Dear all,
I am currently conducting a meta regression in which we are examining the
role of temporal effects (year of study) in the relationship between
organizational attitudes and job performance. Using a mixed-effects model
using ML estimation, our analyses have thus far produced results that do
not appear to be irregular.
Our problem: With one relationship the analysis is showing the following:
tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0152)
tau (square root of estimated tau^2 value): 0
I^2 (residual heterogeneity / unaccounted variability): 0.00%
H^2 (unaccounted variability / sampling variability): 1.00
R^2 (amount of heterogeneity accounted for): 100.00%
However, the significance of the effect of 'year of study' is significant
along with the omnibus Q_M statistic. While I inherently understand this is
due to the way in which these values (R^2, tau^2, I^2, etc.) are calculated
and that it may be due to the smaller than ideal sample size (k =32) as
suggested by L?pez?L?pez and colleagues (2014). I am unsure on how these
findings should be reported, particularly the 100% R^2 with the significant
predictor 'year of study' result.
Thank you for any assistance you may be able to provide.
All the best,
Dustin
Reference:
L?pez?L?pez, J. A., Mar?n?Mart?nez, F., S?nchez?Meca, J., Van den
Noortgate, W., & Viechtbauer, W. (2014). Estimation of the predictive power
of the model in mixed?effects meta?regression: A simulation study. *British
Journal of Mathematical and Statistical Psychology*, *67*(1), 30-48.
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