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. Author manuscript; available in PMC: 2026 Feb 26.
Published before final editing as: J Learn Disabil. 2025 Dec 17:222194251399192. doi: 10.1177/00222194251399192

Comparing Children with and without Learning Disabilities on their Home Literacy Environment and its Association with Pre-Reading Skills

Rachelle M Johnson 1,2, Sara A Hart 3, Richard K Wagner 1,2
PMCID: PMC12935164  NIHMSID: NIHMS2140631  PMID: 41405136

Abstract

Despite children with learning disabilities (LDs) being at high risk for reading delays, how the informal home literacy environment (HLE) of LD children compares to that of their non-LD peers has not previously been investigated. Neither has the extent to which informal HLE is associated with pre-reading skills been compared for these two groups. To address these questions, we analyzed the data of 2,090 U.S. children with and without LDs from the nationally representative Early Childhood Longitudinal Study–Kindergarten Cohort of 2010-2011 (ECLS-K:2011). Children with LDs had a lower informal HLE the summer before kindergarten than those without LDs, although this difference was not independent of group differences in SES. Next, informal HLE was associated with pre-reading skills at the start of kindergarten comparably for children with and without LDs, and this remained true after accounting for SES. In conclusion, LD children experience lower informal HLE than their non-LD peers.

Keywords: home literacy environment (HLE), reading achievement, learning disability, ECLS-K:2011

The home literacy environment is included in risk and resilience models of the reading achievement of children with learning disabilities (Catts & Petscher, 2022). However, this assumption of HLE as a factor of risk and resilience has not been made based on studies of learning disabled1 children but instead based on studies of poor readers and children with a family history of learning disabilities. In fact, to date, no existing studies have empirically measured and compared the HLE of children with and without learning disabilities. The overarching goal of this study was to improve knowledge of the foundational literacy experiences and skills children with learning disabilities are entering kindergarten with. To do so, the current study compared the home literacy environments of children with and without learning disabilities using a large nationally representative U.S. sample. Furthermore, the association between the home literacy environment and pre-reading skills was examined across children with and without learning disabilities. We hope this research will be informative for teachers, parents, and researchers so that they are better equipped to support learning-disabled children as they enter kindergarten.

Home Literacy Environment (HLE)

The home literacy environment (HLE) refers to aspects of the home environment that promote children’s access to and engagement with reading-related materials (Niklas & Schneider, 2017; Puglisi et al., 2017). The most widely recognized model of HLE is the Home Literacy Model by Sénéchal and LeFevre (2002). This model conceptualizes HLE as a culmination of various literacy-related aspects that can be measured in the home environment. The Home Literacy Model further posits two components of HLE: formal and informal literacy experiences. Formal literacy experiences refer to formal instructional activities, such as a parent teaching their child letter sounds (Sénéchal & LeFevre, 2002). Informal home literacy experiences refer to literacy-related activities and aspects of the home environment that can promote literacy but do not involve formal instruction. Examples include shared reading between the parent and child and access to print in the home. In this paper, we specifically focused on informal HLE.

Informal HLE is best known for being a robust correlate of reading achievement and pre-reading skills (Dong et al., 2020). Shared book reading between parents and their children (a part of the informal HLE) is considered the hallmark of family literacy (Roskos & Twardosz, 2004) and is positively correlated with children’s early reading achievement (Sénéchal, 2006; Yeo et al., 2014). Early reading skills also tend to be higher among children with greater access to books (access to books is an aspect of the informal HLE; Aikens & Barbarin, 2008; Bhattacharya, 2010; Zadeh et al., 2010). In particular, informal HLE is associated with pre-reading skills in kindergarten that set the stage for long-term reading development (Burris et al., 2019; Lonigan et al., 2000; Sénéchal & LeFevre, 2014). With the informal HLE serving as a foundation for pre-reading skills and later reading achievement, it is beneficial to know the HLE of various groups of children, especially those at high risk of delayed reading acquisition. With such knowledge, kindergarten teachers can be better equipped to support children who are more likely to struggle in their reading acquisition by being able to anticipate which children are more likely to have lower HLE prior to kindergarten.

The HLE and Pre-Reading Skills of Poor Readers and LD Children

Little is currently known about the HLE of children who have learning disabilities. We do know that poor readers tend to have lower informal HLE when compared to children with typical reading abilities (Dong et al., 2020). Poor readers tend to have fewer books in the home and are read to by their parents less often than students with proficient reading abilities (Tichnor-Wagner et al., 2016). Although there is overlap in group membership between poor readers and LD children, LD children with reading impairments represent a distinct group of students whose literacy skills are a specific area of relative weakness, although co-occurring other areas of specific weakness are common (Grigorenko et al., 2020; Wagner et al., 2020, 2022). Consequently, it is important to examine the HLE experienced by LD children specifically, given that it may or may not be comparable to that of poor readers in general.

