http://ldx.sagepub.com/Journal of Learning Disabilities

http://ldx.sagepub.com/content/41/4/353The online version of this article can be found at:

 DOI: 10.1177/0022219407311747

2008 41: 353 originally published online 8 April 2008J Learn DisabilTolvanen, Minna Torppa and Heikki Lyytinen

Anne Puolakanaho, Timo Ahonen, Mikko Aro, Kenneth Eklund, Paavo H. T. Leppänen, Anna-Maija Poikkeus, AskoOutcomes: Strong to Accuracy but Only Minor to Fluency

Developmental Links of Very Early Phonological and Language Skills to Second Grade Reading  

Published by:

  Hammill Institute on Disabilities

and

http://www.sagepublications.com

can be found at:Journal of Learning DisabilitiesAdditional services and information for    

  http://ldx.sagepub.com/cgi/alertsEmail Alerts:

 

http://ldx.sagepub.com/subscriptionsSubscriptions:  

http://www.sagepub.com/journalsReprints.navReprints:  

http://www.sagepub.com/journalsPermissions.navPermissions:  

http://ldx.sagepub.com/content/41/4/353.refs.htmlCitations:  

What is This? 

- Apr 8, 2008 OnlineFirst Version of Record 

- Jun 17, 2008Version of Record >>

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

353

Developmental Links of Very Early Phonological and Language Skills to Second Grade Reading OutcomesStrong to Accuracy but Only Minor to Fluency

Anne PuolakanahoTimo AhonenMikko AroKenneth EklundPaavo H. T. LeppänenAnna-Maija PoikkeusAsko TolvanenMinna TorppaHeikki LyytinenUniversity of Jyväskylä

The authors examined second grade reading accuracy and fluency and their associations via letter knowledge to phonolog-ical and language predictors assessed at 3.5, 4.5, and 5.5 years in children in the Jyväskylä Longitudinal Study of Dyslexia.Structural equation modeling showed that a developmentally highly stable factor (early phonological and language pro-cessing [EPLP]) behind key dyslexia predictors (i.e., phonological awareness, short-term memory, rapid naming, vocabu-lary, and pseudoword repetition) could already be identified at 3.5 years. EPLP was significantly associated with readingand spelling accuracy and by age with letter knowledge. However, EPLP had only a minor link with reading fluency, whichwas additionally explained by early letter knowledge. The results show that reading accuracy is well predicted by earlyphonological and language skills. Variation in fluent reading skills is not well explained by early skills, suggesting factorsother than phonological core skills. Future research is suggested to explore the factors behind the development of fast andaccurate decoding skills.

Keywords: longitudinal study with SEM analyses; very early phonological and language predictors of dyslexia; readingaccuracy and fluency

Studies conducted in orthographically regular lan-guages, such as Italian, Greek, and Finnish, have

shown that in such language environments, childrenlearning to read can shift the emphasis from decodingaccuracy toward decoding fluency (involving accuracyand rate and the use of prosodic features and text phras-ing; e.g., Kuhn & Stahl, 2003; Torgesen, Rashotte, &Alexander, 2001) during the 1st or 2nd school year. Incontrast, in more irregular languages such as Danish andEnglish, children’s emphasis remains on the accuracyphase of reading, with the eventual shift taking placemuch later. Although several studies have indicated thatpreschool phonological, language, and prereading skillspredict reading accuracy well, it remains unknown

whether these skills are also the best predictors of readingfluency (Aro, 2006; Goswami, 2002; Seymour, 2005;Seymour, Aro, & Erskine, 2003; Wimmer & Mayringer,2002). In a highly regular language such as Finnish,

Journal of Learning DisabilitiesVolume 41 Number 4

July/August 2008 353-370© 2008 Hammill Institute on

Disabilities10.1177/0022219407311747

http://journaloflearningdisabilities.sagepub.com

hosted athttp://online.sagepub.com

Authors’ Note: The authors are grateful to all the families, children,and teachers for their long-lasting cooperation with the JLD project.They would like to offer special thanks to the whole inspirationalresearch team of JLD and the Niilo Mäki Institute. The authors alsowish to thank Professor Rauno Parrila for his comments on this paper.The Jyväskylä Longitudinal Study of Dyslexia (JLD) belonged to theFinnish Center of Excellence Program (2000-2005) and was supportedby the Academy of Finland. This research was also supported by theDepartment of Psychology, Jyväskylä. Please address correspondenceto Anne Puolakanaho, P.O. BOX 35 (Agora), FIN-40351,University ofJyväskylä,Finland; email: anne.puolakanaho@psyka.jyu.fi.

