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Prediction of Reading Comprehension Performance on the Florida Comprehensive Assessment Test in a Dyslexic Population

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PAGE 1

PREDICTION OF READING COMPREHENS ION PERFORMANCE ON THE FLORIDA COMPREHENSIVE ASSESSMENT TES T IN A DYSLEXIC POPULATION By WILLIAM D. WATSON A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2007 1

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2007 William D. Watson 2

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To my Mom and Dad 3

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ACKNOWLEDGMENTS I thank Shelley C. Heaton, Ph.D. and Ti m Conway, Ph.D., for their invaluable mentorship and guidance throughout this project. I also thank the faculty and staff and Einstein Montessori School for their cooper ation and assistance. Finally, I thank my parents, Jethro and Nancie; and my siblings Cody, Emma, and Re ta for their encouragement and support. 4

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TABLE OF CONTENTS page ACKNOWLEDGMENTS ...............................................................................................................4 LIST OF TABLES ...........................................................................................................................7 LIST OF FIGURES .........................................................................................................................8 ABSTRACT .....................................................................................................................................9 CHAPTER 1 INTRODUCTION................................................................................................................. .11 Dyslexia ..................................................................................................................................11 No Child Left Behind .............................................................................................................11 Florida Comprehensive Assessment Test ...............................................................................12 Model of Reading ...................................................................................................................13 Orthographic Processor ...................................................................................................13 Phonological Processor ...................................................................................................14 Semantic Processor ..........................................................................................................14 Model of Successful Reading ..........................................................................................14 Measuring Reading Comprehension .......................................................................................15 Interventions ...........................................................................................................................16 Aims of the Current Study ......................................................................................................17 2 METHODS...................................................................................................................... .......19 Participants .............................................................................................................................19 Measures .................................................................................................................................20 Orthographic ....................................................................................................................20 Phonological ....................................................................................................................21 Semantic ..........................................................................................................................21 State Mandated Reading Assessment (FCAT) ................................................................22 3 RESULTS...................................................................................................................... .........26 Descriptive Statistics and Preliminary Analyses ....................................................................26 Correlation of FCAT to Reading Processors ..........................................................................26 Predictive Variance of Reading Processo rs in FCAT Reading Comprehension ....................27 Predictive Variance of Processor Gains in FCAT Reading Comprehension Gains ...............28 4 DISCUSSION................................................................................................................... ......36 Consistency of the FCAT and Reading Processor tests..........................................................36 Correlation of FCAT wi th reading Processors .......................................................................37 5

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Predictive Variance of the Reading Proce ssors in FCAT Reading Comprehension ..............37 Predictive Variance of Processor Gains in FCAT Reading Comprehension Gains ...............40 Implications ............................................................................................................................41 Limitations and Future Directions ..........................................................................................42 LIST OF REFERENCES ...............................................................................................................43 BIOGRAPHICAL SKETCH .........................................................................................................46 6

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LIST OF TABLES Table page 2-1 Group Mean Age, Gender, SES, a nd FCAT Reading Achievement Level .......................24 2-2 Subtests Used in Composite Make-up of Processors .........................................................25 3-1 Mean FCAT and Reading Processor Scores for Total Sample (N=77) .............................30 3-2 Correlations between Reading Pr ocessor Composites for Total Sample ...........................31 3-3 Multiple Regressions of Composite Processor Scores Predicting FCAT Reading Comprehension Scores for each Grade Level ....................................................................32 3-4 Correlations Between Gains in Reading Processor Composite Scores for Total Sample................................................................................................................................30 7

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LIST OF FIGURES Figure page 3-1 Elementary Students Decomposition of Variance Accounted for by the Composite Processor Scores in FCAT Reading Comprehension Scores .............................................34 3-2 Middle School Students Decomposit ion of Variance Accounted for by the Composite Processor Scores in FCAT Reading Comprehension Scores ..........................35 8

PAGE 9

Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science PREDICTION OF READING COMPREHENS ION PERFORMANCE ON THE FLORIDA COMPREHENSIVE ASSESSMENT TES T IN A DYSLEXIC POPULATION By William D. Watson May 2007 Chair: Shelley C. Heaton Major: Psychology Developmental Dyslexia is characterized by an impaired ability to read words accurately and/or fluently and affects as many as 17% of sc hool-age children. Educat ors seek scientifically based methods of reading instruction to teach these children to read. Children with dyslexia are often unable to learn to read with standard teaching methods due to underlying weaknesses in Phonological, Orthographic and/or Semantic abil ities. Recent legislation mandates that all school-age children reach proficiency in reading. To accomplish this, many states have implemented high-stakes tests, such as the Fl orida Comprehensive Assessment Test (FCAT), that students must pass to be promoted to the ne xt grade-level. Many students with dyslexia are unable to reach the required leve l of proficiency on the FCAT and are not promoted to the next grade. The current archival study examined how reading skills of children with dyslexia influenced their performance on the Florida Co mprehensive Assessment Tests (FCAT) reading comprehension subtest. Reading abilities of child ren with developmental dyslexia (31 in Grades 3-5 and 44 in Grades 6-8) were assessed dur ing two successive school years. Computed composite scores represented Phonological, Or thographic, and Semantic processing domains. Hierarchical regressions identifi ed both unique and shared contributions of these domains to 9

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FCAT reading comprehension performance. Regr ession analyses demonstrated Semantic skills significantly predicted FCAT performance in both groups at initial assessment and in the younger group at second assessment. Phonological processing accounted for more variance only in the older group at second assessment. Semantic and Phonological skill s show varying contributions to FCAT comprehension performance. Changes in FCAT performance without changes in reading abilities may reflect variation in FCAT test construction. Study findings suggest th at a multifaceted approach to reading instruction may best prep are children with reading difficulti es for variations in the design of comprehension tests. Results are discussed in terms of interv ention, development, and future directions. 10

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CHAPTER 1 INTRODUCTION Dyslexia Developmental dyslexia is usually defined as a discrepancy between reading ability and intelligence in children receivi ng adequate reading instruction (Ramus et al., 2003). This neurobiological disorder is characterized by an imp aired ability to read words accurately and/or fluently (Fletcher et al., 1994) a nd it affects as many as 5-17% of school-aged children in the United States (Dombrowski, Kamphaus, & Re ynolds, 2004; Alexander & Slinger-Constant, 2004). As many as 40% of the entire U.S. populat ion read below grade level, yet many of these children go unnoticed in the classroom (U.S. Department of Education, 2005, Katzir 2006). Despite these high figures, some researchers assert that as little as 2% of the population is mentally incapable of learning to read (Torgese n et al., 2001). This estimate highlights the feasibility and importance of reading disorder interventions. No Child Left Behind To target reading proficiency and reading di sabilities such as dyslexia, President Bush passed the No Child Left Behind Act in January 2002. This act requires all states to make continual and substantial progress toward the goal of having 100% of third graders proficient in reading by 2013 (National Assessment of Educational Progress, 2002). To accomplish this, state governments have raised the standards for reading achievement, and school districts that fail to reach the new standards are pena lized financially. Schools are now held more accountable for the achievement of each student, not just the averag e performance of the school as a whole. This has lead states to explore scientifically based reading instruction to bring classroom reading instruction in line with the new standards as quickly as possible (NAE P, 2002). This entails 11

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understanding what abilities are involved in prof icient reading, as well as discovering the best ways to measure and improve these abilities. Florida Comprehensive Assessment Test Proficiency in reading as de scribed in the No Child Left Behind Act is defined by individual states, and Florida uses the Flor ida Comprehensive Assessment Test (FCAT) to evaluate students performance. Under a law pa ssed by the state legislature, third graders in Florida must score at or above the Level 2 benc hmark on the reading portion of this high-stakes test in order to be promoted to the fourth grad e. Students who fail to reach this benchmark are retained and must repeat the third grade. A bout 30% of all U.S. public school systems have adopted similar mandatory promotion tests (Greene & Winters, 2004). The FCAT has several features that present special challenges to many students. It was specifically created to place high demands on voca bulary and reasoning skills. According to the Florida Department of Edu cation, the FCAT demands an in-depth understanding and application of information that is not typical of most standardized tests (Lessons Learned 2002). The skills involved in reading co mprehension tests are complex. Th ey involve being able to read long passages within a time limit, understand th e vocabulary used, and then answer questions about the passages, which places special demands on reading fluency (Torgesen et al., 2004). In the Presidents Commission on Special Education emphasis was placed first on identifying children with reading difficulties, a nd then on closing the gap with their peers (NAEP 2002). This means reducing the discrepancy between a poor readers performance and the expected grade level reading skills. For str uggling readers, this catching up requires an acceleration of development. 12

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According to the new standards, the most important aspect of reading involves ability to comprehend complex text. Therefore, the curre nt study seeks to determine which reading and language skills are required for proficient performance on FCAT measures of reading comprehension, and what skills are particularly de ficient in students who fail to reach the states standards. Model of Reading Research has shown that reading comprehens ion requires both bottom-up processes for identifying words as well as top-down processes for examining the meanings and relationships among the words (Cutting and Scarborough, 2006). Bo ttom-up skills include using visual input (Orthographic aspects) as well as pronunciati on (Phonological aspects) of words to identify them. When bottom-up skills are weak, compre hension will likely suffer. The concentrated effort required to identify words leaves fewer cognitive resources to be devoted to the processing of meaning (Cutting and Scarborough, 2006). Read ers with weak top-down Semantic skills will also struggle, as the meanings of the words they read are not appr eciated (Seidenberg and McClelland, 1989). Adams proposes that profic ient reading must involve the coordinated functioning of Orthographic, Phonological, and Semantic processes (Adams, 1990). Orthographic Processor The Orthographic processor invol ves that ability to rapidly identify visually presented words. As new readers are repeatedly exposed to letters and groups of letters they become able to effortlessly identify words and the associa tions between the visual input and meaning are strengthened. The performance of the Orthographic processor depends on the rapid and accurate perception of individual letters and the familiari ty of the spelling patterns comprising the word (Adams, 1990). If children can r ecognize most of the words in a text by sight, they are likely to be fluent readers (Wagner, Torgesen, & Rashotte, 1994). 13

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Phonological Processor The Phonological processor uses the sound stru cture of oral language when learning how to decode written language (Torgesen, Wagner & Rashotte, 1994). This processor offers a system for identifying visually unfamiliar word s by sounding them out. In this way, the Phonological processor serves to facilitate the Orthographic proce ssor and provide feedback to reinforce the orthographic image of the new word for more effortless subsequent identification. Another function of the Phonologica l processor is to assist comp rehension by increasing readers memory capacity through the articulatory loop which allows verbal information to be rehearsed (Adams, 1990). A great deal of the recent research on reading disabilities has focused on phonological awareness (Katzir, 2006). The Phonological processor ha s been shown to strongly influence the rate at which children acquire impo rtant early reading ski lls (Wagner et al., 1997). To be a fluent reader a child must first acquire effective P honological processing ski lls to support accurate reading (Torgesen et al., 1994). Semantic Processor A correct understanding of the meanings of identified words is the ultimate goal of reading. The Semantic processor performs th is top-down function of determining a words meaning. As readers develop the Orthographic and Phonological processors, they become able to decode and recognize many words by sight. In this way the Semantic processor is influenced by both of the other processors, but it is not dependent on ei ther one alone (Adams, 1990). Model of Successful Reading Taken together, the Orthographic, Phonologi cal, and Semantic processors are all interconnected, and each processor can facilitate the efforts of the others. These connections ensure that all three processors are coordinated and that they ar e working on the same thing at 14

