Correct Confidence Intervals for Various Regression Effect Sizes and Parameters: The Importance of Noncentral Distributions in Computing Intervals

By Michael Smithson. 2001

281 citations recorded by the source corpus.

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Citations in the corpus

Confidence Intervals for Standardized Effect Sizes: Theory, Application, and Implementation (2007, background): “…recently generated much interest in the BESS (e.g., Algina et al. 2006; Cumming and Finch 2001; Kelley 2005; Kelley and Rausch 2006; Steiger and Fouladi 1997; Smithson 2001, 2003; Steiger 2004), where confidence intervals for effect sizes of primary importance are very strongly encouraged (e.g., ).”
Computing Correct Confidence Intervals for Anova Fixed-and Random-Effects Effect Sizes (2001, methodology): “Using the SPSS syntax provided by Smithson (2001), the 90% noncentral CI about this effect size was computed to be CI.90 = .02202 to .49814.”
An introduction to Bayesian hierarchical models with an application in the theory of signal detection (2005, unknown): “Over the last 40 years, researchers have offered various critiques of null-hypothesis significance testing (see, e.g., Cohen, 1994; Cumming & Finch, 2001; Hunter, 1997; Rozeboom, 1960; Smithson, 2001; Steiger & Fouladi, 1997).”
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]).”
Short Article: Coding Strategies in Number Space: Memory Requirements Influence Spatial–Numerical Associations (2008, unknown): “In order to obtain in this type of ANOVA design a standardized estimate of the size of the observed SNARC effect, we calculated the effect size parameter h2 of this interaction and its 95% confi- dence interval, CI (see Smithson, 2001 ).”
A Simple Method for Removing Bias From a Popular Measure of Standardized Effect Size: Adjusted Partial Eta Squared (2019, unknown): “Although this is quite complicated in the case of a population effect size, as it requires the use of the noncentral F distribution (e.g., Smithson, 2001; Steiger, 2004; see Cumming & Finch, 2001, for an introduction), calculation of a confidence interval is highly recommended.”
Confidence intervals about score reliability coefficients, please: An EPM guidelines editorial. (2001, methodology): “To obtain a 95% confidence interval, as Smithson (2001) explains, “When working with statistics for which we deem a one-sided interval appropriate, 100(1-α)% CIs are computed by declaring ‘CONF’ to be 1-[2(α)] (e.g., use ‘CONF’=.90 to obtain a 95% one-sided interval)” (p. 613).”
Person-environment congruence in relation to career change and career persistence (2006, methodology): “The exact 95% confidence interval (Smithson, 2001) for g(2)p ranged from .01 to .08, which indicated that the population effect size was estimated to be small to medium by Cohen’s (1988) standards.”
The Effects of Nonnormal Distributions on Confidence Intervals Around the Standardized Mean Difference: Bootstrap and Parametric Confidence Intervals (2005, background): “…within the behavioral sciences have increasingly emphasized the importance and utility of confidence intervals (Cumming & Finch, 2001; Smithson, 2001), effect sizes (Olejnik & Algina, I would like to thank Ke-Hai Yuan and Joseph R. Rausch, both of the Department of Psychology at the…”
Effect size, confidence interval and statistical significance: a practical guide for biologists (2007, background): “This is because calculations of standardised effect statistics and their CI are not straightforward compared to their unstandardised counterparts (Smithson, 2001; Thompson, 2002b).”
Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs (2013, methodology): “Effect size estimates have their own conﬁdence intervals [for calculations for Cohen’s d , see Cumming (2012), for F -tests, see Smithson (2001)], which are often very large in experimental psychology.”
Evaluating the Quality of Evidence from Correlational Research for Evidence-Based Practice (2005, methodology): “CIs are so appealing because using intervals across studies will ultimately lead us to the correct population value, even if our initial expectations are wildly in error (Schmidt, 1996)! Software for computing confidence intervals for effect sizes is widely available (Algina & Keselman, 2003; Cummin”
The Effects of Stereotypes on the Achievement Gap: Reexamining the Academic Performance of African American High School Students (2008, methodology): “In addition, 95% confidence intervals based on noncentral F distributions were calculated for the effect size estimates using an SPSS routine developed by Smithson (2001).”