Reading Comprehension and Phonics Research: Review of Correlational Analyses with Deaf and Hard-of-Hearing Students

Reading Comprehension and Phonics Research: Review of Correlational Analyses with Deaf and... Abstract This manuscript reviews 28 studies of reading research on deaf and hard-of-hearing (DHH) students published since 2000 that used correlational analyses. The examination focused on assessment issues affecting measurement and analysis of relationships between early phonological or orthographic skills and reading comprehension. Mixed outcomes complicate efforts to determine evidence-based practices, and to develop an accurate model of reading. Across the 28 studies, DHH participants represented a wide age range with potential floor and ceiling effects that reduce score variability for valid correlations. Many studies assessed readers beyond the optimal ages during which early skills develop and are most useful for reading. Reading skills also were assessed using a diverse array of measures and skill definitions. Particularly for reading comprehension, word-level and text-level abilities appear to be different constructs. Suggestions include more consistent skill definitions and differential timing for early- versus later-developing skill assessments to ensure more robust correlational relationships. The Search for Evidence-based Practices in Reading The federal No Child Left Behind Act of (2001) stipulated that educators use “scientifically-based research” to guide choices regarding instructional interventions. Subsequently, the Individuals with Disabilities Education Act (2004) indicated that education for children with disabilities should include the use of scientifically based instructional practices (Section 1400(c)(5)(E)). The difficulty for teachers of deaf and hard-of-hearing (DHH) students is that few scientifically validated practices have been identified. Two meta-analyses of literacy research on DHH students found few practices that met recommended standards identified by Cook et al. (2014) and the Institute for Educational Sciences (U.S. Department of Education, 2003). Luckner, Sebald, Cooney, Young, and Muir’s (2006) reviewed 22 studies of literacy practices recommended by the National Reading Panel (National Institute of Child Health and Human Development, 2000a, 2000b) used with DHH students. None of these studies met standards for strong evidence of effectiveness or even possible evidence of effectiveness. Easterbrooks and Stephenson (2006) reviewed 10 literacy practices routinely cited either in the literature or as field-supported practices with DHH students. Several had weak or developing evidence leading the authors to recommend their use while accumulating further substantiation. One recommended practice for early readers is that they receive instruction in phonemic awareness and phonics (National Institute of Child Health and Human Development, 2000a, 2000b). For students with typical hearing, phonemic awareness and letter knowledge have been the two best school-entry predictors of how well they learn to read during the first 2 years of instruction (Cunningham, 2001; Ehri, Nunes, Willows, Shuster, Yaghoub-Zadeh, & Shanahan, 2001; National Institute of Child Health and Human Development, 2000b). In contrast, research of early phonics-based instruction for DHH individuals has not shown consistent or strong support. Luckner et al.’s (2006) review identified 13 reading practices with large effect sizes; yet, none included use of phonics. Easterbrooks and Stephenson (2006) characterized phonics instruction with DHH students as a developing research base and concluded that instruction using visual and other support strategies allowed some DHH students to develop these skills, although some did not. As these reviews suggest, individual study outcomes on the contributions of phonological skills to reading comprehension of DHH children remain mixed. Several have reported significant relationships (Easterbrooks, Lederberg, Miller, Bergeron, & Connor, 2008; Paul, Wang, Trezek, & Luckner, 2009; Trezek, Wang, Woods, Gampp, & Paul, 2007). Yet, others have found low or non-significant correlations (Alvarado, Puente, & Herrara, 2008; Bélanger, Baum, & Mayberry, 2011; Clark, Gilbert, & Anderson, 2011; Izzo, 2002; Kyle & Harris, 2006; Miller, 2009). The effects of early and substantial hearing loss are likely to affect and perhaps alter, the acquisition and importance of phonics-based skills for young DHH readers. Hearing loss restricts attainment of language fluency if not addressed early and intensively (Anderson, 2006; Boudreault & Mayberry, 2006; Friedmann & Szterman, 2005; Lederberg, 2003; Marschark, Schick, & Spencer, 2006; Mayberry, Chen, Witcher, & Klein, 2011; Moeller, Toblin, Yoshinaga-Itano, Connor, & Jerger, 2007; Nicholas & Geers, 2003). Reduced access to the sounds of language likely affect the brain’s utilization for reading such that the mixed and inconclusive research reflect unique developmental trajectories. Identifying consistent skill relationships for DHH students could provide insights into strategies that may meet standards for evidence-based practice and result in more effective instruction. Concerns with Correlational Research of Phonemic Awareness and Phonological Skills Despite the apparent predictive strength of phonemic awareness and letter knowledge for most young readers with normal hearing, a number of researchers have raised concerns about the National Reading Panel’s conclusions. Several question their statistical assumptions or the practical significance of research cited in support of their findings (Allington, 2013; Almasi, Garas-York, & Shanahan, 2006; Burns, 2003; Hammill & Swanson, 2006; Paris, 2005). One important concern is that the variables used to measure phonemic awareness (PA) and reading comprehension (RC) do not demonstrate properties of equal variability (Paris, 2005). Optimal variability for PA occurs within a very limited time during acquisition. In contrast, RC develops across an individual’s lifespan with increasing and more stable score variability over time. These statistical properties extend to variables beyond PA and RC such that reading skills may be divided in two separate clusters. One cluster consists of constrained skills, characterized by a limited number of elements that typically are learned quickly. Young readers progress quickly from floor to asymptote during early reading instruction. Examples include learning the 26 letter names in English (orthographic awareness, OA) and the 43 letter-sound relationships (phonemic awareness, PA; and visually/print-supported phonological skills, PS). The second cluster consists of unconstrained reading skills that are acquired, developed, and refined over long periods of time. They consist of multiple components that are utilized in various ways, may never be entirely mastered, and comprise non-identical content. Reading comprehension and vocabulary development both are unconstrained skills with each encompassing multiple content domains. Statistical correlations between early- and late-developing and across constrained and unconstrained skills are problematic in that optimal variabilities between these skill clusters have little timing overlap. Analyses may require sophisticated causal modeling or exclusion methods in addition to assuring adequate score dispersion (Thompson, Diamond, McWilliam, Snyder, & Snyder, 2005). Strong correlational analyses require adequate variability among identified factors in order to ensure valid relationships. The need to optimize variability for conducting robust correlational analyses (Gersten et al., 2005; Thompson et al., 2005) is particularly complicated in identifying relationships between these reading skill clusters. Phonological awareness is an early pre-reading skill with its greatest variability during the first 18 months of acquisition, typically first grade and early second grade (Paris, 2005). Other early-acquired and constrained skills have similar developmental trajectories. However early reading comprehension, an unconstrained skill, has low initial variability until students attain initial fluency. Assessment during periods of greatest score variability for early code-based skills occurs when RC variability is minimal due to floor effects; and RC achieves score variability after code-based skills are minimally variable due to asymptote (Paris, 2005). Correlational studies assessing across these categories without adequate variation may be a reason for mixed results with DHH individuals. Wang and Williams’ (2014) meta-analysis identified timing limitations in that phonics- and code-based (early constrained) skills were more effective up to grade one. After this point, comprehension and mixed interventions on language- and thinking-based skills yielded greater effect sizes. Other research has likewise found that mature and experienced readers are less dependent on individual letter codes in comparison with metacognitive and text-based psycholinguistic reading skills (Goodman, 1994/2003; Goodman, Goodman, & Paulson, 2009; Kyle & Harris, 2010; Miller, 2009; Paris, 2005). Differential time-based developmental trajectories appear to effect measurement accuracy when analyzing relationships between various early and later-acquired reading skills. Another complicating factor is that the multiple skill components comprising reading comprehension affect the acquisition processes. Yet, the range of contributing factors may not be consistently included in research models that target strong and direct effects. Storch and Whitehurst’s (2002) longitudinal study of preschool through fourth grade students found that oral language abilities had strong and direct effects on later reading, but only beyond Grade 2. Before this, effects were mediated by code-related skills so that although oral language abilities were essential, they had only an indirect role until Grade 3. Another outcome was that reading accuracy and reading comprehension were found to be two separate abilities, each of which was influenced by different skills. The authors noted a danger in emphasizing phonological processing skills to the extent that other language skills were underestimated. Other studies have identified effects of language abilities on reading acquisition. Cheung, Chen, Lai, Wong, and Hills (2001) found that oral language influenced acquisition of phonological skills, which also were influenced by orthographic abilities. Dickinson, McCabe, Anastasopoulous, Peisner-Feinberg, and Poe (2003) identified shortcomings in vocabulary development that limited aspects of initial literacy development to include phonological sensitivity. Language fluency appears to be a potentially critical, but often under-identified, factor in examining the development of reading skills. The complexity of statistical modeling to reflect both direct and indirect effects, and changing relationships over time leaves most studies unavoidably imperfect (Thompson et al., 2005). The contributions of language fluency to reading skills is of particular importance for DHH individuals. Childhood delays in achieving a first language affect both functional and structural development of the brain with neuroanatomical differences that contrasted with those due to auditory deprivation (Mayberry et al., 2011; Pénicaud et al., 2013). MacSweeney, Waters, Brammer, Woll, and Goswami (2008) found comparable neural networks that supported phonological similarity judgments made in both English and British Sign Language but which were negatively impacted by delayed language acquisition. DHH children’s often-diminished linguistic abilities likely affect acquisition of phonological and code-based processing, and a range of associated reading skills. Yet, early indirect relationships are not consistently assessed in studies of reading development (Storch & Whitehurst, 2002) and may not be of sufficient strength to meet standards for evidence-based practice, potentially eliminating a critical factor in research efforts. An additional issue regards the assessment of research variables. Luckner et al. (2006) found that across 22 studies and 40 years of literacy investigations, no two studies examined the same dimensions of literacy. In that even a single construct can be measured in multiple ways greatly complicates efforts at cross-study comparisons and accruing a body of evidence-based practices. For example, within the domain of reading comprehension, Storch and Whitehurst (2002) found that reading accuracy and text comprehension were separate abilities. Hannon (2012) reported weak relationships between lower, word-level skills in comparison with higher-level reading processes. She suggested that word reading was a construct separate from text-level comprehension skills and each required differentiated instruction. Burns (2003) similarly identified word reading and contextualized reading as very different skills. Overall, the developmental processes for acquiring reading skills in DHH students appears to be multifaceted and complex, and potentially unique. The present study sought to re-examine reading research on DHH children conducted since 2000 that utilized correlational analyses to examine relationships between PA/PS, OA, and RC. An examination of timing and assessment issues could clarify reasons for mixed and inconclusive research results with DHH students. A more accurate model could offer a potential pathway toward evidence-based and effective instructional strategies and improved reading outcomes for these individuals. The research questions examined the timing of assessing component skills of reading, and the measures used to identify and examine these skills. They are as follows: What is the age-based timing of measurements in correlating early-developing and constrained PA/PS and OA skills with later-developing and unconstrained skills of reading comprehension in DHH individuals? In what ways are these reading skill components measured? Method This review identified research articles published between 2000 and 2017 that utilized correlational analyses to examine reading skills of DHH individuals. The search used the university library’s megadatabase “Discovery”, a service that performs a simultaneous search of over 300 individual research databases. The format allows for searches by keywords, title, or author with additional features to further limit the search, as needed. Searches used a range of terms for hearing loss and deafness, reading comprehension, and phonological skills in both the keyword and title functions. Each article’s reference list was reviewed for additional articles to ensure a comprehensive pool. Articles were eliminated that did not include an element of correlational analyses (to include regression) in their statistical procedures, or did not include either phonological or reading comprehension skills. Due to the paucity of research in this low-incidence population category, studies were not confined to a single language (English) but included other alphabetic languages. The acquisition of phonological processes to support reading skills has been identified as similar across alphabetic languages (Cheung et al., 2001; Goswami, Ziegler, & Richardson, 2005). Specifically, phonological processes have been found to remain similar across languages despite some variation in letter-sound consistency. Some of these differences are pronunciation of letter and letter clusters in Greek, Italian, German, Spanish which are more often consistent, whereas English, French, and Hebrew orthographies have less consistency (Goswami et al., 2005). However, the reading acquisition processes use similar alphabetic learning methods. Each article was examined to identify: (a) the age of participants, (b) the analytical methods used, (c) the reported correlational results, (d) the study conclusions regarding PA/PS, OA, with RC, and (e) measures of these skills. The target age for instruction in examining early-developing skills was defined as the 18 months for optimal PA/PS variability which coincides with the period of intensive OA instruction during first and early second grade, calculated to be between 6 and 8 years of age for the DHH population. This also is consistent with studies indicating that early constrained skills were most effective through first grade (National Institute of Child Health and Human Development, 2000b; Storch & Whitehurst, 2002; Wang & Williams, 2014). Examination of the articles could not verify that measurement of phonological awareness (PA) utilized an auditory-only presentation and therefore, the review combined PA with phonological skills (PS). The range of PA/PS are consistently early developing and infrequently utilized by mature readers (Goodman et al., 2009; Goodman, 1994/2003; National Institute of Child Health and Human Development, 2000b; Wang & Williams, 2014). In addition, a number of commonly used tests combine PA with other PS in order to create composite scores of sufficient statistical variability (Comprehensive Test of Phonological Processing, Dynamic Indicators of Basic Literacy Skills, the Texas Primary Reading Inventory, the Phonological Awareness Survey; Paris, 2005). A number of studies included OA in their analyses, also an early-developing constrained skill so this was added to the analyses, when present. Results The megadatabase search and examinations of reference sections of potential studies resulted in a total of 28 journal articles that utilized correlation and/or regression analyses. Table 1 lists the studies and the components examined. The primary language of the participants indicated that 18 used English and the remaining used a variety of languages, all of which had alphabetic orthography. Table 1. Studies of reading comprehension with PA and OA Study  Participants  Method  Reading variables  Correlation or regression results  Study conclusions  Alvarado, Puente, and Herrara (2008)  28 deaf children, 7–16 years old 15 hearing children of for level control  ANOVA Correlation Regression  RC: reading level Phonology Speechreading Orthography Fingerspelling Sign language  Correlation Sig: Reading level with sign language abilities (p < .01); with orthography (p < .05) NS: reading level with phonology, with speechreading, with visual similarityRegression: Sig: age with sign language to predict reading (R2 = .84)  Orthography and fingerspelling offer visual processing strategies for reading comprehension in conjunction with sign language  Bélanger, Baum, and Mayberry (2011)  29 Deaf adults, 22–55 years 16 hearing controls  ANOVA Linear regression  RC: reading level—groups defined by median split Phonology Speechreading Orthography  Regression NS: phonological prediction of reading level (R2 = .001, p = .86)  Less-skilled deaf readers’ reading difficulties are not caused by the lack of use of phonological codes  Clark, Gilbert, and Anderson (2011)  50 DHH college participants 51 hearing college participants  ANOVA Correlation  RC: English reading fluency PA Morphological knowledge ASL (bilingual) Proficiency  Correlation Sig: ASL skills with total score, (r = .34, p = .01); ASL with monomorphemic words (r = .30, p = .03) and with multimorphemic words correct (r = .35, p = .01) NS: ASL with phonology  No clear link between PA, reading, and decoding skills. Some deaf students demonstrate phonological knowledge and skills  Colin, Magnan, Ecalle and Leybaert (2007)  21 deaf 6-year-old prereaders 21 age-matched hearing children  2 year longitudinal study ANOVA Regression  RC: written word test Phoneme identification Speech intelligibility Rhymes  Regression Sig: word recognition score predicted by rhyme decision task (R2 change =28.3%)  PA was a significant correlate even after controlling for hearing loss  Cupples, Ching, Crowe, Day, and Seeto (2013)  101 5-year-old DHH children, 71 with hearing aids, 30 with implants  Correlation Multiple regression  RC: word and nonword reading, passage comprehension PA Letter knowledge Receptive vocabulary  Correlation: NS: PA with passage comprehensionRegression: Sig: PA with real-word reading, with word attack, with letter knowledge (ps < .001) when controlling for HA/CI, communication, cognitive ability, receptive vocabulary, and demographic variables  PA predicts aspects of early reading in 5-year olds with HL who use speech Phonological skills are most important in the earliest stages of reading Vocabulary at 5 years may be associated with reading at later stages of development  Daigle and Armand (2008)  24 DHH children who used sign language, 10–18 years in 3 age groups 24 age-matched hearing peers  ANOVA— repeated measures Correlation  RC zigzag test Graphemic (homophone) similarity of pseudowords Syllabic similarity Response time  Correlation Sig: Age with reading (r = .726, p < .001); DHH graphemic sensitivity with age (r = .492, p = .015), with reading (r = .719, p < .001); DHH syllabic sensitivity with age (r = .485, p = .016), with reading (r = .599, p = .002)  Syllabic sensitivity correlated with age and reading scores DHH had better scores when using both orthography and phonology Not all DHH show graphemic sensitivity  Daza, Phillips-Silver, Ruiz-Cadra, and López-López (2014)  30 DHH students 8–16 years; 23 used spoken language, 7 used sign language; 50% used CIs, 50% used HAs  Correlation Covariates: demographic or clinical variables to differentiate between good and poor readers  RC: sentence-picture matching and sentence completion PA: rhyme judgment Vocabulary Visual attention and memory  Correlation Sig: partial correlations—reading comprehension with vocabulary (r = .56; p = .003), with spatial memory (r = .47; p = .014), with visuospatial memory span (direct order: r = .63; p < .001)  Good readers were better at spoken language, vocabulary, spatial attention, visuospatial short-term and working memory, and executive functions. Differences in RC of readers was not related to PA skills.  Dillon, de Jong, and Pisoni (2012)  27 DHH children 6–14 years (8 = K-2), all used CIs and spoken language  Comparison of standard scores Correlation  RC: letter-phoneme match, word and nonword reading sentence reading PA: isolated sounds, monosyllables & syllable count Vocabulary  Correlation Sig: PA scores with all reading: word reading (r = 1.82, p < .001); word attack (r = 1.74, p < .001); PIAT-RC; PIAT-Total (r = 1.86, p < .001), (r = 1.85, p < .001)  Percentile ranks were in the bottom half on PA and reading Students had poorer vocabulary scores despite relatively high reading Older readers were generally less successful  Dyer, MacSweeney, Szczerbinski, Green, and Campbell (2003)  49 DHH, mean age 13 years, RA ≈ 7 years; all use BSL and TC 81 hearing controls CA matched & RA matched groups  Correlation  RC: cloze PA and decoding Picture rhyme Pseudohomophones Rapid automatized naming Speech or sign repetition  Correlation Sig: Rhyme with reading delay (r = –0.30, p < .05), with RAge (r = .39, p < .01); RAge with pseudohomophones (r = .46, p < .01); Rapid naming-sign and reading delay (r = –.47, p < .01) (unexpected direction) NS: Reading delay with the pseudohomophone matching  Deaf readers can make use of phonological structure to some extent in reading. Strong relationship between pseudohomophone task, reading, and IQ in the BSL-first language subgroup  Easterbrooks, Lederberg, Miller, Bergeron, and Connor (2008)  44 DHH children, 3–6 years, 32 in oral-only classes, 7 in TC, 5 in bilingual/TC classes  T-tests Fall/Spring Correlation  RC: passage comprehension (graphics and words) PA Speech perception Rhyming alliteration Syllable-segmentation Phonological processing  Correlation Sig: Age with all raw scores and between measures: Negative for age with passage comprehension (-.67), with letter-word identification (-.58), with vocabulary (-.30)  All measures generally correlated with Spring word identification and RC Majority of children performed poorly, particularly on rhyming words with little improvement; DHH gaps increased with age  Furlonger, Holmes, and Rickards (2014)  30 DHH adults, ages 18-61, profound HL, 17 preferred Auslan, 13 preferred spoken Hearing controls matched on age, gender, & NVIQ.  ANOVA lo/hi reading groups using medial split Correlation Linear regression  Word reading RC: word reading, passage reading with comprehension questions PA: phoneme, syllable, rhyme; phoneme-grapheme match Sign comprehension (at ceiling). Response time  Correlation Sig: PGC with word reading (r = .41, p < .05); word reading with RC (r = .85, p < .001) NS: PGC and RC (r = .24)Regression Sig: Three PA measures with reading comprehension, (F [3, 27] = 10.17, p < .001); PA measures with orthography information, (F [3, 27] = 10.84, p < .001); rhyme efficiency was the only significant unique predictor of RC (14% & 9%) across tasks  More proficient deaf readers were better at word reading Both deaf groups made rhyme judgments above the level of chance. Deaf participants showed a marked effect for orthographic condition; less-skilled readers showed greater reliance on orthography  Geers (2003)  181 DHH, 8–9 years; all implanted by 5 ½ years; 98 using oral and 83 using Total Communication.  Correlation Multiple regression  RC: modified cloze, sentence with picture Lexical and rhyming tasks— orthographic or phonological strategies  Correlation All significant (p < .0001) for reading with homophones, rhyme, and digit spanRegression Variance in reading principal components score predicted by multiple student and demographic factors  Reading outcome was most highly predicted by linguistic competence. Reading processing skills of word attack, word recognition, and educational placement did not make a substantial contribution to reading outcomes after demographic characteristics were accounted for.  Gibbs (2004)  Group 1: 15 DHH, moderate HL, 7–9 years; Group 2: 15 DHH children 6;2–7;10, all in mainstream and primary language was spoken English 3 groups of 30 hearing children controls  Correlation Hierarchical regression to partial out variance in reading due to PA.  RC: word recognition PA Vocabulary Syntax  Correlation Sig: Vocabulary with word recognition (r = .64, p < .01); vocabulary with word reading (r = .613, p < .01). NS: Initial phonemes or rhymes with single-word reading; DHH reading with PA, with memory spanRegression Vocabulary accounted for a further 27% of the variance in word recognition (F = 7.13; p < .01) following awareness of rhyme scores, 26% of the variance (F = 7.26; p < .01) following awareness of initial phonemes scores Rhyme scores and initial phonemes accounted for 19.5% (F = 4.0; p < .05) and 12% (F = 5.46; p < .05) of the variance in word reading  Ensuring use of language in advance of reading would reduce potential barriers and support acquisition of PA Development of PA may be contingent on vocabulary Some reading may be possible without closely associated phonological skills.  