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

children are at an interesting point in development by theend of second grade. This is because there is still variationin accuracy, whereas fluency can already be measured ina psychometrically valid manner.

Recent longitudinal studies (Carroll & Snowling, 2004;Catts, Fey, Zhang, & Tomblin, 2001; de Jong & van derLeij, 2003; Elbro, Bostrom, & Petersen, 1998; Lyytinen,Aro, et al., 2004; Lyytinen, Eklund, et al., 2004;Pennington & Lefly, 2001; Snowling, Gallagher, & Frith,2003) and earlier findings and meta-analyses byScarborough (1990, 1998, 2001) indicate that the best pre-dictors of future reading achievement are letter knowledge,phonological awareness, short-term memory, rapid serialnaming speed, pseudoword repetition, and expressivevocabulary. A few studies using predictive measuresassessed prior to 4 years of age (e.g., Bryant, MacLean,Bradley, & Crossland, 1990; Gallagher, Frith, & Snowling,2000; Scarborough, 1990; Snowling et al., 2003) suggestthat bivariate correlations between reading skills and theirpredictors are not markedly lower than those between read-ing and the same predictive measures assessed immedi-ately before or at school entry. However, the selectionmeasures typically lack some critical indices, such as rapidautomatic naming (RAN) or short-term memory, and therelationship between measures and developmental continu-ity has rarely been modeled. In the current study, we usedstructural equation modeling (SEM) to assess the predic-tive relations between childhood prereading skills and sec-ond grade reading and spelling accuracy and fluency inchildren with and without familial risk for dyslexia.Participating children belonged to the JyväskyläLongitudinal Study of Dyslexia (JLD; Lyytinen et al.,2001, 2006). A battery of key predictive measures ofdyslexia were administered at three age points (3.5, 4.5,and 5.5 years), 2 to 4 years prior to the start of formalschooling.

Previous studies, predominantly conducted in ortho-graphically regular languages, have suggested that phono-logical awareness is not as good a predictor of readingfluency as reading accuracy (Aro, 2006; Holopainen,Ahonen, & Lyytinen, 2001; Wimmer & Mayringer, 2002;see also de Jong & van der Leij, 1999; Goswami, 2002;Seymour, 2005). Nonetheless, a similar finding is nowbeing reported for irregular languages involving olderchildren (Hogan, Catts, & Little, 2005; Kirby, Parrila, &Pfeiffer, 2003; Parrila, Kirby, & McQuarrie, 2004; vanOrden & Kloos, 2005).

The potentially strong role played by RAN skills as apredictor of reading fluency has lately been raised. In areview, Allor (2002) suggested that in most studies, find-ings have supported the link between RAN and fluencyin both typical and poor readers. However, the findings

mainly come from irregular languages and concernreading accuracy. Recently Georgiou, Parrila, andPapadopoulos (2007) explored RAN in the orthographi-cally regular Greek and irregular English languagesduring the first 2 school years, beginning at age 5. Theiranalyses suggested that RAN is a reliable predictor ofreading fluency (see also Savage & Frederickson, 2005),having a substantial influence on early reading develop-ment (see also Compton, DeFries, & Olson, 2001).Furthermore, RAN may be a stronger predictor of readingin regular languages as opposed to irregular languages(see also Wimmer, Mayringer, & Landerl, 2000). To ourknowledge, studies using measures of RAN obtainedbefore 5 years of age to predict both reading accuracyand fluency do not exist.

Letter knowledge has been highlighted as one of themost important prereading skills at preschool age and apredictor of both typical variance in normal reading andreading disability (e.g., Catts et al., 2001; Pennington, &Lefly, 2001; Wagner, Torgesen, & Rashotte, 1994). Inone of the few studies to investigate the cognitive pre-dictors of letter knowledge, de Jong and Olson (2004)reported predictive links between letter knowledge andphonological memory and RAN. The findings of Torppa,Poikkeus, Laakso, Eklund, and Lyytinen (2006), usingthe same sample of children as the present study, sug-gested that poor letter knowledge development beforeschool age was associated with cognitive predictors suchas phonological sensitivity, phonological memory, andRAN skills. However, poor letter knowledge was alsoconnected to environmental factors such as home-basedletter teaching and mother’s educational level. Again, toour knowledge, no previous study has examined whetherletter knowledge predicts reading accuracy and fluencyin a similar fashion.

