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Journal of Educational Psychology 2004, Vol. 96, No. 1, 43–55

Copyright 2004 by the American Psychological Association, Inc. 0022-0663/04/$12.00 DOI: 10.1037/0022-0663.96.1.43

The Nature of Phonological Awareness: Converging Evidence From Four Studies of Preschool and Early Grade School Children Jason L. Anthony

Christopher J. Lonigan

University of Houston

Florida State University

Significant controversy exists about the nature of phonological awareness, a causal variable in reading acquisition. In 4 studies that included 202 5- to 6-year-old children studied longitudinally for 3 years, 123 2- to 5-year-old children, 38 4-year-old children studied longitudinally for 2 years, and 826 4- to 7-year-old children, the authors examined the relation of sensitivity to rhyme with other forms of phonological awareness. Rhyme sensitivity was indistinguishable from phonemic awareness, segmental awareness, and global phonological sensitivity in younger children. Rhyme sensitivity was distinguishable, although highly correlated, with these phonological skills in older children. Rhyme sensitivity was highly predictive of these other phonological skills. Children’s sensitivity to different linguistic units seems best conceptualized as a single underlying ability.

Current definitions of phonological awareness can be considered on a continuum of generality from highly exclusive to highly inclusive of different types of phonological skills. The most stringent definition defines phonological awareness as the conscious reflection on abstract representations of speech. One operationalization of this definition includes only phoneme level skills (Morais, 1991a). The rationale is that only tasks that involve manipulation of phonemes undoubtedly require reflection on abstract representations because phonemes produced in speech are acoustically inseparable because of coarticulation (A. Liberman, Cooper, Shankweiler, & Studdert-Kennedy, 1967). In contrast, syllables are acoustically marked by changes in amplitude, and rimes may be perceptually marked by their steady-state vowel sound or articulatory cues. In theory, the ability to consciously reflect on phonemes, or phonemic awareness, is a metalinguistic ability that develops alongside general metacognitive control processes during middle childhood (Tunmer & Rohl, 1991). Adherents to this definition differ in whether they conceptualize supraphonemic skills (i.e., sensitivity to linguistic units larger than a phoneme) as supportive of the development of phonemic awareness (e.g., Tunmer & Rohl, 1991) or whether supraphonemic skills are viewed as distinct abilities. For example, Yopp (1988, p. 172) suggested that “rhyme tasks may tap a different underlying ability than other tests of phonemic awareness.” Muter, Hulme, Snowling, and Taylor (1997, p. 386) reported that their study “provides clear evidence for two distinct and more or less independent phonological abilities: [phonemic] segmentation . . . and rhyming.” A second, less stringent definition includes all subsyllabic skills, including phonemic skills, in the construct of phonological awareness. Proponents of this view argue that because subsyllabic units of onset and rime are psychologically based (Treiman, 1983, 1985) that cognitive operations involving these word units also require conscious awareness of abstract representations of speech. Tasks that involve larger linguistic units (i.e., syllables or words) are excluded because they reflect sensitivity to acoustic qualities of speech. Accordingly, phonological awareness, or subsyllabic

Phonological awareness plays a key role in literacy development (for reviews, see Adams, 1990; Whitehurst & Lonigan, 1998). For example, children who are better at detecting rhymes or phonemes are quicker to learn to read, and this relation is present even after variability due to IQ, vocabulary, memory, and social class are statistically controlled (Bryant, MacLean, Bradley, & Crossland, 1990; Wagner & Torgesen, 1987; Wagner, Torgesen, & Rashotte, 1994). Moreover, there are experimental demonstrations that training children in phonological awareness positively affects reading, consistent with a causal relation between phonological awareness and early reading (e.g., Brady, Fowler, Stone, & Winbury, 1994; Byrne & Fielding-Barnsley, 1991, 1993). Even so, disagreements remain about how to best conceptualize phonological awareness. Numerous definitions have been offered, each with relatively well-developed theoretical underpinnings. The overarching goal of the present study was to empirically compare the utility of various definitions of phonological awareness in preschool and early elementary school children. To achieve this goal, we reanalyzed four previously published data sets using a superior statistical approach than that which has been used in prior research aimed at addressing this question.

Preparation of this article was supported, in part, by National Institute of Child Health and Human Development Grants HD36067 and HD36509 and Administration for Children and Families Grant 90-YF-0023 to Christopher J. Lonigan. Views expressed herein are the authors’ and have not been cleared by the grantors. We thank Richard Wagner, Joseph Torgesen, and Carol Rashotte for making their raw data available for Study 1. We thank Sarah Dyer, Brenlee Bloomfield, Crystal Carr, Kimberly Ingram, Danielle Karlau, Nikki Sutton, Emily Shock, and other students at Florida State University for their assistance with data collection for Study 2. Correspondence concerning this article should be addressed to Jason L. Anthony, Department of Psychology, University of Houston, Houston, TX 77204, or to Christopher J. Lonigan, Department of Psychology, Florida State University, Tallahassee, FL 32306-1270. E-mail: jason.anthony@ times.uh.edu or [email protected] 43

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ANTHONY AND LONIGAN

awareness more precisely, can be measured by tasks that require detection or manipulation of onsets, rimes, vowels, or codas, most of which can be more than one phoneme in length. A third definition maintains phonological awareness is the capacity to consciously isolate word segments (Morais, 1991b). In one regard, this definition is more general than the previous two because it includes the ability to identify and manipulate syllables as well as onsets, rimes, codas, and phonemes. In another regard, it is narrower because it excludes the ability to make judgments of phonological similarity or dissimilarity at any level of word structure. For example, a child who can indicate which two of three words rhyme would not be considered phonologically aware unless that child could produce the rime unit of a given word. The reason is that only the latter performance demonstrates conscious representation of the rime unit evidenced through segmentation. Theoretically, segmental awareness is linked with the development of cognitive analytic abilities and experience with or instruction in alphabetic literacy (Morais, 1991b; Morais & Mousty, 1992). Finally, Stanovich (1992) asserted that the construct of phonological awareness should be divorced from the idea of conscious awareness because consciousness is a folk term that cannot be adequately operationalized. Stanovich broadly described phonological sensitivity as along a continuum from a “shallow” sensitivity of large phonological units to a “deep” sensitivity of small phonological units (Stanovich, 1992, p. 317). This general definition includes phonological skills involving manipulation and judgments of any unit of word structure. Adherents to this definition (e.g., Adams, 1990; Bradley, 1988; Bryant et al., 1990; Goswami & Bryant, 1990; Treiman & Zukowski, 1991, 1996) view phonological sensitivity as a single ability taking on different forms during its course of development. In early stages, phonological sensitivity manifests in detection of large phonological units such as words, syllables, onsets, and rimes. In later stages, it manifests in manipulation of phonemes. The developmental hierarchy of phonological sensitivity skills appears to parallel a hierarchical model of word structure, such that children are increasingly sensitive to smaller linguistic units. That is, children achieve syllable sensitivity earlier than subsyllabic sensitivity, and they achieve subsyllabic sensitivity earlier than phonemic sensitivity (Anthony, Lonigan, Driscoll, Phillips, & Burgess, 2003; Fox & Routh, 1975; I. Liberman, Shankweiler, Fischer, & Carter, 1974; Treiman, 1992). Theoretically, the developmental conceptualization of phonological sensitivity proposes that children’s rudimentary phonological skills both set the stage for more advanced phonological skills like phonemic awareness and reflect the same underlying ability as more advanced phonological skills. The four conceptualizations of phonological awareness above agree that there are multiple phonological skills that are distinguished by linguistic complexity and type of operation performed. The key theoretical differences are whether all types of phonological skills belong to the same construct and whether the types are distinct abilities. Accurate specification of phonological awareness is necessary to advance research toward better understanding the causal relation, or relations, between phonological awareness and the acquisition of literacy. This will require determination of the precise number and nature of types of phonological awareness across the early childhood years when this phonological ability or these phonological abilities develop.

