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. Author manuscript; available in PMC: 2011 Jun 9.
Published in final edited form as: Volta Rev. 2006 FALL;106(2):121–145.

Non word Repetition and Reading Skills in Children Who Are Deaf and Have Cochlear Implants

Caitlin M Dillon 1, David B Pisoni 2
PMCID: PMC3111020  NIHMSID: NIHMS277286  PMID: 21666763

Abstract

Reading skills in hearing children are closely related to their phonological processing skills, often measured using a nonword repetition task in which a child relies on abstract phonological representations in order to decompose, encode, rehearse in working memory and reproduce novel phonological patterns. In the present study of children who are deaf and have cochlear implants, we found that nonword repetition performance was significantly related to nonword reading, single word reading and sentence comprehension. Communication mode and nonverbal IQ were also found to be correlated with nonword repetition and reading skills. A measure of the children’s lexical diversity, derived from an oral language sample, was found to be a mediating factor in the relationship between nonword repetition and reading skills. Taken together, the present findings suggest that the construction of robust phonological representations and phonological processing skills may be important contributors to the development of reading in children who are deaf and use cochlear implants.

Introduction

Evidence from many studies suggests that children’s reading skills are strongly related to their phonological processing skills (e.g., Bradley & Bryant, 1983; Byrne, Fielding-Barnsley, & Ashley, 2000; Hansen & Bowey, 1994; Liberman, 1971; Liberman, Shankweiler, & Liberman, 1989; Rapala & Brady, 1990; Scarborough, 1998; Watson & Miller, 1993). The term “phonological processing skills” encompasses a diverse set of cognitive processes measured by a variety of tasks, all of which require phonological encoding and rehearsal in phonological memory and/or accessing of phonological representations from the mental lexicon (see Brady, 1991; McBride-Chang, 1995; Troia, 2004; Wagner & Torgeson, 1987). Phonological processing tasks include, for example, behavioral measures that require phonological sensitivity, phonological awareness, automaticity or fluency in spoken naming, or phonological working memory skills, although sometimes phonological working memory is considered separately from other phonological processing skills (e.g., Gathercole & Baddeley, 1989; Hansen & Bowey, 1994).

Nonword repetition is a complex phonological processing task in which a participant is asked to listen to and then reproduce novel nonsense words. After only one exposure to a novel stimulus, the participant must immediately complete several subtasks: speech perception, decomposition and parsing of the speech stream into phonological units, rehearsal in phonological working memory, reassembly of the phonological units into an articulatory program and speech production. Thus, completion of the nonword repetition task relies upon successful use of several phonological processing skills (Bowey, 2001; Brady, 1997; Chiappe, Chiappe, & Gottardo, 2004; Munson, Edwards, & Beckman, 2005). Nonword repetition performance has been shown to be related to the development of reading skills in children (e.g., Apthorp, 1995; Brady, Poggie, & Rapala, 1989; Savage, 2006; Snowling, 1981; Taylor, Lean, & Schwartz, 1989).

Converging evidence from these and other studies has led several researchers to suggest that an important factor underlying both phonological processing skills and decoding skills is the quality of phonological representations (e.g., Bowey, 2001; Edwards, Beckman, & Munson, 2004; Elbro, Borstrom, & Petersen, 1998; Fowler, 1991; Fowler & Swainson, 2004; Katz, 1986; Metsala, 1999; Mody, Studdert-Kennedy, & Brady, 1997; Snowling, Wagtendonk, & Stafford, 1988; Studdert-Kennedy, 1987, 2002). That is, in broad terms, one possible cause of poor phonological processing and reading is weak or underspecified phonological representations of words in the mental lexicon. As children acquire lexical representations for spoken words, they have the opportunity to make generalizations about the sound patterns that occur in the ambient language so they are able to form abstract segmental phonological representations with increasingly distinct categorical boundaries and increasingly robust within-category representations. The development of robust phonological representations may be necessary for optimal performance on phonological processing tasks and reading tasks (Fowler, 1991; Studdert-Kennedy, 1987).

This hypothesis is consistent with findings demonstrating that children with poorer vocabulary knowledge also tend to perform more poorly on nonword repetition tasks (e.g., Gathercole & Baddeley, 1989; Metsala, 1999) and reading tasks (e.g., Baddeley, Gathercole, & Papagno, 1998; Fowler & Swainson, 2004). For instance, in a study of adults and children, Edwards et al. (2004) found that nonword repetition performance was related to vocabulary size. Furthermore, the nonword stimuli in their study were systematically varied in phonotactic frequency. They found that performance by children who had larger vocabularies was less affected by biphone frequency differences than performance by children who had smaller vocabularies. Using the same set of stimuli, Munson et al. (2005) found that subgroups of children from each of two populations—typically developing children and children with phonological disorders—who demonstrated larger vocabularies also repeated nonwords more accurately than children from the same population who demonstrated smaller vocabularies. Furthermore, they found that nonword repetition performance was not dependent on the speech perception or articulatory skills of the children in their study.

On the basis of their findings, Munson et al. concluded that poorer overall performance by the children with phonological disorders in comparison to typically developing children in the nonword repetition task was not related to difficulties with speech perception, articulation or even the ability to form abstract phonological representations but was associated with the presence of phonological representations that were not as robust or well specified as those of the typically developing children. According to this view, nonword repetition tasks assess the robustness of the participant’s abstract phonological representations, which become more robust as a child makes more generalizations across the items in his/her mental lexicon. Thus, any child who has less-than-average early exposure or access to spoken language may be at a disadvantage for developing robust phonological representations.