Harris and Moreno (2004)  Two groups of 30 DHH British children, 7–8 & 13–14 years CA and RA matched hearing peers  ANOVA MANOVA Regression  Reading age: word reading OA Phonology-spelling  Regression Sig: Reading of younger deaf by age (p < .05) and orthography (p < .01); reading of older deaf for memory span (p < .01) and orthography (p < .05); phonology was marginally significant (p = .06)  Orthography was a significant predictor of reading ability for both older and younger deaf children Phonology was not a significant predictor for younger, and marginally so for older DHH children Results suggest little DHH reliance on phonological coding  Izzo (2002)  29 primary DHH students, 4.33–13.16 years, from residential schools (unlikely to use phonological coding strategies)  Descriptive analyses Multiple regression  RC: retelling Age Language ability (Signed English-to-ASL continuum) PA  Correlation Sig: Reading with language ability = .58 (p ≤.001); with age = .50 (p ≤.001); Language ability with age = .51 (p ≤.001) NS: Reading with PA (r = .09), language ability with PA (r = .03)Regression NS: PA with reading Sig: Language, age, and PA (R2 = .397, p = .005); Individual predictors of language ability (p = .025) NS: Individual predictors: age (p = .1280) and PA (p = .665)  Reading ability was significantly correlated to language ability but not to PA PA may not facilitate reading development for DHH who use other strategies Some DHH with low PA achieved high reading comprehension  Johnson and Goswami (2010)  43 DHH, 5–15 years divided into early/late implant groups. 16 HA user controls 19 RA hearing controls, 6–9 years  ANOVA Multiple Regression  RC: single-word reading, word chains, read-aloud with comprehension questions PA: rhyme, initial and final phonemes Vocabulary Visual and auditory memory Auditory discrimination Speech intelligibility Speechreading of single words  Regression Sig: Composite of 4 reading scores with PA when entered before receptive vocabulary; age at cochlear implantation with vocabulary and reading outcomes when using quotient scores; RC with CI, controlling for age & NVIQ: rhyme =.584 (p < .001), initial phoneme =.364 (p < .05), final phoneme =.594 (p < .001) Receptive vocabulary: up to 38% of additional variance in each reading quotient measure NS: PA with reading composite scores after removing variance due to vocabulary  CI age was associated with development of oral language, auditory memory, and PA skills necessary for developing efficient word recognition skills. There is a benefit to earlier implantation.  Koo, Crain, LaSasso, and Eden (2008)  51 DHH college students grouped into native users of ASL, cued speech, oral Hearing native users of ASL, and hearing native speakers of English  ANOVA across 5 groups Correlation  RC: silent word reading, passage comprehension Phoneme Detection  Correlation Sig: PDT with passage comprehension (p < .006) Sig: Negative RC with reaction time for PDT, with silent word reading (τ = −.291, p < .01) NS: word-recognition fluency with PA  Deaf native ASL users had the lowest PA accuracy (p < .05). Silent word reading utilized more sight words and perhaps was NS for that reason.  Kyle and Harris (2006)  29 British deaf children 7-8 years Hearing controls matched for RA  Descriptive analyses ANOVA Correlation  RC: single-word reading, sentence comprehension PA Speechreading Spelling Vocabulary  Correlation Sig: PA with speechreading (r = .46, p < .05); vocabulary with SWR (r = .46, p < .05), with sentences (r = .70, p < .01), with speechreading (r = .48, p < .01). NS: PA with SWR, sentences, or spelling  DHH and hearing children did better when rime items were orthographically and phonologically congruent After controlling for hearing loss, productive vocabulary and speechreading (language factors) were significant predictors of reading PA did not correlate with RC after controlling for hearing loss  Kyle and Harris (2010)  29 DHH children, 7-8 years of age; 7 = oral, 18 = BSL, 4 = combination  3-year longitudinal study with 4 assessment periods Correlation Multiple regression  RC: single-word reading, cloze sentence comprehension, passage comprehension PA Speechreading Productive vocabulary  Regression Sig: Word reading at T1 with hearing loss (R2 = .77), with word reading at T2; productive vocabulary at T1 accounted for an additional 12% of sentence comprehension at T2; speechreading with productive vocabulary at T1 contributed a further 4% and ≈6%, respectively NS for Word reading at T2: PA at T1, hearing loss, and speechreading at T1  Vocabulary was the strongest and most consistent predictor of all reading measures across all time periods PA not a significant longitudinal predicator after Time1 reading levels DHH children may acquire phonology as a consequence of reading with early reading associated with later PA  Kyle and Harris (2011)  24 British DHH children, 5–6 years 23 hearing children 5–6 years, matched for word recognition  2-year longitudinal comparative study Descriptive analyses Correlation ANOVA  RC: single-word reading PA Letter name and letter-sounds Picture spelling Productive vocabulary Speechreading  Correlation Sig: PA with speechreading vocabulary, spelling, and word reading (ps < .01) NS: PA at T1 was not significantly related to reading at T2 or T3 Sig: Reading score at T1 with PA at T2 (p < .05)  Vocabulary at T1 was the most consistent significant correlate of reading at T2 and T3 even after controlling for NVIQ and T1 reading. Earlier PA was not a longitudinal correlate of reading after controlling for earlier levels Young DHH readers used a whole-word strategy and 2 years later used a more alphabetic strategy.  Luetke-Stahlman and Nielsen (2003)  31 DHH students, 7.9–17.9 years, no CIs; 9 in general education, 22 in self-contained  ANCOVA Correlation  RC-passage comprehension, word comprehension, word identification PA Receptive, expressive and written English  Correlation Sig: Word passage with word comprehension (r = .970, p = .0001), with word identification (r = .832, p = .0001), with phonemic substitution (r = .826, p = .0001) Sig: RC with blending phonemes (r = .723, p = .0001), with syllables (r = .720, p = .0001), with written language (r = .702, p = .0001), with letter identification (r = .700, p = .0001).  Reading strongly correlated with blending phonemes and syllables, segmenting sentences into words, and written English. Length of Signed English exposure did not yield higher reading measures or written language. Deaf students may use some different reading strategies.  Miller (2009)  31 Israeli high school and post-graduate students with prelingual deafness 59 hearing students  Correlation MANOVA  RC of word pairs PA and phonological decoding Response time  Correlation NS: PA, response time, and error rates overall and for each study condition: visual, phonological, and control; DHH for sentence comprehension, response time, and error rate  No significant evidence that DHH participants processed word pairs with less efficiency across conditions Phonemic skills do not significantly impact reading  Miller (2010)  83 Israeli prelingually deaf, 21 in primary school (3rd–4th), 36 in high school (10th–11th), 26 university (21–29 years); 85 control: 29 primary, 29 high school, 27 university  MANOVA Correlation Cluster analysis  RC: sentences and questions, semantically plausible and implausible sentences PA OA  Correlation Sig: OA with PA (r = .54, p < .001), by grade (p ≤ .05); SI with SP sentences (r = .56, p < .001); PA and OA with all sentence types (r = .48, p < .001; r = .36, p < .001)  DHH participants did not demonstrate PA growth over time Sentence-level processing showed individual word meaning using integration with syntactic (structural) knowledge OA plays a central role in processing written text; youngest DHH were non-strategic in their reading DHH participants had poorer PA and OA than hearing peers  Miller and Achmed (2009)  40 prelingually deaf Arab, ½ from primary (9.17–11.08 years) and half from middle-to high school (14.25–16.00 years) 40 control group  MANOVA Correlation  RC: word reading Word categorization: real word and pseudohomophones Rapid word naming  Correlations Sig: Categorization accuracy across real and pseudo conditions for young deaf (r = .84, p < .001) and older deaf (r = .60, p < .01); real word and pseudohomophone categorization for young (r = .39, p < .05) and older deaf (r = .37, p = .05) NS: categorization accuracy associated with speed of processing for the real-word condition  Young and old DHH categorized real words significantly more accurately than pseudohomophones which improved developmentally Older deaf and hearing did not differ in recognition of real words, which was not related to phonological development Older DHH had rates equal to hearing controls  Miller, Kargin, Guldenoglu, Rathmann, Kubus, Hauser, and Spurgeon (2012)  213 DHH, 6th–10th grade; Hebrew, Arabic, English, German participants  Cluster analysis ANOVA Correlation  RC: sentence comprehension with questions: semantically plausible & implausible. Phonology: pseudohomophones and real words Response time Native written language Strategies: syntactic, semantic, or unspecified  Correlations Sig: RC of the 2 sentence types for syntactic readers (r = .47, p < .001) Sig: Negative for syntactic readers for phonological processing with overall sentence comprehension scores (r = −.25, p < .05) NS: RC for semantic or unspecified strategy readers; phonemic processing among the 3 reader profiles despite large intergroup differences  Failure to find significant positive correlations between PA, phonological word decoding skills, and RC. Reading skills appear to develop independently of phonological processing skills. Syntactic deficits offer an explanation for difficulties in reading of DHH students  Most, Aram, and Andorn (2006)  42 DHH, 62–84 months (5–7 years) in 3 placements 11 hearing peers  ANONVA Correlation  RC: word recognition & explanation of choice PA OA Letter identification Word writing Receptive vocabulary  Correlations Sig: PA with vocabulary, with general knowledge, with writing, with reading explanations (ps < .01), with letter identification (p < .05)  PA, letter ID, knowledge and vocabulary was better for those in individual inclusion placements No statistically significant differences between individual and group inclusion programs regarding reading, writing, or OA  Spencer and Oleson (2008)  72 pediatric CI users, tested Simultan-eous (speech & SE) Approximately 5.1–11.3 years (48 months after implant)  Correlation Multiple regression  RC: word comprehension, word identification, word antonyms & synonyms, analogy Speech perception: word-picture identification, vowel perception Speech production: sentence repetition, story retelling Demographic background  Correlation Sig: Speech production measures with each other (r > .73, p < .0001)Multiple Regression Sig: Word reading with correct phonemes produced (short-long & retell), with speech perception, with age at testing (R2 = .59); paragraph comprehension with correct phonemes produced, consonant test, speech perception (R2 = .62)  Standard scores for reading were in the low average range for word identification and passage comprehension Both speech perception and production skills were strongly correlated with word identification and passage comprehension; early speech skills may predict later reading  Spencer and Tomblin (2009)  29 CI children, 7;2–17;8; 32 hearing controls HC, matched on mothers education & word comprehension, nearly 2 years younger than DHH    RC: word comprehension, word attack PA Rhyme judgment Phonological memory Rapid letter and number naming A/O (auditory only) or A/V (auditory-visual) conditions  Correlation Sig: Elision: with word attack (r = .63, p = .01), with word reading (r = .70, p = .01) Sig: Blending and A/V condition with word attack (r = .42, p = .05), with word reading (r = .37, p = .05) Sig: Nonword repetition with A/V (r = .41, p = .05, r = .38, p = .05); with rapid letter naming (r = .49, p = .01, r = .75, p = .01) NS: rapid letter naming, nonword repetition with A/O  The A/V condition was significantly correlated with two word reading tasks and may be a more accurate measure of phonological processing. CI children may hear some sounds (PA) then learn to associate full pronunciation after seeing print form (RC); they may learn PA from print (PS) rather than the reverse  Study  Participants  Method  Reading variables  Correlation or regression results  Study conclusions  Alvarado, Puente, and Herrara (2008)  28 deaf children, 7–16 years old 15 hearing children of for level control  ANOVA Correlation Regression  RC: reading level Phonology Speechreading Orthography Fingerspelling Sign language  Correlation Sig: Reading level with sign language abilities (p < .01); with orthography (p < .05) NS: reading level with phonology, with speechreading, with visual similarityRegression: Sig: age with sign language to predict reading (R2 = .84)  Orthography and fingerspelling offer visual processing strategies for reading comprehension in conjunction with sign language  Bélanger, Baum, and Mayberry (2011)  29 Deaf adults, 22–55 years 16 hearing controls  ANOVA Linear regression  RC: reading level—groups defined by median split Phonology Speechreading Orthography  Regression NS: phonological prediction of reading level (R2 = .001, p = .86)  Less-skilled deaf readers’ reading difficulties are not caused by the lack of use of phonological codes  Clark, Gilbert, and Anderson (2011)  50 DHH college participants 51 hearing college participants  ANOVA Correlation  RC: English reading fluency PA Morphological knowledge ASL (bilingual) Proficiency  Correlation Sig: ASL skills with total score, (r = .34, p = .01); ASL with monomorphemic words (r = .30, p = .03) and with multimorphemic words correct (r = .35, p = .01) NS: ASL with phonology  No clear link between PA, reading, and decoding skills. Some deaf students demonstrate phonological knowledge and skills  Colin, Magnan, Ecalle and Leybaert (2007)  21 deaf 6-year-old prereaders 21 age-matched hearing children  2 year longitudinal study ANOVA Regression  RC: written word test Phoneme identification Speech intelligibility Rhymes  Regression Sig: word recognition score predicted by rhyme decision task (R2 change =28.3%)  PA was a significant correlate even after controlling for hearing loss  Cupples, Ching, Crowe, Day, and Seeto (2013)  101 5-year-old DHH children, 71 with hearing aids, 30 with implants  Correlation Multiple regression  RC: word and nonword reading, passage comprehension PA Letter knowledge Receptive vocabulary  Correlation: NS: PA with passage comprehensionRegression: Sig: PA with real-word reading, with word attack, with letter knowledge (ps < .001) when controlling for HA/CI, communication, cognitive ability, receptive vocabulary, and demographic variables  PA predicts aspects of early reading in 5-year olds with HL who use speech Phonological skills are most important in the earliest stages of reading Vocabulary at 5 years may be associated with reading at later stages of development  Daigle and Armand (2008)  24 DHH children who used sign language, 10–18 years in 3 age groups 24 age-matched hearing peers  ANOVA— repeated measures Correlation  RC zigzag test Graphemic (homophone) similarity of pseudowords Syllabic similarity Response time  Correlation Sig: Age with reading (r = .726, p < .001); DHH graphemic sensitivity with age (r = .492, p = .015), with reading (r = .719, p < .001); DHH syllabic sensitivity with age (r = .485, p = .016), with reading (r = .599, p = .002)  Syllabic sensitivity correlated with age and reading scores DHH had better scores when using both orthography and phonology Not all DHH show graphemic sensitivity  Daza, Phillips-Silver, Ruiz-Cadra, and López-López (2014)  30 DHH students 8–16 years; 23 used spoken language, 7 used sign language; 50% used CIs, 50% used HAs  Correlation Covariates: demographic or clinical variables to differentiate between good and poor readers  RC: sentence-picture matching and sentence completion PA: rhyme judgment Vocabulary Visual attention and memory  Correlation Sig: partial correlations—reading comprehension with vocabulary (r = .56; p = .003), with spatial memory (r = .47; p = .014), with visuospatial memory span (direct order: r = .63; p < .001)  Good readers were better at spoken language, vocabulary, spatial attention, visuospatial short-term and working memory, and executive functions. Differences in RC of readers was not related to PA skills.  Dillon, de Jong, and Pisoni (2012)  27 DHH children 6–14 years (8 = K-2), all used CIs and spoken language  Comparison of standard scores Correlation  RC: letter-phoneme match, word and nonword reading sentence reading PA: isolated sounds, monosyllables & syllable count Vocabulary  Correlation Sig: PA scores with all reading: word reading (r = 1.82, p < .001); word attack (r = 1.74, p < .001); PIAT-RC; PIAT-Total (r = 1.86, p < .001), (r = 1.85, p < .001)  Percentile ranks were in the bottom half on PA and reading Students had poorer vocabulary scores despite relatively high reading Older readers were generally less successful  Dyer, MacSweeney, Szczerbinski, Green, and Campbell (2003)  49 DHH, mean age 13 years, RA ≈ 7 years; all use BSL and TC 81 hearing controls CA matched & RA matched groups  Correlation  RC: cloze PA and decoding Picture rhyme Pseudohomophones Rapid automatized naming Speech or sign repetition  Correlation Sig: Rhyme with reading delay (r = –0.30, p < .05), with RAge (r = .39, p < .01); RAge with pseudohomophones (r = .46, p < .01); Rapid naming-sign and reading delay (r = –.47, p < .01) (unexpected direction) NS: Reading delay with the pseudohomophone matching  Deaf readers can make use of phonological structure to some extent in reading. Strong relationship between pseudohomophone task, reading, and IQ in the BSL-first language subgroup  Easterbrooks, Lederberg, Miller, Bergeron, and Connor (2008)  44 DHH children, 3–6 years, 32 in oral-only classes, 7 in TC, 5 in bilingual/TC classes  T-tests Fall/Spring Correlation  RC: passage comprehension (graphics and words) PA Speech perception Rhyming alliteration Syllable-segmentation Phonological processing  Correlation Sig: Age with all raw scores and between measures: Negative for age with passage comprehension (-.67), with letter-word identification (-.58), with vocabulary (-.30)  All measures generally correlated with Spring word identification and RC Majority of children performed poorly, particularly on rhyming words with little improvement; DHH gaps increased with age  Furlonger, Holmes, and Rickards (2014)  30 DHH adults, ages 18-61, profound HL, 17 preferred Auslan, 13 preferred spoken Hearing controls matched on age, gender, & NVIQ.  ANOVA lo/hi reading groups using medial split Correlation Linear regression  Word reading RC: word reading, passage reading with comprehension questions PA: phoneme, syllable, rhyme; phoneme-grapheme match Sign comprehension (at ceiling). Response time  Correlation Sig: PGC with word reading (r = .41, p < .05); word reading with RC (r = .85, p < .001) NS: PGC and RC (r = .24)Regression Sig: Three PA measures with reading comprehension, (F [3, 27] = 10.17, p < .001); PA measures with orthography information, (F [3, 27] = 10.84, p < .001); rhyme efficiency was the only significant unique predictor of RC (14% & 9%) across tasks  More proficient deaf readers were better at word reading Both deaf groups made rhyme judgments above the level of chance. Deaf participants showed a marked effect for orthographic condition; less-skilled readers showed greater reliance on orthography  Geers (2003)  181 DHH, 8–9 years; all implanted by 5 ½ years; 98 using oral and 83 using Total Communication.  Correlation Multiple regression  RC: modified cloze, sentence with picture Lexical and rhyming tasks— orthographic or phonological strategies  Correlation All significant (p < .0001) for reading with homophones, rhyme, and digit spanRegression Variance in reading principal components score predicted by multiple student and demographic factors  Reading outcome was most highly predicted by linguistic competence. Reading processing skills of word attack, word recognition, and educational placement did not make a substantial contribution to reading outcomes after demographic characteristics were accounted for.  Gibbs (2004)  Group 1: 15 DHH, moderate HL, 7–9 years; Group 2: 15 DHH children 6;2–7;10, all in mainstream and primary language was spoken English 3 groups of 30 hearing children controls  Correlation Hierarchical regression to partial out variance in reading due to PA.  RC: word recognition PA Vocabulary Syntax  Correlation Sig: Vocabulary with word recognition (r = .64, p < .01); vocabulary with word reading (r = .613, p < .01). NS: Initial phonemes or rhymes with single-word reading; DHH reading with PA, with memory spanRegression Vocabulary accounted for a further 27% of the variance in word recognition (F = 7.13; p < .01) following awareness of rhyme scores, 26% of the variance (F = 7.26; p < .01) following awareness of initial phonemes scores Rhyme scores and initial phonemes accounted for 19.5% (F = 4.0; p < .05) and 12% (F = 5.46; p < .05) of the variance in word reading  Ensuring use of language in advance of reading would reduce potential barriers and support acquisition of PA Development of PA may be contingent on vocabulary Some reading may be possible without closely associated phonological skills.  Harris and Moreno (2004)  Two groups of 30 DHH British children, 7–8 & 13–14 years CA and RA matched hearing peers  ANOVA MANOVA Regression  Reading age: word reading OA Phonology-spelling  Regression Sig: Reading of younger deaf by age (p < .05) and orthography (p < .01); reading of older deaf for memory span (p < .01) and orthography (p < .05); phonology was marginally significant (p = .06)  Orthography was a significant predictor of reading ability for both older and younger deaf children Phonology was not a significant predictor for younger, and marginally so for older DHH children Results suggest little DHH reliance on phonological coding  Izzo (2002)  29 primary DHH students, 4.33–13.16 years, from residential schools (unlikely to use phonological coding strategies)  Descriptive analyses Multiple regression  RC: retelling Age Language ability (Signed English-to-ASL continuum) PA  Correlation Sig: Reading with language ability = .58 (p ≤.001); with age = .50 (p ≤.001); Language ability with age = .51 (p ≤.001) NS: Reading with PA (r = .09), language ability with PA (r = .03)Regression NS: PA with reading Sig: Language, age, and PA (R2 = .397, p = .005); Individual predictors of language ability (p = .025) NS: Individual predictors: age (p = .1280) and PA (p = .665)  Reading ability was significantly correlated to language ability but not to PA PA may not facilitate reading development for DHH who use other strategies Some DHH with low PA achieved high reading comprehension  Johnson and Goswami (2010)  43 DHH, 5–15 years divided into early/late implant groups. 16 HA user controls 19 RA hearing controls, 6–9 years  ANOVA Multiple Regression  RC: single-word reading, word chains, read-aloud with comprehension questions PA: rhyme, initial and final phonemes Vocabulary Visual and auditory memory Auditory discrimination Speech intelligibility Speechreading of single words  Regression Sig: Composite of 4 reading scores with PA when entered before receptive vocabulary; age at cochlear implantation with vocabulary and reading outcomes when using quotient scores; RC with CI, controlling for age & NVIQ: rhyme =.584 (p < .001), initial phoneme =.364 (p < .05), final phoneme =.594 (p < .001) Receptive vocabulary: up to 38% of additional variance in each reading quotient measure NS: PA with reading composite scores after removing variance due to vocabulary  CI age was associated with development of oral language, auditory memory, and PA skills necessary for developing efficient word recognition skills. There is a benefit to earlier implantation.  Koo, Crain, LaSasso, and Eden (2008)  51 DHH college students grouped into native users of ASL, cued speech, oral Hearing native users of ASL, and hearing native speakers of English  ANOVA across 5 groups Correlation  RC: silent word reading, passage comprehension Phoneme Detection  Correlation Sig: PDT with passage comprehension (p < .006) Sig: Negative RC with reaction time for PDT, with silent word reading (τ = −.291, p < .01) NS: word-recognition fluency with PA  Deaf native ASL users had the lowest PA accuracy (p < .05). Silent word reading utilized more sight words and perhaps was NS for that reason.  Kyle and Harris (2006)  29 British deaf children 7-8 years Hearing controls matched for RA  Descriptive analyses ANOVA Correlation  RC: single-word reading, sentence comprehension PA Speechreading Spelling Vocabulary  Correlation Sig: PA with speechreading (r = .46, p < .05); vocabulary with SWR (r = .46, p < .05), with sentences (r = .70, p < .01), with speechreading (r = .48, p < .01). NS: PA with SWR, sentences, or spelling  DHH and hearing children did better when rime items were orthographically and phonologically congruent After controlling for hearing loss, productive vocabulary and speechreading (language factors) were significant predictors of reading PA did not correlate with RC after controlling for hearing loss  Kyle and Harris (2010)  29 DHH children, 7-8 years of age; 7 = oral, 18 = BSL, 4 = combination  3-year longitudinal study with 4 assessment periods Correlation Multiple regression  RC: single-word reading, cloze sentence comprehension, passage comprehension PA Speechreading Productive vocabulary  Regression Sig: Word reading at T1 with hearing loss (R2 = .77), with word reading at T2; productive vocabulary at T1 accounted for an additional 12% of sentence comprehension at T2; speechreading with productive vocabulary at T1 contributed a further 4% and ≈6%, respectively NS for Word reading at T2: PA at T1, hearing loss, and speechreading at T1  Vocabulary was the strongest and most consistent predictor of all reading measures across all time periods PA not a significant longitudinal predicator after Time1 reading levels DHH children may acquire phonology as a consequence of reading with early reading associated with later PA  Kyle and Harris (2011)  24 British DHH children, 5–6 years 23 hearing children 5–6 years, matched for word recognition  2-year longitudinal comparative study Descriptive analyses Correlation ANOVA  RC: single-word reading PA Letter name and letter-sounds Picture spelling Productive vocabulary Speechreading  Correlation Sig: PA with speechreading vocabulary, spelling, and word reading (ps < .01) NS: PA at T1 was not significantly related to reading at T2 or T3 Sig: Reading score at T1 with PA at T2 (p < .05)  Vocabulary at T1 was the most consistent significant correlate of reading at T2 and T3 even after controlling for NVIQ and T1 reading. Earlier PA was not a longitudinal correlate of reading after controlling for earlier levels Young DHH readers used a whole-word strategy and 2 years later used a more alphabetic strategy.  Luetke-Stahlman and Nielsen (2003)  31 DHH students, 7.9–17.9 years, no CIs; 9 in general education, 22 in self-contained  ANCOVA Correlation  RC-passage comprehension, word comprehension, word identification PA Receptive, expressive and written English  Correlation Sig: Word passage with word comprehension (r = .970, p = .0001), with word identification (r = .832, p = .0001), with phonemic substitution (r = .826, p = .0001) Sig: RC with blending phonemes (r = .723, p = .0001), with syllables (r = .720, p = .0001), with written language (r = .702, p = .0001), with letter identification (r = .700, p = .0001).  Reading strongly correlated with blending phonemes and syllables, segmenting sentences into words, and written English. Length of Signed English exposure did not yield higher reading measures or written language. Deaf students may use some different reading strategies.  