. Author manuscript; available in PMC: 2023 Apr 1.

Published in final edited form as: Dev Psychol. 2022 Apr;58(4):607–630. doi: 10.1037/dev0001291

Table 3.

LMER results for unrelated trials^*.

A) All participants (N=61) ^**
	Estimate	Std. Error	t-value	p-value
(Intercept)	0.371	0.220	1.684	0.095
Vocabulary	0.023	0.147	0.157	0.875
Semantic category density	0.344	0.138	2.487	0.013
Word Degree	0.298	0.267	1.118	0.264
Global Cluster Coefficient	−0.099	0.088	−1.122	0.262
Age	0.015	0.244	0.061	0.951
Vocab. x Sem. Category Density	−0.154	0.136	−1.136	0.256
Vocab. x Word Degree	−0.210	0.168	−1.253	0.211
Vocab. x GCC	0.026	0.082	0.320	0.749
Vocab. x Age	−0.014	0.210	−0.068	0.946
Age x Sem. Category Density	−0.245	0.192	−1.277	0.202
Age x Word Degree	−0.314	0.267	−1.175	0.240
Age x GCC	0.366	0.322	1.139	0.255
Cat. Density x Age x Vocab.	0.063	0.194	0.325	0.745
Degree x Age x Vocab.	0.150	0.180	0.833	0.405
GCC x Age x Vocab.	0.146	0.333	0.437	0.662

B) Higher vocabulary group (N=30)
	Estimate	Std. Error	t-value	p-value
(Intercept)	0.473	0.159	2.974	0.004
Semantic category density	0.079	0.182	0.432	0.666
Word Degree	0.235	0.137	1.718	0.087
Global Cluster Coefficient	−0.214	0.219	−0.979	0.328
Age	−0.431	0.325	−1.325	0.186
Age x Sem. Category Density	−0.038	0.254	−0.150	0.881
Age x Word Degree	−0.201	0.147	−1.370	0.171
Age x GCC	1.175	0.708	1.661	0.097

C) Lower vocabulary group (N=31)
	Estimate	Std. Error	t-value	p-value
(Intercept)	0.393	0.542	0.724	0.469
Semantic category density	0.567	0.192	2.953	0.003
Word Degree	0.603	0.735	0.820	0.412
Global Cluster Coefficient	−0.114	0.064	−1.785	0.075
Age Group	0.092	0.555	0.166	0.868
Age Grp x Sem. Category Density	−0.508	0.278	−1.825	0.068
Age Grp x Word Degree	−0.499	0.741	−0.673	0.501
Age Grp x GCC	0.162	0.284	0.569	0.570

Panel A illustrates results for all participants, and panels B and C report model outcomes for higher and lower vocabulary groups, respectively. Significant effects are highlighted in bold, marginal effects in italics. Covariance matrices are appear with the analytic code on osf.io/kt9gs

Exploratory analyses also found that category pairings did not contribute to the model, and that removing potential late-talkers below the 10^th percentile on MBCDI did not influence the results, these analyses can be viewed on osf.io

^**

Models were also repeated using robust regression, which controls for potential influence of outliers. Statistical patterns are identical and reported on osf.io