Consistent with this reasoning are findings that children who have hearing loss also demonstrate poorer phonological processing skills and that there is a relationship between amount of hearing loss and phonological coding skills (Conrad, 1979). As in hearing children, studies have shown a relationship between phonological processing and reading skills even in children who are profoundly deaf (e.g., Hanson, Goodell, & Perfetti, 1991; Hanson, Liberman, & Shankweiler, 1984; Lichtenstein, 1985; see Perfetti & Sandak, 2000). Their phonological representations are thought to develop from their experiences with speechreading and articulatory feedback. Children who are deaf generally tend to have poorer reading skills than their hearing peers, and even as adults, often do not reach reading levels beyond that of average fourth graders with typical hearing (see Karchmer, Milone, & Wolk, 1979; Moog & Geers, 1985; Paul, 2003). In the present study, we investigated the relationship between reading and phonological processing skills in children who are deaf and use cochlear implants.

Cochlear implants provide children who are profoundly (or severely) deaf with increased access to spoken language (e.g., Svirsky, Robbins, Kirk, Pisoni, & Miyamoto, 2000). Greater early spoken language experience should therefore contribute to these children’s ability to develop robust phonological representations and better reading skills than have typically been found in children who are deaf and do not use cochlear implants. That is, given greater access to spoken language, children with cochlear implants may have more opportunities to develop lexical representations, make more generalizations across the lexical items they know and develop more robust phonological representations than deaf children have typically had the opportunity to do in the past More robust and highly detailed phonological representations may then allow the children to develop improved phonological processing and reading skills.

Recent studies have found that children with cochlear implants do show improved reading skills compared to earlier findings observed in children who are deaf. For example, Spencer, Tomblin, and Gantz (1997) studied group of 40 deaf children with cochlear implants who were in grades kindergarten through 12 and had 2 to 13 years of experience with their cochlear implant. Almost half of the children in their study were reading within 18 months of their grade level, and about one-fourth of the children achieved reading levels at or above their grade levels. Fifty-four percent of the sub-group of 28 children in grades 4 through 12 achieved scores above the fourth-grade level on the passage comprehension subtest of the Woodcock Reading Mastery Test (Woodcock, 1987). Spencer et al. compared their results to those of previous studies of deaf and hard of hearing children who did not use cochlear implants. They cited earlier findings of Krose, Lotz, Puffer, and Osberger (1986), who found that only 14% of their 9- to 20-year-old participants with hearing loss who did not use cochlear implants achieved scores above the fourth-grade level on the passage comprehension subtest of the Woodcock Reading Mastery Tests (Woodcock, 1973). Spencer et al. concluded that the auditory information about speech provided by a cochlear implant may facilitate deaf child’s ability to decode or recode orthographic representations of speech into a “speech code” (see Conrad, 1979).

Further support for Spencer et al.’s (1997) conclusions was provided more recently by Geers (2003), who studied 181 children with cochlear implants in the Education of the Deaf Child program in St Louis, MO. The children were all 8 or 9 years old and had received their cochlear implant before the age of 5.5 years. Geers found that the children averaged mid to high second-grade reading levels on the Word Attack (nonword reading) subtest of the Wood-cock Reading Mastery Tests-Revised (Woodcock, 1987} and on two subtests of the Peabody Individual Achievement Test-Revised (PIAT-R), Reading Recognition and Reading Comprehension (Dunn & Markwardt, 1989). Geers used the children’s scores on the two PIAT-R measures to calculate total reading scores for each child, for which standard scores are available. The total reading standard scores revealed that 52% of die children scored within the average range of children their age (that is, within one standard deviation of the norm-referenced-sample mean).

In the present study, we report additional analyses of a subset of the children who participated in the larger study reported in Geers (2003). We used existing data to examine the relations between performance on three reading tasks (nonword reading, single word reading and reading sentences for comprehension) and a phonological processing task (nonword repetition). We predicted that better phonological processing skills, as measured by nonword repetition, would be strongly related to better reading skills in deaf children with cochlear implants (CIs). The present study was specifically designed to test this hypothesis. A strong relationship between the children’s nonword repetition performance and their performance on several different reading tasks would indicate that phonological processing skills (as measured by nonword repetition) are related, directly or indirectly, to processes used in reading (as measured by nonword reading, single word reading and reading sentences for comprehension) in the children with cochlear implants who participated in this study.

We also investigated whether the relationships between nonword repetition and reading were mediated by other general underlying cognitive and linguistic factors. We assessed these relations further by computing partial correlations in which we factored out variables that would be likely candidates as mediating factors in the relationship between phonological processing and reading performance. Specifically, we investigated whether demographic variables that were found to be significantly correlated with the children’s outcome performance were also significant mediating factors and whether vocabulary knowledge (as a reflection of the child’s lexicon size) was a mediating factor. An objective measure of vocabulary knowledge was not available for these children, so we used a measure of lexical diversity derived from oral speech samples instead.