Miller (2009)  31 Israeli high school and post-graduate students with prelingual deafness 59 hearing students  Correlation MANOVA  RC of word pairs PA and phonological decoding Response time  Correlation NS: PA, response time, and error rates overall and for each study condition: visual, phonological, and control; DHH for sentence comprehension, response time, and error rate  No significant evidence that DHH participants processed word pairs with less efficiency across conditions Phonemic skills do not significantly impact reading  Miller (2010)  83 Israeli prelingually deaf, 21 in primary school (3rd–4th), 36 in high school (10th–11th), 26 university (21–29 years); 85 control: 29 primary, 29 high school, 27 university  MANOVA Correlation Cluster analysis  RC: sentences and questions, semantically plausible and implausible sentences PA OA  Correlation Sig: OA with PA (r = .54, p < .001), by grade (p ≤ .05); SI with SP sentences (r = .56, p < .001); PA and OA with all sentence types (r = .48, p < .001; r = .36, p < .001)  DHH participants did not demonstrate PA growth over time Sentence-level processing showed individual word meaning using integration with syntactic (structural) knowledge OA plays a central role in processing written text; youngest DHH were non-strategic in their reading DHH participants had poorer PA and OA than hearing peers  Miller and Achmed (2009)  40 prelingually deaf Arab, ½ from primary (9.17–11.08 years) and half from middle-to high school (14.25–16.00 years) 40 control group  MANOVA Correlation  RC: word reading Word categorization: real word and pseudohomophones Rapid word naming  Correlations Sig: Categorization accuracy across real and pseudo conditions for young deaf (r = .84, p < .001) and older deaf (r = .60, p < .01); real word and pseudohomophone categorization for young (r = .39, p < .05) and older deaf (r = .37, p = .05) NS: categorization accuracy associated with speed of processing for the real-word condition  Young and old DHH categorized real words significantly more accurately than pseudohomophones which improved developmentally Older deaf and hearing did not differ in recognition of real words, which was not related to phonological development Older DHH had rates equal to hearing controls  Miller, Kargin, Guldenoglu, Rathmann, Kubus, Hauser, and Spurgeon (2012)  213 DHH, 6th–10th grade; Hebrew, Arabic, English, German participants  Cluster analysis ANOVA Correlation  RC: sentence comprehension with questions: semantically plausible & implausible. Phonology: pseudohomophones and real words Response time Native written language Strategies: syntactic, semantic, or unspecified  Correlations Sig: RC of the 2 sentence types for syntactic readers (r = .47, p < .001) Sig: Negative for syntactic readers for phonological processing with overall sentence comprehension scores (r = −.25, p < .05) NS: RC for semantic or unspecified strategy readers; phonemic processing among the 3 reader profiles despite large intergroup differences  Failure to find significant positive correlations between PA, phonological word decoding skills, and RC. Reading skills appear to develop independently of phonological processing skills. Syntactic deficits offer an explanation for difficulties in reading of DHH students  Most, Aram, and Andorn (2006)  42 DHH, 62–84 months (5–7 years) in 3 placements 11 hearing peers  ANONVA Correlation  RC: word recognition & explanation of choice PA OA Letter identification Word writing Receptive vocabulary  Correlations Sig: PA with vocabulary, with general knowledge, with writing, with reading explanations (ps < .01), with letter identification (p < .05)  PA, letter ID, knowledge and vocabulary was better for those in individual inclusion placements No statistically significant differences between individual and group inclusion programs regarding reading, writing, or OA  Spencer and Oleson (2008)  72 pediatric CI users, tested Simultan-eous (speech & SE) Approximately 5.1–11.3 years (48 months after implant)  Correlation Multiple regression  RC: word comprehension, word identification, word antonyms & synonyms, analogy Speech perception: word-picture identification, vowel perception Speech production: sentence repetition, story retelling Demographic background  Correlation Sig: Speech production measures with each other (r > .73, p < .0001)Multiple Regression Sig: Word reading with correct phonemes produced (short-long & retell), with speech perception, with age at testing (R2 = .59); paragraph comprehension with correct phonemes produced, consonant test, speech perception (R2 = .62)  Standard scores for reading were in the low average range for word identification and passage comprehension Both speech perception and production skills were strongly correlated with word identification and passage comprehension; early speech skills may predict later reading  Spencer and Tomblin (2009)  29 CI children, 7;2–17;8; 32 hearing controls HC, matched on mothers education & word comprehension, nearly 2 years younger than DHH    RC: word comprehension, word attack PA Rhyme judgment Phonological memory Rapid letter and number naming A/O (auditory only) or A/V (auditory-visual) conditions  Correlation Sig: Elision: with word attack (r = .63, p = .01), with word reading (r = .70, p = .01) Sig: Blending and A/V condition with word attack (r = .42, p = .05), with word reading (r = .37, p = .05) Sig: Nonword repetition with A/V (r = .41, p = .05, r = .38, p = .05); with rapid letter naming (r = .49, p = .01, r = .75, p = .01) NS: rapid letter naming, nonword repetition with A/O  The A/V condition was significantly correlated with two word reading tasks and may be a more accurate measure of phonological processing. CI children may hear some sounds (PA) then learn to associate full pronunciation after seeing print form (RC); they may learn PA from print (PS) rather than the reverse  Note. CI = cochlear implant; Sig: = statistically significant; NS = non-significant. Research question one examined the ages of the participants in measuring PA/PS, OA, and RC which affects statistical variability needed for correlational analyses. Few studies reported variabilities of factors and therefore, this could not be directly examined. Across the 28 studies there was broad age range. Half (n = 14) used participants that were within the target ages; however, many included those who were above or below these ages. Three studies included younger participants who were potentially just beginning early reading skill acquisition. Seven studies included older participants expected to have achieved asymptote for early constrained skills. Four studies included both below and above target-age participants. Only three studies used participants solely within the target ages, with 11 using participants who were entirely outside of the target ages, 10 of which were older individuals. The “Participants” column of Table 1 lists the ages of the participants for each study. The examination of ages with correlations indicated that of the 17 studies reporting significant correlations between PA/PS and RC, 1 study used below target-age participants (Cupples et al., 2013); 3 used both younger and target-age participants (Easterbrooks et al., 2008; Kyle & Harris, 2011; Most et al., 2006); 1 used only target-age participants (Colin et al., 2007); 2 used younger, target age, and older participants (Johnson & Goswami, 2010; Spencer & Oleson, 2008); 4 used target age and older participants (Dillon et al., 2012; Geers, 2003; Luetke-Stahlman, & Nielsen, 2003; Spencer & Tomblin, 2009); and 6 used older participants (Daigle & Armand, 2008; Dyer et al., 2003; Furlonger et al., 2014; Koo et al., 2008; Miller & Achmed, 2009; Miller, 2010). A total of 12 studies reported non-significant correlations between PA/PS and RC including 2 that used participants within the target-age range (Kyle & Harris, 2006, 2010); 5 that used within and beyond target-age participants (Alvarado et al., 2008; Daza et al., 2014; Gibbs, 2004; Harris & Moreno, 2004; Izzo, 2002); and 5 that used only participants beyond the target age (Bélanger et al., 2011; Clark et al., 2011; Koo et al., 2008; Miller et al., 2012; Miller, 2009) with Koo et al. (2008) reporting both significant and non-significant correlations. Bélanger et al. (2011) also used linear regression analyses, finding non-significant relationship of phonological skills for predicting reading levels of older (adult) participants (R2 = .001, p = .86). Six studies reported correlations between orthographic awareness (OA) and RC. Significant correlations included two studies using target age and older participants (Alvarado et al., 2008; Harris & Moreno, 2004); and three using older participants (Bélanger et al., 2011; Daigle & Armand, 2008; Furlonger et al., 2014). One study reported non-significant correlations for participants beyond the target ages (Miller, 2010). Several studies reported significant relationships between unconstrained skills that would demonstrate variability long after initial acquisition. Significant relationships between vocabulary and reading comprehension were reported by two studies using target-age participants (Kyle & Harris, 2006, 2010), one using both target age and older participants (Daza et al., 2014), and one using older participants (Miller & Achmed, 2009). Significant correlations between sign language and reading comprehension were reported for mixed younger-through-older participants (Izzo, 2002), and for older participants (Miller, 2009). Alvarado et al. (2008) used regression to find a significant relationship between age and sign language acquisition in predicting reading (t (age) = 4.32, p < .01; t (sign language) = 3.32, p < .01) for target age and older participants. Clark et al. (2011) also found significant relationships between ASL skills and RC (r = .34, p = .01). Vocabulary was significantly correlated with RC in seven studies, and with PA/PS in seven studies. The second research question examined measurements used for targeted reading skills. PA/PS were measured by tests of phoneme detection, elision, blending, matching, phonological similarity, use of words and nonwords, rhyme decision & generation, syllabic similarity, and spelling. Not all studies examined OA skills; however, those that did also used a variety of measures to identify: letters in words, contrasting orthographically similar & dissimilar words & nonwords, phoneme-grapheme correspondence, lexical & rhyming tasks, words in letter strings (word chains), and rapid letter and number naming. A number of studies identified letter naming as an early reading skill and did not report these outcomes as a component of OA (Easterbrooks et al., 2008; Geers, 2003; Most et al., 2006). The present study did not re-categorize outcomes or reclassify measures if not so reported in the original articles. Studies also used a variety of measures for reading comprehension skills. Alvarado et al. (2008) defined this skill based on academic level while controlling for age. Izzo (2002) assessed reading comprehension through story retelling and while Spencer & Oleson (2008) also used story retelling, it was to assess speech/phonemic production skills. Several studies measured RC through letter identification, word and/or pseudoword reading/identification, or word chain tests (Colin et al., 2007; Cupples et al., 2013; Dillon et al., 2012; Easterbrooks et al., 2008; Furlonger et al., 2014; Geers, 2003; Gibbs, 2004; Harris & Moreno, 2004; Johnson & Goswami, 2010; Koo et al., 2008; Kyle & Harris, 2006, 2010, 2011; Luetke-Stahlman & Nielsen, 2003; Miller & Achmed, 2009; Miller, 2009; Spencer & Tomblin, 2009). Most et al. (2006) used word recognition to examine RC but asked participants to explain their answer. Of the 18 studies using letter and word-based measures, all but 5 reported significant correlations between PA/PS and RC (Gibbs, 2004; Harris & Moreono, 2004; Kyle & Harris, 2006, 2010; Miller, 2009). A number of the studies used text-level reading to assess RC such as sentence and short-passage cloze procedures (Cupples et al., 2013; Daza et al., 2014; Dyer et al., 2003; Geers, 2003; Koo et al., 2008; Kyle & Harris, 2010; Luetke-Stahlman & Nielsen, 2003; Spencer & Oleson, 2008) with two of these eight reporting non-significant PA/PS and RC correlations (Daza et al., 2014; Kyle & Harris, 2010). Others used longer text with RC scores based on comprehension questions, correct choices, or pictures (Bélanger et al., 2011; Clark et al., 2011; Cupples et al., 2013; Daigle & Armand, 2008; Daza et al., 2014; Dillon et al., 2012; Easterbrooks et al., 2008; Furlonger et al., 2014; Johnson & Goswami, 2010; Kyle & Harris, 2006, 2010; Luetke-Stahlman & Nielsen, 2003; Miller et al., 2012; Miller, 2009; Miller, 2010; Spencer & Oleson, 2008). Of the 16 studies using text-based RC stimuli, nearly half reported non-significant correlations with PA/PS (Bélanger et al., 2011; Clark et al., 2011; Daza et al., 2014; Kyle & Harris, 2006, 2010; Miller et al., 2012; Miller, 2009). Overall, the 28 studies employed a notable variety of measures across these reading skills. Discussion This study reviewed research on DHH readers that reported on correlations between early- and late-developing constrained (PA/PS, OA) and unconstrained (RC) skills. The purpose was to examine potential effects of assessment timing and constructs that may contribute to inconsistent outcomes regarding these skills. The search of research databases identified 28 studies that fit criteria and reported either significant or non-significant correlations between PA/PS or OA and RC. The first research question examined the age of participants in correlating early-acquired and constrained skills of PA/PS and/or OA with reading comprehension. Across the 28 studies, 10.7% (n = 3) used participants within the target-age group defined as 6–8 years of age; 1 was below the target age (3.6%), 10 were older (35.7%), with most using mixed ages (n = 14, 50%). Of the 17 studies reporting significant correlations between PA/PS and RC, 6 (35.3%) were with older participants and 4 (23.5%) were with target age and older participants. More studies used older readers despite PA/PS and OA being early-acquired skills with attainment of asymptote expected by 8 or 9 years of age. Of the 12 studies that did not report significant PA/PS and RC correlations, 5 (41.7%) were with target age and older participants, 4 (33.3%) were with older participants, with Koo et al. (2008) reporting both significant and non-significant correlations. Six studies reported on relationships between OA and RC with five that were significant (two with target age and older, three with older participants) and one that indicated non-significant correlations using older participants. Those studies using participants within the target ages for PA/PS and OA would not likely have had sufficient variability for the later-developing skill of reading comprehension. This is most salient for correlations with text-based reading which is not attained until a child is beyond floor levels, at age 9 or above (National Institute of Child Health and Human Development, 2000b; Wang & Williams, 2014). Only studies correlating PA/PS or OA with word recognition would tend to have equal variability across these factors. Of the three studies using only target-age participants, one reported a significant correlation between PA/PS and RC (Colin et al., 2007) and two reported non-significant correlations (Kyle & Harris, 2006, 2010). None of the studies examining OA and RC used target-age participants. Outcome patterns are difficult to ascertain and interpret among the remaining studies and varying ages. For those using older participants, six reported significant correlations between PA/PS and RC (Daigle & Armand, 2008; Dyer et al., 2003; Furlonger et al., 2014; Koo et al., 2008; Miller & Achmed, 2009; Miller, 2010) while five reported non-significant correlations (Bélanger et al., 2011; Clark et al., 2011; Koo et al., 2008; Miller et al., 2012; Miller, 2009). Five studies reported significant OA and RC correlations with three that used only older participants (Bélanger et al., 2011; Daigle & Armand, 2008; Furlonger et al., 2014) and two that used target age and older participants (Alvarado et al., 2008; Harris & Moreno, 2004). The correlation results for older participants would be expected to approach asymptote or ceiling effects with reduced variability although few studies identified these. Daigle and Armand (2008) reported ceiling effects although only with hearing participants for graphemic coding. Miller (2009) reported ceiling effects on PA/PS for his older participants and Luetke-Stahlman and Nielsen (2003) reported ceiling effects for initial and final phonemes for their mixed-age participants. Although their study used ANOVA, James, Rajput, Brinton, and Goswami (2008) reported that their results for PA/PS and RC for DHH children approximately 5–10 years of age were impacted by ceiling effects for syllable awareness. These studies suggest that DHH readers experience similar age-based ceiling and asymptote effects although reported by only 3 of the 21 studies using mixed and older participants. Another statistical concern is with floor effects. The National Reading Panel characterized children below first grade as being nonreaders, with this skill being acquired gradually through first and second grade. About 2 of the 28 studies (Cupples et al., 2013; Easterbrooks et al., 2008) reported floor effects with a number of their participants unable to perform several reading tasks. This emphasizes the importance of timing assessments to ensure maximum variability to achieve robust correlations, and avoidance of both floor and ceiling effects among developing skill sequences. Age of participants was not a factor in studies reporting correlations between unconstrained skills. Significant relationships were reported by four studies for vocabulary and RC, two for language and RC, and two for sign language and reading level or RC. Similarly, a meta-analysis of 57 studies examining phonological coding with DHH individuals (Mayberry, del Giudice, & Lieberman, 2011) identified the importance of linguistic skills. Half of the studies had significant effect sizes for PA/PS with RC; however, the mean effect size was .35 (low to medium) and represented just 11% of the variance in RC scores. By comparison, language ability predicted 35% of the variance in RC. The importance of these linguistic skills, also reported by Storch and Whitehurst (2002), suggests a need for further examination of unconstrained skills that may contribute to the reading comprehension of DHH individuals. Such correlations would be less affected by timing once individuals had attained initial fluency. Research question two examined the measures used for assessing component reading skills as a source of potential disparity in reported relationships. PA/PS was measured in more than 10 different ways with this variety suggesting that these discrete skills may be dissimilar enough to warrant caution in making cross-study generalizations. Assessment of OA skills also used approximately 10 different measures with some that contrasted phonemic with graphemic strategies in conjunction with PA/PS development. Spelling was analyzed for orthography in Harris and Moreno (2004) but not in other studies. Orthographic skills were included in several other studies but reported in terms of other linguistic competencies (e.g., Miller & Achmed, 2009). Again, the variety of assessment targets within OA suggests caution in reporting potential acquisition patterns across studies. Notably diverse was the range of skills and the types of measures used to assess reading comprehension. These included individual word or pseudoword reading, sentence reading, and passage comprehension measured by retelling, explanation of answers, correct picture or answer choice, or cloze procedures. Research into testing and test construction has identified particular difficulties for DHH students that result from wording of questions and items, and use of shorter versus longer passages such that scores do not accurately reflect their abilities (Alvermann & Phelps, 2002; Johnson & Mitchell, 2008; Lollis & LaSasso, 2009; Martin & Mounty, 2005; Weinstock & Mounty, 2005). Explanations or responses that are evaluated based on comparisons with hearing participant norms are likely to produce relatively poorer scores. Tests of pseudoword word reading fluency and differentiation between words and pseudowords may be negatively affected by DHH students’ frequently diminished linguistic skills (Luckner, 2013). A panel review of the Dynamic Indicators of Basic Literacy Skills (DIBELS) identified only one of seven subtests as genuinely assessing reading comprehension. Many commercially available reading assessments and several used in these studies have similar tests and subtests. Other test concerns were raised by LaSasso (1980) with regard to cloze procedures such that DHH individuals’ cloze scores did not correlate well with predicted passage difficulty. DHH students tend to have much poorer performance levels when held to typical scoring standards (Kelly & Ewoldt, 1984). A number of the 28 studies utilized cloze procedures. The differences in assessing and comparing comprehension achievement across single-word and connected text reading scores likely contributes to the mixed outcomes reported by these studies. Single-word reading utilizes sight-word and word attack or phonemic skills and is dissimilar to sentence or passage comprehension skills that require multiple psycholinguistic processes (Goodman et al., 2009; Goodman, 1994/2003; Kyle & Harris, 2010; Miller, 2009; Paris, 2005). The National Reading Panel’s conclusion that PA is foundational for assisting with the alphabetic relationships for reading and spelling is based on its utility for word and pseudoword reading, with much smaller effects reported for contextualized reading (National Institute of Child Health and Human Development, 2000b). Word- and passage reading have been identified as separate constructs (Allington, 2013; Burns, 2003; Hannon, 2012; Storch & Whitehurst, 2002) with Dillon et al. (2012) describing even sentence-level comprehension as inferior to passage comprehension in relying more on phonological knowledge than authentic comprehension skills. Among the 17 studies finding significant correlations between PA/PS and RC, more used word-level reading alone (29.4%) or in combination with text reading responses (41.2%) for a total of 82.3%. This is in contrast with studies that used only using text reading with 17.6% reporting significant relationships between PA/PS and RC. For the 11 studies reporting non-significant correlations, 54.6% used word or word measures in combination with others, whereas 45.5% used only text reading measures. The Alvarado et al. (2008) method for determining reading level in correlation with age was not included in these calculations. The five studies examining OA and RC correlations included just one study using only word-level reading to measure RC (Harris & Moreno, 2004), the others using text-level responses alone (Bélanger et al., 2011; Daigle & Armand, 2008; Furlonger et al., 2014, Miller, 2010), and the Miller study reporting non-significant correlations. The difficulty in the current review is that the varying measures likely obscured patterns as well as differences among the reading constructs and PA/PS and OA subskills. This was further complicated by participant age effects on variability across early, constrained and later, unconstrained skills. Even for studies within the target-age range reading measures varied with Colin et al. (2007) using a word recognition test, and with the Kyle & Harris studies using word reading, word recognition, and sentence comprehension. Assessing the contributions of early-acquired skills to those that occur later is important, but require differential timing that may be best addressed using longitudinal methodology, such as used by the Kyle and Harris studies. This would accommodate differential developmental trajectories among component skills while also monitoring fluctuating direct and indirect relationships that are characteristic of developing readers. Summary and Recommendations This review identified several age-based and construct measurement concerns in studies of relationships between early- and later-developing reading skills. Participant ages suggested probable floor and ceiling effects with reduced variability for robust and valid correlations, although relatively few were reported. Most of the 28 studies tested participants outside the optimal ages for PA/PS, OA, or RC. Another issue was with assessments that created composite scores to enhance statistical robustness but that confounded tracking of discrete skills. Reading skills of PA/PS, OA, and RC were not consistently defined among studies or assessments. These issues likely have contributed to inconsistent findings and ongoing obstacles to identifying evidence-based literacy practices with DHH individuals (e.g., Luckner et al., 2006). Although these 28 studies used similar research methodology and skill targets, the variety of skill and construct measurements, ages of participants, and stages of skill development preclude drawing any conclusions. A particular challenge is that the formulation of robust and parsimonious research models limits examination of the potentially multiple related skills that contribute to fluent reading comprehension. One observation is that assessment of skill constructs that reflect age-based timing constraints across early- and later-developing skills, and that capture multiple and varying contributions of component skills across readers’ maturation may be best achieved through longitudinal research designs. One consistent relationship regardless of age, was among unconstrained skills of vocabulary, language (English or sign language), and reading comprehension. Importantly, Miller et al. (2012) found that linguistic skills based on syntactic processing were more effective for adolescent DHH readers than semantic (word-based) strategies. Wang and Williams (2014) also reported that language and thinking skills yielded larger RC effect sizes once readers understood the written code. The difficulty for many DHH readers is with their struggle to attain primary language fluency prior to reading instruction. Studies using hearing children rarely examine linguistic abilities in that most achieve basic skills in early childhood. Yet, Storch and Whitehurst (2002) confirmed the importance of these skills for later reading with normal hearing readers. Linguistic skills are at least as important for DHH readers (Mayberry et al., 2011). This review limited itself to correlational studies which does not allow for identification of causal relationships. The field continues to wrestle with isolating significant contributing factors and the interrelationships that characterize the developmental trajectory of skillful readers. These relationships evolve over time and show unequal rates of growth, with further complications from skill co-dependencies and effects due to co-developing cognitive and linguistics competencies (Paris, 2005). Research needs to move beyond cross-sectional and correlational studies into longitudinal and multi-skill component analyses. Another significant issue raised by this review is