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Noma, Hisashi; Hamura, Yasuyuki; Gosho, Masahiko; Furukawa, Toshi A. – Research Synthesis Methods, 2023
Network meta-analysis has been an essential methodology of systematic reviews for comparative effectiveness research. The restricted maximum likelihood (REML) method is one of the current standard inference methods for multivariate, contrast-based meta-analysis models, but recent studies have revealed the resultant confidence intervals of average…
Descriptors: Network Analysis, Meta Analysis, Regression (Statistics), Error of Measurement
Weber, Frank; Knapp, Guido; Glass, Änne; Kundt, Günther; Ickstadt, Katja – Research Synthesis Methods, 2021
There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study…
Descriptors: Meta Analysis, Computation, Intervals, Statistical Analysis
Dogan, C. Deha – Eurasian Journal of Educational Research, 2017
Background: Most of the studies in academic journals use p values to represent statistical significance. However, this is not a good indicator of practical significance. Although confidence intervals provide information about the precision of point estimation, they are, unfortunately, rarely used. The infrequent use of confidence intervals might…
Descriptors: Sampling, Statistical Inference, Periodicals, Intervals
Zakszeski, Brittany N.; Hojnoski, Robin L.; Wood, Brenna K. – Topics in Early Childhood Special Education, 2017
Classroom engagement is important to young children's academic and social development. Accurate methods of capturing this behavior are needed to inform and evaluate intervention efforts. This study compared the accuracy of interval durations (i.e., 5 s, 10 s, 15 s, 20 s, 30 s, and 60 s) of momentary time sampling (MTS) in approximating the…
Descriptors: Intervals, Time, Sampling, Learner Engagement
Quinn, Anne – Mathematics Teacher, 2016
While looking for an inexpensive technology package to help students in statistics classes, the author found StatKey, a free Web-based app. Not only is StatKey useful for students' year-end projects, but it is also valuable for helping students learn fundamental content such as the central limit theorem. Using StatKey, students can engage in…
Descriptors: Statistics, Computer Oriented Programs, Technology Uses in Education, Teaching Methods
Inzunsa Cazares, Santiago – North American Chapter of the International Group for the Psychology of Mathematics Education, 2016
This article presents the results of a qualitative research with a group of 15 university students of social sciences on informal inferential reasoning developed in a computer environment on concepts involved in the confidence intervals. The results indicate that students developed a correct reasoning about sampling variability and visualized…
Descriptors: Qualitative Research, College Students, Inferences, Logical Thinking
Arzumanyan, George; Halcoussis, Dennis; Phillips, G. Michael – American Journal of Business Education, 2015
This paper presents the Agresti & Coull "Adjusted Wald" method for computing confidence intervals and margins of error for common proportion estimates. The presented method is easily implementable by business students and practitioners and provides more accurate estimates of proportions particularly in extreme samples and small…
Descriptors: Business Administration Education, Error of Measurement, Error Patterns, Intervals
Reardon, Sean F.; Ho, Andrew D. – Journal of Educational and Behavioral Statistics, 2015
In an earlier paper, we presented methods for estimating achievement gaps when test scores are coarsened into a small number of ordered categories, preventing fine-grained distinctions between individual scores. We demonstrated that gaps can nonetheless be estimated with minimal bias across a broad range of simulated and real coarsened data…
Descriptors: Achievement Gap, Performance Factors, Educational Practices, Scores
Reardon, Sean F.; Ho, Andrew D. – Grantee Submission, 2015
Ho and Reardon (2012) present methods for estimating achievement gaps when test scores are coarsened into a small number of ordered categories, preventing fine-grained distinctions between individual scores. They demonstrate that gaps can nonetheless be estimated with minimal bias across a broad range of simulated and real coarsened data…
Descriptors: Achievement Gap, Performance Factors, Educational Practices, Scores
Ruscio, John; Gera, Benjamin Lee – Multivariate Behavioral Research, 2013
Researchers are strongly encouraged to accompany the results of statistical tests with appropriate estimates of effect size. For 2-group comparisons, a probability-based effect size estimator ("A") has many appealing properties (e.g., it is easy to understand, robust to violations of parametric assumptions, insensitive to outliers). We review…
Descriptors: Psychological Studies, Gender Differences, Researchers, Test Results
Padilla, Miguel A.; Divers, Jasmin; Newton, Matthew – Applied Psychological Measurement, 2012
Three different bootstrap methods for estimating confidence intervals (CIs) for coefficient alpha were investigated. In addition, the bootstrap methods were compared with the most promising coefficient alpha CI estimation methods reported in the literature. The CI methods were assessed through a Monte Carlo simulation utilizing conditions…
Descriptors: Intervals, Monte Carlo Methods, Computation, Sampling
Gu, Fei; Skorupski, William P.; Hoyle, Larry; Kingston, Neal M. – Applied Psychological Measurement, 2011
Ramsay-curve item response theory (RC-IRT) is a nonparametric procedure that estimates the latent trait using splines, and no distributional assumption about the latent trait is required. For item parameters of the two-parameter logistic (2-PL), three-parameter logistic (3-PL), and polytomous IRT models, RC-IRT can provide more accurate estimates…
Descriptors: Intervals, Item Response Theory, Models, Evaluation Methods
Curran-Everett, Douglas – Advances in Physiology Education, 2009
Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This third installment of "Explorations in Statistics" investigates confidence intervals. A confidence interval is a range that we expect, with some level of confidence, to include the true value of a population parameter…
Descriptors: Statistics, Intervals, Probability, Computation
Zientek, Linda Reichwein; Ozel, Z. Ebrar Yetkiner; Ozel, Serkan; Allen, Jeff – Career and Technical Education Research, 2012
Confidence intervals (CIs) and effect sizes are essential to encourage meta-analytic thinking and to accumulate research findings. CIs provide a range of plausible values for population parameters with a degree of confidence that the parameter is in that particular interval. CIs also give information about how precise the estimates are. Comparison…
Descriptors: Vocational Education, Effect Size, Intervals, Self Esteem
Kyari, Murat; Buyukozturk, Sener – Educational Sciences: Theory and Practice, 2009
The outcomes which cannot be generalized are specific for a sample but are unable to be reflected to the rest of the population. The parameters that are reached at the end of the statistics that are scarce in sample arise doubts in the aspect of generalization. In these cases, parameter estimation may not be very stable and outlier values can…
Descriptors: Siblings, Intervals, Academic Achievement, Predictor Variables
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