Table 2.

Effects of treatment, framing, and odds of winning on risky choices

Predictors	Odds ratios	95 % CI	p
Int.	1.20	[0.77, 1.89]	.419
Risk	0.57	[0.31, 1.05]	.073
Ambiguity	0.38	[0.21, 0.70]	.002
Framing	1.93	[1.47, 2.54]	<.001
Log-odds of winning	0.89	[0.80, 0.98]	.018
Side	0.95	[0.81, 1.11]	.491
Risk X framing	1.02	[0.70, 1.51]	.907
Ambiguity X Framing	0.90	[0.61, 1.32]	.598
Risk X log-odds of winning	0.94	[0.82, 1.09]	.418
Ambiguity X log-odds of winning	0.91	[0.79, 1.05]	.189
Framing X log-odds of winning	0.99	[0.85, 1.14]	.877
Risk X framing X log-odds of winning	0.98	[0.79, 1.20]	.819
Ambiguity X framing X log-odds of winning	1.07	[0.87, 1.31]	.508
	Random effects
σ2		3.29
τ00 VP_Code		1.16
ICC		0.26
N VP_Code		85
Observations		3,240
Marginal R2/Conditional R2		.080/.319

Note. σ² shows the within-subjects standard deviation. τ₀₀ shows the between-subjects standard deviation. ICC indicates the intraclass correlation, that is, the proportion of variance between individuals (τ₀₀) explained by the overall variance (σ² + τ₀₀). The marginal R² provides the variance explained only by the fixed effects, and the conditional R² provides the variance explained by the entire model (i.e., both fixed effects and random effects)