One approach to addressing the nature of phonological awareness involves correlational research. Researchers interpret high correlations among like measures as indicating the measures tap the same construct. This logic is applied on a larger scale when exploratory factor analysis (EFA) is used to identify measures that empirically cluster together to form a factor that represents a single construct. Some EFA studies have yielded evidence that supports a unitary phonological ability, consistent with Stanovich’s conceptualization of phonological sensitivity (Stanovich, Cunningham, & Cramer, 1984; Wagner & Torgesen, 1987). For example, Stahl and Murray (1994) administered four different tasks, within which linguistic complexity was varied, to 113 kindergarten and firstgrade children. Both EFA of the four tasks across linguistic complexity and EFA of the four levels of linguistic complexity across tasks yielded single-factor solutions that well explained children’s performances. Conversely, some EFA research has provided evidence of more than one phonological ability (Muter, Hulme, & Snowling, 1997; Muter, Hulme, Snowling, & Taylor, 1997; Yopp, 1988). For example, Hoien, Lundberg, Stanovich, and Bjaalid (1995) found separate factors for Phoneme Sensitivity, Syllable Sensitivity, and Rhyme Sensitivity in 6- and 8-year-old Norwegian children. One plausible explanation for the conflicting findings is measurement problems, including ceiling effects, floor effects, and low reliabilities due to multiple-choice formats. Each of these measurement problems bias results against the conceptualization of a global and unified phonological ability. The EFA research has also been plagued with methodological and statistical shortcomings. Most noteworthy are single-item factors, orthogonal rotations, confounding method covariance, and significant cross loadings, all of which have obscured the findings. A second approach used to address the conceptualization of phonological awareness has been to examine whether different phonological skills relate to literacy in different ways. Bryant et al. (1990) found sensitivity to rhyme uniquely predicted reading and spelling in 6-year-old children after controlling for IQ, socioeconomic status, vocabulary, age, and sensitivity to phonemes. In contrast, Muter, Hulme, Snowling, and Taylor (1997) found sensitivity to phonemes uniquely predicted reading and spelling in 5-year-old children after controlling for IQ, letter knowledge, and sensitivity to rhyme (see also Hulme et al., 2002). One interpretation of these results is that sensitivity to rhyme and sensitivity to phonemes are differentially related to reading and spelling and that it is therefore important to consider them separate constructs. However, the fact that Bryant et al. (1990) found sensitivity to rhyme to be most important whereas Muter, Hulme, Snowling, and Taylor found sensitivity to phonemes to be most important suggests that the relative superiority of different types of phonological skills is either small and unreliable or changes for some unidentified reason (e.g., the age of participants). Alternatively, the inconsistent findings could be due to different methodologies. Bryant et al. (1990) used single measures as predictor and control variables, whereas Muter, Hulme, Snowling, and Taylor used latent variables as predictor and control variables. A third and far superior approach was used recently in this area of inquiry by Anthony et al. (2002) who used confirmatory factor analysis (CFA) to compare directly alternative definitions of phonological awareness. CFA allows a priori specification of theoretical models by dictating the factors on which variables load and do not load, making interpretation of factors unambiguous. In con-

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trast, EFA has generated factor solutions in which some variables load on multiple factors and some factors are composed of variables that measure different constructs (e.g., Muter, Hulme, Snowling, & Taylor, 1997). Both situations obscure the nature of factors and require conclusions to be considered tentative. Using CFA, Anthony et al. also were able to model known sources of method covariance as independent of true factor structure. In contrast, systematic method covariance has obscured true factor structure in studies that have used EFA. Finally, Anthony et al. capitalized on CFA’s primary advantage over EFA by empirically evaluating each model and statistically comparing how well the different models described children’s phonological awareness. In summary, Anthony et al. avoided commonplace methodological shortcomings of single-item factors, orthogonal rotations, confounding method covariance, and ambiguous factors by including multiple measures of each phonological skill and using CFA to directly compare the utility of various conceptualizations of phonological awareness. Anthony et al. found that a one-factor model of phonological sensitivity best described the phonological skills of 258 preschool children. In the present report, we used CFA to further evaluate and compare definitions of phonological awareness. We also used structural equation modeling (SEM) with latent variables to more precisely quantify the longitudinal relations among phonological skills than that permitted in prior research by correlational, regression, and path analysis with observed variables. The four data sets we analyzed for this report served to (a) stringently test the replicability of the findings of Anthony et al. (2002) and (b) extend the approach of Anthony et al. to the examination of school-age phonological awareness. We focused on the distinguishability of rhyme sensitivity from more advanced forms of phonological awareness. Specifically, we evaluated competing definitions by testing the hypothesis that rhyme sensitivity was empirically indistinguishable from phonemic awareness, segmental awareness, and global phonological sensitivity.

Method

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Table 1 Sample Characteristics by Study General ability n

Age in months

Gender

Ethnicity

M

SD

Wagner et al. (1997) 202

61–82

53% female

74% Caucasian, 24% African 102a American, 2% Asian or Hispanic

15

Lonigan et al. (1998) 123

25–61

52% female

77% Caucasian, 19% African American, 4% Asian or Hispanic

96b

15

114d

11

—c

—c

Muter, Hulme, Snowling, and Taylor (1997) 38

46–57

—c

—c

Muter, Hulme, and Snowling (1997) 826

48–95

—c

—c

a Mean standard score on Vocabulary subtest of the Stanford-Binet Intelligence Scale (4th ed.; Thorndike, Hagen, & Sattler, 1986). b Mean standard score on Peabody Picture Vocabulary Test—Revised (Dunn & Dunn, 1981). c Information not included in original report. d Mean standard score on Wechsler Preschool and Primary Scale of Intelligence (Wechsler, 1967).

Muter & Snowling, 1998, p. 330). We also included these two studies to highlight methodological and statistical explanations for why the original findings differed from those of other researchers. Finally, the studies by Wagner et al. (1997; Study 1) and Muter, Hulme, Snowling, and Taylor (1997; Study 3) had the added benefit of longitudinal designs that permitted examination of the longitudinal relations among different types of phonological skills to further determine the extent of their distinguishability.

