Hudson Roxanne F

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Relations among reading skills and sub-skills and

text-level reading proficiency in developing readers

Roxanne F. Hudson Joseph K. Torgesen

Holly B. Lane Stephen J. Turner

Published online: 2 December 2010 Springer Science+Business Media B.V. 2010

Abstract Despite the recent attention to text reading fluency, few studies have

studied the construct of oral reading rate and accuracy in connected text in a model

that simultaneously examines many of the important variables in a multi-leveled

fashion with young readers. Using Structural Equation Modeling, this study

examined the measurement and structural relations of the rate and accuracy of

variables important in early reading: phonemic blending, letter sounds, phonograms,

decoding, single-word reading, reading comprehension, and text reading as well asreading comprehension among second grade readers. The effects from phonemic

blending fluency and letter sound fluency to decoding were completely mediated by

phonogram fluency, decoding fluency, single-word reading fluency, and reading

comprehension had direct effects on the text reading fluency of the second grade

students. Understanding the relationship among the many component skills of

readers early in their reading development is important because a deficiency in any

of the component skills has the potential to affect the development of other skills

and, ultimately, the development of the child as a proficient reader.

Keywords Decoding Reading fluency Young readers

R. F. Hudson J. K. Torgesen

Florida Center for Reading Research, Florida State University, Tallahassee, FL, USA

H. B. Lane

Department of Special Education, University of Florida, Gainesville, FL, USA

S. J. Turner

Department of Educational Psychology, Florida State University, Tallahassee, FL, USA

R. F. Hudson (&)

Area of Special Education, University of Washington, Box 353600, Seattle, WA 98195, USA

e-mail: [email protected]

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Read Writ (2012) 25:483507

DOI 10.1007/s11145-010-9283-6


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Modeling individual differences in decoding fluency

in second grade readers

Reading fluency is an important part of reading proficiency and reading a text

fluently is critical for comprehending it (Breznitz, 2006; Daane, Campbell, Grigg,Goodman, & Oranje, 2005; Fuchs, Fuchs, Hosp, & Jenkins, 2001; Samuels &

Farstrup, 2006; Torgesen, Rashotte, & Alexander, 2001). Instruction leading to

fluent text reading is a critical aspect of early reading instruction, and many

researchers and practitioners have questions about the elements that make up

reading fluency and explain the difficulties many children have developing into

proficient readers.

Ehris (1992) phases of word reading development provide a framework for

understanding the development of reading fluency. Children in Ehris pre-alphabetic

phase use cues unrelated to the letters and sounds to read or guess words. As theymove to Ehris partial-alphabetic and full-alphabetic phases they increase in their

phonemic awareness and grasp of the correspondences between graphemes and

phonemes. During this time they often use individual graphemephoneme recoding

to read words, especially unfamiliar ones. As readers develop, they increasingly

unitize, or read in larger letter units, rather than relying on individual graphemes.

This results in increased decoding efficiency and oral reading fluency (Harn,

Stoolmiller, & Chard,2008). It is likely that, as readers reach automaticity with an

increasing number of units, they abandon the letter-by-letter decoding they initially

used, instead of using rimes (McKay &Thompson, 2009; Treiman, Goswami, &Bruck,1990) and other larger letter patterns to read. This will lead to a transition to

Ehris consolidated alphabetic phase where readers are fluent, have a large number

of words they know by sight, and use larger letter patterns to decode unknown

words. The number of unknown words would decrease, making analogy a much

more useful strategy. Evidence of this pattern can be found in the longitudinal

research of Speece and Ritchey (2005). In first grade, letter sound fluency (LSF) was

a unique predictor of oral reading fluency level and slope after other predictors were

in the model. By second grade, however, first grade LSF was no longer uniquely

predictive. Thus, one would expect that early in reading development, subskills such

as phonological awareness and letter sound fluency would be direct contributors to

decoding and oral reading fluency, but later in reading development (e.g., second

grade), the relationship among these variables would be mediated by other variables

that represent more advanced reading such as automaticity with phonograms (i.e.,

letter groups within a word that share a pattern across words such as rimes and

suffixes) and single word reading.

Predicting oral reading rate and accuracy

As seen in Fig.1, text reading fluency, at least as measured by rate and accuracy of

reading in connected text, involves parallel processes at the sub-lexical, lexical,

sentence, text, and discourse level (Hudson, Pullen, Lane, & Torgesen, 2009;

Torgesen et al.,2001). Automaticity in the sub-skills such as letter sound retrieval,

484 R. F. Hudson et al.

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phonemic awareness, reading words by sight, and decoding processes are necessary

for fluent text reading (Biemiller, 19771978). When these processes are not

automatic, reading accuracy and rate suffer, as does comprehension (Perfetti, 1985;Perfetti & Hogaboam,1975). Because the reading processes share limited-capacity

working memory, lack of efficiency in any process is likely to use more of the

resources, starving the resource-intensive processes related to reading comprehension

(Perfetti).

Predictors of decoding fluency

Beginning at the bottom of the model in Fig. 1, we predict that decoding fluency is

explained by several lower-level within-word processes such as automaticity in

letter sounds and phonemic blending. If any of the relevant retrieval processes

operate slowly or inaccurately, decoding will be slowed. Better understanding of the

role each plays can lead to the development of better tools for assessment and

intervention.

Fluency in phonemic blending is critical for decoding success. Over 20 years of

research has established the importance of phonemic awareness in learning to

decode (e.g., Adams,1990; National Reading Panel, 2000; Perfetti, Beck, Bell, &

Hughes,1987; Rayner, Foorman, Perfetti, Pesetsky, & Seidenberg,2001; Wagner &

Torgesen,1987). It is not enough to identify sounds associated with letters in a word

to be a successful decoder. One must also blend those sounds together to produce

the words pronunciation. Readers who are not automatic in this process have

difficulty doing so while decoding unfamiliar words.

Fluency in identifying letter sounds, or quickly and accurately producing

the sounds represented by graphemes, is at the heart of the alphabetic principle.