Earlier exploratory studies have mostly found correla-tive associations between the observed measures at anearly age (e.g., Chaney, 1994; Gallagher et al., 2000;Muter, Hulme, Snowling, & Stevenson, 2004; Snowlinget al., 2003) or have identified several correlated first-order latent factors (e.g., phonological analysis and syn-thesis, coding in working memory, isolated naming, andserial naming [Wagner et al., 1994]). In a few studies, thestructural analyses have led to the identification of a sec-ond-order factor, presumably representing a commoncore (e.g., phonological awareness, letter knowledge,and environmental print [Lonigan, Burgess, & Anthony,2000]; oral language, print principles, and phonologicalawareness [Storch & Whitehurst, 2002]). In their studyusing SEM, Carroll, Snowling, Stevenson and Hulme(2003) followed a sample of children from age 3 years10 months to age 4 years 2 months. They found close

354 Journal of Learning Disabilities

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

relationships between vocabulary, mispronunciationdetection, articulation accuracy, letter knowledge, andsyllable and rhyming skills. Several models were foundto fit, including a model suggesting a single latent factorbehind the measured skills.

The developmental stability of the early skills has alsobeen explored. Wagner, Torgesen, Laughon, and Simmons(1993) found that phonological processing abilities arerelatively stable and form coherent individual attributesfrom kindergarten to second grade. Whitehurst andLonigan (2001) showed that from preschool age onward,some latent skills (including receptive and expressivevocabulary) were highly stable, whereas other latentskills (including latent factor of phonological awareness,print principles, and emergent writing) were somewhatless stable. In the SEM study by Lonigan et al. (2000),consistency was seen in phonological sensitivity andletter knowledge skills in one sample of children fromage 5 onward, but not when the same skills were assessedbetween 3 and 4 years in another sample of children. Thus,information about stability prior to the age of 4 years issparse, and more research is required.

In summary, although the key behavioral-level predic-tors of dyslexia have been identified among preschool-agechildren, and close correlative and sometimes also facto-rial connections have been reported with quite strongdevelopmental stability, studies of children prior to theage of 4 years are rare and their results somewhat con-tradictory. Earlier studies also suggest that preschool-agephonological and language skills predict later readingaccuracy measures, but their links to reading fluency,tapping decoding accuracy and rate, may not be so strong.In addition, although the importance of letter knowledgeas a predictor of reading outcomes has been shown unam-biguously, its role as a mediating factor between theearly skills and later reading accuracy and fluency hasnot been presented explicitly. Although the connectionsbetween familial risk and IQ and reading-related skillshave been consistently shown, the interesting question iswhether the early skills can predict reading outcomesover and above these links.

In this study, we used SEM to examine the stability ofearly literacy-related skills—phonological awareness,verbal short-term memory, rapid serial naming, expres-sive vocabulary, and pseudoword repetition—from 3.5 to5.5 years of age. The degree of shared variance for eachskill and their unique contributions to the prediction ofreading outcomes were also examined. SEM is especiallyuseful when analyzing multiple correlated measures,because it can take into account the correlations betweenthe measures. When several measures of the same under-lying latent factor are used, the measurement error can

be omitted, and the relationships of the latent factors canbe analyzed.

The outcome measures included several tasks that tapreading and spelling accuracy, as well as reading fluencyat the end of second grade. Childhood letter-naming mea-sures were used to tap prereading skills, and their role asa mediating factor between early skills and reading out-comes was explored. A battery of key dyslexia predictorswas administered to participants at the ages of 3.5, 4.5,and 5.5 years. On the basis of the existing literature, thefollowing hypotheses were proposed: (a) Early predic-tors share common variance, and a single latent variablecan be identified as representing core phonological andlanguage ability; (b) with reliable measures, developmen-tal stability will also be observed at the group level fromthe earliest age onward; (c) the key early predictors areconnected to letter-naming skills, which mediate theeffects of these skills on reading accuracy and fluency;and (d) early skills, especially phonological awareness,predict reading accuracy better than reading fluency, whichin turn is predicted by rapid serial naming skills.

