Why poor children are more likely to become poor readers: The school years

Abstract

Socioeconomic status at the individual- and school-level are positively related to literacy achievement in all English-speaking countries. The components of socioeconomic status – income, parent education and parent occupation – are each statistically significant predictors of school literacy achievement but they are primarily a proxy for more directly salient factors. This literature review outlines the factors that are most strongly implicated in literacy achievement. At the individual-level, they are early literacy ability, gene–environment interactions, home learning environment, time spent reading, sleep, school attendance and school mobility. At the school-level, they are school practices and teacher quality, including quality of initial reading instruction. These factors are interactive; not only are socioeconomically disadvantaged children more likely to experience these conditions, they are also more adversely affected by them than their more advantaged peers. This review concludes that understanding the nature of the relationship between socioeconomic status and literacy is the key to mitigating it.

Keywords

Literacy socioeconomic status family environment schools teacher effectiveness reading teaching

Introduction

The statistical relationship between social disadvantage and poor literacy has been well-documented in Australia and around the world (Australian Curriculum, Assessment and Reporting Authority, 2012; Organisation for Economic Cooperation and Development, 2010). A large number of Australian children struggle to learn to read at even a functional level. In the 2012 National Assessment Program for Literacy and Numeracy (NAPLAN), 6.4% of Year 3 students failed to achieve the minimum reading standards expected for their year of education. A further 9.4% achieved only the minimum standard. Children with low parent education levels and low parent occupational status were more likely to be among the group at the minimum benchmark or below. The 2012 NAPLAN report reveals that 33.4% of Year 3 children whose parents had not completed secondary school fell into this category, as did 31.6% of children whose parents had not been in paid work in the previous year (Australian Curriculum, Assessment and Reporting Authority, 2012). An international survey of Year 4 students, Progress in Reading Literacy 2011 (PIRLS) found that 24% of Australian students were below the intermediate international benchmark for literacy, which is deemed the minimum level of competency. PIRLS uses the (self-reported) number of books in the family home as a proxy for socioeconomic status (SES). Forty per cent of students who had ‘few’ (25 or fewer) books at home performed below the intermediate benchmark, compared with 16% of students who had ‘many’ (more than 200) books at home (Thomson et al., 2012).

NAPLAN and PIRLS data are ‘book-ended’ by survey data providing evidence of language and literacy gaps at school entry and at 15 years of age. In the 2009 Australian Early Development Index (AEDI) survey, which assesses children at the beginning of their first year of school, 13.9% of children in the lowest socioeconomic quintile were assessed as being ‘developmentally vulnerable’ in the language and cognitive skills domain, compared with only 4.7% of children in the highest socioeconomic quintile (Centre for Community Child Health and Telethon Institute for Child Health Research, 2009). The Program for International Student Assessment (PISA) assesses literacy skills of 15 year olds. In all countries there was a positive relationship between SES and literacy performance, to varying degrees. The strength of this relationship in Australia declined between PISA 2000 and PISA 2009 to become lower than the international average (Thomson & De Bortoli, 2010). Despite the relative decline, a substantial socioeconomic literacy gap was still evident in PISA 2009. Twenty-five per cent of children in the lowest socioeconomic quartile scored in the lowest ‘Level 1’ literacy band or below, compared with 5% of children in the highest socioeconomic quartile (Thomson & De Bortoli, 2010).

SES is usually a composite variable or index of relative socioeconomic advantage/disadvantage, with household income, parent occupation and parent education as its three components, each of which has been found to correlate significantly with literacy. In some studies, just one or two of these components is used as a measure of SES (Sirin, 2005). Overall, PISA studies find a medium positive correlation (approximately 0.3) between parent occupation and reading literacy scores, putting it within the range of correlations that are typically found for SES and school performance in empirical studies since the 1960s (Coleman et al., 1966; Jencks et al., 1972; Marks, 2009; OECD, 2010).

For many years it was assumed that the educational disadvantage associated with low SES was produced by the circumstances of the individual student, or the aggregate circumstances of individual students in the case of school-level relationships. Recent research on the impact of socioeconomic variables on education shows more complex interactions, however (Aikens & Barbarin, 2008; Holmes-Smith, 2006; Nicoletti & Rabe, 2010). Evidence is accumulating that a student’s achievement is predicted not just by their own SES but additionally, and more powerfully, by the average SES of their school (Holmes-Smith, 2006; Thomson & De Bortoli, 2010). Furthermore, it is becoming clear that SES is primarily a distal factor – a latent construct that acts as proxy for other variables that are more likely to directly affect literacy and academic development at both the individual- and school-level (Fergusson, Horwood, & Boden, 2008).

There are literally hundreds of studies investigating the impact of SES on literacy. Even the best of these, including longitudinal studies, do not demonstrate direct, causal relationships with reading ability. Nonetheless, there are sufficient high-quality studies to produce a compelling picture of the predictive pathways between various factors and literacy development. This literature review outlines the research evidence on the main factors that interact with, or mediate, the influence that social disadvantage at the individual- and school-level exerts on school-age literacy achievement. The articles included in this review were retrieved using a broad search of online databases. Several thousand articles were scanned for relevance and quality, with an emphasis on primary and quantitative research among those selected. For the most part, the review is limited to studies published since 2000, with a few exceptions for major studies which remain an important source of evidence or insight, and only studies conducted with English-speaking subjects have been included. Not every factor linking SES and literacy is discussed; only those for which the literature search revealed good evidence of a significant relationship. Australian data and research are presented wherever possible.

