#statstab #364 Distribution of Cohen's d, p-values, and power curves for an independent two-tailed t-test
Thoughts: Useful for explaining why we use 5% alpha and what power does to p-values.
shinyapps.orgExperience Statistics
Recent searches
Search options
Administered by:
#cohend
1 post1 participant0 posts today
#statstab #294 So You Think You Can Graph - effectiveness of presenting the magnitude of an effect
Thoughts: Competition in the many ways to display effect magnitude. Some cool ideas.
#dataviz #stats #effectsize #effects #plots #figures #cohend
https://amplab.colostate.edu/SYTYCG_S1/SYTYCG_Season1_Results.html
amplab.colostate.eduSo You Think You Can Graph - Season 1
#statstab #281 Correcting Cohen’s d for Measurement Error (A Method!)
Thoughts: Scale reliability can be incorporated into effect size computation (i.e., remove attenuation)
rpubs.comRPubs - Classical Reliability Correction for Cohen's d and the Point-Biserial
#statstab #260 Effect size measures in a two-independent-samples case with nonnormal and nonhomogeneous data
Thoughts: "A_w and d_r were generally robust to these violations"
#robust #effectsize #ttest #2groups #metaanalysis #assumptions #ttest #cohend
SpringerLinkEffect size measures in a two-independent-samples case with nonnormal and nonhomogeneous data - Behavior Research MethodsIn psychological science, the “new statistics” refer to the new statistical practices that focus on effect size (ES) evaluation instead of conventional null-hypothesis significance testing (Cumming, Psychological Science, 25, 7–29, 2014). In a two-independent-samples scenario, Cohen’s (1988) standardized mean difference (d) is the most popular ES, but its accuracy relies on two assumptions: normality and homogeneity of variances. Five other ESs—the unscaled robust d (d r * ; Hogarty & Kromrey, 2001), scaled robust d (d r ; Algina, Keselman, & Penfield, Psychological Methods, 10, 317–328, 2005), point-biserial correlation (r pb ; McGrath & Meyer, Psychological Methods, 11, 386–401, 2006), common-language ES (CL; Cliff, Psychological Bulletin, 114, 494–509, 1993), and nonparametric estimator for CL (A w ; Ruscio, Psychological Methods, 13, 19–30, 2008)—may be robust to violations of these assumptions, but no study has systematically evaluated their performance. Thus, in this simulation study the performance of these six ESs was examined across five factors: data distribution, sample, base rate, variance ratio, and sample size. The results showed that A w and d r were generally robust to these violations, and A w slightly outperformed d r . Implications for the use of A w and d r in real-world research are discussed.
#statstab #226 Standardization and other approaches to meta-analyze differences in means
Thoughts: "standardization after meta-analysis...can be used to assess magnitudes of a meta-analyzed mean effect"
#statstab #153 Difference between Cohen's d and beta coefficient in a standardized regression
Thoughts: This relationship bw (beta) and (d) may be useful in some reporting edge cases, like #metaanalysis