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4
Predictors of Success and Failure in Reading

Of the many conditions that appear to contribute to successful reading by schoolchildren, among the more important are each child's (1) intellectual and sensory capacities, (2) positive expectations about and experiences with literacy from an early age, (3) support for reading-related activities and attitudes so that he or she is prepared to benefit from early literacy experiences and subsequent formal instruction in school, and (4) instructional environments conducive to learning.

This chapter reviews the evidence concerning the predictors of reading achievement: some measurable characteristic of a child or the child's home, school, or community that has been associated with poor progress in learning to read.1It is critical to distinguish predictors from causes or explanations of reading difficulties—predictors are simply correlates. Nor can predictors be interpreted as suggesting the inevitability of poor reading achievement. To the

1 Some sections of this chapter are based closely on a recent review of prediction research by Scarborough (1998), which provides much more detail about the sources and findings that are the basis for many summary statements presented here.



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Page 100 4 Predictors of Success and Failure in Reading Of the many conditions that appear to contribute to successful reading by schoolchildren, among the more important are each child's (1) intellectual and sensory capacities, (2) positive expectations about and experiences with literacy from an early age, (3) support for reading-related activities and attitudes so that he or she is prepared to benefit from early literacy experiences and subsequent formal instruction in school, and (4) instructional environments conducive to learning. This chapter reviews the evidence concerning the predictors of reading achievement: some measurable characteristic of a child or the child's home, school, or community that has been associated with poor progress in learning to read.1It is critical to distinguish predictors from causes or explanations of reading difficulties—predictors are simply correlates. Nor can predictors be interpreted as suggesting the inevitability of poor reading achievement. To the 1 Some sections of this chapter are based closely on a recent review of prediction research by Scarborough (1998), which provides much more detail about the sources and findings that are the basis for many summary statements presented here.

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Page 101 contrary: the whole point of identifying risk factors is to alert parents, physicians, and teachers to potential obstacles children might face so that effective interventions can be devised and implemented. In the absence of other (noncorrelational) evidence, therefore, these predictors cannot be considered causes of reading problems but rather as associated conditions implicated in reading difficulty. Nevertheless, the fact that these characteristics correlate with subsequent reading achievement is potentially very useful for identifying children who may be in the greatest need of intervention. Our goal in this chapter is to present ways of identifying who should receive services to prevent reading difficulties. That an individual or group has been identified as being at risk for reading difficulties has no direct implications for the nature of the appropriate intervention. It is not the case that treating the predictor itself is necessarily the right approach; for instance, if difficulty with letter identification turns out to be a predictor, this does not mean that instruction on letter identification is a sufficient or the best treatment for preventing all reading difficulties (see Adams, 1990). Conversely, the skills that are the focus of treatment may not necessarily be the ones on which the identification of the individual or target group was based. In practice, identification criteria and treatment plans can, and often will, be chosen somewhat independently of each other. It should be borne in mind while reading this chapter that relationships between effective predictors and reading difficulties are markers only and that other mediating variables, which are not measured in a particular research study, may also correlate with reading difficulties.   Again consider letter identification:   Scanlon and Vellutino (1996) found a moderately high correlation (r= .56) between letter identification and reading achievement. In this same study, the correlation between number identification and reading achievement was .59. Since these results indicate that both poor letter identification and poor number identification predict reading difficulty, they weaken or at least complicate the hypothesis that either of them is a direct cause of reading difficulty. Both may be marker variables for another factor that goes further to explain both letter and number identification.