Participants The foremost criterion by which we selected data sets was that they were capable of addressing our hypotheses using latent variable SEM techniques, including CFA. Therefore, selected studies included more than one variable at each of multiple levels of linguistic complexity. We also selected studies that collectively spanned a broad age range to address our research question more comprehensively than had been done in prior research. Finally, we selected studies that complemented each other. For example, an elementary school study was supplemented with a similar preschool study. Two other studies bridged the preschool and early grade school years. One of these later studies had a small sample but a longitudinal design. It was supplemented with a similar study that had a large sample but no longitudinal component. On the basis of the above criteria, we selected the following four studies: Wagner et al. (1997), Lonigan, Burgess, Anthony, and Barker (1998), Muter, Hulme, Snowling, and Taylor (1997), and Muter, Hulme, and Snowling (1997), hereafter referred to as Studies 1 through 4, respectively. Table 1 provides a summary of the sample characteristics of each study. Studies 3 and 4 also were selected to serve as stringent tests of the one-factor model asserted by Anthony et al. (2002) because Muter and colleagues interpreted the findings from Studies 3 and 4 as indicative of “independent skills” (Muter, Hulme, Snowling, & Taylor, 1997, p. 386;

Measures and Procedures Used in the Original Studies In all four original studies, children were assessed individually by trained examiners. Testing was spread over multiple sessions to avoid participant fatigue. All measures had a small number of demonstration items in which an examiner provided feedback regarding the correctness of a participant’s response. One test (rhyme matching in Study 4) involved corrective feedback on a portion of the test items. A description of the measures used in each study is reported in Table 2. Two or more of the following onset–rime-level measures were included in each study. For rhyme oddity, children were required to select the one word from a spoken set of words that did not rhyme. For one of the rhyme oddity tests, an examiner presented pictures that illustrated the auditory stimuli to reduce memory demands on the preschool participants. For rhyme matching, children were presented a set of pictures that were named by an examiner, and children were asked which of the pictures rhymed with a stimulus picture. The alliteration matching test was identical to rhyme matching, except that different stimuli were used and children were required to indicate which picture started with the same sound as the stimulus picture. For rhyme production, an examiner stated a stimulus word and an example of a rhyming word. Children were then allotted 30 s to supply other words and nonwords that rhymed with the stimulus. For blending

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46

Table 2 Types of Measures Used in Each Study Syllable Blending

Phoneme

Segmentation

Onset–rime

Blending

Segmentation

Wagner et al. (1997) Rhyme oddity Phoneme blending Phoneme segmentation Alliteration matching into words Onset and rime blending Phoneme blending into nonwords Lonigan et al. (1998) Syllable blendinga Syllable elisionb Rhyme oddity Word blendinga Word elisionb Rhyme matching

Phoneme blendinga

Phoneme elisionb

Muter, Hulme, Snowling, and Taylor (1997) Rhyme production Rhyme matching

Phoneme blending Phoneme elision Word completion

Muter, Hulme, and Snowling (1997) Word completionc Note.

Rhyme production Rhyme matching

Initial phoneme elisiond Final phoneme elisiond Word completionc

Variables with the same superscript were formed from item sets within a common measure.

onset and rime, children listened to isolated pairs of onsets and rimes and then stated the blended word. Two or more of the following phoneme-level measures were included in each study. For phoneme segmentation, an examiner said a word and instructed children to say each sound in that word in the proper order. For phoneme blending tests, children listened to isolated phonemes and then stated the blended word or nonword. One of the phoneme blending tests included an illustrated multiple-choice format for half of the items to reduce memory demands on preschool participants. For phoneme elision tests, an examiner presented a picture and named it. Children were instructed to say the word without the initial or final phoneme. One of the phoneme elision tests included an illustrated multiple-choice format for half of the items. For word completion, an examiner supplied the first two phonemes of an illustrated word, and children supplied the final phoneme. Two studies included syllable-level measures. Lonigan et al.’s (1998) blending measure had 18 syllable blending and word blending items. In the first half of this test, children were required to listen to isolated syllables or words and then point to one of three pictures that illustrated the blended word or compound word. In the second half, children were required to blend syllables or words without the help of pictures. Lonigan et al.’s (1998) elision measure included 14 syllable elision and word elision items. For half of the items, children were required to point to the one of three pictures that illustrated deletion of a word from a compound word or deletion of a syllable from a multisyllabic word. For the other half, children were required to say the word that remained after the deletion. For Muter, Hulme, and Snowling’s (1997) word completion test, an examiner supplied the first syllable of an illustrated word and children supplied the last syllable.

Results Methods Used to Control for Chronological Age For Studies 1 and 3, we formed age groups with 1-year age bands. The age range and sample size in Study 2 precluded this

approach, so we standardized the variables within the sample by regressing chronological age from the raw scores. Neither approach, however, could be applied to the data in Study 4 because participants’ ages were not made available despite our requests.

Preanalysis Variable Construction Because some measures used a single method to assess multiple levels of linguistic complexity, they supplied values for more than one variable (see Table 2). For example, in Study 4, the first eight items of the word completion test required participants to supply a final syllable, and the last eight items required participants to supply a final phoneme. Thus, we created a word completion– syllables variable and a word completion–phonemes variable from this measure. Also in Study 4, phoneme elision supplied values for initial phoneme elision and final phoneme elision variables. In Study 2, the blending measure supplied values for word blending, syllable blending, and phoneme blending variables. The elision measure likewise supplied values for word elision, syllable elision, and phoneme elision variables. Whenever a measure provided values for multiple variables, none of its items contributed to the score of more than one variable.

Statistical Approach to Data Analysis We used EQS (Bentler, 1995) to perform SEM and CFA of either raw data (Studies 1 and 2) or published correlation matrices (Studies 3 and 4). When we examined the distributional properties of the raw scores in Studies 1 and 2, we found mild and moderate departures from normality but no obvious outliers. Although transformations of the variables improved their distributions, results were identical to those using untransformed variables. The results

THE NATURE OF PHONOLOGICAL AWARENESS

also were unchanged when analyses were conducted using robust maximum likelihood estimation, the Satorra–Bentler scaled chisquare, and adjustments to the standard errors to account for the extent of nonnormality. Thus, for the sake of uniformity, we conducted all analyses using maximum likelihood estimation of either untransformed raw variables or published means and correlations. We modeled correlated residuals among variables that originated from the same measure to avoid confounding true factor structure with method covariance. For example, in Study 4, we modeled correlations among the residuals of the two word completion variables and among the residuals of the two phoneme elision variables. In Study 2, we modeled correlations among residuals of the three elision variables and among residuals of the three blending variables. The a priori models that represented different conceptualizations of phonological awareness fell along a continuum of generality. That is, the one-factor models represented a general single ability conceptualization, the two-factor oblique models represented separate but correlated abilities conceptualizations, and the two-factor orthogonal models represented distinct abilities conceptualizations. These models often were nested in that they could be derived by imposing constraints on other models. For example, constraining the correlation between factors in a two-factor oblique model to 1.0 yields a one-factor model, and constraining the correlation to zero yields a two-factor orthogonal model. This relation between the different models is important because the utility of one model to explain data can be statistically compared with the utility of another model in which it is nested by using chi-square difference tests. The most saturated model always yields the best fit because it is the least restrictive model. However, the most restrictive nested model that fails to yield a significantly worse fit is accepted as superior in the interest of parsimony.