This literature review, which looks specifically at school-age children, is a companion paper to an article focusing on the relationship between SES and early literacy development (Buckingham, Beaman, & Wheldall, 2013). It contributes to the existing literature on SES and literacy by including the most recent published evidence, emphasising the findings that adverse effects of the risk factors for poor literacy are not just cumulative but amplified among socioeconomically disadvantaged students, and drawing conclusions for policy.

Individual factors

Low SES families are generally low income families. Although low income has been found to have a small, significant independent relationship with cognitive development and literacy (Blanden & Gregg, 2004), particularly if it is persistent (Dickerson & Popli, 2012; McLoyd, 1998), it is rarely found to be the most significant factor. Family income and material resources explain a relatively small unique proportion of the variance (Blanden & Gregg, 2004; Fergusson et al., 2008; Marks, Cresswell, & Ainley, 2006). Most research indicates that of the three measures that comprise the tripartite socioeconomic index, parent education has the strongest influence (Cheadle, 2008; Downer & Pianta, 2006; Marks, 2008; Marks et al., 2006; Sutton Trust 2010). Even so, these factors explain only part of the relationship between a student’s socioeconomic background and their literacy achievement at school (Dearden, Sibieta, & Sylva, 2011). Mediating factors include early literacy ability, the quality of the home learning environment, health and sleep, and school attendance and mobility. Research evidence describing these relationships is outlined below.

Early literacy ability

Gaps in children’s literacy abilities are evident when children begin school, with children from low socioeconomic backgrounds tending to demonstrate lower proficiency in the two main aspects of emergent literacy — phonological awareness (Henning, McIntosh, Arnott, & Dodd, 2010; McDowell, Lonigan, & Goldstein, 2007) and vocabulary/oral language competency (Farkas & Beron, 2004; Hart & Risley, 2003; Hay & Fielding-Barnsley, 2009; Locke, Ginsborg, & Peers, 2002; National Institute of Child Health and Human Development (NICHHD), 2005; Washbrook & Waldfogel, 2011). A research review by the authors (Buckingham et al., 2013) describes the risk factors associated with impaired early literacy development among socioeconomically disadvantaged children, including the early (prior to school) home learning environment and preschool attendance and quality.

Early literacy ability is a strong predictor of a child’s literacy performance throughout their school life (Claessens, Duncan, & Engel, 2009; Hecht, Burgess, Torgesen, Wagner, & Rashotte, 2000; Juel, 1988). Lubienski and Crane (2010) found that kindergarten reading score accounted for 25% of the variance in reading gains from Kindergarten to Year 5 in a longitudinal survey of children in the US, while in a separate analysis of the same survey data, Claessens et al. (2009) also found a correlation of 0.5 between kindergarten and Year 5 reading scores. Feinstein and Bynner’s (2004) analysis of British survey data found a 0.5 correlation between cognitive test scores (primarily language and reading) at ages five and 10 years. In a study by Currie and Thomas (1999), reading test scores at age seven significantly predicted reading test scores at age 16 years, explaining around 33% of the variance.

In addition, SES was a significant mediating factor in each of these studies, particularly for the persistence of low reading scores. For example, in Feinstein and Bynner’s (2004) analysis, 67% of low SES children who were in the lowest test score quartile at age five remained in the lowest quartile at age 10, compared with 34% of high SES children. These results show that reading ability is not set at age five – there is substantial mobility in the primary school years – but that low SES students are more likely to remain poor readers if they begin school as poor readers.

Gene–environment interactions

Reading disorders and individual differences within the normal range of reading ability among children are moderately to strongly ‘heritable’ or genetic (Astrom, Wadsworth, Olson, Willcutt, & DeFries, 2011; Byrne et al., 2008; Christopher et al., 2013; Gayan & Olson, 2001, 2003; Hayiou-Thomas, Harlaar, Dale, & Plomin, 2010; McGrath et al., 2007; Soden-Hensler, Taylor, & Schatschneider, 2012). Twin studies have provided estimates of heritability ranging from 30% to 60%, depending on the literacy measure. The remainder of the variance in reading ability is associated with a range of factors in the child’s home and school environment (Berliner, 2005; Guo & Stearns, 2002; Rowe, Jacobson, & van den Oord, 1999; Samuelsson et al., 2008; Taylor, Roehrig, Soden Hensler, Connor, & Schatschneider, 2010).

There is increasing evidence that the influence of genetic and environmental factors on reading ability is not simply additive, and is not present in the same proportions for all children. Rather, genetic factors appear to determine the potential of an individual; the extent to which this potential is realised is dependent on the environmental circumstances. The majority of studies of gene–environment interactions in reading ability support the ‘bioecological model’ (McGrath et al., 2007). In this model of development (Bronfenbrenner & Ceci, 1994), a child’s genetic potential for developing competence is amplified in advantaged environments and suppressed in disadvantaged environments (Turkheimer, Haley, Waldron, D’Onofrio, & Gottesman, 2003). Studies demonstrating the bio-ecological model among adolescent children include measures of verbal IQ (Rowe et al., 1999), receptive vocabulary (Guo & Stearns, 2002), word recognition and phonological decoding (Gayan & Olson, 2003), and word recognition, spelling and reading comprehension in low progress readers (Friend, DeFries, & Olson, 2008). In each of these studies, there was high heritability and low environmental influence of skills among students from advantaged homes, and low heritability and stronger environmental influence among students from disadvantaged homes.

