Early Lit Predictors Dibels1

Predictive Validity of Early Literacy Indicators From the Middle of Kindergarten to Second Grade

The Journal of Special Education Volume 42 Number 4 February 2009 209-226 © 2009 Hammill Institute on Disabilities 10.1177/0022466907313347 http://journalofspecialeducation.sagepub.com hosted at http://online.sagepub.com

Mack D. Burke Shanna Hagan-Burke Oiman Kwok Richard Parker Texas A&M University Research has emphasized the importance of phonological awareness, phonemic decoding, and automaticity in reading development. Special and general education teachers need valid, efficient, and effective early literacy indicators for schoolwide screening and monitoring that adequately predict reading outcomes. The purpose of this study was to examine the interrelationships and predictiveness of kindergarten early literacy indicators from the Dynamic Indicators of Basic Early Literacy Skills (DIBELS) within the context of a path analysis. The results support the validity of kindergarten DIBELS in predicting ever more complex reading skills in a developmental progression from the middle of kindergarten to second grade. Keywords:

M

early literacy; reading assessment; reading fluency; reading acquisition; reading disabilities

any schools are reexamining their current assessment practices in early literacy and reading in response to federal mandates such as the No Child Left Behind Act (NCLB) (Carnine & Granzin, 2001; No Child Left Behind Act, 2002). NCLB’s emphasis on the use of scientifically based practices, along with a focus on prevention in the field of special education, is moving schools toward formative evaluation for early literacy (National Research Council, 1998; National Reading Panel, 2000; No Child Left Behind Act, 2002; Rayner, Foorman, Perfettii, Pesetsky, & Seidenberg, 2001; Torgesen, Wagner, & Rashotte, 1997; Walker & Shinn, 2002). Children who lack adequate reading skills in first grade are less likely to become proficient readers as they advance through higher grades (Cunningham & Stanovich, 1997; Juel, 1988; Kame’enui, 1993; Scarborough & Parker, 2003; Shaywitz et al., 1999; Stanovich, 1986). However, poor reading trajectories may be avoided if critical preskills that are predictive of mature reading can be strengthened during kindergarten (Good, Simmons, & Smith, 1998; Torgesen et al., 2001). The foundation of an effective approach to prevent reading failure and disability is to target early literacy skills that are predictive of later reading success (Good

et al., 1998; Torgesen, 1998, 2002). Three essential areas implicated in successful reading development are (a) phonological awareness, (b) phonetic skills related to the alphabetic principle, and (c) automaticity (National Research Council, 1998; National Reading Panel, 2000).

Phonological Awareness Phonological awareness is the first essential element of a prevention-based approach to reading failure and disability. Phonological awareness focuses on the ability to discriminate and manipulate the sound structure of language (Blachman, 2000; Ehri, Nunes, Stahl, & Willows, 2001; Ehri, Nunes, Willows, et al., 2001; Smith, Simmons, & Kame’enui, 1998). Phonological awareness does not involve orthography (i.e., written language, spelling) and focuses on auditory and oral abilities such as rhyming, alliteration, breaking apart syllables, picking out the initial sounds in words, blending phonemes together, and segmenting words given orally into their speech sounds (Stanovich, Cunningham, & Cramer, 1984). Phonological awareness is one indicator of subsequent reading ability 209

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(Wagner & Torgesen, 1987), with correlations between phonological awareness in kindergarten and wordreading skills at the end of first grade often falling between .4 and .6 (Torgesen, Wagner, & Rashotte, 1994). Because problems with word recognition characterize most children with reading disabilities (Ehri, 1998), monitoring phonological awareness in kindergarten is especially important for prevention. Children entering kindergarten generally have not learned to read. If children who have poor phonological awareness skills can be identified and targeted for instruction, the cognitive framework for learning to read can be “primed,” and the word-reading problems as well as the poor reading trajectories typified by older struggling readers can be prevented.

Alphabetic Principle Phonological awareness is important and should be emphasized in kindergarten; however, awareness of the sound structure of written language by itself is likely insufficient for reading acquisition and alphabetic mastery (Gough & Tunmer, 1986). Research has underscored the second essential element of preventing reading failure, providing cognitive access to the alphabetic principle (Ehri, 1998; Schatschneider & Torgesen, 2004). Young learners must acquire the skills related to the alphabetic writing system and the grapheme-phoneme relationships that correspond to the spellings of words (Ehri, Nunes, Stahl, et al., 2001). Assessment of the alphabetic principle often focuses on letter-sound correspondences, unique letter combinations that make a common sound, and word blending of both regular words and nonwords (Ehri, Nunes, Stahl, et al., 2001). In particular, nonword measures that directly assess phonological recoding ability have been found to be particularly strong discriminators of reading disabilities (Rack, Snowling, & Olson, 1992). For example, a recent meta-analysis of 34 studies found a Cohen’s d (Cohen, 1977) of .65 between the nonword-reading performance of students with reading disabilities and matched control groups (Herrmann, Matyas, & Pratt, 2006). Measures that reflect knowledge of the alphabetic principle should be administered early to monitor the extent to which children are generalizing their phonetic knowledge to written language (Schatschneider & Torgesen, 2004). Mastering the alphabetic principle is critical because reading disabilities are likely to occur when the skills associated with it are not learned.

Phonological and Alphabetic Automaticity Automaticity of those skills related to phonological awareness and the alphabetic principle is the third essential element of prevention (Torgesen, Rashotte, & Alexander, 2001; Wolf & Bowers, 1999). Automaticity in reading seems to occur in developmental phases beginning in preschool and kindergarten and continuing through at least third grade (Ehri & McCormick, 1998). Phonological and alphabetic automaticity is often learned in preschool and kindergarten, where effective instruction results in the rapid, fluent, and context-free retrieval of component skills (Torgesen, 1998). Moreover, automaticity with component skills serves as the foundation for the fast, smooth, coordinated contextual reading typically assessed by the rate of text reading (Berninger, Abbott, Billingsley, & Nagy, 2001; Berninger, Abbott, Vermeulen, & Fulton, 2006). In her discussion of automaticity, Ehri (Ehri, 1995, 2005; Ehri & McCormick, 1998) provided a useful developmental framework for how children become fluent readers. Ehri posited that young learners progress through five phases as they become “automatic” with skills related to the alphabetic principle. Ehri’s developmental framework of reading acquisition consists of the pre-, partial, fully, consolidated, and automatic alphabetic phases.

Prealphabetic Phase Ehri specified that in the prealphabetic phase, children have little to no knowledge of the alphabetic principle. Children are typically in this phase of development in prekindergarten and the early part of kindergarten. During the prealphabetic phase, children do not use alphabetic knowledge to read words. In this phase, young learners lack good phonological awareness and letter knowledge, both of which are important precursors to more advanced phases of alphabetic mastery. Children in the prealphabetic phase are limited to using environmental and contextual cues and selected visual attributes. For example, in a story with a red wagon in a picture, a child may correctly guess the words red wagon or read the word milk because of recognition of the logo but not because of attention to the letters.

