Analysing the impact of the COVID-19 outbreak on everyday travel behaviour in Germany and potential implications for future travel patterns

European Transport Research Review

An Open Access Journal

Table 2 Summary of binary logistic regression analysis for variables predicting whether an individual belongs to the group of people who purchase during the Coronavirus pandemic products online instead of in stores

Predictor	ß	SE	P-value	Exp(ß)
Constant	−3.960	0.505	0.000***	0.019
Gender (female)	0.483	0.157	0.002***	1.621
Age (17 to 24 years old)	1.016	0.215	0.000***	2.762
Living in urban area	0.349	0.169	0.039**	1.418
Previous experience with online shopping	2.079	0.457	0.000***	7.994
Belongs to a risk group	0.467	0.236	0.048**	1.596
Car availability in the household	0.337	0.206	0.102	1.400
−2 Log-Likelihood	1005.315 χ² = 70.863, df = 6, p < .001
R²	0.104