Table 4.

Participant-level comparisons of hand coding vs. ARKit classification

Participant	Total eye-tracking samples	% agreement	χ ² (df=4)	p-value	Cramer’s V	Cohen’s κ
Fam1	3079	92.1	2032.0	<.001	.812	.807
Fam2	3294	78.7	1271.8	<.001	.621	.585
Fam3	3827	27.1	11.1	<.001	.058	.018
Fam4	3109	96.9	2694.0	<.001	.931	.931
Fam5	3077	95.1	2462.8	<.001	.895	.893
Fam6	3322	88.7	1362.5	<.001	.640	.624
Fam7	3347	91.2	2181.6	<.001	.807	.807
Fam8	3289	89.4	2090.8	<.001	.797	.790
Fam9	3069	52.7	294.6	<.001	.310	.199
Fam10	3356	60.3	406.5	<.001	.348	.224

LwL1	3,330	75.6	2521.2	<.001	.615	.601
LwL2	3,139	93.2	4243.8	<.001	.822	.877
LwL3	3,426	93.5	5004.4	<.001	.855	.890
LwL4	3,164	40.2	500.5	<.001	.218	.111
LwL5	2,936	92.0	Fisher’s exact test p<.001			.849
LwL6	3,130	77.6	3069.9	<.001	.700	.664
LwL7	1,950	75.7	822.2	<.001	.459	.468
LwL8	3,365	57.5	1571.0	<.001	.483	.374
LwL9	3,350	78.2	1943.5	<.001	.539	.505
LwL10	3,357	87.3	4476.6	<.001	.817	.806

Note. A significant chi-square rejects the null hypothesis that the two methods are independent from each other; that is, there is an association between the methods.