Examining Multiple Sourcesof Influence on the ReadingComprehension Skills of ChildrenWho Use Cochlear Implants

Carol McDonald ConnorTeresa A. Zwolan

University of Michigan,Ann Arbor

Children with profound deafness are at risk for serious reading difficulties.Multiple factors affect their development of reading skills, including use ofcochlear implants. Further, multiple factors influence the overall success thatchildren experience with their cochlear implants. These factors include the age atwhich they receive an implant, method of communication, vocabulary skills,preoperative residual hearing, and socioeconomic status. Ninety-one childrenwith prelingual and profound hearing impairments who received cochlearimplants at varying ages participated in the study. Structural equation modelingconfirmed that multiple factors affected young cochlear implant users' readingcomprehension skills and that there were significant associations between thepredictors of reading comprehension. Pre-implant vocabulary had an indirectpositive effect on reading through postimplant vocabulary, which had a directpositive effect on reading. Overall, children with stronger language skills demon-strated stronger reading outcomes. Age at implantation both directly andindirectly, through postimplant vocabulary, affected reading outcomes, and thetotal effect was large. Children who were younger when they received theirimplants tended to have higher reading comprehension scores. Socioeconomicstatus negatively affected reading. Children who used total communication priorto implantation tended to have stronger pre-implant vocabulary scores, but thetotal effect of pre-implant communication method on children's reading skills wasnegligible. Research and educational implications are discussed.

KEY WORDS: deafness, literacy, language development, communication, signlanguage, socioeconomic status (SES)

C hildren with profound hearing losses are at particular risk forserious reading difficulties. On average, these children graduatefrom high school with reading skills at a third-grade level (Allen,

1986; Holt, 1994). This gap in reading achievement between childrenwith hearing impairment and children with normal hearing sensitiv-ity widens as severity of hearing loss increases (Karchmer, Milone, &Wolk, 1979). In recent and separate studies, use of cochlear implants (L.Spencer, Tomblin, & Gantz, 1998; Svirsky, Stallings, Lento, Ying, &Leonard, 2002; Szagun, 2001; Tomblin, Spencer, Flock, Tyler, & Ganz,1999) and early exposure to sign language (Padden & Ramsey, 1998, 2000)have been positively associated with vocabulary and literacy skills. Fur-ther, multiple factors affect the reading skills of children with cochlear

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implants (Geers, 2002,2003; Geers et al., 2002; Rayner,Foorman, Perfetti, Pesetsky, & Seidenberg, 2001), justas multiple factors affect the reading skills of childrenwith normal hearing sensitivity (Geers, 2002; Geers etal., 2002; Rayner et al., 2001).

Although there is research that has explored themultiples sources that affect reading skills among chil-dren with profound hearing losses, there is little researchthat has used multilevel modeling. The chief advantageof multilevel models is that they allow for examinationof complex interactions among variables influencingchildren's reading. This study used structural equationmodeling (SEM) to examine multiple factors that mayaffect reading comprehension skills among children withprofound hearing losses. These factors include cochlearimplant use, the age at which children receive implants,vocabulary skills, communication method, and otherchild and family variables.

Cochlear implants provide substantial useable hear-ing to children who derive no significant benefit fromconventional amplification (National Institutes of Health,1995). Accumulating evidence reveals that most childrendemonstrate significant improvement in their speechperception skills (Cheng, Grant, & Niparko, 1999; Meyer,Svirsky, Kirk, & Miyamoto, 1998), speech productionabilities (Tye-Murray, Spencer, & Woodworth, 1995), andoral language development (Bollard, Chute, Popp, &Parisier, 1999; Miyamoto, Kirk, Svirsky, & Sehgal, 1999;Miyamoto, Svirsky, & Robbins, 1997) when they use co-chlear implants.

Further, converging research indicates that multiplefactors affect performance with the cochlear implant,including age at implantation (Connor, 1998; Connor,Hieber, Arts, & Zwolan, 2000; Kileny, Zwolan, &Ashbaugh, 2001; Tyler et al., 2001), age at onset of deaf-ness (Geers, 2002; Geers et al., 2002; Osberger, 1994),pre-implant residual hearing (Zwolan et al., 1997), lengthof cochlear implant use (Tye-Murray et al., 1995), familycharacteristics (Geers, 2002; Geers et al., 2002), type ofdevice (Connor et al., 2000), and device functioning(Geers, 2002; Geers et al., 2002). Research on the effectof communication method has been equivocal (Ash,Hodges, Butts, Schloffman, & Balkany, 1997; Connor etal., 2000; Cullington, Hodges, Butts, Donal-Ash, &Balkany, 2001; Geers et al., 2001; Hodges, Dolan,Balkany, Schloffman, & Butts, 1999; Miyamoto et al.,1999; Ninio, 1983; Zimmerman-Phillips & Murad, 1997).

Only a few published empirical studies have specifi-cally examined the variables affecting the reading skillsof children who use cochlear implants. Recently, Geersand colleagues (Geers, 2002, 2003; Geers & Brenner,2003; Geers et al., 2002) observed that multiple factorsaffect young cochlear implant users' reading skills, in-cluding educational setting and communication method,

family and child characteristics, and implant function-ing. What is not clear is how these key variables inter-act and affect children's reading through multiple path-ways. Another study (Spencer et al., 1998) found that54% of children who used cochlear implants and whowere between Grades 4 and 12 were reading at or abovea fourth grade level. The researchers then comparedtheir results to the results of two similar studies thathad examined the reading skills of children using con-ventional amplification (Furth, 1966; Krose, Lotz, Puffer,& Osberger, 1986). In these latter studies, only 8% and14%, respectively, of the similarly aged children achievedreading skills at or above the fourth grade level.

Although the reading difficulties of children withhearing impairments present unique challenges, re-search on factors affecting the reading development ofchildren with normal hearing sensitivity may be appli-cable to our study. The effects of children's vocabularyskills (Anderson & Freebody, 1981; Rayner et al., 2001)and socioeconomic status (SES; Jencks & Phillips, 1998)on their subsequent literacy development have been wellestablished. Children with stronger vocabulary skillstend to achieve stronger reading skills than do childrenwith weaker vocabulary skills (National Institute ofChild Health and Human Development, 2000; Whitehurst& Lonigan, 2001). Children living in poverty tend toachieve weaker reading skills than do their more afflu-ent peers. Other factors also appear to be crucial for read-ing development, such as phonological awareness andfluency (Rayner et al., 2001); however, examination ofthese variables is beyond the scope of this study.

The purpose of this study was to examine the ef-fects of multiple variables on reading comprehensionskills using multilevel modeling, specifically SEM. Whenusing SEM, researchers first develop an a priori theo-retical or conceptual model to test specific hypotheses(Kline, 1998). Based on current understanding of lan-guage and literacy development for children with nor-mal hearing sensitivity, as well as extant research onvariables affecting outcomes for children who use co-chlear implants, we hypothesize that multiple variableswill affect children's reading comprehension outcomesand present these relations in the conceptual model (seeFigure 1). Although in studies using SEM the concep-tual model is typically presented in the introduction(Hoyle, 1995), for illustrative purposes we discuss ourhypotheses and the conceptual model more fully in theMethod section. The following research questions wereposed in this study:

1. What are the effects of the variables of interest onreading comprehension when key child and familyvariables are controlled?