Studies of the HLE of children with a family history of LDs (i.e., having an LD parent) may be informative because family history is among the most salient risk factors of a child also being LD (Fletcher et al., 2019; Snowling & Melby-Lervåg, 2016). LDs are heritable and tend to run in families, such that it is common for LD children to have an LD parent (Erbeli et al., 2019). Parents create environments in the home that reflect their own genetically-influenced abilities (Plomin et al., 1977; Scarr & McCartney, 1983). This results in a correlation between the genes a child inherits and the home environment their parents cultivate. This phenomenon is referred to as passive gene-environment correlations and, in part, explains the association between HLE and children’s reading achievement (Hart et al., 2021). Children of parents with reading disabilities (RD; a type of LD) tend to have lower informal HLE than children whose parents do not have an RD (Esmaeeli et al., 2018, 2023; Hamilton et al., 2016). However, one study found this was not significant when SES was accounted for (Hamilton et al., 2016).

A reason for the paucity of studies of the HLE of LD children is a practical problem that is difficult to overcome. Ideally, HLE is measured before or during kindergarten, a time at which LDs are not often identified yet (Sanfilippo et al., 2020). Our current study overcomes this barrier by using a large-scale, nationally representative sample of children who’s informal HLE was measured prior to kindergarten and who were followed longitudinally through fifth grade. The longitudinal follow-up made it possible to know which of the children were later diagnosed with LD.

Additionally, whether HLE is equally predictive of pre-reading skills for children with and without LDs is currently unknown. A common characteristic of children with LDs is poor response to reading instruction and intervention (Fletcher et al., 2019). For example, results of a recent model-based meta-analysis showed that response to instruction and intervention, family risk, and poor phonological processing each made independent contributions to predicting dyslexia (Wagner et al., 2023). Whether poor response to instruction and intervention generalizes to response to informal HLE remains an open question. If children with LDs are less responsive to informal HLE, the association between HLE and pre-reading skills may be less for students with LDs compared to students without LDs.

Rashid et. al. (2005) is the only study to date that has measured HLE in LD students; however, they did not include a non-LD comparison group. They examined the association between the current HLE and reading achievement of 65 RD children in middle childhood (mean age 8). They found mixed results on whether HLE was associated with reading achievement; the home literacy activities of the children (e.g., the frequency that the child read alone) were not related to their current reading comprehension or spelling, but the home literacy activities of their parents (e.g., parent’s ownership of a library card) were related to the RD children’s reading comprehension and spelling. However, it is worth noting that Rashid et. al. (2005) did not examine how LD children compare to non-LD children on their HLE, and HLE was measured at an older age than is typically considered salient. This has left a need for further investigation of LD children’s HLE prior to kindergarten and how their HLE compares to non-LD children.

Current Study

The overarching goal of this study was to better understand the foundational literacy experiences LD children possess upon entering kindergarten. There were two specific aims. The first specific aim was to compare the informal HLEs experienced by children with and without LDs before kindergarten. The second specific aim was to compare the extent to which informal HLE is associated with pre-reading skills at the beginning of kindergarten for children with and without LDs. Finally, for both aims, follow-up analyses were performed to account for SES. SES is a known correlate of the home learning environment (Davis-Kean et al., 2021; Van Steensel, 2006; Yarosz & Barnett, 2001; Davis-Kean, 2005; Fletcher & Reese, 2005) and may be particularly important to consider in LD populations, given that LD children tend to come from families of lower SES backgrounds than non-LD children (Hornby & Lafaele, 2011). If LD children experience lower HLE than non-LD children and HLE turns out to be predictive of the pre-reading skills of LD children, the importance of efforts to improve the HLE of children at risk for LDs will be clearer.

Method

Participants

We preregistered this study on Open Science Framework (https://osf.io/vu65w). This study used data from The Early Childhood Longitudinal Study–Kindergarten Cohort of 2010-2011 (ECLS-K:2011; Tourangeau et al., 2017). The ECLS-K:2011 is a nationally-representative sample of approximately 18,170 United States children who were in kindergarten (~age 5) in the 2010-2011 school year and then followed longitudinally through fifth grade (~age 10). The sample selection for ECLS-K:2011 was stratified to be nationally-representative of U.S. children entering kindergarten in fall 2010 with respect to socioeconomic status, racial and ethnic background, region of the country, type of school (private and public schools), and kindergarten type (full and half-day kindergarten). A more detailed description of the ECLS-K:2011 dataset can be found in its publicly available manual and codebook (see Tourangeau et al., 2019). For this paper, we used a subsample of 1,090 children with LDs and 1,000 without LDs in the ECLS-K:2011 dataset. Children who were deaf, blind, or had intellectual disabilities were excluded from the analytic sample. The sample was diverse in terms of gender, race, and socioeconomic status. Table 1 presents the demographics of the LD and non-LD groups for their gender, race, ethnicity, parent education levels, and annual household income. Additionally, the frequencies for each response option within parent education levels and annual household income are presented in Table 2.