the disparity in skill and construct measurement. This likely contributes to the difficulty in accumulating a consistent body of research that: (a) describes the nature and timing of the important factors that contribute to the development of fluent reading, and (b) isolates potential evidence-based instructional practices. Word- and text-level reading typically are treated as interchangeable aspects of “reading comprehension” but instead, are unique skill clusters that should be differentiated for assessment and instruction. The comingling of these two constructs obscures potentially productive research avenues that otherwise could more accurately identify optimal developmental patterns and processes that lead to fluent text reading. To summarize, this review has several potential implications for the planning of future research: Research methodologies should examine reading skills across the developmental trajectory that expand beyond common correlational approaches in order to accurately represent causal connections, maturational change, and varying skill contributions across the acquisition-to-mastery continuum. Models should allow for potential direct and indirect and evolving effects of component skills in order to identify potentially effective and evidence-based practices. Researchers, diagnosticians, and educators need to develop and adhere to consistent definitions of critical reading skills and subskills. For example, Scarborough and Brady (2002) defined reading and speech skill terms beginning with the morpheme “phon” to ensure consistency. At a minimum, word reading should be clearly differentiated from comprehension of connected text, and subskills should be unambiguously identified and defined. Research on instruction should not only distinguish between the two major reading constructs but also reflect the distinctive cognitive skills from which each construct develops. Many early-acquired skills (e.g., OA, PA, PS, and word identification) are learned through memorization, rote practice, and direct instruction processes. In contrast, comprehension of connected text is a higher-order cognitive skill that requires analysis and interpretation through inquiry and discovery learning processes (Borich, 2014). Connected text comprehension is more than a sum of individual word identifications. The historic struggles of DHH individuals to become fluent readers may have created a situation in which singular subskills have become overly emphasized and presumed as prerequisites for text comprehension. Examining the higher-order cognitive skills necessary to develop connected text comprehension may prompt investigations into more effective instructional strategies. These should consider phrase-level reading or “chunking” which provide the foundations for comprehension of sentences, paragraphs, and increasingly larger segments of text. This has been an approach used with hearing students (Barrera, Liu, Thurlow, & Chamberlain, 2006; Rasinski, 1994; Rasinski, Yildirim, & Nageldinger, 2011) or students with learning disabilities (Casteel, 1990). DHH students also have abilities to acquire phrase-level skills (Albertini & Mayer, 2011; Kelly, 2003; Luft & Fochman, accepted with revision) although not consistently (Albertini & Mayer, 2011; Atwell, 2013; Kelly, 2003; Schirmer, Bailey, & Lockman, 2004), perhaps affected by varying levels of language fluency. The goal of reading instruction is to develop fluent comprehension of connected text. This is not achieved through a linear progression that begins with discrete phonological, orthographic, and single-word reading (Hannon, 2012). Nor should it be assumed that phrase-level reading cannot be introduced until after mastery of these discrete and early-developing skills. The complex and interrelated nature of achieving fluent text reading suggests potential insights from Goodman’s sociopsycholinguistic theory of reading (1994/2003). Its foundations in language and cognitive processes of literacy may help in identifying practices that also address the linguistic challenges of DHH readers, and their effects on connected text reading (Kelly, 2003; Mayberry et al., 2011; Miller et al., 2012). We need a better understanding of DHH reading skill development that results in a more accurate model of the relative contributions and timing of skills that comprise fluent reading comprehension and the causal processes by which developing readers assemble related skills into a fluent cognitive package. At that point, educators and researchers can finally remedy the many decades of reading mediocrity that still characterize the abilities of too many DHH students. Conflict of Interest No conflicts of interest were reported. References Albertini, J., & Mayer, C. ( 2011). Using miscue analysis to assess comprehension in deaf college readers. Journal of Deaf Studies and Deaf Education , 16, 35– 46. doi:10.1093/deafed/enq017 Google Scholar CrossRef Search ADS   Allington, R. L. ( 2013). What really matters when working with struggling readers. The Reading Teacher , 66, 520– 530. doi:10.1002/TRTR.1154 Google Scholar CrossRef Search ADS   Almasi, J. F., Garas-York, K., & Shanahan, L. ( 2006). Qualitative research on text comprehension and the report of the National Reading Panel. The Elementary School Journal , 107, 37– 66. doi:10.1086/509526 Google Scholar CrossRef Search ADS   Alvarado, J. M., Puente, A., & Herrara, V. ( 2008). Visual and phonological coding in working memory and orthographic skills of deaf children using Chilean sign language. American Annals of the Deaf , 152, 467– 479. doi:10.1353/aad.2008.0009 Google Scholar CrossRef Search ADS   Alvermann, D. E., & Phelps, S. F. ( 2002). Assessment of students. In Richard-Amato P. A., & Snow M. A. (Eds.), Academic success for English language learners: Strategies for K-12 mainstream teachers  (pp. 103– 110). White Plains, N.Y: Longman. Anderson, D. ( 2006). Lexical development of deaf children acquiring signed languages. In Schick B., Marschark M., & Spencer P. E. (Eds.), Advances in the sign language development of deaf children  (pp. 135– 160). Oxford, Great Britain: Oxford University Press. Atwell, W. R. ( 2013). Deaf readers and phrasal verbs: Instructional refficacy of chunking as a visual tool (Unpublished doctoral dissertation). Lamar University, Beaumont, TX. Barrera, M., Liu, K., Thurlow, M., & Chamberlain, S. ( 2006). Use of chunking and questioning aloud to improve the reading comprehension of English language learners with disabilities (ELLs with Disabilities Report 17). Minneapolis, MN: University of Minnesota, National Center on Educational Outcomes. Borich, G. D. ( 2014). Effective teaching methods: Research-based practice . Boston: Pearson. Boudreault, P., & Mayberry, R. I. ( 2006). Grammatical processing in American Sign Language: Age of first-language acquisition effects in relation to syntactic structure. Language and Cognitive Processes , 21, 608– 635. doi:10.1080/01690960500139363 Google Scholar CrossRef Search ADS   Burns, M. K. ( 2003). Reexamining data from the National Reading Panel’s meta-analysis: Implications for school psychology. Psychology in the Schools , 40, 605– 612. doi:10.1002/pits.10110 Google Scholar CrossRef Search ADS   Bélanger, N. N., Baum, S. R., & Mayberry, R. K. ( 2011). Reading difficulties in adult deaf readers of French: Phonological codes, not guilty! Scientific Studies of Reading , 16, 263– 285. doi:10.1080/10888438.2011.568555 Google Scholar CrossRef Search ADS   Casteel, C. A. ( 1990). Effects of chunked text-material on reading comprehension of high and low ability readers. Reading Improvement , 27, 269– 275. Cheung, H., Chen, H. C., Lai, C. Y., Wong, O. C., & Hills, M. ( 2001). The development of phonological awareness: Effects of spoken language experience and orthography. Cognition , 81, 227– 241. doi:10.1016/S0010-0277(01)00136-6 Google Scholar CrossRef Search ADS   Clark, M. D., Gilbert, G., & Anderson, M. L. ( 2011). Morphological knowledge and decoding skills of deaf readers. Psychology , 2, 109– 116. doi:10.4236/psych.2011.22018 Google Scholar CrossRef Search ADS   Cook, B., Buysse, V., Klinger, J., Landrum, T., McWilliams, R., Tankersley, M., & Test, D. ( 2014). Council for Exceptional Children standards for evidence-based practices in special education . Alexandria, VA: Council for Exceptional Children. Cunningham, J. W. ( 2001). The National Reading Panel report. Reading Research Quarterly , 36, 326– 335. doi:10.1598/RRQ.36.3.5 Google Scholar CrossRef Search ADS   Dickinson, D. K., McCabe, A., Anastasopoulos, L., Peisner-Feinberg, E. S., & Poe, M. D. ( 2003). The comprehensive language approach to early literacy: The interrelationships among vocabulary, phonological sensitivity, and print knowledge among preschool-aged children. Journal of Educational Psychology , 95, 465. doi:10.1037/0022-0663.95.3.465 Google Scholar CrossRef Search ADS   Easterbrooks, S. R., & Stephenson, B. ( 2006). An examination of twenty literacy, science, and mathematics practices used to educate students who are deaf or hard of hearing. American Annals of the Deaf , 151, 386– 397. doi:10.1353/aad.2006.0043 Ehri, L. C., Nunes, S. R., Willows, D. M., Shuster, B. V., Yaghoub-Zadeh, Z., & Shanahan, T. ( 2001). Phonemic awareness instruction helps children learn to read: Evidence from the National Reading Panel’s meta-analysis. Reading Research Quarterly , 36, 250– 287. doi:10.1598/RRQ.36.3.2 Google Scholar CrossRef Search ADS   Friedmann, N., & Szterman, R. ( 2005). Syntactic movement in orally trained children with hearing impairment. Journal of Deaf Studies and Deaf Education , 11, 56– 75. doi:10.1093/deafed/enq052 Google Scholar CrossRef Search ADS   Gersten, R., Fuchs, L. S., Compton, D., Coyne, M., Greenwood, C., & Innocenti, M. S. ( 2005). Quality indicators for group experimental and quasi-experimental research in special education. Exceptional children , 71, 149– 164. 10.1177/001440290507100202 Google Scholar CrossRef Search ADS   Goodman, K. G. ( 1994/2003). Reading, writing, and written texts: A transactional sociopsycholinguistic view. In Flurkey A., & Xu J. (Eds.), On the revolution of reading: The selected writings of Kenneth S. Goodman  (pp. 3– 45). Portsmouth, NH: Heinemann. Goodman, K., Goodman, Y., & Paulson, E. J. ( 2009). Beyond word recognition: How retrospective and future perspectives on miscue analysis can inform our teaching. In Hoffman J. F., & Goodman Y. M. (Eds.), Changing literacies for changing times: An historical perspective on the future of reading research, public policy, and classroom practices  (pp. 146– 161). New York: Routledge. Goswami, U., Ziegler, J. C., & Richardson, U. ( 2005). The effects of spelling consistency on phonological awareness: A comparison of English and German. Journal of Experimental Child Psychology , 92, 345– 365. doi:10.1016/j.jecp.2005.06.002 Google Scholar CrossRef Search ADS   Hammill, D. D., & Swanson, H. L. ( 2006). The National Reading Panel’s meta-analysis of phonics instruction: Another point of view. The Elementary School Journal , 107, 17– 26. 10.1086/509524 Google Scholar CrossRef Search ADS   Hannon, B. ( 2012). Understanding the relative contributions of lower-level word processes, higher-level processes, and working memory to reading comprehension performance in proficient adult readers. Reading Research Quarterly , 47, 125– 152. 10.1002/RRQ.013 Google Scholar CrossRef Search ADS   Individuals with Disabilities Education Act of 2004. 20 U.S.C. § 1400 et seq. Izzo, A. ( 2002). Phonemic awareness and reading ability: An investigation with young readers who are deaf. American Annals of the Deaf , 147, 18– 28. doi:10.1353/aad.2012.0242 Google Scholar CrossRef Search ADS   James, D., Rajput, K., Brinton, J., & Goswami, U. ( 2008). Phonological awareness, vocabulary, and word reading in children who use cochlear implants: Does age of implantation explain individual variability in performance outcomes and growth? Journal of Deaf Studies and Deaf Education , 13, 117– 137. doi:10.1093/deafed/enm042 Google Scholar CrossRef Search ADS   Johnson, R. C., & Mitchell, R. E. ( 2008). Introduction. In Johnson R. C., & Mitchell R. E. (Eds.), Testing deaf students in an age of accountability  (pp. 1– 15). Washington, DC: Gallaudet University. Kelly, L. P. ( 2003). The importance of processing automaticity and temporary storage capacity to the differences in comprehension between skilled and less skilled college-age deaf readers. Journal of Deaf Studies and Deaf Education , 8, 230– 249. 10.1093/deafed/eng013 Google Scholar CrossRef Search ADS   Kelly, L., & Ewoldt, C. ( 1984). Interpreting nonverbatim cloze responses to evaluate program success and diagnose student needs for reading instruction. American Annals of the Deaf , 129, 45– 51. 10.1353/aad.2012.0857 Google Scholar CrossRef Search ADS   Kyle, F. E., & Harris, M. ( 2006). Concurrent correlates and predictors of reading and spelling achievement in deaf and hearing school children. Journal of Deaf Studies and Deaf Education , 11, 273– 288. 10.1093/deafed/enj037 Google Scholar CrossRef Search ADS   Kyle, F. E., & Harris, M. ( 2010). Predictors of reading development in deaf children: A 3-year longitudinal study. Journal of Experimental Child Psychology , 107, 229– 243. 10.1016/j.jecp.2010.04.011 Google Scholar CrossRef Search ADS   Kyle, F. E., & Harris, M. ( 2011). Longitudinal patterns of emerging literacy in beginning deaf and hearing readers. Journal of Deaf Studies and Deaf Education , 16, 289– 304. 10.1093/deafed/enq069 Google Scholar CrossRef Search ADS   LaSasso, C. ( 1980). The validity and reliability of the cloze procedure as a measure of readability for prelingually, profoundly deaf students. American Annals of the Deaf , 125, 559– 563. 10.1353/aad.2012.1497 Google Scholar CrossRef Search ADS   Lederberg, A. R. ( 2003). Expressing meaning: From communicative intent to building a lexicon. In Marschark M., & Spencer P. E. (Eds.), Oxford handbook of deaf studies, language, and education  (pp. 247– 260). Oxford: Oxford University Press. Lollis, J., & LaSasso, C. ( 2009). The appropriateness of the NC state-mandated reading competency test for deaf students as a criterion for high school graduation. Journal of Deaf Studies and Deaf Education , 14, 76– 97. 10.1093/deafed/enn017 Google Scholar CrossRef Search ADS   Luckner, J. L. ( 2013). Using the Dynamic Indicators of Basic Early Literacy Skills with students who are deaf or hard of hearing: Perspectives of a panel of experts. American Annals of the Deaf , 158, 7– 19. 10.1093/deafed/ent015 Google Scholar CrossRef Search ADS   Luckner, J. L., Sebald, A. M., Cooney, J., Young, J., III, & Muir, S. G. ( 2006). An examination of the evidence-based literacy research in deaf education. American Annals of the Deaf , 150, 443– 456. 10.1353/aad.2006.0008 Google Scholar CrossRef Search ADS   Luft, P., & Fochtman, M. (accepted with revision). Strengths-Based reading assessment: Applying miscue analysis to an African American native ASL-user. In Wang, Q. Y., & Andrews, J. F. (Eds.), Toward a global understanding of literacy education for deaf students . Washington, DC: Gallaudet University Press. Targeted publisher. MacSweeney, M., Waters, D., Brammer, M. J., Woll, B., & Goswami, U. ( 2008). Phonological processing in deaf signers and the impact of age of first language acquisition. NeuroImage , 40, 1369– 1379. 10.1016/j.neuroimage.2007.12.047 Google Scholar CrossRef Search ADS   Marschark, M., Schick, B., & Spencer, P. E. ( 2006). Understanding sign language development of deaf children. In Schick B., Marschark M., & Spencer P. E. (Eds.), Advances in the sign language development of deaf children  (pp. 3– 19). Oxford, Great Britain: Oxford University. Martin, D. S., & Mounty, J. L. ( 2005). Overview of the challenge. In Mounty J. L., & Martin D. S. (Eds.), Assessing deaf adults: Critical issues in testing and evaluation  (pp. 3– 10). Washington, DC: Gallaudet University. Mayberry, R. I., Chen, J., Witcher, P., & Klein, D. ( 2011a). Age of acquisition effects on the functional organization of language in the adult brain. Brain and Language , 119, 16– 29. 10.1016/j.banl.2011.05.007 Google Scholar CrossRef Search ADS   Mayberry, R. I., del Giudice, A. A., & Lieberman, A. M. ( 2011b). Reading achievement in relation to phonological coding and awareness in deaf readers: A meta-analysis. Journal of Deaf Studies and Deaf Education , 16, 164– 188. 10.1093/deafed/enq049 Google Scholar CrossRef Search ADS   Miller, P. ( 2009). The nature and efficiency of the word reading strategies of orally raised deaf students. Journal of Deaf Studies and Deaf Education , 14, 344– 361. 10.1093/deafed/enn044 Google Scholar CrossRef Search ADS   Moeller, M. P., Toblin, J. B., Yoshinaga-Itano, C., Connor, C., & Jerger, S. ( 2007). Current state of knowledge: Lanugage and literacy of children with hearing impairment. Ear & Hearing , 28, 740– 753. 10.1097/AUD.0b013e318157f07f Google Scholar CrossRef Search ADS   National Institute of Child Health and Human Development. ( 2000a). Report of the National Reading Panel. Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction (NIH Publication No. 00-4769). Washington, DC: U.S. Government Printing Office. National Institute of Child Health and Human Development. ( 2000b). Report of the National Reading Panel. Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction: Reports of the subgroups (NIH Publication No. 00-4754). Washington, DC: U.S. Government Printing Office. Nicholas, J. G., & Geers, A. E. ( 2003). Hearing status, language modality, and young children’s communicative and linguistic behavior. Journal of Deaf Studies and Deaf Education , 8, 422– 437. 10.1093/deafed/eng029 Google Scholar CrossRef Search ADS   No Child Left Behind Act of 2001, P.L. 107-110, Title IX, Sec 9101 (23)(A&B). Paris, S. G. ( 2005). Reinterpreting the development of reading skills. Reading Research Quarterly , 40, 184– 202. 10.1598/RRQ.40.2.3 Google Scholar CrossRef Search ADS   Paul, P. V., Wang, Y., Trezek, B. J., & Luckner, J. L. ( 2009). Phonology is necessary, but not sufficient: A rejoinder. American Annals of the Deaf , 154, 346– 356. 10.1353/aad.0.0110 Google Scholar CrossRef Search ADS   Pénicaud, S., Klein, D., Zatorre, R. J., Chen, J.-K., Witcher, P., Hyde, K., & Mayberry, R. I. ( 2013). Structural brain changes linked to delayed first language acquisition in congenitally deaf individuals. NeuroImage , 66, 42– 49. 10.1016/j.neuroimage.2012.09.076. Google Scholar CrossRef Search ADS   Rasinski, T. V. ( 1994). Developing syntactic sensitivity in reading through phrase-cued texts. Intervention in School and Clinic , 29, 65– 168. 10.1177/105345129402900307 Google Scholar CrossRef Search ADS   Rasinski, T., Yildirim, K., & Nageldinger, J. ( 2011). Building fluency through the phrased text lesson. The Reading Teacher , 65, 252– 255. 10.1002/TRTR.01036 Google Scholar CrossRef Search ADS   Scarborough, H. S., & Brady, S. A. ( 2002). Toward a common terminology for talking about speech and reading: A glossary of the “phon” words and some related terms. Journal of Literacy Research , 34, 299– 336. 10.1207/s15548430jlr3403_3 Google Scholar CrossRef Search ADS   Schirmer, B. R., Bailey, J., & Lockman, A. S. ( 2004). What verbal protocols reveal about reading strategies of deaf students: A replication study. American Annals of the Deaf , 140, 5– 16. 10.1353/aad.2004.0016 Google Scholar CrossRef Search ADS   Storch, S. A., & Whitehurst, G. J. ( 2002). Oral language and code-related precursors to reading: Evidence from a longitudinal structural model. Developmental Psychology , 38, 934– 947. 10.1037//0012-1649.38.6.934 Google Scholar CrossRef Search ADS   Thompson, B., Diamond, K. E., McWilliam, R., Snyder, P., & Snyder, S. W. ( 2005). Evaluating the quality of evidence from correlational research for evidence-based practice. Exceptional Children , 71, 181– 194. 10.1177/001440290507100204 Google Scholar CrossRef Search ADS   Trezek, B. J., Wang, Y., Woods, D. G., Gampp, T. L., & Paul, P. V. ( 2007). Using visual phonics to supplement beginning reading instruction for students who are deaf or hard of hearing. Journal of Deaf Studies and Deaf Education , 12, 373– 384. 10.1093/deafed/eni028 Google Scholar CrossRef Search ADS   U.S. Department of Education ( 2003). Identifying and implementing educational practices supported by rigorous evidence: A user-friendly guide . Washington, DC: Institute of Educational Sciences. Available from https://ies.ed.gov/ncee/pdf/evidence_based.pdf. Wang, Y., & Williams, C. ( 2014). Are we hammering square pegs into round holes? An investigation of the meta-analyses of reading research with students who are d/Deaf or hard of hearing and students who are hearing. American Annals of the Deaf , 159, 323– 345. 10.1353/aad.2014.0029 Google Scholar CrossRef Search ADS   Weinstock, R. B., & Mounty, J. L. ( 2005). Test-taking for deaf and hard of hearing individuals: Meeting the challenges. In Mounty J. L., & Martin D. S. (Eds.), Assessing deaf adults: Critical issues in testing and evaluation  (pp. 27– 36). Washington, DC: Gallaudet University. Appendix: Reviewed Studies Alvarado, J. M., Puente, A., & Herrara, V., (2008). Visual and phonological coding in working memory and orthographic skills of deaf children using Chilean sign language, American Annals of the Deaf, 152, 467–479. doi: 10.1353/aad.2008.0009 Bélanger, N.N., Baum, S. R., & Mayberry, R. K. (2011). Reading difficulties in adult deaf readers of French: Phonological codes, not guilty! Scientific Studies of Reading, 16, 263–285. doi: 10.1080/10,888,438.2011.568555 Clark, M. D., Gilbert, G., & Anderson, M. L. (2011). Morphological knowledge and decoding skills of deaf readers. Psychology, 2, 109–116. doi:10.4236/psych.2011.22018 Colin, S., Magnan, A., Ecalle, J., & Leybaert, J. (2007) Relation between deaf children’s phonological skills in kindergarten and word recognition performance in first grade. Journal of Child Psychology and Psychiatry 48, 139–146. doi:10.1111/j.1469-7610.2006.01700.x Cupples, L., Ching, T. Y. C., Crowe, K., Day, J., & Seeto, M. (2013). Predictors of early reading skill in 5-year-old children with hearing loss who use spoken language. Reading Research Quarterly, 49, 85–104. doi:10.1002/rrq.60 Daigle, D., & Armand, F. (2008). Phonological sensitivity in severely and profoundly deaf readers of French. Reading and Writing, 21, 669–717. doi: 10.1007/s11145-007-9087-5 Daza, M. T., Phillips-Silver, J., del Mar Ruiz-Cuadra, M., & López-López, F. (2014). Language skills and nonverbal cognitive processes associated with reading comprehension in deaf children. Research in Developmental Disabilities, 35, 3526–3533. doi: 10.1016/j.ridd.2014.08.030 Dillon, C.M., de Jong, K., & Pisoni, D.B. (2012). Phonological awareness, reading skills, and vocabulary knowledge in children who use cochlear implants. Journal of Deaf Studies and Deaf Education, 17, 205–226. doi:10.1093/deafed/enr043 Dyer, A., MacSweeney, M., Szczerbinski, M., Green, L., & Campbell, R. (2003). Predictors of reading delay in deaf adolescents: The relative contributions of rapid automatized naming speed and phonological awareness and decoding. Journal of Deaf Studies and Deaf Education, 8, 215–229. doi: 10.1093/deafed/eng012 Easterbrooks, S. R., Lederberg, A. R., Miller, E. M., Bergeron, J. P., & Connor, C. M. (2008). Emergent literacy skills during early childhood in children with hearing loss: Strengths and weaknesses. The Volta Review, 108, 91–114 Furlonger B., Holmes, V. M., & Rickards, F. W. (2014). Phonological awareness and reading proficiency in adults with profound deafness. Reading Psychology, 35, 357–396. doi: 10.1080/02,702,711.2012.726944 Geers, A. E. (2003). Predictors of reading skill development in children with early cochlear implantation. Ear & Hearing, 24, 595–685. doi: 10.1097/01.AUD.0000051690.43989 Gibbs, S. (2004) The skills in reading shown by young children with permanent and moderate hearing impairment. Educational Research, 46, 17–27. doi: 10.1080/0,013,188,042,000,178,791 Harris, M., & Moreno, C. (2004). Deaf children’s use of phonological coding: Evidence from reading, spelling, and working memory. Journal of Deaf Studies and Deaf Education 9, 253–268. doi: 10.1093/deafed/enh016 Izzo, A. (2002). Phonemic awareness and reading ability: An investigation with young readers who are deaf. American Annals of the Deaf, 147, 18–28. doi: 10.1353/aad.2012.0242 Johnson, C., & Goswami, U. (2010). Phonological awareness, vocabulary, and reading in deaf children with cochlear implants. Journal of Speech, Language, and Hearing Research, 53, 237–261. doi: 10.1044/1092-4388(2009/08-0139) Koo, D. Crain, K., LaSasso, C, & Eden, G. F. (2008). Phonological awareness and short-term memory in hearing and deaf individuals of different communication backgrounds. Annals of the New York Academy of Sciences, 1145, 83–99. doi: 10.1196/annals.1416.025 Kyle, F. E., & Harris, M. (2006). Concurrent correlates and predictors of reading and spelling achievement in deaf and hearing school children. Journal of Deaf Studies and Deaf Education 11, 273–288. doi:10.1093/deafed/enj037 Kyle, F. E., & Harris, M. (2010). Predictors of reading development in deaf children: A 3-year longitudinal study. Journal of Experimental Child Psychology 107, 229–243. doi:10.1016/j.jecp.2010.04.011 Kyle, F. E., & Harris, M. (2011). Longitudinal patterns of emerging literacy in beginning deaf and hearing readers. Journal of Deaf Studies and Deaf Education 16, 289–304. doi:10.1093/deafed/enq069 Luetke-Stahlman, B., & Nielsen, D. C. (2003). The contribution of phonological awareness and receptive and expressive English to the reading ability of deaf students with varying degrees of exposure to accurate English. Journal of Deaf Studies and Deaf Education, 8, 464–484. doi: 10.1093/deafed/eng028 Miller, P. (2009). The nature and efficiency of the word reading strategies of orally raised deaf students. Journal of Deaf Studies and Deaf Education 14, 344–361. doi:10.1093/deafed/enn044 Miller, P. (2010). Phonological, orthographic, and syntactic awareness and their relation to reading comprehension in prelingually deaf individuals: What can we learn from skilled readers. Journal of Developmental and Physical Disabilities, 22, 549–580. doi:10.1007/s10882-010-9195-z Miller, P., & Achmed, R. A. (2009). The development of orthographic knowledge in prelingually deaf individuals: New insight from Arab readers. Journal of Developmental and Physical Disabilities, 22, 11–31. doi: 10.1007/s10882-009-9160-x Miller, P., Kargin, T., Guldenoglu, B., Rathmann, C., Kubus, O., Hauser, P., & Spurgeon, E. (2012). Factors distinguishing skilled and less skilled deaf readers: Evidence from four orthographies. Journal of Deaf Studies and Deaf Education 17, 439–461. doi:10.1093/deafed/ens022 Most, T., Aram, D., & Andorn, T. (2006). Early literacy in children with hearing loss: A comparison between two educational systems. The Volta Review, 106, 5–28. Spencer, L. J., & Oleson, J. J. (2008). Early listening and speaking skills predict later reading proficiency in pediatric cochlear implant users. Ear and Hearing, 29, 270–80. doi: 10.1097/01.aud.0000305158.84403.f7 Spencer, L. J., & Tomblin, J. B. (2009). Evaluating phonological processing skills in children with prelingual deafness who use cochlear implants. Journal of Deaf Studies and Deaf Education 14, 1–21. doi:10.1093/deafed/enn013 © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Deaf Studies and Deaf Education Oxford University Press

