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Donegan, Sarah; Dias, Sofia; Welton, Nicky J. – Research Synthesis Methods, 2019
When numerous treatments exist for a disease (Treatments 1, 2, 3, etc), network meta-regression (NMR) examines whether each relative treatment effect (eg, mean difference for 2 vs 1, 3 vs 1, and 3 vs 2) differs according to a covariate (eg, disease severity). Two consistency assumptions underlie NMR: consistency of the treatment effects at the…
Descriptors: Reliability, Regression (Statistics), Outcomes of Treatment, Statistical Analysis
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van Aert, Robbie C. M.; van Assen, Marcel A. L. M.; Viechtbauer, Wolfgang – Research Synthesis Methods, 2019
The effect sizes of studies included in a meta-analysis do often not share a common true effect size due to differences in for instance the design of the studies. Estimates of this so-called between-study variance are usually imprecise. Hence, reporting a confidence interval together with a point estimate of the amount of between-study variance…
Descriptors: Meta Analysis, Computation, Statistical Analysis, Effect Size
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Wang, Chia-Chun; Lee, Wen-Chung – Research Synthesis Methods, 2019
A systematic review and meta-analysis is an important step in evidence synthesis. The current paradigm for meta-analyses requires a presentation of the means under a random-effects model; however, a mean with a confidence interval provides an incomplete summary of the underlying heterogeneity in meta-analysis. Prediction intervals show the range…
Descriptors: Meta Analysis, Computation, Statistical Analysis, Prediction
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Nissen, Jayson; Donatello, Robin; Van Dusen, Ben – Physical Review Physics Education Research, 2019
Physics education researchers (PER) commonly use complete-case analysis to address missing data. For complete-case analysis, researchers discard all data from any student who is missing any data. Despite its frequent use, no PER article we reviewed that used complete-case analysis provided evidence that the data met the assumption of missing…
Descriptors: Physics, Science Education, Educational Research, Data
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Theobald, Elli J.; Aikens, Melissa; Eddy, Sarah; Jordt, Hannah – Physical Review Physics Education Research, 2019
A common goal in discipline-based education research (DBER) is to determine how to improve student outcomes. Linear regression is a common technique used to test hypotheses about the effects of interventions on continuous outcomes (such as exam score) as well as control for student nonequivalence in quasirandom experimental designs. (In…
Descriptors: Educational Research, Regression (Statistics), Outcomes of Education, Statistical Analysis
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Rustam, Ahmad; Naga, Dali Santun; Supriyati, Yetti – International Journal of Education and Literacy Studies, 2019
Detection of differential item functioning (DIF) is needed in the development of tests to obtain useful items. The Mantel-Haenszel method and standardization are tools for DIF detection based on classical theory assumptions. The study was conducted to highlight the sensitivity and accuracy between the Mantel-Haenszel method and the standardization…
Descriptors: Statistical Analysis, Test Bias, Accuracy, Multiple Choice Tests
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Guskey, Thomas R. – NASSP Bulletin, 2019
School leaders today are making important decisions regarding education innovations based on published average effect sizes, even though few understand exactly how effect sizes are calculated or what they mean. This article explains how average effect sizes are determined in meta-analyses and the importance of including measures of variability…
Descriptors: Effect Size, Educational Innovation, Meta Analysis, Statistical Distributions
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Raykov, Tenko; Marcoulides, George A.; Harrison, Michael – Measurement: Interdisciplinary Research and Perspectives, 2019
Utilizing the perspective of finite mixture modeling, this note considers whether a finding of a plausible one-parameter logistic model could be spurious for a population with substantial unobserved heterogeneity. A theoretically and empirically important setting is discussed involving the mixture of two latent classes, with the less restrictive…
Descriptors: Models, Evaluation Methods, Social Science Research, Statistical Analysis
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Brydges, Christopher R.; Gaeta, Laura – Journal of Speech, Language, and Hearing Research, 2019
Purpose: Null hypothesis significance testing is commonly used in audiology research to determine the presence of an effect. Knowledge of study outcomes, including nonsignificant findings, is important for evidence-based practice. Nonsignificant "p" values obtained from null hypothesis significance testing cannot differentiate between…
Descriptors: Bayesian Statistics, Audiology, Hypothesis Testing, Statistical Significance
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Raykov, Tenko; Marcoulides, George A.; Harrison, Michael; Menold, Natalja – Educational and Psychological Measurement, 2019
This note confronts the common use of a single coefficient alpha as an index informing about reliability of a multicomponent measurement instrument in a heterogeneous population. Two or more alpha coefficients could instead be meaningfully associated with a given instrument in finite mixture settings, and this may be increasingly more likely the…
Descriptors: Statistical Analysis, Test Reliability, Measures (Individuals), Computation
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Shieh, Gwowen – Journal of Experimental Education, 2019
The analysis of covariance (ANCOVA) is a useful statistical procedure that incorporates covariate features into the adjustment of treatment effects. The consequences of omitted prognostic covariates on the statistical inferences of ANCOVA are well documented in the literature. However, the corresponding influence on sample-size calculations for…
Descriptors: Sample Size, Statistical Analysis, Computation, Accuracy
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Trafimow, David; Wang, Tonghui; Wang, Cong – Educational and Psychological Measurement, 2019
Two recent publications in "Educational and Psychological Measurement" advocated that researchers consider using the a priori procedure. According to this procedure, the researcher specifies, prior to data collection, how close she wishes her sample mean(s) to be to the corresponding population mean(s), and the desired probability of…
Descriptors: Statistical Distributions, Sample Size, Equations (Mathematics), Statistical Analysis
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Sülkü, Seher Nur; Koçak, Deniz – International Journal of Assessment Tools in Education, 2019
Performance evaluation functions as an essential tool for decision makers in the field of measuring and assessing the performance under the multiple evaluation criteria aspect of the systems such as management, economy, and education system. Besides, academic performance evaluation is one of the critical issues in higher institution of learning.…
Descriptors: College Students, Student Evaluation, Evaluation Criteria, Statistical Analysis
Pentimonti, J.; Petscher, Y.; Stanley, C. – National Center on Improving Literacy, 2019
Sample representativeness is an important piece to consider when evaluating the quality of a screening assessment. If you are trying to determine whether or not the screening tool accurately measures children's skills, you want to ensure that the sample that is used to validate the tool is representative of your population of interest.
Descriptors: Sampling, Screening Tests, Measurement, Test Validity
Sinharay, Sandip; Johnson, Matthew S. – Grantee Submission, 2019
According to Wollack and Schoenig (2018), score differencing is one of six types of statistical methods used to detect test fraud. In this paper, we suggested the use of Bayes factors (e.g., Kass & Raftery, 1995) for score differencing. A simulation study shows that the suggested approach performs slightly better than an existing frequentist…
Descriptors: Cheating, Deception, Statistical Analysis, Bayesian Statistics
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