Fig. 1 Multi-level model for text reading fluency

Reading proficiency in developing readers 485

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Without the knowledge of how sounds are systematically represented by letters,

children cannot successfully decode (e.g., Adams, 1990; Ehri, 1998; Jenkins,

Bausell, & Jenkins, 1972; National Reading Panel, 2000). Both phonological

awareness and fluency in letter sounds in kindergarten were among the best

predictors of first grade oral reading fluency (Speece, Mills, Ritchey, & Hillman,2003; Stage, Sheppard, Davidson, & Browning, 2001).

Automaticity in recognition of phonograms (i.e., letter groups within a word that

share a pattern across words) is a feature of the more advanced word recognition

characteristic of the consolidated alphabetic reading phase (Ehri, 1992). Without

knowledge of patterns across words, readers are not be able to move to more

advanced, efficient decoding (Ehri, 2002). Both Treiman et al. (1990) and McKay and

Thompson (2009) found that children read words with frequent or familiar rimes (a

vowel plus syllable ending) more accurately than those with infrequent or unfamiliar

ones. In addition, English is more regular at the level of rimes and larger chunks thanat the phoneme-grapheme level (Moats, 2000; Kessler & Treiman, 2003), making

sound-symbol relationships at that level more predictable and useful in reading words.

Readers need to develop context-sensitive mappings of relationships between

phonemes and graphemes as well as larger units to become fluent decoders and

readers (Berninger, Abbott, Vermeulen, & Fulton,2006; Brown & Deavers,1999).

As evidenced by the directional arrows, we propose that the skills develop

sequentially and predict the next set of processes. As readers develop and master

phonemic awareness and individual letter sounds, they move to decoding through

larger letter patterns and analogy.

Decoding fluency

Decoding fluency, defined here as the accuracy and rate of recoding letter sounds

into words, plays a critical role in reading rate and accuracy. It is often considered

an indicator of automaticity in the application of the alphabetic principle and a

bridge to real word reading (Berninger et al., 2006). In addition, it is used by older

and more accomplished readers as a strategy to compensate for lapses in

automaticity in lower-level processes (Walczyk et al., 2007). In order to help

compensate for failure in various automatic processes, decoding itself needs to be

efficient, quick, and use few resources. Because of its role as a means to (a) decipher

previously unseen words (Share & Stanovich, 1995), (b) learn new words (Share,

1995; 1999), or (c) compensate for inefficiencies in other processes in reading

(Walczyk et al.), decoding efficiency is an important area worthy of additional

investigation. Though more frequent an occurrence for beginning readers than

established ones, all readers encounter words that they have not seen before in print

and need to decode. Readers do this by (a) blending together known phoneme-

grapheme correspondences (Adams, 1990), (b) analogizing to other known words

using rimes (Brown & Deavers,1999; Goswami,1988; McKay & Thompson,2009;

Treiman et al.,1990), or (c) blending together amalgamated chunks (Ehri,2002). To

measure this, however, requires something other than real words because

researchers want to ensure the word is truly unknown and is read using letters

and sounds, not accessed from memory.


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Single-word reading fluency

The degree of automaticity with which readers can identify the words in the passage

has a large role to play in how fluently they read. The size of a readers sight word

vocabulary, or the proportion of words in any given passage that can be recognizedby sight, plays a pivotal role in how quick and accurate a reader is (Adams, 1990;

Compton, Appleton, & Hosp, 2004; Torgesen et al., 2001). For students who are

below average in reading rate, this relationship is particularly important. As Ehri

(1998) explains, sight of the written word activates its spelling, pronunciation, and

meaning immediately in memory (p. 8). Sight word fluency is a core component of

reading fluency and important for predicting reading comprehension (Gough,1996;

Perfetti & Hogaboam, 1975). If a student is asked to read a passage in which a

relatively high proportion of the words must be decoded analytically or identified by

contextual inference, this will have an adverse effect on reading fluency, and thus,on comprehension.

Reading comprehension and vocabulary

There is considerable evidence to suggest that the relationship between text reading

fluency and comprehension is reciprocal. Reading rate and accuracy has been

identified as an important facilitator of reading comprehension (Adams,1990; Fuchs

et al.,2001) in average and disabled readers (Breznitz, 1987,1991; Chard, Vaughn,

& Tyler, 2002; Dowhower, 1987). More specifically, individual differences inreading rate and accuracy in third grade were found in one large study to be the

single most important factor in accounting for differences in performance on a

measure of comprehension of complex text (Schatschneider et al., 2004). On the

other hand, it also appears that comprehension facilitates quick and accurate reading

of text. For example, words in context are read faster than the same words out of

context (e.g., Biemiller, 19771978). Jenkins, Fuchs, Van den Broek, Espin, and

Deno (2003) found support for the view that the relationship between reading rate

and comprehension is reciprocal. In examining fourth graders, they found that

reading words in context explained more variance in reading comprehension than

did reading the same words in a list (70% vs. 9%). They also found that the students

reading comprehension score explained more variance in oral reading rate and

accuracy in connected text than did reading the same words in a list (70% vs. 54%).

It seems likely that the speed with which word meanings are identified would

also affect the rate at which a passage is read. Because Perfetti (1985) suggests that

both lexical access (word name) and semantic encoding (contextual word meaning)

processes must be efficient, it is reasonable to think that reading fluency would be

limited if semantic activation is not automatic (Perfetti & Hogaboam, 1975). In

addition to finding that good comprehenders read low-frequency and nonsense

words more quickly than poor comprehenders, Perfetti and Hogaboam also found

that whether a participant knew the meaning of the word significantly affected the

poor readers but not the good ones. When reading words they did not know, poor

comprehenders were both slower and less accurate than when reading words they

knew the meaning of while good readers were equally fast and accurate with both


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types of words. As long as readers are under obligation to be actively thinking about

the meaning of what they are reading, speed of identification of word meanings may

play a role in limiting text reading rate and accuracy.