Method

Participants

Eighty-eight children who participated in an Education and the Deaf Child program in 1999 or 2000 {see Geers & Brenner, 2003) carried out both the nonword repetition task and the reading tasks described below. All children were profoundly deaf and used cochlear implants. Twelve children were excluded from the analysis because they provided responses to less than 75% of the target nonwords. The remaining 76 children were included in the present study. Thirty-six were male and 40 were female. Seventy-four children used a Nucleus 22 CI and the SPEAK coding strategy. One child used a Nucleus 24 implant and one child used a Clarion implant. Table 1 provides a summary of the demographic characteristics of the children. Mean chronological age at the time of testing was 8.9 years (range 7.8–9.9, S.D. = 0.6). Sixty-four of the children were congenitally deaf, six became deaf before age 1 and the remaining six became deaf by age 3. The children’s mean duration of deafness prior to implantation was 37.2 months (range: 7–65, S.D. = 13.1). The children’s mean age at time of implantation was 3.3 years (range: 1.9–5.4, S.D. = 1.0). The children had used their implants for a mean of 5.6 years at the time of testing (range: 3.8–7.5, S.D. = 0.8). The children’s mean communication mode scores were baaed on a parent questionnaire. Children with communication mode scores of 15 or higher were considered oral communication users (i.e., their educational programs emphasized auditory/oral communication methods). Children with communication mode scores below 15 were considered Total Communication users (i.e., both manual and auditory/oral communication methods were used in their educational environment; see Geers & Brenner, 2003). Forty-seven children were oral communication users and 29 were Total Communication users.

Table 1.

Summary of the Demographic Make-Up of the 76 Children

Demographic Variable Mean (S.D.) Range
Age at onset of deafness (months) 2.3 (6.4) 0–36
Duration of deafness prior to implantation (months) 37.2 (13.1) 7–65
Age at implantation (years) 3.3 (1.0) 1.9–5.4
Duration of implant use (years) 5.6 (0.8) 3.8–7.5
Chronological age (years) 8.9 (0.6) 7.8–9.9
Number of active electrodes 18.4 (2.3) 8–22
Communication mode scores 19.8 (7.7) 6–30
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Note: S.D., standard deviation.

Nonword Repetition Task

Stimulus Materials and Procedure

The 20 target nonwords used in the present study were a subset of the nonwords included in the Children’s Test of Nonword Repetition (Gathercole, Willis, Baddeley, & Emslie, 1994; see also Carlson, Cleary, & Pisoni, 1998). The nonwords, shown in Table 2, were balanced in terms of syllable number and included 112 target consonants and 68 target vowels. Each child was asked to listen to each nonword, presented one at a time, and then to attempt to repeat the nonword aloud back to the experimenter. The children heard digital recordings of a female native speaker of American English played over a loudspeaker at approximately 70 dB SPL. The original nonword stimuli and the children’s responses were recorded onto digital audio tape for later analysis.

Table 2.

The 20 Nonwords Used in the Present Study

Number of Syllables
2 3 4 5
ballop bannifer Comisitate altupatory
prindle berrizen contramponist detratapillic
rubid doppolate emplifervent pristeractional
sladding glistering fennerizer versatrationist
tafflist skiticult penneriful voltularity
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Note: Nonwords from Gathercole et al., 1994; see also Carlson et al., 1998.

Nonword Transcriptions

All of the nonword repetition responses were independently transcribed by two phonetically trained listeners. Disagreements were resolved by consensus (92% agreement). A third listener resolved the remaining 8% disagreements. These phonemic transcriptions were then used to calculate a “suprasegmental score” for each child, the percent of imitations with the correct number of syllables. The phonemic transcriptions were also used to calculate two “segmental scores” for all 76 children: (1) percent consonants correct based on the number of consonants reproduced with correct place (labial, coronal, dorsal), manner (stop, fricative, liquid, nasal) and voicing (voiced or voiceless), both out of the total number of target consonants (N = 112) and out of the total number of target consonants in the nonwords for which the child provided a response; and (2) percent vowels correct based on the number of vowels reproduced with correct height (high, mid or low) and backness (front, central or back), both out of the total number of target vowels (N = 68) and out of the total number of target vowels in the nonwords for which the child provided a response. More details regarding the suprasegmental scores and segmental consonant scores for a subset of the children in the present study are reported in Carter, Dillon and Pisoni (2002) and Dillon, Cleary, Pisoni and Carter (2004b). Dillon, Pisoni, Cleary and Carter (2004c) describe in more detail the segmental consonant accuracy scores and error analyses of the children in the present study.

Scores Based on Perceptual Accuracy Ratings

In addition to these transcription-based scores, the children’s nonword responses were also played back to naive listeners to obtain perceptual goodness ratings. The target nonword patterns and the child’s attempted nonword repetitions were played back to groups of hearing college-age adult listeners who were asked to make similarity judgments. On each trial, the listener heard the target nonword stimulus followed by a child’s response to the target nonword. Listeners were asked to provide goodness ratings of the child’s response on a scale of one (poor) to seven (perfectly accurate), which were used to calculate a mean rating score per child. All nonword responses provided by each child were rated by 10 listeners. The results were highly reliable both within and between listeners. More details on the perceptual ratings scores are reported in Dillon, Burkholder, Cleary and Pisoni (2004a).