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Page 102 When deciding which factors to use to identify children who are at risk for reading difficulties, the main determinant should be the strength of the association. (Of course, other practical matters, such as cost and ease of assessment, also affect assessment decisions.) One way to measure the strength of the relationship between a kindergarten predictor and a later reading score is to compute a "correlation" statistic (symbolized by r), which takes a value of zero when there is no predictive relationship at all and takes a value of 1.0 when there is perfect predictability. In between, the higher the correlation, the stronger the tendency for children who did well on the predictor measure to become good readers, and for children who did poorly initially to end up with lower reading achievement scores later. For example, when reading is measured yearly, correlations between scores in one year with scores in the next year are typically in the .60 to .80 range; in other words, they are quite strong but not perfect. As will be seen, correlations between the best kindergarten predictors and later reading scores are not quite as strong (in the .40 to .60 range) but still provide a great deal of useful predictive information. For other predictors, however, the correlations tend to be lower. Because correlations summarize the strength of the relationship across the full range of children's abilities, their use is consistent with a dimensional account of individual differences in reading discussed in Chapter 3. Another way to look at the strength of prediction instead reflects the categorical model, which continues to predominate in educational practice. In this approach, an at-risk subgroup of kindergartners is designated based on their scores on the predictor measure, and a reading disability subgroup is identified based on later achievement scores. The percentage of children whose outcome classification was correctly predicted is an overall measure of prediction accuracy. Furthermore, a predictor is said to have high sensitivity if most of the disabled readers had been correctly identified as at risk at the outset and to have high specificity if most nondisabled readers had been classified as not at risk. It is also informative to examine errors of prediction, including false positives (children deemed at risk who did not develop reading problems) and false negatives (those who did not meet the risk criterion but nevertheless had difficulty learning to read).

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Page 103 In what follows we attempt to estimate the degree of risk associated with many kinds of predictor measures, alone and in combination. Sometimes the magnitude of risk can be estimated quite closely on the basis of an abundance of longitudinal findings. For other factors, far less information is available regarding the degree of risk they pose. For each predictor, we describe the average strength of its correlation with future reading achievement and, when possible, estimate the probabilities of prediction errors and correct predictions from studies in which risk status has been examined in relation to outcome classifications. We have organized this chapter by first considering predictors that are intrinsic to the individual and would be identified by assessing the child. We then move to a discussion of factors identified in the household and then to factors associated with the child's larger environment—the neighborhood, the school, and the community. CHILD-BASED RISK FACTORS Physical and Clinical Conditions Some primary organic conditions are associated with the development of learning problems as secondary symptoms. That is, the child's reading and more general learning problems are thought to result from cognitive or sensory limitations that follow from the primary diagnosis. These primary conditions include: ·      severe cognitive deficiencies, ·      hearing impairment, ·      chronic otitis media, ·      (specific) early language impairment, and ·      attention deficit/hyperactivity disorder. Cognitive Deficiencies Children with severe cognitive deficiencies usually develop very low, if any, reading achievement. Other factors that are associated with developmental delays in cognitive abilities include severe nutri-

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Page 104 tional deficiency, very low birthweight, fetal alcohol syndrome, lead poisoning, and severe psychopathological conditions that emerge in early childhood. Hearing Impairment Hearing impairment or deafness is another condition well documented to be associated with reading difficulty (Conrad, 1979; Karchmer et al., 1978; Waters and Doehring, 1990). Chronic ear infections (chronic otitis media) often lead to intermittent hearing loss during the early years. Concern has thus been raised regarding the effects of this on language development and, later, on reading. For chronic otitis media and reading difficulties, results are mixed. Wallace and Hooper (1997) reviewed 18 studies examining chronic otitis media and reading and noted a modest association between the two for language-based skills such as reading. Early Language Impairment Although there is tremendous variability in the rate with which children acquire language during their first four years of life, some children are so clearly behind by age 3 that it arouses concern on the part of their parents, neighbors, preschool teachers, pediatricians, or others. In many such cases, delayed language development is the first indication of a broader primary condition, such as a general developmental disability, autism, hearing impairment, or neurological condition, which is likely to be associated with reading difficulty. In other cases, however, an evaluation by a speech-language professional results in a diagnosis of "(specific) early language impairment"(ELI) and usually the initiation of a course of therapy designed to stimulate language growth in one or more domains. There have been more than a dozen follow-up studies of the later academic achievements of children who were clinically identified as having specific early language impairment. In this work, the sampling criteria, the initial skill levels of the children, and the measures of outcome status have not always been well specified and are rarely comparable from study to study; nevertheless, several general trends