2. How do these variables relate and interact with eachother?

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Figure 1. Conceptual model (SEM Model 1): Path diagram for testing the effects of low socioeconomic status (LSES), age implanted,communication method, and pre- and postimplant vocabulary on reading comprehension. This is the theoretically constructed modeldesigned to test our hypotheses. All of the variables are observed as indicated by the rectangles surrounding the variable names. Latent andunobserved variables are indicated by circles. In this model, the disturbances or error are unobserved, as indicated by the circles. Unidirec-tional straight arrows indicate the predicted direction of the hypothesized effect. For example, in this model, we predict that LSES will directlyaffect pre-implant vocabulary and reading comprehension (Reading Comp). This is indicated by the arrows leading from LSES to PreimplantVocabulory and to Reading Comp. Curved bidirectional arrows indicate a correlation. In this model, we predict that communication, speechdetection threshold (SDT), and LSES will be correlated.

3. What are the combined direct and indirect effectsof these variables on reading comprehension scores?

MethodParticipants

Ninety-one children using cochlear implants (45boys and 46 girls) participated in this study. Descrip-tive statistics for the groups of participants are presentedin Table 1. All of the children participating in this studydemonstrated cognitive abilities within normal limits.Cognitive ability was assessed by a licensed clinicalpsychologist at the medical center where the cochlearimplant center was located. A number of different as-sessments were used, including the nonverbal portions

of the Kaufman Assessment Battery for Children(Kaufman & Kaufman, 1983) and the Stanford-BinetIntelligence Scale (4th ed.; Thormdike, Hagen, & Sattler,1986).

Preoperative unaided speech detection thresholds(SDTs) were greater than 80 dB HL for all participants.On average, children were 11 years old and had usedtheir implant for more than 4 years at the time of theirreading evaluation. Postoperatively, all children dem-onstrated SDTs between 15 and 30 dB HL when usingtheir cochlear implants. Of the 91 children, 88 used theNucleus 22 device and three children used the Nucleus24 device. All of the children used the SPEAK process-ing strategy at the time of their reading evaluation. How-ever, older children used MPEAK after activation until

Connor & Zwolan: Reading and Cochlear Irnplants 511

Table 1. Descriptive statistics for participants by age at implantation (preschool 5 5 years; school age > 5 years) and by communicationgroup.

Preschool age School ageTotal at implantation at implantation OC TC

(n = 91) (n = 40) (n = 51J (n = 48) (n = 43)

M SD M SD M SD M SD M SD

Age at reading evaluation (years) 10.98 2.67 9.45 1.63 12.03 2.77 10.81 2.53 11.16 2.83Pre-implant aided SDT (dB HL) 53.37 14.01 57.50 16.53 50.72 11 .54 51.72 13.08 55.22 14.95Ageatonsetofdeafness(years) 0.16 0.24 0.22 0.29 0.13 0.20 0.19 0.28 0.12 0.19Age at implantation (yearsl 6.78 3.06 3.85 0.89 8.66 2.40 6.23 2.98 7.40 3.06Length of implant use at reading

evaluation (years) 4.24 2.15 5.63 1.56 3.36 2.00 4.63 2.51 3.82 1.56Pre-implant vocabulary (SS) 59.73 16.53 62.63 21.16 58.08 13.17 53.08 13.55 66.54 16.67Postimplant vocabulary (SS) 70.77 17.73 75.90 19.81 67.46 15.74 67.12 16.14 74.60 18.66Reading comprehension (55) 69.78 13.40 76.42 13.5 65.02 11.28 69.21 11.83 70.40 15.04

Note. OC = oral communication; TC = total communication; SDT = speech detection threshold; SS = stondard score with a mean of 100 and astandard deviation of 15 for children with normal hearing sensitivity.

the newer processing strategy was available. Length oftime with SPEAK did not significantly predict readingcomprehension skills and so was removed from themodel.

Children received special education services soonafter their hearing impairment was diagnosed. Theyattended public schools and were enrolled in oral or to-tal communication educational programs. These pro-grams emphasized use of spoken language and providedauditory training as well as speech and language therapy.Based on school visit observations, both the total and oralprograms provided early intervention, self-containedclassrooms, and opportunities for mainstreaming (witheither oral or sign language interpreters, based on thechild's communication method). Reading curricula var-ied among schools but, in general, included both mean-ing-based and code-based instruction (see Rayner et al.,2001, for a review). This instruction was implementedusing commercially available programs, such as Mile-stones (Quigley & King, 1985), as well as school-designedactivities.

Data Collection and ScoringAn audiological and speech and language evalua-

tion was completed for each child prior to receiving a co-chlear implant and approximately yearly thereafter.Speech-language pathologists and audiologists admin-istered all of the tests and conducted school visits andparent interviews. The children's pre-implant and mostrecent evaluation results, which included a reading evalu-ation, were used for this study. Variables included in thestatistical models are described below; descriptive sta-tistics for these variables are displayed in Table 1.

Reading ComprehensionThe Passage Comprehension subtest of the Wood-

cock Reading Mastery Test-Revised (WRMT; Woodcock,1987) was used to assess reading comprehension. Thetesting was conducted in a quiet room according to stan-dardized procedures, but with the following changes: (a)Children gave their answers using signed and/or spo-ken language according to their preference and (b) werepermitted to read aloud using signed and/or spoken lan-guage if they wished. Raw scores were converted to stan-dardized scores (M = 100, SD = 15) for statistical analy-ses. This conversion permitted us to compare the readingcomprehension skills of children of varying ages and tocompare their scores with the test standardizationsample (i.e., children with normal hearing sensitivity).

The Passage Comprehension subtest is a modifiedcloze procedure. In a cloze task, the student is shown abrief passage of text and asked to report the missingword (e.g., The duck is swimming in the _ .). The testis designed so that the child has to understand the en-tire passage, exercising decoding, vocabulary, and com-prehension strategies to determine the correct response.Additionally, the cloze task implicitly requires an ad-equate grasp of the syntactic structure of English.

Pre- and Postimplant VocabularyLinks between vocabulary and reading skill have

been well documented for children with normal hearingsensitivity (Anderson & Freebody, 1981) and with pro-found hearing losses (Oakhill & Cain, 2000). Pre-implantvocabulary was evaluated using the Picture Vocabularytest of the Woodcock-Johnson Test of Cognitive Ability(Woodcock & Mather, 1989) and the Expressive One-Word

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Picture Vocabulary Test (EOWPVT; Gardner, 1990; n =11). Postimplant, all children were assessed using theWoodcock-Johnson measure. Both tests incorporatedpicture identification tasks, in which the children werepresented with a target picture and asked to name it.Standard administration procedures were followed ex-cept that both signed and spoken responses were ac-cepted. Every effort was made to administer the tests tochildren who used signed and/or spoken language in anequivalent manner. Iconic gestures were not consideredto be correct responses; only the exact spoken, signed,and/or finger-spelled target word was accepted as cor-rect. Nevertheless, test scores for children with differ-ing communication methods must be compared withcaution because translation from sign language to spo-ken language may differ depending on the sign systemused (e.g., Signed English, Signing Exact English [SEE])and geographic region. The difficulties are similar tothose encountered in cross-linguistic studies (Berman& Slobin, 1994; Tardif, 1996). Further, the tests werenormed for children with normal hearing sensitivity,so comparing the scores of the children in this studywith the standardized norms should be approached cau-tiously. Raw scores were converted to standard scores.There were no significant differences between pre-im-plant vocabulary scores of children tested using theEOWPVT and the Woodcock-Johnson measure (p > .05),so the test scores were combined into one variable, pre-implant vocabulary.