Partial Alphabetic Phase Ehri pointed out that during the partial alphabetic phase, children have some insight that letters and

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sounds are related, but this insight is not fully developed (Ehri, 2005; Ehri & McCormick, 1998). This phase often characterizes kindergarten students. Children in the partial alphabetic phase can begin to detect letters in words, match some letters to their sounds, and identify the initial or final sounds in words. Letters whose names contain their sounds may be known (e.g., b, d, f, j). However, mastery of the alphabetic principle is still not fully developed. For example, children are likely to have difficulties with medial sounds in words, even if the first and last sounds are correctly identified. Children in this phase still may rely heavily on context, mistake similarly spelled words, and attempt to read words right to left. Moreover, they may lack a strategy for sounding out words and have no knowledge of spelling combinations that have more than one letter to represent a phoneme (e.g., sh, ck).

Full Alphabetic Phase Ehri explained that the full alphabetic phase is the essential starting point for acquiring a solid foundation and mastering the mature reading skills required in an alphabetic writing system (Ehri, 2005; Ehri & McCormick, 1998). Mastery of the full alphabetic phase often occurs during first grade, when lettersound and cipher knowledge are developed. During the full alphabetic phase, children begin reading words in print that they have not seen before by using grapheme-phoneme correspondences. They read common words by sounding them out and begin to develop a working knowledge of the alphabetic writing system. It is at this phase, Ehri explained, that children start to build their sight word vocabularies as the words they recognize and practice through grapheme-phoneme decoding become automatically recognized.

Consolidated Alphabetic Phase The consolidated alphabetic phase characterizes children during second grade who are able to recognize entire words automatically, have large sight word vocabularies, and use unique spelling patterns to assist in reading unfamiliar words (Ehri, 2005; Ehri & McCormick, 1998). Ehri indicated that in this phase, word parts are used for decoding multisyllabic words. Affixes, syllables, and recurring spelling patterns are used by learners for word reading. Furthermore, whereas in the full alphabetic phase, word reading occurs sequentially, left to right, children in the consolidated alphabetic phase begin to use spelling rules and their mastery of the alphabetic system. For example, a

child in this phase may be able to read a word with a long vowel sound and silent final e (e.g., kite).

Automatic Alphabetic Phase Once the automatic alphabetic phase is reached, children have mastered the critical components needed to be successful in an alphabetic writing system (Ehri & McCormick, 1998). Children are able to read known and unknown words accurately and effortlessly. Most words encountered by children in this phase are now part of their sight word vocabularies. When unknown, highly technical words are encountered, children in this phase are able to use multiple strategies for reading them. In this phase of reading development, in which words are recognized by sight, cognitive resources can be applied to higher level cognitive process related to understanding text (LaBerge & Samuels, 1974).

Sublexical Fluency Becoming a fluent reader with good reading comprehension is a developmental process requiring alphabetic mastery (Ehri & McCormick, 1998). Phonological awareness, alphabetic understanding, and automaticity in sublexical skills facilitate movement through the alphabetic phases of reading development (Torgesen et al., 2001; Wolf & Bowers, 1999; Wolf & Katzir-Cohen, 2001). Reading fluency and comprehension are functionally interdependent. Fluency in connected, authentic text serves as a bridge between decoding and reading comprehension (Chard, Vaughn, & Tyler, 2002; Pikulski & Chard, 2005). Many researchers are coming to the conclusion that to obtain a high degree of fluency in connected text and reach what Ehri and McCormick (1998) referred to as the automatic alphabetic phase, strong, automatic connections are also needed between the phonological and alphabetic processes necessary for fluent reading to occur. In particular, fluent readers must be able to automatically retrieve phonological codes and their corresponding orthographies from long-term memory (Wagner & Torgesen, 1987). Ritchey and Speece (2006) referred to the rapid processing of phonological and alphabetic relationships as sublexical fluency, which they defined as “the speed and accuracy with which subword skills can be accessed and produced” (p. 302). The automatic retrieval of phonemes, letter names, and letter sounds and the fluent application of phonological and

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alphabetic knowledge provide the basis for indexing sublexical fluency (Coyne, Kame’enui, & Simmons, 2001; Good, Gruba, & Kaminski, 2001; Ritchey & Speece, 2006). Children who have phonological processing deficits and naming speed difficulties are said to have a “double deficit” (Wolf & Bowers, 1999; Wolf, Bowers, & Biddle 2000). The speed with which children can generate the names of a randomly repeated series of items has become an important index for measuring automaticity and the rate of retrieval of phonological codes. Studies have typically measured automaticity through naming speed of letters, objects, colors, and digits (Cutting & Denckla, 2001; Denckla & Rudel, 1974). However, an alternative approach is to identify and monitor children who may have acquisition and/or fluency problems with sublexical skills by directly measuring the rapid processing of phonological and grapheme-phoneme relationships (Good & Kaminski, 1996; Good, Simmons, & Kame’enui, 2001). Indicators of sublexical fluency that index phonological and alphabetic skills along with accuracy are a fairly new innovation for kindergarten assessment (Ritchey & Speece, 2006). The research reviewed in this article focuses on early literacy indicators and emphasizes only those measures that focus on sublexical fluency in kindergarten. As students move beyond kindergarten, other fluency indicators, such as oral reading fluency (Fuchs, Fuchs, Hosp, & Jenkins, 2001) and word identification fluency (Fuchs, Fuchs, & Compton, 2004), become more viable measures. In kindergarten, however, the alphabetic principle has not been mastered, and sublexical fluency must be monitored to ensure that children progress at an adequate rate toward alphabetic mastery.

Dynamic Indicators of Basic Early Literacy Skills Research The adoption of early literacy indicators that focus on the direct measurement of sublexical fluency is increasing among school systems. The direct measurement of academic skills has a long history in special education (Deno, 1985; Lindsley, 1972; Lovitt, 1967; Shapiro, 1996). However, the development and adoption of fluency-based measures, particularly in kindergarten and within the context of a prevention-based approach, is a relatively new innovation (Good & Kaminski, 1996; Kaminski & Good, 1996). The Dynamic Indicators of Basic Early Literacy Skills (DIBELS) are a set of sublexical fluency measures

developed for identifying whether children are mastering the necessary skills to become successful readers (Good, Simmons, et al., 2001). Aspects of validity have been examined for DIBELS at the beginning (Hagan-Burke, Burke, & Crowder, 2006) and middle (Burke & Hagan-Burke, 2007) of first grade. However, kindergarten is where a preventionoriented approach such as DIBELS can have the most impact. This review of the literature yielded six published studies examining aspects of the validity of those DIBELS measures designed to be administered in kindergarten. First, Kaminski and Good (1996) published reports of low-moderate to strong reliability coefficients ranging from .43 to .90 for letter naming fluency and phoneme segmentation fluency. Their findings, however, were limited by their study’s small number of participants (n = 37 in kindergarten, n = 41 in first grade). A modified version of DIBELS was examined by Elliott, Lee, and Tollefson (2001) using letter naming fluency, sound naming fluency, initial phoneme ability, and phonemic segmentation ability with 75 kindergarteners. The concurrent criterion-related validity of the modified measures was examined, with significant correlations ranging from .12 to .81 reported between predictor and criterion achievement measures. However, those findings were limited because two of their four measures were accuracy based, differing from the fluency-based design of DIBELS. Good, Simmons, et al. (2001) published findings examining DIBELS within their outcomes-based problem-solving system. These researchers reported correlations of .34 between midyear initial sound fluency scores and end-of-the-year phoneme segmentation fluency, .38 between end-of-kindergarten phoneme segmentation fluency and middle-of-firstgrade nonsense word fluency, and .78 between nonsense word fluency in the middle of first grade and end-of-first-grade oral reading fluency. In that same study, Good et al. reported a correlation of .82 between students’ first and second grade oral reading fluency performance and a correlation of .67 between third graders’ oral reading fluency and performance on Oregon’s third grade state assessment test. Furthermore, 96% of those students classified as not at risk from their third grade oral reading fluency performance met or exceeded the state’s reading standards. Hintze, Ryan, and Stoner (2003) examined the concurrent validity and diagnostic accuracy of DIBELS and the Comprehensive Test of Phonological Processing (CTOPP; Wagner, Torgesen, & Rashotte,