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Page 105 are evident. First, between 40 and 75 percent of preschoolers with early language impairment develop reading difficulties later, often in conjunction with broader academic achievement problems (Aram and Hall, 1989; Bashir and Scavuzzo, 1992). Second, the risk for reading problems appears to be lowest among those whose early language weaknesses were relatively mild or were confined to a narrow domain (especially to speech production alone). Nevertheless, some children with only mild-to-moderate language delays, who appear to overcome their spoken-language difficulties by the end of the preschool period, remain at greater risk than other youngsters for the development of a reading difficulty (e.g., Scarborough and Dobrich, 1990; Stark et al., 1984; Stothard et al., in press). Third, regardless of a child's general cognitive abilities or therapeutic history, in general the risk for reading problems is greatest when a child's language impairment is severe in any area, broad in scope, or persistent over the preschool years (e.g., Stark et al., 1984; Bishop and Adams, 1990). Attention Deficits Although good evidence indicates that attention deficit/hyperactivity disorder and reading disability are distinct disorders, they frequently co-occur. Longitudinal follow-up indicates that, from the beginning of formal schooling, reading disability is relatively common in children with inattention problems (31 percent in first grade), becoming even more frequent as the child matures (over 50 percent in ninth grade—S.E. Shaywitz et al., 1994; B.A. Shaywitz et al., 1995a). Other Conditions A visual impairment is not in itself a predictor of reading difficulty. If not correctable, it makes the reading of printed text impossible, so the visually impaired child must instead learn to read Braille manually. Because Braille notation for English text is alphabetic, and because discovering the alphabetic principle is often the biggest obstacle to children in learning to read, many of the same risk fac-

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Page 106 tors that have been identified for sighted children also presumably apply. Unless these or some other Braille-specific processing difficulties (such as poor manual discrimination) are present, there is probably no higher risk for reading difficulties among blind children than among sighted children, provided that early and adequate instruction in reading Braille is provided. Developmental Differences in Language and Linguistic Development Children who are developing normally achieve certain milestones of motor, linguistic, and cognitive development at predictable ages. Children who show delays in language development in particular have been studied to determine whether these early language delays relate to literacy problems later on. As described earlier, clinical follow-up studies of preschoolers who had been diagnosed as having ELI indicate that this diagnosis is associated with considerable risk. Even among children who do not receive an ELI diagnosis, there is tremendous variation in language skills. Only a handful of longitudinal prediction studies have initially assessed children from birth through age 4, in part because of the difficulty of testing children accurately in this age range. The main focus of these investigations has been to describe the development of various linguistic and metalinguistic abilities in very young children and then follow them up through their early school years. To our knowledge, only one study has directly examined the prediction of reading from language and linguistic developmental differences among infants (Shapiro et al., 1990). A composite measure of infant achievement was found to predict reading status (reading disability or not) with .73 sensitivity (i.e., 73 percent of children with reading disability had been classified initially as at risk) and .74 specificity (i.e., 74 percent of nondisabled readers had been classified as not at risk). Individually, the expressive language milestones made a particularly strong contribution to prediction; including IQ in the composite measure did not improve accuracy. Although not sufficiently accurate for practical use, this degree of predictive success is nevertheless remarkably high, particularly in comparison to

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Page 107 the results emerging from studies predicting reading difficulty from kindergarten (see section below). Walker et al. (1994) cumulatively monitored mean utterance length and number of vocabulary words produced, two developmentally sensitive aspects of emerging language. The two early-language measures, which were highly intercorrelated, correlated moderately well with reading scores in grades 1 through 3, as did the preschool IQ scores. Bryant et al. (1989, 1990) tested young children on several phonological awareness measures, as well as IQ. Performance on reading tests was predicted by receptive vocabulary, expressive language ability, receptive language ability, nursery rhyme recitation, and IQ. Correlations of the rhyme-matching measure with later reading were not reported, and this measure was only weakly related to the tests of phonological awareness at 40-55months, the last of which were strongly predictive of reading. Scarborough (1991) considered several language and IQ measures and reading outcomes at the end of grade 2 for a sample of 62 children, about half of whom had parents and/or older siblings with reading problems. IQ scores correlated moderately with later reading, as did scores on receptive language. Expressive vocabulary skill at age 42 months predicted reading a bit more strongly than did receptive vocabulary scores at the same age. In addition, for a subset of 52 children at age 2.5 years (20 from affected families who became reading disabled; 20 similar in sex, socioeconomic status (SES), and IQ; nonreading disability cases from unaffected families; and 12 who became good readers despite a family history of reading disability), measures of expressive phonological (pronunciation accuracy), syntactic (length/complexity of sentences), and lexical (word diversity) abilities were derived from naturalistic observations of children's language during play sessions with their mothers (Scarborough, 1990). The children who became poor readers were much weaker than the other groups on the syntactic and phonological measures. At ages 3, 3.5, and 4 years, however, only the syntactic differences were evident. What is most striking about the results of the preceding studies is the power of early preschool language to predict reading three to five