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Hypothesis 1: The Distinguishability of Onset⫺Rime Sensitivity From Phonemic Awareness CFA. CFA of data from Studies 1, 3, and 4 tested whether a single Subsyllabic Sensitivity factor better described children’s performances on the measures or whether separate Onset⫺Rime Sensitivity and Phonemic Awareness factors better described these data. The a priori model representing subsyllabic sensitivity consisted of a single factor defined by variables that indexed sensitivity to onset, rime, or phonemes. A second a priori model consisted of two correlated factors, an Onset⫺Rime factor defined by onset–rime-level variables and a Phonemic Awareness factor defined by phoneme-level variables. This model represented the conceptualization in which onset⫺rime sensitivity and phonemic awareness are separate but correlated abilities. A third a priori model consisted of the same Onset⫺Rime and Phonemic factors, but they were uncorrelated. This model represented the more conservative position within the specific abilities conceptualization in which onset⫺rime sensitivity and phonemic awareness are considered distinct abilities. We compared the utility of these alternative models at each wave of assessment in Study 1, at each wave of assessment in Study 3, and at the one wave of assessment in Study 4. Given that good model fits are indicated by comparative fit indices and Tucker–Lewis indices greater than .90, root-meansquare errors of approximation less than .08, nonsignificant chisquare values, and a relatively low Akaike information criterion, it is apparent from Table 3 that in Study 1 the one-factor and two-factor oblique models provided very good fits at all grades. In contrast, the two-factor orthogonal model poorly described these data at all grades. This was due to the Onset⫺Rime Sensitivity factor and the Phonemic Awareness factor of the two-factor oblique model being highly correlated in all grades (rs ⫽ .90 to

Table 3 Fit Indices for Models of Kindergarten, First-Grade, and Second-Grade Children’s Phonological Sensitivity (Study 1) Model

␹2

df

CFI

TLI

RMSEA

AIC

⌬␹2

.96 .96 .45

.10 .10 .36

8.5 7.5 229.2

2.98† 223.71***

.97 .97 .45

.08 .09 .37

3.7 4.0 238.2

1.69† 236.15***

.94 .96 .47

.11 .09 .33

13.5 4.5 181.7

11.06*** 179.18***

Kindergarten 1-factor (OR ⫹ P) 2-factor oblique (OR, P) 2-factor orthogonal (OR, P)

26.48** 23.50** 247.21***

9 8 9

.98 .98 .67

First grade 1-factor (OR ⫹ P) 2-factor oblique (OR, P) 2-factor orthogonal (OR, P)

21.74** 20.05** 256.20***

9 8 9

.98 .98 .67

Second grade 1-factor (OR ⫹ P) 2-factor oblique (OR, P) 2-factor orthogonal (OR, P)

31.53*** 20.47** 199.65***

9 8 9

.96 .98 .68

Note. N ⫽ 202. Chi-square difference tests are comparisons to the previous model. Letters in parentheses indicate combinations of variables on factors (variables separated by a plus sign composed a factor; variables separated by a comma were on different factors). CFI ⫽ comparative fit index; TLI ⫽ Tucker–Lewis index; RMSEA ⫽ root-mean-square error of approximation; AIC ⫽ Akaike information criterion; OR ⫽ onset–rimelevel variables; P ⫽ phoneme-level variables. †p ⬎ .05. ** p ⬍ .01. *** p ⬍ .001.

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.97, ps ⬍ .001). Moreover, the difference between fits of the nested one-factor and two-factor oblique models was not significant in the kindergarten data or in the first-grade data, ⌬␹2(1, N ⫽ 202) ⫽ 2.98 and 1.69, ps ⬎ .10, respectively. Consequently, the one-factor Subsyllabic Sensitivity model was judged the most parsimonious, well-fitting characterization of the kindergarten and first-grade data. However, the fit of the two-factor oblique model was reliably better than that of the one-factor model in the second-grade data, ⌬␹2(1, N ⫽ 202) ⫽ 11.06, p ⬍ .001. All factor loadings were substantial and reliable (␭s ⫽ .41 to .95, ps ⬍ .001) in both models at every grade. In Study 3, we could not evaluate the a priori models in the 4-year-olds’ data because the models did not converge cleanly (i.e., a parameter estimate was out of range; March, 1994). The variance of the error term for the rhyme matching measure was negative, even though criteria were met for proper identification. Therefore, none of the a priori models appropriately described the 4-year-olds’ data, and the residual of the rhyme matching measure shared variance with another variable. We evaluated an a posteriori model that posited a single factor identified by all four phonological sensitivity measures and that allowed the residuals of the two rhyme measures to correlate. This one-factor model provided an excellent fit (see upper panel of Table 4). The correlation between residuals of the rhyme measures was significant (r ⫽ .61, p ⬍ .01) as were three of the four loadings on the Subsyllabic Sensitivity factor (␭s ⫽ .44 to .78, ps ⬍ .05). Rhyme production, however, did not significantly load on Subsyllabic Sensitivity (␭ ⫽ .13, p ⬎ .50). We also examined a second a posteriori model that consisted of a single factor defined by all phonological sensitivity variables

except rhyme production. The rationale for excluding the rhyme production variable was that it poorly indexed the construct(s) of interest. This was evidenced by (a) only moderate internal consistency, (b) low cross-time stability (e.g., rs ⫽ .10 and ⫺.02), (c) a nonsignificant loading on Subsyllabic Sensitivity, and (d) lower correlations with other phonological sensitivity variables (average r2 ⫽ .09) than those among the other phonological sensitivity variables (average r2 ⫽ .16). The a posteriori one-factor model that excluded rhyme production provided an excellent fit to the 4-yearolds’ data (see Table 4). Concerning the 5- and 6-year-olds’ data in Study 3, the a priori one-factor model provided a reasonably good fit (see middle and lower panels of Table 4). However, the a priori two-factor oblique model that had separate Rhyme Sensitivity and Phonemic Awareness factors provided a better fit to both of these datasets, ⌬␹2(1, N ⫽ 38) ⫽ 4.03 and 4.77, ps ⬍ .05, for 5- and 6-year-olds, respectively. The two-factor orthogonal model could not be examined directly because the measurement portion of this model was underidentified. However, the substantial correlation between factors in the two-factor oblique model (rs ⫽ .63 and .75, ps ⬍ .001) was not consistent with a two-factor orthogonal model. The a posteriori one-factor model that excluded rhyme production provided a very good fit to both datasets. A statistical comparison of this one-factor model with the other models was not appropriate because these models were not nested. However, the fit indices showed that the a posteriori one-factor model was an improvement over the one-factor model that included rhyme production, and its fit was equivalent to that of the two-factor oblique model at both ages. Factor loadings for the various models were substantial and