Table 1.

Demographics of the LD and Non-LD sample

LD group
 Non-LD group
Mean/% Median Mean/% Median Min Max
Parent education+ Vocational program, but no diploma Vocational program diploma Vocational program diploma Some college, but no degree Grade 7 or less Professional degree after bachelor’s
Household income+ $45,000 $35,000 $50,000 $55,000 Less than $5,000 Over $200,000
Male 65% 51%
White 47% 48%
Black 14% 12%
Hispanic 29% 25%
Asian 3% 7%
Pacific Islander 1% 1%
Native American 1% 1%
Multiracial 5% 5%
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Note. LD = learning disability. + Frequencies for each of the response options for parent education and household income are reported in Table 2.

Table 2.

Percentage of Children in each Category of Household Income and Parent Education Level

LD group Non-LD group
Income
  1. $5,000 or less 6% 3%
  2. $5,001-10,000 6% 5%
  3. $10,001-15,000 10% 5%
  4. $15,001-20,000 10% 5%
  5. $20,001-25,000 10% 8%
  6. $25,001-30,000 6% 7%
  7. $30,001-35,000 6% 5%
  8. $35,001-40,000 5% 5%
  9. $40,001-45,000 3% 3%
  10. $45,001-50,000 4% 4%
  11. $50,001-55,000 3% 4%
  12. $55,001-60,000 3% 2%
  13. $60,001-65,000 2% 3%
  14. $65,001-70,000 2% 3%
  15. $70,001-75,000 2% 4%
  16. $75,001-100,000 9% 13%
  17. $100,001-200,000 10% 17%
  18. $200,001 or more 4% 5%
Parent education level
  1. Grade 7 or less 5% 3%
  2. Grade 8 2% 2%
  3. Grade 9 4% 2%
  4. Grade 10 4% 2%
  5. Grade 11 6% 4%
  6. Grade 12 but no diploma 4% 2%
  7. High school diploma/equivalent 3% 4%
  8. High school diploma 18% 15%
  9. Vocational program, but no diploma 2% 1%
  10. Vocational program diploma 5% 4%
  11. Some college but no degree 18% 17%
  12. Associate’s degree 9% 9%
  13. Bachelor’s degree 13% 22%
  14. Attended graduate school, but no degree 1% 2%
  15. Master’s degree 6% 10%
  16. Doctorate degree 0.1% 1%
  17. Professional degree after bachelor’s 1% 2%
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Note. LD = learning disability.

Measures and Procedure

LD status was determined using both parent and teacher reports. First, parents reported whether their child had ever been diagnosed with an LD. However, due to the skip logic of the parent interview, some parents of LD students were likely not asked if their child had an LD. Thus, it was determined that this parent report measure alone was not adequate for identifying which students in the sample were LD. A teacher report of LD was collected as well. For children with an IEP, the special education teacher/provider reported the disability categories under which the child was classified. Children who were reported to be receiving disability services under the disability category of LD were placed in the LD group. Both parent and teacher report of LD status was collected every year (kindergarten through fifth grade) of data collection. If a child was identified as being LD during any year, kindergarten through fifth grade, by either parent or teacher report, they were considered LD for the purposes of this paper. LD status was binary coded (LD = 1, non-LD = 0). The data collected did not allow for further differentiation of children into their specific LDs, and thus, LD was operationalized as one larger umbrella category of LD. There is high co-morbidity across the subtypes of LDs, and LD students tend to struggle in multiple academic subject areas, but LDs in reading are the most common by far (Compton et al., 2012; Cortiella & Horowitz, 2014). Using this described process, an LD sample of about2 1,090 was identified. We decided to limit the size of the non-LD sample to have comparable sensitivity for analyses on both groups. Thus, a random sample of 1,000 was selected from the remaining non-LD students to create the non-LD comparison group.

Informal HLE was assessed in an interview with children’s parents during the fall of kindergarten. Parents were asked five questions about their children’s HLE during the summer before kindergarten. All five questions pertained to the informal HLE, as opposed to the formal HLE. First, they were asked how often family members read with their child in a typical week (HLE1). This was measured on a four-point Likert scale (1 = not at all, 4 = every day). Second (HLE2), they were asked “how long is [child] read to at each of these times?” (reported in minutes). Third (HLE3), they reported the number of children’s books in the home (including library books). Fourth (HLE4) they were asked, in the past week “how often did [child] look at picture books outside of school.” This was measured on a four-point Likert scale (1 = not at all, 4 = every day). Fifth (HLE5), they were asked “in the past week, how often did [child] read to or pretend to read to [himself/herself] or to others outside of school?” This was measured on a four-point Likert scale (1 = not at all, 4 = every day).