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Page 108 years later. In fact, the correlations between reading and early preschool measures are almost as high as those between kindergarten predictors and reading (see next section). Predictors at School Entry Acquired Proficiency in Language Spoken language and reading have much in common. If the printed words can be efficiently recognized, comprehension of connected text depends heavily on the reader's oral-language abilities, particularly with regard to understanding the meanings of words that have been identified and the syntactic and semantic relationships among them. Indeed, many early research reports called attention to the differences between good and poor readers in their comprehension and production of structural relations within spoken sentences. Given the close relationship between reading and language, we should expect that normally occurring variations in language differences would be related to speed or ease of the acquisition of reading. Earlier, we reviewed the empirical data indicating that language development in the preschool years is indeed related to later reading achievement and that preschoolers with language disabilities are highly likely to show reading problems as well. Here we consider whether variation in language abilities at the time children typically begin to receive formal reading instruction also relates to variability in reading outcomes. Verbal Memory The ability to retain verbal information in working memory is essential for reading and learning, so it might be expected that verbal memory measures would be effective predictors of future reading achievement. Many prediction studies have included such measures within their predictor batteries. From the results of those studies (Scarborough, 1998), it is clear that, on average, kindergartners' abilities to repeat sentences or to recall a brief story that was just read aloud to them are more strongly related to their future reading achievement than are their scores on digit span, word span,

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Page 109 and pseudo-word repetition measures. Sentence or story recall (r = .45), in fact, compares favorably with other predictors of reading (see Table 4-1). Lexical and Syntactic Skills Several kinds of vocabulary measures have been examined as predictors of future reading achievement. On each trial of a ''receptive" vocabulary test, the child must indicate which of several pictures best corresponds to the word (usually a noun, adjective, or gerund) spoken by the examiner. A long series of items of increasing difficulty is available, and testing terminates when the child's vocabulary level is exceeded. As shown in Table 41, in 20 prediction studies the mean correlation between receptive vocabulary scores in kindergarten and subsequent reading scores in the first three grades is .36. With regard to lexical abilities, one can also examine expressive, rather than receptive, vocabulary, which is also sometimes referred to as "confrontation naming" or simply "object naming." On such tests, the child is shown a series of drawings of objects and is asked to name each one. Compared with receptive tests, these measures place greater demands on accurate retrieval of stored phonological representations of lexical items and on the formulation and production of spoken responses. To our knowledge, only five kindergarten prediction studies have included confrontation naming measures in the predictor battery, but the magnitude and consistency of the results of those studies suggest that naming vocabulary is a reliable predictor of future reading ability. On average, expressive vocabulary measures are associated (r = .45) with a considerable amount of variance in subsequent reading scores, which compares favorably with the effect sizes for receptive vocabulary and IQ. Not only the accuracy of name production but also its speed can be measured. Rapid serial naming speed has been shown to correlate with concurrent and future reading ability but not with IQ in several dozen studies of schoolchildren (e.g., Ackerman et al., 1990; Bowers and Swanson, 1991; Cornwall, 1992; Denckla and Rudel, 1976b; Felton et al., 1987; Spring and Davis, 1988; Wolf and Obregon, 1992). Rapid serial naming speed has been found to be