Table 4 Fit Indices for Models of Late-Preschool and Early Grade School Phonological Sensitivity (Study 3) ␹2

Model

df

⌬␹2

CFI

TLI

RMSEA

AIC

1.00 1.00

1.21 1.30

.00 .00

⫺1.9 ⫺1.9

0.94 1.00 1.00

0.88 1.10 1.18

.11 .00 .00

⫺3.0 ⫺5.0 ⫺3.6

4.03*

0.93 0.98 0.98

0.85 0.94 0.94

.16 .10 .12

⫺0.3 ⫺2.5 ⫺1.0

4.77*

Four-year-old children 1-factor 2-factor 1-factor 1-factor

(RM ⫹ RP ⫹ WC ⫹ PE) oblique (RM ⫹ RP, WC ⫹ PE) (RM ⫹ RP ⫹ WC ⫹ PE)b (RM ⫹ WC ⫹ PE)

—a —a .14† .02†

1 1

Five-year-old children 1-factor (RM ⫹ RP ⫹ WC ⫹ PE ⫹ SB) 2-factor oblique (RM ⫹ RP, WC ⫹ PE ⫹ SB) 1-factor (RM ⫹ WC ⫹ PE ⫹ SB)

6.98† 2.95† 0.45†

5 4 2

Six-year-old children 1-factor (RM ⫹ RP ⫹ WC ⫹ PE ⫹ SB) 2-factor oblique (RM ⫹ RP, WC ⫹ PE ⫹ SB) 1-factor (RM ⫹ WC ⫹ PE ⫹ SB)

9.75† 5.48† 2.98†

5 4 2

Note. N ⫽ 38. Chi-square difference tests are comparisons to the prior model. Letters in parentheses indicate combinations of tasks on factors (variables separated by a plus sign composed a factor; variables separated by a comma were on different factors). RM ⫽ rhyme matching; RP ⫽ rhyme production; WC ⫽ word completion; PE ⫽ phoneme elision; SB ⫽ sound blending; CFI ⫽ comparative fit index; TLI ⫽ Tucker–Lewis index; RMSEA ⫽ root-mean-square error of approximation; AIC ⫽ Akaike information criterion. a Model with out-of-range parameter values. b One-factor model that includes correlated residuals for the two rhyme measures. † p ⬎ .05. * p ⬍ .05.

THE NATURE OF PHONOLOGICAL AWARENESS

reliable (␭s ⫽ .43 to .92, ps ⬍ .05), and the smallest loadings were for rhyme production. In Study 4, the a priori one-factor model of Subsyllabic Sensitivity described the performances of the late-preschool through early grade school children reasonably well (see upper panel of Table 5). However, the a priori two-factor oblique model that had separate Rhyme Sensitivity and Phonemic Awareness factors reliably described these data better, ⌬␹2(1, N ⫽ 826) ⫽ 72.82, p ⬍ .001. The substantial correlation between Rhyme Sensitivity and Phonemic Awareness in the two-factor oblique model (r ⫽ .74, p ⬍ .001) was inconsistent with a two-factor orthogonal model. The a posteriori one-factor model that excluded rhyme production provided an excellent fit that was an improvement over the a priori one-factor model and that was equivalent to the two-factor oblique model. Factor loadings for the various models were substantial and reliable (␭s ⫽ .53 to .85, ps ⬍ .001), and the smallest loadings were for rhyme production. SEM. Longitudinal analyses of Studies 1 and 3 examined the independence of rhyme sensitivity from phonemic awareness. In Study 1, Onset⫺Rime Sensitivity, Phonemic Awareness, and Subsyllabic Sensitivity were significantly correlated across kindergarten, first grade, second grade, and third grade (see Table 6). Onset⫺Rime Sensitivity measured in kindergarten and first grade was a better predictor of all subsequent phonological skills than was Phonemic Awareness measured in kindergarten and first grade. At each grade level, Onset⫺Rime Sensitivity was a strong predictor of subsequent Phonemic Awareness, indicating that Onset⫺Rime Sensitivity and Phonemic Awareness were not independent across time: In fact, 77%, 78%, and 57% of the variance in first, second, and third grade Phonemic Awareness were predicted by the previous year’s Onset⫺Rime Sensitivity, respectively.

49

In Study 3, Rhyme Sensitivity, Phonemic Awareness, and Subsyllabic Sensitivity were significantly intercorrelated within and across late preschool and early elementary school (see Table 7). Time 2 Rhyme Sensitivity was a better predictor of Time 3 Rhyme Sensitivity than was Time 2 Phonemic Awareness. Conversely, Time 2 Phonemic Awareness was a better predictor of Time 3 Phonemic Awareness than was Time 2 Rhyme Sensitivity. Most important, each of these factors was a significant and substantial predictor of the other, indicating Rhyme Sensitivity and Phonemic Awareness were not independent across time.

Hypothesis 2: The Distinguishability of Rhyme Sensitivity From Segmental Awareness Because some theories propose distinguishing segmentation from rhyming (Morais, 1991b; Muter & Snowling, 1998; Muter, Hulme, Snowling, & Taylor, 1997), we used CFA of data from Studies 2 and 4 to examine whether a single Subsyllabic Sensitivity factor better described children’s scores or whether separate Rhyme Sensitivity and Segmental Awareness factors better described these data. One a priori model consisted of two correlated factors. Rhyme Sensitivity was defined by rhyme oddity, rhyme matching, and/or rhyme production variables, and Segmental Awareness was defined by elision variables. A second a priori model, representing subsyllabic sensitivity, consisted of a single factor identified by rhyme and elision variables. A two-factor orthogonal model could not be examined directly because it was underidentified in both data sets. In Study 2, the one-factor and two-factor oblique models described preschoolers’ performances on the rhyme and elision tasks very well (see upper panel of Table 8). Rhyme Sensitivity and Segmental Awareness in the two-factor oblique model were cor-

Table 5 Model Fit Indices for Structure of Late-Preschool and Early Grade School Children’s Phonological Sensitivity (Study 4) ␹2

Model

df CFI

TLI RMSEA AIC

⌬␹2

Rhyme Sensitivity and Phonemic Awareness 1-factor (RM ⫹ RP ⫹ WC:P ⫹ PE) 2-factor oblique (RM ⫹ RP, WC:P ⫹ PE) 1-factor (RM ⫹ WC:P ⫹ PE)

76.00*** 2.98† 0.35†

4 3 1

0.95 0.88 1.00 1.00 1.00 1.00

.15 .00 .00

67.8 ⫺3.0 72.82*** ⫺1.7

.21 .00 .00

69.9 ⫺1.6 73.47*** ⫺1.7

.13 .06 .07

84.0 13.9 72.08*** 11.6

Rhyme Sensitivity and Segmental Awareness 1-factor (RM ⫹ RP ⫹ PE) 2-factor oblique (RM ⫹ RP, PE) 1-factor (RM ⫹ PE)