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1999) with 86 kindergarteners. Hintze et al. found significant correlations among letter naming fluency, initial sound fluency, and phoneme segmentation fluency and the CTOPP subtests of (a) elision, (b) rapid color naming, (c) blending words, (d) sound matching, (e) rapid object naming, (f) memory for digits, and (g) nonsense word repetition. In addition, Hintze et al. reported significant correlations between letter naming fluency, initial sound fluency, and phoneme segmentation fluency and the CTOPP composites of phonological awareness, phonological memory, and rapid naming. Although the strength of correlations ranged from .09 to .63, Hintze et al. concluded that initial sound fluency and phoneme segmentation fluency were associated more closely with the CTOPP measures of phonological awareness, and letter naming fluency was associated with both phonological awareness and rapid naming. Speece, Mills, Ritchey, and Hillman (2003) investigated modified letter naming fluency (composed of only lowercase letters) and nonsense word fluency with the Woodcock Johnson–Revised (WJ-R) letter Word Identification and Word Attack subtests and oral reading fluency. Thirty-nine children were administered a battery of measures at the end of kindergarten and again at the end of first grade. Of interest, letter naming fluency at the end of kindergarten was correlated with WJ-R letter word identification at .55, WJ-R word attack at .44, and oral reading fluency at .69. The predictive criterion-related validity coefficients for end-of-first-grade nonsense word fluency correlated with WJ-R letter word identification at .59, WJ-R word attack at .59, and oral reading fluency at .71. Although the sample size was small (n = 39), Speece et al. concluded that both nonsense word fluency and letter naming fluency were valid measures. More recently, Rouse and Fantuzzo (2006) investigated the convergent and predictive validity of letter naming fluency, phoneme segmentation fluency, and nonsense word fluency from the end of kindergarten to the end of first grade. The results of a canonical correlation analysis indicated that letter naming fluency was strongly associated with the structure of the Test of Early Reading Ability (Reid, Hresko, & Hammill, 2001). All three measures together explained 51.9% of the variance in instructional reading from the Developmental Reading Assessment (Beaver, 1997). Letter naming fluency was the strongest predictor of instructional reading level, followed by nonsense word fluency and phoneme segmentation fluency. Interestingly, letter naming fluency and

phoneme segmentation fluency were better predictors than nonsense word fluency on the vocabulary, language, and reading subtests of the Terra Nova (CTB/ McGraw-Hill, 1997).

Rationale for Study There are two compelling rationales for the current study. First, understanding how early literacy indicators can be used to model reading acquisition is an important part of establishing their validity (Good, Simmons, et al., 2001). DIBELS is currently under scrutiny because of their widespread adoption as part of the federal government’s Reading First Initiative (Manzo, 2005). The validity of DIBELS has been recently criticized by several prominent researchers in the field of reading (Goodman, 2005; Pressley, Hilden, & Shankland, 2005; Shanahan, 2005). Despite criticism, the construction and development of DIBELS is conceptually sound, reflecting constructs for which there is a wealth of evidence to support (Ehri, Nunes, Stahl, et al., 2001; Ehri, Nunes, Willows, et al., 2001; Rayner et al., 2001; Smith et al., 1998). Clearly, the core skills targeted by DIBELS have empirical support underscoring their relevance (Ehri, Nunes, Stahl, et al., 2001; Grossen, 1997; Simmons, & Kame’enui, 1998; Smith et al., 1998; Torgesen et al., 1994, 2001; Wolf et al., 2000). However, there is less validity research on DIBELS in the existing published research literature than one would surmise for a set of measures in such widespread use. The validity evidence for DIBELS could be enhanced by a longitudinal examination that makes more explicit the developmental connections between the early literacy indicators and reading acquisition. A second rationale for conducting this study is the need to examine the process of reading acquisition itself and whether sublexical fluency indicators are adequate for modeling reading development. Theoretically, the act of reading involves two processes referred to in the research literature as the simple view of reading (Gough & Tunmer, 1986; Hoover & Gough, 1990). The first process in this view of reading is decoding words from print. The second reading process is cognitively extracting meaning from the words that are read. Arguably, a breakdown in reading words from print is the primary cause for most reading disabilities and is a necessary requirement for the application of listening comprehension processes to reading (Gough & Tunmer, 1986; Hoover & Gough, 1990; Metsala & Ehri, 1998). Thus, mastering the alphabetic principle

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is a key “linchpin” in preventing reading disabilities (National Research Council, 1998; National Reading Panel, 2000). Developmentally, efforts to develop the sublexical phonological and alphabetic skills that are required to read words from print must begin at the beginning of the reading process (Wolf & KatzirCohen, 2001). Kindergarten, then, becomes a key time to start using indicators of sublexical fluency such as DIBELS to directly monitor progress toward mastery of the alphabetic principle (Ehri, 1998; Smith et al., 1998). Early literacy indicators hold much promise because they allow for the ongoing evaluation of progress toward alphabetic mastery as skills change with the passage of time (Good et al., 1998). Therefore, the purpose of the present study was to determine whether early literacy indicators from kindergarten DIBELS can be used to model reading acquisition and, if they can, what their predictive relationships are when ordered in a developmental progression. The predictive validity of DIBELS, and the validity of using early literacy indicators in general, can be augmented by fitting a path model that focuses on sublexical fluency in the process of reading acquisition. Path analysis is a multivariate technique for establishing the predictive validity of DIBELS as well as providing insights on how phonological and alphabetic skills are related over time (Pedhazur, 1997). Some early literacy variables may predict more complex reading outcomes but may do so because of the mediating effect of a third variable. For example, phonological awareness as a construct is theorized to be an important variable in reading acquisition (Torgesen et al., 1997), but its effect is likely mediated by print-related variables related to phoneme decoding and word recognition (Ehri, 1998). Examination of direct and indirect effects can be used to examine how DIBELS and other reading measures might “work together” to predict reading competence. The model proposed is based on a theoretical assumption that there is a developmental progression of sublexical reading skills and processes in reading acquisition that build on one another and ultimately result in reading fluency and comprehension (Ehri, 1995). Modeling reading acquisition based on a developmental progression of phonological and alphabetic skills provides a robust test of the predictive validity of kindergarten DIBELS. Moreover, by adding into the path model well-established external criteria, a path model can be developed to examine the degree to which kindergarten DIBELS predict phonemic decoding, automatic word recognition, fluent oral reading, and reading comprehension.

Method Participants and Setting This study occurred at a large, rural primary school in northern Georgia. The school served all students in prekindergarten through second grade in the school district. The sample included 218 kindergarteners, for whom demographic data were available for 159. Of those 159, 89 (55.97%) were boys and 70 (44.03%) girls. Ninety-eight students (61.64%) were Caucasian, 47 (29.56%) were African American, 3 were Hispanic (1.89%), 2 were Asian (1.26%), and 9 (5.66%) were of mixed ethnicities. Sixty (37.74%) of the participants for whom demographic data were available were eligible for free lunch. Another 9 (5.03%) were eligible for reduced-price lunch. Ninety-one participants (57.23%) were not eligible for free or reduced-price lunch.