Method

Participants

The present data were drawn from the follow-up pro-ject of the JLD, which has explored early language devel-opment and the precursors of reading skills (for a reviewof the sample and earlier results, see Lyytinen et al., 2001,2006; Lyytinen, Aro, et al., 2004; Lyytinen, Eklund, et al.,2004). Altogether, 214 families joined the study before thebirths of their children. Half of the participating childrenhave parents who were diagnosed with dyslexia and whoalso reported similar problems among immediate rela-tives. The control group consists of children from familieswhose parents reported no problems in learning to read orspell among first- or second-degree relatives. The parents’educational distribution was characteristic of the Finnishpopulation (for details, see Leinonen et al., 2001).

These data were drawn from 198 children participatingin the JLD (all the participants from whom full data setswere available), of whom 106 had backgrounds of familialrisk (the at-risk sample, including 53 girls and 53 boys)and 92 had no familial risk (the control sample, including40 girls and 52 boys). All the children and their parentscame from the city of Jyväskylä and its surroundingcommunities in the province of Central Finland. All thechildren are Caucasian, speak Finnish as their native lan-guage, and have no reported mental, physical, or sensoryhandicaps. Most of the children attended kindergartenprior to the start of formal schooling. The kindergarten

Puolakanaho et al. / Language Skills 355

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

curriculum does not focus on literacy skills; rather, itfocuses on social skills as well as on play and motoractivities. The children participating in the JLD projectentered a mainstream elementary school.

Predictive Measures Prior to School Age

The early predictors of reading and spelling werederived from the individual assessments at the ages of3.5 years (M = 3.53 years, SD = 18 days), 4.5 years (M =4.51 years, SD = 11 days), and 5.5 years (M = 5.50 years,SD = 11 days). The predictive measures tapped the following skill areas.

Phonological Awareness Tasks

Both tasks embedded in a computer animation programcalled Heps-Kups Land (created especially for this pur-pose; for details, see Puolakanaho, Poikkeus, Ahonen,Tolvanen, & Lyytinen, 2003) and more traditional phono-logical awareness (see Note 1) tasks were used. The age-specific composites were formed on the basis of thefollowing subtasks.

Subtask 1: Word-level segment identification (eightitems at the age of 3.5 years). The child was presentedwith three pictures of objects on the screen, immediatelyfollowed by the name of each object (all compoundwords), and asked to identify the picture containing aspecified part of the compound (e.g., for the three wordslentokone [airplane], soutuvene [rowboat], and polkupyörä[bicycle], the question was “In which picture can youhear the sound kone [plane]?”).

Subtask 2: Syllable-level segment identification (8items at the age of 3.5 years, 16 items at the age of 4.5years, and 22 items at the age of 5.5 years). The task wasthe same as for Subtask 1 but with the requirement toidentify the subword-level units (syllables or phonemes)within the target (e.g., koi in the word koira [dog]).

Subtask 3: Synthesis of phonological units (12 itemsat the age of 3.5 years). Segments (syllables orphonemes) were presented to the child, each separatedby 750 milliseconds, with the requirement to blend thesegments to produce the resulting word (e.g., per-ho-nen[butterfly], m-u-n-a [egg]). Only a response containingthe correctly assembled form was coded as correct.

Subtask 4: Continuation of phonological units (8items at the age of 3.5 years and 12 items at the age of 4.5years). The child was presented with the beginning of a“secret” word and asked to guess how the word would

continue (e.g., mu-). Only continuations that were mean-ingful words were coded as correct.

Subtask 5: Initial phoneme identification (nine itemsat the ages of 4.5 and 5.5 years). The child was shownfour pictures of objects with the simultaneous presenta-tion of the objects’ names. The child was then required toselect the correct picture on the basis of the oral pre-sentation of a subsequent initial phoneme relating to onetarget (e.g., “In the beginning of which word do you hear[initial phoneme]?”).

Subtask 6: Production of the first phoneme (eight itemsat the ages of 4.5 and 5.5 years). The experimentershowed a picture to the child and asked what he or she sawin the picture. The child was then asked to listen to theword and then articulate the first sound (phoneme or lettername) of the object. The sum of the correct phonemes orinitial letter answers formed the score of the task.