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the same time (Adams 1990). The bottom-up skills of the Orthographic and Phonological processors contribute to the top-down efforts of the Semantic processor (Torgesen, Rashotte, & Alexander, 2001). The skills involved in each le vel are built progressively on one another (Kameenui et al.,2000). As suc h, fluent and efficient reading is a complex activity that requires the parallel and interconnected operation of bottom-up and top-dow n abilities (Wolf and KatzirCohen, 2001). These skills complement and compensate for each others vulnerabilities and weaknesses in the course of reading (Katzir et al., 2006). Measuring Reading Comprehension A recent study by Cutting and Scarborough investigated the contributions of these three processors to reading comprehe nsion. This study found that th e Orthographic, Phonological and Semantic processors each made unique and shared contributions to comprehension (Cutting and Scarborough, 2006). However, the findings regardi ng the relative contribu tions of top-down vs. bottom-up skills were mixed. Complicating this question further, researchers have discovered that the contributions of the different processors depend on the char acteristics of the reading task used. Variables such as passage length, comp lexity, and vocabulary can greatly influence the role of these underlying processors These researchers determined that the underlying skills that are most influential in reading comprehension are test specific (Cutti ng and Scarborough, 2006). A test authors construct of r eading comprehension can greatly influence what underlying skills are necessary to succeed on a given test. With this in mind, if teachers and legislators want children to succeed on the FCAT and target in terventions appropriately on the relevant processors toward optimizing performance on the FCAT, a close look needs to be taken at the characteristics of the FCAT to understand which processors are the most important. 15

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Interventions Children with dyslexia can have deficits in any or all of the reading processors which impede them from acquiring proficient reading skills from the same type of instruction as their normal reading peers. However, some educator s now suppose that student s who cannot read at a proficient level at the end of third grade would benefit by simply retaking the same material again (Greene & Winters, 2004). This, however, is not necessarily the case and has little empirical support. Rather, there is evidence from several sources that typical public school interventions for children with dyslexia can most accurately be characterized as stabilizing their reading failure rather than remediating their reading skills (Schatschne ider & Torgesen, 2004) Research has shown that children with dys lexia show Phonological processing problems at the onset of reading instruc tion and rarely acquire normal skills in this area (Francis et al., 1988). For children with dyslexia to achieve proficient reading sk ills, they must receive more intensive, explicit, and systematic instruction in word-level skills than is typically provided in schools (Torgesen 2001). Studies show that the interventions that produced the most powerful effects on growth in reading pr oficiency are those that combined training in Phonological processing with explicit training in the appl ication of these skills (Lindamood et al., 1997;Torgesen et al., 1999). One study in particul ar (Iverson & Tunmer, 1993) provided specific evidence that the effectiveness of an early-inter vention program for at-ri sk children could be enhanced substantially by the addition of explic it instruction in Phonol ogical processing skills. The evidence suggests that in order to be su ccessful in narrowing the gap for children with dyslexia, interventions must contain powerfu l instruction and effective practice at the Phonological level (Torgesen et al., 1999, Rayner et al., 2001). 16

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The current study will address the issue of appropr iate intervention methods for dyslexia, by examining which of the processor abilities ha ve the greatest impact on FCAT performance. Based on the abundance of evidence provided by pr evious research it is hypothesized that the Phonological processor will play the greatest role. If this is i ndeed the case, the current study stands to inform educators how to more effec tively provide remediation for students who fail to reach proficiency on the FCAT and suggest a means of preventing deficiencies in the first place by identifying at risk children early. Aims of the Current Study The overall aim of the present study was to increase understanding of the underlying reading skills that contribute to the performance of childre n with dyslexia on the reading comprehension portion of the Florida Comprehens ive Assessment Test (FCAT). We sought to examine the relative contributions of the Orthographic, Phonological and Semantic processors to FCAT performance. Finally, we wanted to investigate which gains in processor abilities lead to the greatest gains in FCAT performance. We hypothesized that because the Orthographi c, Phonological, and Semantic processors are known to underlie reading, FCAT reading scor es will correlate with measures of these abilities in dyslexic readers. Further, based on the literature, we hypothesize d that deficits in the Phonological processor will have th e greatest impact on FCAT read ing scores. Therefore, we anticipated that children with deficiencies in this domain would exhib it the greatest overall reading deficiency as measured by the FCAT Finally, we hypothesized that gains in Phonological processor abilities woul d have the greatest influence on FCAT gains. Here again, based on previous research, we predicted that re mediation targeted at these core Phonological 17

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skills, when effective, will yiel d the greatest improvement in read ing ability as measured by the FCAT (Torgesen et al., 1999). 18

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CHAPTER 2 METHODS Participants The present study used archival data from 77 children with dyslexia, who were attending a local charter Montessori School specializing in the remediation of dyslexia. These students ranged in age from 8 to 14-years-old, represen ting grades 3 through 8. Of these students, 46 were male and 31 were female. Ethnicity repr esentation for these students was predominantly Caucasian (n=55), followed by African-American (n=18), Hispanic (n=2) and mixed ethnicity (n=2). Students were separated into two groups based on grade level (e lementary school: grades 3-5 (n=31) and middle school: grades 6-8 (n=46)). Table 2-1 presents demographic information of this sample. Data on student reading was obtained from school psychoeducational records at the Montessori school for both the 2003/2004 a nd 2004/2005 school years. Children who had complete data on all measures for both years we re used in this study. This Montessori school implements the Lindamood Phoneme Sequencin g (LiPS) program, which addresses the development of Phonological awaren ess skills to facilitate accur ate reading and spelling. The LiPS Program focuses on visual, auditory, and oral-motor feedback in reading instruction. Students were initially referred to the Montessori school fo r specialized education by parents or teachers because of observed difficult ies in reading. Prior to admittance, students reading test performances and sc hool records were evaluated to ve rify the presence of a reading disorder and ensure that their reading difficultie s were primarily language-based. Specifically, identification or confirmation of a primary langua ge-based learning disab ility (characterized by reading, spelling, written language, or comprehens ion difficulties) was established on the basis of one or more of the following composite sc ores: Woodcock Reading Mastery Test Revised 19

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Basic Skills Cluster, Reading Comprehension Cluster, or Comp rehensive Test of Phonological Processing, Phonological Awareness composite. The Montessori school does not accept childr en whose learning disability stems primarily from attention-deficit hyperactiv ity disorder (ADHD), behavior pr oblems, or any other sensory, psychiatric, or neurological conditions. However, common comorbid disorders such as ADHD did not serve as exclusionary criteria for this study. Furt her, based on students school psychoeducational records, none of the children in this sample had been diagnosed with mental retardation. Measures All study participants in the ar chival sample completed a batte ry of reading tests at the end of each school year as a part of annual re ading progress assessments at the Montessori school. A subset of measures from this batt ery was selected to comp rise the three reading processors evaluated in this study: Orthograp hic, Phonological, and Semantic. Thus, study measures consisted of scores from four subtes ts from the Comprehensive Test of Phonological Processing (CTOPP, Wagner, Torgesen, & Rasho tte, 1999), three subtes ts from the Woodcock Reading Mastery Test-Revised (WRMT-R, Woodcoc k, 1987), and the two subtests of the Test of Word Reading Efficiency (TOWRE, Torgesen, Wa gner, & Rashotte, 1999). Scores from these tests were then used to represent each of the three reading processors (Table 2-2). Orthographic At the most basic level, th e Orthographic processor depends on the speed and adequacy with which individual letters ar e perceived. Therefore, the Rapid Letter Naming and Rapid Digit Naming subtests of the CTOPP were included, where students were to name the stimuli in each subtest as quickly and accurately as possible. Each of these tasks contains 50 stimuli (letters or digits) arranged randomly in a 10 x 5 matrix. At the word level, the Word Identification subtest 20

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of the WRMT-R requires the child to identify re gular and irregular sight words within a fivesecond limit per word. This task forces students to rely heavily on the Orthographic processor, as words must be identified without context. Finally, the Sight Word Efficiency subtest of the TOWRE was included to measure how quickly and accurately students could identify sight words. This task contains a list of regular and irregular sight words of increasing level of difficulty. Children are require d to read as many words as possible within 45 seconds. Phonological Four measures of the Phonological processor we re collected from the archival data. The Elision and Blending Words subtests of the CTOPP were included to assess participants Phonological awareness. The Elision task requires th e child to say a word produced by the experimenter and then repeat the word after de leting either a syllable or a phoneme specified by the experimenter, the correct response forms a real word. The Blending task involves a series of orally presented isolated syllables or phonemes, which the child must blend together to form a word. The Word Attack subtest from the WRMT-R was include d as a measure of participants ability to sound out words (i.e., decode). This task assesses a childs ability to analyze and pronounce phonetically regular non-wor ds (e.g., frith). Lastly, the Phonemic Decoding Efficiency subtest from the TOWRE was included to measure how quickly and accurately students could utilize their decoding skills. This task contains a list of non-words of increasing level of difficulty. Children are required to r ead as many words as possible within 45 seconds. Semantic The Passage Comprehension subtest from the WRMT-R wa s included as a measure of the Semantic processor. This subtest uses a cl oze procedure that requires the subject to read sentences missing a word that is important to th e meaning of the passage. Subjects must supply a word that fits the meaning of each sentence or passage. 21

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Age based standard scores (mean of 100 and a standard deviation of 15) based on national norms were analyzed for the Woodcoc k Johnson Reading Mastery Test Revised and Test of Word Reading Efficiency while age based scaled scores (mean 10 and a standard deviation of 3) were used for the Comprehensiv e Test of Phonological Processing. To reduce the overall number of reading variable s, standard scores from all measures were collapsed into a single composite score for the given domain. For example, scaled scores for each participant from the four Phonological tasks were summed a nd divided by four, yielding a single composite Phonological score. Because the scales making up the composites were on different metrics, variables were converted to standa rd scores prior to compositing. State Mandated Reading Assessment (FCAT) All study participants in the archival sample also completed the FCAT reading comprehension test. This test consists of seve ral literary and informa tional passages followed by multiple-choice, short answer, and extended response questions. Passage length varies by grade level, with grades 3-5 averaging 450 word s, and grades 6-8 averaging 600 words. This exam is administered annually, in Februa ry or March, to all public school students in grades 3 through 11. The total amount of time allowed to complete the FCAT depends on guidelines prescribed according to students grade level. In gene ral students spend approximately ten hours over a two-week period ta king different parts of the FCAT. Many of the students at the Montessori school have Individual Education Plans (IEP) or 504 pl ans that stipulate that they receive accommodations allowing unlimited time to complete exam sections and/or individual administration to decrease distra ctions. The FCAT is administered in group format to up to 29 students. These test sections are proctored by classroom teachers who are required to remain in the testing room at all times. Students test do cuments are sent to a test-scoring contractor where 22

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multiple-choice and gridded-response answers are machine graded and performance task items are hand scored. The FCAT uses scaled scores that range from 100 to 500 for the reading test for each grade tested. FCAT scaled scores are the result of a two-step process that analyzes student responses using Item Response Theory (IRT) and uses the resulting item parameters to convert student responses to a scaled score that is comp arable across test years. The students in the current study have an averag e reading score of (220.09, sd = 68.186) which is well below the state cutoff of 300 necessary for grade promotion each year. 23

