The Role of Linguistic Diversity in the Prediction of Early Reading Comprehension: A Quantile Regression Approach

Figures & data

Table 1. Demographics for L1 Readers and L2 Readers.

	L1 Readers (n = 83)		L2 Readers (n = 78)
	Frequency	%	Frequency	%
Country of birth
NL	82	98.8%	72	92.3%
Turkey	0	0.0%	4	5.1%
Other	1	1.2%	2	2.6%
Parents’ county of birth
Both parents born in NL	59	71.1%	6	7.7%
One parent born in NL	16	19.3%	30	38.4%
Neither parent born in NL	7	8.4%	39	50.0%
Home language
Only Dutch	83	100.0%	0	0.0%
Turkish and Dutch	0	0.0%	76	97.4%
Only Turkish	0	0.0%	2	2.6%

Note. L1 = first language; L2 = second language; NL = The Netherlands.

Table 2. Descriptive Statistics for All Measures and Composite Scores for L1 Readers and L2 Readers.

	L1 Readers (n = 83)		L2 Readers (n = 78)		M Differences
Measures/Potential Range	M (SD)	Range	M (SD)	Range	t	p	d
DEC
Words^a/0–420	136.9 (54.8)	37–309	130.5 (47.9)	40–245	0.80	.428	0.12
Pseudo words^a/0–420	100.6 (43.0)	28–266	101.9 (37.8)	22–199	–0.20	.840	0.03
Composite score^b	0.0 (1.1)		0.0 (0.9)		0.30	.765	0.04
VOCAB
Receptive^a/0–64	44.1 (5.5)	27–56	33.3 (7.1)	10–48	10.84	< .001*	1.70
Productive^a/0–70	23.0 (6.1)	11–36	16.6 (4.5)	6–28	7.67	< .001*	1.19
Composite score^b	0.6 (0.7)		−0.6 (0.7)		10.43	< .001*	1.64
MSK
Morphology^a/0–36	25.7 (4.5)	14–35	18.9 (5.2)	3–32	8.86	< .001*	1.40
Syntax^a/0–33	29.1 (2.7)	19–33	26.3 (3.5)	15–31	5.65	< .001*	0.90
Composite score^b	0.5 (0.7)		−0.5 (0.8)		8.37	< .001*	1.32
WM^a/0–14	3.5 (1.1)	1–6	3.4 (1.1)	1–6	0.92	.358	0.15
RC
Semantic relations^a/0–27	23.3 (2.6)	14–27	21.0 (4.1)	9–27	4.24	< .001*	0.67
Anaphoric relations^a/0–24	20.2 (3.1)	6–24	18.6 (3.9)	9–24	2.74	.007*	0.45
CITO test^a/0–252	137.7 (25.8)	84–210	114.7 (21.3)	70–190	6.13	< .001*	0.97
Composite score^b	0.3 (0.7)		−0.4 (0.9)		5.51	< .001*	0.87

Note. L1 = first language; L2 = second language; DEC = decoding skills, VOCAB = vocabulary, MSK = morphosyntactic knowledge, WM = working memory, RC = reading comprehension.

^a Values are based on raw scores. ^bValues are based on composite scores, calculated by the average z score on both tests.

*Mean difference is significant based on critical p values from linear step-up procedure (Benjamini & Hochberg, 1995) to correct for multiple testing.

Table 3. Correlations Across Variables Based on Composite Scores.

	DEC	VOCAB	MSK	WM	RC
DEC	—
VOCAB	.05	—
MSK	.05	.78***	—
WM	.19*	.17*	.19*	—
RC	.23**	.62***	.67***	.24**	—

Note. DEC = decoding, VOCAB = vocabulary knowledge, MSK = morphosyntactic knowledge, WM = working memory, RC = reading comprehension.

*p < .05. **p < .01. ***p < .001.

Table 4. Ordinary Least Squares Multiple Regression Analysis for Early Reading Comprehension Performance.

