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Hansen, Spencer; Rice, Kenneth – Research Synthesis Methods, 2022
Meta-analysis of proportions is conceptually simple: Faced with a binary outcome in multiple studies, we seek inference on some overall proportion of successes/failures. Under common effect models, exact inference has long been available, but is not when we more realistically allow for heterogeneity of the proportions. Instead a wide range of…
Descriptors: Meta Analysis, Effect Size, Statistical Inference, Intervals
Brannick, Michael T.; French, Kimberly A.; Rothstein, Hannah R.; Kiselica, Andrew M.; Apostoloski, Nenad – Research Synthesis Methods, 2021
Tolerance intervals provide a bracket intended to contain a percentage (e.g., 80%) of a population distribution given sample estimates of the mean and variance. In random-effects meta-analysis, tolerance intervals should contain researcher-specified proportions of underlying population effect sizes. Using Monte Carlo simulation, we investigated…
Descriptors: Meta Analysis, Credibility, Intervals, Effect Size
Pantelis, Peter C.; Kennedy, Daniel P. – Autism: The International Journal of Research and Practice, 2016
Two-phase designs in epidemiological studies of autism prevalence introduce methodological complications that can severely limit the precision of resulting estimates. If the assumptions used to derive the prevalence estimate are invalid or if the uncertainty surrounding these assumptions is not properly accounted for in the statistical inference…
Descriptors: Foreign Countries, Pervasive Developmental Disorders, Autism, Incidence
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
Calzada, Maria E.; Gardner, Holly – Mathematics and Computer Education, 2011
The results of a simulation conducted by a research team involving undergraduate and high school students indicate that when data is symmetric the student's "t" confidence interval for a mean is superior to the studied non-parametric bootstrap confidence intervals. When data is skewed and for sample sizes n greater than or equal to 10,…
Descriptors: Intervals, Effect Size, Simulation, Undergraduate Students
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
Bonett, Douglas G. – Psychological Methods, 2009
L. Wilkinson and the Task Force on Statistical Inference (1999) recommended reporting confidence intervals for measures of effect sizes. If the sample size is too small, the confidence interval may be too wide to provide meaningful information. Recently, K. Kelley and J. R. Rausch (2006) used an iterative approach to computer-generate tables of…
Descriptors: Intervals, Sample Size, Effect Size, Statistical Inference
Byrd, Jimmy; Eddy, Colleen – Journal of Educational Administration, 2009
Purpose: The purpose of this study was to review research published by "Journal of Educational Administration (JEA)" and the "Educational Administration Quarterly (EAQ)" over the past ten years to examine the type of research reported and to determine if confidence intervals and effect sizes were being reported as recommended by the American…
Descriptors: Educational Administration, Periodicals, Journal Articles, Educational Research
Levine, Timothy R.; Weber, Rene; Park, Hee Sun; Hullett, Craig R. – Human Communication Research, 2008
This paper offers a practical guide to use null hypotheses significance testing and its alternatives. The focus is on improving the quality of statistical inference in quantitative communication research. More consistent reporting of descriptive statistics, estimates of effect size, confidence intervals around effect sizes, and increasing the…
Descriptors: Intervals, Communication Research, Testing, Statistical Significance
Capraro, Robert M.; Capraro, Mary Margaret – Middle Grades Research Journal, 2009
This study examines two journals specific to the middles grades where original quantitative empirical articles are published, Research in Middle Level Education and Middle Grades Research Journal to determine what quantitative statistics are used, how they are used, and what study designs are used. Important for those who write for the…
Descriptors: Periodicals, Research Methodology, Social Science Research, Effect Size
Byrd, Jimmy K. – Educational Administration Quarterly, 2007
Purpose: The purpose of this study was to review research published by Educational Administration Quarterly (EAQ) during the past 10 years to determine if confidence intervals and effect sizes were being reported as recommended by the American Psychological Association (APA) Publication Manual. Research Design: The author examined 49 volumes of…
Descriptors: Research Design, Intervals, Statistical Inference, Effect Size
Schafer, William D.; Papapolydorou, Maria; Rahman, Taslima; Parker, Lori – Online Submission, 2005
Possible relationships between five test examiner characteristics (gender, race, tenure, experience as a test administrator, and experience as a test developer or scorer) and six student achievement scores (reading, writing, language usage, mathematics, science, and social studies) were studied at the school level in a statewide assessment. The…
Descriptors: Intervals, Academic Achievement, Test Validity, Examiners