In other words, children whose home lives are characterised by social disadvantage are less likely to achieve to their genetic potential, as environmental factors impede their development, while their more advantaged peers tend to be limited only by their innate ability.

Home learning environment

For school-age children, most of their formal learning takes place in the classroom but there is still potential for the home environment to be influential. If the way children spend their after-school time is a factor in their reading development, and if children’s after-school experiences and activities differ with SES, these factors would be expected to interact in their effect on reading performance.

The research literature on the impact of children’s home learning environment once they reach school age is dominated by studies on the amount of reading at home. This literature is described below. Another set of studies, however, looks at a broader set of home characteristics and parenting practices that impact on children’s reading achievement. These studies examine factors such as parents’ academic aspirations and expectations for their children, their encouragement of intellectuality and reading, and students’ inclination for independent study and good work habits.

A research synthesis by Hattie (2009) includes only two meta-analyses of studies on the home learning environment of school-age children. One of these studies, by Iverson and Walberg (1982), summarised the evidence as finding that sociopsychological or ‘process’ characteristics of the home have a stronger association with academic ability and achievement than socioeconomic or ‘status’ characteristics. This suggests that values and parenting practices are stronger factors than income or parent education levels. Hattie’s synthesis estimates the effect size of home environment on academic achievement (not restricted to reading ability) to be in the medium–high range compared to other factors but is not specific about which aspects of the home environment are most influential.

Where aspects of parenting practices have been investigated more closely, research has generally supported Parcel and Dufur’s (2001) conclusion that beyond a certain level of basic expenditure, home environments that positively impact on education are characterised by ‘parental orientation to providing the types of interpersonal resources that favour child development’ (p. 883). Home environment factors which have been shown to be strong predictors of reading achievement are parents’ educational aspiration and expectations, and encouragement of intellectuality and reading (Fan & Chen, 2001; Fergusson et al., 2008; Greaney & Hegarty, 1987; Wilder, 2013). It seems that these characteristics, rather than help with homework or direct supervision of literacy activities, may positively impact reading ability among school-age children by developing their motivation to read (Guthrie, Wigfield, Metsala, & Cox, 1999; Mucherah & Yoder, 2008; Petscher, 2010), their self-concept as readers (Katzir, Lesaux, & Kim, 2009), and their capacity for self-regulated learning (Xu, Kushner Benson, Mudrey-Camino, & Steiner, 2010), all of which have been shown to mediate the relationship between home learning environment and reading performance.

In one of the few studies with Australian data, Evans, Kelly, Sikora, and Treiman (2010) describe the quality of the home learning environment as its ‘scholarly culture’. Using data from literacy assessments in 27 countries, they found that the number of books in the home (their proxy measure of scholarly culture) was significantly positively related to the literacy scores of 15 year old students, net of socioeconomic factors. Among Australian students in the sample, the number of books in the home was the second strongest unique predictor of literacy scores after IQ. Furthermore, there was an interactive effect – having books in the home had a greater impact on children whose parents had the lowest levels of education than on children with university-educated parents.

The Longitudinal Study of Australian Children has found that both parental expectations and the number of books in the home have a significant relationship with SES. Ninety-three per cent of children in the highest SES quartile had more than 30 books at home, compared with 65% of children in the lowest SES quartile. Less than 10% of mothers with tertiary education expected that their children would go no further in their education than completing school, compared with 36% of mothers who had not completed school themselves (Australian Institute of Family Studies, 2011). Chowdry, Crawford, and Goodman (2011) found that parents’ and students’ educational expectations were strong predictors of student achievement and each explained around 16% of the test-score gap between the lowest and highest socioeconomic groups of 16 year olds in England.

Time spent reading

The majority of studies examining the association between the amount of time children spend reading outside of school and various measures of reading ability find medium but statistically significant positive relationships (Anderson, Wilson, & Fielding 1988; Cheng, Kinger, & Zheng, 2009; Cunningham & Stanovich, 1997, 1998; Greaney & Hegarty, 1987; Mol & Bus, 2011; Watkins & Edwards, 1992). In contrast, Taylor, Frye, and Maruyama (1990) found that only time spent reading in school positively affected reading scores, and Lawrence (2009) found that the time children spent reading books during their summer holidays was predictive of improved vocabulary but not comprehension.

In several studies, the contribution of time spent reading to variance in children’s reading scores was considerably (but not completely) reduced after taking children’s prior reading ability into account (Cunningham & Stanovich, 1998; Taylor et al.,1990; Watkins & Edwards, 1992). Even after controlling for prior achievement, however, the association between reading time and reading achievement remains practically important for all ability levels. In Taylor et al.’s (1990) study, just 10 min per day of reading outside of school was related to a one-quarter standard deviation improvement in reading skill for below average and average readers, and a half standard deviation improvement for above average readers, over the course of the school year.