	ΔR^2	B	SE	β
Step 1	.513***
Decoding skills		.16	.05	.18**
Vocabulary knowledge		.21	.08	.23*
Morphosyntactic knowledge		.44	.09	.46***
Working memory		.06	.05	.08
Step 2	.000
Decoding skills		.16	.05	.18**
Vocabulary knowledge		.22	.09	.24*
Morphosyntactic knowledge		.44	.09	.47***
Working memory		.06	.05	.08
Language background		.03	.12	.02
Step 3
Decoding skills		.09	.06	.11
Vocabulary knowledge	.019	.36	.11
Morphosyntactic knowledge		.26	.13	.39**
Working memory		.11	.07	.27*
LB		−.01	.12	.13
Decoding skills × LB		.12	.10	−.01
Vocabulary knowledge × LB		−.33	.19	.09
Morphosyntactic knowledge × LB		.35	.18	−.22
Working memory × LB		−.10	.10	.26
				−.08

Note. LB = language background.

*p < .05. **p < .01. ***p < .001.

Figure 1. Distribution of reading comprehension performance across quartiles (low = 0–25%, below average = 25–50%, above average = 50–75%, high = 75–100%) split by language background.

Table 5. Individual Quantile Regression Coefficients and Comparisons of Coefficients for Poor (P), Average (A), and Good (G) Comprehenders.

	Quantile Regression Coefficients [95% CI]			p Value of Comparisons
	0.25 (P)	0.50 (A)	0.75 (G)	P vs. A	P vs. G	A vs. G
DEC	0.36* [0.07, 0.42]	0.22* [0.03, 0.30]	0.06 [−0.03, 0.21]	.189	.010^a	.001^a
VOC	0.68* [0.49, 0.77]	0.51* [0.37, 0.64]	0.45* [0.34, 0.58]	.029^a	.013^a	.444
MSK	0.72* [0.60, 0.77]	0.63* [0.56, 0.72]	0.57* [0.44, 0.76]	.256	.107	.322
WM	0.22* [0.13, 0.40]	0.20* [0.05, 0.39]	0.19* [0.09, 0.32]	.811	.761	.857
LB	−0.99* [−1.34, −0.62]	−0.56* [−0.84, −0.42]	−0.38* [−0.55, −0.26]	.024^a	.007^a	.188

Note. CI = confidence interval; DEC = decoding; VOC = vocabulary knowledge; MSK = morphosyntactic knowledge; WM = working memory; LB = language background.

^a Between-quantile difference is significant based on critical p values from linear step-up procedure (Benjamini & Hochberg, 1995) to correct for multiple testing.

*Coefficient is significant (p < .05) because the confidence interval does not include zero.

Table 6. Multiple OLS Regression and Multiple Quantile Regression Analyses for Poor (P), Average (A), and Good (G) Comprehenders.

		Quantile Regression [95% CI]
	OLS [95% CI]	0.25 (P)	0.50 (A)	0.75 (G)
Intercept	−0.03 [−0.18, 0.12]	−0.34 [−0.57, −0.22]	−0.07 [−0.18, 0.15]	0.30 [0.19, 0.56]
DEC	0.16* [0.06, 0.25]	0.16* [0.03, 0.23]	0.16* [0.05, 0.26]	0.11* [0.02, 0.21]
VOC	0.22* [0.04, 0.40]	0.23* [0.08, 0.39]	0.19 [−0.11, 0.35]	0.09 [−0.12, 0.36]
MSK	0.44* [0.27, 0.61]	0.51* [0.33, 0.70]	0.50* [0.30, 0.66]	0.55* [0.27, 0.72]
WM	0.06 [−0.03, 0.16]	0.08 [−0.02, 0.19]	0.10 [−0.01, 0.20]	0.09* [0.02, 0.18]
LB	0.03 [−0.21, 0.27]	−0.02 [−0.22, 0.26]	0.12 [−0.17, 0.34]	0.09 [−0.26, 0.44]

Note. OLS = ordinary least squares; CI = confidence interval; DEC = decoding; VOC = vocabulary knowledge; MSK = morphosyntactic knowledge; WM = working memory; LB = language background.

*Coefficient is significant (p < .05) because the confidence interval does not include zero.

Benjamini, Y., & Hochberg, Y. (1995). Controlling for the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistics Society, 57, 289–300.

 Google Scholar