[R-sig-ME] Help with determining effect sizes
Francesco Romano
|brom@no77 @end|ng |rom gm@||@com
Sat Oct 5 10:00:54 CEST 2019
Dear all,
A journal has asked that I determine the effect sizes for a series of
dummy-coded contrasts from the following ME model:
RT ~ Group * Grammaticality + (1 + Grammaticality | Participant) +
(1 + Group | item)
Here RT is my continuous outcome variable measured in milliseconds, Group
is a factor with 3 levels (NS, L2, and HL), and Grammaticality a factor
with 2 levels (gr and ungr). After relevelling —NOTE: I am deliberately
omitting the call for each new relevelled model here— I obtained a series
of contrasts which are tabulated below (not sure you can view this whole):
Reference level
Contrasts
Estimate
(ms)
Effect size
(Cohen’s *d*)
SE
df
*t*
*p*
HL
GR vs UNGR
-213
89
72.13
-2.399
< .05*
L2
GR vs UNGR
-408
90
74.18
-4.513
< .001***
L1
GR vs UNGR
-111
73
70.02
-1.520
> .05
HL > L2
-25
191
43.48
-.135
> .05
GR
L1 > HL
400
175
43.81
2.286
< .05*
L1 > L2
374
179
43.59
2.092
< .05*
HL > L2
-219
179
42.70
-1.226
> .05
UNGR
L1 > HL
298
164
43
1.817
> .05
L1> L2
77
166
42.03
.469
> .05
How would I go about determining the Cohen's *d* for each of the contrasts?
The model call is:
Linear mixed model fit by REML. t-tests use Satterthwaite's method
['lmerModLmerTest']
Formula: RT ~ Group * Grammaticality + (1 + Grammaticality | Participant) +
(1 + Group | item)
Data: RTanalysis
REML criterion at convergence: 52800
Scaled residuals:
Min 1Q Median 3Q Max
-2.1696 -0.6536 -0.1654 0.5060 5.0134
Random effects:
Groups Name Variance Std.Dev. Corr
item (Intercept) 71442 267.29
GroupL2 1144 33.82 0.80
GroupNS 9951 99.76 -0.43 -0.88
Participant (Intercept) 235216 484.99
Grammaticalityungr 50740 225.25 -0.39
Residual 378074 614.88
Number of obs: 3342, groups: item, 144; Participant, 46
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2801.98 136.70 48.85 20.498 <2e-16 ***
GroupL2 -25.86 191.20 43.48 -0.135 0.8931
GroupNS -400.63 175.22 43.81 -2.286 0.0271 *
Grammaticalityungr -213.87 89.17 72.13 -2.399 0.0190 *
GroupL2:Grammaticalityungr -194.57 107.25 42.55 -1.814 0.0767 .
GroupNS:Grammaticalityungr 102.31 99.39 43.45 1.029 0.3090
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) GropL2 GropNS Grmmtc GrL2:G
GroupL2 -0.672
GroupNS -0.744 0.526
Grmmtcltyng -0.404 0.222 0.260
GrpL2:Grmmt 0.259 -0.391 -0.205 -0.589
GrpNS:Grmmt 0.299 -0.202 -0.392 -0.702 0.540
convergence code: 0
Model failed to converge with max|grad| = 0.0477764 (tol = 0.002, component
1)
The distribution of the outcome is fairly normal and the overall mean,
without considering the two fixed effects, is very close to the means of
each of the three groups (without considering the effect of Grammaticality)
as well as the means of the two levels of grammaticality (without
considering the effect of group).
The package simR can simulate data to determine power, amongst other things,
but I am not sure how to do this for models with interactions such as mine.
Use of simR is recommended in Brysbaert and Stevens (2018)
https://www.journalofcognition.org/articles/10.5334/joc.10/. Perhaps there
is a simpler way of extracting *d *from the stats I already know?
Any help would be greatly appreciated,
Francesco
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models
mailing list