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Table 2-1. Group Mean Age, Gender, SES, and FCAT Reading Achievement Level Elementary School Middle School p -value ( N = 31) ( N = 46) Chronological Age (CA) 10.40 (.80) 12.60 (0.99) 0.166 Gender Male 20 26 Female 11 20 Ethnicity White 19 36 Black 8 10 Hispanic 2 0 Mixed 2 0 Socioeconomic Status Free Lunch 9 17 Reduced Lunch 8 5 Ineligible 14 24 FCAT Achievement Level 1 24 37 2 1 7 3 3 1 4 3 1 24

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Table 2-2. Subtests Used in Co mposite Make-up of Processors Processor Task Orthographic Rapid Digit Naming 1 Rapid Letter Naming 1 Word Identification 2 Sight Word Efficiency 3 Phonological Elision 1 Blending Words 1 Word Attack 2 Phonemic Decoding Efficiency 3 Semantic Passage Comprehension 2 1 Comprehensive Test of Phonological Processing (CTOPP) 2 Woodcock Reading Mastery Test-Revised (WRMT-R) 3 Test of Word Reading Efficiency (TOWRE) 25

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CHAPTER 3 RESULTS The current study explored the relationship be tween reading abilities and performance on the FCAT reading comprehension test in a sa mple of school age children diagnosed with dyslexia. First, these analyses examined the correl ation between composite scores of underlying reading processors and FCAT reading comprehe nsion scores. Second, the unique and shared predictive variance in FCAT performance among these composites for both the elementary and middle school age groups was examined. Finally, the unique and shared predictive variance among gains in FCAT performance for these groups was examined. Descriptive Statistics a nd Preliminary Analyses Table 3-1 provides descriptive statistics for all measures of reading for all students. Distributions of scores on each test were exam ined for skewness, kurtosis, and outliers and none were found. Students showed impaired reading on several of the measures as mean scores for WRMT-R Word ID (M = 82.99, sd = 10.144) and passage comprehension (M = 83.72, sd = 11.330) as well as the TOWRE Sight Word Ef ficiency (M = 83.48, sd = 10.837) and Phonemic Decoding Efficiency (M = 83.75, sd = 11.635) subtes ts fell more than one standard deviation below the average of the normative sample. Correlation of FCAT to Reading Processors Initial analyses focused on determining the consistency of the FCAT and the reading domain composites from year to year. Table 3-2 displays the correlations between the composite scores and the FCAT for the two time points. The composite scores displayed high test-retest correlations for all three do mains: Phonological (r = .89, p < .001), Orthographic (r = .90, p < .001), and Semantic (r = .90, p < .001). The correlation of FCAT scores was likewise significant for the two time points but was of a substantially lower magnitude (r = .66, p < .001). 26

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As predicted the FCAT correlates signifi cantly with all three domains of underlying reading ability. Table 3-2 shows that the correl ation between the reading domain composites and the FCAT for both time points. The Semantic composites for each year were significantly and positively related to the FCAT score ( r = .61 to .64, p < .001) the Phonological composite likewise correlated highly with the FCAT (r = .56 to .60, p < .001), and finally, Orthographic with a similar magnitude (r = .50 to .52, p < .001). Predictive Variance of Reading Proce ssors in FCAT Reading Comprehension To investigate both the unique and shar ed contributions of the Orthographic, Phonological, and Semantic processors to read ing comprehension, a series of hierarchical multiple regressions was conducted (summarized in Table 3-3). We conducted the analyses separately by grade groups (i.e., grades 3-5 a nd grades 6-8) in order to examine whether the unique and shared predictive variance associ ated with reading do mains differed between elementary and middle school participants. Independent samples t -tests revealed that these groups did not differ significantly on the FCAT or any measures of reading. The regressions were moderate fits (R 2 = 47-49%) and FCAT reading comprehension scores were positively related to the three proces sor composite scores. This high proportion of unexplained variance must be attr ibutable to other skills, asid e from these underlying reading processors examined in this study. Subtracting variance estimates for each individual composite from the shared variance of the model yields estimates of the variance un iquely associated with each composite. These results are summarized in Figures 3-1 and 3-2. All three composites made unique as well as shared contributions to FCAT scores for both grade groups in 2003/2004. For the elem entary group in 2004/2005, Phonological failed to 27

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contribute significantly to the pr edictive power of the model. Similarly, for the middle school group in 2004/2005, Orthographic failed to contribute significantly. As Figure 3-1 shows, in the elementary sample for the 2003/2004 school year, Semantic skills uniquely accounted for about 10% of the ex plained variance in FCAT. In contrast, only about 1% of the predictive variance in FCAT was made up of Phonologica l variance and another 1% was Orthographic variance. Similarly for the 2004/2005 school year Semantic skills dominated the variance and uniquely accounted for 13% of the variance while Orthographic skills accounted for 4%. Thus, for elementary aged students with dyslexia, those with adequate Semantic skills are best equipped to do we ll on the FCAT reading comprehension test. In the middle school group, the pattern rema ins the same for the 2003/2004 school year, with Semantic skills uniquely accounting for 11% of the variance in FCAT scores. Orthographic skills accounted for 4% of the variance and Phonological accounted for 1%. Here again Semantic is shown to be the strongest predictor of FCAT performance. However, for the 2004/2005 school year, the pattern changed as P honological skills dominated explaining 9% of the variance with Semantic now explaining just 4% and Orthographic skills not accounting for any unique variance. These dissimilar results su ggest that for these students, at this time, Phonological skills are the best predictor of FCAT performance. Predictive Variance of Processor Gains in FCAT Reading Comprehension Gains Finally, to examine how well FCAT readi ng gains can be predicted, a series of hierarchical regression analyses was conducted. Adding these composite gain scores to the model produced increases of less than 1% in the proportion of variance accounted for and thus did not significantly improve prediction. 28

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Paired t -test analyses were carried out on the reading processo r composite scores for the two years of testing. To pr otect the error rate for th e three tests an adjusted of .016 (.05/3) was used. The results suggested that the Orthographic processo r composite remained stable, whereas the Phonological pro cessor improved significantl y over the year (t = 4.17, p < .001) as did the Semantic processor (t = 2.56, p = .014). Significant gains were made by the students on the FCAT reading comprehension test as well as on several of the measures of reading. However, these correlations were much lower. Only the Semantic processor reached significance at p < .05 and then only at r = .24 (Table 3-4). When put into a regression these gain scores we re not able to explain the variance with any degree of significance. Thus, the gains that we re observed in underlying reading abilities were not useful in predicting improve ment in FCAT performance. 29

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Table 3-1. Mean FCAT and Reading Proce ssor Scores for Total Sample (N=77) 2003/2004 Test Scores (N=77) 2004/2005 Test Scores (N=77) Mean SD Mean SD FCAT scaled score Orthographic WRMT-R Word ID CTOPP Rapid Digit Naming CTOPP Rapid Letter Naming TOWRE Sight Word Efficiency Phonological WRMT-R Word Attack CTOPP Elision CTOPP Blending Words TOWRE Phonemic Decoding Efficiency Semantic WRMT-R Passage Comprehension 220.09 82.99 7.75 7.68 83.48 90.14 7.35 9.14 83.75 83.72 68.19 10.14 2.37 2.55 10.84 9.91 2.93 2.72 11.64 11.33 244.64 83.70 7.95 7.65 85.48 91.82 8.57 9.99 84.88 85.87 54.68 11.56 2.49 3.08 10.20 9.80 2.95 2.79 12.35 10.48 30

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Table 3-2. Correlations between Reading Processor Composites for Total Sample FCAT 2004 FCAT 2005 Phono 2004 Phono 2005 Ortho 2004 Ortho 2005 Sem 2004 FCAT 2005 .656 Phonological 2004 .556 .511 Phonological 2005 .557 .600 .888 Orthographic 2004 .502 .433 .549 .578 Orthographic 2005 .537 .515 .669 .725 .905 Semantic2004 .642 .636 .744 .756 .634 .646 Semantic 2005 .544 .608 .664 .731 .486 .542 .904 All correlations were significant at p < .001 31

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Table 3-3. Multiple Regressions of Composite Processor Scores Predicting FCAT Reading Comprehension Scores for each Grade Level Elementary School (N=31) Middle School (N=46) Testing Year Processor R R 2003/2004 Semantic Phonological Orthographic .535* .117 .092 .479 .555** .109 .089 .487 2004/2005 Semantic Phonological .556* -.086 Orthographic .300 .490 .275~ .470* .003 .471 ~ p < .10. p < .05. **p < .01. 32

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Table 3-4. Correlations Between Gains in Reading Processor Composite Scores for Total Sample FCAT Ortho Phono Orthographic .196 Phonological .038 .181 Semantic .242* .228 .082 p < .05. 33

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Figure 3-1. Elementary Students Decomposition of Variance Accounted for by the Composite Processor Scores in FCAT R eading Comprehension Scores 0 0.1 0.2 0.3 0.4 0.5 0.6 2003/2004 2004/2005Proportion of Variance Accounted For 0.36 0.32 0.01 0.01 0.10 0.04 0.13 Shared Variance Phonological Orthographic Semantic 34

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Figure 3-2. Middle School Students Decomposition of Variance Accounted for by the Composite Processor Scores in FCAT Reading Comprehension Scores 0 0.1 0.2 0.3 0.4 0.5 0.6 2003/2004 2004/2005Proportion of Variance Accounted For 0.36 0.34 0.01 0.01 0.09 0.11 0.04 Shared Variance Phonological Orthographic Semantic 35

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CHAPTER 4 DISCUSSION A major aim of this study was to analyze th e performance of children with dyslexia on the Florida Comprehensive Assessment Test (FCAT) We sought to analy ze the correlation of the FCAT to measures of underlying the reading abilities of the Orthograp hic, Phonological and Semantic processors. Further, we aimed to dete rmine the extent to which the underlying reading processors contribute to successf ul FCAT performance. Finally, we wanted to investigate which gains in reading processor abilities lead to the greatest gains in FCAT scores. In line with the predictions, FCAT read ing scores correlated with measures of Orthographic, Phonological, and Semantic processors demonstrating the measurement of a similar construct of reading. Ho wever, the results also suggest that the performance of these children was variable and that th e Phonological processor did not ha ve the primary role that we hypothesized. Further, the study revealed that the FCATs em phasis on the different reading processor abilities varies from year to year for each age group. Finally, due to irregular patterns of gains in FCAT reading comprehension scores and Reading Processor scores, the current study was not able to determine the best pred ictor of improvement FCAT performance. Consistency of the FCAT and Reading Processor Tests Results of the current study demonstrated that the composite scores for the reading processor measures are highly c onsistent across the two years measured in this study. The examination of the FCAT scores, on the other hand, did not yield th is level of consistency. In fact, the magnitude of the correl ation of the students scores fo r the two FCAT administrations, while statistically significant, was much lower ( r = .65). The stability of the composite scores in contrast to the relative instability of the FCAT scores suggest variab ility that can be attributed to the FCAT test properties rather than to the stud ents performances. These initial analyses suggest 36

