Dear James,
Thank you so much for the detailed response! I apologize for the delay in
getting back to you; my graduate school applications got in the way of
this. Your suggestion is exactly what we have been looking for and your
blogpost has been very informative. I do have a couple of follow-up
questions and would be curious to hear what you think:
*Calculating Cohen?s d and its variance for mixed-effects models*
Initially, we planned to follow Brysbaert and Stevens' (2018) suggestion
to calculate Cohen?s d for mixed-effects models using:
d = difference in means / sqrt(sum of all variance components).
Hedges (2007) proposes three approaches toward scaling the treatment
effect in mixed-effects models, namely by standardizing the mean difference
by the total variance (i.e., sum of the within- and between-cluster
components), the within-cluster variance, or the between-cluster variance.
Intuitively, I understood that Brysbaert and Stevens? approach also uses
the total variance to scale the treatment effect since **all** variance
components are summed up. However, Hedges seems to use another formula
for deriving dTotal, namely:
dT = difference in means / sqrt (between-cluster components + ((n-1) / n)
* within-cluster components).
Can you help me understand in which cases it would make sense to scale the
difference in means by sqrt(sum of all variance components) and in which
cases it would be more reasonable to use sqrt (between-cluster components +
((n-1) / n) * within-cluster components)?
You also provided information on an alternative approach
towards calculating the variance of Cohen?s d using:
Vd = (SEb / S)^2 + d^2 / (2 v)
For our mixed-effects models, I could derive SEb directly from the lme4
output, and I could substitute the standardizer used for calculating Cohen?s
d for S (sqrt(sum of all variance components) or sqrt (between-cluster
components + ((n-1) / n) * within-cluster components). In an effort to be
as conservative as possible, I would use the number of participants as the
degrees of freedom (v). Does this make sense?
*Comparability of effect sizes derived from between- and within-subjects
designs*
Finally, I wonder to which extent the alternative formulas suggested in
the blogpost allow for comparison across different experimental designs. In
our meta-analysis, we aim at including effect sizes derived from between-
and within-subjects designs. To be able to synthesize the results from both
types of designs in one analysis, we make sure to meet the three criteria
outlined in Morris and DeShon (2002): 1) all effect sizes were ultimately
transformed into a common metric (between-subjects metric); 2) the same
effect of interest was measured in both types of studies; and 3) sampling
variances for all effect sizes were estimated based on the original design
of the study (Table 2). Comparing the variance formulas provided in the
blogpost to the ones provided in Morris & DeShon, it seems like the latter
ones are slightly larger (and thus more conservative, which seems fine).
However, I am uncertain about mixing the Morris & DeShon formulas for
within- and between-subjects designs (to allow for comparison) with
the alternative formulas you provided for calculating Cohen?s d and its
respective variance for mixed-effects models. Do you think this might cause
any problems for the comparability of our effect sizes? I wonder whether
you have some intuition on whether effect sizes derived using the
alternative formulas proposed in the blogpost can be across different study
designs.
Thank you so much for your help. Your time and effort are very much
appreciated!
Best wishes,
Lena Schaefer
On behalf of a collaborative team that additionally includes Leah
Somerville (head of the Affective Neuroscience and Development Laboratory),
Katherine Powers (former postdoc in the Affective Neuroscience and
Development Laboratory) and Bernd Figner (Radboud University).