Stimulus Materials and Procedures

Three measures of reading were also obtained from these children. The Word Attack subtest of the Woodcock Reading Mastery Tests-Revised (WRMT-R; Woodcock, 1987) was administered to all of the children. The Word Attack subtest is a nonword reading task that includes 45 nonwords or extremely rare real words. Each child was asked to read aloud the nonwords one at a time to the experimenter. The child cannot complete this task by relying on visual recognition or reading skills alone because the stimuli are unfamiliar nonwords. Instead, the Word Attack subtest measures the child’s “ability to apply phonic and structural analysis skills to pronouncing words that are not recognizable by sight” (Woodcock, 1987).

The children also completed the two subtests of the Peabody Individual Achievement Test-Revised (PIAT-R; Dunn & Markwardt 1989). The Reading Recognition subtest of the PIAT-R includes 100 items. The first 16 items consist of four-alternative, forced-choice questions requiring a pointing response. This measure was designed to test “reading readiness” skills, which are assumed to be essential prerequisites for a child learning to read (Markwardt, 1998). Several types of items are included in the reading readiness part of the PIAT-R Reading Recognition subtest. For example, the child is shown a letter or word such as “B,” “GO” or “to” and is asked to point to one like it from among four choices; or, the child is asked to name the object shown in four pictures and then choose the picture of an object whose name does not start with the same sound as the other three objects, such as “ball” from among pictures of a ball, pencil, pan and pie. Several other items require the child to choose an item that begins with the same sound as a stimulus picture from among four pictures or four written words. Items 17–100 all involve single real-word reading. The questions are ordered in terms of increasing difficulty, ranging from kindergarten level to twelfth grade level. In the Reading Recognition subtest, the child earns one point for every correct answer to items 1 through 16, and one point for every correct pronunciation of items 17 through 100, with each pronunciation counted as either correct or incorrect after one attempted pronunciation. Because most items in the Reading Recognition subtest require the child to read single real words, we refer to this measure as a single word reading or visual word recognition task.

The Reading Comprehension subtest of the PIAT-R was also given to all of the children. This reading measure includes 82 four-alternative, forced-choice items that require a pointing response. The test items are meaningful narrative sentences designed to test literal reading comprehension (as opposed to interpretation of information or recognition of inferences; Markwardt, 1998). For each item, the child is shown a sentence and is told to read it to him/herself only once. Then the child is shown a page with four pictures and is asked to point to the picture that best represents the meaning of the sentence. As in the Reading Recognition subtest, the items in the Reading Comprehension subtest are ordered in terms of increasing difficulty over a wide range. Some examples of the test sentences include: “There is the sun,” “The eagle floats on its wings as it travels in search of a feast” and “The residence has been essentially reduced to rubble, the remainder being only the foundation.” The child is given one point for each correct response.

Reading Scores

Grade-equivalent scores were computed for the Word Attack (Woodcock, 1998), the Reading Recognition and Reading Comprehension tasks (Markwardt, 1998). A Total Reading standard score was also calculated for each child. The child’s raw scores on the two PIAT-R reading subtests were summed and converted to a standard score using the child’s age (Markwardt, 1998).

Lexical Diversity

An objective behavioral measure of vocabulary knowledge was not available for children who are deaf and use cochlear implants in the present study. However, the children had participated in a conversational oral interview as part of the larger CID study and provided a sample of spontaneous speech at that time (Geers, Nicholas, & Sedey, 2003). The number of different words used by each child during the interview was calculated and used as a measure of “lexical diversity,” which is assumed to reflect overall vocabulary knowledge. In the present study, we used this measure to assess the relations between lexical diversity, nonword repetition performance, and reading skills.

Results

Nonword Repetition Task

The children’s nonword responses varied in terms of suprasegmental, consonant, vowel and overall perceptual accuracy. A summary of the children’s responses is provided in Table 3. More detailed summaries of the nonword repetition task results are reported in the earlier studies cited above. A brief preliminary report of the present study was provided in a conference proceeding (Dillon & Pisoni, 2004).

Table 3.

Summary of Nonword Repetition Scores Provided by the Children (N=76)

Nonword Repetition Score Mean (S.D) Range
Percent correct number of syllables out of all 20 target NWs 55% (18%) 10%–95%
Percent correct number of syllables out of number of responses 59% (19%) 12%–95%
Percent correct Cs out of Cs in all 20 target NWs 30% (17%) 1%–76%
Percent correct Cs out of target Cs in responses 33% (17%) 1%–76%
Percent correct Vs out of Vs in all 20 target NWs 44% (17%) 9%–75%
Percent correct Vs out of target Vs in responses 48% (17%) 13%–78%
Mean accuracy ratings 3.1 (1.1) 1.1–5.7
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Note: SD. Standard deviation; NWs, nonwords: Vs, vowels: Cs, consonants.

The children’s suprasegmental and segmental accuracy scores were initially calculated out of the total number of targets. The suprasegmental scores (proportion of imitations with the correct number of syllables) were then calculated for each child by dividing the number of responses with the correct number of syllables by the number of stimuli (N = 20). Percent consonants (Cs) and vowels (Vs) correct scores were calculated out of the total number of target Cs in all 29 target nonwords (N = 112) and target Vs in all 20 target nonwords (N = 68). respectively. Because some of the 76 children did not produce a repetition response to all 20 target nonwords, we also calculated individual scores based only on the target nonwords for which the child provided a response. That is, each child’s suprasegmental score was calculated by dividing the number of responses with the correct number of syllables by the total number of responses provided by that child. Similarly, each child’s percent Cs correct and percent Vs correct scores were calculated out of the total number of target Cs or Vs, respectively, only in the target nonwords for which the child provided a response. A summary of the children’s non-word repetition scores is shown in Table 3. The different methods of scoring the nonword repetition task yielded scores that were strongly intercorrelated with each other, as shown in Table 4.

