RT, being a ratio scale, which is the highest level of measurement, is one of the few variables in psychological research that could justify the use of covariances instead of correlations in a factor analysis or components analysis. It makes no sense to factor analyze a covariance matrix composed of raw-score variables that are not all on a scale with the same equal units of measurement. The Pearson correlation coefficient is simply the standardized covariance, i.e., Cov XY = /N Correlation r xy = Cov XY/σ x* σ y. It should be noted that RT variables are particularly well suited to the factor analysis or principal components analysis of their raw-score variance-covariance matrix rather than the correlation matrix. These results show that while green spaces have an effect on urban areas, this effect is significant in terms of a reduced negative sentiment, but not significant in terms of an increase in positive sentiments. 00655 for sadness), while joy is positively correlated ( P < 0. Table 6.5 shows that anger, anticipation, fear, sadness, and trust are negatively correlated with green space proximity ( P < 0. 00,017), but none for positive sentiments.
0001) and a significant positive correlation between sentiment polarity and green space proximity ( P = 0. The results ( Table 6.4) show a significant negative correlation between negative sentiments and green space proximity ( P < 0. The bold numbers indicate a statistically significant correlation. R XY = 1, the dependent variable Y is perfectly correlated positively with the independent variable X.Ġ.8 0.70 and very satisfactory when r XY > 0.85.