A meta-analysis by Mol and Bus (2011) incorporated 99 studies of the relationship between print exposure and reading ability, which tend to report stronger relationships than studies using self-reports of time spent reading. Print exposure is considered by some researchers to be more reliable than self-reports of time spent reading. Print exposure is most often measured by a Title Recognition Test, with the premise that respondents who can identify more real book titles will be those who read more books. Mol and Bus (2011) found that correlations between print exposure and reading ability became higher with age. Print exposure explained 12% of language skills in preschool and kindergarten, 13% in primary school, 19% in middle school and 30% in high school. At all ages the correlations were significant. If the amount of reading students do, including reading at home, is related to their reading achievement, might some of the literacy disadvantage associated with socioeconomic disadvantage be attributable to differences in reading at home? Few published studies investigate this possibility directly. Those which measure SES use it as a control factor rather than as an independent variable. One study which did examine socioeconomic groups, by McKool (2007), found no significant differences in the amount of voluntary after-school reading by students in low and middle/high income families, and echoes other studies in finding that reading time is instead more directly related to a ‘positive educational home environment and to the value placed on reading in the home’ (p. 119).

Statistics from the PISA provide contrasting evidence of significant differences among students. SES was significantly positively related to the PISA Enjoyment of Reading (EoR) Index. Thirty-three per cent of Australian students in the lowest SES quartile reported that they did not read for enjoyment, compared with 17% of the highest SES quartile. Twenty per cent of students in the lowest SES quartile reported reading for up to 1 h a day, with 21% reading more than 1 h a day. In comparison, 29% of students in the highest SES quartile reported reading up to 1 h a day; 31% read more than 1 h a day (Thomson & De Bortoli, 2010).

Differences in time spent reading for enjoyment appear to translate into literacy performance. There was a significant correlation between the PISA 2009 EoR Index and literacy performance. Students with the highest EoR Index had a higher mean literacy performance, equivalent to four more years of schooling, than students with the lowest EoR Index (Thomson & De Bortoli, 2010).

Furthermore, according to the PISA data, both quantity and quality of reading are associated with reading performance, and both are related to SES. The strongest association between text type and reading performance was for fiction books (a medium correlation) and non-fiction books (a small correlation). Magazines, newspapers and comics had very small positive or even negative correlations with reading performance in PISA (Thomson & De Bortoli, 2010). Lawrence (2009) similarly found that reading books outside of school time – fiction and non-fiction – was the strongest predictor of vocabulary growth, while reading magazines and comics was associated with a decline in vocabulary.

Time spent reading and ‘Matthew effects’

Studies showing an interaction between reading ability and time spent reading are congruent with the substantial body of evidence supporting a reciprocal relationship (Cunningham & Stanovich, 1998), or as Mol and Bus (2011) put it, a ‘spiral of causality’ (p. 267). The accumulation of skills and knowledge in some students and the deficit in others creates a widening reading gap that becomes increasingly difficult to close as children get older (Cunningham & Stanovich, 1997; Mol & Bus, 2011; Stanovich, 1986). Often described as the ‘Matthew effect’, inspired by a verse in St Matthew’s gospel which is translated as ‘the rich get richer, the poor get poorer’, this theory posits that children who do not quickly acquire the fundamental skills of reading tend to read less than their peers with higher reading skills. This initiates a ‘feedback loop’ of low reading experience and slow reading acquisition, the result of which is lower vocabulary and lower comprehension (Stanovich, 1986). In this theory, reading begets reading (Cunningham & Stanovich, 1997).

The Matthew effect theory is acknowledged as offering a highly plausible explanation of reading development (Cain & Oakhill, 2011; Kempe, Eriksson-Gustavsson, & Samuelsson, 2011; Mol & Bus, 2011; Sideridis, 2011). SES is pertinent to the Matthew effects theory insofar as it influences the development of emergent literacy skills (Buckingham et al., 2013) and is associated with the quality of home learning environment. There is limited evidence that some subpopulations of students exhibit Matthew effects more reliably, including low ability readers from low income families (Morgan, Farkas, & Hibel, 2008) and children with learning difficulties, who are disproportionately from low income families (Morgan, Farkas, & Wu, 2011).

Physical health and sleep

A socioeconomic gradient to child health has been found in numerous studies; child health scores decline with SES (Bradley & Corwyn, 2002; Braveman & Barclay, 2009; Braveman, Cubbin, Egerter, Williams, & Pamuk, 2010; Chen, 2004; Cushon, Vu, Janzen, & Nazeem, 2011; Fletcher & Wolfe, 2013; Waldfogel & Washbrook, 2010; Zwi & Henry 2005). Australian survey data confirm this link for young children. In the AEDI, which assesses children at the beginning of school, children in the lowest socioeconomic quartile were twice as likely to be rated as developmentally vulnerable in the physical health and wellbeing domain (Centre for Community Child Health & Telethon Institute for Child Heath Research, 2009).

Although there is an extensive literature documenting the relationships between low SES mothers, adverse infant health conditions (AIFS, 2011; AIHW, 2011; Berliner, 2005), and cognitive outcomes (Anders et al., 2011; Julvez et al., 2007; Litt, Taylor, Klein, & Hack, 2005), there is limited evidence of socioeconomic differentials in the prevalence of specific illnesses or physical impairments in school-age children. A Western Australian study found that children who had always lived in low income families were twice as likely to have developed persistent asthma by the age of 14 as children who had never been in a low income family (Kozyrskyj, Kendall, Jacoby, Sly, & Zubrick, 2010) and a national report indicates that children in low socioeconomic households are twice as likely be hospitalised for asthma (AIHW, 2011). These studies do not indicate whether the overall prevalence of asthma is related to SES, but they do show that the extent and severity, and therefore the burden of the disease, is greater for socioeconomically disadvantaged students.