Reading Comprehension and Phonics Research: Review of Correlational Analyses with Deaf and Hard-of-Hearing Students

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Abstract

Abstract This manuscript reviews 28 studies of reading research on deaf and hard-of-hearing (DHH) students published since 2000 that used correlational analyses. The examination focused on assessment issues affecting measurement and analysis of relationships between early phonological or orthographic skills and reading comprehension. Mixed outcomes complicate efforts to determine evidence-based practices, and to develop an accurate model of reading. Across the 28 studies, DHH participants represented a wide age range with potential floor and ceiling effects that reduce score variability for valid correlations. Many studies assessed readers beyond the optimal ages during which early skills develop and are most useful for reading. Reading skills also were assessed using a diverse array of measures and skill definitions. Particularly for reading comprehension, word-level and text-level abilities appear to be different constructs. Suggestions include more consistent skill definitions and differential timing for early- versus later-developing skill assessments to ensure more robust correlational relationships. The Search for Evidence-based Practices in Reading The federal No Child Left Behind Act of (2001) stipulated that educators use “scientifically-based research” to guide choices regarding instructional interventions. Subsequently, the Individuals with Disabilities Education Act (2004) indicated that education for children with disabilities should include the use of scientifically based instructional practices (Section 1400(c)(5)(E)). The difficulty for teachers of deaf and hard-of-hearing (DHH) students is that few scientifically validated practices have been identified. Two meta-analyses of literacy research on DHH students found few practices that met recommended standards identified by Cook et al. (2014) and the Institute for Educational Sciences (U.S. Department of Education, 2003). Luckner, Sebald, Cooney, Young, and Muir’s (2006) reviewed 22 studies of literacy practices recommended by the National Reading Panel (National Institute of Child Health and Human Development, 2000a, 2000b) used with DHH students. None of these studies met standards for strong evidence of effectiveness or even possible evidence of effectiveness. Easterbrooks and Stephenson (2006) reviewed 10 literacy practices routinely cited either in the literature or as field-supported practices with DHH students. Several had weak or developing evidence leading the authors to recommend their use while accumulating further substantiation. One recommended practice for early readers is that they receive instruction in phonemic awareness and phonics (National Institute of Child Health and Human Development, 2000a, 2000b). For students with typical hearing, phonemic awareness and letter knowledge have been the two best school-entry predictors of how well they learn to read during the first 2 years of instruction (Cunningham, 2001; Ehri, Nunes, Willows, Shuster, Yaghoub-Zadeh, & Shanahan, 2001; National Institute of Child Health and Human Development, 2000b). In contrast, research of early phonics-based instruction for DHH individuals has not shown consistent or strong support. Luckner et al.’s (2006) review identified 13 reading practices with large effect sizes; yet, none included use of phonics. Easterbrooks and Stephenson (2006) characterized phonics instruction with DHH students as a developing research base and concluded that instruction using visual and other support strategies allowed some DHH students to develop these skills, although some did not. As these reviews suggest, individual study outcomes on the contributions of phonological skills to reading comprehension of DHH children remain mixed. Several have reported significant relationships (Easterbrooks, Lederberg, Miller, Bergeron, & Connor, 2008; Paul, Wang, Trezek, & Luckner, 2009; Trezek, Wang, Woods, Gampp, & Paul, 2007). Yet, others have found low or non-significant correlations (Alvarado, Puente, & Herrara, 2008; Bélanger, Baum, & Mayberry, 2011; Clark, Gilbert, & Anderson, 2011; Izzo, 2002; Kyle & Harris, 2006; Miller, 2009). The effects of early and substantial hearing loss are likely to affect and perhaps alter, the acquisition and importance of phonics-based skills for young DHH readers. Hearing loss restricts attainment of language fluency if not addressed early and intensively (Anderson, 2006; Boudreault & Mayberry, 2006; Friedmann & Szterman, 2005; Lederberg, 2003; Marschark, Schick, & Spencer, 2006; Mayberry, Chen, Witcher, & Klein, 2011; Moeller, Toblin, Yoshinaga-Itano, Connor, & Jerger, 2007; Nicholas & Geers, 2003). Reduced access to the sounds of language likely affect the brain’s utilization for reading such that the mixed and inconclusive research reflect unique developmental trajectories. Identifying consistent skill relationships for DHH students could provide insights into strategies that may meet standards for evidence-based practice and result in more effective instruction. Concerns with Correlational Research of Phonemic Awareness and Phonological Skills Despite the apparent predictive strength of phonemic awareness and letter knowledge for most young readers with normal hearing, a number of researchers have raised concerns about the National Reading Panel’s conclusions. Several question their statistical assumptions or the practical significance of research cited in support of their findings (Allington, 2013; Almasi, Garas-York, & Shanahan, 2006; Burns, 2003; Hammill & Swanson, 2006; Paris, 2005). One important concern is that the variables used to measure phonemic awareness (PA) and reading comprehension (RC) do not demonstrate properties of equal variability (Paris, 2005). Optimal variability for PA occurs within a very limited time during acquisition. In contrast, RC develops across an individual’s lifespan with increasing and more stable score variability over time. These statistical properties extend to variables beyond PA and RC such that reading skills may be divided in two separate clusters. One cluster consists of constrained skills, characterized by a limited number of elements that typically are learned quickly. Young readers progress quickly from floor to asymptote during early reading instruction. Examples include learning the 26 letter names in English (orthographic awareness, OA) and the 43 letter-sound relationships (phonemic awareness, PA; and visually/print-supported phonological skills, PS). The second cluster consists of unconstrained reading skills that are acquired, developed, and refined over long periods of time. They consist of multiple components that are utilized in various ways, may never be entirely mastered, and comprise non-identical content. Reading comprehension and vocabulary development both are unconstrained skills with each encompassing multiple content domains. Statistical correlations between early- and late-developing and across constrained and unconstrained skills are problematic in that optimal variabilities between these skill clusters have little timing overlap. Analyses may require sophisticated causal modeling or exclusion methods in addition to assuring adequate score dispersion (Thompson, Diamond, McWilliam, Snyder, & Snyder, 2005). Strong correlational analyses require adequate variability among identified factors in order to ensure valid relationships. The need to optimize variability for conducting robust correlational analyses (Gersten et al., 2005; Thompson et al., 2005) is particularly complicated in identifying relationships between these reading skill clusters. Phonological awareness is an early pre-reading skill with its greatest variability during the first 18 months of acquisition, typically first grade and early second grade (Paris, 2005). Other early-acquired and constrained skills have similar developmental trajectories. However early reading comprehension, an unconstrained skill, has low initial variability until students attain initial fluency. Assessment during periods of greatest score variability for early code-based skills occurs when RC variability is minimal due to floor effects; and RC achieves score variability after code-based skills are minimally variable due to asymptote (Paris, 2005). Correlational studies assessing across these categories without adequate variation may be a reason for mixed results with DHH individuals. Wang and Williams’ (2014) meta-analysis identified timing limitations in that phonics- and code-based (early constrained) skills were more effective up to grade one. After this point, comprehension and mixed interventions on language- and thinking-based skills yielded greater effect sizes. Other research has likewise found that mature and experienced readers are less dependent on individual letter codes in comparison with metacognitive and text-based psycholinguistic reading skills (Goodman, 1994/2003; Goodman, Goodman, & Paulson, 2009; Kyle & Harris, 2010; Miller, 2009; Paris, 2005). Differential time-based developmental trajectories appear to effect measurement accuracy when analyzing relationships between various early and later-acquired reading skills. Another complicating factor is that the multiple skill components comprising reading comprehension affect the acquisition processes. Yet, the range of contributing factors may not be consistently included in research models that target strong and direct effects. Storch and Whitehurst’s (2002) longitudinal study of preschool through fourth grade students found that oral language abilities had strong and direct effects on later reading, but only beyond Grade 2. Before this, effects were mediated by code-related skills so that although oral language abilities were essential, they had only an indirect role until Grade 3. Another outcome was that reading accuracy and reading comprehension were found to be two separate abilities, each of which was influenced by different skills. The authors noted a danger in emphasizing phonological processing skills to the extent that other language skills were underestimated. Other studies have identified effects of language abilities on reading acquisition. Cheung, Chen, Lai, Wong, and Hills (2001) found that oral language influenced acquisition of phonological skills, which also were influenced by orthographic abilities. Dickinson, McCabe, Anastasopoulous, Peisner-Feinberg, and Poe (2003) identified shortcomings in vocabulary development that limited aspects of initial literacy development to include phonological sensitivity. Language fluency appears to be a potentially critical, but often under-identified, factor in examining the development of reading skills. The complexity of statistical modeling to reflect both direct and indirect effects, and changing relationships over time leaves most studies unavoidably imperfect (Thompson et al., 2005). The contributions of language fluency to reading skills is of particular importance for DHH individuals. Childhood delays in achieving a first language affect both functional and structural development of the brain with neuroanatomical differences that contrasted with those due to auditory deprivation (Mayberry et al., 2011; Pénicaud et al., 2013). MacSweeney, Waters, Brammer, Woll, and Goswami (2008) found comparable neural networks that supported phonological similarity judgments made in both English and British Sign Language but which were negatively impacted by delayed language acquisition. DHH children’s often-diminished linguistic abilities likely affect acquisition of phonological and code-based processing, and a range of associated reading skills. Yet, early indirect relationships are not consistently assessed in studies of reading development (Storch & Whitehurst, 2002) and may not be of sufficient strength to meet standards for evidence-based practice, potentially eliminating a critical factor in research efforts. An additional issue regards the assessment of research variables. Luckner et al. (2006) found that across 22 studies and 40 years of literacy investigations, no two studies examined the same dimensions of literacy. In that even a single construct can be measured in multiple ways greatly complicates efforts at cross-study comparisons and accruing a body of evidence-based practices. For example, within the domain of reading comprehension, Storch and Whitehurst (2002) found that reading accuracy and text comprehension were separate abilities. Hannon (2012) reported weak relationships between lower, word-level skills in comparison with higher-level reading processes. She suggested that word reading was a construct separate from text-level comprehension skills and each required differentiated instruction. Burns (2003) similarly identified word reading and contextualized reading as very different skills. Overall, the developmental processes for acquiring reading skills in DHH students appears to be multifaceted and complex, and potentially unique. The present study sought to re-examine reading research on DHH children conducted since 2000 that utilized correlational analyses to examine relationships between PA/PS, OA, and RC. An examination of timing and assessment issues could clarify reasons for mixed and inconclusive research results with DHH students. A more accurate model could offer a potential pathway toward evidence-based and effective instructional strategies and improved reading outcomes for these individuals. The research questions examined the timing of assessing component skills of reading, and the measures used to identify and examine these skills. They are as follows: What is the age-based timing of measurements in correlating early-developing and constrained PA/PS and OA skills with later-developing and unconstrained skills of reading comprehension in DHH individuals? In what ways are these reading skill components measured? Method This review identified research articles published between 2000 and 2017 that utilized correlational analyses to examine reading skills of DHH individuals. The search used the university library’s megadatabase “Discovery”, a service that performs a simultaneous search of over 300 individual research databases. The format allows for searches by keywords, title, or author with additional features to further limit the search, as needed. Searches used a range of terms for hearing loss and deafness, reading comprehension, and phonological skills in both the keyword and title functions. Each article’s reference list was reviewed for additional articles to ensure a comprehensive pool. Articles were eliminated that did not include an element of correlational analyses (to include regression) in their statistical procedures, or did not include either phonological or reading comprehension skills. Due to the paucity of research in this low-incidence population category, studies were not confined to a single language (English) but included other alphabetic languages. The acquisition of phonological processes to support reading skills has been identified as similar across alphabetic languages (Cheung et al., 2001; Goswami, Ziegler, & Richardson, 2005). Specifically, phonological processes have been found to remain similar across languages despite some variation in letter-sound consistency. Some of these differences are pronunciation of letter and letter clusters in Greek, Italian, German, Spanish which are more often consistent, whereas English, French, and Hebrew orthographies have less consistency (Goswami et al., 2005). However, the reading acquisition processes use similar alphabetic learning methods. Each article was examined to identify: (a) the age of participants, (b) the analytical methods used, (c) the reported correlational results, (d) the study conclusions regarding PA/PS, OA, with RC, and (e) measures of these skills. The target age for instruction in examining early-developing skills was defined as the 18 months for optimal PA/PS variability which coincides with the period of intensive OA instruction during first and early second grade, calculated to be between 6 and 8 years of age for the DHH population. This also is consistent with studies indicating that early constrained skills were most effective through first grade (National Institute of Child Health and Human Development, 2000b; Storch & Whitehurst, 2002; Wang & Williams, 2014). Examination of the articles could not verify that measurement of phonological awareness (PA) utilized an auditory-only presentation and therefore, the review combined PA with phonological skills (PS). The range of PA/PS are consistently early developing and infrequently utilized by mature readers (Goodman et al., 2009; Goodman, 1994/2003; National Institute of Child Health and Human Development, 2000b; Wang & Williams, 2014). In addition, a number of commonly used tests combine PA with other PS in order to create composite scores of sufficient statistical variability (Comprehensive Test of Phonological Processing, Dynamic Indicators of Basic Literacy Skills, the Texas Primary Reading Inventory, the Phonological Awareness Survey; Paris, 2005). A number of studies included OA in their analyses, also an early-developing constrained skill so this was added to the analyses, when present. Results The megadatabase search and examinations of reference sections of potential studies resulted in a total of 28 journal articles that utilized correlation and/or regression analyses. Table 1 lists the studies and the components examined. The primary language of the participants indicated that 18 used English and the remaining used a variety of languages, all of which had alphabetic orthography. Table 1. Studies of reading comprehension with PA and OA Study  Participants  Method  Reading variables  Correlation or regression results  Study conclusions  Alvarado, Puente, and Herrara (2008)  28 deaf children, 7–16 years old 15 hearing children of for level control  ANOVA Correlation Regression  RC: reading level Phonology Speechreading Orthography Fingerspelling Sign language  Correlation Sig: Reading level with sign language abilities (p < .01); with orthography (p < .05) NS: reading level with phonology, with speechreading, with visual similarityRegression: Sig: age with sign language to predict reading (R2 = .84)  Orthography and fingerspelling offer visual processing strategies for reading comprehension in conjunction with sign language  Bélanger, Baum, and Mayberry (2011)  29 Deaf adults, 22–55 years 16 hearing controls  ANOVA Linear regression  RC: reading level—groups defined by median split Phonology Speechreading Orthography  Regression NS: phonological prediction of reading level (R2 = .001, p = .86)  Less-skilled deaf readers’ reading difficulties are not caused by the lack of use of phonological codes  Clark, Gilbert, and Anderson (2011)  50 DHH college participants 51 hearing college participants  ANOVA Correlation  RC: English reading fluency PA Morphological knowledge ASL (bilingual) Proficiency  Correlation Sig: ASL skills with total score, (r = .34, p = .01); ASL with monomorphemic words (r = .30, p = .03) and with multimorphemic words correct (r = .35, p = .01) NS: ASL with phonology  No clear link between PA, reading, and decoding skills. Some deaf students demonstrate phonological knowledge and skills  Colin, Magnan, Ecalle and Leybaert (2007)  21 deaf 6-year-old prereaders 21 age-matched hearing children  2 year longitudinal study ANOVA Regression  RC: written word test Phoneme identification Speech intelligibility Rhymes  Regression Sig: word recognition score predicted by rhyme decision task (R2 change =28.3%)  PA was a significant correlate even after controlling for hearing loss  Cupples, Ching, Crowe, Day, and Seeto (2013)  101 5-year-old DHH children, 71 with hearing aids, 30 with implants  Correlation Multiple regression  RC: word and nonword reading, passage comprehension PA Letter knowledge Receptive vocabulary  Correlation: NS: PA with passage comprehensionRegression: Sig: PA with real-word reading, with word attack, with letter knowledge (ps < .001) when controlling for HA/CI, communication, cognitive ability, receptive vocabulary, and demographic variables  PA predicts aspects of early reading in 5-year olds with HL who use speech Phonological skills are most important in the earliest stages of reading Vocabulary at 5 years may be associated with reading at later stages of development  Daigle and Armand (2008)  24 DHH children who used sign language, 10–18 years in 3 age groups 24 age-matched hearing peers  ANOVA— repeated measures Correlation  RC zigzag test Graphemic (homophone) similarity of pseudowords Syllabic similarity Response time  Correlation Sig: Age with reading (r = .726, p < .001); DHH graphemic sensitivity with age (r = .492, p = .015), with reading (r = .719, p < .001); DHH syllabic sensitivity with age (r = .485, p = .016), with reading (r = .599, p = .002)  Syllabic sensitivity correlated with age and reading scores DHH had better scores when using both orthography and phonology Not all DHH show graphemic sensitivity  Daza, Phillips-Silver, Ruiz-Cadra, and López-López (2014)  30 DHH students 8–16 years; 23 used spoken language, 7 used sign language; 50% used CIs, 50% used HAs  Correlation Covariates: demographic or clinical variables to differentiate between good and poor readers  RC: sentence-picture matching and sentence completion PA: rhyme judgment Vocabulary Visual attention and memory  Correlation Sig: partial correlations—reading comprehension with vocabulary (r = .56; p = .003), with spatial memory (r = .47; p = .014), with visuospatial memory span (direct order: r = .63; p < .001)  Good readers were better at spoken language, vocabulary, spatial attention, visuospatial short-term and working memory, and executive functions. Differences in RC of readers was not related to PA skills.  Dillon, de Jong, and Pisoni (2012)  27 DHH children 6–14 years (8 = K-2), all used CIs and spoken language  Comparison of standard scores Correlation  RC: letter-phoneme match, word and nonword reading sentence reading PA: isolated sounds, monosyllables & syllable count Vocabulary  Correlation Sig: PA scores with all reading: word reading (r = 1.82, p < .001); word attack (r = 1.74, p < .001); PIAT-RC; PIAT-Total (r = 1.86, p < .001), (r = 1.85, p < .001)  Percentile ranks were in the bottom half on PA and reading Students had poorer vocabulary scores despite relatively high reading Older readers were generally less successful  Dyer, MacSweeney, Szczerbinski, Green, and Campbell (2003)  49 DHH, mean age 13 years, RA ≈ 7 years; all use BSL and TC 81 hearing controls CA matched & RA matched groups  Correlation  RC: cloze PA and decoding Picture rhyme Pseudohomophones Rapid automatized naming Speech or sign repetition  Correlation Sig: Rhyme with reading delay (r = –0.30, p < .05), with RAge (r = .39, p < .01); RAge with pseudohomophones (r = .46, p < .01); Rapid naming-sign and reading delay (r = –.47, p < .01) (unexpected direction) NS: Reading delay with the pseudohomophone matching  Deaf readers can make use of phonological structure to some extent in reading. Strong relationship between pseudohomophone task, reading, and IQ in the BSL-first language subgroup  Easterbrooks, Lederberg, Miller, Bergeron, and Connor (2008)  44 DHH children, 3–6 years, 32 in oral-only classes, 7 in TC, 5 in bilingual/TC classes  T-tests Fall/Spring Correlation  RC: passage comprehension (graphics and words) PA Speech perception Rhyming alliteration Syllable-segmentation Phonological processing  Correlation Sig: Age with all raw scores and between measures: Negative for age with passage comprehension (-.67), with letter-word identification (-.58), with vocabulary (-.30)  All measures generally correlated with Spring word identification and RC Majority of children performed poorly, particularly on rhyming words with little improvement; DHH gaps increased with age  Furlonger, Holmes, and Rickards (2014)  30 DHH adults, ages 18-61, profound HL, 17 preferred Auslan, 13 preferred spoken Hearing controls matched on age, gender, & NVIQ.  ANOVA lo/hi reading groups using medial split Correlation Linear regression  Word reading RC: word reading, passage reading with comprehension questions PA: phoneme, syllable, rhyme; phoneme-grapheme match Sign comprehension (at ceiling). Response time  Correlation Sig: PGC with word reading (r = .41, p < .05); word reading with RC (r = .85, p < .001) NS: PGC and RC (r = .24)Regression Sig: Three PA measures with reading comprehension, (F [3, 27] = 10.17, p < .001); PA measures with orthography information, (F [3, 27] = 10.84, p < .001); rhyme efficiency was the only significant unique predictor of RC (14% & 9%) across tasks  More proficient deaf readers were better at word reading Both deaf groups made rhyme judgments above the level of chance. Deaf participants showed a marked effect for orthographic condition; less-skilled readers showed greater reliance on orthography  Geers (2003)  181 DHH, 8–9 years; all implanted by 5 ½ years; 98 using oral and 83 using Total Communication.  Correlation Multiple regression  RC: modified cloze, sentence with picture Lexical and rhyming tasks— orthographic or phonological strategies  Correlation All significant (p < .0001) for reading with homophones, rhyme, and digit spanRegression Variance in reading principal components score predicted by multiple student and demographic factors  Reading outcome was most highly predicted by linguistic competence. Reading processing skills of word attack, word recognition, and educational placement did not make a substantial contribution to reading outcomes after demographic characteristics were accounted for.  Gibbs (2004)  Group 1: 15 DHH, moderate HL, 7–9 years; Group 2: 15 DHH children 6;2–7;10, all in mainstream and primary language was spoken English 3 groups of 30 hearing children controls  Correlation Hierarchical regression to partial out variance in reading due to PA.  RC: word recognition PA Vocabulary Syntax  Correlation Sig: Vocabulary with word recognition (r = .64, p < .01); vocabulary with word reading (r = .613, p < .01). NS: Initial phonemes or rhymes with single-word reading; DHH reading with PA, with memory spanRegression Vocabulary accounted for a further 27% of the variance in word recognition (F = 7.13; p < .01) following awareness of rhyme scores, 26% of the variance (F = 7.26; p < .01) following awareness of initial phonemes scores Rhyme scores and initial phonemes accounted for 19.5% (F = 4.0; p < .05) and 12% (F = 5.46; p < .05) of the variance in word reading  Ensuring use of language in advance of reading would reduce potential barriers and support acquisition of PA Development of PA may be contingent on vocabulary Some reading may be possible without closely associated phonological skills.  