The phonological awareness score at 3.5 years wascomputed from the sum of Subtasks 1, 2, 3, and 4; thescore at 4.5 years was a composite of Subtasks 2, 4, 5,and 6; and the score at 5.5 years was a composite ofSubtasks 2, 5, and 6.

Rapid Automatic Naming

RAN of objects was assessed at the ages of 3.5 and 5.5years using the standard procedure (see Denckla &Rudel, 1976), in which the child was asked to name, asrapidly as possible, a series of five visual-object stimuli.The child was first familiarized with the stimuli. A stan-dard name was given if the child offered another name(e.g., “We could say that this is a ball!”). Total matrix (30items; 5 stimuli randomly presented 6 times) completiontime (in seconds) was used as the measure.

Short-Term Memory

The Digit Span subtest of the Wechsler IntelligenceScale for Children, Third Edition (WISC-III; Wechsler,1991) was administered at 3.5 and 5.0 years of age (thelatter score was included in the measures of the 4.5-yearbattery) using the typical procedure described in the lit-erature (e.g., Gathercole & Adams, 1994), whereby thechild is required to repeat a series of spoken digits ofnumbers of increasing length. Beginning with one-digitlength, two lists were administered at each length, with athird added if only one of the two lists was recalled. Testcutoff followed two consecutive failures at a similarlength. The score used in the analyses was the number ofcorrectly repeated lists. The Memory for Names subtestof the NEPSY was administered at the age of 5.5 years

356 Journal of Learning Disabilities

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

(Korkman, Kirk, & Kemp, 1998). In this test, the childwas required to recall names read aloud by the examiner.One point was awarded for each correct repetition.

Expressive Vocabulary

The Boston Naming Test (BNT; Kaplan, Goodglass, &Weintraub, 1983) was used to obtain a measure ofproductive vocabulary at 3.5 and 5.5 years of age. TheFinnish version of the BNT contained 60 pictured items,which the child was asked to name. Testing continueduntil six consecutive errors were incurred. Scores werecomputed by summing the number of items (maximumof 60) that the child spontaneously named correctly andthe number of items correctly named following a seman-tic stimulus cue (e.g., for violin, “an instrument”; fortennis racket, “you play a game with it”). Vocabularydevelopment was assessed using the Vocabulary subtestof the Wechsler Preschool and Primary Scale ofIntelligence–Revised (WPPSI-R; Wechsler, 1989) at5.0 years of age. The children’s performance in the taskwas estimated on the basis of this subtest according tothe standard guidelines outlined in the manual. In theanalyses, this subtest was included in the measures of the4.5-year battery.

Pseudoword Repetition

In the pseudoword repetition task (18 partly differentitems administered at the ages of 3.5 and 4.5 years),pseudowords were embedded in a computer animationstory, and after the child had repeated a pseudoword, ahidden animal would appear on the screen. At 5.5 years,the Nonword Repetition task of the Finnish version ofthe NEPSY (Korkman et al., 1998) was presented. Thestimuli were arranged in series of increasing complexityand length from monosyllabic items (e.g., nas) to poly-syllabic items (e.g., plotsiskäntsigis). One point wasawarded for each correct repetition.

Letter Naming

In the letter-naming task, the child was asked toname letters written in large capitals and presented oneat a time, each on its own page. At the age of 3.5 years,the child was presented with 16 letters organized inthree sets (6, 6, and 4 letters), and at 4.5 and 5.5 years,the child was presented with 23 letters arranged in foursets (6, 6, 6, and 5 items). The child received one pointfor each correct response (the use of a phoneme and the use of a letter name were both coded as correctresponses). The testing always began by presenting the

child with the letter that was expected to be the mostfamiliar to child (i.e., the first letter of his or her ownfirst name).

Performance IQ

A short-form of the WPPSI-R (Wechsler, 1989) wasadministered at 5.0 years of age, and three performancequotient subtests (Block Design, Object Assembly, andPicture Completion) constituted the performance IQmeasure. The WISC-III (Wechsler, 1991) was adminis-tered at the age of 8.0 years, and four performancequotient subtests were used to compute the performanceIQ measure. The scores were estimated on the basis ofthese subtests according to the standard guidelines out-lined in the manual.