Rapid automatized naming

How quickly one can access names of familiar stimuli has proven to be an important

predictor of reading and decoding achievement. The relation between Rapid

Automatized Naming (RAN) and reading achievement has repeatedly been

demonstrated across various samples of typical and atypical readers, even after

IQ, processing speed, and phonological skill have been partialed out (Denckla &

Rudel, 1976; Kail & Hall, 1994; Manis, Doi, & Bhadha, 2000; Schatschneider,

Fletcher, Francis, Carlson, & Foorman, 2004; Wolf, 1997; Wolf & Bowers,1999;Wolf, Bowers, & Biddle, 2000). This relationship varies according to the stimuli

used. Naming letters or digits is more related to reading achievement than naming of

pictures or colors (Schatschneider et al., 2004) and RAN-digits is more related to

reading speed than reading accuracy (Savage & Frederickson, 2005). Whether it is

thought of as a measure of lexical access (Wagner, Torgesen, Laughon, Simmons, &

Rashotte, 1993), a marker of orthographic processing (Manis et al., 1999; Wolf

et al.,2000), or the speed of processing information (Catts, Gillispie, Leonard, Kail,

& Miller,2002; Kail & Hall, 1994), RAN is more than simply naming stimuli and

needs to be included in any model of reading fluency.

Purpose of the study

Despite the recent attention to text reading fluency (e.g., Fuchs et al., 2001; Hudson

et al.,2009; Kameenui & Simmons,2001; Samuels & Farstrup,2006), few studies

have examined the construct of oral reading rate and accuracy in connected text in a

model that simultaneously examines many of the important variables in a multi-

leveled fashion. Some researchers have looked at the relation between rate,

accuracy, prosody, and reading comprehension (Daane et al.,2005; Schwanenflugel,

Hamilton, Kuhn, Wisenbaker, & Stahl, 2004) while others have looked at the

relation between text reading fluency and reading comprehension (Berninger et al.,

2006; Jenkins et al., 2003; Schwanenflugel, Meisinger, Wisenbaker, Kuhn, Strauss,

& Morris,2006). Some have looked at the predictive validity of decoding fluency to

text reading fluency in young children (Good, Simmons, & Kameenui, 2001;

Speece et al., 2003; Speece & Ritchey, 2005), while another examined the

development of lexical and sub-lexical reading skills and their contributions to text

reading fluency in beginning readers (Burke, Crowder, Hagan-Burke, & Zou,2009),

however, to our knowledge, no one has examined the components of text reading

fluency in children who are neither established readers nor beginners. Also, among

the previously-cited research, Berninger et al., Jenkins et al., and Speece and

colleagues have all focused on poor readers while the current study has a wide range

of excellent to poor readers.


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We had three goals in this study: (a) identify salient variables that explain

individual differences in the text reading and decoding fluency of a sample of young

readers, (b) create a model of the structural relations among the variables, and (c)

empirically test the model presented in Fig. 1. We planned to determine which

component lexical and sub-lexical skills best explain the processes underlying textreading fluency in second graders.

First, we sought to determine the most reasonable measurement model. In

particular, we asked whether the eight constructs of interest in this study constitute

different factors or whether a more parsimonious model is more appropriate. In

particular, we asked whether phonogram fluency (PGF) and decoding are two

different factors or measures of the same construct. We hypothesized the presence

of two separate factors because of the increasing consolidation of letter patterns as

readers develop. Ehri (1992) suggests that first young children learn the associations

between individual letters and sounds, and then over time, consolidate theseassociations into larger letter patterns. We see this interim step between individual

letters and entire words as an important variable to examine. When decoding,

children could use either single letter sounds or larger letter patterns and it is

possible that phonogram fluency is a way to capture this transition.

Second, we investigated the structural relations among the variables predicting

decoding fluency. These relations are represented in Fig. 2. We predicted mediated

effects from phonemic blending fluency (PBF) to letter sound fluency (LSF) to

phonogram fluency (PGF) to decoding because of our proposed pattern of reading

development. Third, we examined the structural relations among the variablespredicting text reading fluency represented in Fig.2. Based on our theoretical

framework, we expected direct effects from single-word reading fluency (SWF) and

reading comprehension to text reading fluency (TRF), decoding to TRF, and

decoding to SWF.

Fig. 2 Hypothesized structural

model of decoding and oral

reading rate and accuracy in

second grade readers. Paths with

adashed arroware hypothesizedto be close to zero. RANRapid

automatized naming


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Methods

Participants

All second grade students in five schools in a north Florida school district wereinvited to participate in this study. The demographic characteristics of each of the

schools are reported in Table1.

The parents of 214 children gave permission for their children to participate; due

to mobility, 198 students completed all of the assessments. The sample of 97 boys

and 101 girls had a mean chronological age of 8 years 5 months. Data from a

parental questionnaire identified 47% of the children as Caucasian, 38% as African

American, 4% as Asian/Pacific Islanders, 3.5% as Hispanic, 4.5% as Multiracial,

.5% as American Indian, and 2.5% did not report. About 12% of the participants

primary caregivers (mother or grandmother) indicated that they had less than a highschool education, 44% graduated from high school or attended additional training

beyond high school, 22% graduated from college or university, and 19% had a

graduate education. According to school records, 75% (150) of the children had no

identified disability label, .5% had a primary disability identification of mild mental

retardation, 14% of speech impairment, 7% of language impairment, and 2.5% with

a specific learning disability. The vast majority (98%) were competent speakers of

English.

Measures

Measures were selected to provide assessments of each variable hypothesized to be

important to decoding and reading fluency. Attention was paid to the reliability of

the scores from each measure and validity for the intended purpose and sample

(Thompson & Vacha-Haase, 2000). All measures were given at the end of second

grade within a 3-week period.

Table 1 School demographics in percentages

Model School A School B School C School D School E

Race/ethnicity

White 76.7 17.1 61.5 4.6 58.5

African-American 13.0 74.7 33.7 83.7 24.8

Hispanic 2.2 3.5 1.3 7.1 9.7

Asian 5.8 .3 .2 2.8 2.5

Native American .2 .3 1.3 .2 .8

Multiracial 2.0 4.1 3.1 1.6 3.7Female 47.8 47.7 48.3 48.6 50.2

Special education 19.7 25.1 29.9 20.0 8.5

English language learners 1.2 3.0 .6 7.8 2.4

Free or reduced-price lunch 8.7 87.3 62.1 82.1 23.2


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Reading comprehension

Two measures were used to form the reading comprehension latent variable. First,

the Gray Oral Reading Test, 4th edition (Weiderholt & Bryant, 2001) uses 14

developmentally sequenced reading passages with five comprehension questionsfollowing each passage. Participants were asked to read progressively more difficult

passages and answer questions about each one until the ceiling was reached,

yielding a reading comprehension score. Second, the picture vocabulary subtest of

the Woodcock-Johnson Test of Cognitive Abilities, 3rd edition was used. This

measures expressive vocabulary by asking for a label for a series of pictures.

