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R 2 was calculated to explain the percentage of between study variance explained by any particular co-variate.
Within study variance.
In this study, variance is maximised in two ways.
A generalised I statistic, say, could easily be defined for a meta-analysis with typical within study variance s as for any estimate of the between study variance.
We used the random effect model to pool results across studies, accounting for between study variance.
Factor 2 has 24 defining participants and accounts for 23% of the study variance.
Similar(47)
The random effects model provides a more conservative estimate because it accounts for between-study variance.
The between-study variance was assessed using Cochran Q test and I2.
A model that includes an estimate for the between-study variance is commonly referred to as random effects model.
However, between-study variance is significant, and there are signs of publication bias among published studies on pair programming.
Alternatively, the between-study variance could be restricted to non-negative values.
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