TABLE 3.

Model parameters and summaries of linear and polynomial regression analyses of Sentence ID predicting SLS-Berlin sentence characteristics.

	Model parameters				Model summary
	Estimate	SE	t-value	p-value	adj. R2	F-value	p-value
Number of characters
Linear	2.27	0.57	4.01	<0.001	−	−	−
Squared	–0.06	0.02	–3.82	<0.001	−	−	−
Cubic	0.0006	0.0001	4.33	<0.001	0.65	47.77	<0.001
Number of syllables
Linear	0.55	0.19	2.82	0.006	−	−	−
Squared	–0.01	0.006	–2.57	0.010	−	−	−
Cubic	0.0001	0.00005	3.0	0.004	0.59	36.99	<0.001
Number of words
Linear	0.41	0.12	3.15	0.002	−	−	−
Squared	–0.01	0.004	–3.26	0.002	−	−	−
Cubic	0.0001	0.00003	3.51	<0.001	0.27	10.46	<0.001
Number of long wordsa (>6 letters)
Linear	0.03	0.006	4.79	<0.001	0.23	22.99	<0.001
Squared	−	−	−	−	−	−	−
Cubic	−	−	−	−	−	−	−
Number of nouns
Linear	0.02	0.004	4.59	<0.001	0.21	21.05	<0.001
Squared	−	−	−	−	−	−	−
Cubic	−	−	−	−	−	−	−
Number of punctuation marks
Linear	−	−	−	−		<1	n.s.
Squared	−	−	−	−
Cubic	−	−	−	−
Fleschb
Linear	–0.52	0.14	–3.72	<0.001	0.15	13.81	<0.001
Squared	−	−	−	−	−	−	−
Cubic	−	−	−	−	−	−	−
Complexity rating*
Linear	0.06	0.019	3.44	<0.001	−	−	−
Squared	–0.002	0.0006	–3.34	0.001	−	−	−
Cubic	0.00002	0.000005	3.71	<0.001	0.51	27.35	<0.001
Point of decision*c
Linear	−	−	−	−		<1	n.s.
Squared	−	−	−	−
Cubic	−	−	−	−

^*In a prestudy 19 participants (different from the norm sample) rated the complexity (on a five-point scale ranging from one to five) and marked the point of semantic decision for all 77 sentences. ^aBest model fit after excluding influential cases (i.e., sentence 77) due to large residuals (< ± 3). ^bBest model fit after excluding influential cases (i.e., sentences 8 and 28) due to large residuals (< ± 3). ^cBest model fit after excluding influential cases (i.e., sentences 10 and 33) due to large residuals (< ± 3).