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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,
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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
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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
494 R. F. Hudson et al.
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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
Reading proficiency in developing readers 495
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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
496 R. F. Hudson et al.
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(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
Reading proficiency in developing readers 497
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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
498 R. F. Hudson et al.
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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
Reading proficiency in developing readers 499
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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.
500 R. F. Hudson et al.
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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
Reading proficiency in developing readers 501
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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
502 R. F. Hudson et al.
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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.
Reading proficiency in developing readers 503
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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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