Age at ImplantationAge at implantation, in years, was calculated by

subtracting each child's date of birth from the date thatshe or he received and used the external parts of theircochlear implant device. Forty children received im-plants at age 5 years or younger; of these, 10 childrenreceived their implants before age 3 years, and 14 chil-dren were implanted between ages 3 and 4 years. Fifty-one received their implants after age 5 years; of these,32 received the implant before age 9 years, and 19 chil-dren were implanted between 11 and 14 years of age.

Pre-Implantation CommunicationMethod

Children were assigned to one of two groups basedon their primary method of communication (communi-cation method) prior to receiving a cochlear implant.They were placed in the total communication (TC) group(n = 43) if they used sign language in combination withspoken language. Children in the TC group used signsystems based on English, including Signed English andSEE, which strive to replicate visually the structure ofspoken English (Nevins & Chute, 1996; Ratner, 1997).Children were assigned to the oral communication (OC)

group (n = 48) if they used only spoken language. Groupplacement decisions were based on pre-implant clinicalobservation as well as parent report. If the parent re-ported using sign language or that the child was in aTC educational program, or if the child used any formalsign language with the examiner (e.g., Signed English,SEE), then the child was placed in the TC group. If theparent reported using only spoken language with thechild or reported that the child was in an OC educa-tional program, then the child was placed in the OCgroup. If the parent reported using an oral approach,then the examiner did not use any form of sign languagewith the child.

Primary communication method and school place-ment were highly related for those children for whomexaminers completed school observations. In general,children continued to use their pre-implant communi-cation method after receiving an implant and none ofthe children in this sample had their educational place-ment changed for at least 3 years following implanta-tion. Based on clinical observation, all of the childrenincreased their use of spoken English postimplant andwere using spoken English (either with or without signlanguage) at the time of the reading evaluation. In themodel, the OC group was coded 0 and the TC group wascoded 1 (Cohen & Cohen, 1983).

Pre-Implant Aided SDTsPre-implant binaural aided SDTs were included in

our model to control for the varying levels of audiologicalinformation that the children were receiving from con-ventional amplification (i.e., hearing aids) before implan-tation. SDTs represent the softest speech sounds thatchildren can detect. The binaural aided SDTs (reportedin dB HL) were obtained using live-voice stimuli in asoundproof booth while children used appropriate ampli-fication. Prior to receiving their implants, the childrendemonstrated mean binaural aided SDTs of 53 dB HL.Their unaided SDTs all were greater than 80 dB HL.

Length of Use/AgeLength of implant use (LOU) in years was calcu-

lated by subtracting the date at which children firstreceived their cochlear implant (age at implantation)from the date of their reading evaluation. There is goodevidence that LOU affects cochlear implant perfor-mance. Children demonstrate superior speech percep-tion (Osberger, 1994), speech production (Tye-Murrayet al., 1995), vocabulary (Connor et al., 2000; Connor,Raudenbush, & Craig, 2001), and reading comprehen-sion skills (L. Spencer et al., 1998) after sustained co-chlear implant use, and these skills continue to improveover time. It is important to note, however, that by in-cluding both LOU and age at implantation in the SEM,

Connor & Zwolan: Reading and Cochlear implants 51 3

the combined variable (LOU/age) represents the child'sage at the time of the reading evaluation. Further, be-cause we used standardized scores, the coefficient be-tween LOU/age and the outcomes represents differences,by age, in the disparity in achievement between chil-dren with cochlear implants and their same age peerswith normal hearing sensitivity. A positive effect wouldindicate that the achievement gap was less for olderchildren than for younger children; a negative effectwould indicate that the achievement gap was greaterfor older children than for younger children.

Age at Onset of Deafness

All of the children in this study demonstratedprelingual onset of deafness. This included children whowere diagnosed with a profound sensorineural hearingloss prior to their first birthday. In our sample, 84% ofthe children were congenitally deaf. Research indicatesthat age at onset of deafness significantly predicts co-chlear implant performance, including reading outcomes(Geers, 2002). However, using this sample, age at onset(in years) did not significantly predict reading compre-hension and so was not included in the model.

Demographics: SES, Gender,Race/Ethnicity

Previous studies that have examined the effects ofSES and the mother's educational level (a key indica-tor of SES) have revealed no significant effect on read-ing for children with cochlear implants (Geers, 2002;Hodges et al., 1999; L. Spencer et al., 1998; Tomblin &Spencer, 1997). However, Geers (2003) found that SES,using income as the marker, did uniquely explain read-ing variance. In studies of children with normal hear-ing sensitivity, SES is a consistent and robust predic-tor of reading skills (Jencks & Phillips, 1998; Snow,Burns, & Griffin, 1998) and, therefore, it was includedin our analyses. The type of medical insurance cover-age used by children's families was noted at the time ofthe pre-implant evaluation. Children who qualified forMedicaid and state-funded insurance were consideredto be from low SES (LSES) families (n = 23). Childrenwhose families had private insurance, with or withoutstate funded insurance, were assigned to the middle SES(MSES) group (n = 68). This variable was coded as fol-lows: 1 = LSES and 0 = MSES.

According to published research, there does not ap-pear to be a gender gap on measures of achievementfor children who are deaf (Slate & Fawcett, 1996) or forperformance with their cochlear implant (Osberger,1994). However, to rule out any gender effect, we in-cluded gender as a variable, where girls were coded 1and boys were coded 0. The addition of the gender vari-able to our model during exploratory data analyses did

not explain a significant amount of variance in stan-dard scores on reading comprehension (critical ratio [CR]< 1.96). The gender variable, therefore, was not includedin the final model.

With regard to racial/ethnic groups, our sample in-cluded only a small proportion of children from AfricanAmerican (n = 2) and other ethnic groups (n = 3). Thereis evidence that "deaf and hard of hearing children whoare members of racial, linguistic or ethnic minorities[leave school] with fewer language skills than theirWhite counterparts even when adjustments for otherdemographic differences are made" (Kluwin & Corbett,1998, p. 426). However, adding minority as a coded vari-able (White = 0; minority = 1) to our model during ex-ploratory data analyses did not explain a significantamount of variance in standard scores on reading com-prehension (CR < 1.96). Therefore, this variable also wasnot included in the final models.