It is reasonable to expect that poor health might affect literacy through its impact on school attendance. There is evidence to support the relationship between poor health and school attendance in the specific case of dental health – children with poor oral health scores were more likely to miss school (Berg & Coniglio, 2006; Jackson, Vann, Kotch, Pahel, & Lee, 2011). Children from low socioeconomic backgrounds, on average, suffer from poorer oral health (AIHW, 2011). Numerous studies have found that students with asthma have higher rates of absenteeism (Basch, 2011; Collins et al., 2008; Milton, Whitehead, Holland, & Hamilton, 2004; Moonie, Sterling, Figgs, & Castro, 2006; Taras & Potts-Datema, 2005) but have found only a weak or non-existent relationship between asthma and school performance generally, or literacy in particular (Krensitsky-Korn, 2011; Milton et al., 2004; Moonie, Sterling, Figgs, & Castro, 2008; Taras & Potts-Datema, 2005). There are no studies exploring a possible interaction between asthma, SES and academic achievement.

Otitis media (middle ear infection) is a childhood illness which is common across the population. There is mixed evidence whether otitis media is more prevalent among children of low SES overall (Berliner, 2005; Kong & Coates, 2009; Williams & Jacobs, 2009) but there are stronger findings suggesting that it is likely to have earlier onset, be more frequent and less treated, resulting in a greater probability of hearing loss among extremely disadvantaged children, especially Aboriginal children in remote communities (Williams & Jacobs, 2009). Children who experience even temporary partial hearing loss due to otitis media while they are developing their oral language abilities can experience speech perception and language delays (Winskel, 2006), which may be especially important for non-English-speaking Aboriginal children learning a different phonological system (Williams & Jacobs, 2009). Hearing loss due to otitis media would be expected to affect literacy development at school, especially if the hearing loss is permanent, but this has not been established empirically (Roberts, Rosenfeld, & Zeisel, 2004), nor are there data showing whether hearing impairment is more prevalent among socially disadvantaged children.

In short, while the available data indicate that socially disadvantaged children are at greater risk of poor health, evidence of an educational impact is weak. This does not mean the relationship is non-existent, just that it has not been established empirically.

An emerging area of research suggests that less sleep might be a factor in the lower school performance of children from low SES families. There is some evidence that lower SES children get less sleep than higher SES children (El-Sheikh, Kelly, Buckhalt, & Hinnant, 2010), and a series of studies have found that sleep duration and quality is positively associated with cognitive functioning (Buckhalt, El-Sheikh, & Keller, 2007), intellectual ability (Buckhalt, El-Sheikh, Keller, & Kelly, 2009), verbal comprehension (Bub, Buckhalt, & El-Sheikh, 2011), and letter–word recognition and passage comprehension (Eide & Showalter, 2012) in school-age children. Once again, there also appears to be an interaction between SES and sleep-related variations in performance. Poor quality sleep seems to have a more detrimental effect on low SES children and children whose parents have low education levels (Buckhalt et al., 2007; Buckhalt et al., 2009; El-Sheikh et al., 2010).

Behaviour

The link between behavioural problems and poor reading achievement is well-established. Reviews of the literature by Morgan, Farkas, Tufis, and Sperling (2008) and Limbrick, Wheldall, and Madelaine (2011) report evidence of relationships between behaviour and reading in both directions separately (behaviour problems predict low reading ability and vice versa) as well as a bidirectional relationship. Smart, Prior, Sanson, and Oberklaid (2001) found that behaviour problems contributed to the persistence of reading difficulties over a six-year period, for boys only. McIntosh, Sadler, and Brown (2012)’s longitudinal study found that low phonological awareness at the beginning of kindergarten predicted chronic behaviour problems in Year 5, but that this was mediated by progress in literacy skills over the kindergarten year, indicating that effective initial instruction can mitigate behaviour issues.

The importance of SES in this relationship is not clear. The AEDI shows that the proportion of children assessed as developmentally vulnerable on the ‘emotional maturity’ domain (which includes sociability, anxiety, aggression, hyperactivity and inattention) increased as SES decreased (Centre for Community Child Health and Telethon Institute for Child Health Research, 2009). Morgan et al. (2008) found that while children from low income families were significantly more likely to have reading problems in third grade, they did not have a higher prevalence of behavioural problems than middle income children. In contrast, Hay and Fielding-Barnsley (2009) found that low SES children had lower average early reading and language skills and that there was a significant positive relationship between these skills and students’ in-class on-task behaviour. This indicates correlation but not the direction of the interrelationships.