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potential changes in the content and emphasis of th e FCAT for each year or grade level. Overall, this finding raises questions about th e psychometric properties of the FCAT. Correlation of FCAT with Reading Processors Based on prior studies, it was hypothesized that the Orthographic, Phonological, and Semantic processors would correlate highly with FCAT reading comprehension scores (Katzir et al., 2006, Wolf & Katzir-Cohen, 2001). As pred icted, the FCAT scores did correlate significantly with all th ree underlying reading processor com posites. Qualitative review of the correlation magnitudes further reve aled several important findings. Consistent with previous research, the top-down skills of the Semantic processor had the highest correlations with the reading comprehension measure for both year s of data (Cutting and Scarborough, 2006). The bottom-up skills of the Phonological and Orthogra phic processors were likewise significantly correlated with the FCAT, albeit to a lesser ex tent. This pattern of correlations has been demonstrated by previous studies (Torgeson et al., 1999) and supports the three processor model of reading. Overall, this fi nding demonstrates that the FCAT reading comprehension test measures a comparable construct to the other meas ures that have been sh own to underlie reading. Predictive Variance of the Reading Pro cessors in FCAT Reading Comprehension In the current study, the m odel of reading including th e three composites of the Orthographic, Phonological, and Semantic processor measures predicted only a modest 47% of the variance in FCAT reading comprehension perf ormance. It is not clear what other skills, aside from those that we examined, may be re sponsible for the especially high proportion of unexplained variance in the FCAT. A very s ubstantial amount of the variance between Orthographic, Phonological, and Semantic processors was shared rather th an unique when FCAT reading comprehension scores ar e predicted. Similar findings were found by other researchers 37

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yet the basis remains unclear and should be investigated further (Catts et al., 2003; Cutting & Scarborough, 2006). The findings of this study demonstrated that al l three processors made unique as well as shared contributions to FCAT scores for bot h grade groups in 2003/2004. In contrast to our hypothesis that the Phonological processor would be the primar y predictor of FCAT reading comprehension scores, the Semantic processor dominated the variance for both grade groups in this school year. Consistent with previous res earch, these results suggest that Semantic top down processes best predic t successful reading comprehension performance (Cutting and Scarborough, 2006). Thus, for students with dyslexia in the cu rrent study, those who dem onstrated mastery of Semantic abilities performed best on the FCAT. Consistent with previous research, the findings of the current study generally show that, wh ile the bottom-up skills of Orthographic and Phonological processors contri buted to reading comprehens ion, they only accounted for a minimal amount of the variance (Katzir et al., 2006). The pattern of results changed dramatic ally for the subsequent 2004/2005 FCAT administration. For the elementary group, th e Phonological processor failed to contribute significantly to the predictive power of the model. Recent studies have found similar results. Researchers have begun to speculate that although Phonological processing is an important skill for reading comprehension, its role may not always be detectable due to its shared variance with other processing skills such as Or thographic (Katzir et al., 2006). For the middle school group in 2004/2005, th e profile changed its emphasis from Semantic to Phonological. The Orthographic processor failed to contribute significantly to the model. The Phonological processor, rather than the Semantic, dominated the variance for these students, suggesting that for this year middle school students adept at Phonological skills were 38

PAGE 39

best equipped to succeed on the FCAT reading co mprehension test. The patterns of prediction for the reading processors changed for both age groups for the two FCAT administrations but in different ways. This is not wholly unusual, as some previous studies have similarly found evidence of age differences in reading comprehension prediction (Catts et al., 2003, Francis et al., 2005). The discrepancy in relevant processors be tween the two administ rations of the FCAT cannot be definitively explained by the current st udy. However there are several possibilities of why this may be the case. One possible explanati on for this change is th at the alternate version of FCAT administered in the 2004/2005 school year was not equitable to the previous years version. This explanation seems possible given the moderate correlation between the two versions. Given our lack of a comparison group this discrepancy could be sample specific; therefore, this explanation c ould only be confirmed or disconf irmed with the inclusion of a comparison group of non-dyslexic children. A second explanation for this discrepancy could be that the specific intervention these students received was effective to the extent that the targeted Phonol ogical processor became more skilled and therefore had a greater infl uence on reading compre hension. Again, this possibility could only be confirme d or rejected with the addition of a comparison group. In this instance, a group of dyslexic children not receiving the Phonological in tervention would be needed to tease apart the discrepancy. These findings suggest that versions of the FCAT for each school year may make differential demands on Phonological and Semantic processing abilities. These findings are similar to prior reading comprehension prediction research, where investigators showed that the relative contributions of these ab ilities affect performance on some reading comprehension tests 39

PAGE 40

more than on others (Cutting and Scarborough, 2 006). Varying forms of the FCAT could mean that educators working with childr en with dyslexia are trying to h it a moving target. If the skills emphasis of the FCAT continually changes, interventions cannot be catered to specifically target the necessary reading processors. Predictive Variance of Processor Gains in FCAT Reading Comprehension Gains The examination of gains on these measures assesses the progress these children make toward narrowing the gap with normal readers. Th e standard scores on the reading measures and the FCAT scale scores are calculated by referenc ing an age or grade matched normative sample. Therefore, students making expected yearly academ ic progress should have the same scores each year. In order to see gains in these scores students must go beyond the expected yearly progress and learn at an accelerated rate; a task that is difficult for normal readers let alone a students with dyslexia. Although repeated measures analyses revealed significant gains for these students on average on both reading processor composite scores as well as FCAT reading comprehension scores, they did not correlate hi ghly. Therefore, it was impossibl e to test a model to predict FCAT gains. As shown in the previous analyses the profiles of FCAT prediction were different for the two test administrations. It is likely that these discrepancies led to inconsistent patterns as gains scores were calculated. Therefore, althoug h specific processors were targeted in this sample and gains were made, there is no way of determining whether this progress helped students perform better on the FCAT. Research ers and educators have created effective strategies that can be empl oyed to improve the Phonological, Orthographic, or Semantic processors. Again, not knowing which gains will have the most impact FCAT reading comprehension scores makes it impossible to de termine which processors should be targeted. 40

PAGE 41

Implications Overall, the results of this study show that generally Semantic, top-down abilities are most critical for struggling readers to succeed on the FCAT. However, as the profile of reading contributions suggested Phonological abilities are close behind and therefor e cannot be neglected for this population. This knowledge can be used to implement prevention screenings and in further developing effective inte rventions. Examining a student s profile on these or similar measures of underlying reading abil ities can identify those who need extra help before they enter the high stakes world of the FCAT. This can lead to interventions that are created to specifically target their needs. However, based on the differing profiles among middle school stude nts, there is a possibility that the alternate vers ion administered this year wasnt successful in maintaining the emphasis. The results raise a concern that th e FCAT reading comprehension test does not necessarily tap the same array of cognitive processes each year. Further, it may be influenced to different degrees by particular skills that can affect comprehension. The effects of changes in test format and passage characteristics across ve rsions of the FCAT need to be examined and disentangled. There are also some important practical imp lications of the findings. First, whether a child will be retained or promoted as determin ed by the FCAT reading comprehension test may depend on the test given that particular year. Furthermore, different test versions may provide discrepant information about wh ich processors need to be ta rgeted for remediation. These findings are disconcerting consid ering the FCAT was designed to improve reading instruction and ensure that no child is left behind. 41

PAGE 42

Limitations and Future Directions The implications of our findings are tempered by the limitations of the current study. The archival data limited the information available about the students. In formation regarding IQ, medications, comorbidities, and socioeconomic stat us of the participants was not available for this study. It would be important to include this data in any future studies, as it is possible that these factors affected the results. Further, it was impossible to confirm the diagnosis of dyslexia for these students. Thus, the sample may have been relatively heterogeneous. Additionally, without compar ison groups it is impossible to determine whether the discrepancy in FCAT profiles was due to a characte ristic of the current st udy participants or of the test itself. In order to better tease th is apart, we intend to expa nd the current study to include comparison groups. These include non-dyslexic ageand grade-matched peers, as well as a group of children with dyslexia who do not rece ive the Phonological intervention. The addition of these groups will make it possible to determine whether differences in profiles can be attributed to the sample or to the FCAT itself. Further, the current study was limited by the te st measures available. The expanded study will include a variety of other measures of rele vant cognitive constructs (i.e., working memory, IQ, executive functioning, etc.) in addition to reading processors to get a more comprehensive profile of the reading and related abilities th at contribute to comp rehension on the FCAT. 42

PAGE 43

LIST OF REFERENCES Adams, M.J., (1990). Beginning to Read: Thinki ng and Learning about Print. Cambridge, MA: MIT Press. Alexander, A.W., & Slinger-Constant, A. (2004). Current Status of Treatments for Dyslexia. Journal of Child Neurology 19, 744-758. Catts, H. W., Fey, M. E., Zhang, X., & Tomblin, J.B. (1999). Language Basis of Reading and Reading Disabilities: Evidence fr om a Longitudinal Investigation. Scientific Studies of Reading, 3, 331-361. Cutting, L.E., & Scarborough, H.S., (2006). Predicti on of Reading Comprehension: Relative Contributions of Word Recognition, Language Proficiency, and Other Cognitive Skills Can Depend on How Comprehension Is Measured. Scientific Studies of Reading 10, 277299. Dombrowski, S.C., Kamphaus, R.W., & Reynolds, C.R. (2004). After the Demise of the Discrepancy: Proposed Learning Di sabilities Diagnos tic Criteria. Professional Psychology: Research and Practice 35, 364-372. Fletcher, J.M., Shaywitz, S.E., Shankweiler, D.P., Katz, L., Liberman, I.Y., Stuebing, K.K., Francis, D.J., Fowler, A.E., & Shaywitz, B.A., (1994). Cognitive Profiles of Reading Disability: Comparisons of Discrepanc y and Low Achievement Definitions. Journal of Educational Psychology, 86, 6-23. Francis, D.J., Shaywitz, S.E., Stuebing, K.K., Shaywitz, B.A., & Fletcher, J.M. (1996). Developmental Lag Versus Deficit Models of Reading Disability: A Longitudinal, Individual Growth Curves Analysis. Journal of Educational Psychology 88, 3-17. Greene, J.P., & Winters, M.A. (2004). An Ev aluation of Floridas Program to End Social Promotion. Education Working Paper, 7, 1-16. Heilman, K., Alexander, A.W., & Voeller, K.K. S. (1996). A Motor Articulatory Feedback Hypothesis. Annals of Neurology, 39, 407-413. Iversen, S., & Tunmer, W. (1993). Phonological processing skills and th e Reading Recovery Program. Journal of Educational Psychology 85, 112-126. Kameenui, E.J., Simmons, D.C ., Good, R.H., & Harn, B.A. (20010. The Use of Fluency-based Measures in Early Identification and Evaluation of Intervention Efficacy in Schools. In M. Wolf (Ed.), Time, Fluency, and Dyslexia (pp. 307-333). Parton, MD: York Press. Katzir, Tami, Kim, Y., Wolf, M., O'Brien, B ., Kennedy, B., Lovett, M., Morris, R., (2006). Reading Fluency: The Whole Is More than the Parts. Annals of Dyslexia 56, 51-82. 43