Analytic StrategyOur analytic strategy incorporated path analysis

using SEM to test our hypothesis that multiple factorsinfluence children's reading comprehension. "Path analy-sis can be viewed as an extension of multiple regression"(Klem, 2000, p. 65), but where there is more than onevariable. With SEM, using AMOS software (Arbuckle,1994-1999), we statistically compared the covariancesbetween the variables in the theory-based model (seeFigure 1) with the actual relations between the vari-ables in our data set. If the theory-based model fits thedata well, then the covariances implied by the modelshould not differ significantly from the covariances inthe observed data. Goodness-of-fit measures are usedto test the fit (Hoyle, 1995). If the model fits the datawell, it is considered plausible. This notion of plausibil-ity is important because there may be alternative mod-els that fit the data equally well (Klem, 2000). For ex-ample, if reading comprehension actually predictspostimplant vocabulary, then such a model might fit thedata as well as a model hypothesizing that postimplantvocabulary predicts reading comprehension, or, as wehypothesize, that there is a reciprocal relation betweenthe two. SEM can reveal which models are plausible,but not which ones are correct.

Although in this study we use SEM to test a pathanalysis (Klem, 2000), where all variables are observed,more elaborate models including latent variables mayalso be examined using SEM. However, larger samplesof more than 100 are required for these models (Kline,1998).

Cognizant that our sample size is small for SEM,we selected several measures of goodness of fit for ourmodel, including an estimate of chi square, which is

514 Journal of Speech, Language, and Hearing Research * Vol. 47 * 509-526 * June 2004

sensitive to sample size. We also we report the Tucker-Lewis fit index (TLI) and the comparative fit index (CFI).A nonsignificant chi square indicates a good model fitwith the data (i.e., the theoretical model does not differsignificantly from the data driven model). Higher TLIand CFI values also suggest a good fit. Additionally, wereport the root mean square error of approximation(RMSEA), which is an absolute fit index and takes intoaccount the complexity of the model. Values below .05represent a good fit. Descriptive statistics are providedin Table 1 and the correlation matrix of the variablesincluded in the SEM is provided in Table 2.

In addition to overall model fit, SEM can provideinformation regarding the significance of direct effectsor path coefficients (e.g., age at implantation to readingcomprehension in Figure 1). The direction of the arrow,usually left to right, implies the flow of the causal ef-fect. Path coefficients, which may be unstandardized(each measure on its own scale) or standardized (allvariables on the same scale falling between -1 and 1),are interpreted in the same way that multiple regres-sion coefficients (unstandardized and standardized) areinterpreted-"they control for correlations among mul-tiple presumed causes of the endogenous variable"(Kline, 1998, p. 52). For example, the path coefficientbetween age at implantation and reading comprehen-sion controls for the correlation of variables in themodel-communication, SDT, vocabulary, LOU, and soon. Unstandardized path coefficients' CRs above 1.96may be considered to differ significantly from zero atthe .05 level (i.e., 95% confidence level). Standardizedpath coefficients of less than .10 are generally consid-ered small, those around .30 are considered medium,and those above .50 are considered large (Kline, 1998).Indirect paths (communication to pre-implant vocabu-lary to postimplant vocabulary to reading comprehen-sion) also are estimated in SEM, which will be discussedmore completely in the Results section.

Also included in the models (see Figure 1) are thedisturbances, or error. These are designated by a largeD (i.e., Dl, D2, etc.). In SEM path analysis, every en-dogenous variable has a disturbance, which is analo-gous to the residual in a multiple regression (Klem, 2000;Kline, 1998). Theoretically, a disturbance may be con-sidered to represent the influence of all of the unmea-sured variables that affect the variable. For example,many variables may influence the age at which childrenreceive cochlear implants over and above their methodof communication, SES, and pre-implant SDT. Suchvariables include the age at which the child is diagnosedand the availability of an implant center (or, historically,the implant itself). The disturbance can be seen as acomposite variable that represents these unmeasuredcauses of age at implantation (Kline, 1998). Standard-ized disturbances represent the unexplained variance(1 - 2; Klem, 2000). R2 values, also called the squaredmultiple correlations (SMCs), are the percentage of vari-ance explained for each endogenous variable in themodel, and these are reported in the standardized pathdiagram (see Figure 2).

Rationale for the ConceptualModels

The path diagram for our conceptual model is pre-sented in Figure 1. Note that the disturbances for LOU/age and age (implanted) are assumed to covary because,together, they represent a child's age at the time of thereading evaluation. Further, the disturbances betweenpre-implant vocabulary and age at implantation areassumed to covary because we predict a negative rela-tion between them. This is based on research that in-dicates the language skills of children using hearingaids grow at about half the rate of those of childrenwith normal-hearing sensitivity (Miyamoto et al., 1997,Svirsky, 2001). Thus, there is an achievement gap thatincreases over time. Communication method, LSES, and

Table 2. Correlation coefficients for variables in the model.

1 2 3 4 5 6 7 8

1. Communication method -

2. SDT .125 -3. LSES .115 -.146 -

4. Pre-implant vocabulary .410- .065 -.1215. Age at implantation .192 -.182 .081 -.0886. Length of use -.189- .256* -.042 -.205- -.51" -

7. Postimplant vocabulary .212* -.100 -.239' .400- -.23' .0168. Reading comprehension .044 .062 -.28- .33A-' -.61* -.116 .516"9. Age atonset -.146 .250* -.133 -.058 -.066 .231 -.271 -.166

Note. LSES = low socioeconomic status.

-p<. 1 0. 'p<.05. *'p<.Ol.

Connor & Zwolan: Reading and Cochlear Implants 51 5

Figure 2. Structural equation model-path diagram for final model [SEM Model 2), including standardized path coefficients, correlations,and squared multiple correlations (SMCs) for each variable. For communication, total communication (TC) group = 1, oral communication(OC) group = 0. For LSES, LSES = 1, MSES = 0. All test scores are standardized (M = 100, SD= 15). Standardized path coefficients arepresented at the midpoint of the unidirectional arrow paths. Correlations are presented at the midpoint of the bidirectional arrow curvedpaths. Values noted beside the endogenous observed variables are the SMCs, which represent the percentage of variance explained for eachvariable. Note that this model explains 75% of the variability in children's reading comprehension scores.

.1

preoperative SDT are unanalyzed (i.e., allowed to covary)because, in this model, whatever causes them is un-known (Kline, 1998). Also, a reciprocal relation betweenreading and vocabulary was hypothesized based on re-search with children with normal hearing (Anderson &Freebody, 1981). Because we predict this reciprocal re-lation and have correlated disturbances, this model isconsidered nonrecursive. In this kind of model, variablesmay be both a cause and an effect of each other (e.g.,reading comprehension and postimplant vocabulary).Without the reciprocal relation, the model can be con-sidered partially recursive (although the literature isnot consistent in its designation of models with unidi-rectional effects and correlated disturbances; Kline,1998). Only recursive models, where the effects are uni-directional, where there are no correlated disturbances,and which involve only observed variables, can be testedusing multiple regression. SEM must be used for thenonrecursive model we present.

When using nonrecursive models, the researchermust attend to the identification of the model. "A modelis said to be identified if it is theoretically possible to calcu-late a unique estimate of every one of its parameters"(Kline, 1998, p. 108). If not, then it is underidentified orunidentified. In this study, the model is identified be-cause we use instrumental variables, which predict pre-implant vocabulary but not reading comprehension(pre-implant vocabulary) and that predict reading com-prehension but not postimplant vocabulary (LSES andLOU). If we did not have the instrumental variables,then it would be impossible to find one unique solutionfor our model (there could be an infinite number of solu-tions), which is not permissible (Klem, 2000).

