Untitled - Journal of Lighting Engineering

Although the areas within the
neighbourhoods were rather similar,
different neighbourhood and lighting
components may yield different
connections between the factors.
Regression analysis examines a group of
independent variables at the same time and
seeks the independent explanatory effects
(controlling for other variables in the
model) instead of mere zero-order
correlations; it was therefore considered
suitable for an exploratory study.
Possible connections between the
lighting attributes and safety/pleasantness
were first explored with linear regression
Lighting promoting safety and creating a sense of pleasantness
analysis (enter method)/standard multiple
regression; the analysis revealed significant
relations between the lighting attributes and
the dependent variables. Only these
attributes were included in the subsequent
regression analysis (enter). Finally, the
suggested connections were tested using the
Spearman correlation as well.
As with in some of the statements, the
scale ranged from negative to positive and
again back to negative (e.g. ‘lighting on the
pathway is too strong – totally inadequate’):
Nonlinear regression was also checked, but
no significant nonlinear connections were
detected.
Table 3 Mean values (M) (on 0-6 scale) and standard deviations (SD) for all rating items in the five test areas
(A-E).
4. Results
Descriptive statistics are presented in Table 3.
Brightness perceptions corresponded well
with the luminance measurements whereas
there were considerable differences
between the TI values and perceived glare
measurements. However, as stated before,
TI values describe disability glare in road
lighting conditions, whereas perceived glare
describes the subjective feeling of
discomfort.
Data on the significant (p≤0.05)
connections and coefficients are presented
in Table 4 (perceived safety) and Table 5
(pleasant lighting). The tables present the
significant coefficients indicated by the
linear regression analysis (enter method).
After conducting regression analysis, we
used the Spearman correlation test. Data on
the significant correlations (Spearman
correlation, p≤0.05) between the variables
are provided in Table 6 (perceived safety)
and Table 7 (pleasant lighting).
Ingineria Iluminatului 2012; 14, 2: 33-46 39