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Joshi, Megha; Pustejovsky, James E.; Beretvas, S. Natasha – Research Synthesis Methods, 2022
The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include multiple effect size estimates per primary study, leading to dependence in the estimates. Some…
Descriptors: Meta Analysis, Regression (Statistics), Models, Effect Size
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Mortaz Hejri, Sara; Yazdani, Kamran; Labaf, Ali; Norcini, John J.; Jalili, Mohammad – Advances in Health Sciences Education, 2016
In a sequential OSCE which has been suggested to reduce testing costs, candidates take a short screening test and who fail the test, are asked to take the full OSCE. In order to introduce an effective and accurate sequential design, we developed a model for designing and evaluating screening OSCEs. Based on two datasets from a 10-station…
Descriptors: Models, Instructional Design, Sequential Approach, Medical Students
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Baker, Rose D.; Jackson, Dan – Research Synthesis Methods, 2013
The use of meta-analysis in medicine and epidemiology really took off in the 1970s. However, in high-energy physics, the Particle Data Group has been carrying out meta-analyses of measurements of particle masses and other properties since 1957. Curiously, there has been virtually no interaction between those working inside and outside particle…
Descriptors: Meta Analysis, Physics, Error of Measurement, Statistical Analysis
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Lang, Kyle M.; Little, Todd D. – International Journal of Behavioral Development, 2014
We present a new paradigm that allows simplified testing of multiparameter hypotheses in the presence of incomplete data. The proposed technique is a straight-forward procedure that combines the benefits of two powerful data analytic tools: multiple imputation and nested-model ?2 difference testing. A Monte Carlo simulation study was conducted to…
Descriptors: Hypothesis Testing, Data Analysis, Error of Measurement, Computation
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Raykov, Tenko – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…
Descriptors: Correlation, Models, Vertical Organization, Predictor Variables
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Curran-Everett, Douglas – Advances in Physiology Education, 2011
Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This seventh installment of "Explorations in Statistics" explores regression, a technique that estimates the nature of the relationship between two things for which we may only surmise a mechanistic or predictive…
Descriptors: Regression (Statistics), Statistics, Models, Correlation
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Raykov, Tenko; Penev, Spiridon – Structural Equation Modeling: A Multidisciplinary Journal, 2010
A latent variable analysis procedure for evaluation of reliability coefficients for 2-level models is outlined. The method provides point and interval estimates of group means' reliability, overall reliability of means, and conditional reliability. In addition, the approach can be used to test simple hypotheses about these parameters. The…
Descriptors: Reliability, Evaluation, Models, Intervals
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Jenson, William R.; Clark, Elaine; Kircher, John C.; Kristjansson, Sean D. – Psychology in the Schools, 2007
Evidence-based practice approaches to interventions has come of age and promises to provide a new standard of excellence for school psychologists. This article describes several definitions of evidence-based practice and the problems associated with traditional statistical analyses that rely on rejection of the null hypothesis for the…
Descriptors: School Psychologists, Statistical Analysis, Hypothesis Testing, Intervention
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Calkins, Dick S. – 1974
The mathematical derivation of the statistics used for inference in some linear models assumes that the values of the independent variables are measured without error. This assumption is often disregarded when these models are utilized in research. This study is an investigation of the consequences of the violation of this assumption for one…
Descriptors: Analysis of Covariance, Computer Programs, Error of Measurement, Error Patterns