Pre-reading skills were directly assessed in kindergarten at the beginning of the school year (fall 2010) using a computer-adaptive measure (Tourangeau et al., 2017). During kindergarten, the questions assessed basic skills, including print familiarity, letter recognition, beginning and ending sounds, and vocabulary. Pre-reading skills were reported as a composite score, rather than children’s scores on each of these basic reading skills separately. Specifically, we used the Item Response Theory (IRT) based theta score, which accounts for item-level difficulty, allowing the pre-reading skills of children who received different questions to be compared.

Socioeconomic status was a composite measure of SES that included father’s education level, mother’s education level, father’s occupational prestige, mother’s occupational prestige, and household income. Of note, SES was lower in children with (mean = −0.34) than without (mean = −0.04) LDs, t(1800) = 8.352, p < .001.

Analytic Plan

As a first step, we examined the HLE data for missingness and outliers. Parents of children with and without LDs had a similar response rate of about 73% on the HLE survey questions. All five HLE questions were checked for outliers and the shape of their distributions. For each variable, datapoints outside of the median plus or minus twice the interquartile range for that variable were considered outliers and adjusted to be set at the edge of that range. For each HLE question, 1-5% of the data points were identified as outliers and adjusted accordingly. Afterwards, descriptive statistics and correlations between all pairs of HLE variables were examined. These steps were done using R (R Core Team, 2021).

Confirmatory Factor Analysis of the Home Literacy Environment

Next, confirmatory factor analysis (CFA) was carried out to examine the factor structure of HLE for students with and without LDs. For each group, a single factor CFA– with all five HLE questions loaded onto a single latent HLE factor – was run and the model fit was examined. We then ran a multi-group CFA to test if the factor structure was the same across the LD and non-LD groups. Two CFA models were run as multi-group CFAs. For the first (unconstrained) model, the loadings of the indicators onto the HLE latent factor were allowed to differ for the two groups. For the second (constrained) model, the groups were constrained to have identical loadings. For both models, scalar invariance was established by constraining the item intercepts to be identical across groups. A chi-square difference test was used to determine if constraining the indicator loadings to be the same for children with and without LDs significantly reduced model fit. In the case that the constrained model did not have significantly worse fit than the unconstrained model, the constrained model would be accepted. In the case of a significant difference in model fit, the unconstrained model would be retained; this would indicate that the LD and non-LD groups had different HLE factor structures. Provided the groups had comparable factor structures, the means of the latent variables could be compared between the LD and non-LD groups.

We performed an additional analysis to determine if LD status predicted HLE after controlling for SES. Scores on the latent HLE factor were predicted by both LD status and SES. For this model, LD status was binary coded 1 = LD group and 0 = non-LD group. This analysis was suggested by a reviewer and, therefore, was not included in the preregistration.

Structural Equation Model of the Association between HLE and Pre-Reading Skills

We performed follow-up analyses to examine HLE as a predictor of pre-reading skills at the start of kindergarten.3 A structural equation model (SEM) was conducted with the latent factor HLE as a predictor of fall kindergarten pre-reading skills. This model was run once as an unconstrained model and once as a constrained model. In the unconstrained model, the parameter estimates of the association between pre-reading skills and latent HLE were allowed to differ for the LD and non-LD groups. In the constrained model, these parameter estimates were forced to be identical for the LD and non-LD groups. The fit of the constrained and unconstrained models were then compared using a chi-square difference test. If the constrained model had a significantly worse fit than the unconstrained model, then we would retain the unconstrained model. This would indicate that the strength of the association between HLE and pre-reading skills differed for children with and without LDs. Conversely, if the constrained model did not have a significantly worse fit than the unconstrained model, the constrained model would be accepted, indicating no differences between the LD and non-LD groups in the association between HLE and pre-reading skills. Finally, the accepted model (constrained or unconstrained) was rerun with SES included as a secondary predictor of pre-reading skills to test whether HLE predicts pre-reading skills independently of SES.

Fit Statistics

For all the models, analyses were conducted in Mplus (Muthén & Muthén, 2017), using full information maximum likelihood to handle missing data. For all CFA models, the fit of the models run was evaluated using several fit statistics. First, the chi-squared test of model fit gives the probability that the model perfectly fits the population covariance matrix. A higher p-value indicated a better fitting model, as it is the probability that the model fits the data in the population. If the model provided a perfect fit, the expected chi-squared value would be approximately equal to its degrees of freedom. However, the chi-squared test is sensitive to sample size. With large samples, it almost always will be significant, indicating misfit between the model and the population covariances. Additional fit statistics examined include the Root Mean Square Error of Approximation (RMSEA), for which a value of under 0.05 indicates good fit and over 0.1 indicates poor fit. The Tucker-Lewis Index (TLI) compares the independence model to the proposed model; if the proposed model fits as poorly as the independence model it would have a value closer to 0, indicating poor fit. A Tucker-Lewis Index (TLI) value greater than .90 indicates a moderate fitting model and a value greater than .95 is an indicator of a good fitting model. The Comparative Fit Index (CFI) was examined, for which a value of greater than .90 indicates moderate fit and greater than .95 indicates a good fitting model. Finally, the standardized mean square residual (SRMR) was examined, which is the average distance between the values of the observed and implied correlation matrix. For this test, values less than .10 indicate a good fitting model.