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Lessons Learned-FCAT, Sunshine State Standa rds and Instructional Implications (2002) Florida Department of Education Lindamood, P., Bell, N., & Lindamood, P. ( 1997). Sensory-Cognitive Factors in the Controversy over Reading Instruction, Journal of Developmental and Learning Disorders 1, 1-37. Morris, R.D., Stuebing, K.K.S., Fletcher, J.M., Shaywitz, S.E., Lyon, G.R., Shankweiler, D.P., Katz, L., Francis, D.J., & Shaywitz, B.A. (1998). Subtypes of Reading Disability: Variability Around a Phonological Core. Journal of Educational Psychology 90, 347-373. National Assessment of Edu cational Progress, (2002). National Center for Education Statistics Ramus, F., Rosen, S., Dakin, S.C., Day, B.L., Castellote, J.M., White, S., & Frith, U. (2003). Theories of developmental dyslexia: insights from a multiple case study of dyslexic adults. Brain 126, 841-865. Rayner, K., Foorman, B.R., Perfetti, C.A., Pesetsky, D., & Seidenberg, M.S. (2001). How Psychological Science Informs the Teaching of Reading. Psychological Science in the Public Interest, 2, 31-72. Schatschneider, C., & Torgeson, J.K. (2004). Us ing Our Current Understanding of Dyslexia to Support Early Identification and Intervention. Journal of Child Neurology, 19, 759-765. Seidenberg, M.S., & McClelland, J.L. (1989). A Distributed, Developmental Model of Word Recognition and Naming. Psychological Review, 34, 33-58. Smart, D., Sanson, A., & Prior, M. (1996). Connections Between Reading Disability and Behavior Problems: Testing Tem poral and Causal Hypothesis. Journal of Abnormal Child Psychology, 24, 363-383. Sundheim, S.T.P.V., & Voeller, K.K.S. (2004). Ps ychiatric Implications of Language Disorders and Learning Disabilities. Journal of Child Neurology 19, 814-826. Torgesen, J.A., Alexander, A.W., Wagner, R.K., Rashotte, C.A, Voeller, K.K.S., & Conway, T. (2001). Intensive Remedial Instruction for Children with Severe Reading Disabilities: Immediate and Long-term Outcomes Two Instructional Approaches. Journal of Learning Disabilities 34, 33-58. Torgesen, J.A., Wagner, R.K., & Rashotte, C.A. (1994). Longitudinal Studies of Phonological Processing and Reading. Journal of Reading Disabilities 27, 276-286. Torgesen, J.A., Wagner, R.K., Rashotte, C.A., Rose, E., Lindamood, P., Conway, T., & Garvan, C. (1999). Preventing Reading Failure in Young Children With Phonological Processing Disabilites: Group and Individua l Responses to Instruction. Journal of Educational Psycho logy, 91, 579-593. 44

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Wagner, R.K., Torgesen, J.A., & Rashotte, C.A. (1994). Development of Reading-Realated Phonolgical Processing Abilities: New Evidence of Bidirectional Causality From a Latent Variable Longitudinal Study. Developmental Psychology 30, 73-87. Wagner, R.K., Torgesen, J.A., & Rashotte, C.A. (1999). Comprehensive Test of Phonological Processing (CTOPP). Austin, TX: Pro-Ed. Wolf, M., & Katzir-Cohen, T., (2001). Reading Fluency and its Intervention, Scientific Studies of Reading 5, 211-239. Woodcock, R.W., & Johnson, M.B. (1989). Woodcock-Johnson Reading Mastery Test Revised. Circle Pines, MN: American Guidance Service. 45

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BIOGRAPHICAL SKETCH William Watson was born and raised in Ogden, UT, the third of four children of Jethro and Nancie Watson. He graduated from the University of Utah (Salt Lake City), with bachelors degrees in psychology and German with a minor in chemistry. William entered the Clinical and Health Psychology program at the University of Florida (UF) in 2005. Du ring his study at UF, he worked in a pediatric neuropsychology lab. Williams mentor is Shelley C. Heaton, Ph.D. Williams interests include childhood disorder s such as dyslexia and attention deficit hyperactivity disorder. He plans to work in a c linical research setting after earning his doctoral degree in clinical psychology. 46


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Title: Prediction of Reading Comprehension Performance on the Florida Comprehensive Assessment Test in a Dyslexic Population
Physical Description: Mixed Material
Copyright Date: 2008

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PREDICTION OF READING COMPREHENSION PERFORMANCE ON THE FLORIDA
COMPREHENSIVE ASSESSMENT TEST IN A DYSLEXIC POPULATION


















By

WILLIAM D. WATSON


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2007



































0 2007

William D. Watson



































To my Mom and Dad









ACKNOWLEDGMENTS

I thank Shelley C. Heaton, Ph.D. and Tim Conway, Ph.D., for their invaluable

mentorship and guidance throughout this project. I also thank the faculty and staff and Einstein

Montessori School for their cooperation and assistance. Finally, I thank my parents, Jethro and

Nancie; and my siblings Cody, Emma, and Reta for their encouragement and support.










TABLE OF CONTENTS

page

ACKNOW LEDGM ENTS ....................................................... ... .... ............. 4

L IST O F T A B L E S .......... .... ......................................................................................... 7

LIST OF FIGURES .................................. .. ..... ..... ................. .8

A B S T R A C T ................................ .................. .......................... ................ .. 9

CHAPTER

1 INTRODUCTION ............... ................. ........... ................................. 11

D y slex ia ................... ...................1...................1..........
N o Child Left Behind ............................ .... .................................. .. 11
Florida Com prehensive A ssessm ent Test................................................................... .... ..12
M odel of R leading ............................................................... ..... ..... ......... 13
O rthographic P processor .................................................................. ........................13
Phonological Processor .......................................... .. .. .... ........ ......... 14
Sem antic P rocessor.......... .................................................................... ......... ........ 14
M odel of Successful R leading .................................................... ................................ 14
Measuring Reading Comprehension...................... ....... ............................ 15
In terv e n tio n s .............................................................................. 16
A im s of the C current Study .............................................................................. ..................17

2 M E TH O D S ....................... ..........................19..........

P articip an ts .........................................................................19
M e a su re s ....................... ................... ... ............................................................................ 2 0
O rthographic .............................................. 20
P h o n o lo g ical ................................................................2 1
S e m a n tic ......................................................................................2 1
State Mandated Reading Assessment (FCAT) ...... ............................................22

3 R E SU L T S .............. ... ................................................................26

Descriptive Statistics and Preliminary Analyses .......................................26
Correlation of FCAT to Reading Processors ............................................... ..................26
Predictive Variance of Reading Processors in FCAT Reading Comprehension ....................27
Predictive Variance of Processor Gains in FCAT Reading Comprehension Gains ............28

4 D IS C U S S IO N ........................................................................................................3 6

Consistency of the FCAT and Reading Processor tests ................................................... 36
Correlation of FCAT with reading Processors ....................................................... 37









Predictive Variance of the Reading Processors in FCAT Reading Comprehension .............37
Predictive Variance of Processor Gains in FCAT Reading Comprehension Gains ..............40
Im p location s .............................................................. ................ 4 1
Lim stations and Future D directions .......................................................... ............... 42

L IST O F R E F E R E N C E S .............................................................................. ...........................43

B IO G R A PH IC A L SK E T C H .............................................................................. .....................46









LIST OF TABLES


Table page

2-1 Group Mean Age, Gender, SES, and FCAT Reading Achievement Level.....................24

2-2 Subtests Used in Composite Make-up of Processors .....................................................25

3-1 Mean FCAT and Reading Processor Scores for Total Sample (N=77).............................30

3-2 Correlations between Reading Processor Composites for Total Sample.........................31

3-3 Multiple Regressions of Composite Processor Scores Predicting FCAT Reading
Com prehension Scores for each Grade Level............................................ ................... 32

3-4 Correlations Between Gains in Reading Processor Composite Scores for Total
S am p le ........................................................................................... 3 0









LIST OF FIGURES


Figure page

3-1 Elementary Students Decomposition of Variance Accounted for by the Composite
Processor Scores in FCAT Reading Comprehension Scores...................................34

3-2 Middle School Students Decomposition of Variance Accounted for by the
Composite Processor Scores in FCAT Reading Comprehension Scores ........................35









Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

PREDICTION OF READING COMPREHENSION PERFORMANCE ON THE FLORIDA
COMPREHENSIVE ASSESSMENT TEST IN A DYSLEXIC POPULATION

By

William D. Watson

May 2007

Chair: Shelley C. Heaton
Major: Psychology

Developmental Dyslexia is characterized by an impaired ability to read words accurately

and/or fluently and affects as many as 17% of school-age children. Educators seek scientifically

based methods of reading instruction to teach these children to read. Children with dyslexia are

often unable to learn to read with standard teaching methods due to underlying weaknesses in

Phonological, Orthographic and/or Semantic abilities. Recent legislation mandates that all

school-age children reach proficiency in reading. To accomplish this, many states have

implemented high-stakes tests, such as the Florida Comprehensive Assessment Test (FCAT),

that students must pass to be promoted to the next grade-level. Many students with dyslexia are

unable to reach the required level of proficiency on the FCAT and are not promoted to the next

grade.

The current archival study examined how reading skills of children with dyslexia

influenced their performance on the Florida Comprehensive Assessment Test's (FCAT) reading

comprehension subtest. Reading abilities of children with developmental dyslexia (31 in Grades

3-5 and 44 in Grades 6-8) were assessed during two successive school years. Computed

composite scores represented Phonological, Orthographic, and Semantic processing domains.

Hierarchical regressions identified both unique and shared contributions of these domains to









FCAT reading comprehension performance. Regression analyses demonstrated Semantic skills

significantly predicted FCAT performance in both groups at initial assessment and in the

younger group at second assessment. Phonological processing accounted for more variance only

in the older group at second assessment.

Semantic and Phonological skills show varying contributions to FCAT comprehension

performance. Changes in FCAT performance without changes in reading abilities may reflect

variation in FCAT test construction. Study findings suggest that a multifaceted approach to

reading instruction may best prepare children with reading difficulties for variations in the design

of comprehension tests. Results are discussed in terms of intervention, development, and future

directions.









CHAPTER 1
INTRODUCTION

Dyslexia

Developmental dyslexia is usually defined as a discrepancy between reading ability and

intelligence in children receiving adequate reading instruction (Ramus et al., 2003). This

neurobiological disorder is characterized by an impaired ability to read words accurately and/or

fluently (Fletcher et al., 1994) and it affects as many as 5-17% of school-aged children in the

United States (Dombrowski, Kamphaus, & Reynolds, 2004; Alexander & Slinger-Constant,

2004). As many as 40% of the entire U.S. population read below grade level, yet many of these

children go unnoticed in the classroom (U.S. Department of Education, 2005, Katzir 2006).

Despite these high figures, some researchers assert that as little as 2% of the population is

mentally incapable of learning to read (Torgesen et al., 2001). This estimate highlights the

feasibility and importance of reading disorder interventions.

No Child Left Behind

To target reading proficiency and reading disabilities such as dyslexia, President Bush

passed the "No Child Left Behind" Act in January 2002. This act requires all states to make

continual and substantial progress toward the goal of having 100% of third graders proficient in

reading by 2013 (National Assessment of Educational Progress, 2002). To accomplish this, state

governments have raised the standards for reading achievement, and school districts that fail to

reach the new standards are penalized financially. Schools are now held more accountable for

the achievement of each student, not just the average performance of the school as a whole. This

has lead states to explore "scientifically based reading instruction" to bring classroom reading

instruction in line with the new standards as quickly as possible (NAEP, 2002). This entails









understanding what abilities are involved in proficient reading, as well as discovering the best

ways to measure and improve these abilities.

Florida Comprehensive Assessment Test

Proficiency in reading as described in the "No Child Left Behind" Act is defined by

individual states, and Florida uses the Florida Comprehensive Assessment Test (FCAT) to

evaluate students' performance. Under a law passed by the state legislature, third graders in

Florida must score at or above the 'Level 2 benchmark' on the reading portion of this high-stakes

test in order to be promoted to the fourth grade. Students who fail to reach this benchmark are

retained and must repeat the third grade. About 30% of all U.S. public school systems have

adopted similar mandatory promotion tests (Greene & Winters, 2004).