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Page 110 TABLE 4-1 Prediction of Reading Difficulties at School Entry Factors Identified in the Child Number of Samples Strength of Relationship Language Verbal memory for stories/sentences 11 Median r = .49 mean r = .45 (SD = .14) Lexical skills 1. Receptive vocabulary 20 Median r = .33 mean r = .36 (SD = .17) 2. Confrontation naming 5 Median r = .49 mean r = .45 (SD = .07) 3. Rapid serial naming 14 Median r = .40 mean r = .38 (SD = .09) Receptive language, syntax/morphology 9 Median r = .38 mean r = .37 (SD = na) Expressive language 11 Median r = .37 mean r = .32 (SD = .16) Overall language 4 Median r = .47 mean r = .46 (SD = .15) Phonological awareness 27 Median r = .42 mean r = .46 (SD = .13) Early Literacy-Related Skills Reading "readiness" 21 Median r = .56 mean r = .57 (SD = .12) Letter identification 24 Median r = .53 mean r = .52 (SD = .14) Concepts of print 7 Median r = .49 mean r = .46 (SD = .20) NOTE: Only studies with sample sizes of 30 or more were considered. At least one of the risk factors of interest had to be assessed initially when the children were within about one year of beginning formal schooling in reading, and at least one assessment of reading skills had to be obtained after one, two, or occasionally three years of instruction. If a word recognition measure was used in a prediction study, its correlation(s) with predictors was used; otherwise, a composite reading score or, rarely, a reading comprehension measure was instead accepted as the criterion variable. When more than one correlation value per risk factor was available in a given sample of children (because multiple reading assessments were conducted and/or because multiple measures of the predictor were used), the average correlation for the sample was used for aggregation. To obtain the average correlations across samples, therefore, each contributing sample contributed only one independent observation. SOURCE: adapted from Scarborough (1998).

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Page 124 Use of a Nonstandard Dialect of English in the Home Dialect differences among English speakers are widely recognized—for example, a Boston accent or a Southern drawl. There is ample evidence that listeners make stereotyped judgments about speakers of particular dialects. Of greater concern here, however, is that some dialect differences are viewed by some not as regional variations but as ''incorrect" English, connoting aberrant or delayed language development, poor learning, lazy or sloppy articulation, or even purposeful insolence. Particularly under these conditions, the differences between a young child's dialect and the standard classroom English dialect may become a risk factor for reading difficulties. With regard to reading instruction in particular, the risk for confusion is considerable. For example, if the teacher is pointing out the letter-sound correspondences within a word that is pronounced quite differently in the child's dialect than in the teacher's, the lesson could confuse more than enlighten. Moreover, teachers who are insensitive to dialect differences may develop negative perceptions of children and low expectations for their achievement, and they may adjust their teaching downward in accord with those judgments. Although these situations undeniably occur, there are many difficulties in measuring the extent to which they happen and the degree to which their occurrence is correlated with, and may contribute to, poor reading achievement. As is the case for children with limited English proficiency, dialect differences are often confounded with poverty, cultural differences, substandard schooling, and other conditions that may themselves impose very high risks for reading difficulties. Even measuring the phenomena and their relation to achievement is confounded by the risk factor itself (Labov, 1966; Smitherman, 1977; Wolfram, 1991). The knowledge base, therefore, is spotty. Some dialects have been researched more thoroughly than others.

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Page 125 Socioeconomic Status Socioeconomic differences are conventionally indexed by such demographic variables as household income and parents' education and occupation, alone or in some weighted combination. In educational studies, furthermore, the socioeconomic level of a school or district may be estimated by the percentage of the enrollment qualifying for federal lunch subsidies. (For a critique and a discussion of some recommended modifications of current methods of measuring SES, see Entwisle and Astone, 1994). Families rated low in SES are not only less affluent and less educated than other families but also tend to live in communities in which the average family SES is low and tend to receive less adequate nutrition and health services, including prenatal and pediatric care. In other ways, too, low SES often encompasses a broad array of conditions that may be detrimental to the health, safety, and development of young children, which on their own may serve as risk factors for reading difficulties. Teasing apart the various aspects of the environment associated with low SES is virtually impossible, and this should be borne in mind as we discuss some particular risk factors that are linked to poverty. As far back as Galton's (1874) studies of English scientists, SES has consistently been shown to predict cognitive and academic outcomes (Hess and Holloway, 1984; White, 1982, Pungello et al., 1996). Although reliable, the relationship between SES and reading achievement is more complex than is generally realized. Consider, for example, how the findings of Alexander and Entwisle (1996)—that low SES students progress at identical rates as middle and high SES students during the school year, but they lose ground during the summer—shed light on the relationship between SES and reading achievement. The degree of risk associated with the SES of the individual child's family differs considerably from the degree of risk associated with the SES level of the group of students attending a particular school. The evidence for this, and its implications for the prevention of reading difficulties among such students, is reviewed here. In an earlier section, we turned our attention to aspects of the home envi-