Text reading fluency (rate and accuracy)

Two assessments were used to measure oral reading rate and accuracy. Studentswere asked to read 3 s-grade passages from the Oral Reading Fluency subtest of the

Dynamic Indicators of Basic Early Literacy Skills, 6th edition (DIBELS; Good &

Kaminski, 2002a) and the number of correct words read in 1 min was used. The

three passages were selected to have the same reading difficulty level as reported by

the developers (Good & Kaminski, 2002b) and contain topics that second graders

in Florida would likely be familiar with regardless of their socio-economic level.

The Gray Oral Reading Test, 4th edition (Weiderholt & Bryant, 2001) uses 14

developmentally sequenced reading passages with five comprehension questions

following each passage. Participants were asked to read progressively more difficultpassages until the ceiling was reached, yielding a fluency score based on time and

accuracy.

Single-word reading fluency

Two forms of the Sight Word Efficiency (SWE) subtest of theTest of Word Reading

Efficiency(Torgesen, Wagner, & Rashotte,1999) were used to measure single-word

reading fluency. Participants were asked to read as many real words as possible from

a word list that increased in difficulty in 45 s. The alternate form reliability obtained

in this study was .95.

Decoding fluency (rate and accuracy)

Two assessments were used to measure decoding fluency. In the Phonemic

Decoding Efficiency (PDE) subtest of the Test of Word Reading Efficiency

(Torgesen et al., 1999), participants were asked to read as many nonsense words

presented in list format that increased in difficulty as possible in 45 s. Only fully

blended responses were considered correct. Both form A and form B were given;

alternate form reliability obtained in this study was .94. In the Nonsense Word

Fluency (NWF) test of the DIBELS (Good & Kaminski, 2002a), students were

asked to read randomly ordered VC and CVC nonsense words presented in rows.

The difficulty of the items stayed constant. The score is the number of correct

sounds read per minute, with a correct response consisting either of sounds read


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individually, partially blended, or fully blended. The median 1-month alternate form

reliability reported by the developers was .83 (Dynamic Measurement Group,

2008).

Phonemic blending fluency (PBF)

A measure consisting of three- and four-phoneme words was constructed for this

study to measure phonemic blending rate and accuracy. Examiners orally presented

each item sound by sound with a short pause between sounds (e.g., f-a-t) and the

participants then blended the sounds into a whole word. Examiners timed the

latency of response for each item using a stop watch until a minute was reached,

yielding a score of the number of correctly blended words per minute. The obtained

corrected Spearman-Brown split half coefficient was .78.

Letter sound fluency (LSF)

In order to measure rate and accuracy in graphemephoneme connections,

researchers constructed two forms of randomly ordered lowercase single letters,

digraphs, and r-controlled vowels (e.g.,a,f,ch,ee,ar) presented in rows. All single

letters and digraphs in random order were represented on both forms for a total of

48 items per form. Five practice items, including a long vowel digraph and a single

short vowel were administered before the test and feedback given. Students were

given 1 min to say the sound represented by each letter or digraph, yielding a scoreof correct letter sounds per minute. For single vowels, short sounds were counted as

correct. Obtained alternate form reliability was .73.

Phonogram fluency (PGF)

In order to measure childrens fluency with common larger within-word letter

patterns, researchers constructed two forms consisting of randomly ordered common

rimes (e.g.,eed,op,um,at,unch,arp) presented in rows. To ensure that participants

understood the task, three practice items with feedback were given using these

directions, I am going to ask you to read as many of these phonograms as you can. A

phonogram is a set of letters we see a lot in words, so you may have seen them before

as a part of words youve read. Try to read each one like you would in a word, but

dont make it into a real word if it isnt one. Ill show you how with the first one.

Students were given 1 min to read as many phonograms as possible. Only fully-

blended responses were coded as correct, yielding a score of correct phonograms per

minute. The alternate form reliability obtained in this study was .89.

Rapid automatized naming

The Rapid Letter Naming subtest of the Comprehensive Test of Phonological

Processing (Wagner, Torgesen, & Rashotte, 1999) was used to measure RAN.

Several rows of six lower case letters that repeat across the page are presented

and participants are asked to name them as quickly as possible. The raw score is the


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total number of seconds needed to name the letters. The alternate form reliability

obtained in this study was .83.

Administration

All measures were administered in the spring of second grade and given individually

in a quiet place in several sessions to minimize participant distraction and fatigue. In

addition to the first author, assessors were graduate students with training in school

psychology or education. In order to maintain assessment fidelity, assessors were (a)

given 8 h of training, (b) required to demonstrate correct assessment procedures

before they tested participants, (c) assessed with the first or last author before

working independently, (d) attended weekly meetings with follow-up training, and

(e) were observed at least twice while they assessed. Interrater reliability was

calculated on 10% of the participants, yielding coefficients that ranged from .90 to1.0 agreement. All raw data protocols were scored a second time by the first or last

author to ensure correct scoring. All of the data were entered twice by different

research assistants and discrepancies corrected on a case by case basis.

Results

Data analysis

Structural Equation Modeling (SEM; Kline, 2005) using AMOS 7.0 and maximum

likelihood estimation was used to analyze the data. This occurred in two phases; first

a confirmatory factor analysis was conducted to determine the measurement model

and second, this validated measurement model was used to test structural

hypotheses in relation to the directional relations between phonemic blending

fluency, letter sound fluency, RAN, phonogram fluency, single-word reading

fluency, text reading fluency, and reading comprehension. It is important to test the

measurement model before the structural model, since the structural relations make

no sense if the constructs are not reasonable (Thompson, 2000). Raw scores

uncorrected for age were used in all SEM analyses.

Descriptive statistics and correlations

Descriptive statistics and Pearson product-moment correlations among the indicator

measures are presented in Table2. These statistics support the psychometric

adequacy of the tasks for the children in our study. Correlations were in expected

directions, with magnitudes in line with those reported in other studies (e.g.,

Compton, 2000; Speece et al., 2003; Wagner et al., 1994).

