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Kulinskaya, Elena; Hoaglin, David C. – Research Synthesis Methods, 2023
For estimation of heterogeneity variance T[superscript 2] in meta-analysis of log-odds-ratio, we derive new mean- and median-unbiased point estimators and new interval estimators based on a generalized Q statistic, Q[subscript F], in which the weights depend on only the studies' effective sample sizes. We compare them with familiar estimators…
Descriptors: Q Methodology, Statistical Analysis, Meta Analysis, Intervals
Trafimow, David – Educational and Psychological Measurement, 2017
There has been much controversy over the null hypothesis significance testing procedure, with much of the criticism centered on the problem of inverse inference. Specifically, p gives the probability of the finding (or one more extreme) given the null hypothesis, whereas the null hypothesis significance testing procedure involves drawing a…
Descriptors: Statistical Inference, Hypothesis Testing, Probability, Intervals
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
Ferrando, Pere J. – Psicologica: International Journal of Methodology and Experimental Psychology, 2010
This article proposes a procedure, based on a global statistic, for assessing intra-individual consistency in a test-retest design with a short-term retest interval. The procedure is developed within the framework of parametric item response theory, and the statistic is a likelihood-based measure that can be considered as an extension of the…
Descriptors: Item Response Theory, Intervals, Psychometrics, Testing
A Generally Robust Approach for Testing Hypotheses and Setting Confidence Intervals for Effect Sizes
Keselman, H. J.; Algina, James; Lix, Lisa M.; Wilcox, Rand R.; Deering, Kathleen N. – Psychological Methods, 2008
Standard least squares analysis of variance methods suffer from poor power under arbitrarily small departures from normality and fail to control the probability of a Type I error when standard assumptions are violated. This article describes a framework for robust estimation and testing that uses trimmed means with an approximate degrees of…
Descriptors: Intervals, Testing, Least Squares Statistics, Effect Size
Lee, Won-Chan; Brennan, Robert L.; Kolen, Michael J. – Journal of Educational and Behavioral Statistics, 2006
Assuming errors of measurement are distributed binomially, this article reviews various procedures for constructing an interval for an individual's true number-correct score; presents two general interval estimation procedures for an individual's true scale score (i.e., normal approximation and endpoints conversion methods); compares various…
Descriptors: Probability, Intervals, Guidelines, Computer Simulation