Predictors Four subtests from DIBELS designed for administration during the middle of kindergarten were used as predictors (Good & Kaminski, 2002): (a) Initial Sound Fluency, (b) Phoneme Segmentation Fluency, (c) Letter Naming Fluency, and (d) Nonsense Word Fluency. Initial sound fluency. Initial sound fluency is a measure of emerging phonological awareness. The examiner presents and labels a series of pictures as stimulus items for which the child identifies their initial sounds. For example, “This is ball, rock, tree, and horse. Which picture begins with /r/?” The student is instructed to point to the picture that matches the initial sound. After the beginning sounds for three of four of the pictures are queried, the student is asked to produce the initial sound of the remaining picture. “What sound does horse begin with?” The cumulative latency of response is recorded so that the final score can be reported in total correct initial sounds per minute. Good and Kaminski (2002) reported an alternate-forms reliability for initial sound fluency of .72 in the middle of kindergarten. Phoneme segmentation fluency. Phoneme segmentation fluency is a measure of phonological awareness that assesses a student’s ability to break three- and four-phoneme words into their individual phonemes fluently (Good & Kaminski, 1996). The examiner orally presents words of three to four phonemes and asks the student verbally to produce each phoneme. For example, if the examiner says “dog,” the student

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says “/d/ /o/ /g/” to receive three points for this word. Each phoneme spoken correctly is scored as a point, and incorrect phonemes are not counted. The total number of correctly spoken phonemes in 1 minute is the score for phoneme segmentation fluency. Testretest reliability for phoneme segmentation fluency is .85 (Elliott et al., 2001). Equivalent-forms reliability ranged from .79 (Good & Kaminski, 1996) to .88 (Good, Kaminski, Laimon, & Johnson, 1992). Letter naming fluency. Letter naming fluency is assessed at three points in time during kindergarten (i.e., the beginning, middle, and end of the year). Letter naming fluency is useful as a general measure of risk (Kaminski & Good, 1996). During the letter naming fluency administration, the examiner presents the student with a series of uppercase and lowercase letters randomly arranged in rows on a standard 8.5inch × 11-inch sheet of paper. The student is asked to orally identify the names of as many letters as he or she can and is stopped at the end of 1 minute. The score is the number of correctly named letters in 1 minute. Reliability estimates for letter naming fluency range from .88 to .93 (Good & Kaminski, 1996). Nonsense word fluency. Nonsense word fluency assesses a student’s knowledge of the alphabetic principle. Specifically, nonsense word fluency is a measure of a student’s ability to identify common letter-sound correspondences and/or blend letter sounds into words (Kaminski & Good, 1996). The student is presented with a list of vowel-consonant and consonant-vowelconsonant nonsense words (e.g., wuj, ig, tav) and is asked to correctly read the entire word or verbally produce each letter sound. For example if the word is wuj, the student could generate the separate sounds “/w/ /u/ /j/” or pronounce them as a blended word, wuj. Each correctly produced letter sound, whether produced individually or as a blended unit (i.e., “word”), is counted. A student is given 1 minute to produce as many sounds as possible, and the total number of correct sounds generated within 1 minute is the final score. Reliability estimates for nonsense word fluency average near .80 (Elliott et al., 2001).

Outcome Measures Four outcome measures that were administered across 3 years are reported in this study: phonemic decoding efficiency (Torgesen, Wagner, & Rashotte, 1999), sight word efficiency (Torgesen et al., 1999), alternate forms of oral reading fluency from DIBELS

(Good & Kaminski, 2002), and the Passage Comprehension subtest from the Woodcock Reading Mastery Test–Revised (WRMT-R; Woodcock, 1987). Each of these criterion measures represents valued outcomes of concern to special educators. Phonemic Decoding Efficiency and Sight Word Efficiency are subtests of the Test of Word Reading Efficiency (TOWRE) and represent two outcomes important to fluent reading and automatic word recognition (Torgesen et al., 1999). The TOWRE is a criterion measure with established reliability and validity and has been used in other longitudinal studies (Schatschneider, Flecher, Francis, Carlson, & Foorman, 2004; Wagner et al., 1997). Oral reading fluency has a well-established history in special education as an outcome for measuring rate of reading (Fuchs et al., 2001). Last, the Passage Comprehension subtest of the WRMT-R was used as an outcome of reading comprehension (Woodcock, 1987). DIBELS oral reading fluency. The DIBELS oral reading fluency measure is based on the oral reading fluency literature (Marston, 1989). Oral reading fluency is a general indicator of the ability to recognize words fluently within connected text. Alternate forms of the oral reading fluency from DIBELS were used in this study (Good & Kaminski, 2002). Students are timed on three 1-minute samples of reading connected text. Measures of oral reading fluency have been the subject of a substantial amount of research on their technical adequacy. Test-retest reliability has been reported to range between .92 and .97, and alternateforms reliability from different reading passages was found to range from .89 to .94 (Tindal, Marston, & Deno, 1983). Studies examining the concurrent validity of oral reading fluency have reported coefficients ranging from .52 to .91 (Good & Jefferson, 1998). TOWRE phonetic decoding efficiency. Phonetic Decoding Efficiency is a measure constructed of nonwords (e.g., knip, plood) from the TOWRE (Torgesen, et al., 1999). The examinee is shown a list of nonwords that progress in difficulty and asked to read aloud as many words as possible within a 45second period. A critical difference between the Phonetic Decoding Efficiency measure and the Nonsense Word Fluency subtest of the DIBELS is that for Nonsense Word Fluency, credit is given for the student’s ability to read individual phonemes correctly, whereas the student must decode the whole nonword accurately for the examiner to score a response as correct for Phonetic Decoding Efficiency.

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The total number of words spoken correctly within 45 seconds constitutes a student’s final score for Phonetic Decoding Efficiency. This subtest’s concurrent validity with the WRMT-R word attack subtest is .89. Alternate-forms reliability for Phonetic Decoding Efficiency is .97, and test-retest reliability is .90 (Torgesen et al., 1999). TOWRE sight word efficiency. Sight Word Efficiency is a measure of accuracy and fluency in reading phonetically regular and irregular words from the TOWRE (Torgesen et al., 1999). Examinees are shown a list of words that progress in difficulty, beginning with single-syllable words, and are asked to read aloud as many words as possible until told to stop. The number of words spoken correctly within 45 seconds is counted, and this constitutes the final score for sight word efficiency. This subtest’s concurrent validity with the WRMT-R Word Identification subtest is .92. Alternate form reliability for the Sight Word Efficiency subtest is .97 and test-retest reliability is .96 (Torgesen et al., 1999). WRMT-R passage comprehension subtest. The Passage Comprehension subtest of the WRMT-R was used as a measure of reading comprehension (Woodcock, 1987). The Passage Comprehension subtest of the WRMT-R uses a modified cloze procedure in which a student has to silently read a passage and supply the missing word. The median reliability reported for the Passage Comprehension subtest is .92.