School attendance and mobility

Common sense dictates that, on average, children are more likely to learn to read if they attend school. This is borne out by research showing a significant positive relationship between school attendance and literacy achievement from Kindergarten and Year 1 (Attendance Works, 2011; Balfanz & Byrnes, 2012; Chang & Romero, 2008) through primary school (Chang & Romero, 2008; Roby, 2004) and into the final years of high school (Dunn, Kadane, & Garrow, 2003). Rothman (2001) suggests that chronic absenteeism is both a cause and effect of low academic achievement. Children who are struggling at school seek to avoid it, and this exacerbates the problem. Although there has been a strong and justified focus on school attendance to close the literacy gap for indigenous Australians (Australian Government, 2012), there is mixed and limited evidence of the impact of attendance for this group of children. Zubrick et al. (2006) found a significant relationship between attendance and academic performance (not literacy specifically) for indigenous Australian children but not for non-indigenous children. Cowey, Harper, Dunn, and Wolgemuth (2009) found inconsistent evidence of a relationship between school attendance and reading scores. They suggest that attendance is only one part of the solution, and that quality of instruction and effective use of class time are mediating factors. In another study, in which students were participants in a reading intervention, attendance was strongly positively related to gains in phonological processing and early literacy skills (Ehrich et al., 2010)

Several studies show the importance of good school attendance in the year of initial reading instruction, finding that chronic absenteeism in Kindergarten is associated with lower reading test scores in Year 3 (Attendance Works, 2011; Balfanz & Byrnes, 2012; Chang & Romero, 2008). Again, there is evidence of an interaction, with absenteeism being particularly detrimental to children from socially disadvantaged families (Balfanz & Byrnes, 2012; Chang & Romero, 2008).

Children from low income and low SES families have much lower average attendance rates and a higher prevalence of chronic absenteeism (usually defined as missing >10% of the school year), placing them at a higher risk for reading failure. In Australia, Rothman (1999, 2001) reports that low SES children had significantly higher school absence rates than middle and high SES children across all school years, with student SES predicting approximately 30% of variance in absence rates. An additional 8% was predicted by the school average SES, indicating a peer effect. A New Zealand survey found that ‘justified’ (explained, acceptable absences) were similar in all schools, irrespective of average SES. However, schools in the two lowest SES deciles had rates of ‘unjustified’ absences around three times higher than schools in the two highest SES deciles. Rates of frequent truancy were almost five times higher in low decile schools (New Zealand Ministry of Education, 2011). In the United States, Romero and Lee (2008) found that 21% of low income Kindergarten children were chronic absentees, compared with 8% of higher income children. Similar absentee ratios were found for children with low maternal education and unemployed mothers, and each of these risk factors had a cumulative impact on absenteeism.

School mobility – the number of times a student changes schools – is also correlated with reading and general school achievement. Higher school mobility rates are significantly associated with lower reading achievement in Kindergarten (Burkam, Lee, & Dwyer, 2009) and throughout primary school (Mehana & Reynolds, 2004; Thompson, Meyers, & Oshima, 2011), as well as high school English grades (Dunn, Kadane, & Garrow, 2003; Reynolds, Chen, & Herbers, 2009). These studies each found medium correlations between SES and school mobility. Mehana and Reynolds (2004) and Burkam et al. (2009) again found interactive effects – the relationship between mobility and reading was stronger for children of low SES families.

School factors

The correlation between SES and literacy is well-established in studies conducted since the 1960s and 1970s (e.g. Coleman et al., 1966; Currie & Thomas, 1999; Jencks et al., 1972), but recent research on the impact of socioeconomic variables on education shows complex multi-layered relationships. Improvements in the quality of data and in statistical techniques have allowed the separate effects on achievement of SES at the student- and school-levels to be investigated. Over the past decade or so, a number of studies have shown that socioeconomic variables are stronger at the school-level than the student-level, that is, the mean SES of a school has a larger impact on a student’s achievement than their own SES (Holmes-Smith, 2006; OECD, 2010; Rothman, 2002). Similar to individual SES, school-level SES seems to affect literacy mostly indirectly, operating through its association with school practices rather than resource levels alone (Sirin, 2005).

School-level SES

A number of large-scale studies have found that school-level SES has an effect on literacy achievement in addition to the effect of student-level SES. The largest international study, PISA, found that in most OECD countries, the literacy performance of 15-year-old students was more strongly related to the SES of their school than their own SES. This was true for all English-speaking countries (OECD, 2010).

Studies analysing data from the Longitudinal Surveys of Australian Youth confirm the significant impact of school-level SES, finding furthermore that the independent influence of individual SES decreased between 1975 and 1998, while the influence of school-level SES increased (Rothman, 2002; Rothman & McMillan, 2003). Other studies indicate that the relationship between school-level SES and student literacy becomes stronger as students progress through school (Holmes-Smith, 2006; New South Wales Department of Education & Training, 2011). Research in the UK and USA has also provided evidence of a significant school SES effect on literacy scores and reading growth that is equivalent to, or exceeds, the effect of student SES (Cassen & Kingdom, 2004; Palardy, 2008; Rumberger & Palardy, 2005; Sirin, 2005). Sirin’s (2005) meta-analysis describes the effect size of student-level SES as medium and the effect size of school-level SES as large.

Like individual SES, school-level SES can, to some extent, be viewed as a proxy for other more directly salient factors – the conditions and experiences that influence achievement (Barton & Coley, 2009). According to Palardy (2008), schools with higher proportions of students from socioeconomically disadvantaged backgrounds have an ‘educational milieu’ that presents a ‘consistent barrier to learning’ (p. 31). That is, rather than school-level SES being simply a concentration of individual disadvantage, schools serving disadvantaged students are characterised by conditions less conducive to educational success. Cassen and Kingdom (2004) put it this way: students with lower SES are more often found in lower quality schools.

