The data collected from the survey is as follows:
\[
\text{Data} = [5, 8, 12, 4, 11, 6, 7, 2, 3, 5, 4, 9, 9, 13, 12, 1, 1, 3, 8, 8, 13, 4, 5, 6, 2, 10, 9, 21, 3, 3, 5, 1, 14, 7, 12, 0]
\]
After sorting, the data becomes:
\[
\text{Sorted Data} = [0, 1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 5, 6, 6, 7, 7, 8, 8, 8, 9, 9, 9, 10, 11, 12, 12, 12, 13, 13, 14, 21]
\]
To find \( Q_1 \), we use the formula:
\[
\text{Rank} = Q \times (N + 1) = 0.25 \times (36 + 1) = 9.25
\]
Since the rank is not an integer, we average the values at positions 9 and 10:
\[
Q_1 = \frac{X_{\text{lower}} + X_{\text{upper}}}{2} = \frac{3 + 3}{2} = 3.0
\]
Thus,
\[
Q_1 = 3.0
\]
To find \( Q_3 \), we use the formula:
\[
\text{Rank} = Q \times (N + 1) = 0.75 \times (36 + 1) = 27.75
\]
Again, since the rank is not an integer, we average the values at positions 27 and 28:
\[
Q_3 = \frac{X_{\text{lower}} + X_{\text{upper}}}{2} = \frac{9 + 10}{2} = 9.5
\]
Thus,
\[
Q_3 = 9.5
\]
The IQR is calculated as:
\[
\text{IQR} = Q_3 - Q_1 = 9.5 - 3.0 = 6.5
\]
The lower and upper bounds for identifying outliers are calculated as follows:
\[
\text{Lower Bound} = Q_1 - 1.5 \times \text{IQR} = 3.0 - 1.5 \times 6.5 = -6.75
\]
\[
\text{Upper Bound} = Q_3 + 1.5 \times \text{IQR} = 9.5 + 1.5 \times 6.5 = 19.25
\]
Any data point below the lower bound or above the upper bound is considered an outlier. In this case, the outlier is:
\[
\text{Outliers} = [21]
\]
The outlier in the data is:
\[
\boxed{21}
\]