We hypothesized that multiple descriptive vari-ables, including family SES, age at onset of deafness,and gender may affect reading performance with thecochlear implant. Exploratory analyses revealed that

516 Joumal of Speech, Language, and Hearing Research * Vol. 47 * 509-526 * June 2004

Table 3. Unstandardized path coefficients from structural equation modeling (SEM Model 1).

Model 1

Variable paths Esfimate SE CR

SDTto LOU 0.045 0.016 2.825-LSES to LOU 0.258 0.511 0.505Communication method to LOU -1.003 0.440 -2.279Communication method to age implanted 1.162 0.653 1.779SDT to age implanted -0.045 0.024 -1.916LSES to age implanted 0.261 0.758 0.344LSES to pre-implant vocabulary -7.378 3.818 -1.932SDT to pre-implant vocabulary 0.024 0.119 -0.201Communication method to pre-implant vocabulary 14.073 3.294 4.272-Pre-implant vocabulary to postimplant vocabulary 0.322 0.111 2.992*Reading comprehension to postimplant vocabulary 0.210 0.240 0.874Age implanted to postimplant vocabulary -0.839 0.823 -1.021LOU to reading comprehension -3.298 0.405 -8.149-LSES to reading comprehension -5.234 1.685 -3.106'Age implanted to reading comprehension -3.573 0.303 -11.804*Communication method to reading comprehension 1.109 1.513 0.733Postimplant vocabulary to reading comprehension 0.177 0.061 2.880'

Note. Communication method: TC = 1, OC = 0. LOU = length of implant use.

*CR> 1.96. p < .05.

age at onset of deafness and gender did not contributesignificantly (CR < 1.96, p > .05) to variability in theoutcomes, so they were trimmed from the model.

Again, the small sample size limits the number ofparameters that can be estimated, so only the descrip-tive variable, family SES, was included in the finalmodel. In this case, based on research with children withnormal hearing sensitivity, SES is expected to affect notonly reading comprehension, but vocabulary as well(Hart & Risley, 1995; Snow et al., 1998). Further, usingthis model, we can examine whether children from LSEShomes have the same opportunity to receive and usecochlear implants as children from MSES homes. Theseresults can be obtained by examining the paths fromLSES to age at implantation and to LOU/age; nonsig-nificant paths would suggest equal opportunity. We canalso examine whether a particular communicationmethod is related to SES by examining the covariancebetween LSES and communication method.

ResultsStructural Equation Modeling ofVariables Affecting ReadingComprehension

Age at implantation, pre-implant vocabulary, post-implant vocabulary, communication method, pre-im-plant SDT, and LSES significantly affected children's

reading comprehension. In turn, they significantly af-fected each other as well (see Table 3). This model fitthe data well (see Table 4) for the small sample size.Small sample size increases the likelihood of finding anonsignificant chi-square value, suggesting a good fitthat does not exist. The opposite case exists for very largesamples. With large samples, chi square can be signifi-cant although the model actually fits the data well. Thatis why several measures of goodness of fit are provided.

Frequently, SEM is used to test competing theoriesand researchers present alternative models (Kline,1998). To test alternative theories (e.g., communicationmethod has a direct versus an indirect effect on reading),

Table 4. Summary of structural equation model goodness of fit.

Chi square(x21 TLI CFI RMSEA

SEM Model 1 Measure of fit 10.752 .982 .997 .094df 6p .096Pclose .185

SEM Model 2 Measure of fit 12.348 .995 .998 .051df 10p .262Pclose .436

Note. TLI = Tucker-Lewis fit index; CFI = comparative fit index.X2

0 rnce(4) = 1. 596, p > .05

Connor & Zwolan: Reading and Cochlear Implants 51 7

the conceptual model was refined or trimmed (see Fig-ure 2 and Table 4). We deleted only theoretically unim-portant paths. For example, comparing Figures 1 and2, we hypothesized that children from LSES familieswould have equal opportunity to receive and use theircochlear implants (we deleted LSES to LOU and LSESto age implanted paths from the conceptual model).LSES should affect vocabulary (Hart & Risley, 1995) andso the LSES to pre-implant vocabulary path was left inthe final model, even though in the conceptual modelthe path coefficient was not significant (see Table 4).Because our alternative model was nested within theconceptual model, our alternative model could be testedby comparing the chi-square estimate of the conceptualmodel with the chi-square value of the more parsimoni-ous model (Kline, 1998). A comparison of these modelsindicated that goodness of fit was maintained in the moreparsimonious model; the x2 difference (X2 for Model 1minus x2 for Model 2) was not significant, X2dMfference(4) =1.596, p > .05. Indeed, comparison of the absolute good-ness-of-fit measure, RSMEA, which takes into accountthe complexity of the model, revealed a better fit withthe data than was obtained with the conceptual model(see Table 5). The results of the final model are presentedbelow (see Table 5 and Figure 2).

Pre- and Postimplant VocabularyChildren's vocabulary, both pre- and postimplant,

had a significant effect on their reading comprehensionscores. Children's vocabulary just prior to receiving theirimplants significantly predicted their vocabulary at thetime of the reading evaluation (see Figure 2 and Table5, total standardized effect = .105), suggesting some sta-bility in vocabulary over time. Their postimplant vocabu-lary, in turn, significantly predicted their reading com-prehension score (see Figure 2 and Table 5). Of thevariables, only communication method contributed topre-implant vocabulary. In addition to pre-implant vo-cabulary, age at implantation significantly negativelypredicted postimplant vocabulary. Children who wereyounger when they received their implants tended tohave higher postimplant vocabulary scores than didchildren who were older at implantation; for every yearyounger children were at implantation, their fittedpostimplant vocabulary score was 1.37 points higher(standardized effect = -.241). Reading comprehensiondid not significantly predict postimplant vocabulary.

Age at ImplantationAge at implantation directly and negatively affected

reading comprehension (see Table 5 and Figure 2). The

Table 5. Unstandardized path coefficients and disturbances from structural equation modeling (SEMModel 2).

Model 2

Variable paths Estimate SE CR

SDT to LOU 0.043 0.016 2.775Communication method to LOU -0.971 0.436 -2.224Communication method to age implanted 1.186 0.649 1.828SDT to age implanted -0.047 0.023 -2.000-LSES to pre-implant vocabulary -6.778 3.663 -1.850SDT to pre-implant vocabulary -0.021 0.119 -0.179Communication method to pre-implant vocabulary 14.027 3.291 4.262-Pre-implant vocabulary to postimplant vocabulary 0.383 0.107 3.573'Age implanted to postimplant vocabulary -1.373 0.558 -2.462-LOU to reading comprehension -3.291 0.397 -8.281 *LSES to reading comprehension -4.757 1.664 -2.859*Age implanted to reading comprehension -3.484 0.282 -12.369*Postimplant vocabulary to reading comprehension 0.219 0.043 5.135*

Disturbances

Dl 4.026 0.614 6.559'D2 43.894 6.801 6.454'D3 216.932 33.908 6.398'D4 9.053 1.353 6.689-D5 249.931 38.252 6.534-

Note. Communication method: TC = 1, OC = 0.

