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Showing all 11 results Save | Export
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Mara, Constance A.; Cribbie, Robert A. – Journal of Experimental Education, 2018
Researchers are often interested in establishing equivalence of population variances. Traditional difference-based procedures are appropriate to answer questions about differences in some statistic (e.g., variances, etc.). However, if a researcher is interested in evaluating the equivalence of population variances, it is more appropriate to use a…
Descriptors: Statistical Analysis, Differences, Comparative Analysis, Research Problems
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Wilhelm, Anne Garrison; Gillespie Rouse, Amy; Jones, Francesca – Practical Assessment, Research & Evaluation, 2018
Although inter-rater reliability is an important aspect of using observational instruments, it has received little theoretical attention. In this article, we offer some guidance for practitioners and consumers of classroom observations so that they can make decisions about inter-rater reliability, both for study design and in the reporting of data…
Descriptors: Interrater Reliability, Measurement, Observation, Educational Research
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Gwet, Kilem L. – Educational and Psychological Measurement, 2016
This article addresses the problem of testing the difference between two correlated agreement coefficients for statistical significance. A number of authors have proposed methods for testing the difference between two correlated kappa coefficients, which require either the use of resampling methods or the use of advanced statistical modeling…
Descriptors: Differences, Correlation, Statistical Significance, Statistical Analysis
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Pedersen, Ellen Raben; Juhl, Peter Møller – Journal of Speech, Language, and Hearing Research, 2017
Purpose: Critical differences state by how much 2 test results have to differ in order to be significantly different. Critical differences for discrimination scores have been available for several decades, but they do not exist for speech reception thresholds (SRTs). This study presents and discusses how critical differences for SRTs can be…
Descriptors: Speech, Simulation, Differences, Test Results
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Muñoz, J. F.; Álvarez-Verdejo, E.; García-Fernández, R. M. – Sociological Methods & Research, 2018
Many poverty measures are estimated by using sample data collected from social surveys. Two examples are the poverty gap and the poverty severity indices. A novel method for the estimation of these poverty indicators is described. Social surveys usually contain different variables, some of which can be used to improve the estimation of poverty…
Descriptors: Poverty, Simulation, Income, Socioeconomic Status
Feng, Junchen – ProQuest LLC, 2017
The future of education is human expertise and artificial intelligence working in conjunction, a revolution that will change the education as we know it. The Intelligent Tutoring System is a key component of this future. A quantitative measurement of efficacies of practice to heterogeneous learners is the cornerstone of building an effective…
Descriptors: Intelligent Tutoring Systems, Learning Processes, Bayesian Statistics, Models
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Kang, Yoonjeong; Harring, Jeffrey R.; Li, Ming – Journal of Experimental Education, 2015
The authors performed a Monte Carlo simulation to empirically investigate the robustness and power of 4 methods in testing mean differences for 2 independent groups under conditions in which 2 populations may not demonstrate the same pattern of nonnormality. The approaches considered were the t test, Wilcoxon rank-sum test, Welch-James test with…
Descriptors: Comparative Analysis, Monte Carlo Methods, Statistical Analysis, Robustness (Statistics)
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McNeish, Daniel M. – Journal of Educational and Behavioral Statistics, 2016
Mixed-effects models (MEMs) and latent growth models (LGMs) are often considered interchangeable save the discipline-specific nomenclature. Software implementations of these models, however, are not interchangeable, particularly with small sample sizes. Restricted maximum likelihood estimation that mitigates small sample bias in MEMs has not been…
Descriptors: Models, Statistical Analysis, Hierarchical Linear Modeling, Sample Size
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Dirghangi, Shrija; Kahn, Gilly; Laursen, Brett; Brendgen, Mara; Vitaro, Frank; Dionne, Ginette; Boivin, Michel – Developmental Psychology, 2015
This study tested 2 related hypotheses. The first holds that high co-rumination anticipates heightened internalizing problems. The second holds that positive relationships with friends exacerbate the risk for internalizing problems arising from co-rumination. A sample of MZ twins followed from birth (194 girls and 170 boys) completed (a)…
Descriptors: Early Adolescents, Friendship, Peer Influence, Anxiety
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Prindle, John J.; McArdle, John J. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
This study used statistical simulation to calculate differential statistical power in dynamic structural equation models with groups (as in McArdle & Prindle, 2008). Patterns of between-group differences were simulated to provide insight into how model parameters influence power approximations. Chi-square and root mean square error of…
Descriptors: Statistical Analysis, Structural Equation Models, Goodness of Fit, Monte Carlo Methods
Hummel, Thomas J.; Johnston, Charles B. – 1986
This study investigated seven methods for analyzing multivariate group differences. Bonferroni t statistics, multivariate analysis of variance (MANOVA) followed by analysis of variance (ANOVA), and five other methods were studied using Monte Carlo methods. Methods were compared with respect to (1) experimentwise error rate; (2) power; (3) number…
Descriptors: Analysis of Variance, Comparative Analysis, Correlation, Differences