The FCAT has several features that present special challenges to many students. It was

specifically created to place high demands on vocabulary and reasoning skills. According to the

Florida Department of Education, the FCAT "demands an in-depth understanding and

application of information that is not typical of most standardized tests" (Lessons Learned 2002).

The skills involved in reading comprehension tests are complex. They involve being able to read

long passages within a time limit, understand the vocabulary used, and then answer questions

about the passages, which places special demands on reading fluency (Torgesen et al., 2004).

In the President's Commission on Special Education emphasis was placed first on

identifying children with reading difficulties, and then on closing the gap with their peers (NAEP

2002). This means reducing the discrepancy between a poor reader's performance and the

expected grade level reading skills. For struggling readers, this catching up requires an

acceleration of development.









According to the new standards, the most important aspect of reading involves ability to

comprehend complex text. Therefore, the current study seeks to determine which reading and

language skills are required for proficient performance on FCAT measures of reading

comprehension, and what skills are particularly deficient in students who fail to reach the state's

standards.

Model of Reading

Research has shown that reading comprehension requires both 'bottom-up processes' for

identifying words as well as 'top-down processes' for examining the meanings and relationships

among the words (Cutting and Scarborough, 2006). Bottom-up skills include using visual input

(Orthographic aspects) as well as pronunciation (Phonological aspects) of words to identify

them. When bottom-up skills are weak, comprehension will likely suffer. The concentrated

effort required to identify words leaves fewer cognitive resources to be devoted to the processing

of meaning (Cutting and Scarborough, 2006). Readers with weak top-down Semantic skills will

also struggle, as the meanings of the words they read are not appreciated (Seidenberg and

McClelland, 1989). Adams proposes that proficient reading must involve the coordinated

functioning of Orthographic, Phonological, and Semantic processes (Adams, 1990).

Orthographic Processor

The Orthographic processor involves that ability to rapidly identify visually presented

words. As new readers are repeatedly exposed to letters and groups of letters they become able

to effortlessly identify words and the associations between the visual input and meaning are

strengthened. The performance of the Orthographic processor depends on the rapid and accurate

perception of individual letters and the familiarity of the spelling patterns comprising the word

(Adams, 1990). If children can recognize most of the words in a text by sight, they are likely to

be fluent readers (Wagner, Torgesen, & Rashotte, 1994).









Phonological Processor

The Phonological processor uses the sound structure of oral language when learning how

to decode written language (Torgesen, Wagner & Rashotte, 1994). This processor offers a

system for identifying visually unfamiliar words by sounding them out. In this way, the

Phonological processor serves to facilitate the Orthographic processor and provide feedback to

reinforce the orthographic image of the new word for more effortless subsequent identification.

Another function of the Phonological processor is to assist comprehension by increasing reader's

memory capacity through the articulatoryy loop" which allows verbal information to be

rehearsed (Adams, 1990).

A great deal of the recent research on reading disabilities has focused on phonological

awareness (Katzir, 2006). The Phonological processor has been shown to strongly influence the

rate at which children acquire important early reading skills (Wagner et al., 1997). To be a fluent

reader a child must first acquire effective Phonological processing skills to support accurate

reading (Torgesen et al., 1994).

Semantic Processor

A correct understanding of the meanings of identified words is the ultimate goal of

reading. The Semantic processor performs this top-down function of determining a word's

meaning. As readers develop the Orthographic and Phonological processors, they become able to

decode and recognize many words by sight. In this way the Semantic processor is influenced by

both of the other processors, but it is not dependent on either one alone (Adams, 1990).

Model of Successful Reading

Taken together, the Orthographic, Phonological, and Semantic processors are all

interconnected, and each processor can facilitate the efforts of the others. These connections

ensure that all three processors are coordinated and that they are working on the same thing at









the same time (Adams 1990). The bottom-up skills of the Orthographic and Phonological

processors contribute to the top-down efforts of the Semantic processor (Torgesen, Rashotte, &

Alexander, 2001). The skills involved in each level are built progressively on one another

(Kame'enui et al.,2000). As such, fluent and efficient reading is a complex activity that requires

the parallel and interconnected operation of bottom-up and top-down abilities (Wolf and Katzir-

Cohen, 2001). These skills complement and compensate for each other's vulnerabilities and

weaknesses in the course of reading (Katzir et al., 2006).

Measuring Reading Comprehension

A recent study by Cutting and Scarborough investigated the contributions of these three

processors to reading comprehension. This study found that the Orthographic, Phonological and

Semantic processors each made unique and shared contributions to comprehension (Cutting and

Scarborough, 2006). However, the findings regarding the relative contributions of top-down vs.

bottom-up skills were mixed. Complicating this question further, researchers have discovered

that the contributions of the different processors depend on the characteristics of the reading task

used. Variables such as passage length, complexity, and vocabulary can greatly influence the

role of these underlying processors. These researchers determined that the underlying skills that

are most influential in reading comprehension are test specific (Cutting and Scarborough, 2006).

A test author's construct of reading comprehension can greatly influence what underlying skills

are necessary to succeed on a given test. With this in mind, if teachers and legislators want

children to succeed on the FCAT and target interventions appropriately on the relevant

processors toward optimizing performance on the FCAT, a close look needs to be taken at the

characteristics of the FCAT to understand which processors are the most important.









Interventions

Children with dyslexia can have deficits in any or all of the reading processors which

impede them from acquiring proficient reading skills from the same type of instruction as their

normal reading peers. However, some educators now suppose that students who cannot read at a

proficient level at the end of third grade would benefit by simply retaking the same material

again (Greene & Winters, 2004). This, however, is not necessarily the case and has little

empirical support. Rather, there is evidence from several sources that typical public school

interventions for children with dyslexia can most accurately be characterized as stabilizing their

reading failure rather than remediating their reading skills (Schatschneider & Torgesen, 2004)

Research has shown that children with dyslexia show Phonological processing problems

at the onset of reading instruction and rarely acquire normal skills in this area (Francis et al.,

1988). For children with dyslexia to achieve proficient reading skills, they must receive more

intensive, explicit, and systematic instruction in word-level skills than is typically provided in

schools (Torgesen 2001). Studies show that the interventions that produced the most powerful

effects on growth in reading proficiency are those that combined training in Phonological

processing with explicit training in the application of these skills (Lindamood et al.,

1997;Torgesen et al., 1999). One study in particular (Iverson & Tunmer, 1993) provided specific

evidence that the effectiveness of an early-intervention program for 'at-risk children' could be

enhanced substantially by the addition of explicit instruction in Phonological processing skills.

The evidence suggests that in order to be successful in narrowing the gap for children with

dyslexia, interventions must contain powerful instruction and effective practice at the

Phonological level (Torgesen et al., 1999, Rayner et al., 2001).









The current study will address the issue of appropriate intervention methods for dyslexia,

by examining which of the processor abilities have the greatest impact on FCAT performance.

Based on the abundance of evidence provided by previous research it is hypothesized that the

Phonological processor will play the greatest role. If this is indeed the case, the current study

stands to inform educators how to more effectively provide remediation for students who fail to

reach proficiency on the FCAT and suggest a means of preventing deficiencies in the first place

by identifying at risk children early.

Aims of the Current Study

The overall aim of the present study was to increase understanding of the underlying

reading skills that contribute to the performance of children with dyslexia on the reading

comprehension portion of the Florida Comprehensive Assessment Test (FCAT). We sought to

examine the relative contributions of the Orthographic, Phonological and Semantic processors to

FCAT performance. Finally, we wanted to investigate which gains in processor abilities lead to

the greatest gains in FCAT performance.

We hypothesized that because the Orthographic, Phonological, and Semantic processors

are known to underlie reading, FCAT reading scores will correlate with measures of these

abilities in dyslexic readers. Further, based on the literature, we hypothesized that deficits in the

Phonological processor will have the greatest impact on FCAT reading scores. Therefore, we

anticipated that children with deficiencies in this domain would exhibit the greatest overall

reading deficiency as measured by the FCAT. Finally, we hypothesized that gains in

Phonological processor abilities would have the greatest influence on FCAT gains. Here again,

based on previous research, we predicted that remediation targeted at these core Phonological









skills, when effective, will yield the greatest improvement in reading ability as measured by the

FCAT (Torgesen et al., 1999).









CHAPTER 2
METHODS

Participants

The present study used archival data from 77 children with dyslexia, who were attending

a local charter Montessori School specializing in the remediation of dyslexia. These students

ranged in age from 8 to 14-years-old, representing grades 3 through 8. Of these students, 46

were male and 31 were female. Ethnicity representation for these students was predominantly

Caucasian (n=55), followed by African-American (n=18), Hispanic (n=2) and mixed ethnicity

(n=2). Students were separated into two groups based on grade level (elementary school: grades

3-5 (n=31) and middle school: grades 6-8 (n=46)). Table 2-1 presents demographic information

of this sample.

Data on student reading was obtained from school psychoeducational records at the

Montessori school for both the 2003/2004 and 2004/2005 school years. Children who had

complete data on all measures for both years were used in this study. This Montessori school

implements the Lindamood Phoneme Sequencing (LiPS) program, which addresses the

development of Phonological awareness skills to facilitate accurate reading and spelling. The

LiPS Program focuses on visual, auditory, and oral-motor feedback in reading instruction.

Students were initially referred to the Montessori school for specialized education by

parents or teachers because of observed difficulties in reading. Prior to admittance, students'

reading test performances and school records were evaluated to verify the presence of a reading

disorder and ensure that their reading difficulties were primarily language-based. Specifically,

identification or confirmation of a primary language-based learning disability (characterized by

reading, spelling, written language, or comprehension difficulties) was established on the basis

of one or more of the following composite scores: Woodcock Reading Mastery Test Revised









Basic Skills Cluster, Reading Comprehension Cluster, or Comprehensive Test of Phonological

Processing, Phonological Awareness composite.

The Montessori school does not accept children whose learning disability stems primarily

from attention-deficit hyperactivity disorder (ADHD), behavior problems, or any other sensory,

psychiatric, or neurological conditions. However, common comorbid disorders such as ADHD

did not serve as exclusionary criteria for this study. Further, based on students' school

psychoeducational records, none of the children in this sample had been diagnosed with mental

retardation.

Measures

All study participants in the archival sample completed a battery of reading tests at the

end of each school year as a part of annual reading progress assessments at the Montessori

school. A subset of measures from this battery was selected to comprise the three reading

processors evaluated in this study: Orthographic, Phonological, and Semantic. Thus, study

measures consisted of scores from four subtests from the Comprehensive Test of Phonological

Processing (CTOPP, Wagner, Torgesen, & Rashotte, 1999), three subtests from the Woodcock

Reading Mastery Test-Revised (WRMT-R, Woodcock, 1987), and the two subtests of the Test of

Word Reading Efficiency (TOWRE, Torgesen, Wagner, & Rashotte, 1999). Scores from these

tests were then used to represent each of the three reading processors (Table 2-2).

Orthographic

At the most basic level, the Orthographic processor depends on the speed and adequacy

with which individual letters are perceived. Therefore, the Rapid Letter Naming and Rapid Digit

Naming subtests of the CTOPP were included, where students were to name the stimuli in each

subtest as quickly and accurately as possible. Each of these tasks contains 50 stimuli (letters or

digits) arranged randomly in a 10 x 5 matrix. At the word level, the Word identification subtest









of the WRMT-R requires the child to identify regular and irregular sight words within a five-

second limit per word. This task forces students to rely heavily on the Orthographic processor, as

words must be identified without context. Finally, the Sight Word Efficiency subtest of the

TOWRE was included to measure how quickly and accurately students could identify sight

words. This task contains a list of regular and irregular sight words of increasing level of

difficulty. Children are required to read as many words as possible within 45 seconds.