73.90*** 0.43† 0.36†

2 1 1

0.93 0.79 1.00 1.00 1.00 1.00

Rhyme Sensitivity and Phonological Sensitivity 1-factor (RM ⫹ RP ⫹ WC:S ⫹ WC:P ⫹ PE) 2-factor oblique (RM ⫹ RP, WC:S ⫹ WC:P ⫹ PE) 1-factor (RM ⫹ WC:S ⫹ WC:P ⫹ PE)

97.98*** 25.90*** 17.60***

7 6 3

0.96 0.91 0.99 0.98 0.99 0.97

Note. N ⫽ 826. Chi-square difference tests are comparisons to the one-factor model. Letters in parentheses indicate combinations of variables on factors (variables separated by a plus sign composed a factor; variables separated by a comma were on different factors). RM ⫽ rhyme matching; RP ⫽ rhyme production; WC:S ⫽ word completion:syllable; WC:P ⫽ word completion:phoneme; PE ⫽ both phoneme elision variables; CFI ⫽ comparative fit index; TLI ⫽ Tucker–Lewis index; RMSEA ⫽ root-mean-square error of approximation; AIC ⫽ Akaike information criterion. † p ⬎ .05. *** p ⬍ .001.

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50

Table 6 Concurrent and Longitudinal Intercorrelations Among Latent Phonological Sensitivity Variables in Kindergarten, First-, Second-, and Third-Grade Children (Study 1) Factor 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Kindergarten Onset–Rime Sensitivity Kindergarten Phonemic Awareness Kindergarten Subsyllabic Sensitivity First-grade Onset–Rime Sensitivity First-grade Phonemic Awareness First-grade Subsyllabic Sensitivity Second-grade Onset–Rime Sensitivity Second-grade Phonemic Awareness Second-grade Subsyllabic Sensitivity Third-grade Phonemic Awareness

1

2

3

4

5

6

7

8

9

.96 — .85 .88 .82 .54 .69 .60 .67

— .57 .72 .69 .32 .55 .49 .59

.63 .75 .72 .38 .56 .53 .58

.97 — .93 .89 .91 .77

— .67 .85 .80 .79

.70 .85 .83 .78

.90 — .76

— .94

.89

10

Note. N ⫽ 202. Onset–Rime Sensitivity was composed of the three onset- and rime-level variables. Phonemic Awareness was composed of three phoneme-level variables. Subsyllabic Sensitivity was composed of all six variables. All correlations were significant at the p ⬍ .001 level. Dashes indicate that correlations were not computed because of lack of independence.

related at .97. Obviously, a two-factor orthogonal model would not have been a viable characterization of these data. Because the oneand two-factor models provided equivalent fits, ⌬␹2(1, N ⫽ 123) ⫽ .004, p ⬎ .25, the more parsimonious one-factor model that represented Subsyllabic Sensitivity was judged superior for these preschoolers. Factor loadings ranged from .29 to .73 ( ps ⬍ .01). In Study 4, the a priori one-factor model described the performances of the late-preschool through early grade school children reasonably well (see middle panel of Table 5). However, the two-factor oblique model provided a very good fit to these data that was reliably better than that of the one-factor model, ⌬␹2(1, N ⫽ 826) ⫽ 73.47, p ⬍ .001. The substantial correlation between Rhyme Sensitivity and Segmental Awareness (r ⫽ .74, p ⬍ .001) was inconsistent with a two-factor orthogonal model. The a posteriori one-factor model that excluded rhyme production also provided an excellent fit. Moreover, this one-factor Subsyllabic Sensitivity model represented a substantial improvement over the one-factor model that included rhyme production, and it provided a fit that was similar to that of the two-factor oblique model. Factor

loadings ranged from .53 to .86 ( ps ⬍ .001), and the smallest loadings were those for rhyme production.

Hypothesis 3: The Distinguishability of Rhyme Sensitivity From Phonological Sensitivity We used CFA of data from Studies 2 and 4 to examine whether a single Phonological Sensitivity factor better described children’s performances on a broad range of syllabic and subsyllabic phonological tasks or whether separate Rhyme Sensitivity and Phonological Sensitivity factors better described children’s performances. One a priori model consisted of a single factor indexed by all of the phonological sensitivity variables in a given study. Because Studies 2 and 4 included syllable-level tasks, this model represented general phonological sensitivity. A second a priori model consisted of two correlated factors, one defined by rhyme variables and the other defined by blending, elision, and word completion variables. This later model tested a conceptualization in which rhyming abilities were separate from but related to general phonological sensitivity. A two-factor orthogonal model

Table 7 Concurrent and Longitudinal Correlations Among Latent Phonological Sensitivity Variables for Late-Preschool and Early Grade School Children (Study 3) Factor 1. 2. 3. 4. 5. 6. 7.

T1 T2 T2 T2 T3 T3 T3

Subsyllabic Sensitivity Rhyme Sensitivity Phonemic Awareness Subsyllabic Sensitivity Rhyme Sensitivity Phonemic Awareness Subsyllabic Sensitivity

1

2

3

4

5

6

.82 .82 .87 .53 .58 .60

.63 — .80 .64 .66

— .52 .91 .64

.57 .90 .93

.75 —



7

Note. N ⫽ 38. Subsyllabic Sensitivity factor was composed of all the phonological sensitivity variables except rhyme production. Rhyme Sensitivity factor was composed of the rhyme matching and rhyme production variables. Phonemic Awareness factor was composed of word completion, phoneme elision, and sound blending variables. Sound blending was not administered at Time 1. Correlations of Time 1 Rhyme Sensitivity and Time 1 Phonemic Awareness with the other latent variables were not available because these models did not converge cleanly. Dashes indicate correlations were not computed because of lack of independence. All correlations were significant at the p ⬍ .05 level. T1 ⫽ Time 1; T2 ⫽ Time 2; T3 ⫽ Time 3.

THE NATURE OF PHONOLOGICAL AWARENESS

51

Table 8 Fit Indices for Models of Preschool Phonological Sensitivity (Study 2) Model

␹2

df

CFI

TLI

RMSEA

AIC

⌬␹2

⫺3.93 ⫺1.93

.004†

⫺6.35 —b

—b

Rhyme Sensitivity and Segmental Awareness 1-factor (R ⫹ E) 2-factor (R, E)

6.07† 6.07†

5 4

1.00 0.99

.99 .98

.04 .07

Rhyme Sensitivity and Phonological Sensitivity 1-factor (R ⫹ B ⫹ E)a 2-factor (R, B ⫹ E)a

21.65† —b

14 —b

0.99 —b

.97 —b

.07 —b

Note. N ⫽ 123. Letters in parentheses indicate combinations of variables on factors (variables separated by a plus sign composed a factor; variables separated by a comma were on different factors). R ⫽ rhyme variables; B ⫽ blending variables; E ⫽ elision variables; CFI ⫽ comparative fit index; TLI ⫽ Tucker–Lewis index; AIC ⫽ Akaike information criterion; RMSEA ⫽ root-mean-square error of approximation. a Models that included correlated residuals between like variables (i.e., blending, elision). b Correlation between factors constrained to 1.0, implying a one-factor solution. † p ⬎ .05.