Table 4.

Intercorrelations Among the Children’s Nonword Repetition Scores (N = 76]

Nonword Repetition Score 1. Syl 1 2. Syl 2 3. Cs 1 4. Cs 2 5. Vs 1 6. Vs 2 7. Ratings
1. Percent correct number of syllables our of all 20 target NWs 1 0.97* 0.80* 0.79* 0.84* 0.81* 0.81
2. Percent correct number of syllables out of number of responses 1 0.75* 0.77* 0.80* 0.81* 0.79*
3. Percent correct Cs out of Cs in all 20 target NWs 1 0.99* 0.90* 0.86* 0.92*
4. Percent correct Cs out of target Cs in responses 1 0.88* 0.87* 0.92*
5. Percent correct Vs out of Vs in all 20 target NWs 1 0.98* 0.88*
6. Percent correct Vs out of target Vs in responses 1 0.86*
7. Mean accuracy ratings 1
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Note: Column headings include abbreviations that correspond to the same-number row heading. Abbreviations: Syl, syllable score; Cs, percent consonants correct score; Vs, percent vowels correct score.

*

P < 0.001

Correlations between the measures of nonword repetition accuracy and age at implantation, duration of CI use, chronological age at the time of testing and number of active electrodes did not reach significance. Correlations between nonword repetition accuracy and the demographic factors that did reach significance are shown in Table 5. The significant correlations between nonword repetition scores and age at onset of deafness are due to the some-what better overall nonword repetition performance by children who were not congenitally deaf. These correlations indicate that children who had some early exposure to natural speech before becoming deaf tended to provide better nonword repetition responses. In addition, children whose educational environments emphasized the use of oral language for communication, instead of a combination of both manual and oral communication, tended to obtain better nonword repetition scores. Finally, we observed relatively weak correlations between the children’s nonword repetition scores and their performance IQ scores, suggesting that higher nonverbal IQ was related to better nonword repetition performance in our group of children.

Table 5.

Significant Correlations Between the Children’s Demographic Characteristics and Their Nonword Repetition Scores (N = 76)

Nonword Repetition Score Age at Onset of Deafness Communcation Mode PIQ
Percent correct number of syllables out of all 20 target NWs 0.28* 0.37** 0.28*
Percent correct number of syllables out of number of responses 0.23* 0.34** 0.30**
Percent correct Cs out of Cs in all 20 target NWs 0.31** 0.54*** 0.22
Percent correct Cs out of target Cs in responses 0.28* 0.54*** 0.22
Percent correct V» out of Vain all 20 target NWs 0.24* 0.47*** 0.24*
Percent correct Vs out of target Vs in responses 0.20 0.45*** 0.26*
Mean accuracy ratings 0.32** 0.51*** 0.26*
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Note: PIQ, performance IQ; NWs, nonwords; Cs, consonants; and Vs, vowels.

*

p < 0.05,

**

p < 0.01,

***

p < 0.001.

Reading Outcome Measures

A summary of the children’s scores on the reading outcome measures is shown in Table 6. We report grade equivalent scores for the WRMT-R Word Attack subtest and the PIAT-R Recognition and Comprehension subtests. FIAT-R Total Reading standard scores were calculated using the children’s ages (cf. Geers, 2003). Fifty-three children (70%) obtained Total Reading standard scores within the normal range for children their age (i.e., within one standard deviation of the norm-sample mean). The remaining 23 children (30%) obtained Total Reading standard scores that were below the normal range for children their age (based on norms from Markwardt, 1998; Geers, 2003, results are based on norms from Dunn & Markwardt, 1989).

Table 6.

Summary of the Children’s Performance on the Reading Outcome Measures (N = 76)

Reading Outcome Measure Mean (S.D.) Range
WRMT-R Word Attack (grade equivalent scores) 3.3 (2.8) 0.0–12.6
PIAT-R Recognition (grade equivalent scores) 2.9 (1.0) 0.4–6.1
PIAT-R Comprehension (grade equivalent scores) 2.9 (1.5) 0.0–8.7
PIAT-R Total Reading (standard scores) 95.3 (8.4) 80–116
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Note: S.D., standard deviation.

The number and corresponding percentage of children that performed at several grade levels on the WRMT-R Word Attack subtest and the PIAT-R Recognition and Comprehension subtests are shown in Table 7. Eleven children (14%) scored above the fourth-grade level on the Word Attack two children (3%) on Reading Recognition and eight children (11%) on Reading Comprehension. Ten children (13%) scored below the first-grade level on at least one of the Word Attack, Reading Recognition and Reading Comprehension tasks. As shown in Table 8, scores on the reading outcome measures were all highly intercorrelated with each other.

Table 7.