Procedures Administration protocols were strictly followed using the guidelines described in the DIBELS, TOWRE, and WRMT-R administration procedures (Good & Kaminski, 2002; Torgesen et al., 1999; Woodcock, 1987). The administration procedures recommended by Good and Kaminski (2002) were followed for DIBELS. Student participants were assessed at the midpoint of the kindergarten school year (i.e., just after the winter break in January). The administration procedures for DIBELS oral reading fluency likewise followed the recommendations of Good and Kaminski in first and second grade. Administration procedures for the TOWRE were followed for the Phonetic Decoding Efficiency and Sight Word Efficiency subtests (Torgesen et al., 1999). The WRMT-R procedures were followed when the passage comprehension subtest was administered in the middle of second grade.

Training procedures. Data collectors were trained at the university to administer the DIBELS measures as well as each of the criterion measures prior to each administration period. The data collectors were students in a graduate special education program. Because of the longitudinal nature of the project, some of the data collectors during given assessment periods were new to the project. Accordingly, training sessions tailored for the returning data collectors as well as the new data collectors were held before each administration period. The first time a graduate student assisted with the project, the student completed a series of three 2-hour training sessions. A fourth session then occurred in which the student administered portions of each measure to one of the authors or a doctoral student with prior experience. Students who failed to administer each measure using the standardized procedures were provided additional practice and feedback until their delivery was accurate. Only then was a student scheduled to administer any measure on site. On-site procedures. It took 2 to 3 weeks to assess all of the students during each administration. Throughout administration periods at the school, the authors of the study and one senior data collector rotated among data collectors conducting fidelity checks. Debriefings occurred immediately after any fidelity check if procedural administration errors were observed. Data collectors were also observed scoring each measure. Again, any errors were corrected, and additional feedback occurred as necessary. The school provided schedules for all classrooms containing students involved in the study. This information allowed project coordinators to efficiently schedule administrations and maximize the number of children who could be assessed within a given day. The majority of test administration occurred in empty classrooms designated by the school. One graduate student with previous experience administering DIBELS and prior experience working in the school would pull students from their respective classrooms and escort them to one of the testing stations. Between three and six of the trained graduate students were administering measures at any given time on a typical administration day. Participating children were immediately escorted back to their classrooms after their testing sessions.

Data Analysis Data from the different assessment periods were uploaded into MPLUS Version 4.2 (Muthén &

Burke et al. / Predictive Validity 217

Table 1 Means, Standard Deviations, and Correlations of the Predictor and Outcome Variables Grade

Variable

1

2

3

4

5

6

7

8

9

K K K K 1 1 1 2 2

1. ISF 2. LNF 3. PSF 4. NWF 5. PDE 6. SWE 7. ORF1 8. PC 9. ORF2 M SD n

— .50 .51 .51 .32 .45 .43 .46 .38 21.92 11.88 218

— .46 .67 .59 .72 .71 .51 .62 31.64 15.19 218

— .59 .48 .44 .49 .48 .42 13.41 12.28 218

— .67 .67 .73 .56 .58 16.86 15.80 218

— .79 .81 .59 .68 12.38 6.96 180

— .89 .63 .81 29.12 12.20 180

— .61 .81 34.66 26.27 179

— .69 28.32 6.00 167

— 78.78 31.48 165

Note: N = 162 to 218, depending on measure for correlations. ISF = initial sound fluency; LNF = letter naming fluency; PSF = phoneme segmentation fluency; NWF = nonsense word fluency; PDE = phoneme decoding efficiency; SWE = sight word efficiency; ORF1 = oral reading fluency in first grade; PC = Woodcock Reading Mastery Test–Revised passage comprehension; ORF2 = oral reading fluency in second grade.

Muthén, 2007). Scatterplots were generated between independent and dependent variables and visually examined for linearity. Descriptive statistics and an intercorrelation matrix were generated and examined. Correlations were examined for the strength of the associations between the predictor variables with the criterion measures. Moreover, to supplement the interpretation of the direct effects found in the path model, simultaneous regressions were also conducted (Pedhazur, 1997). Table 1 includes the means and standard deviations for each measure. An initial hypothesized model was developed on the basis of previous research on DIBELS (Burke & Hagan-Burke, 2007; Good, Simmons, et al., 2001; Hagan-Burke et al., 2006; Hintze et al., 2003; Speece et al., 2003) and reading acquisition (Carver, 1993; Coyne et al., 2001; Ehri, 1999; Ehri & McCormick, 1998; Gough & Tunmer, 1986; Grossen, 1997; Torgesen, 2002). The early literacy predictors and reading outcome measures were placed in a developmental progression to model reading acquisition. In testing the model, the robust maximum likelihood estimation method was used because two variables (nonsense word fluency and first grade oral reading fluency) were not normally distributed. The robust maximum likelihood estimation method can provide “maximum likelihood parameter estimates with standard errors and a chi-square test statistics that are robust to non-normality and non-independence of observations when used with TYPE = COMPLEX” (Muthén & Muthén, 2007, p. 426). Because of the nonindependent observations (i.e., students nested

within 11 different classrooms) and the existence of some missing data, the TYPE = COMPLEX MISSING H1 procedure was adopted to analyze the hypothesized model. Missing data were handled using the missing-data method (MISSING H1), which is a full information maximum likelihood estimation method for analyses with missing data. Hence, all 218 children were included in the analyses. Estimated models were evaluated using the χ2 statistic, which represents the difference between the observed covariance matrix and the model-implied covariance matrix on the basis of the hypothesized model (Bollen, 1989; Hoyle & Panter, 1995). A low χ2 value indicates that the hypothesized model accounted for a majority of the covariance in the data. Although the χ2 statistic can reveal useful information regarding overall fit, it does not verify the direction or strength of individual path values. For the current model, this limitation was somewhat less critical for most of the variables because the timing of test administrations determined the directionality. Other stand-alone fit indices were used to supplement the overall χ2 statistic, including the Comparative Fit Index (CFI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR) (Hoyle & Panter, 1995; McDonald & Ho, 2002). The SRMR reflects the difference between the residual elements of the original and reproduced covariance matrix. Standardized values range from 0 to 1. Hu and Bentler (1998) recommend that values below .08 indicate relatively good fit. The RMSEA is also reported, reflecting standardized values

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that can range from 0 to 1. Browne and Cudeck (1993) recommended a cutoff value of .08 or below to indicate a reasonable fit of a model. The CFI compares the misfit of the hypothesized model to a baseline model in which all variables are assumed to be uncorrelated (Bentler, 1990). The CFI statistic will approach a maximum value of 1 depending on how much better the hypothesized model fits the data than a baseline model. A cutoff value of .95 or above for the CFI is recommended (Hu & Bentler, 1998). After specifying an initial model, it was modified using a two-index approach (i.e., modification index [Sörbom, 1989] and expected parameter change [Kaplan, 1989]). The modification index measures the change in the overall model χ2 value when a particular constrained parameter is free for estimation. Expected parameter change is the estimated change of the parameter value when a particular parameter is set free for estimation. Generally, the freely estimated parameter has to be in the expected direction (Jöreskog, 1993). Consideration was given only to a well-defined subset of parameters with strong theoretical justifications to be free for estimation (MacCallum, 1986; MacCallum, Roznowski, & Necowitz, 1992).