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Page 126 ronment that may be responsible for the degree of risk posed to the individual child from a low SES home. In principle, low SES could potentially carry risk for reading difficulty for an individual child and for entire groups of children. That is, low SES is an individual risk factor to the extent that among children attending the same schools, youngsters from low-income families are more likely to become poorer readers than those from high-income families. Low SES is also a group risk factor because children from low-income communities are likely to become poorer readers than children from more affluent communities. Because the former are more likely to attend substandard schools, the correlation between SES and low achievement is probably mediated, in large part, by differences in the quality of school experiences. It is thus not very surprising that the strength of the correlation between SES and achievement is stronger when the unit of analysis is the school than when the unit of analysis is the individual child (Bryk and Raudenbush, 1992, on multilevel measures of school effects). When the average SES of a school (or district) and the average achievement level of the students attending that school are obtained for a large sample of schools, a correlation between SES and achievement can be calculated using the school as the unit of analysis. In a meta-analytic review of the findings for 93 such samples, White (1982) found that the average size of the correlation was .68, which is substantial and dovetails with the conclusion of the section below that attending a substandard school (which is usually one whose students tend to be low in both SES and achievement) constitutes a risk factor for the entire group of children in that school. When achievement scores and SES are measured individually for all children in a large sample, however, the strength of the association between SES and achievement is far lower. In White's (1982) meta-analysis, for instance, the average correlation between reading achievement and SES across 174 such samples was .23. Similarly, the correlation was .22 in a sample of 1,4599-year-old students whose scores were obtained through the National Assessment of Educational Progress (NAEP) evaluations (Walberg and Tsai, 1985). In a meta-analysis of longitudinal prediction studies, Horn and

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Page 127 O'Donnell (1984) obtained a correlation that was only slightly higher (.31) between SES and early school achievement. Similar SES findings were found in population-representative studies in the United States and in other English-speaking countries (e.g., Alwin and Thornton, 1984; Estrada et al., 1987; Richman et al., 1982; Rowe, 1991; Share et al., 1984; Wells, 1985). In other words, within a given school or district, or across many districts within a country, SES differences among children are relatively weak predictors of achievement. Thus, all else being equal, coming from a family of low SES (defined according to income, education, and occupation of the parents) does not by itself greatly increase a child's risk for having difficulty in learning to read after school income level has been accounted for. We are not saying here that SES is not an important risk marker. What we are saying is that its effects are strongest when it is used to indicate the status of a school or a community or a district, not the status of individuals. A low-status child in a generally moderate or upper-status school or community is far less at risk than that same child in a whole school or community of low-status children. Analysis of Family-Based Risk Factors Parents' reading disabilities predict a higher than normal rate of reading disabilities in their children (31 to 62 percent versus 5 to 10 percent). Although parental reading disabilities are not completely predictive of their children's reading disabilities, the substantially greater risk at least warrants very close monitoring of their children's progress in early language and literacy development. Lack of English proficiency for a Hispanic child is a strong indication that he or she is at risk for reading difficulty; however, linguistic differences appear to be less responsible than other co-occurring group risk factors, particularly school quality. In a similar manner, the occurrence of family use of nonstandard dialect and individual family SES covary considerably with factors such as school quality, which is discussed in the next major section of this chapter. The quantity of verbal interaction in families constitutes a risk factor primarily in that it relates closely to child vocabulary scores.

