TABLE 7.

Does comprehension of relative clause structure interact with bilingualism for cognitive function? glm() regression with Poisson error were used to model cognitive parameters.

	Dependent variable:
	TEA-1	TEA-2	TEA-3	Backward digit span
	(1)	(2)	(3)	(4)
(Intcpt)/Monolingual	1.897	1.917	1.660	1.692
	t = 19.539	t = 19.646	t = 14.784	t = 15.165
	p = 0.000	p = 0.000	p = 0.000	p = 0.000
Bilingual	0.021	0.239	0.322	0.132
	t = 0.159	t = 1.939	t = 2.312	t = 0.920
	p = 0.874	p = 0.053	p = 0.021	p = 0.358
Relative-Clause	0.043	−0.007	−0.045	−0.073
	t = 0.412	t = −0.064	t = −0.371	t = −0.603
	p = 0.681	p = 0.949	p = 0.711	p = 0.547
Bilingual: Rel-Clause	−0.034	−0.055	−0.010	0.087
(Interaction)	t = −0.251	t = −0.432	t = −0.071	t = 0.583
	p = 0.802	p = 0.666	p = 0.944	p = 0.560
Observations	48	48	48	48
Log Likelihood	−91.323	−108.801	−131.834	−93.458
Akaike Inf. Crit.	190.647	225.601	271.668	194.915

Model 1: TEA-1 ∼ Monoling/Biling × Relative Clause, Poisson error. Model 2: TEA-2 ∼ Monoling/Biling × Relative Clause, Poisson error. Model 3: TEA-3 ∼ Monoling/Biling × Relative Clause, Poisson error. Model 4: Backward digit span ∼ Monoling/Biling × Relative Clause, Poisson error.