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Article Excerpt
Abstract. Which children are most at risk of experiencing a Matthew effect in reading? We investigated this question using population-based methodology. First, we identified children entering kindergarten on socio-demographic factors (i.e., gender, race/ethnicity, and socioeconomic status) known to index the relative risks and resources available to them as beginning readers. Second, we fired growth curve models to the kindergarten-third-grade reading scores of these children as they participated in the Early Childhood Longitudinal Study-Kindergarten Class (ECLS-K) study. Third, we compared the relative reading achievement (as measured in standard deviation units from the sample's overall mean across the study's time points) of the children who were most and least at risk for reading disabilities. We found that the population subgroups most at risk for reading disabilities fell further behind typical readers over time. By contrast, those least at risk for reading disabilities did not move further ahead. Based on these findings, we conclude that a one-sided Matthew effect exists and that, moreover, it is likely to be experienced by children who are at greatest risk for reading disabilities.
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The "Matthew effect" refers to a pattern of increasing advantage or disadvantage following initial advantage or disadvantage. The term comes from the Gospel according to Matthew: "For unto one that hath shall be given, and he shall have abundance: but from him that hath not shall be taken away even that which he hath" (XXV: 29, New Analytical). In reading, the "Matthew effect" (Stanovich, 1986) refers to the notion that "over time, better readers get even better, and poorer readers become relatively poorer" (Bast & Reitsma, 1998, p. 1373).
A specific developmental cycle, termed the Matthew effects model (Bast & Reitsma, 1998; Stanovich, 2000), is thought to result in the fan spread effect. That is, children in homes and schools fostering rapid acquisition of reading skills--the reading rich--should begin to enjoy reading from a very young age, and thus practice it more frequently. Frequent reading practice then fuels further skill acquisition (e.g., Cunningham & Stanovich, 1997; Guthrie, Schafer, & Huang, 2001). These children should spiral upward as increasingly competent and motivated readers. Hence, the "rich get richer."
Children experiencing consistent difficulty acquiring reading skills--the reading poor--should follow a different trajectory. Their reading difficulties should lead Hem to develop more negative attitudes towards reading (e.g., Lepola, Salonen, & Vauras, 2000), and practice it far less (Anderson, Wilson, & Fielding, 1988; Scarborough & Parker, 2003). Over time, the "poor get poorer" because of this increasing avoidance of reading practice (Cunningham & Stanovich, 1997; Senechal, LeFevre, Hudson, & Lawson, 1996; Stanovich, 1986). Children with phonological deficits and other language or reading disabilities should be especially likely to experience this "poor get poorer" effect. Stanovich used the model to explain why children with learning disabilities can display increasingly generalized cognitive, motivational, and behavioral deficits.
Children with learning disabilities should be especially at risk of experiencing Matthew effects. This is not to say that learning disability is synonymous with reading disability (Lyon, 1996). Learning disabilities can occur in other skill areas, such as written expression or mathematical reasoning (Hallahan, Lloyd, Kauffman, Weiss, & Martinez, 2004), However, most children with learning disabilities have primary deficits in reading (Lyon, 1995). Snow, Burns, and Griffin (1998) estimated that about 80% of children with learning disabilities are poor readers. Stanovich (1986) put forth the Matthew effect theory as an etiological account of children's identification as having learning disabilities.
Which groups of children are likely to experience the hypothesized Matthew effect in reading? We investigated this question using population-based methodology. First, we categorized children on the basis of their socioeconomic status (SES), gender, and race/ethnicity, Second, we used these factors to predict the intercepts and slopes in growth curve models of the children's reading achievement from kindergarten through third grade. Third, we used the resulting estimated coefficients to calculate beginning and ending reading scores for subgroups of children categorized by differing combinations of SES, gender, and race/ethnicity. After standardizing the resulting scores using the standard deviations of reading proficiency in kindergarten and third grade (separately), we examined which, if any, of these population subgroups experienced Matthew effects in reading.
This methodology was not designed to focus on the detailed mechanisms by which Matthew effects may occur. Instead, we simply asked whether any of the population subgroups whose exogenous characteristics should predispose them to low or high reading performance indeed do experience a Matthew effect. Epidemiologically, the answer to this question is important in order to identify population subgroups that are most at risk of experiencing the model's predicted generalized cognitive, motivational, and behavioral deficits. Identifying young children who are likely to lag increasingly behind in becoming readers should help researchers, practitioners, and policy-makers more effectively target early intervention services (McCoach, O'Connell, Reis, & Levitt, 2006; Parrila, Aunola, Leskinen, Nurmi, & Kirby, 2005). As detailed below, previous investigations of the Matthew effect have yielded inconsistent findings. A necessary first step in evaluating the validity of Stanovich's (1986) theoretical model is to determine whether and for whom the predicted Matthew effect occurs.
Prior Studies of the Existence of Matthew Effects
Whether a Matthew effect truly occurs in reading has been much debated (e.g., Bast & Reitsma, 1997, 1998; Leppanen, Niemi, Aunola, & Nurmi, 2004; Parrila et al., 2005; Scarborough & Parker, 2003; Shaywitz et al., 1995; Stanovich, 2000). For such an effect to exist, two phenomena should be evident.
First, skill differences between good and poor readers should remain stable (i.e., "rich" readers remain rich whereas "poor" readers remain poor). Much empirical evidence exists for this phenomenon (e.g., Anderson et al., 1988; Baker, Decker, & DeFries, 1984; Jordan, Kaplan, & Hanich, 2002; Juel, 1988; McGee, Williams, Share, Anderson, & Silva, 1986; Scarborough, 1998; Shaywitz et al., 1995; but also see Phillips, Norris, Osmond, & Maynard, 2002). For example, Smith (1998) reported that, of children with the highest and lowest preschool assessment scores, 93% and 71% were reading above or below grade level, respectively, in the third grade. Juel found that 87% and 88%, respectively, of average-to-good and poor readers in first grade remained average-to-good and poor readers in fourth grade. Such stability led Juel to conclude that poor readers in her sample appeared "doomed" (p. 444).
Second, skill differences between good and poor readers should increase over time (i.e., rich readers become richer whereas poor readers become poorer). This increasing gap is sometimes referred to as the fan spread effect. The metaphor is apt, in that end points towards the base of an open fan are relatively close together but, traveling away from the base, they grow further apart. Much of the controversy surrounding the Matthew effect is due to inconsistent evidence for the fan spread effect (e.g., Leppanen et al., 2004). For example, whereas Bast and Reitsma (1997, 1998) reported that the gap in word recognition skills between good and poor readers widened over time, Aarnoutse and van Leeuwe (2000), Baker et al. (1984), and McCoach et al. (2006) did not find this to be the case.
Instead, poor readers have often been observed to narrow the reading achievement gap (e,g., Aarnoutse & van Leeuwe, 2000; Catts, Hogan, & Fey, 2003; Jordan et al., 2002; Parrila et al., 2005; Phillips et al., 2002; Shaywitz et al., 1995). This was the case in the only study to date to investigate the Matthew effect using a sample of children with learning disabilities.
Scarborough and Parker (2003) tracked a small sample (N = 57) of children with and without learning disabilities (i.e., reading disabilities, mathematics disabilities) from grade 2 to grade 8. Their analyses yielded a correlation of -.77 between the children's beginning reading scores and their reading growth. Given their own and the findings of others, Scarborough and...
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