Results

After handling outliers, all five HLE variables had acceptably low skew and kurtosis values. The distributions of the data for the questions were examined using histograms. Scatterplots of the pairs of HLE variables were examined for nonlinearity and bivariate outliers. See Table 3 for descriptive statistics and Table 4 for correlations between the five HLE variables.

Table 3.

Descriptive Statistics of Home Literacy Environment Questions for Each Group

LD group
 Non-LD group
Mean Standard
Deviation		 N Mean Standard
Deviation		 N
HLE1 3.31 0.76 790 3.37 0.71 740
HLE2 20.11 9.68 780 20.06 9.01 730
HLE3 64.21 57.37 790 74.82 60.68 740
HLE4 3.22 0.80 790 3.28 0.78 740
HLE5 2.83 0.95 790 3.04 0.87 740
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Note. Per ECLS-K guidelines, all N values have been rounded to the nearest 10’s place. HLE = home literacy environment. LD = learning disability.

Table 4.

Bivariate Correlations Among all Pairs of Variables by Group

HLE1 HLE2 HLE3 HLE4 HLE5
HLE1 .13*** .29*** .38*** .29***
HLE2 .13*** .09** .12** .12**
HLE3 .26*** .06 .28*** .18***
HLE4 .41*** .08* .22*** .47***
HLE5 .29*** .17*** .13*** .47***
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Note. Above the diagonal is the non-LD group and below the diagonal is the LD group. * p < .05, ** p < .01, *** p < .001. HLE = home literacy environment. LD = learning disability.

Confirmatory Factor Analysis of the HLE

Model fit statistics for all models are reported in Table 5. Results of single-factor CFAs run separately for the LD and non-LD groups suggested that a single-factor model was appropriate for both groups. The factor loadings of the HLE items onto the HLE factor were comparable across the LD and non-LD group (see Table 6). Turning to multi-group models, the model constraining the factor loadings to be identical for the two groups did not have significantly worse fit than the unconstrained model, with a non-significant chi-square difference of 3.661 with 4 degrees of freedom. The model fit statistics for the constrained model indicated that it was an adequately fitting model (see Table 5). Thus, it was determined that there was full measurement invariance for the LD and non-LD groups, and we used the constrained model moving forward.

Table 5.

Model Fit Statistics

X 2 df p-value CFI TLI RMSEA SRMR
LD group, single-factor CFA model of HLE 29.579 5 < .001 0.944 0.887 0.079 0.033
Non-LD group, single-factor CFA model of HLE 16.961 5 .005 0.971 0.943 0.057 0.026
Multi-group constrained CFA model of HLE 68.158 18 < .001 0.941 0.935 0.060 0.039
Multi-group unconstrained CFA model of HLE 64.497 14 < .001 0.941 0.915 0.069 0.035
Association between HLE and LD status, accounting for SES 303.582 13 < .001 0.761 0.633 0.121 0.067
Multi-group constrained SEM model of the association between HLE and reading 136.002 27 < .001 0.887 0.875 0.066 0.049
Multi-group constrained SEM model of the association between HLE and reading, accounting for SES 440.568 38 < .001 0.720 0.690 0.113 0.102
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Note. CFA = confirmatory factor analysis. HLE = home literacy environment. LD = learning disability. SES = socioeconomic status. Reading = pre-reading skills. SEM = structural equation model. CFI = Comparative Fit Index. TLI = Tucker-Lewis Index. RMSEA = Root Mean Square Error of Approximation. SRMR = Standardized Root Mean Square Residual.

Table 6.