was not examined directly because it was underidentified in both data sets. In Study 2, the one-factor model described the preschoolers’ performances very well (see lower panel of Table 8). The twofactor oblique model yielded a solution in which the correlation between Rhyme Sensitivity and Phonological Sensitivity was constrained to 1.0, indicating that all variables indexed a single factor. Given these results, the two-factor oblique and two-factor orthogonal models were obviously not viable models for describing these preschoolers’ phonological skills. Factor loadings for the onefactor model ranged from .29 to .73 ( ps ⬍ .01). In Study 4, the a priori one-factor model described latepreschool through early grade school children’s performances reasonably well (see lower panel of Table 5). However, the two-factor oblique model that had a separate Rhyme Sensitivity factor provided a better fit, ⌬␹2(1, N ⫽ 826) ⫽ 72.08, p ⬍ .001. The substantial correlation between Rhyme Sensitivity and Phonological Sensitivity (r ⫽ .74, p ⬍ .001) was inconsistent with a two-factor orthogonal model. The a posteriori one-factor model that excluded rhyme production provided an excellent fit. This Phonological Sensitivity model was an improvement over the one-factor model that included rhyme production, and it provided a fit that was equivalent to that of the two-factor oblique model. Factor loadings ranged from .52 to .81 ( ps ⬍ .001), and the smallest loading was for rhyme production.

Discussion Results from four independent studies converged on the conclusion that sensitivity to rhyme and sensitivity to other linguistic units are not distinct phonological abilities. In most of the studies, rhyme sensitivity, phonemic awareness, segmental awareness, and phonological sensitivity were best characterized as manifestations of the same phonological ability. These results are consistent with the conceptualization of phonological sensitivity. In other words, phonological sensitivity is a single ability that can be measured by a variety of tasks (e.g., detection, blending, and elision) that differ in linguistic complexity (e.g., syllables, rimes, onsets, and phonemes). Support for a unified conceptualization of phonological

sensitivity is accumulating from among comprehensive and welldesigned studies (Anthony et al., 2002; Schatschneider, Francis, Foorman, Fletcher, & Mehta, 1999; Stahl & Murray, 1994; Wagner et al., 1997). This growing body of research provides a sound empirical basis to contest the commonplace notion that sensitivity to syllables, onsets, and rimes should be excluded from the construct of phonological awareness based on theoretical grounds. Instead, theories of phonological sensitivity development and its relations with literacy acquisition need to accommodate this empirical research (e.g., see Anthony et al., 2003). Although the findings from these four studies were largely congruent, there was a systematic difference between studies concerning the degree of association between rhyme sensitivity and other forms of phonological sensitivity. Rhyme sensitivity correlated less highly with other forms of phonological sensitivity in Studies 3 and 4 than it did in Studies 1 and 2. The difference, as small as it was, appeared attributable to the rhyme production task used in Studies 3 and 4. Performance on this task was minimally related to phonological sensitivity. In fact, when rhyme production was excluded from analyses of Studies 3 and 4, a one-factor model provided an excellent characterization of children’s phonological sensitivity across all levels of linguistic complexity and across all levels of task complexity, making the results of all four studies virtually identical. Many researchers have found that rhyme production measures are problematic. Analysis of their research yields some reasons why rhyme production (and alliteration production) appears to be a questionable measure of phonological sensitivity. First, the descriptive statistics reported by Muter, Hulme, Snowling, and Taylor (1997) were consistent with many of the 4-, 5-, and 6-year-old children performing at floor levels on this task. Second, in our earlier piloting of a rhyme production task with children of the same age, we found this task more often brought tears than scorable responses. The children in our pilot study obviously experienced rhyme production as frustrating and too difficult; floor effects were abundant. Third, Chaney (1992) and MacLean, Bryant, and Bradley (1987) also found that rhyme production and alliteration production measures yielded severe floor effects, and

52

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some of their 3-year-olds refused to complete these tasks. Fourth, Chaney found that children’s performances on these production measures were unrelated to their performances on rhyme detection and alliteration detection measures. She suggested that performance on these production tasks may reflect rote learning of word pairs rather than metalinguistic competence. Alternatively, children’s performances on these production tasks may be heavily influenced by the size of their expressive vocabulary, their ability to search the lexicon phonologically, or instruction in the concepts of rhyme or “sounds like.” Regardless, given the empirical findings of the present report and prior research, we believe that rhyme production and probably alliteration production should not be used as measures of phonological sensitivity. Younger children’s phonological sensitivity was best explained by one-factor models, but older children’s phonological sensitivity was best explained by two-factor models that specified a separate Rhyme Sensitivity factor that was highly correlated with other types of phonological sensitivity. One interpretation of this pattern of results is that as children’s phonological sensitivity develops, it differentiates into rhyme sensitivity and more advanced forms of phonological sensitivity. This interpretation would be somewhat consistent with theories that posit the emergence of phonemic awareness and segmental awareness is related to the development of cognitive analytic skills, the development of metacognitive control processes, or exposure to letter sounds. However, the supposed differentiation occurred much later than these theories would predict (i.e., in second grade, according to Study 1). Alternatively, the distinguishability of rhyme sensitivity from more advanced forms of phonological sensitivity in older children could be a measurement artifact. For example, many of the older children in Studies 1, 3, and 4 performed at near ceiling levels on the rhyme matching, rhyme oddity, alliteration matching, and blending onset and rime tests. Ceiling effects on these onset–rime-level tasks most likely attenuated their relations with the other phonological sensitivity variables and rendered these tasks unable to differentiate children at the upper end of the distribution of phonological sensitivity performance from each other. Consequently, the perfect relation between latent Rhyme Sensitivity and other latent phonological sensitivity variables was attenuated. In short, ceiling effects may have artificially made rhyme sensitivity a distinguishable construct in the older children. The same explanation probably accounts for why rhyme tasks did not load on the phonological sensitivity factors of Hoien et al. (1995), Stanovich et al. (1984), and Yopp (1988) and why some of these authors understandably suggested that rhyme skills were independent of phonological sensitivity. Future studies that address the structure of phonological sensitivity, the relative importance of different phonological sensitivity skills, or the issue of how specific phonological sensitivity skills aid reading should be careful to control for measurement artifacts, like floor effects, ceiling effects, multiple-choice formats, and differential reliabilities. Because it is less influenced by measurement problems, item response theory (IRT) holds promise as an appropriate means to investigate these questions. Muter and colleagues (Muter, Hulme, & Snowling, 1997; Muter, Hulme, Snowling, & Taylor, 1997) asserted that rhyme skills were independent of phonemic segmentation; however, our reanalyses of their data led us to a different conclusion. The discrepancy was due to different approaches to data analysis. Muter and colleagues included rhyme production in their analyses