*CR > 1.96. p < .05.

518 Journal ofSpeech, Longuage, and Hearing Research * Vol. 47 * 509-526 . June 2004

younger the age of the children when they received theirimplant, the higher were their predicted reading com-prehension scores. The total unstandardized effect, in-cluding both direct and indirect paths, was -3.785; forevery year younger children were when they receivedtheir implant, the model predicted that their readingscore increased more than 31/2 points (total standard-ized effect = -.900, which is large). Age at implantationsignificantly affected postimplant vocabulary. Theyounger the age of the children when they received theirimplants, the greater were their postimplant vocabu-lary scores. SDT significantly predicted age at implan-tation. Children with higher SDTs (i.e., more severe pre-implant hearing loss) tended to be younger when theyreceived their implants. Communication method did notsignificantly predict age at implantation.

Communication MethodThere was no significant direct path between com-

munication method and reading comprehension (seeTable 5 and Figure 2). Indeed, as discussed previously,removing the path from the model improved the good-ness of fit. Thus, classification in the TC group was as-sociated with higher pre-implant vocabulary, but thetotal direct and indirect effect of communication methodon reading comprehension was negligible (total stan-dardized effect = -.004). Hence, assignment to the TCgroup, per se, did not significantly predict stronger read-ing comprehension.

LOU/AgeThe negative effect of LOU/age on reading compre-

hension (see Table 5 and Figure 2) appears to run counterto results suggesting that longer cochlear implant useis related to increasing scores over time. This effect ac-tually reflects an achievement gap between children whouse cochlear implants and children of the same age withnormal hearing sensitivity. The negative effect of LOU/age indicates that, on average, older deaf children dem-onstrate a greater disparity between their scores andthe scores of their age-matched peers with normal hear-ing sensitivity than do younger deaf children. The nega-tive association does not suggest that the older children'sreading raw scores were lower than younger children's;visual inspection of the data indicated that, on average,raw scores were higher for older children.

The achievement gap was moderated by other vari-ables. For example, children with larger vocabularieswho received their implants at a younger age exhibiteda smaller achievement gap than did children withsmaller vocabularies who were older when they receivedtheir implants. Further, the achievement gap was ap-parent before children received implants, as evidencedby the significantly negatively correlated disturbances

for age at implantation and pre-implant vocabulary (r =-.26, covariance = -11.519, CR = -2.591).

LSES

The LSES children in this study appeared to haveequal opportunity to receive (path to age at implanta-tion) and use (path to LOU) their cochlear implants, andthe model fit improved when these paths were removed.Furthermore, LSES was not significantly associatedwith communication method (covariance = .025, SE =.024, CR = 1.031; r = .113). Nevertheless, on average,children from LSES families achieved significantly lowerreading comprehension scores than did children fromMSES families (see Figure 2 and Table 5), and this ef-fect was direct. Although the trend was negative, LSESdid not significantly predict pre-implant vocabulary. Thetotal effect of LSES was -5.326. This effect means thatchildren from LSES families, on average, achieved read-ing comprehension scores more than five points lowerthan did children from MSES families, when all othervariables were held constant (standardized total effect= -.180).

DiscussionOur findings demonstrate that children can follow

multiple pathways to. stronger reading comprehensionskills, with paths that include vocabulary, age at im-plantation, communication method, and SES. Each pathhas implications for reading development for childrenwho use cochlear implants. The results of this studyunderscore the interrelatedness of factors that affectcochlear implant performance, the importance of earlyimplantation, the important role of vocabulary, and thecomplexity of comparing the reading skills of young co-chlear implant users who use different methods of com-munication. Multilevel modeling techniques, such asSEM, help to elucidate these complex relations.

Age at Implantation EffectThe age at which children received their implants

strongly affected their reading comprehension. Specifi-cally, children who were younger when they receivedimplants achieved higher reading comprehensionscores and this effect was large. In addition to the di-rect effect of age at implantation on reading compre-hension, children who were younger when they receivedtheir implants generally had stronger pre-implant vo-cabulary skills. This trend predicted stronger post-implant vocabulary, which in turn predicted readingcomprehension. Further, age at implantation had adirect effect on postimplant vocabulary. Thus, thereappeared to be a lasting effect of early implantation

Connor & Zwolon: Reoding and Cochlear Implants 519

that had an impact on both vocabulary growth and read-ing outcomes.

The direct effect of age at implantation on readingwas important. Regardless of children's vocabulary orother factors, children who were younger when theyreceived their implants achieved higher reading com-prehension scores. The direct effect of age at implan-tation on reading is intriguing and opens several in-teresting lines of speculation. The cochlear implant,and the age at which children receive them, may pro-vide a natural experiment. This experiment can be usedto examine how the age at which children obtain audi-tory information has an impact on their developingauditory and language systems (i.e., age at implanta-tion is considered a naturally randomly occurring in-dependent variable; Cook & Campbell, 1979). Clearlyage at implantation is affecting important factors, whichare not included in our model, that contribute to read-ing success. These factors include access to sound andaccess to spoken English.

Access to sound may have affected other factors re-lated to reading success, including children's interac-tions with caregivers (Sroufe, 1988), joint attention (P.E. Spencer, 2000), and their overall connection to theworld around them (Neuman & Dickinson, 2001; Snowet al., 1998). Further, the salient auditory informationprovided by their implants may have supported thechildren's acquisition of phonological awareness, whichis closely related to reading success (Bradley & Bryant,1983; Perfetti, Beck, Bell, & Hughes, 1987; Rego, 1997).It should be easier for children to develop phonologicalawareness if they can hear and discriminate phonologi-cal information (e.g.,Alegria, 1998). Furthermore, grasp-ing the alphabetic principal relies on the understand-ing that letters in the alphabet are associated with aparticular set of speech sounds (i.e., phonemes). Again,access to auditory phonemic information might be ex-pected to facilitate children's grasp of the alphabeticprincipal and, hence, reading.

The implant provides greater access to spoken En-glish and its complex phonological, prosodic, intona-tional, and morphosyntactic structure. Each of thesefactors independently and together may support read-ing development. There is emerging evidence for chil-dren with normal hearing sensitivity that the use of moretypical intonation and prosody during oral reading isassociated with stronger reading comprehension (Kuhn& Stahl, 2002). Morphosyntactic knowledge is also re-lated to stronger reading comprehension (Neuman &Dickinson, 2001; Snow et al., 1998). The younger the chil-dren are when they receive their implants, the longerthey have access to this information as they develop theirliteracy skills. Moreover, early exposure to language, re-gardless of modality, may be critical for typical language

development (Bates, 1999; Connor et al., 2001; Connor,Raudenbush, Zwolan, Heavner, & Craig, 2004; Elmanet al., 1996; Hart & Risley, 1995; Lenneberg, 1967; Locke,1997). The effect of early implantation on growth in vo-cabulary (postimplant vocabulary controlling for pre-implant vocabulary) also supports early sensitive periodtheories. Early implantation provides access to spokenEnglish during the important early language develop-ment phase, and the resulting stronger language skillswould tend to support stronger reading skills.