Measurement model

We conducted a confirmatory factor analysis to test the adequacy of our

measurement model. Results of the model fitting process are summarized in


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Table2

Correlationsandrawscoredescriptivestatisticsforallobservedindicators

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

1.GORT

comprehensionSS

2.Picturevocabulary

.242

3.DORFpassage

1

.372

.292

4.DORFpassage

2

.364

.272

.935

5.DORFpassage

3

.357

.304

.950

.9

44

6.GORTfluencySS

.397

.273

.876

.8

66

.862

7.SWEformA

.322

.239

.881

.8

62

.868

.842

8.SWEformB

.288

.247

.874

.8

62

.876

.832

.948

9.PDEformA

.326

.247

.818

.8

19

.802

.837

.849

.841

10.

PDEformB

.336

.249

.798

.8

09

.789

.820

.831

.822

.940

11.

NWF

.246

.256

.702

.7

01

.690

.702

.709

.696

.793

.791

12.

Phonogram

fluencyA

.290

.170

.804

.8

00

.781

.797

.808

.802

.876

.852

.754

13.

Phonogram

fluencyB

.269

.212

.790

.7

94

.788

.793

.820

.830

.869

.864

.763

.88

9

14.

Lettersound

fluencyA

.049

.098

.183

.2

32

.188

.171

.278

.266

.322

.300

.393

.37

2

.335

15.

Lettersound

fluencyB

.056

.042

.226

.2

81

.240

.211

.335

.334

.358

.366

.429

.32

4

.431

.728

16.

Phonemic

blendingfluency

.171

.303

.145

.1

27

.148

.153

.131

.107

.162

.162

.175

.14

5

.156

.292

.320

17.

RANlettersA

-.1

63

-.0

36

-.5

65

-.5

91

-.5

82-.5

12

-.5

96

-.6

10

-.5

26

-.5

17

-.4

88

-.55

9

-.5

76

-.3

21

-.4

05

-.232

18.

RANlettersB

-.1

23

-.0

02

-.5

36

-.5

68

-.5

67-.4

87

-.5

56

-.6

05

-.4

95

-.5

05

-.4

83

-.55

3

-.5

37

-.3

14

-.3

78

-.212

.817


1 3


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Table2

continue

d

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

M

8.60

14.0

6

94.6

102.8

103.4

9.6

5

56.0

55.6

26.5

25.5

103.5

39.2

39.6

38.1

38.6

28.7

22.6

22.9

SD

3.97

1.73

37.6

0

40.8

9

38.71

3.6

7

13.10

13.8

3

11.5

8

12.3

6

48.3

6

18.62

18.2

4

12.3

4

13.2

0

12.08

6.6

7

7.16

Allcorrelationsab

ove.1

4aresignificantatp\

.05(2

-tailed)

GORT=

GrayOralReadingTest,4thed.,

Picturevocabulary=

picturevocabularysubtestoftheWoodcock-JohnsonTestof

CognitiveAbilities,

3rded.,

DORF

=

Oralreading

fluencyfromtheD

ynamicIndicatorsofBasicEarlyL

iteracySkills,

SWE=

Sightword

efficiencysubtestoftheTestofWo

rdReadingEfficiency,

PDE=

Pho

nemicdecoding

efficiencysubtestfromtheTestofWordReadingEffic

iency,NWF=

NonsensewordfluencyfromtheDynamicIndicatorsof

BasicEarlyLiteracySkills,

RAN

letters=

Rapid

letternamingsubtestoftheComprehensiveTestofPh

onologicalProcessing


1 3


14/25

Table3. To assess whether a newly specified model shows an improvement in fit

over its predecessor, we examined the difference in v2 (Dv2) between the two nested

models. A significant reduction in Dv2 (p\ .05) indicates a substantial improve-

ment in model fit while a significant increase in Dv2 (p\ .05) indicates a substantial

decrement in model fit. We did not use an exploratory factor analysis because we

used theory to determine our constructs and their indicators.

In order to evaluate the model predicted by our theory, Model 1 was fitted. The

factor loadings were strong, ranging from .81 to .98 except those for readingcomprehension (.45 for reading comprehension, .54 for vocabulary; all regression

estimates presented are standardized). The R2 values for the indicators were also

large, ranging from .67 (DIBELS NWF) to .98 (DIBELS ORF 1 & 3, PDE, A),

except again for the reading comprehension measures (.20 for GORT and .29 for

vocabulary). The fit of this model was good, with a v932 of 148.25, a comparative

fit index (CFI) of .986, and a root mean square error of approximation (RMSEA)

of .055.

Information from the modification indices showed that adding a path that allowed

DIBELS NWF to load on both the Decoding and the Letter-Sound Factors was

worth considering. Because children can earn a correct item for providing either a

single letter sound or a blended response, this assessment does appear to measure

both individual letter sound knowledge and decoding of nonsense words. Thus we

added the cross-loading and Model 2 was estimated. The v2 was significantly lower,

Dv2=11.72, Ddf = 1,p = .0006 and the model fit was good, with v92

2 of 136.53, a

CFI of .989, and a RMSEA of .050. The R2 for DIBELS NWF increased to .70,

indicating that 3% additional variance was explained by the added path. The first

order correlations between the factors are presented in Table 4.

To test whether phonogram fluency (PGF) and decoding fluency are measures of

two distinct factors or are the same one, a model with seven factors was fit with

decoding predicting the indicators of phonogram fluency as well as its own

indicators (Byrne, 2000; Kline, 2005). The resulting model (Model 3) had a

significantly worse fit (Dv2 =52.11, Ddf =8, p\ .0001, CFI = .977,

RMSEA = .068), indicating that they are two separate, but highly related

Table 3 Test statistics for measurement models

Model v2 df CFIa RMSEAb AIC Dv2c Ddfd Dp

1. 8 Factors 148.25 93 .986 .055 [.038.071]e 268.25

2. 8 Factors with NWFcross-loaded

136.53 92 .989 .050 [.031.067]e

258.53 11.72 1 =.0006

3. 7 Factors with phonogram

fluency, NWF, and PDE

predicted by decoding fluency

188.64 99 .977 .068 [.053.083]e 296.64 52.11 8 \.0001

a Comparative fit indexb Root mean square error of approximationc Difference in v2

d Difference in degrees of freedome

90% confidence interval for RMSEA


1 3


15/25

(r = .95), factors. Thus we conclude that the eight-factor measurement model has

the most support; it will be used in all future analyses.