Harris and Moreno (2004)  Two groups of 30 DHH British children, 7–8 & 13–14 years CA and RA matched hearing peers  ANOVA MANOVA Regression  Reading age: word reading OA Phonology-spelling  Regression Sig: Reading of younger deaf by age (p < .05) and orthography (p < .01); reading of older deaf for memory span (p < .01) and orthography (p < .05); phonology was marginally significant (p = .06)  Orthography was a significant predictor of reading ability for both older and younger deaf children Phonology was not a significant predictor for younger, and marginally so for older DHH children Results suggest little DHH reliance on phonological coding  Izzo (2002)  29 primary DHH students, 4.33–13.16 years, from residential schools (unlikely to use phonological coding strategies)  Descriptive analyses Multiple regression  RC: retelling Age Language ability (Signed English-to-ASL continuum) PA  Correlation Sig: Reading with language ability = .58 (p ≤.001); with age = .50 (p ≤.001); Language ability with age = .51 (p ≤.001) NS: Reading with PA (r = .09), language ability with PA (r = .03)Regression NS: PA with reading Sig: Language, age, and PA (R2 = .397, p = .005); Individual predictors of language ability (p = .025) NS: Individual predictors: age (p = .1280) and PA (p = .665)  Reading ability was significantly correlated to language ability but not to PA PA may not facilitate reading development for DHH who use other strategies Some DHH with low PA achieved high reading comprehension  Johnson and Goswami (2010)  43 DHH, 5–15 years divided into early/late implant groups. 16 HA user controls 19 RA hearing controls, 6–9 years  ANOVA Multiple Regression  RC: single-word reading, word chains, read-aloud with comprehension questions PA: rhyme, initial and final phonemes Vocabulary Visual and auditory memory Auditory discrimination Speech intelligibility Speechreading of single words  Regression Sig: Composite of 4 reading scores with PA when entered before receptive vocabulary; age at cochlear implantation with vocabulary and reading outcomes when using quotient scores; RC with CI, controlling for age & NVIQ: rhyme =.584 (p < .001), initial phoneme =.364 (p < .05), final phoneme =.594 (p < .001) Receptive vocabulary: up to 38% of additional variance in each reading quotient measure NS: PA with reading composite scores after removing variance due to vocabulary  CI age was associated with development of oral language, auditory memory, and PA skills necessary for developing efficient word recognition skills. There is a benefit to earlier implantation.  Koo, Crain, LaSasso, and Eden (2008)  51 DHH college students grouped into native users of ASL, cued speech, oral Hearing native users of ASL, and hearing native speakers of English  ANOVA across 5 groups Correlation  RC: silent word reading, passage comprehension Phoneme Detection  Correlation Sig: PDT with passage comprehension (p < .006) Sig: Negative RC with reaction time for PDT, with silent word reading (τ = −.291, p < .01) NS: word-recognition fluency with PA  Deaf native ASL users had the lowest PA accuracy (p < .05). Silent word reading utilized more sight words and perhaps was NS for that reason.  Kyle and Harris (2006)  29 British deaf children 7-8 years Hearing controls matched for RA  Descriptive analyses ANOVA Correlation  RC: single-word reading, sentence comprehension PA Speechreading Spelling Vocabulary  Correlation Sig: PA with speechreading (r = .46, p < .05); vocabulary with SWR (r = .46, p < .05), with sentences (r = .70, p < .01), with speechreading (r = .48, p < .01). NS: PA with SWR, sentences, or spelling  DHH and hearing children did better when rime items were orthographically and phonologically congruent After controlling for hearing loss, productive vocabulary and speechreading (language factors) were significant predictors of reading PA did not correlate with RC after controlling for hearing loss  Kyle and Harris (2010)  29 DHH children, 7-8 years of age; 7 = oral, 18 = BSL, 4 = combination  3-year longitudinal study with 4 assessment periods Correlation Multiple regression  RC: single-word reading, cloze sentence comprehension, passage comprehension PA Speechreading Productive vocabulary  Regression Sig: Word reading at T1 with hearing loss (R2 = .77), with word reading at T2; productive vocabulary at T1 accounted for an additional 12% of sentence comprehension at T2; speechreading with productive vocabulary at T1 contributed a further 4% and ≈6%, respectively NS for Word reading at T2: PA at T1, hearing loss, and speechreading at T1  Vocabulary was the strongest and most consistent predictor of all reading measures across all time periods PA not a significant longitudinal predicator after Time1 reading levels DHH children may acquire phonology as a consequence of reading with early reading associated with later PA  Kyle and Harris (2011)  24 British DHH children, 5–6 years 23 hearing children 5–6 years, matched for word recognition  2-year longitudinal comparative study Descriptive analyses Correlation ANOVA  RC: single-word reading PA Letter name and letter-sounds Picture spelling Productive vocabulary Speechreading  Correlation Sig: PA with speechreading vocabulary, spelling, and word reading (ps < .01) NS: PA at T1 was not significantly related to reading at T2 or T3 Sig: Reading score at T1 with PA at T2 (p < .05)  Vocabulary at T1 was the most consistent significant correlate of reading at T2 and T3 even after controlling for NVIQ and T1 reading. Earlier PA was not a longitudinal correlate of reading after controlling for earlier levels Young DHH readers used a whole-word strategy and 2 years later used a more alphabetic strategy.  Luetke-Stahlman and Nielsen (2003)  31 DHH students, 7.9–17.9 years, no CIs; 9 in general education, 22 in self-contained  ANCOVA Correlation  RC-passage comprehension, word comprehension, word identification PA Receptive, expressive and written English  Correlation Sig: Word passage with word comprehension (r = .970, p = .0001), with word identification (r = .832, p = .0001), with phonemic substitution (r = .826, p = .0001) Sig: RC with blending phonemes (r = .723, p = .0001), with syllables (r = .720, p = .0001), with written language (r = .702, p = .0001), with letter identification (r = .700, p = .0001).  Reading strongly correlated with blending phonemes and syllables, segmenting sentences into words, and written English. Length of Signed English exposure did not yield higher reading measures or written language. Deaf students may use some different reading strategies.  Miller (2009)  31 Israeli high school and post-graduate students with prelingual deafness 59 hearing students  Correlation MANOVA  RC of word pairs PA and phonological decoding Response time  Correlation NS: PA, response time, and error rates overall and for each study condition: visual, phonological, and control; DHH for sentence comprehension, response time, and error rate  No significant evidence that DHH participants processed word pairs with less efficiency across conditions Phonemic skills do not significantly impact reading  Miller (2010)  83 Israeli prelingually deaf, 21 in primary school (3rd–4th), 36 in high school (10th–11th), 26 university (21–29 years); 85 control: 29 primary, 29 high school, 27 university  MANOVA Correlation Cluster analysis  RC: sentences and questions, semantically plausible and implausible sentences PA OA  Correlation Sig: OA with PA (r = .54, p < .001), by grade (p ≤ .05); SI with SP sentences (r = .56, p < .001); PA and OA with all sentence types (r = .48, p < .001; r = .36, p < .001)  DHH participants did not demonstrate PA growth over time Sentence-level processing showed individual word meaning using integration with syntactic (structural) knowledge OA plays a central role in processing written text; youngest DHH were non-strategic in their reading DHH participants had poorer PA and OA than hearing peers  Miller and Achmed (2009)  40 prelingually deaf Arab, ½ from primary (9.17–11.08 years) and half from middle-to high school (14.25–16.00 years) 40 control group  MANOVA Correlation  RC: word reading Word categorization: real word and pseudohomophones Rapid word naming  Correlations Sig: Categorization accuracy across real and pseudo conditions for young deaf (r = .84, p < .001) and older deaf (r = .60, p < .01); real word and pseudohomophone categorization for young (r = .39, p < .05) and older deaf (r = .37, p = .05) NS: categorization accuracy associated with speed of processing for the real-word condition  Young and old DHH categorized real words significantly more accurately than pseudohomophones which improved developmentally Older deaf and hearing did not differ in recognition of real words, which was not related to phonological development Older DHH had rates equal to hearing controls  Miller, Kargin, Guldenoglu, Rathmann, Kubus, Hauser, and Spurgeon (2012)  213 DHH, 6th–10th grade; Hebrew, Arabic, English, German participants  Cluster analysis ANOVA Correlation  RC: sentence comprehension with questions: semantically plausible & implausible. Phonology: pseudohomophones and real words Response time Native written language Strategies: syntactic, semantic, or unspecified  Correlations Sig: RC of the 2 sentence types for syntactic readers (r = .47, p < .001) Sig: Negative for syntactic readers for phonological processing with overall sentence comprehension scores (r = −.25, p < .05) NS: RC for semantic or unspecified strategy readers; phonemic processing among the 3 reader profiles despite large intergroup differences  Failure to find significant positive correlations between PA, phonological word decoding skills, and RC. Reading skills appear to develop independently of phonological processing skills. Syntactic deficits offer an explanation for difficulties in reading of DHH students  Most, Aram, and Andorn (2006)  42 DHH, 62–84 months (5–7 years) in 3 placements 11 hearing peers  ANONVA Correlation  RC: word recognition & explanation of choice PA OA Letter identification Word writing Receptive vocabulary  Correlations Sig: PA with vocabulary, with general knowledge, with writing, with reading explanations (ps < .01), with letter identification (p < .05)  PA, letter ID, knowledge and vocabulary was better for those in individual inclusion placements No statistically significant differences between individual and group inclusion programs regarding reading, writing, or OA  Spencer and Oleson (2008)  72 pediatric CI users, tested Simultan-eous (speech & SE) Approximately 5.1–11.3 years (48 months after implant)  Correlation Multiple regression  RC: word comprehension, word identification, word antonyms & synonyms, analogy Speech perception: word-picture identification, vowel perception Speech production: sentence repetition, story retelling Demographic background  Correlation Sig: Speech production measures with each other (r > .73, p < .0001)Multiple Regression Sig: Word reading with correct phonemes produced (short-long & retell), with speech perception, with age at testing (R2 = .59); paragraph comprehension with correct phonemes produced, consonant test, speech perception (R2 = .62)  Standard scores for reading were in the low average range for word identification and passage comprehension Both speech perception and production skills were strongly correlated with word identification and passage comprehension; early speech skills may predict later reading  Spencer and Tomblin (2009)  29 CI children, 7;2–17;8; 32 hearing controls HC, matched on mothers education & word comprehension, nearly 2 years younger than DHH    RC: word comprehension, word attack PA Rhyme judgment Phonological memory Rapid letter and number naming A/O (auditory only) or A/V (auditory-visual) conditions  Correlation Sig: Elision: with word attack (r = .63, p = .01), with word reading (r = .70, p = .01) Sig: Blending and A/V condition with word attack (r = .42, p = .05), with word reading (r = .37, p = .05) Sig: Nonword repetition with A/V (r = .41, p = .05, r = .38, p = .05); with rapid letter naming (r = .49, p = .01, r = .75, p = .01) NS: rapid letter naming, nonword repetition with A/O  The A/V condition was significantly correlated with two word reading tasks and may be a more accurate measure of phonological processing. CI children may hear some sounds (PA) then learn to associate full pronunciation after seeing print form (RC); they may learn PA from print (PS) rather than the reverse  Study  Participants  Method  Reading variables  Correlation or regression results  Study conclusions  Alvarado, Puente, and Herrara (2008)  28 deaf children, 7–16 years old 15 hearing children of for level control  ANOVA Correlation Regression  RC: reading level Phonology Speechreading Orthography Fingerspelling Sign language  Correlation Sig: Reading level with sign language abilities (p < .01); with orthography (p < .05) NS: reading level with phonology, with speechreading, with visual similarityRegression: Sig: age with sign language to predict reading (R2 = .84)  Orthography and fingerspelling offer visual processing strategies for reading comprehension in conjunction with sign language  Bélanger, Baum, and Mayberry (2011)  29 Deaf adults, 22–55 years 16 hearing controls  ANOVA Linear regression  RC: reading level—groups defined by median split Phonology Speechreading Orthography  Regression NS: phonological prediction of reading level (R2 = .001, p = .86)  Less-skilled deaf readers’ reading difficulties are not caused by the lack of use of phonological codes  Clark, Gilbert, and Anderson (2011)  50 DHH college participants 51 hearing college participants  ANOVA Correlation  RC: English reading fluency PA Morphological knowledge ASL (bilingual) Proficiency  Correlation Sig: ASL skills with total score, (r = .34, p = .01); ASL with monomorphemic words (r = .30, p = .03) and with multimorphemic words correct (r = .35, p = .01) NS: ASL with phonology  No clear link between PA, reading, and decoding skills. Some deaf students demonstrate phonological knowledge and skills  Colin, Magnan, Ecalle and Leybaert (2007)  21 deaf 6-year-old prereaders 21 age-matched hearing children  2 year longitudinal study ANOVA Regression  RC: written word test Phoneme identification Speech intelligibility Rhymes  Regression Sig: word recognition score predicted by rhyme decision task (R2 change =28.3%)  PA was a significant correlate even after controlling for hearing loss  Cupples, Ching, Crowe, Day, and Seeto (2013)  101 5-year-old DHH children, 71 with hearing aids, 30 with implants  Correlation Multiple regression  RC: word and nonword reading, passage comprehension PA Letter knowledge Receptive vocabulary  Correlation: NS: PA with passage comprehensionRegression: Sig: PA with real-word reading, with word attack, with letter knowledge (ps < .001) when controlling for HA/CI, communication, cognitive ability, receptive vocabulary, and demographic variables  PA predicts aspects of early reading in 5-year olds with HL who use speech Phonological skills are most important in the earliest stages of reading Vocabulary at 5 years may be associated with reading at later stages of development  Daigle and Armand (2008)  24 DHH children who used sign language, 10–18 years in 3 age groups 24 age-matched hearing peers  ANOVA— repeated measures Correlation  RC zigzag test Graphemic (homophone) similarity of pseudowords Syllabic similarity Response time  Correlation Sig: Age with reading (r = .726, p < .001); DHH graphemic sensitivity with age (r = .492, p = .015), with reading (r = .719, p < .001); DHH syllabic sensitivity with age (r = .485, p = .016), with reading (r = .599, p = .002)  Syllabic sensitivity correlated with age and reading scores DHH had better scores when using both orthography and phonology Not all DHH show graphemic sensitivity  Daza, Phillips-Silver, Ruiz-Cadra, and López-López (2014)  30 DHH students 8–16 years; 23 used spoken language, 7 used sign language; 50% used CIs, 50% used HAs  Correlation Covariates: demographic or clinical variables to differentiate between good and poor readers  RC: sentence-picture matching and sentence completion PA: rhyme judgment Vocabulary Visual attention and memory  Correlation Sig: partial correlations—reading comprehension with vocabulary (r = .56; p = .003), with spatial memory (r = .47; p = .014), with visuospatial memory span (direct order: r = .63; p < .001)  Good readers were better at spoken language, vocabulary, spatial attention, visuospatial short-term and working memory, and executive functions. Differences in RC of readers was not related to PA skills.  Dillon, de Jong, and Pisoni (2012)  27 DHH children 6–14 years (8 = K-2), all used CIs and spoken language  Comparison of standard scores Correlation  RC: letter-phoneme match, word and nonword reading sentence reading PA: isolated sounds, monosyllables & syllable count Vocabulary  Correlation Sig: PA scores with all reading: word reading (r = 1.82, p < .001); word attack (r = 1.74, p < .001); PIAT-RC; PIAT-Total (r = 1.86, p < .001), (r = 1.85, p < .001)  Percentile ranks were in the bottom half on PA and reading Students had poorer vocabulary scores despite relatively high reading Older readers were generally less successful  Dyer, MacSweeney, Szczerbinski, Green, and Campbell (2003)  49 DHH, mean age 13 years, RA ≈ 7 years; all use BSL and TC 81 hearing controls CA matched & RA matched groups  Correlation  RC: cloze PA and decoding Picture rhyme Pseudohomophones Rapid automatized naming Speech or sign repetition  Correlation Sig: Rhyme with reading delay (r = –0.30, p < .05), with RAge (r = .39, p < .01); RAge with pseudohomophones (r = .46, p < .01); Rapid naming-sign and reading delay (r = –.47, p < .01) (unexpected direction) NS: Reading delay with the pseudohomophone matching  Deaf readers can make use of phonological structure to some extent in reading. Strong relationship between pseudohomophone task, reading, and IQ in the BSL-first language subgroup  Easterbrooks, Lederberg, Miller, Bergeron, and Connor (2008)  44 DHH children, 3–6 years, 32 in oral-only classes, 7 in TC, 5 in bilingual/TC classes  T-tests Fall/Spring Correlation  RC: passage comprehension (graphics and words) PA Speech perception Rhyming alliteration Syllable-segmentation Phonological processing  Correlation Sig: Age with all raw scores and between measures: Negative for age with passage comprehension (-.67), with letter-word identification (-.58), with vocabulary (-.30)  All measures generally correlated with Spring word identification and RC Majority of children performed poorly, particularly on rhyming words with little improvement; DHH gaps increased with age  Furlonger, Holmes, and Rickards (2014)  30 DHH adults, ages 18-61, profound HL, 17 preferred Auslan, 13 preferred spoken Hearing controls matched on age, gender, & NVIQ.  ANOVA lo/hi reading groups using medial split Correlation Linear regression  Word reading RC: word reading, passage reading with comprehension questions PA: phoneme, syllable, rhyme; phoneme-grapheme match Sign comprehension (at ceiling). Response time  Correlation Sig: PGC with word reading (r = .41, p < .05); word reading with RC (r = .85, p < .001) NS: PGC and RC (r = .24)Regression Sig: Three PA measures with reading comprehension, (F [3, 27] = 10.17, p < .001); PA measures with orthography information, (F [3, 27] = 10.84, p < .001); rhyme efficiency was the only significant unique predictor of RC (14% & 9%) across tasks  More proficient deaf readers were better at word reading Both deaf groups made rhyme judgments above the level of chance. Deaf participants showed a marked effect for orthographic condition; less-skilled readers showed greater reliance on orthography  Geers (2003)  181 DHH, 8–9 years; all implanted by 5 ½ years; 98 using oral and 83 using Total Communication.  Correlation Multiple regression  RC: modified cloze, sentence with picture Lexical and rhyming tasks— orthographic or phonological strategies  Correlation All significant (p < .0001) for reading with homophones, rhyme, and digit spanRegression Variance in reading principal components score predicted by multiple student and demographic factors  Reading outcome was most highly predicted by linguistic competence. Reading processing skills of word attack, word recognition, and educational placement did not make a substantial contribution to reading outcomes after demographic characteristics were accounted for.  Gibbs (2004)  Group 1: 15 DHH, moderate HL, 7–9 years; Group 2: 15 DHH children 6;2–7;10, all in mainstream and primary language was spoken English 3 groups of 30 hearing children controls  Correlation Hierarchical regression to partial out variance in reading due to PA.  RC: word recognition PA Vocabulary Syntax  Correlation Sig: Vocabulary with word recognition (r = .64, p < .01); vocabulary with word reading (r = .613, p < .01). NS: Initial phonemes or rhymes with single-word reading; DHH reading with PA, with memory spanRegression Vocabulary accounted for a further 27% of the variance in word recognition (F = 7.13; p < .01) following awareness of rhyme scores, 26% of the variance (F = 7.26; p < .01) following awareness of initial phonemes scores Rhyme scores and initial phonemes accounted for 19.5% (F = 4.0; p < .05) and 12% (F = 5.46; p < .05) of the variance in word reading  Ensuring use of language in advance of reading would reduce potential barriers and support acquisition of PA Development of PA may be contingent on vocabulary Some reading may be possible without closely associated phonological skills.  Harris and Moreno (2004)  Two groups of 30 DHH British children, 7–8 & 13–14 years CA and RA matched hearing peers  ANOVA MANOVA Regression  Reading age: word reading OA Phonology-spelling  Regression Sig: Reading of younger deaf by age (p < .05) and orthography (p < .01); reading of older deaf for memory span (p < .01) and orthography (p < .05); phonology was marginally significant (p = .06)  Orthography was a significant predictor of reading ability for both older and younger deaf children Phonology was not a significant predictor for younger, and marginally so for older DHH children Results suggest little DHH reliance on phonological coding  Izzo (2002)  29 primary DHH students, 4.33–13.16 years, from residential schools (unlikely to use phonological coding strategies)  Descriptive analyses Multiple regression  RC: retelling Age Language ability (Signed English-to-ASL continuum) PA  Correlation Sig: Reading with language ability = .58 (p ≤.001); with age = .50 (p ≤.001); Language ability with age = .51 (p ≤.001) NS: Reading with PA (r = .09), language ability with PA (r = .03)Regression NS: PA with reading Sig: Language, age, and PA (R2 = .397, p = .005); Individual predictors of language ability (p = .025) NS: Individual predictors: age (p = .1280) and PA (p = .665)  Reading ability was significantly correlated to language ability but not to PA PA may not facilitate reading development for DHH who use other strategies Some DHH with low PA achieved high reading comprehension  Johnson and Goswami (2010)  43 DHH, 5–15 years divided into early/late implant groups. 16 HA user controls 19 RA hearing controls, 6–9 years  ANOVA Multiple Regression  RC: single-word reading, word chains, read-aloud with comprehension questions PA: rhyme, initial and final phonemes Vocabulary Visual and auditory memory Auditory discrimination Speech intelligibility Speechreading of single words  Regression Sig: Composite of 4 reading scores with PA when entered before receptive vocabulary; age at cochlear implantation with vocabulary and reading outcomes when using quotient scores; RC with CI, controlling for age & NVIQ: rhyme =.584 (p < .001), initial phoneme =.364 (p < .05), final phoneme =.594 (p < .001) Receptive vocabulary: up to 38% of additional variance in each reading quotient measure NS: PA with reading composite scores after removing variance due to vocabulary  CI age was associated with development of oral language, auditory memory, and PA skills necessary for developing efficient word recognition skills. There is a benefit to earlier implantation.  Koo, Crain, LaSasso, and Eden (2008)  51 DHH college students grouped into native users of ASL, cued speech, oral Hearing native users of ASL, and hearing native speakers of English  ANOVA across 5 groups Correlation  RC: silent word reading, passage comprehension Phoneme Detection  Correlation Sig: PDT with passage comprehension (p < .006) Sig: Negative RC with reaction time for PDT, with silent word reading (τ = −.291, p < .01) NS: word-recognition fluency with PA  Deaf native ASL users had the lowest PA accuracy (p < .05). Silent word reading utilized more sight words and perhaps was NS for that reason.  Kyle and Harris (2006)  29 British deaf children 7-8 years Hearing controls matched for RA  Descriptive analyses ANOVA Correlation  RC: single-word reading, sentence comprehension PA Speechreading Spelling Vocabulary  Correlation Sig: PA with speechreading (r = .46, p < .05); vocabulary with SWR (r = .46, p < .05), with sentences (r = .70, p < .01), with speechreading (r = .48, p < .01). NS: PA with SWR, sentences, or spelling  DHH and hearing children did better when rime items were orthographically and phonologically congruent After controlling for hearing loss, productive vocabulary and speechreading (language factors) were significant predictors of reading PA did not correlate with RC after controlling for hearing loss  Kyle and Harris (2010)  29 DHH children, 7-8 years of age; 7 = oral, 18 = BSL, 4 = combination  3-year longitudinal study with 4 assessment periods Correlation Multiple regression  RC: single-word reading, cloze sentence comprehension, passage comprehension PA Speechreading Productive vocabulary  Regression Sig: Word reading at T1 with hearing loss (R2 = .77), with word reading at T2; productive vocabulary at T1 accounted for an additional 12% of sentence comprehension at T2; speechreading with productive vocabulary at T1 contributed a further 4% and ≈6%, respectively NS for Word reading at T2: PA at T1, hearing loss, and speechreading at T1  Vocabulary was the strongest and most consistent predictor of all reading measures across all time periods PA not a significant longitudinal predicator after Time1 reading levels DHH children may acquire phonology as a consequence of reading with early reading associated with later PA  Kyle and Harris (2011)  24 British DHH children, 5–6 years 23 hearing children 5–6 years, matched for word recognition  2-year longitudinal comparative study Descriptive analyses Correlation ANOVA  RC: single-word reading PA Letter name and letter-sounds Picture spelling Productive vocabulary Speechreading  Correlation Sig: PA with speechreading vocabulary, spelling, and word reading (ps < .01) NS: PA at T1 was not significantly related to reading at T2 or T3 Sig: Reading score at T1 with PA at T2 (p < .05)  Vocabulary at T1 was the most consistent significant correlate of reading at T2 and T3 even after controlling for NVIQ and T1 reading. Earlier PA was not a longitudinal correlate of reading after controlling for earlier levels Young DHH readers used a whole-word strategy and 2 years later used a more alphabetic strategy.  Luetke-Stahlman and Nielsen (2003)  31 DHH students, 7.9–17.9 years, no CIs; 9 in general education, 22 in self-contained  ANCOVA Correlation  RC-passage comprehension, word comprehension, word identification PA Receptive, expressive and written English  Correlation Sig: Word passage with word comprehension (r = .970, p = .0001), with word identification (r = .832, p = .0001), with phonemic substitution (r = .826, p = .0001) Sig: RC with blending phonemes (r = .723, p = .0001), with syllables (r = .720, p = .0001), with written language (r = .702, p = .0001), with letter identification (r = .700, p = .0001).  Reading strongly correlated with blending phonemes and syllables, segmenting sentences into words, and written English. Length of Signed English exposure did not yield higher reading measures or written language. Deaf students may use some different reading strategies.  