Table 2.

Follow-up mixed logit analysis

Predictor		Coefficient	SE	Z	p Value
Mixed Logit Model
	Intercept	0.17	0.31	<1	.59
	Bias condition	1.13	0.30	3.81	<.001
	Current Run	−0.017	0.016	−1.09	.27
	Bias × Current Run	0.19	0.03	6.33	<.001
DO-Only Analysis
	Intercept	0.75	0.30	2.50	.01
	Cumulative priming	0.05	0.018	2.80	<.01
	Current Run	0.04	0.03	1.46	.14
PO-Only Analysis
	Intercept	−0.42	0.37	−1.13	.26
	Cumulative priming	0.083	0.025	3.26	.001
	Current Run	−0.09	0.028	−3.22	.001

Raw and Estimated Mean Proportions of DO Target Completions (per Run Length Bin)
	Raw Means			Estimated Means
	−5 to −1	1–15	16–25	−5 to −1	1–15	16–25
DO bias	.36	.63	.87	.49	.68	.82
PO bias	.65	.45	.19	.67	.40	.18

Coefficients express log odds. For the purposes of generating raw and estimated means, we split the “Current run” variable into three bins, with one bin representing runs of the opposite construction (i.e., negative numbers), and the remaining two bins splitting the positive runs. The numbers of observations per bin were as follows: DO bias, −5 to −1 = 70, 1–15 = 106, 16–25 = 76; PO bias, −5 to −1 = 97, 1–15 = 97, 16–25 = 78. The numbers of observations vary across bins because (a) we needed to keep all observations with the same run length in the same bin, and (b) we wanted to have the same bin parameters for both conditions. To generate the estimated means, we used run values corresponding to the mean run length within each bin. The mean run lengths within each bin were slightly different for the DO and PO Bias conditions, so we rounded to the nearest whole number between the two condition means, and then converted this value to its corresponding “centered” value (because the variables in the regression were centered). Thus, we employed run values of −10, 0, and 10 to generate the estimated means.