Because the vast majority of reading research in-cludes participants with full and ongoing access to sound,there may be other acoustic aspects of reading that re-main undiscovered. Investigation of the reading skills ofchildren who receive cochlear implants at different agesmay help us to understand the complex links betweenacoustic aspects of speech, language, and literacy.

The average age at which the children in this studyreceived their implants was 6.7 years and most of thechildren were implanted between 1991 and 1997 (5 re-ceived them between 1988 and 1990). These two factorshave important implications for our findings. First, atthe time these children were implanted, FDA guidelinesstated that cochlear implants were appropriate for usein children 2 years of age or older. Recently, the FDAdecreased the minimum age for implantation to 12months. This change has resulted in an increase in thenumber of children receiving implants at younger ages.Because this change in policy was a fairly recent occur-rence, reading comprehension data are not yet avail-able for children who received implants prior to age 2years.' It is likely that future studies will demonstrateeven stronger effects of age at implantation on readingcomprehension than did our study.

Secondly, several recent technological advancementshave been made with cochlear implant devices. Becausethe children in this study received their implants sev-eral years ago, the effects of these advancements arenot reflected in this study. For example, all of the chil-dren utilized the SPEAK strategy at the time of theirreading evaluation, but some spent substantial timeusing MPEAK before SPEAK was available. It is likelythat future studies, which will include participants whohave had years of experience using more technologicallyadvanced devices, will demonstrate even stronger out-comes for children using cochlear implants.

Family SESIt is encouraging that children from LSES and

MSES families appeared to have equal access to cochlear

'Children must be 7 years old to obtain a standard score on the WRMTPassage Comprehension subtest.

520 Journal of Speech, Longuoge, and Hearing Research * Vol. 47 * 509-526 * June 2004

implants and that SES was not related to communica-tion method. Surprisingly, although the trend was nega-tive, family SES did not significantly predict pre-implantvocabulary. A relation was expected based on prior re-search on children with normal hearing sensitivity (Hart& Risley, 1995). Nevertheless, the reading comprehen-sion scores of LSES children were significantly lowerwhen compared to children from MSES families. Thisresult is a consistent finding in the literature for chil-dren with normal hearing sensitivity (Hart & Risley,1995; Neuman & Dickinson, 2001; Snow et al., 1998)and with a recent finding for children with cochlearimplants (Geers, 2003). Many factors appear to be asso-ciated with LSES. These factors can have a deleteriouseffect on children's reading outcomes; such factors in-clude underachievement, reduced academic opportunity,unstable housing, family stress, health issues, and singleparent households. Additionally, there may be factorsunique to young cochlear implant users. For example,transportation problems that preclude regular appoint-ments for implant programming have been negativelyassociated with reading success (Geers, 2002). Clearly,more research is needed to evaluate the effects of SESon children's performance with cochlear implants.

Vocabulary and CommunicationMethod

SEM results indicated that a child's early vocabu-lary and ongoing vocabulary growth are important pre-dictors of reading comprehension. These results supportprevious and recent findings for deaf children (Geers,2003; Padden & Ramsey, 1998, 2000) and for childrenwith normal hearing senstivity (Anderson & Freebody,1981; Loban, 1976; Snow et al., 1998) and underscoreimportant but complex links between language and lit-eracy. Children with stronger language skills tend tohave stronger reading skills. Specific aspects of theirlanguage systems, including lexicon, syntax, morphol-ogy, metalinguistic awareness, and pragmatic use of lan-guage, are interrelated and are, in turn, also related toreading comprehension (Connor, Morrison, & Petrella,in press; National Institute of Child Health and HumanDevelopment, 2000; Snow, 2001).

Because both pre-implant and current vocabularywere endogenous variables in our model, factors thatwere related to stronger vocabulary (e.g., communica-tion method [see below] and early implantation [seeabove]) also were related to stronger reading outcomes.Supporting deaf children's language development maybe a critical first step toward preventing later difficul-ties with reading. Early cochlear implantation, pre-im-plant exposure to visuospatial language, maximizingpre-implant residual hearing, and intensive language

and auditory therapy to support language developmentare all methods to be considered. Further, these arenot mutually exclusive endeavors. A combination ofstrategies might prove to be most effective; however,more research is needed, including experiments usingrandom assignment to pre-implant language supporttreatments.

A child's communication method, whether OC or TC,did not directly affect his or her reading comprehensionscores when other variables were controlled. Indeed, thetotal effect was negligible. In our model, communica-tion method was associated with early vocabulary skills,which were associated with vocabulary growth, which,in turn, predicted reading comprehension. Children inthe TC group tended to have stronger pre-implant vo-cabulary skills than did children in the OC group. Thismay have been an artifact of our vocabulary tests andthe difficulties inherent in cross-linguistic studies (seeTardif, 1996). It may also be that using a visuospatiallanguage prior to having access to auditory languageinformation, via the cochlear implant, supported childlanguage development, including vocabulary (Mayberry,Lock, & Kazmi, 2002). Neuropsychological studies ofnative sign-language users indicated that use and un-derstanding of British Sign Language followed the lo-calization patterns of spoken language (MacSweeney etal., 2002). Mayberry and colleagues (Mayberry, 1993;Mayberry et al., 2002) observed that late acquisition ofa first language appeared to result in long-term defi-cits, whereas early acquisition of a first language facili-tated the acquisition of a second language. Overall, earlyaccess to language, regardless of modality, appears tobe important for ongoing language and literacy devel-opment (Hart & Risley, 1995; Huttenlocher, Haight,Bryk, Seltzer, & Lyons, 1991; Locke, 1997; Shonkoff &Phillips, 2000; Snow et al., 1998).

Because the effect of communication method onreading comprehension was indirect and a function ofpre-implant vocabulary, our results suggest that if par-ents decide to use sign language with their young chil-dren, then they should learn sign language well, use itconsistently, and use it as soon as possible to buildchildren's language skills (Moeller, 2002). Assignmentto the OC or TC group was based on the child's pre-implant communication system. Our results, therefore,may allow us to generalize about the effects of teachingchildren sign language before they receive an implant.These results, however, do not suggest the extent towhich an educational program's emphasis on a particu-lar communication mode benefits or detracts from theoverall postimplant effect. Almost all of the children in-creased their use of spoken English postimplant. Fur-thermore, given that all of the TC group children in thisstudy used sign and spoken language together, these

Connor & Zwolon: Reacding and Cochlear implants 521

results may not generalize to children who use ASL tocommunicate.

Consider also that, according to our model, childrenin the OC group with stronger vocabulary skills wouldbe expected to achieve stronger reading comprehensionskills than would children in the TC group with weakervocabulary skills. Overall, then, children's vocabulary,and by proxy, their language skills, appear to be stron-ger predictors of their reading comprehension skills thanare the methods of communication. Parents who decideto use OC with their child should be cognizant of theirchild's early vocabulary development. Programs thatfocus on language stimulation (e.g., auditory-verbalmethods) including vocabulary have demonstratedstrong outcomes (Goldberg & Flaxer, 2001).