Miller (2009)  31 Israeli high school and post-graduate students with prelingual deafness 59 hearing students  Correlation MANOVA  RC of word pairs PA and phonological decoding Response time  Correlation NS: PA, response time, and error rates overall and for each study condition: visual, phonological, and control; DHH for sentence comprehension, response time, and error rate  No significant evidence that DHH participants processed word pairs with less efficiency across conditions Phonemic skills do not significantly impact reading  Miller (2010)  83 Israeli prelingually deaf, 21 in primary school (3rd–4th), 36 in high school (10th–11th), 26 university (21–29 years); 85 control: 29 primary, 29 high school, 27 university  MANOVA Correlation Cluster analysis  RC: sentences and questions, semantically plausible and implausible sentences PA OA  Correlation Sig: OA with PA (r = .54, p < .001), by grade (p ≤ .05); SI with SP sentences (r = .56, p < .001); PA and OA with all sentence types (r = .48, p < .001; r = .36, p < .001)  DHH participants did not demonstrate PA growth over time Sentence-level processing showed individual word meaning using integration with syntactic (structural) knowledge OA plays a central role in processing written text; youngest DHH were non-strategic in their reading DHH participants had poorer PA and OA than hearing peers  Miller and Achmed (2009)  40 prelingually deaf Arab, ½ from primary (9.17–11.08 years) and half from middle-to high school (14.25–16.00 years) 40 control group  MANOVA Correlation  RC: word reading Word categorization: real word and pseudohomophones Rapid word naming  Correlations Sig: Categorization accuracy across real and pseudo conditions for young deaf (r = .84, p < .001) and older deaf (r = .60, p < .01); real word and pseudohomophone categorization for young (r = .39, p < .05) and older deaf (r = .37, p = .05) NS: categorization accuracy associated with speed of processing for the real-word condition  Young and old DHH categorized real words significantly more accurately than pseudohomophones which improved developmentally Older deaf and hearing did not differ in recognition of real words, which was not related to phonological development Older DHH had rates equal to hearing controls  Miller, Kargin, Guldenoglu, Rathmann, Kubus, Hauser, and Spurgeon (2012)  213 DHH, 6th–10th grade; Hebrew, Arabic, English, German participants  Cluster analysis ANOVA Correlation  RC: sentence comprehension with questions: semantically plausible & implausible. Phonology: pseudohomophones and real words Response time Native written language Strategies: syntactic, semantic, or unspecified  Correlations Sig: RC of the 2 sentence types for syntactic readers (r = .47, p < .001) Sig: Negative for syntactic readers for phonological processing with overall sentence comprehension scores (r = −.25, p < .05) NS: RC for semantic or unspecified strategy readers; phonemic processing among the 3 reader profiles despite large intergroup differences  Failure to find significant positive correlations between PA, phonological word decoding skills, and RC. Reading skills appear to develop independently of phonological processing skills. Syntactic deficits offer an explanation for difficulties in reading of DHH students  Most, Aram, and Andorn (2006)  42 DHH, 62–84 months (5–7 years) in 3 placements 11 hearing peers  ANONVA Correlation  RC: word recognition & explanation of choice PA OA Letter identification Word writing Receptive vocabulary  Correlations Sig: PA with vocabulary, with general knowledge, with writing, with reading explanations (ps < .01), with letter identification (p < .05)  PA, letter ID, knowledge and vocabulary was better for those in individual inclusion placements No statistically significant differences between individual and group inclusion programs regarding reading, writing, or OA  Spencer and Oleson (2008)  72 pediatric CI users, tested Simultan-eous (speech & SE) Approximately 5.1–11.3 years (48 months after implant)  Correlation Multiple regression  RC: word comprehension, word identification, word antonyms & synonyms, analogy Speech perception: word-picture identification, vowel perception Speech production: sentence repetition, story retelling Demographic background  Correlation Sig: Speech production measures with each other (r > .73, p < .0001)Multiple Regression Sig: Word reading with correct phonemes produced (short-long & retell), with speech perception, with age at testing (R2 = .59); paragraph comprehension with correct phonemes produced, consonant test, speech perception (R2 = .62)  Standard scores for reading were in the low average range for word identification and passage comprehension Both speech perception and production skills were strongly correlated with word identification and passage comprehension; early speech skills may predict later reading  Spencer and Tomblin (2009)  29 CI children, 7;2–17;8; 32 hearing controls HC, matched on mothers education & word comprehension, nearly 2 years younger than DHH    RC: word comprehension, word attack PA Rhyme judgment Phonological memory Rapid letter and number naming A/O (auditory only) or A/V (auditory-visual) conditions  Correlation Sig: Elision: with word attack (r = .63, p = .01), with word reading (r = .70, p = .01) Sig: Blending and A/V condition with word attack (r = .42, p = .05), with word reading (r = .37, p = .05) Sig: Nonword repetition with A/V (r = .41, p = .05, r = .38, p = .05); with rapid letter naming (r = .49, p = .01, r = .75, p = .01) NS: rapid letter naming, nonword repetition with A/O  The A/V condition was significantly correlated with two word reading tasks and may be a more accurate measure of phonological processing. CI children may hear some sounds (PA) then learn to associate full pronunciation after seeing print form (RC); they may learn PA from print (PS) rather than the reverse  Note. CI = cochlear implant; Sig: = statistically significant; NS = non-significant. Research question one examined the ages of the participants in measuring PA/PS, OA, and RC which affects statistical variability needed for correlational analyses. Few studies reported variabilities of factors and therefore, this could not be directly examined. Across the 28 studies there was broad age range. Half (n = 14) used participants that were within the target ages; however, many included those who were above or below these ages. Three studies included younger participants who were potentially just beginning early reading skill acquisition. Seven studies included older participants expected to have achieved asymptote for early constrained skills. Four studies included both below and above target-age participants. Only three studies used participants solely within the target ages, with 11 using participants who were entirely outside of the target ages, 10 of which were older individuals. The “Participants” column of Table 1 lists the ages of the participants for each study. The examination of ages with correlations indicated that of the 17 studies reporting significant correlations between PA/PS and RC, 1 study used below target-age participants (Cupples et al., 2013); 3 used both younger and target-age participants (Easterbrooks et al., 2008; Kyle & Harris, 2011; Most et al., 2006); 1 used only target-age participants (Colin et al., 2007); 2 used younger, target age, and older participants (Johnson & Goswami, 2010; Spencer & Oleson, 2008); 4 used target age and older participants (Dillon et al., 2012; Geers, 2003; Luetke-Stahlman, & Nielsen, 2003; Spencer & Tomblin, 2009); and 6 used older participants (Daigle & Armand, 2008; Dyer et al., 2003; Furlonger et al., 2014; Koo et al., 2008; Miller & Achmed, 2009; Miller, 2010). A total of 12 studies reported non-significant correlations between PA/PS and RC including 2 that used participants within the target-age range (Kyle & Harris, 2006, 2010); 5 that used within and beyond target-age participants (Alvarado et al., 2008; Daza et al., 2014; Gibbs, 2004; Harris & Moreno, 2004; Izzo, 2002); and 5 that used only participants beyond the target age (Bélanger et al., 2011; Clark et al., 2011; Koo et al., 2008; Miller et al., 2012; Miller, 2009) with Koo et al. (2008) reporting both significant and non-significant correlations. Bélanger et al. (2011) also used linear regression analyses, finding non-significant relationship of phonological skills for predicting reading levels of older (adult) participants (R2 = .001, p = .86). Six studies reported correlations between orthographic awareness (OA) and RC. Significant correlations included two studies using target age and older participants (Alvarado et al., 2008; Harris & Moreno, 2004); and three using older participants (Bélanger et al., 2011; Daigle & Armand, 2008; Furlonger et al., 2014). One study reported non-significant correlations for participants beyond the target ages (Miller, 2010). Several studies reported significant relationships between unconstrained skills that would demonstrate variability long after initial acquisition. Significant relationships between vocabulary and reading comprehension were reported by two studies using target-age participants (Kyle & Harris, 2006, 2010), one using both target age and older participants (Daza et al., 2014), and one using older participants (Miller & Achmed, 2009). Significant correlations between sign language and reading comprehension were reported for mixed younger-through-older participants (Izzo, 2002), and for older participants (Miller, 2009). Alvarado et al. (2008) used regression to find a significant relationship between age and sign language acquisition in predicting reading (t (age) = 4.32, p < .01; t (sign language) = 3.32, p < .01) for target age and older participants. Clark et al. (2011) also found significant relationships between ASL skills and RC (r = .34, p = .01). Vocabulary was significantly correlated with RC in seven studies, and with PA/PS in seven studies. The second research question examined measurements used for targeted reading skills. PA/PS were measured by tests of phoneme detection, elision, blending, matching, phonological similarity, use of words and nonwords, rhyme decision & generation, syllabic similarity, and spelling. Not all studies examined OA skills; however, those that did also used a variety of measures to identify: letters in words, contrasting orthographically similar & dissimilar words & nonwords, phoneme-grapheme correspondence, lexical & rhyming tasks, words in letter strings (word chains), and rapid letter and number naming. A number of studies identified letter naming as an early reading skill and did not report these outcomes as a component of OA (Easterbrooks et al., 2008; Geers, 2003; Most et al., 2006). The present study did not re-categorize outcomes or reclassify measures if not so reported in the original articles. Studies also used a variety of measures for reading comprehension skills. Alvarado et al. (2008) defined this skill based on academic level while controlling for age. Izzo (2002) assessed reading comprehension through story retelling and while Spencer & Oleson (2008) also used story retelling, it was to assess speech/phonemic production skills. Several studies measured RC through letter identification, word and/or pseudoword reading/identification, or word chain tests (Colin et al., 2007; Cupples et al., 2013; Dillon et al., 2012; Easterbrooks et al., 2008; Furlonger et al., 2014; Geers, 2003; Gibbs, 2004; Harris & Moreno, 2004; Johnson & Goswami, 2010; Koo et al., 2008; Kyle & Harris, 2006, 2010, 2011; Luetke-Stahlman & Nielsen, 2003; Miller & Achmed, 2009; Miller, 2009; Spencer & Tomblin, 2009). Most et al. (2006) used word recognition to examine RC but asked participants to explain their answer. Of the 18 studies using letter and word-based measures, all but 5 reported significant correlations between PA/PS and RC (Gibbs, 2004; Harris & Moreono, 2004; Kyle & Harris, 2006, 2010; Miller, 2009). A number of the studies used text-level reading to assess RC such as sentence and short-passage cloze procedures (Cupples et al., 2013; Daza et al., 2014; Dyer et al., 2003; Geers, 2003; Koo et al., 2008; Kyle & Harris, 2010; Luetke-Stahlman & Nielsen, 2003; Spencer & Oleson, 2008) with two of these eight reporting non-significant PA/PS and RC correlations (Daza et al., 2014; Kyle & Harris, 2010). Others used longer text with RC scores based on comprehension questions, correct choices, or pictures (Bélanger et al., 2011; Clark et al., 2011; Cupples et al., 2013; Daigle & Armand, 2008; Daza et al., 2014; Dillon et al., 2012; Easterbrooks et al., 2008; Furlonger et al., 2014; Johnson & Goswami, 2010; Kyle & Harris, 2006, 2010; Luetke-Stahlman & Nielsen, 2003; Miller et al., 2012; Miller, 2009; Miller, 2010; Spencer & Oleson, 2008). Of the 16 studies using text-based RC stimuli, nearly half reported non-significant correlations with PA/PS (Bélanger et al., 2011; Clark et al., 2011; Daza et al., 2014; Kyle & Harris, 2006, 2010; Miller et al., 2012; Miller, 2009). Overall, the 28 studies employed a notable variety of measures across these reading skills. Discussion This study reviewed research on DHH readers that reported on correlations between early- and late-developing constrained (PA/PS, OA) and unconstrained (RC) skills. The purpose was to examine potential effects of assessment timing and constructs that may contribute to inconsistent outcomes regarding these skills. The search of research databases identified 28 studies that fit criteria and reported either significant or non-significant correlations between PA/PS or OA and RC. The first research question examined the age of participants in correlating early-acquired and constrained skills of PA/PS and/or OA with reading comprehension. Across the 28 studies, 10.7% (n = 3) used participants within the target-age group defined as 6–8 years of age; 1 was below the target age (3.6%), 10 were older (35.7%), with most using mixed ages (n = 14, 50%). Of the 17 studies reporting significant correlations between PA/PS and RC, 6 (35.3%) were with older participants and 4 (23.5%) were with target age and older participants. More studies used older readers despite PA/PS and OA being early-acquired skills with attainment of asymptote expected by 8 or 9 years of age. Of the 12 studies that did not report significant PA/PS and RC correlations, 5 (41.7%) were with target age and older participants, 4 (33.3%) were with older participants, with Koo et al. (2008) reporting both significant and non-significant correlations. Six studies reported on relationships between OA and RC with five that were significant (two with target age and older, three with older participants) and one that indicated non-significant correlations using older participants. Those studies using participants within the target ages for PA/PS and OA would not likely have had sufficient variability for the later-developing skill of reading comprehension. This is most salient for correlations with text-based reading which is not attained until a child is beyond floor levels, at age 9 or above (National Institute of Child Health and Human Development, 2000b; Wang & Williams, 2014). Only studies correlating PA/PS or OA with word recognition would tend to have equal variability across these factors. Of the three studies using only target-age participants, one reported a significant correlation between PA/PS and RC (Colin et al., 2007) and two reported non-significant correlations (Kyle & Harris, 2006, 2010). None of the studies examining OA and RC used target-age participants. Outcome patterns are difficult to ascertain and interpret among the remaining studies and varying ages. For those using older participants, six reported significant correlations between PA/PS and RC (Daigle & Armand, 2008; Dyer et al., 2003; Furlonger et al., 2014; Koo et al., 2008; Miller & Achmed, 2009; Miller, 2010) while five reported non-significant correlations (Bélanger et al., 2011; Clark et al., 2011; Koo et al., 2008; Miller et al., 2012; Miller, 2009). Five studies reported significant OA and RC correlations with three that used only older participants (Bélanger et al., 2011; Daigle & Armand, 2008; Furlonger et al., 2014) and two that used target age and older participants (Alvarado et al., 2008; Harris & Moreno, 2004). The correlation results for older participants would be expected to approach asymptote or ceiling effects with reduced variability although few studies identified these. Daigle and Armand (2008) reported ceiling effects although only with hearing participants for graphemic coding. Miller (2009) reported ceiling effects on PA/PS for his older participants and Luetke-Stahlman and Nielsen (2003) reported ceiling effects for initial and final phonemes for their mixed-age participants. Although their study used ANOVA, James, Rajput, Brinton, and Goswami (2008) reported that their results for PA/PS and RC for DHH children approximately 5–10 years of age were impacted by ceiling effects for syllable awareness. These studies suggest that DHH readers experience similar age-based ceiling and asymptote effects although reported by only 3 of the 21 studies using mixed and older participants. Another statistical concern is with floor effects. The National Reading Panel characterized children below first grade as being nonreaders, with this skill being acquired gradually through first and second grade. About 2 of the 28 studies (Cupples et al., 2013; Easterbrooks et al., 2008) reported floor effects with a number of their participants unable to perform several reading tasks. This emphasizes the importance of timing assessments to ensure maximum variability to achieve robust correlations, and avoidance of both floor and ceiling effects among developing skill sequences. Age of participants was not a factor in studies reporting correlations between unconstrained skills. Significant relationships were reported by four studies for vocabulary and RC, two for language and RC, and two for sign language and reading level or RC. Similarly, a meta-analysis of 57 studies examining phonological coding with DHH individuals (Mayberry, del Giudice, & Lieberman, 2011) identified the importance of linguistic skills. Half of the studies had significant effect sizes for PA/PS with RC; however, the mean effect size was .35 (low to medium) and represented just 11% of the variance in RC scores. By comparison, language ability predicted 35% of the variance in RC. The importance of these linguistic skills, also reported by Storch and Whitehurst (2002), suggests a need for further examination of unconstrained skills that may contribute to the reading comprehension of DHH individuals. Such correlations would be less affected by timing once individuals had attained initial fluency. Research question two examined the measures used for assessing component reading skills as a source of potential disparity in reported relationships. PA/PS was measured in more than 10 different ways with this variety suggesting that these discrete skills may be dissimilar enough to warrant caution in making cross-study generalizations. Assessment of OA skills also used approximately 10 different measures with some that contrasted phonemic with graphemic strategies in conjunction with PA/PS development. Spelling was analyzed for orthography in Harris and Moreno (2004) but not in other studies. Orthographic skills were included in several other studies but reported in terms of other linguistic competencies (e.g., Miller & Achmed, 2009). Again, the variety of assessment targets within OA suggests caution in reporting potential acquisition patterns across studies. Notably diverse was the range of skills and the types of measures used to assess reading comprehension. These included individual word or pseudoword reading, sentence reading, and passage comprehension measured by retelling, explanation of answers, correct picture or answer choice, or cloze procedures. Research into testing and test construction has identified particular difficulties for DHH students that result from wording of questions and items, and use of shorter versus longer passages such that scores do not accurately reflect their abilities (Alvermann & Phelps, 2002; Johnson & Mitchell, 2008; Lollis & LaSasso, 2009; Martin & Mounty, 2005; Weinstock & Mounty, 2005). Explanations or responses that are evaluated based on comparisons with hearing participant norms are likely to produce relatively poorer scores. Tests of pseudoword word reading fluency and differentiation between words and pseudowords may be negatively affected by DHH students’ frequently diminished linguistic skills (Luckner, 2013). A panel review of the Dynamic Indicators of Basic Literacy Skills (DIBELS) identified only one of seven subtests as genuinely assessing reading comprehension. Many commercially available reading assessments and several used in these studies have similar tests and subtests. Other test concerns were raised by LaSasso (1980) with regard to cloze procedures such that DHH individuals’ cloze scores did not correlate well with predicted passage difficulty. DHH students tend to have much poorer performance levels when held to typical scoring standards (Kelly & Ewoldt, 1984). A number of the 28 studies utilized cloze procedures. The differences in assessing and comparing comprehension achievement across single-word and connected text reading scores likely contributes to the mixed outcomes reported by these studies. Single-word reading utilizes sight-word and word attack or phonemic skills and is dissimilar to sentence or passage comprehension skills that require multiple psycholinguistic processes (Goodman et al., 2009; Goodman, 1994/2003; Kyle & Harris, 2010; Miller, 2009; Paris, 2005). The National Reading Panel’s conclusion that PA is foundational for assisting with the alphabetic relationships for reading and spelling is based on its utility for word and pseudoword reading, with much smaller effects reported for contextualized reading (National Institute of Child Health and Human Development, 2000b). Word- and passage reading have been identified as separate constructs (Allington, 2013; Burns, 2003; Hannon, 2012; Storch & Whitehurst, 2002) with Dillon et al. (2012) describing even sentence-level comprehension as inferior to passage comprehension in relying more on phonological knowledge than authentic comprehension skills. Among the 17 studies finding significant correlations between PA/PS and RC, more used word-level reading alone (29.4%) or in combination with text reading responses (41.2%) for a total of 82.3%. This is in contrast with studies that used only using text reading with 17.6% reporting significant relationships between PA/PS and RC. For the 11 studies reporting non-significant correlations, 54.6% used word or word measures in combination with others, whereas 45.5% used only text reading measures. The Alvarado et al. (2008) method for determining reading level in correlation with age was not included in these calculations. The five studies examining OA and RC correlations included just one study using only word-level reading to measure RC (Harris & Moreno, 2004), the others using text-level responses alone (Bélanger et al., 2011; Daigle & Armand, 2008; Furlonger et al., 2014, Miller, 2010), and the Miller study reporting non-significant correlations. The difficulty in the current review is that the varying measures likely obscured patterns as well as differences among the reading constructs and PA/PS and OA subskills. This was further complicated by participant age effects on variability across early, constrained and later, unconstrained skills. Even for studies within the target-age range reading measures varied with Colin et al. (2007) using a word recognition test, and with the Kyle & Harris studies using word reading, word recognition, and sentence comprehension. Assessing the contributions of early-acquired skills to those that occur later is important, but require differential timing that may be best addressed using longitudinal methodology, such as used by the Kyle and Harris studies. This would accommodate differential developmental trajectories among component skills while also monitoring fluctuating direct and indirect relationships that are characteristic of developing readers. Summary and Recommendations This review identified several age-based and construct measurement concerns in studies of relationships between early- and later-developing reading skills. Participant ages suggested probable floor and ceiling effects with reduced variability for robust and valid correlations, although relatively few were reported. Most of the 28 studies tested participants outside the optimal ages for PA/PS, OA, or RC. Another issue was with assessments that created composite scores to enhance statistical robustness but that confounded tracking of discrete skills. Reading skills of PA/PS, OA, and RC were not consistently defined among studies or assessments. These issues likely have contributed to inconsistent findings and ongoing obstacles to identifying evidence-based literacy practices with DHH individuals (e.g., Luckner et al., 2006). Although these 28 studies used similar research methodology and skill targets, the variety of skill and construct measurements, ages of participants, and stages of skill development preclude drawing any conclusions. A particular challenge is that the formulation of robust and parsimonious research models limits examination of the potentially multiple related skills that contribute to fluent reading comprehension. One observation is that assessment of skill constructs that reflect age-based timing constraints across early- and later-developing skills, and that capture multiple and varying contributions of component skills across readers’ maturation may be best achieved through longitudinal research designs. One consistent relationship regardless of age, was among unconstrained skills of vocabulary, language (English or sign language), and reading comprehension. Importantly, Miller et al. (2012) found that linguistic skills based on syntactic processing were more effective for adolescent DHH readers than semantic (word-based) strategies. Wang and Williams (2014) also reported that language and thinking skills yielded larger RC effect sizes once readers understood the written code. The difficulty for many DHH readers is with their struggle to attain primary language fluency prior to reading instruction. Studies using hearing children rarely examine linguistic abilities in that most achieve basic skills in early childhood. Yet, Storch and Whitehurst (2002) confirmed the importance of these skills for later reading with normal hearing readers. Linguistic skills are at least as important for DHH readers (Mayberry et al., 2011). This review limited itself to correlational studies which does not allow for identification of causal relationships. The field continues to wrestle with isolating significant contributing factors and the interrelationships that characterize the developmental trajectory of skillful readers. These relationships evolve over time and show unequal rates of growth, with further complications from skill co-dependencies and effects due to co-developing cognitive and linguistics competencies (Paris, 2005). Research needs to move beyond cross-sectional and correlational studies into longitudinal and multi-skill component analyses. Another significant issue raised by this review is the disparity in skill and