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Page 128 Findings related to home literacy environments are mixed. Many of the large-scale studies (Walberg and Tsai, 1984; White, 1982) of the correlations between home environment and school achievement have focused primarily on samples of children in elementary school (or older). Because the focus of this report is on the prevention of reading difficulties in young children, it is especially important to consider the different roles that home environment may play at different ages. In particular, the opportunities provided in the home for literacy acquisition during the preschool years may contribute primarily to the child's acquisition of attitudes toward literacy, of knowledge about the purpose and mechanics of reading, and of skills (such as vocabulary growth and letter knowledge) that may facilitate learning when school instruction begins. Once the child has begun to attend school and has started to learn to read, the contributions of home and parents may be somewhat different; assistance with homework, listening to the child's efforts at reading aloud, the availability of resources such as a dictionary and an encyclopedia, and so forth may be particularly important for fostering high achievement in school.  NEIGHBORHOOD, COMMUNITY, AND SCHOOL-BASED RISK FACTORS As is clear from our discussion of the family-based factors that constitute risks, it is extremely difficult to disentangle the effects of family practices from factors such as the neighborhood where the family lives, the cultural and economic community of which the family is a part, and the school the child attends. In this section, we focus on these issues, noting that more research has addressed schooling rather than environmental risks to reading development. A school in which students are performing at a much higher (or much lower) level than might be predicted using such standard measures as family SES is often described as an "outlier." Studies of outlier schools have overwhelmingly concentrated on positive outlier schools. Variously referred to as studies of "exemplary schools" (Weber, 1971), "unusually effective schools" (Levine and Lezotte, 1990), and ''high-flying" schools (Anderson et al., 1992), these posi-

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Page 129 tive outlier studies have made important contributions to the field (for a review, see Stringfield, 1994). Of the studies that have examined both positive and negative outlier schools, the largest and longest running has been the Louisiana School Effectiveness Study (Stringfield and Teddlie, 1988, 1991; Teddlie and Stringfield, 1993). Classroom practices in ineffective schools (regardless of community SES) were characterized by significantly lower rates of student time on task, less teacher presentation of new material, lower rates of teacher communication of high academic expectations, fewer instances of positive reinforcement, more classroom interruptions, more discipline problems, and a classroom ambiance generally rated as less friendly (Teddlie et al., 1989). Stringfield and Teddlie (1991) also conducted detailed qualitative analyses of the 16 case studies. Those analyses added significantly to the quantitative findings. Qualitative differentiations were made at three levels: the student, the classroom, and the school. At the level of student activities, ineffective schools were found to be different from more effective, demographically matched schools in two ways. First, students' time-on-task rates were either uniformly low or markedly uneven. Time on task is a good predictor of achievement gain (Stallings, 1980). In some schools, very few academic tasks were put before any students, and in other schools there were marked differences in the demands made of students, with only some students being required to make a concerted academic effort. Students in positive outlier schools were more uniformly engaged in academic work. The second student-level variable was whether tasks were put before the students in what appeared to the students to be an organized and goal-oriented fashion. When interviewed, students at ineffective schools were much less likely to be aware of why they were being asked to do a task, how the task built on prior schoolwork, and how it might be expected to lay a foundation for future work. At the classroom level, ineffective schools were characterized by a leisurely pace, minimal moderate-to-long-term planning, low or uneven rates of interactive teaching, and a preponderance of "ditto sheets" and other relatively unengaging tasks. One of the most readily observable of the classroom differences was that teachers in

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Page 130 ineffective schools simply failed to cover all of the district-mandated materials by year's end. These students were not being provided equal "opportunity to learn." (For a discussion of the power of opportunity to learn, see Muthen et al., 1991). Finally, ineffective schools were structured such that teachers almost invariably taught in isolation from one another; there was little focus on building a professional knowledge base within the school. An additional factor, class size, is related to achievement (Mosteller et al., 1996). During the kindergarten year, there is evidence that teacher-child relationships are important for later school achievement. Studies have defined the significant qualities of these relationships (Howes and Hamilton, 1992; Howes and Matheson, 1992). One study used a scale based on these findings that describes teachers' perceptions of different qualities of their relationships with their students (Pianta and Steinberg, 1992). Another study compared results on this scale and readiness tests and found that two global qualities of the teacher-child relationship, dependency or conflict, were related to poor performance (Birch and Ladd, 1997). Dependency is an index of the child's overdependence on the teacher; conflict is an index of friction in the teacher-child relationship. Closeness in the teacher-child relationship was associated with better readiness performance. Closeness is an index of warmth and open communication in the teacher-child relationship. At the school level, ineffective schools were observed to be different from their demographically matched peers along seven dimensions: (1) they were not academically focused; (2) the school's daily schedule was not an accurate guide to academic time usage; (3) resources often worked at cross-purposes instructionally; (4) principals seemed uninterested in curricula; (5) principals were relatively passive in the recruitment of new teachers, in the selection of professional development topics and opportunities for the teachers, and in the performance of teacher evaluations; (6) libraries and other media resources were rarely used to their full potential; and (7) there were few systems of public reward for students' academic excellence. Similar descriptions of a smaller set of negative outlier schools have been provided by Venezky and Winfield (1979).