Results Descriptive Statistics and Correlations Descriptive statistics were calculated for the early literacy measures administered at the midpoint of kindergarten as well as each of the criterion measures. Table 1 provides a summary of the descriptive statistics for these measures. An intercorrelation matrix was generated that examined the intercorrelations of the predictors and criterions, which are also provided in Table 1. Overall, correlations ranged from .32 to .89, and all were significant. Correlations with oral reading fluency in second grade ranged from .38 to .81, with letter naming fluency in kindergarten being a notable correlate at .62. The kindergarten correlations with passage comprehension in second grade ranged from .46 to .63, with nonsense word fluency being the strongest correlate at .56. Of the two phonological measures, phoneme segmentation fluency was slightly better on most criteria than initial sound fluency, but neither was a better correlate than the alphabetic measures.

Overall Path Model The hypothesized path model was analyzed using MPLUS Version 4.2 (Muthén & Muthén, 2007), and

Table 2 Explained Variance of Predictors on the Basis of Significant Direct Effects Dependent Variable

R2

Predictor(s)

PSF NWF PDE SWE ORF1 ORF2 PC

.26 .56 .50 .73 .84 .69 .48

ISF ISF, PSF, LNF NWF, LNF LNF, PDE PDE, NWF, SWE ORF1, SWE ORF2

Note: PSF = phoneme segmentation fluency; ISF = initial sound fluency; NWF = nonsense word fluency; LNF = letter naming fluency; PDE = phoneme decoding efficiency; SWE = sight word efficiency; ORF1 = oral reading fluency in first grade; ORF2 = oral reading fluency in second grade; PC = Woodcock Reading Mastery Test–Revised passage comprehension.

the final model is presented in Figure 1. According to the overall model test and the fit indices, the fit of the original hypothesized model to the data was not fully satisfactory, χ2(20) = 76.02, p < .005, CFI = .94, RMSEA = .113, SRMR = .078. To improve the overall model fit, the hypothesized model was modified by freely estimating three more parameters: (a) correlating kindergarten initial sound fluency and second grade passage comprehension, (b) adding a path from kindergarten letter naming fluency to first grade phonetic decoding efficiency, and (c) adding a path from kindergarten nonsense word fluency to first grade oral reading fluency. The modified model fit the data adequately well, χ2(17) = 33.87, p < .005, CFI = .98, RMSEA = .067, SRMR = .039. The predictive relationships between kindergarten, first grade, and second grade measures are honored by the directional arrows. All path coefficients illustrated in Figure 1 are standardized coefficients and were significant (p < .05). Moreover, to supplement the interpretation of the direct effects found in the path model, simultaneous regressions were conducted and are displayed in Table 2. Direct effects on phoneme segmentation fluency. Regarding the theoretical ordering, initial sound fluency was placed in the path model to predict phoneme segmentation fluency because it reflects a less complex phonemic awareness skill (matching the initial sound to a picture). Phoneme segmentation fluency is a more difficult and complex task consisting of the ability to orally segment three- and four-phoneme words into their parts. Initial sound fluency was found to have a direct effect on phoneme segmentation fluency (β = .51). Moreover, initial sound fluency explained 26% of the variance in phoneme segmentation fluency.

Burke et al. / Predictive Validity 219

Figure 1 Path Model of Reading Acquisition .17 .51 ISF .11

.49

.52

PDE

.51

.50

.44

.52

NWF

PC

.21

.26

.32

.13

.16 .17

.56

ORF1

.08

PSF

.46 .61

.47

ORF2

.74 SWE

.21

.36 LNF

.40 .31

.27

Note: N = 218 children. The modified model fit the data adequately, χ2(17) = 33.87, p < .005, comparative fit index = .98, root mean square error of approximation = .067, standardized root mean square residual = .039. All path coefficients were significant at p < .05 (two tailed) except the dashed paths from nonsense word fluency (NWF) to sight word efficiency (SWE) and from oral reading fluency in first grade (ORF1) to Woodcock Reading Mastery Test–Revised passage comprehension (PC). All path coefficients are standardized coefficients. ISF = initial sound fluency; LNF = letter naming fluency; ORF2 = oral reading fluency in second grade; PDE = phoneme decoding efficiency; PSF = phoneme segmentation fluency.

Direct effects on nonsense word fluency. Initial sound fluency, phoneme segmentation fluency, and letter naming fluency were used to predict nonsense word fluency. Previous research in reading acquisition has found phonological awareness and letter names to be predictive of word-level reading (Torgesen et al., 1994). Initial sound fluency had a direct effect on nonsense word fluency (path coefficient = .11). Phoneme segmentation fluency had an even stronger effect (path coefficient = .32). Letter naming fluency had the strongest effect on nonsense word fluency, with a path coefficient of .47. Together, initial sound fluency, phoneme segmentation fluency, and letter naming fluency explained 56% of the variance in students’ nonsense word fluency. Direct effects on phonetic decoding efficiency. Two direct paths were fitted from nonsense word fluency and letter naming fluency to phonetic decoding efficiency. A path coefficient of .50 was found for nonsense word fluency, and a path coefficient of .26 was found for letter naming fluency to phonetic decoding efficiency. Together, nonsense word fluency and letter naming fluency explained 50% of the variance in phonetic decoding efficiency. Direct effects on sight word efficiency. Two direct paths were also fitted for sight word efficiency. The

coefficient for the direct path from phonetic decoding efficiency to sight word efficiency was .52. The direct effect from letter naming fluency on sight word efficiency was .36. Together, phonetic decoding efficiency and letter naming fluency explained 73% of the variance of sight word fluency. Direct effects on oral reading fluency. Three paths to oral reading fluency in first grade were fitted from nonsense word fluency in kindergarten and sight word efficiency and phonetic decoding efficiency. Directional paths were used for sight word efficiency and phonetic decoding efficiency on oral reading fluency even though all three measures were collected around the same point in time, primarily because oral reading fluency represents a more complex skill set than either sight word efficiency or phoneme decoding alone. Path coefficients were .21 for oral reading fluency from phonetic decoding efficiency and .61 for oral reading fluency from sight word efficiency. Nonsense word fluency, sight word efficiency, and phonetic decoding efficiency explained 84% of the variance in first grade oral reading fluency. Oral reading fluency was also administered in second grade. Directional paths were fitted to second grade oral reading fluency from first grade oral reading fluency and sight word efficiency. The path coefficient from first to second grade oral reading fluency was found

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Table 3 Mediational Paths From Middle Kindergarten DIBELS to Passage Comprehension ISF to PC

PSF to PC

LNF to PC

NWF to PC

Mediational Path

Result

1. ISF → PSF → NWF → PDE → SWE → ORF2 → PC 2. ISF → PSF → NWF → PDE → SWE → ORF1 → ORF2 → PC 3. ISF → NWF → ORF1 → ORF2 → PC 4. ISF → PSF → NWF → ORF1 → ORF2 → PC 5. ISF → NWF → PDE → ORF1 → ORF2 → PC 6. ISF → PSF → NWF → PDE → ORF1 → ORF2 → PC 7. PSF → NWF → PDE → SWE → ORF2 → PC 8. PSF → NWF → PDE → ORF1 → ORF2 → PC 9. PSF → NWF → PDE → SWE → ORF1 → ORF2 → PC 10. PSF → NWF → ORF1 → ORF2 → PC 11. LNF → SWE → ORF2 → PC 12. LNF → PDE → SWE → ORF1 → ORF2 → PC 13. LNF → NWF → PDE → SWE → ORF2 → PC 14. LNF → NWF → PDE → SWE → ORF1 → ORF2 → PC 15. LNF → NWF → ORF1 → ORF2 → PC 16. LNF → PDE → ORF1 → ORF2 → PC 17. LNF → SWE → ORF1 → ORF2 → PC 18. LNF → NWF → PDE → ORF1 → ORF2 → PC 19. NWF → PDE → SWE → ORF2 → PC 20. NWF → PDE → SWE → ORF1 → ORF2 → PC 21. NWF → ORF1 → ORF2 → PC 22. NWF → PDE → ORF1 → ORF2 → PC