Tests of direct and mediated effects on decoding and text reading fluency

We then addressed the next set of research questions. Results of the model

comparisons are summarized in Table5 and the initial model is represented in

Fig.2. The fit of Model 1, with both direct and mediated paths from all variables,

was adequate (v1192 = 258.46, CFI = .967, RMSEA = .077). The R2 for decoding

fluency was .91 and for ORF, .89. As predicted, the direct paths phonemic blending

fluency (PBF) ? decoding fluency (.03), letter sound fluency (LSF) ? decoding

Table 4 Maximum likelihood correlations among all latent variables, whole sample

1 2 3 4 5 6 7

1. Reading comprehension

2. Text reading fluencya

.666

3. Decoding fluencya .587 .856

4. Phonogram fluencya .486 .864 .949

5. Letter sound fluencya .102 .273 .410 .455

6. Single word reading fluency .553 .921 .886 .888 .370

7. RAN -.165 -.645 -.584 -.653 -.469 -.673

8. Phonemic blending fluency .544 .156 .180 .167 .333 .129 -.217

All correlations above .13 are significant at p\ .05 (two tailed)a Defined as rate and accuracy

Table 5 Test statistics for structural models

Model v2 df CFIa RMSEAb Dv2c Ddfd Dp

1. Initial 258.46 119 .967 .077 [.064.090]e

2. Fixed PBF?

PGF path to 0 258.56 120 .967 .077 [.064.090]e

.10 1 .7523. Fixed LSF ? decoding,

PBF ? decoding paths to 0

259.83 122 .968 .076 [.063.089]e 1.27 2 .530

4. Deleted all non-significant paths

and those set to 0

264.50 124 .967 .076 [.063.089]e 4.67 2 .100

5. Added path from PGF ? TRF 260.68 123 .968 .076 [.063.089]e 3.82 2 .148

6. Fixed PGF ? decoding,

decoding ? TRF paths to 0

603.86 124 .887 .141 [.129.152]e 343.18 1 \.00001

PBF Phonemic blending fluency, PGF phonogram fluency, LSFletter sound fluency, RANrapid auto-

matized naming, TRFtext reading fluency

a Comparative fit indexb Root mean square error of approximationc Difference in v2

d Difference in degrees of freedome 90% confidence interval for RMSEA


1 3


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fluency (-.04), and PBF ? PGF (-.02) were not significant (all regression

estimates presented are standardized). The paths from RAN ? decoding fluency

(.07) and text reading fluency (-.04) were also not significant.

In order to test our hypothesis that there would be no unique, direct effects from

phonemic blending (PBF) and letter sounds (LSF) to decoding fluency, the path

PBF ? PGF was set to 0 and the model estimated (Model 2 in Table 5). The paths

PBF ? decoding and LSF? decoding were then set to 0 and the model was

Decoding

Fluency

nwf e5

pdea e6

pdeb e7

Letter Sound

Fluency

lsfa e1

lsfb e2

.81

.90

phonfa e3

phonfb e4

Phonemic Blending

Fluency

res2

res1

Text Reading

Fluency

orf1 e8.97

orf2 e9.96

orf3 e10.97

res3 GORTFluency

e11.89

PGF

.94

.95

res4

.75

.97

.96

.95

.25

.18

RAN

rlna

e12

rlnb

e13

.92.89

-.56

-.41

.20Single Word

Reading Fluency

sweb

e15

swea

e14

.97.97

-.23

.75

.74

res5

-.21

res6

.18

Reading

Comprehension

GORT

RC e22

vocab e33

.55

.44

.18

Fig. 3 Final structural model of decoding and text reading rate and accuracy in second grade readers.All regression weights are significant (p\ .05). lsf =Letter sound fluency; rln =Rapid letter naming

subtest of the Comprehensive Test of Phonological Processing; nwf =Nonsense word fluency from the

Dynamic Indicators of Basic Early Literacy Skills; pde =Phonemic decoding efficiency subtest from

the Test of Word Reading Efficiency, phonf=Phonogram fluency, orf=Oral reading fluency from the

Dynamic Indicators of Basic Early Literacy Skills, GORT =Gray Oral Reading Test, 4th ed.,

swe =Sight word reading subtest of the Test of Word Reading Efficiency, vocab =Picture vocabulary

subtest from the Woodcock-Johnson Test of Cognitive Abilities, 3rd ed. RANRapid automatized naming


1 3


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re-estimated (Model 3 in Table 5). The lack of change in fit from these two models

demonstrated that there are indirect effects from PBF and LSF to decoding fluency,

but no unique direct contribution once the variance due to the other variables is

partialed out.

Next we examined the structural relations among the variables predicting textreading fluency. In order to do this with the most parsimonious model, we removed

all non-significant paths, including those from RAN to decoding and text reading

fluency and those set to 0 and estimated the model (Model 4 in Table 5). The fit was

not significantly different from the previous model (v1242

=264.50, CFI = .967,

RMSEA = .076). The R2 for decoding fluency was .90 and for ORF, .89. As

predicted, the path from single-word reading fluency (SWF) ? text reading fluency

(TRF) showed a strong association (.74). The path decoding fluency ? SWF also

showed an equally strong association (.75) while that from decoding fluency ? text

reading fluency was smaller (.21) as was the path from reading comprehension (RC)(.18). RAN predicted all the lower-level processes and SWR, but did not show a

direct path to decoding fluency or text reading fluency (Fig. 3).

In order to further examine the role phonogram fluency plays in the prediction of

decoding fluency, we estimated a model with an additional path from phonogram

fluency (PGF) directly to text reading fluency in addition to the path mediated by

decoding. There was no significant change in model fit (Model 5 in Table5). We then

set the paths from PGF ? decoding and from decoding ? text reading fluency to 0 to

determine if PGF would account for the same variance as decoding. That is, we asked

the question whether both variables uniquely related to text reading fluency (Model 6in Table5). This resulted in a model with significantly worse fit (Dv2 = 343.18,

Ddf = 1,p \ .0001, CFI =.887, RMSEA = .146), indicating that both phonogram

fluency and decoding play a unique role in explaining text reading fluency.