The research literature often considers the factors associated with school quality as forming three categories: material resources, structural characteristics and school practices. Material resources include funding to schools, the school’s physical environment and educational resources such as technology. Structural characteristics include class sizes and academic streaming. School practices include teachers’ expectations of students’ ability and achievement levels, rigour of the curriculum, disciplinary climate and homework requirements.

Rumberger and Palardy (2005) found that the material and structural features of schools did not significantly contribute to school-level socioeconomic effects on academic achievement, including reading. Four school practice variables in combination predicted all of the variance in test scores between schools with different levels of mean SES: teacher expectations; curriculum rigour, how safe students felt at school and the amount of homework completed by students. Similarly, Marks (2009) concludes that the academic context of the school is most important, rather than SES. Resource levels may contribute indirectly to achievement in systems with a high degree of school choice, if low SES students become concentrated in low-resource schools (Perry & McConney, 2010).

Palardy (2008) found that school practices varied significantly with school-level SES, but also identified significant differences in teacher qualifications and experience. In Palardy’s study, there was an interactive effect – school practices were found to have a greater impact in schools with lower mean SES, suggesting that disadvantaged students are more vulnerable to the effect of low-quality schools. In contrast, however, Perry and McConney (2010) found that school-level SES in Australia was positively associated with reading scores to a similar extent for students from all socioeconomic backgrounds.

Teacher quality

Multi-level analyses of student performance have found significant differences between classes within schools, leading some researchers to argue that classroom/teacher effects are stronger than any school-level effects (Hill & Rowe, 1996; Rowe, 2002). One interpretation is that lower average results in schools with lower average SES might be attributed to lower average quality of teaching.

It is important to make a distinction between teacher quality and teaching quality. Although these terms are often used interchangeably, and education policy debates have been framed around the notion of teacher quality, it is difficult to identify a ‘high-quality teacher’ using measurable characteristics. There is some evidence that student outcomes are positively associated with teachers’ years of experience (peaking at around five years) and verbal and intellectual aptitude (Leigh & Mead, 2005; National Council on Teacher Quality, 2004; Rivkin, Hanushek, & Kain, 2005). Yet, somewhat counter-intuitively, research has found that teachers’ formal educational qualifications were not strongly related to student performance (Hattie, 2009; Hess, Rotherham, & Walsh, 2005), including reading test scores (Chingos & Peterson, 2011). This does not mean that teacher education and training is unnecessary or unimportant; a more likely explanation is that the effectiveness of teacher training is variable (Boyd, Grossman, Lankford, Loeb, & Wyckoff, 2009; Buckingham, 2005). Likewise, although teacher employment statistics indicate that schools with the most educational challenges (schools in disadvantaged and/or rural and remote communities) on average have less experienced and less qualified (Auguste, Kihn, & Miller, 2010; Freedman, Lipson, & Hargreaves, 2008; Productivity Commission, 2012), the evidence that this results in lower quality classroom instruction in such schools is limited.

Quality of teaching – lesson content and pedagogy used by teachers – is a stronger predictor of student achievement than teacher characteristics (though they are plausibly connected). Hattie’s (2009) research synthesis found effects in the high range for ‘quality teaching’ as rated by students, and for specific aspects of teaching practice including direct instruction, teacher–student relationships, reciprocal teaching and feedback. There is little evidence of variation in quality of teaching practice associated with SES, but in one Australian study, Griffiths, Amosa, Ladwig, and Gore (2007) conducted classroom observations to investigate teaching practice in schools with large numbers of students from disadvantaged backgrounds. They found that the quality of pedagogy was low and stated that ‘the link between SES and pedagogy at the class level is disturbing’ (p. 9).

Initial reading instruction

Effective reading instruction in the early years of schooling is critical. An extensive body of research shows that quality, comprehensive literacy programmes develop children’s skills in five essential areas: phonemic awareness, phonics, fluency, vocabulary and comprehension (Department of Education, Science & Training, 2005; NICHHD, 2000; Rose, 2006). Although these five ‘big ideas’ of reading are now widely accepted, the quality of initial reading instruction in schools has still been variable (Coltheart & Prior, 2007; Duke & Block, 2012; Lesaux, 2012; Office for Standards in Education, 2011; Patel, 2010).

Phonemic awareness and phonics instruction are essential components of effective initial reading programmes. Phonemic awareness is the ability to identify and manipulate the discrete sounds in words and is a necessary skill in the early and successful acquisition of decoding ability. Phonics instruction teaches children the relationships between the sounds in speech and letters (and groups of letters) in print, providing them with the ability to decode or ‘sound out’ words using their knowledge of letter–sound correspondences (Snow, Burns, & Griffin, 1998). Numerous research studies, reviews and meta-analyses have shown that the most effective way to teach phonics is with a ‘systematic’ approach (e.g. Chall, 1983; de Lemos, 2005; Ehri, Nunes, Stahl, & Willows, 2001; Louden et al., 2005).

