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Siemens, Waldemar; Meerpohl, Joerg J.; Rohe, Miriam S.; Buroh, Sabine; Schwarzer, Guido; Becker, Gerhild – Research Synthesis Methods, 2022
Using the Hartung-Knapp method and 95% prediction intervals (PIs) in random-effects meta-analyses is recommended by experts but rarely applied. Therefore, we aimed to reevaluate statistically significant meta-analyses using the Hartung-Knapp method and 95% PIs. In this methodological study, three databases were searched from January 2010 to July…
Descriptors: Cancer, Meta Analysis, Medical Research, Patients
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Rosenthal, Jeffrey S. – Teaching Statistics: An International Journal for Teachers, 2018
This article advocates that introductory statistics be taught by basing all calculations on a single simple margin-of-error formula and deriving all of the standard introductory statistical concepts (confidence intervals, significance tests, comparisons of means and proportions, etc) from that one formula. It is argued that this approach will…
Descriptors: Statistics, Introductory Courses, Computation, Statistical Analysis
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Gorard, Stephen – International Journal of Social Research Methodology, 2019
This paper compares the use of confidence intervals (CIs) and a sensitivity analysis called the number needed to disturb (NNTD), in the analysis of research findings expressed as 'effect' sizes. Using 1,000 simulations of randomised trials with up to 1,000 cases in each, the paper shows that both approaches are very similar in outcomes, and each…
Descriptors: Intervals, Statistics, Social Sciences, Foreign Countries
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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
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Rutherford, Marion; Burns, Morag; Gray, Duncan; Bremner, Lynne; Clegg, Sarah; Russell, Lucy; Smith, Charlie; O'Hare, Anne – Journal of Autism and Developmental Disorders, 2018
The 'autism diagnosis crisis' and long waiting times for assessment are as yet unresolved, leading to undue stress and limiting access to effective support. There is therefore a significant need for evidence to support practitioners in the development of efficient services, delivering acceptable waiting times and effectively meeting guideline…
Descriptors: Autism, Pervasive Developmental Disorders, Clinical Diagnosis, Efficiency
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Vaughan, Timothy S. – Journal of Statistics Education, 2015
This paper introduces a dataset and associated analysis of the scores of National Football League (NFL) games over the 2012, 2013, and first five weeks of the 2014 season. In the face of current media attention to "lopsided" scores in Thursday night games in the early part of the 2014 season, t-test results indicate no statistically…
Descriptors: Team Sports, Success, Scores, Statistics
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Velicer, Wayne F.; Brick, Leslie Ann D.; Fava, Joseph L.; Prochaska, James O. – Multivariate Behavioral Research, 2013
Testing Theory-based Quantitative Predictions (TTQP) represents an alternative to traditional Null Hypothesis Significance Testing (NHST) procedures and is more appropriate for theory testing. The theory generates explicit effect size predictions and these effect size estimates, with related confidence intervals, are used to test the predictions.…
Descriptors: Smoking, Statistical Significance, Confidence Testing, Effect Size
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Gu, Lin; Lockwood, John; Powers, Donald E. – ETS Research Report Series, 2015
Standardized tests are often designed to provide only a snapshot of test takers' knowledge, skills, or abilities at a single point in time. Sometimes, however, they are expected to serve more demanding functions, one of them is assessing change in knowledge, skills, or ability over time because of learning effects.The latter is the case for the…
Descriptors: Language Tests, Second Language Learning, English (Second Language), Standardized Tests
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Liu, Xiaofeng Steven – International Journal of Mathematical Education in Science and Technology, 2012
The statistical power of a significance test is closely related to the length of the confidence interval (i.e. estimate precision). In the case of a "Z" test, the length of the confidence interval can be expressed as a function of the statistical power. (Contains 1 figure and 1 table.)
Descriptors: Statistical Analysis, Intervals, Statistical Significance, Statistics
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Doebler, Anna; Doebler, Philipp; Holling, Heinz – Psychometrika, 2013
The common way to calculate confidence intervals for item response theory models is to assume that the standardized maximum likelihood estimator for the person parameter [theta] is normally distributed. However, this approximation is often inadequate for short and medium test lengths. As a result, the coverage probabilities fall below the given…
Descriptors: Foreign Countries, Item Response Theory, Computation, Hypothesis Testing
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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
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Linting, Marielle; van Os, Bart Jan; Meulman, Jacqueline J. – Psychometrika, 2011
In this paper, the statistical significance of the contribution of variables to the principal components in principal components analysis (PCA) is assessed nonparametrically by the use of permutation tests. We compare a new strategy to a strategy used in previous research consisting of permuting the columns (variables) of a data matrix…
Descriptors: Intervals, Simulation, Statistical Significance, Factor Analysis
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Gelman, Andrew; Hill, Jennifer; Yajima, Masanao – Journal of Research on Educational Effectiveness, 2012
Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian…
Descriptors: Intervals, Comparative Analysis, Inferences, Error Patterns
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Taub, Gordon E.; McGrew, Kevin S.; Keith, Timothy Z. – Journal of Research in Childhood Education, 2015
This article examines the effect of improvements in timing/rhythmicity on mathematics achievement. A total of 86 participants attending 1st through 4th grades completed pre- and posttest measures of mathematics achievement from the Woodcock-Johnson III Tests of Achievement. Students in the experimental group participated in a 4-week intervention…
Descriptors: Elementary School Students, Elementary School Mathematics, Mathematics Achievement, Intervals
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Dunst, Carl J.; Hamby, Deborah W. – Journal of Intellectual & Developmental Disability, 2012
This paper includes a nontechnical description of methods for calculating effect sizes in intellectual and developmental disability studies. Different hypothetical studies are used to illustrate how null hypothesis significance testing (NHST) and effect size findings can result in quite different outcomes and therefore conflicting results. Whereas…
Descriptors: Intervals, Developmental Disabilities, Statistical Significance, Effect Size
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