Confirmatory Factor Analysis HLE Results of the LD and Non-LD groups run in two Separate Models

LD group
 Non-LD group
Unstandardized
Loading
(p-value)		 Standard
error		 Standardized
Loading
(p-value)		 Unstandardized
Loading
(p-value)		 Standard
error		 Standardized
Loading
(p-value)
Loadings on HLE
HLE 1 1.000 (999.000) 0.000 0.542 (<.001) 1.000 (999.000) 0.000 0.523 (<.001)
HLE 2 4.251 (<.001) 1.057 0.180 (<.001) 4.633 (<.001) 1.119 0.190 (<.001)
HLE 3 43.407 (<.001) 6.297 0.311 (<.001) 62.786 (<.001) 8.019 0.383 (<.001)
HLE 4 1.472 (<.001) 0.145 0.755 (<.001) 1.587 (<.001) 0.165 0.751 (<.001)
HLE 5 1.376 (<.001) 0.135 0.595 (<.001) 1.410 (<.001) 0.144 0.603 (<.001)
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Note. HLE = home literacy environment. LD = learning disability

All five observed HLE questions significantly loaded onto the latent HLE factor (see Table 7). In looking at the standardized loadings, the second and third HLE questions had the smallest loadings on the latent factor, compared to the other three observed HLE questions. Each of the observed HLE questions had significant residual variance (all p’s < .001; see Table 7).

Table 7.

Confirmatory Factor Analysis Results of the Constrained Model

Unstandardized
Estimate
(p-value)		 Standard error Standardized
Estimate
(p-value)
Loadings on HLE
HLE 1 1.000 (999.000) 0.000 0.528 (<.001)
HLE 2 4.395 (<.001) 0.763 0.181 (<.001)
HLE 3 53.170 (<.001) 5.029 0.363 (<.001)
HLE 4 1.497 (<.001) 0.104 0.745 (<.001)
HLE 5 1.415 (<.001) 0.100 0.592 (<.001)
Residual variances
HLE 1 0.407 (<.001) 0.024 0.720 (<.001)
HLE 2 90.574 (<.001) 4.652 0.967 (<.001)
HLE 3 2944.750 (<.001) 157.173 0.868 (<.001)
HLE 4 0.284 (<.001) 0.028 0.445 (<.001)
HLE 5 0.587 (<.001) 0.038 0.650 (<.001)
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Note. HLE = home literacy environment.

With full measurement invariance established, it was possible to compare the means of the latent HLE factor for the two groups. The LD group had a significantly lower mean score on the latent HLE factor by 0.208 standard deviations compared with the non-LD group (standard error = 0.061, p < .001).

To test whether LD status was associated with HLE when SES was accounted for, an exploratory model was then constructed with LD status and SES as predictors of the latent HLE factor. Notably, this model had a poor fit to the data (see Table 5), and the results should be viewed as tentative at best and in need of replication. Keeping these caveats in mind, as predicted, the regression coefficient for SES was significantly greater than 0 (standardized estimate = 0.302, p < .001). Before including SES in the model, the regression coefficient for LD status was negative as predicted (standardized estimate = −0.105, p < .001). When SES was included as a second predictor, the magnitude of the regression coefficient for LD status was reduced and became no longer a statistically significant predictor of the latent HLE factor (standardized estimate −0.049, p = .105).

Structural Equation Model of the Association between HLE and Pre-Reading Skills

First, the normality of the data for the variable of pre-reading skills was confirmed for both students with and without LDs. No floor effects were observed (mean = −1.55, SD = 0.80, min = −3.73, max = 0.99). Fall pre-reading skills in kindergarten were significantly lower in the LD group (mean = −1.89, n = 910) than the non-LD group (mean = −1.19, n = 850; t = 20.23, p < .001).

In an SEM model, latent HLE (from the multi-group constrained CFA model) was modeled as a predictor of fall kindergarten pre-reading skills. The constrained model did not have a significantly worse fit than the unconstrained model. Thus, we proceeded with the constrained model for interpreting the results. This constrained model had an acceptable fit to the data (see Table 5). HLE was a significant positive predictor of pre-reading skills (standardized estimate = 0.220, S.E. = 0.030, p < .001).

This constrained SEM model was then run with SES as an additional predictor of pre-reading skills. However, adding SES resulted in poor model fit (see Table 5). SES significantly predicted pre-reading skills (standardized estimate = 0.323, S.E. = 0.024, p < .001). When accounting for SES, HLE still significantly predicted pre-reading skills (standardized estimate = 0.141, S.E. = 0.029 p < .001).

Discussion

The overarching goal of this paper was to better understand the foundational literacy experiences that children with LDs are entering kindergarten with. To do this, the first step was to establish any differences in the components that constitute the informal HLE of children with and without LDs. We then determined whether children with and without LDs differed in their mean level of informal HLE experienced before entering formal schooling. Next, we examined the extent to which informal HLE is associated with pre-reading skills at the start of kindergarten, and if this differed for children with and without LDs. Finally, we examined whether our results were robust to potential confounding by SES. We found that children’s informal HLE the summer before kindergarten was lower for children with than without LDs; however, this was not independent of SES. Furthermore, informal HLE was positively associated with start-of-kindergarten pre-reading skills at the same strength for children with and without LDs; this remained a significant finding after controlling for SES. The hope is that the knowledge gained from this study will further understandings of the role of HLE in preparing students with LDs for reading instruction.