There is also evidence that effective reading instruction is especially important for children at-risk of reading failure (Lesaux, 2012; Samuelsson et al., 2008; Taylor et al., 2010). Phonics instruction has been shown to be beneficial to all students, but with stronger effects for students from low socioeconomic backgrounds (NICHHD, 2000) and children who begin school with low levels of phonological awareness and pre-literacy skills, who are disproportionately from low socioeconomic backgrounds (Foorman, Francis, Fletcher, Schatschneider, & Mehta, 1998; Savage, Carless, & Erten, 2009; Sonnenschein, Stapleton, & Benson, 2010; Xue & Meisels, 2004). Phonic skills should not be taught in isolation, however, as socioeconomically disadvantaged children are also more likely to have low oral language ability and vocabulary knowledge. The provision of a literacy programme that is equally strong in reading practice and developing vocabulary and comprehension is essential (Adams, 1990; Beverly, Giles, & Buck, 2009; Hamston & Scull, 2007; Rupley, Blair, & Nichols, 2009; Teale, Paciga, & Hoffman, 2007).

The potential for high-quality reading instruction to attenuate the relationship between literacy and socioeconomic disadvantage is evident in several studies (Chatterji, 2006; Magnuson, Ruhm, & Waldfogel, 2007). In one longitudinal study of children from Kindergarten to Grade 5, initial literacy gaps associated with SES progressively dissipated and were no longer evident in Grade 3 when children were provided with a ‘rich’ initial and on-going literacy programme, which included explicit instruction in phonemic awareness and phonics (D’Angiulli, Siegel, & Hertzman, 2004; D’Angiulli, Siegel, & Maggi, 2004). In another longitudinal study – the ‘Clackmannshire study’ – there were no literacy gaps between socioeconomic groups among children who had been given synthetic phonics instruction as part of a balanced literacy program, until Grade 5 for comprehension and Grade 7 for reading and spelling (Johnston & Watson, 2005). If effective literacy methods are more beneficial for struggling readers, particularly those from socioeconomically disadvantaged backgrounds, the corollary is that they are more adversely impacted by the absence of high-quality literacy instruction. Consistent findings that teachers are not adequately prepared to teach reading in schools (Binks-Cantrell, Washburn, Joshi, & Hougen, 2012; Fielding-Barnsley, 2010; Rowe, 2006; Walsh, Glaser, & Dunne Wilcox, 2006) points to literacy instruction as a mediating factor in the relationship between low SES and poor literacy.

Conclusion

A persistently large number of children struggle to learn to read even at a basic level, and these children are disproportionately from socially disadvantaged families. Not only is a student’s reading achievement predicted by their own socioeconomic background, but it is also, and even more strongly, associated with the average SES of the school they attend. At both the individual- and school-level, beyond a minimum, financial resources make a relatively small contribution. At both the individual- and the school-level, the relationship between SES and literacy is significantly mediated by its association with other more proximal factors.

According to Snow, Burns, and Griffin (1998), it is ‘virtually impossible’ to tease out all of the factors that play a role in creating literacy gaps between children from different socioeconomic groups, and empirical studies can only point to statistical associations rather than prove causality (p. 125). However, the extent and quality of research in this area is gradually building up evidence of the predictive pathways, showing how these factors accumulate and interact to multiply the impact of disadvantage on some children, leading to greater risk of reading problems.

At the individual-level, the impact of SES on school-age reading achievement seems to be largely exerted through its relationship with early literacy. Children’s literacy proficiency at the beginning of formal schooling is a powerful predictor of reading achievement throughout their schooling. Children from low SES backgrounds typically start school with lower literacy skills and are more likely to remain poor readers as they progress through school. A number of mediating variables are implicated in this pattern of poor reading development: less time spent reading, less sleep, higher rates of absenteeism and mobility, and less parental encouragement of academic pursuits. Not only are these mediating factors more likely to be experienced by children from low socioeconomic backgrounds, research indicates there is often an interactive effect – socially disadvantaged children suffer more adverse effects from these risk factors than other children.

At the school-level, the relationship between the SES of the school population and the performance of individual students is more closely associated with school practices and academic culture than school resources and structures. Given the importance of quality of teaching, differences in teaching quality is potentially involved. But although low socioeconomic schools tend to have less experienced, less qualified teachers, there is little evidence of how this translates into differences in quality of teaching between schools with different levels of social disadvantage.

There is more evidence to indicate that reading instruction in the first years of school plays a major role in literacy achievement in general, and literacy gaps in particular. The impact of effective instruction including, but not limited to, systematic and explicit phonics instruction is greater for children with low initial levels of literacy and children from low socioeconomic backgrounds. Phonics programs work best when embedded in a rich literacy programme that provides ample time for practice (so that code-related skills can be generalised) and plenty of exposure to real books to develop vocabulary and comprehension, and to foster enjoyment of good literature. If the most effective instruction has not been routinely provided to children when they begin formal schooling, and there is good reason to believe that it has not, this is a potent area for reform.

Of course, low SES does not destine a child to poor literacy achievement, but to argue that it is not important is to misconstrue the research. That the relationship between SES and literacy is attributable, at least in part, to other variables does not negate its impact, it merely explains the process by which SES influences educational performance. Identifying the multiple, cumulative and interactive effects of factors associated with socioeconomic disadvantage, and understanding the processes by which they work to increase the risk of poor literacy, is the key to reducing its impact.

Footnotes

Declaration of conflicting interest

None declared.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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