Table 4.

Summary of multiple regression analysis for variables predicting weight bias (N = 1007).

	Model 1			Model 2			Model 3			Model 4
	β	95% CI	p	β	95% CI	p	β	95% CI	p	β	95% CI	p
Age	−0.12	[−0.25, −0.07]	<0.001	−0.13	[−0.25, −0.07]	0.001	−0.09	[−0.22, −0.04]	0.005	−0.08	[−0.20, −0.01]	0.026
Gender	−0.20	[−10.04, −5.49]	<0.001	−0.20	[−10.01, −5.44]	<0.001	−0.23	[−10.99, −6.45]	<0.001	−0.24	[−11.46, −6.84]	<0.001
BMI	−0.12	[−0.44, −0.14]	<0.001	−0.12	[−0.44, −0.14]	<0.001	−0.04	[−0.26, 0.07]	0.279	−0.05	[−0.36, 0.09]	0.245
Education	−0.08	[−1.86, −0.29]	0.008	−0.08	[−1.84, −0.26]	0.009	−0.09	[−1.87, −0.32]	0.006	−0.10	[−2.00, −0.45]	0.002
National obesity prevalence				−0.02	[−2.22, 1.37]	0.644	−0.01	[−2.17, 1.36]	0.651	−0.02	[−2.36, 1.25]	0.548
Health normalisation							−0.10	[−2.18, −0.34]	0.007	−0.10	[−2.26, −0.32]	0.009
Attractiveness normalisation							−0.14	[−2.72, −0.76]	0.001	−0.13	[−2.73, −0.66]	0.001
Personal contact
Own body shape exposure										0.05	[−0.45, 1.23]	0.363
Daily exposure										0.08	[0.03, 1.78]	0.043
Friends exposure										−0.11	[−2.28, −0.48]	0.003
Family exposure										−0.02	[−0.82, 0.37]	0.459
Adjusted R2	0.10			0.09			0.13			0.13
F for change in R2	27.45***			0.21			19.51***			2.97*
Model Fit	F (4,1003) = 27.45, p < 0.001			F (5,1002) = 21.98, p < 0.001			F (7,1000) = 21.85, p < 0.001			F (11,996) = 15.10, p < 0.001

*p < 0.05, ***p < 0.001