construct measurement. This likely contributes to the difficulty in accumulating a consistent body of research that: (a) describes the nature and timing of the important factors that contribute to the development of fluent reading, and (b) isolates potential evidence-based instructional practices. Word- and text-level reading typically are treated as interchangeable aspects of “reading comprehension” but instead, are unique skill clusters that should be differentiated for assessment and instruction. The comingling of these two constructs obscures potentially productive research avenues that otherwise could more accurately identify optimal developmental patterns and processes that lead to fluent text reading. To summarize, this review has several potential implications for the planning of future research: Research methodologies should examine reading skills across the developmental trajectory that expand beyond common correlational approaches in order to accurately represent causal connections, maturational change, and varying skill contributions across the acquisition-to-mastery continuum. Models should allow for potential direct and indirect and evolving effects of component skills in order to identify potentially effective and evidence-based practices. Researchers, diagnosticians, and educators need to develop and adhere to consistent definitions of critical reading skills and subskills. For example, Scarborough and Brady (2002) defined reading and speech skill terms beginning with the morpheme “phon” to ensure consistency. At a minimum, word reading should be clearly differentiated from comprehension of connected text, and subskills should be unambiguously identified and defined. Research on instruction should not only distinguish between the two major reading constructs but also reflect the distinctive cognitive skills from which each construct develops. Many early-acquired skills (e.g., OA, PA, PS, and word identification) are learned through memorization, rote practice, and direct instruction processes. In contrast, comprehension of connected text is a higher-order cognitive skill that requires analysis and interpretation through inquiry and discovery learning processes (Borich, 2014). Connected text comprehension is more than a sum of individual word identifications. The historic struggles of DHH individuals to become fluent readers may have created a situation in which singular subskills have become overly emphasized and presumed as prerequisites for text comprehension. Examining the higher-order cognitive skills necessary to develop connected text comprehension may prompt investigations into more effective instructional strategies. These should consider phrase-level reading or “chunking” which provide the foundations for comprehension of sentences, paragraphs, and increasingly larger segments of text. This has been an approach used with hearing students (Barrera, Liu, Thurlow, & Chamberlain, 2006; Rasinski, 1994; Rasinski, Yildirim, & Nageldinger, 2011) or students with learning disabilities (Casteel, 1990). DHH students also have abilities to acquire phrase-level skills (Albertini & Mayer, 2011; Kelly, 2003; Luft & Fochman, accepted with revision) although not consistently (Albertini & Mayer, 2011; Atwell, 2013; Kelly, 2003; Schirmer, Bailey, & Lockman, 2004), perhaps affected by varying levels of language fluency. The goal of reading instruction is to develop fluent comprehension of connected text. This is not achieved through a linear progression that begins with discrete phonological, orthographic, and single-word reading (Hannon, 2012). Nor should it be assumed that phrase-level reading cannot be introduced until after mastery of these discrete and early-developing skills. The complex and interrelated nature of achieving fluent text reading suggests potential insights from Goodman’s sociopsycholinguistic theory of reading (1994/2003). Its foundations in language and cognitive processes of literacy may help in identifying practices that also address the linguistic challenges of DHH readers, and their effects on connected text reading (Kelly, 2003; Mayberry et al., 2011; Miller et al., 2012). We need a better understanding of DHH reading skill development that results in a more accurate model of the relative contributions and timing of skills that comprise fluent reading comprehension and the causal processes by which developing readers assemble related skills into a fluent cognitive package. At that point, educators and researchers can finally remedy the many decades of reading mediocrity that still characterize the abilities of too many DHH students. Conflict of Interest No conflicts of interest were reported. References Albertini, J., & Mayer, C. ( 2011). Using miscue analysis to assess comprehension in deaf college readers. Journal of Deaf Studies and Deaf Education , 16, 35– 46. doi:10.1093/deafed/enq017 Google Scholar CrossRef Search ADS   Allington, R. L. ( 2013). What really matters when working with struggling readers. The Reading Teacher , 66, 520– 530. doi:10.1002/TRTR.1154 Google Scholar CrossRef Search ADS   Almasi, J. F., Garas-York, K., & Shanahan, L. ( 2006). Qualitative research on text comprehension and the report of the National Reading Panel. The Elementary School Journal , 107, 37– 66. doi:10.1086/509526 Google Scholar CrossRef Search ADS   Alvarado, J. M., Puente, A., & Herrara, V. ( 2008). Visual and phonological coding in working memory and orthographic skills of deaf children using Chilean sign language. American Annals of the Deaf , 152, 467– 479. doi:10.1353/aad.2008.0009 Google Scholar CrossRef Search ADS   Alvermann, D. E., & Phelps, S. F. ( 2002). Assessment of students. In Richard-Amato P. A., & Snow M. A. (Eds.), Academic success for English language learners: Strategies for K-12 mainstream teachers  (pp. 103– 110). White Plains, N.Y: Longman. Anderson, D. ( 2006). Lexical development of deaf children acquiring signed languages. In Schick B., Marschark M., & Spencer P. E. (Eds.), Advances in the sign language development of deaf children  (pp. 135– 160). Oxford, Great Britain: Oxford University Press. Atwell, W. R. ( 2013). Deaf readers and phrasal verbs: Instructional refficacy of chunking as a visual tool (Unpublished doctoral dissertation). Lamar University, Beaumont, TX. Barrera, M., Liu, K., Thurlow, M., & Chamberlain, S. ( 2006). Use of chunking and questioning aloud to improve the reading comprehension of English language learners with disabilities (ELLs with Disabilities Report 17). Minneapolis, MN: University of Minnesota, National Center on Educational Outcomes. Borich, G. D. ( 2014). Effective teaching methods: Research-based practice . Boston: Pearson. Boudreault, P., & Mayberry, R. I. ( 2006). Grammatical processing in American Sign Language: Age of first-language acquisition effects in relation to syntactic structure. Language and Cognitive Processes , 21, 608– 635. doi:10.1080/01690960500139363 Google Scholar CrossRef Search ADS   Burns, M. K. ( 2003). Reexamining data from the National Reading Panel’s meta-analysis: Implications for school psychology. Psychology in the Schools , 40, 605– 612. doi:10.1002/pits.10110 Google Scholar CrossRef Search ADS   Bélanger, N. N., Baum, S. R., & Mayberry, R. K. ( 2011). Reading difficulties in adult deaf readers of French: Phonological codes, not guilty! Scientific Studies of Reading , 16, 263– 285. doi:10.1080/10888438.2011.568555 Google Scholar CrossRef Search ADS   Casteel, C. A. ( 1990). Effects of chunked text-material on reading comprehension of high and low ability readers. Reading Improvement , 27, 269– 275. Cheung, H., Chen, H. C., Lai, C. Y., Wong, O. C., & Hills, M. ( 2001). The development of phonological awareness: Effects of spoken language experience and orthography. Cognition , 81, 227– 241. doi:10.1016/S0010-0277(01)00136-6 Google Scholar CrossRef Search ADS   Clark, M. D., Gilbert, G., & Anderson, M. L. ( 2011). Morphological knowledge and decoding skills of deaf readers. Psychology , 2, 109– 116. doi:10.4236/psych.2011.22018 Google Scholar CrossRef Search ADS   Cook, B., Buysse, V., Klinger, J., Landrum, T., McWilliams, R., Tankersley, M., & Test, D. ( 2014). Council for Exceptional Children standards for evidence-based practices in special education . Alexandria, VA: Council for Exceptional Children. Cunningham, J. W. ( 2001). The National Reading Panel report. Reading Research Quarterly , 36, 326– 335. doi:10.1598/RRQ.36.3.5 Google Scholar CrossRef Search ADS   Dickinson, D. K., McCabe, A., Anastasopoulos, L., Peisner-Feinberg, E. S., & Poe, M. D. ( 2003). The comprehensive language approach to early literacy: The interrelationships among vocabulary, phonological sensitivity, and print knowledge among preschool-aged children. Journal of Educational Psychology , 95, 465. doi:10.1037/0022-0663.95.3.465 Google Scholar CrossRef Search ADS   Easterbrooks, S. R., & Stephenson, B. ( 2006). An examination of twenty literacy, science, and mathematics practices used to educate students who are deaf or hard of hearing. American Annals of the Deaf , 151, 386– 397. doi:10.1353/aad.2006.0043 Ehri, L. C., Nunes, S. R., Willows, D. M., Shuster, B. V., Yaghoub-Zadeh, Z., & Shanahan, T. ( 2001). Phonemic awareness instruction helps children learn to read: Evidence from the National Reading Panel’s meta-analysis. Reading Research Quarterly , 36, 250– 287. doi:10.1598/RRQ.36.3.2 Google Scholar CrossRef Search ADS   Friedmann, N., & Szterman, R. ( 2005). Syntactic movement in orally trained children with hearing impairment. Journal of Deaf Studies and Deaf Education , 11, 56– 75. doi:10.1093/deafed/enq052 Google Scholar CrossRef Search ADS   Gersten, R., Fuchs, L. S., Compton, D., Coyne, M., Greenwood, C., & Innocenti, M. S. ( 2005). Quality indicators for group experimental and quasi-experimental research in special education. Exceptional children , 71, 149– 164. 10.1177/001440290507100202 Google Scholar CrossRef Search ADS   Goodman, K. G. ( 1994/2003). Reading, writing, and written texts: A transactional sociopsycholinguistic view. In Flurkey A., & Xu J. (Eds.), On the revolution of reading: The selected writings of Kenneth S. Goodman  (pp. 3– 45). Portsmouth, NH: Heinemann. Goodman, K., Goodman, Y., & Paulson, E. J. ( 2009). Beyond word recognition: How retrospective and future perspectives on miscue analysis can inform our teaching. In Hoffman J. F., & Goodman Y. M. (Eds.), Changing literacies for changing times: An historical perspective on the future of reading research, public policy, and classroom practices  (pp. 146– 161). New York: Routledge. Goswami, U., Ziegler, J. C., & Richardson, U. ( 2005). The effects of spelling consistency on phonological awareness: A comparison of English and German. Journal of Experimental Child Psychology , 92, 345– 365. doi:10.1016/j.jecp.2005.06.002 Google Scholar CrossRef Search ADS   Hammill, D. D., & Swanson, H. L. ( 2006). The National Reading Panel’s meta-analysis of phonics instruction: Another point of view. The Elementary School Journal , 107, 17– 26. 10.1086/509524 Google Scholar CrossRef Search ADS   Hannon, B. ( 2012). Understanding the relative contributions of lower-level word processes, higher-level processes, and working memory to reading comprehension performance in proficient adult readers. Reading Research Quarterly , 47, 125– 152. 10.1002/RRQ.013 Google Scholar CrossRef Search ADS   Individuals with Disabilities Education Act of 2004. 20 U.S.C. § 1400 et seq. Izzo, A. ( 2002). Phonemic awareness and reading ability: An investigation with young readers who are deaf. American Annals of the Deaf , 147, 18– 28. doi:10.1353/aad.2012.0242 Google Scholar CrossRef Search ADS   James, D., Rajput, K., Brinton, J., & Goswami, U. ( 2008). Phonological awareness, vocabulary, and word reading in children who use cochlear implants: Does age of implantation explain individual variability in performance outcomes and growth? Journal of Deaf Studies and Deaf Education , 13, 117– 137. doi:10.1093/deafed/enm042 Google Scholar CrossRef Search ADS   Johnson, R. C., & Mitchell, R. E. ( 2008). Introduction. In Johnson R. C., & Mitchell R. E. (Eds.), Testing deaf students in an age of accountability  (pp. 1– 15). Washington, DC: Gallaudet University. Kelly, L. P. ( 2003). The importance of processing automaticity and temporary storage capacity to the differences in comprehension between skilled and less skilled college-age deaf readers. Journal of Deaf Studies and Deaf Education , 8, 230– 249. 10.1093/deafed/eng013 Google Scholar CrossRef Search ADS   Kelly, L., & Ewoldt, C. ( 1984). Interpreting nonverbatim cloze responses to evaluate program success and diagnose student needs for reading instruction. American Annals of the Deaf , 129, 45– 51. 10.1353/aad.2012.0857 Google Scholar CrossRef Search ADS   Kyle, F. E., & Harris, M. ( 2006). Concurrent correlates and predictors of reading and spelling achievement in deaf and hearing school children. Journal of Deaf Studies and Deaf Education , 11, 273– 288. 10.1093/deafed/enj037 Google Scholar CrossRef Search ADS   Kyle, F. E., & Harris, M. ( 2010). Predictors of reading development in deaf children: A 3-year longitudinal study. Journal of Experimental Child Psychology , 107, 229– 243. 10.1016/j.jecp.2010.04.011 Google Scholar CrossRef Search ADS   Kyle, F. E., & Harris, M. ( 2011). Longitudinal patterns of emerging literacy in beginning deaf and hearing readers. Journal of Deaf Studies and Deaf Education , 16, 289– 304. 10.1093/deafed/enq069 Google Scholar CrossRef Search ADS   LaSasso, C. ( 1980). The validity and reliability of the cloze procedure as a measure of readability for prelingually, profoundly deaf students. American Annals of the Deaf , 125, 559– 563. 10.1353/aad.2012.1497 Google Scholar CrossRef Search ADS   Lederberg, A. R. ( 2003). Expressing meaning: From communicative intent to building a lexicon. In Marschark M., & Spencer P. E. (Eds.), Oxford handbook of deaf studies, language, and education  (pp. 247– 260). Oxford: Oxford University Press. Lollis, J., & LaSasso, C. ( 2009). The appropriateness of the NC state-mandated reading competency test for deaf students as a criterion for high school graduation. Journal of Deaf Studies and Deaf Education , 14, 76– 97. 10.1093/deafed/enn017 Google Scholar CrossRef Search ADS   Luckner, J. L. ( 2013). Using the Dynamic Indicators of Basic Early Literacy Skills with students who are deaf or hard of hearing: Perspectives of a panel of experts. American Annals of the Deaf , 158, 7– 19. 10.1093/deafed/ent015 Google Scholar CrossRef Search ADS   Luckner, J. L., Sebald, A. M., Cooney, J., Young, J., III, & Muir, S. G. ( 2006). An examination of the evidence-based literacy research in deaf education. American Annals of the Deaf , 150, 443– 456. 10.1353/aad.2006.0008 Google Scholar CrossRef Search ADS   Luft, P., & Fochtman, M. (accepted with revision). Strengths-Based reading assessment: Applying miscue analysis to an African American native ASL-user. In Wang, Q. Y., & Andrews, J. F. (Eds.), Toward a global understanding of literacy education for deaf students . Washington, DC: Gallaudet University Press. Targeted publisher. MacSweeney, M., Waters, D., Brammer, M. J., Woll, B., & Goswami, U. ( 2008). Phonological processing in deaf signers and the impact of age of first language acquisition. NeuroImage , 40, 1369– 1379. 10.1016/j.neuroimage.2007.12.047 Google Scholar CrossRef Search ADS   Marschark, M., Schick, B., & Spencer, P. E. ( 2006). Understanding sign language development of deaf children. In Schick B., Marschark M., & Spencer P. E. (Eds.), Advances in the sign language development of deaf children  (pp. 3– 19). Oxford, Great Britain: Oxford University. Martin, D. S., & Mounty, J. L. ( 2005). Overview of the challenge. In Mounty J. L., & Martin D. S. (Eds.), Assessing deaf adults: Critical issues in testing and evaluation  (pp. 3– 10). Washington, DC: Gallaudet University. Mayberry, R. I., Chen, J., Witcher, P., & Klein, D. ( 2011a). Age of acquisition effects on the functional organization of language in the adult brain. Brain and Language , 119, 16– 29. 10.1016/j.banl.2011.05.007 Google Scholar CrossRef Search ADS   Mayberry, R. I., del Giudice, A. A., & Lieberman, A. M. ( 2011b). Reading achievement in relation to phonological coding and awareness in deaf readers: A meta-analysis. Journal of Deaf Studies and Deaf Education , 16, 164– 188. 10.1093/deafed/enq049 Google Scholar CrossRef Search ADS   Miller, P. ( 2009). The nature and efficiency of the word reading strategies of orally raised deaf students. Journal of Deaf Studies and Deaf Education , 14, 344– 361. 10.1093/deafed/enn044 Google Scholar CrossRef Search ADS   Moeller, M. P., Toblin, J. B., Yoshinaga-Itano, C., Connor, C., & Jerger, S. ( 2007). Current state of knowledge: Lanugage and literacy of children with hearing impairment. Ear & Hearing , 28, 740– 753. 10.1097/AUD.0b013e318157f07f Google Scholar CrossRef Search ADS   National Institute of Child Health and Human Development. ( 2000a). Report of the National Reading Panel. Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction (NIH Publication No. 00-4769). Washington, DC: U.S. Government Printing Office. National Institute of Child Health and Human Development. ( 2000b). Report of the National Reading Panel. Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction: Reports of the subgroups (NIH Publication No. 00-4754). Washington, DC: U.S. Government Printing Office. Nicholas, J. G., & Geers, A. E. ( 2003). Hearing status, language modality, and young children’s communicative and linguistic behavior. Journal of Deaf Studies and Deaf Education , 8, 422– 437. 10.1093/deafed/eng029 Google Scholar CrossRef Search ADS   No Child Left Behind Act of 2001, P.L. 107-110, Title IX, Sec 9101 (23)(A&B). Paris, S. G. ( 2005). Reinterpreting the development of reading skills. Reading Research Quarterly , 40, 184– 202. 10.1598/RRQ.40.2.3 Google Scholar CrossRef Search ADS   Paul, P. V., Wang, Y., Trezek, B. J., & Luckner, J. L. ( 2009). Phonology is necessary, but not sufficient: A rejoinder. American Annals of the Deaf , 154, 346– 356. 10.1353/aad.0.0110 Google Scholar CrossRef Search ADS   Pénicaud, S., Klein, D., Zatorre, R. J., Chen, J.-K., Witcher, P., Hyde, K., & Mayberry, R. I. ( 2013). Structural brain changes linked to delayed first language acquisition in congenitally deaf individuals. NeuroImage , 66, 42– 49. 10.1016/j.neuroimage.2012.09.076. Google Scholar CrossRef Search ADS   Rasinski, T. V. ( 1994). Developing syntactic sensitivity in reading through phrase-cued texts. Intervention in School and Clinic , 29, 65– 168. 10.1177/105345129402900307 Google Scholar CrossRef Search ADS   Rasinski, T., Yildirim, K., & Nageldinger, J. ( 2011). Building fluency through the phrased text lesson. The Reading Teacher , 65, 252– 255. 10.1002/TRTR.01036 Google Scholar CrossRef Search ADS   Scarborough, H. S., & Brady, S. A. ( 2002). Toward a common terminology for talking about speech and reading: A glossary of the “phon” words and some related terms. Journal of Literacy Research , 34, 299– 336. 10.1207/s15548430jlr3403_3 Google Scholar CrossRef Search ADS   Schirmer, B. R., Bailey, J., & Lockman, A. S. ( 2004). What verbal protocols reveal about reading strategies of deaf students: A replication study. American Annals of the Deaf , 140, 5– 16. 10.1353/aad.2004.0016 Google Scholar CrossRef Search ADS   Storch, S. A., & Whitehurst, G. J. ( 2002). Oral language and code-related precursors to reading: Evidence from a longitudinal structural model. Developmental Psychology , 38, 934– 947. 10.1037//0012-1649.38.6.934 Google Scholar CrossRef Search ADS   Thompson, B., Diamond, K. E., McWilliam, R., Snyder, P., & Snyder, S. W. ( 2005). Evaluating the quality of evidence from correlational research for evidence-based practice. Exceptional Children , 71, 181– 194. 10.1177/001440290507100204 Google Scholar CrossRef Search ADS   Trezek, B. J., Wang, Y., Woods, D. G., Gampp, T. L., & Paul, P. V. ( 2007). Using visual phonics to supplement beginning reading instruction for students who are deaf or hard of hearing. Journal of Deaf Studies and Deaf Education , 12, 373– 384. 10.1093/deafed/eni028 Google Scholar CrossRef Search ADS   U.S. Department of Education ( 2003). Identifying and implementing educational practices supported by rigorous evidence: A user-friendly guide . Washington, DC: Institute of Educational Sciences. Available from https://ies.ed.gov/ncee/pdf/evidence_based.pdf. Wang, Y., & Williams, C. ( 2014). Are we hammering square pegs into round holes? An investigation of the meta-analyses of reading research with students who are d/Deaf or hard of hearing and students who are hearing. American Annals of the Deaf , 159, 323– 345. 10.1353/aad.2014.0029 Google Scholar CrossRef Search ADS   Weinstock, R. B., & Mounty, J. L. ( 2005). Test-taking for deaf and hard of hearing individuals: Meeting the challenges. In Mounty J. L., & Martin D. S. (Eds.), Assessing deaf adults: Critical issues in testing and evaluation  (pp. 27– 36). Washington, DC: Gallaudet University. Appendix: Reviewed Studies Alvarado, J. M., Puente, A., & Herrara, V., (2008). Visual and phonological coding in working memory and orthographic skills of deaf children using Chilean sign language, American Annals of the Deaf, 152, 467–479. doi: 10.1353/aad.2008.0009 Bélanger, N.N., Baum, S. R., & Mayberry, R. K. (2011). Reading difficulties in adult deaf readers of French: Phonological codes, not guilty! Scientific Studies of Reading, 16, 263–285. doi: 10.1080/10,888,438.2011.568555 Clark, M. D., Gilbert, G., & Anderson, M. L. (2011). Morphological knowledge and decoding skills of deaf readers. Psychology, 2, 109–116. doi:10.4236/psych.2011.22018 Colin, S., Magnan, A., Ecalle, J., & Leybaert, J. (2007) Relation between deaf children’s phonological skills in kindergarten and word recognition performance in first grade. Journal of Child Psychology and Psychiatry 48, 139–146. doi:10.1111/j.1469-7610.2006.01700.x Cupples, L., Ching, T. Y. C., Crowe, K., Day, J., & Seeto, M. (2013). Predictors of early reading skill in 5-year-old children with hearing loss who use spoken language. Reading Research Quarterly, 49, 85–104. doi:10.1002/rrq.60 Daigle, D., & Armand, F. (2008). Phonological sensitivity in severely and profoundly deaf readers of French. Reading and Writing, 21, 669–717. doi: 10.1007/s11145-007-9087-5 Daza, M. T., Phillips-Silver, J., del Mar Ruiz-Cuadra, M., & López-López, F. (2014). Language skills and nonverbal cognitive processes associated with reading comprehension in deaf children. Research in Developmental Disabilities, 35, 3526–3533. doi: 10.1016/j.ridd.2014.08.030 Dillon, C.M., de Jong, K., & Pisoni, D.B. (2012). Phonological awareness, reading skills, and vocabulary knowledge in children who use cochlear implants. Journal of Deaf Studies and Deaf Education, 17, 205–226. doi:10.1093/deafed/enr043 Dyer, A., MacSweeney, M., Szczerbinski, M., Green, L., & Campbell, R. (2003). Predictors of reading delay in deaf adolescents: The relative contributions of rapid automatized naming speed and phonological awareness and decoding. Journal of Deaf Studies and Deaf Education, 8, 215–229. doi: 10.1093/deafed/eng012 Easterbrooks, S. R., Lederberg, A. R., Miller, E. M., Bergeron, J. P., & Connor, C. M. (2008). Emergent literacy skills during early childhood in children with hearing loss: Strengths and weaknesses. The Volta Review, 108, 91–114 Furlonger B., Holmes, V. M., & Rickards, F. W. (2014). Phonological awareness and reading proficiency in adults with profound deafness. Reading Psychology, 35, 357–396. doi: 10.1080/02,702,711.2012.726944 Geers, A. E. (2003). Predictors of reading skill development in children with early cochlear implantation. Ear & Hearing, 24, 595–685. doi: 10.1097/01.AUD.0000051690.43989 Gibbs, S. (2004) The skills in reading shown by young children with permanent and moderate hearing impairment. Educational Research, 46, 17–27. doi: 10.1080/0,013,188,042,000,178,791 Harris, M., & Moreno, C. (2004). Deaf children’s use of phonological coding: Evidence from reading, spelling, and working memory. Journal of Deaf Studies and Deaf Education 9, 253–268. doi: 10.1093/deafed/enh016 Izzo, A. (2002). Phonemic awareness and reading ability: An investigation with young readers who are deaf. American Annals of the Deaf, 147, 18–28. doi: 10.1353/aad.2012.0242 Johnson, C., & Goswami, U. (2010). Phonological awareness, vocabulary, and reading in deaf children with cochlear implants. Journal of Speech, Language, and Hearing Research, 53, 237–261. doi: 10.1044/1092-4388(2009/08-0139) Koo, D. Crain, K., LaSasso, C, & Eden, G. F. (2008). Phonological awareness and short-term memory in hearing and deaf individuals of different communication backgrounds. Annals of the New York Academy of Sciences, 1145, 83–99. doi: 10.1196/annals.1416.025 Kyle, F. E., & Harris, M. (2006). Concurrent correlates and predictors of reading and spelling achievement in deaf and hearing school children. Journal of Deaf Studies and Deaf Education 11, 273–288. doi:10.1093/deafed/enj037 Kyle, F. E., & Harris, M. (2010). Predictors of reading development in deaf children: A 3-year longitudinal study. Journal of Experimental Child Psychology 107, 229–243. doi:10.1016/j.jecp.2010.04.011 Kyle, F. E., & Harris, M. (2011). Longitudinal patterns of emerging literacy in beginning deaf and hearing readers. Journal of Deaf Studies and Deaf Education 16, 289–304. doi:10.1093/deafed/enq069 Luetke-Stahlman, B., & Nielsen, D. C. (2003). The contribution of phonological awareness and receptive and expressive English to the reading ability of deaf students with varying degrees of exposure to accurate English. Journal of Deaf Studies and Deaf Education, 8, 464–484. doi: 10.1093/deafed/eng028 Miller, P. (2009). The nature and efficiency of the word reading strategies of orally raised deaf students. Journal of Deaf Studies and Deaf Education 14, 344–361. doi:10.1093/deafed/enn044 Miller, P. (2010). Phonological, orthographic, and syntactic awareness and their relation to reading comprehension in prelingually deaf individuals: What can we learn from skilled readers. Journal of Developmental and Physical Disabilities, 22, 549–580. doi:10.1007/s10882-010-9195-z Miller, P., & Achmed, R. A. (2009). The development of orthographic knowledge in prelingually deaf individuals: New insight from Arab readers. Journal of Developmental and Physical Disabilities, 22, 11–31. doi: 10.1007/s10882-009-9160-x Miller, P., Kargin, T., Guldenoglu, B., Rathmann, C., Kubus, O., Hauser, P., & Spurgeon, E. (2012). Factors distinguishing skilled and less skilled deaf readers: Evidence from four orthographies. Journal of Deaf Studies and Deaf Education 17, 439–461. doi:10.1093/deafed/ens022 Most, T., Aram, D., & Andorn, T. (2006). Early literacy in children with hearing loss: A comparison between two educational systems. The Volta Review, 106, 5–28. Spencer, L. J., & Oleson, J. J. (2008). Early listening and speaking skills predict later reading proficiency in pediatric cochlear implant users. Ear and Hearing, 29, 270–80. doi: 10.1097/01.aud.0000305158.84403.f7 Spencer, L. J., & Tomblin, J. B. (2009). Evaluating phonological processing skills in children with prelingual deafness who use cochlear implants. Journal of Deaf Studies and Deaf Education 14, 1–21. doi:10.1093/deafed/enn013 © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Published: Apr 1, 2018

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