Z = 1.81, p < .10 Z = 2.57, p < .05 Z = 2.04, p < .05 Z = 1.87, p < .10 Z = 2.24, p < .05 Z = 2.61, p < .05 Z = 2.01, p < .05 Z = 2.61, p < .05 Z = 2.82, p < .05 Z = 1.97, p < .05 Z = 1.90, p < .10 Z = 1.92, p < .10 Z = 1.79, p < .10 Z = 3.76, p < .05 Z = 1.92, p < .10 Z = 2.30, p < .05 Z = 2.93, p < .05 Z = 2.80, p < .05 Z = 2.29, p < .05 Z = 3.38, p < .05 Z = 2.56, p < .05 Z = 3.57, p < .05

Note: DIBELS = Dynamic Indicators of Basic Early Literacy Skills; ISF = initial sound fluency; PC = Woodcock Reading Mastery Test–Revised passage comprehension; PSF = phoneme segmentation fluency; NWF = nonsense word fluency; PDE = phoneme decoding efficiency; SWE = sight word efficiency; ORF2 = oral reading fluency in second grade; ORF1 = oral reading fluency in first grade; LNF = letter naming fluency.

to be .46, and the path from first grade sight word efficiency was found to be .40. First grade oral reading fluency and sight word efficiency together explained 69% of second grade oral reading fluency. Direct effects on passage comprehension. A direct path was modeled from oral reading fluency in second grade to passage comprehension. A path coefficient of .56 was found, with second grade oral reading fluency explaining 48% of the variance in passage comprehension. A directional path was used even though both passage comprehension and oral reading fluency were administered at around the same time, because of previous research indicating that the rate of reading affects comprehension (Carver, 1993; Fuchs et al., 2001). Nondirectional paths. Several correlated nondirectional paths were fitted in the model. A path coefficient of .49 was found between initial sound fluency and letter naming fluency. Research has found that there is a relationship between phonemic awareness and letter names, even though the directionality of the relationship is not clear (Blaiklock, 2004; Treiman &

Rodriguez, 1999). A smaller correlated path coefficient was found between phoneme segmentation fluency and letter naming fluency at .21. A correlated nondirectional path was also fitted “post hoc” between initial sound fluency and passage comprehension to improve model fit. Modifying the model to fit a nondirectional path between initial sound fluency and passage comprehension resulted in a path coefficient of .17 between the two variables.

Mediation Analysis Additional tests were conducted to test for mediation (Muthén & Muthén, 2007). The primary focus of the mediation analysis was to examine whether kindergarten DIBELS were mediated by the other early literacy and reading measures administered in first and second grade. Letter naming and initial sound fluency, phoneme segmentation, and nonsense word fluency were all tested for mediation in predicting passage comprehension. Predictors are frequently referred to in the path analysis literature as exogenous variables and will often be mediated through intervening endogenous variables (variables within the model that have

Burke et al. / Predictive Validity 221

both incoming and outgoing causal arrows) and dependents (variables at the end of the model) (Jöreskog, 1993; Muthén & Muthén, 2007). The results of the mediation tests are displayed in Table 3. Four of the paths from letter naming fluency to passage comprehension were significant. Four of the paths from initial sound fluency to passage comprehension were also significant, including the path that contained all intervening variables. All paths from phoneme segmentation and nonsense word fluency were significant.

Discussion A large body of reading literature indicates that reading is a developmental process. Identifying quality measures to monitor alphabetic mastery becomes paramount if reading disabilities are to be prevented. The purpose of this study was to use a path analytic approach to examine the interrelationships and predictiveness of early literacy indicators from kindergarten that represent sublexical fluency in skills required for alphabetic mastery. Modeled were the sublexical fluency and reading acquisition of young learners as they progressed from kindergarten to second grade. Thus, measures of sublexical fluency (i.e., DIBELS) were placed in a developmental progression to model how well they predicted reading development. DIBELS measures are distinct from other reading measures because they monitor reading acquisition by directly measuring the rapid processing of phonological and grapheme-phoneme relationships. A breakdown in the processing of phonological and grapheme-phoneme relationships is the primary cause of reading disabilities (Metsala & Ehri, 1998). Specifically, early reading difficulties are manifested primarily in an inadequate facility in word identification and related sublexical skills (Vellutino, Fletcher, Snowling, & Scanlon, 2004). If the initial development of sublexical skills does not occur, it is likely that young learners will have difficulty progressing from one reading stage to another and that mastery of the alphabetic writing system will be stunted.

Overall Model of Reading Acquisition The first research question focused on whether early literacy indicators from kindergarten DIBELS can be used to model reading acquisition, and if they can, what their predictive relationships are when ordered in a developmental progression. The results of the model fit indicated that middle-of-kindergarten

DIBELS scores are valid for predicting the more complex alphabetic skills that typify the developmental phases of reading development (Metsala & Ehri, 1998). The path model that was developed was based in a large part on Ehri’s theoretical model of reading acquisition. Ehri theorized that reading development occurs in phases (i.e., prealphabetic, partial alphabetic, fully alphabetic, consolidated alphabetic, and automatic alphabetic). Of these phases, the partial and full alphabetic phases are the most transitory yet the most important for the prevention of reading disabilities (Ehri, 2005; Ehri & McCormick, 1998). The model fit indices would indicate the overall model is a good fit of the data and that statistically the theoretical orderings are valid and represent a plausible model of reading acquisition. Given that the model is plausible, it seems reasonable to assume that initial sound fluency, phoneme segmentation fluency, letter naming fluency, and nonsense word fluency in kindergarten are valid early literacy measures that can be used to represent sublexical skills for children who are in the partial alphabetic phase of development and for predicting literacy outcomes for children as they pass to the next alphabetic phase (Ehri, 2005; Ehri & McCormick, 1998). Although the emphasis of the study was on the predictive validity of kindergarten DIBELS, the results suggest that phonetic decoding efficiency, sight word efficiency, and oral reading fluency can be used to represent children who are in the full alphabetic phase. Children in the full alphabetic stage are developing a working knowledge of the alphabetic writing system. Results also suggest that oral reading fluency could be used in the first and second grade to represent children who would be in the automatic and consolidated alphabetic phases (Ehri & McCormick, 1998).

Mediational Analysis of Kindergarten DIBELS The study’s second research question focused on how kindergarten DIBELS scores are mediated by the intervening variables in predicting passage comprehension. To further examine the predictive validity of DIBELS in kindergarten, follow-up mediational analyses were conducted, and the amount of explained variance was examined. All paths for phoneme segmentation fluency and nonsense word fluency were significant. Four of the paths from initial sound fluency and from letter naming fluency were significant, while the others approached significance. This result supports the predictive validity of the DIBELS kindergarten measures but also

222 The Journal of Special Education

emphasizes the developmental nature of reading acquisition (Cunningham & Stanovich, 1997; Ehri, 1998; Scarborough & Parker, 2003; Schatschneider et al., 2004; Shaywitz et al., 1999). Furthermore, the study adds to the current literature indicating the predictiveness of the kindergarten early literacy indicators from DIBELS in forecasting important reading outcomes (Elliott et al., 2001; Good, Simmons, et al., 2001; Hintze et al., 2003; Kaminski & Good, 1996; Rouse & Fantuzzo, 2006; Speece et al., 2003).