Phonological

Four measures of the Phonological processor were collected from the archival data. The

Elision and Blending Words subtests of the CTOPP were included to assess participants'

Phonological awareness. The Elision task requires the child to say a word produced by the

experimenter and then repeat the word after deleting either a syllable or a phoneme specified by

the experimenter, the correct response forms a real word. The Blending task involves a series of

orally presented isolated syllables or phonemes, which the child must blend together to form a

word. The WordAttack subtest from the WRMT-R was included as a measure of participants'

ability to sound out words (i.e., decode). This task assesses a child's ability to analyze and

pronounce phonetically regular non-words (e.g., frith). Lastly, the Phonemic Decoding

Efficiency subtest from the TOWRE was included to measure how quickly and accurately

students could utilize their decoding skills. This task contains a list of non-words of increasing

level of difficulty. Children are required to read as many words as possible within 45 seconds.

Semantic

The Passage Comprehension subtest from the WRMT-R was included as a measure of

the Semantic processor. This subtest uses a cloze procedure that requires the subject to read

sentences missing a word that is important to the meaning of the passage. Subjects must supply a

word that fits the meaning of each sentence or passage.









Age based standard scores (mean of 100 and a standard deviation of 15) based on

national norms were analyzed for the Woodcock Johnson Reading Mastery Test Revised and

Test of Word Reading Efficiency while age based scaled scores (mean 10 and a standard

deviation of 3) were used for the Comprehensive Test of Phonological Processing. To reduce the

overall number of reading variables, standard scores from all measures were collapsed into a

single composite score for the given domain. For example, scaled scores for each participant

from the four Phonological tasks were summed and divided by four, yielding a single composite

Phonological score. Because the scales making up the composites were on different metrics,

variables were converted to standard scores prior to compositing.

State Mandated Reading Assessment (FCAT)

All study participants in the archival sample also completed the FCAT reading

comprehension test. This test consists of several literary and informational passages followed by

multiple-choice, short answer, and extended response questions. Passage length varies by grade

level, with grades 3-5 averaging 450 words, and grades 6-8 averaging 600 words.

This exam is administered annually, in February or March, to all public school students in

grades 3 through 11. The total amount of time allowed to complete the FCAT depends on

guidelines prescribed according to students' grade level. In general students spend approximately

ten hours over a two-week period taking different parts of the FCAT. Many of the students at the

Montessori school have Individual Education Plans (IEP) or 504 plans that stipulate that they

receive accommodations allowing unlimited time to complete exam sections and/or individual

administration to decrease distractions. The FCAT is administered in group format to up to 29

students. These test sections are proctored by classroom teachers who are required to remain in

the testing room at all times. Students' test documents are sent to a test-scoring contractor where









multiple-choice and gridded-response answers are machine graded and performance task items

are hand scored.

The FCAT uses scaled scores that range from 100 to 500 for the reading test for each

grade tested. FCAT scaled scores are the result of a two-step process that analyzes student

responses using Item Response Theory (IRT) and uses the resulting item parameters to convert

student responses to a scaled score that is comparable across test years. The students in the

current study have an average reading score of (220.09, sd = 68.186) which is well below the

state cutoff of 300 necessary for grade promotion each year.









Table 2-1. Group Mean Age,


Chronological Age (CA)
Gender
Male
Female
Ethnicity
White
Black
Hispanic
Mixed
Socioeconomic Status
Free Lunch
Reduced Lunch
Ineligible
FCAT Achievement Level


Gender, SES, and FCAT Reading Achievement Level
Elementary School Middle School p-value
(N= 31) (N = 46)
10.40 (.80) 12.60 (0.99) 0.166









Table 2-2. Subtests Used in Composite Make-up of Processors
Processor Task
Rapid Digit Naming1
Rapid Letter Naming1
Orthographic Word Identification2
Sight Word Efficiency3
Elision1
Blending Words1
Phonological Word Attack2
Word Attack
Phonemic Decoding Efficiency3
Semantic Passage Comprehension2
1 Comprehensive Test of Phonological Processing (CTOPP)
2 Woodcock Reading Mastery Test-Revised (WRMT-R)
3 Test of Word Reading Efficiency (TOWRE)









CHAPTER 3
RESULTS

The current study explored the relationship between reading abilities and performance on

the FCAT reading comprehension test in a sample of school age children diagnosed with

dyslexia. First, these analyses examined the correlation between composite scores of underlying

reading processors and FCAT reading comprehension scores. Second, the unique and shared

predictive variance in FCAT performance among these composites for both the elementary and

middle school age groups was examined. Finally, the unique and shared predictive variance

among gains in FCAT performance for these groups was examined.

Descriptive Statistics and Preliminary Analyses

Table 3-1 provides descriptive statistics for all measures of reading for all students.

Distributions of scores on each test were examined for skewness, kurtosis, and outliers and none

were found. Students showed impaired reading on several of the measures as mean scores for

WRMT-R Word ID (M = 82.99, sd = 10.144) and passage comprehension (M = 83.72, sd =

11.330) as well as the TOWRE Sight Word Efficiency (M = 83.48, sd = 10.837) and Phonemic

Decoding Efficiency (M = 83.75, sd = 11.635) subtests fell more than one standard deviation

below the average of the normative sample.

Correlation of FCAT to Reading Processors

Initial analyses focused on determining the consistency of the FCAT and the reading

domain composites from year to year. Table 3-2 displays the correlations between the composite

scores and the FCAT for the two time points. The composite scores displayed high test-retest

correlations for all three domains: Phonological (r = .89, p < .001), Orthographic (r = .90, p <

.001), and Semantic (r = .90, p < .001). The correlation of FCAT scores was likewise significant

for the two time points but was of a substantially lower magnitude (r = .66, p < .001).









As predicted the FCAT correlates significantly with all three domains of underlying

reading ability. Table 3-2 shows that the correlation between the reading domain composites and

the FCAT for both time points. The Semantic composites for each year were significantly and

positively related to the FCAT score (r = .61 to .64, p < .001) the Phonological composite

likewise correlated highly with the FCAT (r = .56 to .60, p < .001), and finally, Orthographic

with a similar magnitude (r = .50 to .52, p < .001).

Predictive Variance of Reading Processors in FCAT Reading Comprehension

To investigate both the unique and shared contributions of the Orthographic,

Phonological, and Semantic processors to reading comprehension, a series of hierarchical

multiple regressions was conducted (summarized in Table 3-3). We conducted the analyses

separately by grade groups (i.e., grades 3-5 and grades 6-8) in order to examine whether the

unique and shared predictive variance associated with reading domains differed between

elementary and middle school participants. Independent samples t-tests revealed that these

groups did not differ significantly on the FCAT or any measures of reading.

The regressions were moderate fits (R2 = 47-49%) and FCAT reading comprehension

scores were positively related to the three processor composite scores. This high proportion of

unexplained variance must be attributable to other skills, aside from these underlying reading

processors examined in this study. Subtracting variance estimates for each individual composite

from the shared variance of the model yields estimates of the variance uniquely associated with

each composite. These results are summarized in Figures 3-1 and 3-2.

All three composites made unique as well as shared contributions to FCAT scores for

both grade groups in 2003/2004. For the elementary group in 2004/2005, Phonological failed to









contribute significantly to the predictive power of the model. Similarly, for the middle school

group in 2004/2005, Orthographic failed to contribute significantly.

As Figure 3-1 shows, in the elementary sample for the 2003/2004 school year, Semantic

skills uniquely accounted for about 10% of the explained variance in FCAT. In contrast, only

about 1% of the predictive variance in FCAT was made up of Phonological variance and another

1% was Orthographic variance. Similarly for the 2004/2005 school year Semantic skills

dominated the variance and uniquely accounted for 13% of the variance while Orthographic

skills accounted for 4%. Thus, for elementary aged students with dyslexia, those with adequate

Semantic skills are best equipped to do well on the FCAT reading comprehension test.

In the middle school group, the pattern remains the same for the 2003/2004 school year,

with Semantic skills uniquely accounting for 11% of the variance in FCAT scores. Orthographic

skills accounted for 4% of the variance and Phonological accounted for 1%. Here again

Semantic is shown to be the strongest predictor of FCAT performance. However, for the

2004/2005 school year, the pattern changed as Phonological skills dominated explaining 9% of

the variance with Semantic now explaining just 4% and Orthographic skills not accounting for

any unique variance. These dissimilar results suggest that for these students, at this time,

Phonological skills are the best predictor of FCAT performance.

Predictive Variance of Processor Gains in FCAT Reading Comprehension Gains

Finally, to examine how well FCAT reading gains can be predicted, a series of

hierarchical regression analyses was conducted. Adding these composite gain scores to the

model produced increases of less than 1% in the proportion of variance accounted for and thus

did not significantly improve prediction.









Paired t-test analyses were carried out on the reading processor composite scores for the

two years of testing. To protect the error rate for the three tests an adjusted a of .016 (.05/3) was

used. The results suggested that the Orthographic processor composite remained stable,

whereas the Phonological processor improved significantly over the year (t = 4.17, p < .001) as

did the Semantic processor (t = 2.56, p = .014).

Significant gains were made by the students on the FCAT reading comprehension test as

well as on several of the measures of reading. However, these correlations were much lower.

Only the Semantic processor reached significance atp < .05 and then only at r = .24 (Table 3-4).

When put into a regression these gain scores were not able to explain the variance with any

degree of significance. Thus, the gains that were observed in underlying reading abilities were

not useful in predicting improvement in FCAT performance.











Table 3-1. Mean FCAT and Reading Processor Scores for Total Sample (N=77)


2003/2004
Test Scores
(N=77)


2004/2005
Test Scores
(N=77)


Mean SD Mean SD
FCAT scaled score 220.09 68.19 244.64 54.68
Orthographic
WRMT-R Word ID 82.99 10.14 83.70 11.56
CTOPP Rapid Digit Naming 7.75 2.37 7.95 2.49
CTOPP Rapid Letter Naming 7.68 2.55 7.65 3.08
TOWRE Sight Word Efficiency 83.48 10.84 85.48 10.20
Phonological
WRMT-R Word Attack 90.14 9.91 91.82 9.80
CTOPP Elision 7.35 2.93 8.57 2.95
CTOPP Blending Words 9.14 2.72 9.99 2.79
TOWRE Phonemic Decoding Efficiency 83.75 11.64 84.88 12.35
Semantic
WRMT-R Passage Comprehension 83.72 11.33 85.87 10.48









Table 3-2. Correlations between Reading Processor Composites for Total Sample

FCAT FCAT Phono Phono Ortho Ortho Sem
2004 2005 2004 2005 2004 2005 2004
FCAT 2005 .656
Phonological 2004 .556 .511
Phonological 2005 .557 .600 .888
Orthographic 2004 .502 .433 .549 .578
Orthographic 2005 .537 .515 .669 .725 .905
Semantic2004 .642 .636 .744 .756 .634 .646
Semantic 2005 .544 .608 .664 .731 .486 .542 .904
All correlations were significant at p < .001









Table 3-3. Multiple Regressions of Composite Processor Scores Predicting FCAT Reading
Comprehension Scores for each Grade Level

Elementary School Middle School
(N=31) (N=46)
Testing Year Processor P R2 P R2
2003/2004 Semantic .535* .479 .555** .487
Phonological .117 .109
Orthographic .092 .089
2004/2005 Semantic .556* .490 .275- .471
Phonological -.086 .470*
Orthographic .300 .003
-p < .10. *p < .05. **p < .01.