Reading and Spelling Measures

The reading and spelling measures were administeredindividually at the end of second grade (M = 8.9 years,SD = 0.5 years). The first four tasks were developedto tap the accuracy component of reading and spellingand the remaining tasks to tap the fluency component ofreading.

R1: Reading Three- and Four-Syllable Words and Nonwords

Items (10 of each type, altogether 40 items from fourdifferent tasks) were presented separately via a computer.The number of the correctly read items was used as ameasure.

R2: Spelling Words (Six Items) and Nonwords (Six Plus Six Items)

Altogether, 18 four-syllable items from three differ-ent sets were presented by a computer via headphones.The number of correctly written items was used as ameasure.

R3: Reading Nonword Text Vinnittäjä Tenkoja (19 Words, 141 Characters)

The percentage value of the correctly read nonwordswas used as a measure.

R4: Reading Text Jännittävät Matkat (124 Words, 901 Characters)

The percentage value of the correctly read words wasused as a measure.

Puolakanaho et al. / Language Skills 357

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

R5: Reading Fluency,Standardized Test Luki-Lasse

Children read aloud a list of words on which the itemsgradually became longer and more difficult. A fluencyscore was obtained by calculating the correctly readwords within 2 minutes, according to which a standardscore was derived from the manual.

R6: Reading Three- and Four-Syllable Words and Nonwords (Same Items as in R1)

The mean of the response times (reaction time plusresponse duration) to separately presented and correctlyread items was used as a measure.

R7: Reading Text Jännittävät Matkat (124 Words, 901 Characters)

A score of fluency (words read per minute) was cal-culated by dividing the number of words read by the timespent on reading.

R8: Reading Nonword Text Vinnittäjä Tenkoja (19 Words, 141 Characters)

Again, a score of fluency (read nonwords/minute) wascalculated by dividing the number of nonwords read withthe time spent on reading.

Backgrounds of the Children

Parental education was classified using a 7-point scale.This scale was constructed by combining the informationthe parents had given concerning their general educationand their upper secondary vocational education andtertiary education. These two scales were combined intoone 7-point scale as follows: 1 = comprehensive schooleducation without any vocational education; 2 = compre-hensive school education combined with short-term voca-tional courses; 3 = comprehensive school educationcombined with a vocational school degree; 4 = compre-hensive school education combined with a vocationalcollege degree; 5 = comprehensive school educationcombined with a lower university degree (bachelor’s) or adegree at a polytechnic institute; 6 = upper secondarygeneral school diploma combined with a lower universitydegree (bachelor’s) or a degree at a polytechnic institute;and 7 = comprehensive school or upper secondary generalschool diploma combined with a higher university degree(master’s or doctorate).

The at-risk and control groups did not differ from eachother at the mean level of parental education as assessedon the 7-point scale. There were no group differences in

the children’s performance IQ at 5 or 8 years of age (seeTable 1). All the participants had either verbal IQ or per-formance IQ equal to or above 80. The statistical differ-ence between girls and boys on all the predictive andreading related skills was explored using a two-tailed ttest. The Memory for Names task at 5.5 years of ageshowed that girls (M = 11.3, SD = 5.1) outperformedboys (M = 8.3, SD = 4.4), t(196) = 4.3, p < .001, on thistask. Because no other differences were found, girls andboys are combined in the following analyses. The back-grounds of parents and children are presented in Table 1.

Results

Descriptive Analyses

Ceiling or floor effects were absent for the majority ofvariables, and the distributions were normal or close tonormal (Table 2).

The distributions of reading accuracy measures andletter naming at 3.5 years of age, however, were skewed.Very few outliers were found, and they were relocated tothe tails of the distributions before the analyses. The mod-els were built with all children (see Note 2) (n = 198),including a few missing values (M = 4.4%). Both theearly predictive measures and the second grade readingand spelling measures were correlated within themselvesand with each other (see Table 3).

Age-Specific Predictive Models of Reading Outcomes

To examine the intercorrelations between the mea-sures in a comprehensive model and to explore thepotential latent factors, the SEM approach was appliedusing Mplus Version 3.13 (Muthén, 2004) on theobserved variables. The model was estimated using max-imum likelihood with robust standard error estimationbecause of the skewness of reading accuracy and letterknowledge tasks.