Limitations As with most studies, this study also had several limitations. First, the data were gathered as part of a technical assistance project. As part of the outreach effort and consistent with human participants protocols used in this project, the researchers shared the data and corresponding instructional recommendations described by Good, Simmons, et al. (2001) with the school administration and teachers. Such sharing of data could have influenced interventions developed by the school and subsequently influenced the results observed. Second, anecdotally, the school had a multisensory phonological awareness program in place at the kindergarten level and used an adapted reading recovery program at the first grade level for those students identified as at risk by teachers. Moreover, the school had several prekindergarten classes that served many of the student participants prior to their entry into kindergarten. Variables that were not examined, such as curricular effects, additional intervention, and the quality of preschool experiences, could have affected the data used in this study and could be a factor in the low correlations for initial sound fluency and phoneme segmentation fluency. For example, the preschools from which many of the participating students transitioned from had phonological awareness as part of their daily instruction. Inferences to other populations should be made with caution, and replication studies are needed that involve other schools that use programs with different curricular emphases. Finally, the present study did not formally document and analyze interrater agreement data. As described in the method section, data collection procedures were standardized and clearly followed. Training, feedback, and review sessions occurred, and carefully trained members of the research team (rather than teachers) collected the data. However, the interobserver agreement during the data collection process was not formally documented and is thus a limitation that should be considered.

Practice Implications This study documented the predictiveness of early literacy indicators from kindergarten to second grade within the context of a path analytic model. Clearly, the primary implication for practice is that brief, fluency-based indicators of early literacy can be constructed with good technical adequacy. Moreover, schools currently implementing DIBELS should be reassured by the results of this study. DIBELS provides a fairly good picture of reading acquisition and has good predictive validity from a developmental reading perspective. Two particularly strong aspects of this study are the use of nonsense word fluency in kindergarten and the use of a comprehension measure as the ultimate criterion. Nonsense word fluency is a measure of a phonological recoding. Generally, early literacy tests, especially those designed for kindergarten, have focused on phonological processing without an explicit phonological recoding or lettersound task. The results of this study indicate that nonsense word and letter-sound tasks are viable measures in kindergarten. The second particularly strong area is the inclusion of passage comprehension from the WRMT-R as the final criterion, as opposed to a strict word reading measure. Many studies examining reading acquisition have justifiably focused on word reading. However, it is important to remember that comprehension is the goal, and alphabetic mastery is a required but not ultimate outcome of reading development. Special educators should be comforted in knowing that although mediated, the emphasis placed on phonological and alphabetic mastery in kindergarten is related to improved word reading and comprehension skills.

Research Implications Several areas warrant continued exploration as researchers continue to develop sublexical fluency measures. First, researchers should continue to try to improve the ability of early literacy indicators to forecast reading outcomes. In the present study, DIBELS had reasonably good predictive validity when placed within a developmental framework. However, none of the early literacy indicators in the current study displayed the robust technical adequacy of measures used to predict reading outcomes for older children, such as oral reading fluency (Fuchs et al., 2001; Good & Jefferson, 1998). The closest measures to displaying such robust predictions were letter naming fluency and nonsense word fluency.

Burke et al. / Predictive Validity 223

Second, an area requiring further discussion by the field at large regards interpretation: How strong do correlational relationships from early literacy indicators need to be in order to be considered valid? In particular, discussion should occur regarding phonological measures such as initial sound fluency and phoneme segmentation fluency. Measures that contain orthography (e.g., letter naming) or require the mapping of phonological relationships onto print (e.g., nonsense word fluency) generally have better predictions than phonological awareness alone (Bishop, 2003; O’Conner & Jenkins, 1999; Schatschneider & Torgesen, 2004). Torgesen et al. (1994) indicated that the relationships among phonological skills in kindergarten and word reading are often not very strong despite the hypothesized causal nature of phonological processing in reading acquisition. However, even early literacy indicators such as phoneme segmentation with less robust correlational relationships can still be important and often have good predictive utility and diagnostic accuracy (e.g., see results of cut scores by Good, Simmons, et al., 2001). A third area that warrants further research regards how the field should interpret the fluency component of early literacy indicators such as DIBELS. Fluency is often viewed as an outcome of accuracy in word recognition and the rate of sublexical skills. Fluency at the phoneme and letter levels is often neglected in discussions of reading acquisition (Katzir et al., 2006). Furthermore, automaticity has been defined in some research studies by using naming speed as a proxy for the rate of retrieval of phonological codes (Torgesen, 2002; Wolf & Bowers, 1999). For example, the CTOPP (Wagner et al., 1999) uses tests of rapid object naming and rapid color naming to measure automaticity. Both object naming and color naming do not directly use phonological information or orthography to measure automaticity. DIBELS focuses on the rapid rate of retrieval on skills that are directly measured instead of using proxies of automaticity. This focus on automaticity is true of the phonological indicators (e.g., initial sound fluency, phoneme segmentation fluency) as well as the alphabetic indicators (e.g., letter naming fluency, nonsense word fluency). Future research should determine whether rapid automatic naming measures are needed as part of a battery for early literacy indicators (Wagner et al., 1997; Wolf & Bowers, 1999) or whether the direct measurement approach emphasized in a sublexical fluency battery such as DIBELS is sufficient.

Conclusion The development and validation of critical indicators of early literacy skills for children for the purpose of preventing reading failure and disability should continue to be a priority. The results of this study offer strong support for the predictive validity of DIBELS, especially when performance on these measures is considered within a developmental model of reading acquisition. This study adds to the growing research literature on sublexical fluency and the development and validation of early indicators in beginning reading.

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Wolf, M., & Bowers, P. G. (1999). The double-deficit hypothesis for the developmental dyslexias. Journal of Educational Psychology, 91, 415–438. Wolf, M., Bowers, P. G., & Biddle, K. (2000). Naming-speed processes, timing, and reading: A conceptual review. Journal of Learning Disabilities, 33, 387–407. Wolf, M., & Katzir-Cohen, T. (2001). Reading fluency and its intervention. Scientific Studies of Reading, 5, 211–238. Woodcock, R. M. (1987). Woodcock Reading Mastery Test– Revised. Circle Pines, MN: American Guidance Corporation. Mack D. Burke, PhD, is an assistant professor in the Department of Educational Psychology, Special and Bilingual Education Program, at Texas A&M University at College Station. His research interests include response to intervention and integrated models of academic and behavioral support. Shanna Hagan-Burke, PhD, is an associate professor in the Department of Educational Psychology, Special and Bilingual Education Program, at Texas A&M University at College Station. Her current interests include early literacy, the relation between academic failure and problem behavior, and the application of data-based decisions in schools. Oiman Kwok, PhD, is an assistant professor in the Department of Educational Psychology, Educational Psychology Foundations, at Texas A&M University at College Station. His current research interests include structural equation models, multilevel models, and the application of these two modeling approaches on analyzing longitudinal data. Richard Parker, PhD, is an associate professor in the Department of Educational Psychology, Special and Bilingual Education Program, at Texas A&M University at College Station. His research interests include measurement and scaling, program evaluation, and single case research methodology. He also has an active agenda for development projects in Latin America, where he has lectured and trained teachers.