Structure of Informal HLE

Informal HLE is conceptualized as the culmination of literacy-related materials and activities in the home (Puglisi et al., 2017). In alignment with the Home Literacy Model (Sénéchal & LeFevre, 2002), we found that all five HLE questions loaded onto a single shared latent factor of informal HLE. An advantage of measuring informal HLE as a latent factor is that the effects of measurement error and other question-specific variance is minimized or eliminated because the latent variable represents common variance. In our study we found evidence of full measurement invariance, indicating that this single factor structure of informal HLE was equivalent for children with and without LDs. Hamilton et al. (2016), also found full measurement invariance across children with and without an RD parent in a two-factor (i.e., informal and formal literacy experiences) model. In conclusion, the components in the home environment that together make up the informal HLE are similar for children with and without LDs. Furthermore, this allows for meaningful comparisons of the informal HLE between the two groups using the latent factor.

Comparing the HLE of Children with and without LDs

We found that LD children had lower informal HLE than their non-LD peers. These results suggest that even prior to entering kindergarten, LD students have had less exposure to literacy-related materials and activities than their peers without LDs. This aligns with past research documenting lower HLE for poor readers than children with reading abilities that would be typical for their age (Tichnor-Wagner et al., 2016). Our results demonstrate that low HLE is also observed in LD children. Although LD children and poor readers are distinct groups, most LD students are delayed in their early acquisition of reading skills in kindergarten and sustain lower reading performance than their non-LD peers throughout elementary school (Sullivan et al., 2017). Thus, in addition to LD students facing difficulties with reading acquisition due to their disabilities, our results indicate that LD students are also faced with the barrier of having fewer home literacy experiences to build their reading skills upon.

Socioeconomic Status

Past research has shown that HLE is associated with but not fully explained by SES (Carroll et al., 2019). Children with LDs tend to come from lower SES families than non-LD children (Hornby & Lafaele, 2011), which was also observed in our sample. Notably, after accounting for SES, we found the effect of LD status on informal HLE was reduced and no longer significant. However, the inclusion of this model with SES was an exploratory analysis (i.e., not preregistered), and this model had a poor fit to the data. Poor fit in an SEM model can mean that the parameter estimates are not trustworthy (Hoyle, 2023). Therefore, our finding regarding SES (i.e., that children with and without LDs had equivalent informal HLE when SES is accounted for) should be interpreted with extreme caution. Replications of this analysis with other datasets are needed to strengthen confidence in the role of SES in the HLE of children with LDs. Although our study is the first to compare the HLE of children with and without identified LDs, similar results to ours have been documented in children with a family history of RD. Informal HLE tends to be lower among children with than without an RD parent (Esmaeeli et al., 2018, 2023; Hamilton et al., 2016). However, Hamilton et al. (2016) found that this group difference in the informal HLE of children with and without an RD parent was not independent of differences in SES. The findings by previous studies on children with a family history of LDs and our tentative findings for LD children warrant further investigations into the unique variance that is attributable to informal HLE versus SES, especially in LD children and children at risk for LDs (e.g., children with a family history of LD). Nevertheless, we argue that the finding of lower informal HLE in children with than without LDs is a noteworthy finding, regardless of the extent to which this is attributable to the lower SES of children with than without LDs. The finding that LD children are entering kindergarten with lower informal HLE than their peers is of concern and warrants further investigation into how to support the foundational literacy experiences of LD children.

Future Directions

Family history of LDs is a potential explanation for our findings. Parents with LDs are likely to have children with LDs (Erbeli et al., 2019) and have lower educational attainment and vocational outcomes (Cortiella & Horowitz, 2014; de Beer et al., 2014). Parents’ genes related to their LDs influence both the home environment that they create (e.g., HLE) and their child being more likely to be LD, a passive gene-environment correlation (Hart et al., 2021, p. 20; Plomin et al., 1977; Scarr & McCartney, 1983). Supporting this, children of parents with LDs tend to have lower informal HLE than children of parents without LDs (Hamilton et al., 2016; Esmaeeli et al., 2018, 2023). Furthermore, among children with a family history of LD, mean HLE has been measured at equivalent levels in children with low and average reading achievement (Esmaeeli et al., 2019; van Bergen et al., 2011). Finally, in alignment with our study, Hamilton et al. (2016) found that group differences in informal HLE of children with and without an LD parent were no longer significant when SES was accounted for. As such, family history of LD could be an explanation for our findings that LD students have lower HLE than non-LD students. Unfortunately, we did not have a measure of family history to include in this study. Future research is needed that examines the role of family history in the HLE of LD children, while also accounting for SES.

