein, J. Fredericks; Yin, Alexander C. – New Directions for Institutional Research, 2010
This chapter summarizes ten selected issues and common problems that arise in most assessment research projects. These include: (1) the uses of grades in assessment; (2) institutional review boards; (3) research design as a compromise; (4) standardized testing; (5) self-reported measures; (6) missing data; (7) weighting data; (8) conditional…
Descriptors: Research Design, Research Methodology, Standardized Tests, Least Squares Statistics
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Butts, David P. – Journal of Research in Science Teaching, 1983
Basic characteristics of survey as a research study are discussed. Issues associated with survey research, potential uses of survey research as a strategy in science education, and potential problems jeopardizing survey studies are also discussed. (JN)
Descriptors: Educational Research, Error of Measurement, Research Design, Research Methodology
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Loo, Robert – Perceptual and Motor Skills, 1983
In examining considerations in determining sample sizes for factor analyses, attention was given to the effects of outliers; the standard error of correlations, and their effect on factor structure; sample heterogeneity; and the misuse of rules of thumb for sample sizes. (Author)
Descriptors: Correlation, Error of Measurement, Evaluation Methods, Factor Analysis
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Good, Ron – 1980
Knowledge of the magnitude of effect(s) of an experimental study in science education should be of utmost concern to researchers in the field, but is often not reported. This document describes the concept of "explained variance" in analysis of variance designs and then explains how it can be calculated and reported. Reporting the magnitude of…
Descriptors: Analysis of Variance, Error of Measurement, Research, Research Design
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