In this study, vocabulary, age at implantation, andfamily SES were significant predictors of reading,whereas communication method was not. In this regard,these results differed from Geers and colleagues (Geers,2002, 2003; Geers, Nicholas, & Sedey, 2003). In theirstudies, communication method significantly predictedreading but age at implantation did not (Geers 2002,2003). There were, however, important differences be-tween our study and theirs in the covariates included,the sample child characteristics, and in the analyticstrategies used. Communication method was defined andcoded differently in the two studies. Studies by Geersand colleagues (e.g., Geers, 2002; Geers & Brenner, 2003;Geers et al., 2003) defined communication method basedon the child's educational program. They coded commu-nication method using a noninterval ranking from 1 to6 and included children who used a variety of communi-cation methods, such as auditory-verbal (rank = 6), pri-marily sign language (rank = 1), and cued speech (rank= 4). This study used two groups, OC and TC; none ofthe children used cued speech. The two studies used dif-ferent markers for SES, which may have contributed todifferent results. Participants in the studies by Geersand colleagues (Geers, 2002; Geers & Brenner, 2003;Geers et al., 2003) tended to be more affluent than ours.Furthermore, it is not clear whether SES and commu-nication method were related in the Geers et al. stud-ies. If SES and communication method were related toeach other, then the relation between communicationmethod and reading outcomes may have been biased(i.e., the results related to the SES effect rather than tocommunication method effects). Additionally, some chil-dren in the Geers et al. (Geers, 2002; Geers & Brenner,2003; Geers et al., 2003) studies had IQs falling below85, which was not the case in this study. All of the chil-dren in this study had cognitive abilities within nor-mal limits. No measure of pre-implant hearing levelswas included in the Geers et al. (Geers, 2002; Geers &Brenner, 2003; Geers et al., 2003) studies, which may

be important because pre-implant residual hearing hasbeen positively associated with postimplant speech per-ception (Zwolan et al., 1997). Moreover, the Geers et al.(Geers, 2002; Geers & Brenner, 2003; Geers et al., 2003)studies included covariates that we did not include (e.g.,hours of therapy, parent participation in therapy, andtherapist experience). Finally, the two studies used dif-ferent analytic strategies. Geers et al. (Geers, 2002;Geers & Brenner, 2003; Geers et al., 2003) relied on re-gression and we used SEM. Both strategies are useful,but for different kinds of research questions. All of thesedifferences could contribute to the different results inour study and in Geers et al. (Geers, 2002; Geers &Brenner, 2003; Geers et al., 2003).

The different effects for age at implantation areprobably due to the different participant criteria usedin the studies. Only children who received their implantby age 5 years were included in the Geers et al. study(Geers & Brenner, 2003), whereas children who receivedtheir implants by age 14 years were included in our in-vestigation. The difference in the age ranges, 3 versus11 years, could lead to different results. Additionally,age at implantation effects have been found for otheroutcomes in other studies (Connor et al., 2000; Kilenyet al., 2001; Kirk et al., 2002; Sharma, Dorman, & Spahr,2002; Tyler et al., 2001). Importantly, these studies allshared the common finding that stronger language skills(vocabulary or syntax) are associated with stronger read-ing skills (Geers, 2003; Geers et al., 2003).

ImplicationsOverall, the children in this study were not reading

as well as their peers with normal hearing sensitivity.In our model, the achievement gap between childrenwith implants and children with normal hearing sensi-tivity was evident for both vocabulary and reading com-prehension. Nevertheless, the cochlear implant doesappear to support reading development, especially ifreceived early. There is evidence that children who usecochlear implants attain higher reading comprehensionscores and that these scores grow more rapidly over timewhen compared to their peers who use conventional am-plification (L. Spencer et al., 1998). Furthermore, strongearly vocabulary skills, strong vocabulary growth, andearly implantation appear to lessen the achievement gap.Nevertheless, the difficulties that these deaf childrenexperience in learning to read remain important andperplexing. In recent years, we have learned more aboutimportant factors that contribute to children's readingsuccess (see Rayner et al., 2001), including phonologi-cal awareness (Bryant, Maclean, & Bradley, 1990), earlyintervention (S. W. Barnett, 1995), and parent involve-ment (Neuman & Dickinson, 2001). Programs based onthis research may prove effective.

522 Journal of Speech, Language, and Hearing Research * Vol. 47 * 509-526 * June 2004

Educational recommendations should not be madeon the basis of this study alone. Further research isclearly needed. Many factors, such as the quality of achild's educational program, early identification of hear-ing loss and early intervention, parental support, thestudent's motivation, and his or her speed of auditoryprocessing and working memory, may all contribute toreading performance (Alderman, 1999; W. S. Barnett,Frede, Mobasher, & Mohr, 1987; Bus & van Ijzendoorn,1995; Calderon, Bargones, & Sidman, 1998; Catts, 1997;Pisoni & Geers, 2002). We do suggest that all schoolsserving children with cochlear implants provide well-designed and implemented auditory training, speechand language development programs, and strong lit-eracy instruction. Reading curricula should includespecific focus on phonological awareness, decoding, flu-ency, reading comprehension, and writing (Adams,1990; Neuman & Dickinson, 2001; Snow et al., 1998).Recommendations provided to parents should be madethoughtfully, based on the extant research, and withcareful focus on the best interest and needs of the indi-vidual child within the context of the family, school, andcommunity (Bronfenbrenner, 1986). Additionally, edu-cational, clinical, and medical professionals should dis-cuss the documented benefits of early cochlear implan-tation with parents and advise them accordingly. Thisopen discussion will be especially important as univer-sal newborn hearing screening promotes very early in-tervention for more deaf children.

Studies of children who use cochlear implants arecomplex, and converging evidence reveals that multiplechild, family, device, and educational factors contributeto successful outcomes. The results of this study under-score and expand these findings while illustrating theusefulness of multilevel statistical modeling techniquesto examine the multifaceted relations between variablesthat influence a child's performance with a cochlearimplant.

AcknowledgmentsThe Spencer Foundation provided part of the funding

for this research. We are grateful to Laura Klem for herguidance regarding structural equation modeling and hercomments on the final draft of the manuscript, to FrederickMorrison and Joanne Carlisle for their keen insightsregarding reading development, to Seung-Hee Son for adviceon the models, and to Steve Telian for his ongoing support ofthis research. We thank the children and families whoparticipated in this research and members of the cochlearimplant team at University of Michigan for their help withdata collection. We also thank Sara Hieber for her earlywork with the manuscript. An early version of this paperwas presented at the Fifth International Cochlear ImplantConference, New York (October 1997), and parts of thisstudy were presented at the annual convention of the

American Speech-Language-Hearing Association, Atlanta,GA (November 2002).

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Received February 9, 2003

Accepted January 5, 2004

DOI: 10.1044/1092-4388(2004/040)

Contact author: Carol McDonald Connor, Department ofPsychology, University of Michigan, 525 E. University,Suite 1346, Ann Arbor, MI 48109.E-mail: cconnor@umich.edu

526 Journal of Speech, Language, and Hearing Research * Vol. 47 * 509-526 * June 2004

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TITLE: Examining Multiple Sources of Influence on the ReadingComprehension Skills of Children Who Use CochlearImplants

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