HLE and Pre-Reading Skills

A secondary aim was added to investigate the extent to which HLE is associated with pre-reading skills at the beginning of kindergarten for children with and without LDs. Aligning with past research on typical children, we found that informal HLE is positively associated with pre-reading skills (Burris et al., 2019; Sénéchal & LeFevre, 2014). To date, only one existing study has measured HLE in LD children, and that study did not include a non-LD comparison group. Rashid et. al. (2005) found mixed results on whether HLE is significantly associated with reading achievement in LD children. A possible explanation for the inconsistent results of Rashid et. al. (2005) is that they measured HLE in older children (mean age 8). In our study, we found that HLE measured the summer before kindergarten was a significant predictor of LD children’s pre-reading skills at the start of kindergarten, and the strength of this association was equivalent to that of non-LD children. Furthermore, this result remained significant after controlling for SES. Based on our findings, we conclude that HLE is equally important to the initial reading skills of children with and without LDs. Children whose parents are providing exposure to text through literacy experiences during the summer before kindergarten are starting kindergarten ahead in their pre-reading skills compared to their classmates with lower HLE. Additionally, even LD children, who are likely to struggle with reading due to their disabilities, appear to benefit from high HLE. As such, it appears that when LD children receive high HLE prior to kindergarten, their HLE may act as a resilience factor in their pre-reading skills, aligning with theoretical models of risk and resilience (Catts & Petscher, 2022). This finding that HLE is equally associated with the pre-reading skills of children with and without LDs is encouraging. The poor response to reading instruction that is characteristic of LDs (Fletcher et al., 2019) does not appear to extend to informal HLE.

Limitations

This was a secondary data analysis of the ECLS-K:2011, which brought both advantages and limitations to our analyses. Beginning with advantages, this is a large nationally representative sample of U.S. children, which provides confidence that our findings apply to U.S. children in general. A second advantage is that it is a longitudinal dataset that covered the time frame from measurement of HLE prior to kindergarten entry through subsequent LD identification in later grades, a requirement for the present study. Turning to limitations, there are several types of LDs that impact different academic skills. It would be of interest to compare informal HLE across various subtypes of LDs. However, the way in which LD status was collected in the ECLS-K:2011 surveys did not allow for differentiating among types of LDs.

Furthermore, our measurement of informal HLE captured the quantity but not the quality of informal HLE provided. However, the informal HLE provided to children by their parents can be conceptualized as a combination of the frequency of activities and amount of resources available as well as how often the children are engaging with these informal HLE activities and resources (Arya et al., 2014). Quantity and quality provide distinct insights into the informal HLE children are receiving (Fletcher & Reese, 2005; Marjanovič Umek et al., 2005). From our study, we know the quantity of their informal HLE, such as how often they were read to and how many books they had in their home, but we do not know the quality of the texts they are reading nor the quality of the children’s interactions with the text while engaging with the informal HLE. Investigation of the quality of informal HLE children with LDs are receiving prior to kindergarten is a direction for future research.

Conclusions

With HLE being an established predictor of reading achievement and LD students being at high risk for delayed reading acquisition, the aim of this study was to provide needed knowledge of the HLE LD students experience prior to entering formal reading instruction in kindergarten. This research provides helpful insights for teachers on the literacy foundations children at risk of having learning disabilities are entering kindergarten with so that teachers will be better able to anticipate the literacy supports these children will need. We found that when LD children entered formal reading instruction in kindergarten, they were already starting with less of a literacy foundation than their non-LD peers in the form of their informal HLE. Given that the difference in HLE was not independent of SES, an important area for future research is to better understand relations between SES and HLE for LD children. For example, HLE might be one mediator of the relation between SES and both pre-reading skills and subsequent reading achievement. Second, HLE was associated with pre-reading skills at the beginning of kindergarten at the same strength for children with and without LDs, and this remained significant when SES was accounted for. Our findings support the value of future studies that attempt to improve the informal HLE for children at risk of developing learning disabilities.

Acknowledgments

This study was preregistered on the Open Science Framework (https://osf.io/vu65w). This work was supported by grant P50HD52120 from the National Institute of Child Health and Human Development. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. In addition, this research was undertaken, in part, thanks to funding from the Canada Excellence Research Chairs Program. Rachelle Johnson was supported by the FIREFLIES training fellowship funded by the Institute of Education Sciences grant R305B200020. There are no conflicts of interest to declare.

Footnotes

1.

Because linguistic preferences vary among those in the disability community (Andrews et al., 2022; Best et al., 2022; Gernsbacher, 2017; Johnson, 2023), we intentionally use both identity-first (learning disabled children) and person-first (children with learning disabilities) language throughout this paper.

2.

Per the data security guidelines required by the ECLS-K:2011, all N sizes throughout the paper have been rounded to the nearest ten’s place.

3.

Examining HLE predicting pre-reading skills in fall of kindergarten was not part of the original preregistration, as this was requested by reviewers. However, these follow-up analyses were added to the preregistration before running these models.

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