Hi everyone,
I am currently working on a meta-analysis and need some advice on calculating an effect size for a repeated-measures study using metafor.
The study I am extracting data from uses a within-subjects design. The outcome reported in the paper is already a calculated difference score (i.e., each participant has a score based on the difference between two measurements). The authors provide the mean and SD of this difference score separately for each condition.
For example:
- Condition A: M = ..., SD = ...
- Condition B: M = ..., SD = ...
The same participants completed both conditions.
I am planning to calculate a standardised mean change effect size using:
escalc(measure = "SMCC", ...)
My understanding is that the correlation (ri) required for this calculation should be the correlation between the outcome scores across conditions, i.e. the correlation between the difference scores:
cor((Measure1 - Measure2) in Condition A,
(Measure1 - Measure2) in Condition B)
However, the authors have only provided correlations for the two individual components that make up the difference score (i.e., the correlation for Measure 1 across conditions and the correlation for Measure 2 across conditions).
I am unsure whether these correlations can be used instead, or whether I specifically need the correlation between the derived difference scores themselves.
Could someone clarify what the appropriate approach would be in this situation?
Many thanks!