Discussion

This study examined a multi-level model of text reading fluency, which we define as

reading rate and accuracy in connected text. In the current study, we addressed three

goals: (a) identify salient variables that explain individual differences in the text

reading and decoding fluency of a sample of young readers, (b) create a model of the

structural relations among the variables, and (c) test specific theoretical hypotheses.

Limitations

As with any research study, the findings of this project are limited in several ways.

One of the largest is that these findings are based on a single sample of 198 s graders

in 5 schools. As can be seen in the descriptive statistics found in Table2, they fall in

the average range with a large amount of variability and represent a wide range of

readers, however another group of second graders may well produce a different set

of findings. Second, except for reading comprehension and vocabulary, we used

only speeded measures. It is likely that some of the predictions we saw are due to

the fact the measures were all based on rate. Including accuracy measures would


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help to better sort out the relative contributions of the variables. In addition, none of

the variables tap into orthographic processes important to reading, leaving questions

about what other explanatory variables are not included. Our use of only rimes in

the phonogram fluency measure added unintended complications in the interpre-

tations of our results. Including affixes in addition to common rimes may help settlethe issue of the role larger-letter patterns plays in decoding. In addition, there were

not multiple indicators of all variables, and the indicators had variable levels of

reliability, which could account for some of the findings. We are also limited in our

findings by the single age of our participants. We do not have a cross-sectional

sample, or better yet, a longitudinal sample to study the development of these

processes. This limits our understanding to a small snapshot in the reading of young

children. Finally, there are other alternate models that could explain the data equally

well that are not examined here. Despite all of these limitations, the findings of our

study provide important insights into the nature and measurement of decodingfluency as well as variables important for text reading fluency.

Measurement of text reading fluency and decoding fluency

First we examined the appropriate measurement of the constructs of interest using a

confirmatory factor analysis. In keeping with Ehris (1992) phases of development

and other evidence that proposed an interim step between single letter sounds and

decoding of whole words (i.e., within-word letter patterns), we hypothesized that

there were eight separate factors. We fit the hypothesized model, which fitsignificantly well, had strong loadings in most cases, and explained a great deal of

the variance in the various indicators. We then determined the most appropriate

measurement model included a cross-loading of both Letter Sound Fluency and

Decoding Fluency on the DIBELS Nonsense Word Fluency (NWF) measure. This is

interesting because when using NWF, the first author has noticed that it does appear

to be measuring both letter sound knowledge and blending of sounds into words.

This measurement model confirms this observation.

A measurement concern about the nature of decoding fluency was addressed next.

Using our model and the hypothesized pattern of development from single letter

sounds to within-word patterns, to reading whole words as units (Ehri, 1992), we

thought that the automaticity of reading rimes (e.g., eet, igh, eem, otch) would be

separate from, but highly related to, reading of nonsense words (e.g., dat, mis, stree,

vog, tel, zul). We see the first task as measuring familiar letter units that could be read

quickly by sight while the other two tasks are designed to facilitate recoding, or

sounding out the nonsense words. To test this, we fit a model with the indicators of

decoding fluency and PGF loaded onto the same factor per Byrne ( 2000) and Kline

(2005). This model was significantly worse fitting than the full model, indicating that

while phonogram and decoding fluency were highly related (r = .95), they are not

the same factor. This was confirmed during the structural model analysis when

setting the paths from decoding to text reading fluency resulted in a much worse

fitting model. Setting the paths to zero had the effect of bypassing decoding, with a

single route from phonogram fluency (PGF) to text reading fluency. Both appear to

explain unique variance in the model.


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It is possible that the high correlation between the two factors is due to an overlap

of items between the three measures (DIBELS Nonsense Word Fluency (NWF),

TOWRE Phonemic Decoding Efficiency (PDE), and Phonogram Fluency). We

examined this and found little overlap between the Phonogram Fluency and

DIBELS NWF measures (two rimes occurred twice in NWF and four occurredonce). However, there was some overlap between the TOWRE PDE and phonogram

fluency. There were 48 occurrences of rimes in the phonogram fluency measure in

the two forms of the TOWRE PDE, with the three rimes -at, -ip, and -inaccounting

for 11 of them. Clearly there is some overlap, both in items and in the method of

measurement (speeded, use of a timer, same assessors), and it is possible that the

phonogram fluency is nothing more than a measure of non-words, however, there is

also still unshared variance that we believe is due to the underlying differences in

the constructs. To further examine these two factors, we conducted a CFA with just

the two factors. We estimated a model and found that it explained the data very well(v4

2=6.28, p = .179, CFI = .998, RMSEA = .054) with high loadings onto each

factor. We then fixed the two factors to be equal to test whether a one-factor solution

is truly better. This model had a significantly worse fit (Dv2 =33.75, Ddf = 1,

p\ .0001, CFI = .972, RMSEA = .189).

Students can approach unknown words, represented in these measures as

nonsense words, in several ways: pronounce individual letter sounds, pronounce

individual sounds and then blend them, blend them without the initial attempt, or

pronounce them through an analogy. DIBELS NWF gives full credit for the first,

second, and third options though students pay a rate penalty for the second, theTOWRE PDE gives full credit for the second through fourth options, again with a

penalty for the second, and the Phonogram Fluency encourages the use of the fourth

option, analogy. Perhaps it is that use of analogy that is at the heart of the unique

variance that we are detecting in these constructs, and perhaps it is the shared nature

of the nonsense word reading strategies that children may apply that leads to the

high correlation.

Mediated and direct predictors of decoding fluency

Using two different models, we established that the lower-level skills of phonemic

blending and letter-sound correspondences are not uniquely related to decoding

fluency when phonogram fluency is accounted for; they are mediated by fluency in

reading these larger letter patterns. If a reader can read using larger letter patterns,

then it makes sense that isolated letter sounds and phonemic blending would no

longer have a direct effect on decoding; the variation would appear in the higher

level skill. We found evidence that phonemic blending (PBF) predicts letter sound

fluency (LSF), which predicts phonogram fluency (PGF), which predicts decoding

fluency. This sequence follows the expected direction of development among young

readers (Ehri, 1992).

