# The Impact of Noise on Learning in Children and Adolescents: A Meta-Analysis
## 3 Results
### Quantitative Synthesis  
The Restricted Maximum Likelihood (REML) method was used to estimate random effects. REML is a robust and suitable technique for meta-analyses when variability is expected between studies, as it adjusts the estimation of the variance of random effects, providing a more precise estimate compared to less sophisticated methods. This method is particularly useful when sample sizes vary between studies and an unbiased estimation of heterogeneity is desired. However, REML assumes that effect sizes follow a normal distribution, which may not always hold in cases of strong skewness or extreme heterogeneity. Additionally, its accuracy depends on the correct specification of its moderators. The Omnibus Test of Model Coefficients was performed to evaluate the overall significance of the model. The test yielded a Q value of 36.921 with 17 degrees of freedom, and the result was statistically significant (\(p<0.001\)). This indicates that the model coefficients all collectively contribute to the prediction of the outcome variable, as shown in Table 1.  
The fit measures for the REML estimation method indicate the adequacy of the model in explaining the data's variability (Table 2). The Log-Likelihood value was -95.417, the Deviance was 190.834, the Akaike Information Criterion (AIC) was 283.834, the Bayesian Information Criterion (AIC) was 235.500. These measures suggest that the model provides a reasonable fit for the data, with lower AIC, BIC, and AICc values indicating good model performance. In the analysis of the coefficients (see Table 3, and note that it is split across two pages due to space limitations), the intercept was statistically significant, with an estimate of \(-0.352\) (\(p=0.020\)), indicating a negative baseline effect when all other variables are held constant. This suggests that even in the absence of specific moderating factors such as noise type or student age, noise exposure has a detrimental impact. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Q** & **df** & \(p\) \\ \hline Omnibus Test of Model Coefficients & 36.921 & 17 & \textless{}0.001 \\ \hline \hline Note. Q = Omnibus test statistic; df = degrees of freedom; \(p=p\)-value. The model was estimated using Restricted ML method. & \\ \end{tabular}
\end{table} 
Table 1: Fixed and random effects. The age category \"Children\" (6-12 years) showed a highly significant negative effect (\(-0.350\), \(p\) = 0.007), reinforcing previous findings that younger students are more vulnerable to noise-related disruptions. This may be attributed to the developing of executive functions in this age group, making it harder for them to filter out auditory distractions. Regarding noise type, traffic noise exhibited a positive effect (0.477, \(p\) = 0.088), which is unexpected given the typically negative effects of environmental noise on learning. Although this result does not reach statistical significance, it may reflect complex compensatory mechanisms, where students exposed to traffic noise over time develop adaptive cognitive strategies. Alternatively, it could indicate that certain studies included in the meta-analysis measured background traffic noise differently, leading to variability in the reported effects. Further research is needed to explore whether low-level traffic noise could serve as a masking noise, reducing the impact of disruptive intermittent sounds, as observed in some studies on white noise. On the other hand, classroom noise exhibited a negative effect (\(-0.161\), \(p\) = 0.436), consistent with expectations that higher noise levels within the learning environment can impair student concentration and performance. However, the high \(p\)-value indicates substantial variability across studies, suggesting that not all types of classroom noise have the same effect. Factors such as the source of noise (e.g., teacher vs. students talking, external disturbances), classroom design (acoustics, size), and task complexity could be influencing the degree of impairment observed.