Grade Equivalent Scores on Reading Outcome Measures (N = 76)

Reading Outcome Measure Below First Grade Level First Grade Level Second Grade Level Third Grade Level Fourth Grade Level Above Fourth Grade Level
WRMT-R Word Attack (grade equivalent scores) 11 (14%) 31 (41%) 14 (18%) 5 (7%) 4 (5%) 11 (14%)
PIAT-R Recognition (grade equivalent scores) 2 (3%) 10 (13%) 29 (38%) 25 (33%) 8 (11%) 2 (3%)
PIAT-R Comprehension (grade equivalent scores) 6 (8%) 6 (8%) 37 (49%) 16 (21%) 3 (4%) 8 (11%)
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Note: The table shows the number and corresponding percentage of children with grade equivalent scores below first grade level to above fourth grade level on the three reading outcome measures for which grade equivalency scores were available.

Table 8.

Intercorrelations Among the Reading Outcome Measures (N = 76)

Reading Outcome Measure WRMT-R Word Attack PIAT-R Reading Recognition PIAT-R Reading Comprehension PIAT-R Total Reading
WRMT-R Word Attack 1 0.83* 0.68* 0.82*
PIAT-R Reading Recognition 1 0.78* 0.88*
PIAT-R Reading Comprehension 1 0.89*
PIAT-R Total Reading 1
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Note:

*

p < 0.001.

We did not find any significant correlations between the reading measures and the conventional demographic variables (age at onset of deafness, duration of deafness prior to implantation, age at implantation, duration of CI use or chronological age at the time of testing) (all p values are greater than 0.11) or number of electrodes (after one outlier was removed, all p values are greater than 0.08). A t test revealed no differences in performance by gender (p = 0.69, 0.71, 0.96, 0.81 and 0.09 for the WRMT-R Word Attack, PIAT-R Recognition, PIAT-R Comprehension, PIAT-R Total Reading and Rhyme Errors tasks, respectively). However, correlations between the reading measures and both communication mode and performance IQ (Wechsler, 1991) reached significance (see Table 9). These correlations indicated weak tendencies for children who achieved better reading scores to also use oral communication and to have higher nonverbal intelligence scores.

Table 9.

Significant Correlations Between the Children’s Demographic Characteristics and Their Scores on the Reading Outcome Measures (N = 76)

Reading Outcome Measure Communication Mode PIQ
WRMT-R Word Attack 0.41*** 0.34**
PIAT-R Reading Recognition 0.26* 0.35**
PIAT-R Reading Comprehension 0.15 0.39***
PIAT-R Total Reading 0.25* 0.45***
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Note: PIQ, performance IQ.

*

p < 0.05,

**

p < 0.01,

***

p < 0.001.

Correlational Analysis

Because the different methods of scoring the nonword repetition task were all highly intercorrelated with each other, we report only the correlations between the nonword repetition accuracy ratings and the reading outcome measures. As shown in Table 5, we found that age at onset of deafness, communication mode and performance IQ (measured using the WISC III, Wechsler, 1991; see also Geers, 2003, and Dillon et al., 2004a) were all significantly correlated with nonword repetition performance. We also computed partial correlations between nonword repetition accuracy ratings and the reading measures to control for these demographic variables. After these potentially confounding demographic factors were removed, we found that the children’s performance on nonword repetition (a phonological processing task) and their performance on the three reading measures still remained significantly correlated (see Table 10).

Table 10.

Correlations Between Nonword Repetition Accuracy Ratings and Reading Outcome Measures (N = 76)

Reading Outcome Measure Bivariate Correlation Partial Correlation 1 Partial Correlation 2
WRMT-R Word Attack 0.61*** 0.49*** 0.23
PIAT-R Reading Recognition 0.57*** 0.50*** 0.26*
PIAT-R Reading Comprehension 0.43*** 0.41*** 0.15
PIAT-R Total Reading 0.59*** 0.55*** 0.32**
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Note: The table shows simple bivariate correlations, partial correlations 1 (controlling for age at onset of deafness, communication mode, and performance IQ) and partial correlations 2 (controlling for age at onset of deafness, communication mode, performance IQ, and lexical diversity).

*

p < 0.05,

**

p < 0.01,

***

p < 0.001.

Finally, we computed partial correlations in which lexical diversity was also controlled, in addition to the potentially confounding demographic characteristics (age at onset of deafness, communication mode and performance IQ). When lexical diversity was removed, several of the correlations between children’s nonword repetition performance and their reading scores no longer reached significance. However, the Reading Recognition scores and Total Reading scores still remained significantly correlated with non-word repetition performance, but were substantially reduced in magnitude (Table 10).

Summary and Discussion

We investigated the relationship between reading skills, as measured by nonword reading, single word reading and sentence comprehension tasks, and phonological processing skills, as measured by a nonword repetition task, in a group of 8- and 9-year-old children who are deaf and are experienced cochlear implant users. Twelve children who provided responses to fewer than 15 out of the 20 target nonword stimuli in the nonword repetition task were excluded from the analyses. The 76 children who were included in the final data analyses demonstrated a wide range of individual variability in their performance on all outcome measures.