Instructionally, this would suggest that teachers need to ensure their young

students become automatic in oral blending of sounds, individual letter sounds, and

larger letter patterns in order to be successful decoders. Instruction that provides

enough practice to move beyond accuracy to automaticity is needed. Given the role


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phonogram fluency played in predicting text reading fluency, instruction in

recognizing words with shared phonograms accurately and quickly would be likely

to promote the development of text reading fluency. Too often, teachers focus on

decoding accuracy at the letter level, with little or no attention devoted to the

development of automaticity in decoding skills. Judging from the methods includedin most basal series, little instructional time is spent developing familiarity with

phonograms. By including instruction in these component skills, teachers can play a

more active role in their students reading fluency development.

Mediated and direct predictors of text reading fluency

Looking at the portion of the model directly predicting text reading fluency, it

appears that both single-word reading fluency and decoding fluency are strong

predictors of text reading fluency and that reading comprehension also plays animportant role. Single-word reading had a unique strong relation to text reading

fluency, which is consistent with the findings of Compton et al. (2004) and Torgesen

et al. (2001), who found that the percentage of sight words in a passage predicts the

text reading fluency of elementary readers.

Not unexpectedly, decoding fluency also had a direct relation to text reading

fluency and single-word reading fluency, a finding consistent with Gough and Walsh

(1991). As Ehri (2002) explained, words become sight words when they have been

practiced sufficiently to be fully amalgamated in memory. It stands to reason, then,

that automaticity in decoding allows for more efficient practice of words whichwould, in turn, lead to greater single-word reading fluency. In addition, these young

readers are likely to still encounter unknown words that need to be decoded, so

decoding continues to play a strong role in text reading fluency in addition to that

played by single-word reading fluency.

The findings in the current study are somewhat inconsistent with those of Burke

et al. (2009). Like this study, Burke et al. found that decoding fluency in first grade

predicted single-word reading fluency (SWF) in that same grade and SWF in first

grade had a strong effect on text reading fluency in second grade. Unlike this study,

they did not find a link between decoding fluency in first grade and text reading

fluency. This is perhaps due to the longitudinal nature of their study; perhaps if they

had measured DIBELS Nonsense Word Fluency (NWF) and DIBELS Oral Reading

Fluency (ORF) concurrently, our results would have been more similar. In addition,

perhaps the relative simplicity of the items on the DIBELS NWF measure did not

capture as much advanced decoding as the TOWRE Phonemic Decoding Efficiency

subtest, which has considerably more difficult items. In the current study, decoding

fluency explains 96 and 97% of the variance in the PDE, but only 75% of the

variance in DIBELS NWF, perhaps demonstrating this difference.

At least among our sample, it appears that the direct effects of RAN on decoding

and reading fluency found by many researchers (Georgiou, Parrila, Kirby, &

Stephenson,2008; Manis et al.,2000; Savage & Frederickson,2005) are not found

when other mediator variables are present in the model. This lack of direct effect is

interesting because we found a rather strong and similar total effect (maximum

likelihood correlation) of RAN on text reading fluency (-.58) and on decoding


1 3


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fluency (-.54). It would appear that the total effect was decomposed through

phonemic blending (PBF), letter sounds (LSF), phonogram fluency (PGF), and in

the case of text reading fluency, single-word reading fluency. RAN uniquely

explained 6% of variance in PBF, 15% in LSF, and 26% in PGF, even after all the

other variance was partialed out.Our finding of a significant relation from reading comprehension to text reading

supports the contention that reading comprehension plays a role in how quickly and

accurately one reads connected text (Jenkins et al., 2003). The relation was small

but significant. When the direction was reversed, and text reading fluency predicted

reading comprehension, the relation was larger, providing support for the notion

fluency is a necessary, but not sufficient condition for deep understanding (Breznitz,

1987,1991; Laberge & Samuels, 1974; Perfetti, 1985).

Implications for research

We are left with several unanswered research questions that are raised by these

results. There was considerable variability in the accuracy of students on the fluency

measures, leading us to wonder if the results were due to accuracy or fluency

differences in the childrens scores. We also wonder if some of the prediction

provided by the lower-level skills would be found if accuracy measures were

included. We are intrigued by the question of whether fluency in these subskills

accounts for additional variance above and beyond accuracy in these processes. We

also wonder if the structural relationships we observed between variables are thesame in children at different levels of reading development or achievement level.

Given that all the variables in the current study are sound-based, we wonder about

the role orthographic knowledge plays in the decoding and text reading fluency of

young readers given all of these other variables. This model of decoding and text

reading fluency will need to be replicated and validated with other samples of

readers, especially readers at other points in their development. We wonder if first

graders or poor readers show similar indirect effects, or would they have additional

variance that uniquely contributes to decoding fluency.

We chose to conduct this study with second graders because children at that age

are typically in a steep trajectory of development in reading and thus may show a

range of mastery of the processes under examination. Most have mastered the

beginning skills related to phonemic awareness and letter knowledge, but few have

reached their potential in text reading fluency. It is important to understand the

relationship among the many component skills of readers during this phase of their

development because a deficiency in any of the component skills has the potential to

affect the development of other skills and, ultimately, the development of the child

as a proficient reader. As evidenced by the wide variability in competence in the

second graders in our sample, developmental phases in reading may be more

important than age or grade level to the understanding of how these factors are

related. Examination of the changes that occur in these component skills as children

move from Ehris (1992) full-alphabetic phase to her consolidated-alphabetic phase

could yield important findings that would further our understanding of how these

skills lead to proficient reading.


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Acknowledgments The work presented in this article was supported by Grant H324N040039 from the

US Department of Education, Office of Special Education Programs. This article does not necessarily

reflect the positions or policies of this funding agency and no official endorsement should be inferred. We

are grateful for the generous assistance of Richard K. Wagner and Robert Abbott in this project and the

invaluable comments of Joseph Jenkins and anonymous reviewers on an earlier draft. We also appreciate

Laura Snyder, Jennifer Wolvin, Jennifer Tow, Anna Ylakotola, Christan Grygas, Yi Pan, PatriciaShubrick, and Brian Mincey for their work collecting the data in this study. We especially thank the

children, parents, teachers, and principals in the Leon County School District and Florida State University

School who made this research possible.

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