Table 3-4. Correlations Between Gains in Reading Processor Composite Scores for Total Sample


FCAT Ortho Phono
Orthographic .196
Phonological .038 .181
Semantic .242* .228 .082
*p < .05.








Figure 3-1. Elementary Students Decomposition of Variance Accounted for by the Composite
Processor Scores in FCAT Reading Comprehension Scores


0.6-




0.5-




0.4


<

0.3




S0.2
0


0.1 oo


2003/2004


2004/2005


N Shared Variance
D Phonological
E Orthographic
* Semantic








Figure 3-2. Middle School Students Decomposition of Variance Accounted for by the
Composite Processor Scores in FCAT Reading Comprehension Scores


0.6




0.5




0.4




0.3 --




0.2




0.1 0.01.


2003/2004


2004/2005


Shared Variance
Phonological
Orthographic
Semantic









CHAPTER 4
DISCUSSION

A major aim of this study was to analyze the performance of children with dyslexia on

the Florida Comprehensive Assessment Test (FCAT). We sought to analyze the correlation of

the FCAT to measures of underlying the reading abilities of the Orthographic, Phonological and

Semantic processors. Further, we aimed to determine the extent to which the underlying reading

processors contribute to successful FCAT performance. Finally, we wanted to investigate which

gains in reading processor abilities lead to the greatest gains in FCAT scores.

In line with the predictions, FCAT reading scores correlated with measures of

Orthographic, Phonological, and Semantic processors demonstrating the measurement of a

similar construct of reading. However, the results also suggest that the performance of these

children was variable and that the Phonological processor did not have the primary role that we

hypothesized. Further, the study revealed that the FCAT's emphasis on the different reading

processor abilities varies from year to year for each age group. Finally, due to irregular patterns

of gains in FCAT reading comprehension scores and Reading Processor scores, the current study

was not able to determine the best predictor of improvement FCAT performance.

Consistency of the FCAT and Reading Processor Tests

Results of the current study demonstrated that the composite scores for the reading

processor measures are highly consistent across the two years measured in this study. The

examination of the FCAT scores, on the other hand, did not yield this level of consistency. In

fact, the magnitude of the correlation of the students' scores for the two FCAT administrations,

while statistically significant, was much lower (r = .65). The stability of the composite scores in

contrast to the relative instability of the FCAT scores suggest variability that can be attributed to

the FCAT test properties rather than to the students' performances. These initial analyses suggest









potential changes in the content and emphasis of the FCAT for each year or grade level. Overall,

this finding raises questions about the psychometric properties of the FCAT.

Correlation of FCAT with Reading Processors

Based on prior studies, it was hypothesized that the Orthographic, Phonological, and

Semantic processors would correlate highly with FCAT reading comprehension scores (Katzir et

al., 2006, Wolf & Katzir-Cohen, 2001). As predicted, the FCAT scores did correlate

significantly with all three underlying reading processor composites. Qualitative review of the

correlation magnitudes further revealed several important findings. Consistent with previous

research, the top-down skills of the Semantic processor had the highest correlations with the

reading comprehension measure for both years of data (Cutting and Scarborough, 2006). The

bottom-up skills of the Phonological and Orthographic processors were likewise significantly

correlated with the FCAT, albeit to a lesser extent. This pattern of correlations has been

demonstrated by previous studies (Torgeson et al., 1999) and supports the three processor model

of reading. Overall, this finding demonstrates that the FCAT reading comprehension test

measures a comparable construct to the other measures that have been shown to underlie reading.

Predictive Variance of the Reading Processors in FCAT Reading Comprehension

In the current study, the model of reading including the three composites of the

Orthographic, Phonological, and Semantic processor measures predicted only a modest 47% of

the variance in FCAT reading comprehension performance. It is not clear what other skills,

aside from those that we examined, may be responsible for the especially high proportion of

unexplained variance in the FCAT. A very substantial amount of the variance between

Orthographic, Phonological, and Semantic processors was shared rather than unique when FCAT

reading comprehension scores are predicted. Similar findings were found by other researchers









yet the basis remains unclear and should be investigated further (Catts et al., 2003; Cutting &

Scarborough, 2006).

The findings of this study demonstrated that all three processors made unique as well as

shared contributions to FCAT scores for both grade groups in 2003/2004. In contrast to our

hypothesis that the Phonological processor would be the primary predictor of FCAT reading

comprehension scores, the Semantic processor dominated the variance for both grade groups in

this school year. Consistent with previous research, these results suggest that Semantic top down

processes best predict successful reading comprehension performance (Cutting and Scarborough,

2006). Thus, for students with dyslexia in the current study, those who demonstrated mastery of

Semantic abilities performed best on the FCAT. Consistent with previous research, the findings

of the current study generally show that, while the bottom-up skills of Orthographic and

Phonological processors contributed to reading comprehension, they only accounted for a

minimal amount of the variance (Katzir et al., 2006).

The pattern of results changed dramatically for the subsequent 2004/2005 FCAT

administration. For the elementary group, the Phonological processor failed to contribute

significantly to the predictive power of the model. Recent studies have found similar results.

Researchers have begun to speculate that although Phonological processing is an important skill

for reading comprehension, its role may not always be detectable due to its shared variance with

other processing skills such as Orthographic (Katzir et al., 2006).

For the middle school group in 2004/2005, the profile changed its emphasis from

Semantic to Phonological. The Orthographic processor failed to contribute significantly to the

model. The Phonological processor, rather than the Semantic, dominated the variance for these

students, suggesting that for this year middle school students adept at Phonological skills were









best equipped to succeed on the FCAT reading comprehension test. The patterns of prediction

for the reading processors changed for both age groups for the two FCAT administrations but in

different ways. This is not wholly unusual, as some previous studies have similarly found

evidence of age differences in reading comprehension prediction (Catts et al., 2003, Francis et

al., 2005).

The discrepancy in relevant processors between the two administrations of the FCAT

cannot be definitively explained by the current study. However there are several possibilities of

why this may be the case. One possible explanation for this change is that the alternate version

of FCAT administered in the 2004/2005 school year was not equitable to the previous year's

version. This explanation seems possible given the moderate correlation between the two

versions. Given our lack of a comparison group, this discrepancy could be sample specific;

therefore, this explanation could only be confirmed or disconfirmed with the inclusion of a

comparison group of non-dyslexic children.

A second explanation for this discrepancy could be that the specific intervention these

students received was effective to the extent that the targeted Phonological processor became

more skilled and therefore had a greater influence on reading comprehension. Again, this

possibility could only be confirmed or rejected with the addition of a comparison group. In this

instance, a group of dyslexic children not receiving the Phonological intervention would be

needed to tease apart the discrepancy.

These findings suggest that versions of the FCAT for each school year may make

differential demands on Phonological and Semantic processing abilities. These findings are

similar to prior reading comprehension prediction research, where investigators showed that the

relative contributions of these abilities affect performance on some reading comprehension tests









more than on others (Cutting and Scarborough, 2006). Varying forms of the FCAT could mean

that educators working with children with dyslexia are trying to hit a moving target. If the skills

emphasis of the FCAT continually changes, interventions cannot be catered to specifically target

the necessary reading processors.

Predictive Variance of Processor Gains in FCAT Reading Comprehension Gains

The examination of gains on these measures assesses the progress these children make

toward narrowing the gap with normal readers. The standard scores on the reading measures and

the FCAT scale scores are calculated by referencing an age or grade matched normative sample.

Therefore, students making expected yearly academic progress should have the same scores each

year. In order to see gains in these scores students must go beyond the expected yearly progress

and learn at an accelerated rate; a task that is difficult for normal readers let alone a students with

dyslexia. Although repeated measures analyses revealed significant gains for these students on

average on both reading processor composite scores as well as FCAT reading comprehension

scores, they did not correlate highly. Therefore, it was impossible to test a model to predict

FCAT gains. As shown in the previous analyses, the profiles of FCAT prediction were different

for the two test administrations. It is likely that these discrepancies led to inconsistent patterns as

gains scores were calculated. Therefore, although specific processors were targeted in this

sample and gains were made, there is no way of determining whether this progress helped

students perform better on the FCAT. Researchers and educators have created effective

strategies that can be employed to improve the Phonological, Orthographic, or Semantic

processors. Again, not knowing which gains will have the most impact FCAT reading

comprehension scores makes it impossible to determine which processors should be targeted.









Implications

Overall, the results of this study show that generally Semantic, top-down abilities are

most critical for struggling readers to succeed on the FCAT. However, as the profile of reading

contributions suggested Phonological abilities are close behind and therefore cannot be neglected

for this population. This knowledge can be used to implement prevention screenings and in

further developing effective interventions. Examining a student's profile on these or similar

measures of underlying reading abilities can identify those who need extra help before they enter

the high stakes world of the FCAT. This can lead to interventions that are created to specifically

target their needs.

However, based on the differing profiles among middle school students, there is a

possibility that the alternate version administered this year wasn't successful in maintaining the

emphasis. The results raise a concern that the FCAT reading comprehension test does not

necessarily tap the same array of cognitive processes each year. Further, it may be influenced to

different degrees by particular skills that can affect comprehension. The effects of changes in

test format and passage characteristics across versions of the FCAT need to be examined and

disentangled.

There are also some important practical implications of the findings. First, whether a

child will be retained or promoted as determined by the FCAT reading comprehension test may

depend on the test given that particular year. Furthermore, different test versions may provide

discrepant information about which processors need to be targeted for remediation. These

findings are disconcerting considering the FCAT was designed to improve reading instruction

and ensure that no child is left behind.









Limitations and Future Directions

The implications of our findings are tempered by the limitations of the current study. The

archival data limited the information available about the students. Information regarding IQ,

medications, comorbidities, and socioeconomic status of the participants was not available for

this study. It would be important to include this data in any future studies, as it is possible that

these factors affected the results. Further, it was impossible to confirm the diagnosis of dyslexia

for these students. Thus, the sample may have been relatively heterogeneous.

Additionally, without comparison groups it is impossible to determine whether the

discrepancy in FCAT profiles was due to a characteristic of the current study participants or of

the test itself. In order to better tease this apart, we intend to expand the current study to include

comparison groups. These include non-dyslexic age- and grade-matched peers, as well as a

group of children with dyslexia who do not receive the Phonological intervention. The addition

of these groups will make it possible to determine whether differences in profiles can be

attributed to the sample or to the FCAT itself.

Further, the current study was limited by the test measures available. The expanded study

will include a variety of other measures of relevant cognitive constructs (i.e., working memory,

IQ, executive functioning, etc.) in addition to reading processors to get a more comprehensive

profile of the reading and related abilities that contribute to comprehension on the FCAT.









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BIOGRAPHICAL SKETCH

William Watson was born and raised in Ogden, UT, the third of four children of Jethro and

Nancie Watson. He graduated from the University of Utah (Salt Lake City), with bachelor's

degrees in psychology and German with a minor in chemistry. William entered the Clinical and

Health Psychology program at the University of Florida (UF) in 2005. During his study at UF,

he worked in a pediatric neuropsychology lab. William's mentor is Shelley C. Heaton, Ph.D.

William's interests include childhood disorders such as dyslexia and attention deficit

hyperactivity disorder. He plans to work in a clinical research setting after earning his doctoral

degree in clinical psychology.