[R-meta] Dependent Measure Modelling Question

Grace Hayes gr@ce@h@ye@3 @end|ng |rom my@cu@edu@@u
Mon Feb 11 07:47:58 CET 2019

Hi all,

I have a question regarding a meta-analysis of multiple dependent outcomes that I would like to conduct using metafor.

For this meta-analysis of emotion recognition in ageing, I'm interested in age-effects (young adults vs. older adults) on four different emotion recognition tasks (Task A, Task B, Task C, Task D). Studies in this area typically compare older adults' performance to younger adults' performance on more than one of these emotion recognition task.

For each task there are also multiple outcomes.  Each task produces an accuracy age-effect for each emotion type included (I.e., anger, sadness, fear). Up to 6 different emotions are included (Emotion 1, Emotion 2, Emotion 3, Emotion 4, Emotion 5, Emotion 6). I therefore have some studies with, for example, 6 different age-effects from 3 different emotions tasks; a total of 18 dependent outcomes.

Ideally I would like to investigate age-effects for each of the six emotion types seperately (with Tasks A, B, C and D combined), and age-effects for each task type seperately (with Emotions 1-6 combined). I would then like to compare the effects for each emotion type (Emotions 1-6 separately) produced by each task  (Measure A, B, C, D separately).

My question is, can I have a model that analyses emotion type and task type all together? Is this possible and statistically appropriate? Will it tell me the age-effects produced for each emotion by each task, or will it only tell me if task type and emotion type are significant moderators?

I am also interested to know if I can add additional moderators such as number of emotions included in the task and year of publication?

One concern that has been brought to my attention is overfitting from too many factors. Another is that output would be difficult too interpret, and thus it has been recommended that I perhaps run separately analyses for each task.

Any advice would be much appreciated.


Grace Hayes

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