based on the assumption that rhyme production was a valid measure of onset–rime sensitivity, an assumption we believe is less credible now in light of accumulated research. Also, Muter and colleagues used EFA with an orthogonal rotation that forced the Rhyme and Segmentation factors to be uncorrelated. Although the rotation probably improved interpretability of the factors, this imposed condition may have been inappropriate because (a) rhyming and segmenting scores were correlated in their data, (b) a substantial body of research indicates that rhyme- and phonemelevel skills tend to be correlated both concurrently and longitudinally (e.g., Bryant et al., 1990; Lonigan et al., 1998; Stanovich et al., 1984; Walton, 1995), and (c) the four studies herein showed that rhyming and segmenting both reflect phonological sensitivity. As a consequence of the orthogonal rotation, Muter and colleagues’ Rhyme and Segmentation factors maximally underrepresented the naturally occurring overlap among these phonological sensitivity skills, which understandably led to their conclusion that these constructs were independent and had different predictive relations with literacy. However, our CFAs of their data demonstrated that the Rhyme and Segmentation factors were highly correlated. Moreover, if one accepts our position that rhyme production is a poor measure of young children’s ability to detect and manipulate rime, then rhyme and phonemic segmentation tasks were clearly markers of the same phonological ability. Some researchers have stressed the importance of rhyme sensitivity in beginning reading (Bryant et al., 1990; Goswami & Bryant, 1990, 1992), whereas others have stressed phonemic sensitivity (Hulme et al., 2002; Morais, 1991a; Muter, Hulme, Snowling, & Taylor, 1997; Nation & Hulme, 1997). Our repeated finding that measures of rhyme sensitivity (excluding rhyme production) and phonemic sensitivity were indicators of the same underlying ability leads us to think that the effects of rhyme sensitivity and phonemic sensitivity are unlikely to be unique to situations in which words can be read by analogy or letter–sound correspondences. Instead, our results imply that phonological sensitivity may influence reading acquisition through multiple pathways, including reading by analogy and reading by letter–sound correspondences. Our conceptualization of phonological sensitivity permits rhyme sensitivity to add a slight advantage to reading by analogy and phonemic sensitivity to add a slight advantage to reading by letter–sound correspondences, but it also recognizes that most of the influence on reading is shared by these two phonological sensitivity skills. This is consistent with findings that phonemic sensitivity helps children read by analogy (Goswami, 1986; Goswami & Mead, 1992; Walton, 1995) and with our unreported reanalysis of the longitudinal data from Muter, Hulme, Snowling, and Taylor (1997), which found most of the variance that was predictive of reading and spelling was shared by Rhyme and Segmentation factors. We hold a developmental view of phonological sensitivity as a single ability that develops from sensitivity to words to sensitivity to phonemes (Adams, 1990; Bryant et al., 1990; Goswami & Bryant, 1990; Stanovich, 1992) in a quasi-parallel progression rather than a temporally discrete, sequential progression (Anthony, 2000; Anthony et al., 2003). Within and across the four present studies, younger children were sensitive to larger linguistic units but less so to smaller linguistic units, and older children were sensitive to both larger and smaller linguistic units. This agerelated trend is also evident within and across a number of other

THE NATURE OF PHONOLOGICAL AWARENESS

studies (Lonigan et al., 1998; Rohl & Pratt, 1995; Stanovich et al., 1984; Treiman, 1992; Treiman & Zukowski, 1996; Wagner et al., 1987). In a systematic investigation of this trend, Anthony et al. (2003) found that children generally mastered word-level skills before syllable-level skills, syllable-level skills before onset–rimelevel skills, and onset–rime-level skills before phoneme-level skills, controlling for task complexity. Anthony et al. also found that children were able to detect phonological information before they could manipulate it and that children were able to blend phonological information before they could delete phonological information of the same level of linguistic complexity. The developmental conceptualization of phonological sensitivity was further supported by the longitudinal results of Studies 1 and 3 that demonstrated a high level of stability in phonological sensitivity across the late-preschool and early grade school years. Moreover, rhyme sensitivity was a significant and substantial predictor of phonemic sensitivity. Similarly, in a recent 1-year longitudinal study of 4-year-old children, we found that a latent Phonological Sensitivity variable comprising tasks similar to those used in Study 2 perfectly predicted a latent Phonological Sensitivity variable that was composed of primarily phoneme-level items (Lonigan, Burgess, & Anthony, 2000). Moreover, either of these latent variables accounted for approximately 50% of the variance in children’s decoding skills in kindergarten and first grade. In summary, it appears that debate over whether sensitivity to rhyme or sensitivity to phonemes is most important for reading and spelling has led researchers and theorists astray in their conceptualization of phonological sensitivity because of the implicit or explicit assumption of different types of phonological sensitivity. Instead, we found that different phonological skills represent either the same ability or highly correlated abilities and that it is, therefore, children’s general sensitivity to the sound structure of language that is important for learning to read and spell in an alphabetic system. We believe that the results of many different studies, including those in the present report, are consistent with the view that phonological sensitivity can be indexed by a variety of measures if administered at the proper point in a given child’s development. The important question, therefore, is not what type of phonological sensitivity is most important for literacy? but which measures of phonological sensitivity are developmentally appropriate for this particular child? Children’s phonological sensitivity would be best indexed by an IRT-based measure of phonological sensitivity that spans the task demands and levels of linguistic complexity from those that have been recently mastered to those that are just emerging. The practical importance of this article lies on the assumption that prereaders’ phonological sensitivity is an early manifestation of the same ability that plays an important causal role in learning to read. If this is the case—as suggested by the four present studies, similar CFA research (Anthony, 2000; Anthony et al., 2002), and longitudinal studies (Bryant, Bradley, MacLean, & Crossland, 1989; Bryant et al., 1990; Lonigan et al., 2000)—then it is plausible to conduct early phonological sensitivity screenings that identify children at risk for reading disability. Following early identification, at-risk children’s phonological sensitivity deficits could be remediated before these children experience reading failure and its associated behavioral, social, academic, and psychological difficulties (e.g., Allington, 1984; Brown, Palincsar, &

53

Purcell, 1986; Hinshaw, 1992; Light, Pennington, Gilger, & DeFries, 1995; Lonigan et al., 1999; Shaywitz & Shaywitz, 1993; Velting & Whitehurst, 1997). Research on early identification, early intervention, and prevention seem the logical next steps in light of the stability of preschoolers’ phonological sensitivity and letter knowledge (Burgess & Lonigan, 1998; Lonigan et al., 2000), given that prereaders’ phonological sensitivity and letter knowledge influence literacy acquisition (e.g., Bradley & Bryant, 1983; Bryant et al., 1989, 1990; Lonigan et al., 2000; MacLean et al., 1987; Wagner et al., 1994, 1997) and given the stability of reading skills and phonological processing in school-age children (Juel, 1988; Torgesen & Burgess, 1998; Wagner et al., 1997).

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Received March 16, 2001 Revision received May 20, 2003 Accepted May 21, 2003 䡲