Out with .05, in with Replication and Measurement: Isolating and Working with the Particular Effect Sizes that are Troublesome for Inferential Statistics
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
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Yn: The Journal of General Psychology, Cyfrol 144, Rhif 4, 11.2017, t. 309-316.
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
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TY - JOUR
T1 - Out with .05, in with Replication and Measurement
T2 - Isolating and Working with the Particular Effect Sizes that are Troublesome for Inferential Statistics
AU - Bradley, Michael T
AU - Brand, Andrew
PY - 2017/11
Y1 - 2017/11
N2 - It is difficult to obtain adequate power to test a small effect size with a set criterion alpha of 0.05. Probably an inferential test will indicate non-statistical significance and not be published. Rarely, statistical significance will be obtained, and an exaggerated effect size calculated and reported. Accepting all inferential probabilities and associated effect sizes could solve exaggeration problems. Graphs, generated through Monte Carlo methods, are presented to illustrate this. The first graph presents effect sizes (Cohen's d) as lines from 1 to 0 with probabilities on the Y axis and the number of measures on the X axis. This graph shows effect sizes of .5 or less should yield non-significance with sample sizes below 120 measures. The other graphs show results with as many as 10 small sample size replications. There is a convergence of means with the effect size as sample size increases and measurement accuracy emerges.
AB - It is difficult to obtain adequate power to test a small effect size with a set criterion alpha of 0.05. Probably an inferential test will indicate non-statistical significance and not be published. Rarely, statistical significance will be obtained, and an exaggerated effect size calculated and reported. Accepting all inferential probabilities and associated effect sizes could solve exaggeration problems. Graphs, generated through Monte Carlo methods, are presented to illustrate this. The first graph presents effect sizes (Cohen's d) as lines from 1 to 0 with probabilities on the Y axis and the number of measures on the X axis. This graph shows effect sizes of .5 or less should yield non-significance with sample sizes below 120 measures. The other graphs show results with as many as 10 small sample size replications. There is a convergence of means with the effect size as sample size increases and measurement accuracy emerges.
KW - effext sizes
KW - Replication
KW - Maeasurement
KW - Inferential probabilites
U2 - 10.1080/00221309.2017.1381496
DO - 10.1080/00221309.2017.1381496
M3 - Article
C2 - 29023206
VL - 144
SP - 309
EP - 316
JO - The Journal of General Psychology
JF - The Journal of General Psychology
SN - 0022-1309
IS - 4
ER -