Performance on Phonological Processing: Nonword Repetition Task

Nonword repetition is a difficult information processing task for a young child because it requires immediate and rapid phonological processing of novel phonological patterns. Nevertheless, most of the children with cochlear implants in the present study were able to complete the nonword repetition task with some measurable level of accuracy, although their performance was substantially worse than would be expected from typically-developing age-matched hearing peers who typically perform nonword repetition tasks with very high accuracy {e.g., Gathercole et al., 1994; Carlson et al., 1998; see Gathercole, 2006, for a review). We found that several different methods of scoring the children’s nonword repetition performance, including both transcription-based and perceptual accuracy scores, were strongly intercorrelated (all r values are greater than 0.75). These intercorrelations suggest that all of the nonword repetition accuracy measures reported in the present study provide similar information about the children’s accuracy levels on the non-word repetition task.

Performance on Reading Outcome Measures

The children with cochlear implants in the present study achieved better reading scores than would typically be expected for children who are profoundly deaf (e.g., Krose et al., 1986). The fact that the children in this study could complete the nonword reading task indicates that they had developed phonological and lexical decoding skills (not simply visual word recognition skills), which are a necessary part of becoming an optimally proficient (skilled) reader (see Shankweiler & Fowler, 2004). It is encouraging that 70% of the children in the present study achieved Total Reading scores (a composite including single word reading and read-sentence comprehension) that were within the normal range for hearing children of their age. In addition, a small proportion of these 8- and 9-year-olds achieved scores above the fourth-grade level, which has been shown to be rare among individuals who are profoundly deaf (see Paul, 2003). However, 30% of the children’s Total Reading scores were below average compared to hearing children their age, and all three reading tests elicited scores that were below a first-grade level from a small proportion of the children. We also found that all of the reading measures were significantly intercorrelated with each other (all r values were greater than 0.68).

Correlations with Demographic Characteristics

Overall, the children’s demographic characteristics were not found to be significantly correlated with their nonword repetition performance or their scores on the reading outcome measures. This finding indicates that the group of children in the present study was sufficiently homogeneous in terms of deafness-, implantation- and age-related characteristics and that variability in their outcome measures was not significantly related to variability in their demographic characteristics. The exception to this general finding was the finding of weak correlations between nonword repetition scores and age at onset of deafness. These correlations were due to the overall better performance by the small number of children in the study who were not congenitally deaf (N = 12). This result indicates that having some early auditory experience with spoken language prior to becoming deaf is advantageous to the development of the phonological skills used in nonword repetition even after a period of deafness and cochlear implantation.

In addition, we found that both the nonword repetition scores and the reading scores tended to be significantly correlated with both communication mode and performance IQ. Children whose educational environment focused primarily on speaking and listening skills (oral communication mode) also tended to demonstrate better nonword repetition accuracy than children whose educational environments focused on both spoken and manual communication (Total Communication mode). Similarly, children with higher nonverbal intelligence scores also tended to perform the reading tasks better than children with lower nonverbal intelligence scores.

Previous studies have shown that the extent to which a child benefits from cochlear implantation in terms of speech, language and literacy development is related to demographic characteristics such as age at implantation and communication mode (e.g., Kirk et al., 2002). We found weaker or nonsignificant relationships between the children’s demographic characteristics and their phonological processing and reading scores because the demographic characteristics of the children in the present sample were more homogeneous: Most of the children were congenitally deaf and received cochlear implants at an early age, and were close in chronological age.

Correlations Between Phonological Processing and Reading Outcome Measures

We found that the children’s nonword repetition performance was significantly correlated with their scores on measures of nonword reading, single word reading and sentence comprehension. As in hearing children, the phonological processing skills (as measured by nonword repetition) of children who use cochlear implants were found to be related to their reading skills. This finding provides further support for previous research showing that phonological processing skills are related to reading not only in hearing children but also in children who are hard of hearing or deaf (e.g., Conrad, 1979; see Perfetti & Sandak, 2000).

Furthermore, although some traditional demographic variables—specifically age at onset of deafness, communication mode and performance IQ—were found to be significantly related to the children’s phonological processing and reading skills, they did not emerge as the primary mediating factors in the relationships between nonword repetition and the reading tasks. That is, the distributions of the children’s communication modes and performance IQ scores were not underlying contributors to the relationship between the children’s phonological processing and reading skills. Although the children varied in their demographic characteristics, it is possible that the group as a whole was too demographically homogeneous for the demographic characteristics to make a more substantial contribution to explaining the variance in the children’s reading and phonological processing skills.

The measure of lexical diversity, however, was found to be a mediating factor in the relationship between the children’s nonword repetition and reading skills, explaining most of the overlapping variance in the distribution of nonword repetition and reading scores. This finding indicates that, especially given demographic characteristics that are fairly homogeneous as in the present group of children, a child’s lexical diversity and expressive vocabulary knowledge may reflect an important underlying factor in the development of both phonological processing and reading skills, If our measure of lexical diversity—the number of different words a child produced during an oral interview—reflects vocabulary knowledge, this finding is consistent with earlier studies of hearing children that have shown that individual variability in vocabulary knowledge explains much of the variance in phonological processing (e.g., Gathercole, & Baddeley, 1989; Metsala, 1999) and reading tasks (e.g., Baddeley et al., 1998; Fowler & Swainson, 2004). As discussed above, these studies and others have led to the hypothesis that better vocabulary knowledge provides a child with more opportunities to make generalizations across items in the mental lexicon, thereby developing more robust phonological representations (e.g., Fowler, 1991). In turn, more robust phonological representations may allow a child to develop better (more efficient) phonological processing skills, which contribute to the development of better reading skills (Brady, 1997). The present findings suggest that children with cochlear implants exhibit relationships among phonological processing, reading and vocabulary knowledge that are similar to those found in hearing children.