Am 22.10.2019 um 05:12 schrieb James Pustejovsky <jepusto at gmail.com>:
Lena,
The formula you tried from Hedges 2007 is derived under the assumption
that treatment assignment is at the cluster level, so I don't think it will
work for your mixed design. The following post might be useful to answer
your questions:
https://www.jepusto.com/alternative-formulas-for-the-smd/
In it, I suggest a quite general approach to estimating the variance of a
standardized mean difference effect size, even if it is based on a complex
experimental design. Suppose that you calculate the SMD estimate as
d = b / S,
where b is the unstandardized mean difference (which in your design
involves a combination of within- and between-Ss comparisons) and S is the
standard deviation of the outcome, which generally might involve a sum of
multiple variance components. A delta-method approximation to the variance
of d is
Vd = (SEb / S)^2 + d^2 / (2 v),
where SEb is the standard error of b, S is the denominator of the effect
size estimate, d is the effect size estimate, and v is the degrees of
freedom of S^2, defined by v = 2[ E(S^2)]^2 / Var(S^2). The SEb should
usually be reported in primary studies (or can be back-calculated from t
statistics or CIs). Thus, the only tricky bit is to find the degrees of
freedom for the standardizing variance S^2. You might need to just make a
rough approximation, based on for instance the total number of
participants. Using a rough approximation (e.g., v = 30) should not have
much effect on the total estimated variance Vd unless d is very large, so
personally I would not worry too much about getting it perfect.
As I explain in the post, you can also use the degrees of freedom v to do
Hedges' g correction, taking
g = J(v) * d,
where J(v) = 1 - 3 / (4 * v - 1). Again, it's not worth worrying about
getting the degrees of freedom perfect. Consider that J(30) = 0.9748 and
J(60) = 0.9874, so the g estimate will differ by only a tiny amount
depending on the degrees of freedom you use.
James
On Sat, Oct 19, 2019 at 2:41 PM Lena Sch?fer <lenaschaefer2304 at gmail.com>
wrote:
1. Variance for complex mixed-effects models
While the calculation of Cohen?s d is unproblematic (formula 8 on page
346 in Hedges, 2007), the calculation of the respective variance turned out
to be difficult for complex study designs. Hedge?s provided the following
formula () to derive V(dw):
V(dw) = ((NT + NC) / (NT * NC)) * ((1+(n-1)p)/(1-p)) + ((dw^2) / (2(N ?
M)))
with NT referring to the number of observations in the treatment group,
NC referring to the number of observations in the control group, N
referring to the total number of observations (NT + NC = N), n referring
to the number of observations per cluster, p referring to the ICC, and M
referring to the number of clusters.
For our meta-analysis, we want to derive the variance related to Cohen?s
d for a mixed-subjects design with some participant conducting a task only
in the control condition and other participants conducting the task in the
control and in the experimental condition (within-subjects design). Since
the number of observations per cluster differs (some participants have 30
observations, others have 60) we decided to use the variance formula for
unequal cluster sample sizes in which n is substituted with the cluster
sample size ? (formula 18 on page 350):
? = ((NC * ?mTi = 1 (nTi)^2) / (NT * N)) + ((NT * ?mCi = 1 (nCi)^2) /
(NC * N))
iWhile we expected that this formula would yield an unequal cluster
sample size between 30 and 60, it gives us a value of 30 (which is equal to
the cluster sample size if this would be a between-subjects design). This
suggests that the formula cannot account for the participants which are
both in the control and the experimental condition. Do you have any advice
on how we could derive an accurate variance estimate for such a design?
2. Turning Cohen?s d into Hedge?s g for mixed-models
Finally, we want to transform Cohen?s d into Hedge?s g using:
g(d) = d * (1- ((3) / (4 * df - 1))
We are uncertain how to best estimate the dfs in our mixed-models. We
considered using Kenward-Roger approximated dfs but this does not seem
feasible since we only have access to parts of the raw data-sets used to
derive dw and V{dw}. Potentially, another option would be to estimate the
dfs via the effective sample size. This seems more feasible since the
authors of primary papers provided us with the ICC related to each model.
What do you think about this option?
If you have any thoughts on this, we would greatly appreciate it if you
could let us know what you think. Thank you for taking the time to consider
our request, and please don?t hesitate to reach out if anything is unclear.
Thank you very much and best regards,
Lena Sch?fer
On behalf of a collaborative team that additionally includes Leah
Somerville (head of the Affective Neuroscience and Development Laboratory),
Katherine Powers (former postdoc in the Affective Neuroscience and
Development Laboratory) and Bernd Figner (Radboud University).
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