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Page 131 SUMMARY In this chapter we have examined information about risk factors to determine what kinds of risk are so strongly related to reading difficulties that they can potentially be used to identify children in need of prevention and early intervention. It is clear that the relationships between risk factors and reading achievement are continuous and probabilistic, not categorical or deterministic. Misleading conclusions can be reached if risk factors are not interpreted in this light. It must always be borne in mind that many children whose language and literacy skills are weak at the outset of schooling become successful readers. A majority, however, do not, giving rise to the correlational evidence we have reviewed. It bears repeating, also, that a causal relationship to reading has been shown for only some, but not all, of the measures that best predict future reading ability. Our review of prediction studies indicates clearly that no single risk factor, on its own, is sufficiently accurate to be of practical use for predicting reading difficulties. In combination, however, measures of various kinds of risk—individual, familial, and demographic—can provide useful estimates of future achievement levels. Although prediction accuracy is far from perfect, errors of prediction can be tolerated as long as children's progress is carefully monitored during kindergarten and beyond. As discussed below, how different school systems can best use the available information about risk indicators must be tailored to their particular needs, goals, and resources. Group Risk Factors It is abundantly clear that some groups of children are at risk for reading difficulties because they are affected by any or all of the following conditions: 1. They are expected to attend schools in which achievement is chronically low, 2. they reside in low-income families and live in poor neighborhoods, 3. they have limited proficiency in spoken English, and

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Page 132 4. they speak a dialect of English that differs substantially from the one used in school. Individual Risk Factors The evidence also indicates that individual children, whether or not faced with the adverse conditions just mentioned, may be at greater risk than other otherwise-comparable children for reading difficulties for any or all of the following reasons: 1. They are children of parents with histories of reading difficulty; 2. they have acquired less knowledge and skill pertaining to literacy during the preschool years, either through lack of appropriate home literacy experiences and/or as a result of some inherent cognitive limitations; 3. they lack age-appropriate skills in literacy-related cognitivelinguistic processing, especially phonological awareness, confrontational naming, sentence/story recall, and general language ability; 4. they have been diagnosed as having specific early language impairment; 5. they have a hearing impairment; and 6. they have a primary medical diagnosis with which reading problems tend to occur as a secondary symptom. Practical Use of This Information Detecting problems early, in order to avoid other problems later on, is the most practical course. The ease, cost, and reliability with which various risk factors can be measured are therefore a central concern. Many of the group factors named above (e.g., a child is expected to attend a school in which achievement is chronically low, the child lives in a low-income family and neighborhood) are easily accessible measures. When they are present, effective preventions and early interventions can be provided throughout the age span we are addressing in this report—birth through grade 3.

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Page 133 Pediatric screening tools are effective in identifying children who have severe sensory or developmental impairments (hearing impairment, specific language impairment). When these are present, preventions and early interventions can be provided. There is less practical utility in conducting population-wide individual screening of infants, toddlers, and preschoolers who have acquired less knowledge and skills pertaining to literacy during the preschool years, either through lack of appropriate home literacy experiences or as a result of some inherent cognitive limitations, or of those who lack age-appropriate skill in literacy-related cognitive-linguistic processing, for the purpose of identifying those who are at greatest risk for reading difficulties. Some screening (i.e., language milestones) is already part of regular well-baby visits; in this case the information could help to define risk, especially when aggregated with other risk factors. Kindergarten screening, in contrast, has become reasonably accurate when a combination of skills is measured (although the optimal combination is not yet identified). Ideally, screening procedures should be quick and inexpensive; they should identify all or most children who have the specific problem; and they should mistakenly detect none or few children who do not have the problem. To achieve the goal of preventing reading difficulties, it will not be feasible or appropriate to provide the same sort of intervention to all of these groups and individuals, although some kinds of programs may be of benefit to all. In the next chapter, we review and evaluate the possible approaches that can be taken toward addressing the problems of groups and individuals who have been identified as being at risk.

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