for each study 2. Pool effect sizes across studies, weighting by sample size (+ look for moderators) Studies … … … Study characteristics Stats Effect Size + Var. d d_var
analysis: Quantitative, scale-free measure of “success” 0.25 0.50 0.75 1.00 Prop. trials fixating novel object chance d Observed mean diff. between means standard dev. Effect Size =
“small” Diff. between the heights of 15 yo and 16 yo girls in the US Bouba-kiki effect in kids (~.15; Lammertink, et al. 2016) .5 “medium” Diff. between the heights of 14 yo and 18 yo girls. Cognitive behavioral therapy on anxiety (~ .4; (Belleville, et al., 2004) Sex difference in implicit math attitudes (~.5; Klein, et al., 2013) .8 “large” Diff. between the heights of 13 yo and 18 yo girls. Syntactic Priming (~.9; Mahowald, et al., under review) Mutual exclusivity ( ~1.0; Lewis & Frank, in prep) (Cohen, 1969)
design (e.g., within vs. between subject) – Many effect size metrics (Hedge’s g for small samples) – Can convert between ES metrics (compute.es package in R; AC Del Re, 2013) – Can calculate via different pieces of raw data Multiple Types: – the difference is between groups (t-test, d) – the relationship between variables (correlation, r) – the amount of variance accounted for by a factor (ANOVA, regression, f) – Associations between categorical variables (odds ratio, risk difference, log odds) – More generally, for any statistical test you conduct, can compute effect size (some more straight-forwardly than others) http://rpsychologist.com/d3/cohend/
for each study 2. Pool effect sizes across studies, weighting by sample size (+ look for moderators) Studies … … … Study characteristics Stats Effect Size + Var. d d_var
In a study, sample participants and pool to get estimate of effect in study (unweighted mean) – In meta-analysis, sample studies to get estimate of grand effect (weighted mean) Just as for models across participants, two models for pooling: – Fixed effect: One true population effect – Random effect: Random sample of many population effects, estimates mean
= 5.97 N = 16 SD = .079 baseline = 0 d = 1.49 Example meta-analysis with two studies (1) Markman and Wachtel (1988) Reported M = 4.9 t = 3.94 N = 10 baseline = 3
in R with the metafor package. Journal of Statistical Software, 36(3), 1-48. URL: http://www.jstatsoft.org/v36/i03/ … Studies … … … Study characteristics Stats Effect Size + Var. d d_var