Computing Correct Confidence Intervals for Anova Fixed-and Random-Effects Effect Sizes

By Fiona Fidler, B. Thompson. 2001

122 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d8c5befe4f0b5a1d60542a71cfa5c7849d43ada4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Inference by eye: confidence intervals and how to read pictures of data. (2005, background): “…and Fouladi (1997) for CIs requiring noncentral distributions; Cumming and Finch (2001) for the standardized effect size measure known as Cohen's d; Fidler and Thompson (2001), Bird (2002), and Steiger (2004) for some analysis of variance effect sizes; and Smithson (2001) for some regression…”
A confidence interval approach to data analysis (2004, methodology): “Fidler and Thompson (2001) focused on the use of PEV effect size measures and described the use of non-central distributions in confidence interval calculation.”
Confidence Intervals for Effect Sizes in Analysis of Variance (2002, background): “Fidler and Thompson (2001) provided further discussion of the distinctions between these models.”
Confidence Intervals for Effect Sizes (2001, methodology): “Several of the articles in the present EPM issue discuss the computation of confidence intervals in ANOVA and related applications (cf. Cumming & Finch, 2001; Fidler & Thompson, 2001; Smithson, 2001 [all in this issue]).”
Improving narrative skills in young children with delayed language development (2004, background): “The figure in brackets shows the 95% confidence interval for this difference assuming a central confidence interval (Fidler & Thompson, 2001).”
A Communication Researchers' Guide to Null Hypothesis Significance Testing and Alternatives. (2008, background): “These calculations often require computer patches, many of which are provided by Smithson, Bird (2002), and Fidler and Thompson (2001).”
Effect Sizes in Single Case Research: How Large is Large? (2005, background): “Statisticians ( Fidler & Thompson, 2001 ) now recommend that effect size presentation include an index of reliability such as confidence intervals (CIs; Fowler, 1985).”
Computation of measures of effect size for neuroscience data sets (2011, methodology): “Formulae for a number of MES to convert ncp to CIs were taken from these studies, as well as from Fidler & Thompson (2001).”
Confidence Intervals for Effect Sizes: Applying Bootstrap Resampling. (2016, methodology): “…multi-step formulae have been used to compute confidence intervals by hand (Cumming & Finch, 2001; Published by ScholarWorks@UMass Amherst, 2016 Practical Assessment, Research & Evaluation, Vol 21, No 5 Page 2 Banjanovic & Osborne, Confidence Intervals for Effect Sizes Fidler & Thompson, 2001).”
Effect size measures for mediation models: quantitative strategies for communicating indirect effects. (2011, unknown): “…confidence intervals for effect sizes because the real interest lies not in the estimated value but in the population value (Balluerka, Gómez, & Hidalgo, 2005; Bird, 2002; Cumming & Finch, 2001; Fidler & Thompson, 2001; Henson, 2006; Kelley, 2007a; Kirk, 1996; Smithson, 2001; Thompson, 2002, 2007).”
A Primer on the Understanding, Use, and Calculation of Confidence Intervals that are Based on Central and Noncentral Distributions (2001, background): “…psychological literature have described procedures for calculating CIs for effect size measures and have explained that, in many situations, noncentral distributions are needed for calculating CIs (e.g., Fidler & Thompson, 2001 [this issue]; Smithson, 2001 [this issue]; Steiger & Fouladi, 1997).”
Matrix Summaries Improve Research Reports: Secondary Analyses Using Published Literature (2009, background): “We hope our few examples illustrate how “meta-analytic thinking” (cf. Cumming & Finch, 2001; Fidler & Thompson, 2001; Thompson, 2006b) can be encouraged when researchers have the ability to conduct the same analyses on multiple studies and then compare these findings across studies and over time.”