. 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 4.

LMER results for related trials^*.

A) All participants (N=61) ^**
	Estimate	Std. Error	t-value	p-value
(Intercept)	0.746	0.205	3.646	0.0003
Vocabulary	−0.032	0.152	−0.210	0.834
Semantic category density	−0.316	0.143	−2.214	0.027
Word Degree	0.277	0.271	1.024	0.306
Global Cluster Coefficient	0.034	0.091	0.370	0.711
Age	−0.707	0.252	−2.806	0.005
Vocab. x Sem. Category Density	−0.167	0.139	−1.202	0.230
Vocab. x Word Degree	−0.140	0.174	−0.805	0.421
Vocab. x GCC	−0.003	0.085	−0.031	0.975
Age x Vocab.	−0.183	0.219	−0.836	0.403
Age x Sem. Category Density	0.398	0.199	1.996	0.046
Age x Word Degree	−0.324	0.276	−1.172	0.241
Age x GCC	0.677	0.337	2.008	0.045
Cat. Density x Age x Vocab.	0.128	0.200	0.638	0.524
Degree x Age x Vocab.	0.153	0.186	0.820	0.412
GCC x Age x Vocab.	0.401	0.346	1.161	0.246
B) Higher vocabulary group (N=30)

	Estimate	Std. Error	t-value	p-value
(Intercept)	0.823	0.154	5.343	<0.0001
Semantic category density	−0.368	0.186	−1.979	0.048
Word Degree	0.016	0.135	0.117	0.907
Global Cluster Coefficient	−0.398	0.227	−1.750	0.081
Age Group	−1.152	0.337	−3.416	0.0007
Age Grp x Sem. Category Density	0.535	0.265	2.024	0.043
Age Grp x Word Degree	−0.123	0.151	−0.813	0.416
Age Grp x GCC	1.997	0.745	2.681	0.008

C) Lower vocabulary group (N=31)

	Estimate	Std. Error	t-value	p-value
(Intercept)	0.907	0.562	1.615	0.107
Semantic category density	−0.148	0.200	−0.742	0.458
Word Degree	0.691	0.776	0.891	0.374
Global Cluster Coefficient	0.031	0.066	0.475	0.635
Age Group	−0.664	0.587	−1.131	0.258
Age Grp x Sem. Category Density	0.171	0.287	0.594	0.553
Age Grp x Word Degree	−0.634	0.787	−0.805	0.421
Age Grp x GCC	0.271	0.291	0.931	0.352

Panel A illustrates results for all participants, and panels B and C include model outcomes for higher and lower vocabulary groups, respectively. Significant effects are highlighted in bold, marginal effects (.05<p<.1) in italics. Covariance matrices are posted 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