Children who were better able to use the phonological processing skills necessary to complete the auditory-only nonword repetition task (which did not involve any reading) performed better on the word recognition and sentence comprehension tasks. The sentence comprehension task was a visual reading task that required only a pointing response (not necessarily any auditory or phonological processing). In addition, as discussed above, children who were better able to decode novel grapheme sequences in the non-word reading task—which requires mapping phonological representations onto graphemic representations (Liberman, Shankweiler, & Liberman, 1989)—also demonstrated better visual word recognition skills and better reading comprehension of sentences. That is, children who were able to make better use of phonological representations and/or phonological processing skills also demonstrated better word recognition and sentence comprehension skills. Taken together, these findings suggest that the development of robust phonological representations and phonological processing skills underlies the development of reading skills in children who are deaf and use cochlear implants. However, further investigation of the relations among phonological processing skills, reading skills and vocabulary knowledge in a larger, more diverse group of children who use cochlear implants is necessary.

Future Directions

Two specific aspects of the present study warrant further investigation. First, we used a measure of lexical diversity—the number of different words the child used in a sample of spontaneous speech obtained from an oral interview—in place of a more direct objective measure of vocabulary knowledge. However, lexical diversity is not necessarily reflective of vocabulary knowledge or mental lexicon size. At least two studies have examined the relationship between vocabulary size (as measured by standardized vocabulary tests) and several different measures of spontaneous (elicited) speech. The results provide some converging evidence that lexical diversity (number of different words used in a conversation) may be related to vocabulary size, but the correlations in those studies were not strong. Ukrainetz and Blomquist (2002) obtained several vocabulary measures from a group of 28 hearing, typically developing children approximately ages 4 to 6. They found that the number of different words produced in an elicited speech sample was correlated with the children’s scores on the Peabody Picture Vocabulary Test-III (PPVT-III; Dunn & Dunn, 1997), a measure of receptive vocabulary size (r = 0.36, p < 0.05). In the task used to elicit the speech sample from the children, most children described materials used as conversational prompts: a farm set (including animals, a tractor, etc.), pictures in a wordless storybook and drawings in an activity book. Some children told stories about these prompts. In contrast, for the lexical diversity measure used in the present study, the examiner led the child in a conversation by asking him/her open-ended questions about many different things including personal interests.

In a study of 15 hearing, typically-developing children ages 27 to 47 months, Silverman and Ratner (2002) obtained speech samples from the children while they played with their parents using common toys (blocks, play food, etc.). A vocabulary diversity measure was calculated by applying an algorithm to type-token ratios in the children’s language samples. The authors found that a correlation between this measure of the children’s vocabulary diversity and receptive vocabulary as measured by the Peabody Picture Vocabulary Test-Revised (Dunn & Markwardt, 1989) scores was positive but did not reach significance (r = 0.33, p = 0.08). However, the correlation between the children’s vocabulary diversity scores and their Expressive One-Word Picture Vocabulary Test (Gardner, 1990) was significant (r = 0.48, p < 0.01). The type-token ratios themselves were not significantly correlated with the children’s receptive or expressive vocabulary scores. The absence of stronger correlations in this study may simply be due to the small sample size used.

The number of different words in the speech sample (which would be more similar to our lexical diversity measure) was not calculated in either of the above studies; the participants were different ages from those in the present study and were hearing. Thus, the relationship between lexical diversity and vocabulary knowledge, particularly in children who use cochlear implants, is not clear. A future study of reading and phonological processing skills in children with cochlear implants should include direct objective norm-referenced measures of both expressive and receptive vocabulary knowledge as well as measures of lexical diversity obtained from samples of spontaneous speech.

Second, phonological processing is a general term that encompasses a variety of skills that have also been studied extensively as separate skills in hearing children. Different phonological processing skills have been found to be related in varying degrees to other phonological processing skills and to reading performance. For example, phonemic awareness in particular has been found to be closely related to concurrent and later reading skills in beginning readers (see Gillon, 2004). Additional research is needed on the relative contributions of various phonological processing skills (e.g., phonological awareness, phonological working memory skills, lexical access) to reading skills in children who are deaf and use cochlear implants. Further exploration of these relations may provide additional insights into optimal methods and strategies for teaching reading to children who are deaf and use cochlear implants.

Acknowledgments

This work was supported by NIH-NIDCD Research Grant R01 DC00111 and T32 Training Grant DC00012 to Indiana University. We would like to thank Ann Geers, Chris Brenner and the research staff at Central Institute for the Deaf in St. Louis, Mo., in 1999 and 2000 for testing the cochlear implant users and making the literacy data available to us for this study. We are also grateful to Ken de Jong and Rose Burkholder for their valuable insights and help with this investigation.

Contributor Information

Caitlin M. Dillon, Doctorate in linguistics from Indiana University and is a postdoctoral fellow at Haskins Laboratories in New Haven, Conn.

David B. Pisoni, Chancellors’ Professor of Psychology and Cognitive Science at Indiana University, Bloomington, and an adjunct professor of Otolaryngology–Head and Neck Surgery at the Indiana School of Medicine in Indianapolis, Ind.

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