358 Journal of Learning Disabilities

Table 1Statistical Background of Children

Belonging to Analyzed Sample

At Risk Control (n = 106) (n = 92)

Variable M SD M SD

Performance IQ at 5 years of age 101.2 13.7 102.2 13.3Performance IQ at 8 years of age 100.0 12.9 102.2 13.5Father’s education 3.6 1.3 3.8 1.4Mother’s education 4.1 1.5 4.5 1.4

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

359

Tabl

e 2

Des

crip

tive

Sta

tist

ics

of E

arly

Mea

sure

s an

d Se

cond

Gra

de R

eadi

ng

Out

com

es in

the

At-

Ris

k an

d C

ontr

ol G

roup

s

At R

isk

(n=

106)

Con

trol

(n

=92

)

Mea

sure

sM

inim

umM

axim

umM

SDSk

ewne

ssK

urto

sis

MSD

Skew

ness

Kur

tosi

s

Age

3.5

yea

rsPh

onol

ogic

al a

war

enes

s7

3421

.46.

5–0

.19

–0.4

624

.35.

7–0

.65

0.40

Shor

t-te

rm m

emor

y2

85.

01.

60.

27–0

.43

5.4

1.5

–0.2

7–0

.17

Rap

id n

amin

g33

148

72.9

22.8

0.85

0.65

69.7

23.5

1.50

2.28

Voc

abul

ary

737

17.4

5.7

0.5

–0.3

20.1

5.8

0.78

0.45

Pseu

dow

ord

repe

titio

n3

1811

.73.

2–0

.53

–0.1

013

.42.

8–0

.34

–0.6

2L

ette

r la

min

g0

161.

93.

52.

445.

862.

74.

52.

063.

12A

ge 4

.5 y

ears

Phon

olog

ical

aw

aren

ess

944

24.9

6.5

0.27

–0.2

127

.95.

40.

120.

94Sh

ort-

term

mem

orya

08

3.1

1.6

–0.0

5–0

.53

3.7

1.5

–0.2

10.

55V

ocab

ular

ya5

1911

.33.

60.

12–0

.63

11.7

2.9

–0.1

5–0

.71

Pseu

dow

ord

repe

titio

n1

168.

93.

4–0

.36

–0.5

69.

32.

7–0

.09

–0.4

6L

ette

r na

min

g0

236.

37.

10.

96–0

.34

8.9

7.6

0.38

–1.3

8A

ge 5

.5 y

ears

Phon

olog

ical

aw

aren

ess

936

21.4

6.7

0.41

–0.4

124

.14.

80.

38–0

.35

Shor

t-te

rm m

emor

y0

228.

95.

00.

55–0

.03

10.7

4.8

0.19

–0.2

7R

apid

nam

ing

2085

47.8

14.0

0.96

0.65

42.4

8.9

0.25

–0.2

4V

ocab

ular

y14

4833

.56.

8–0

.72

0.42

35.9

5.5

–0.4

2–0

.31

Pseu

dow

ord

repe

titio

n1

137.

62.

1–0

.51

0.78

7.7

2.4

–0.1

4–0

.40

Let

ter

nam

ing

023

11.3

7.9

0.02

–1.5

114

.86.

8–0

.42

–1.1

6R

eadi

ng o

utco

mes

(m

ean

age

8.9

year

s)R

1:re

adin

g w

ords

and

non

wor

ds:a

ccur

acy

16.0

40.0

33.8

5.0

–1.2

91.

5136

.43.

1–2

.33

9.92

R2:

spel

ling

wor

ds a

nd n

onw

ords

:acc

urac

y0.

018

.012

.24.

7–0

.91

–0.0

214

.13.

4–1

.97

5.30

R3:

read

ing

nonw

ord

text

:% c

orre

ct10

.510

0.0

74.9

20.4

–0.8

60.

2484

.012

.1–1

.01

0.77

R4:

read

ing

text

:% c

orre

ct61

.310

0.0

91.0

8.3

–1.8

23.

4294

.34.

8–1

.51

1.90

R5:

Luk

i-L

asse

:sta

ndar

d sc

ore

1.0

14.0

8.5

3.4

0.54

–0.9

610

.53.

1–0

.56

–0.3

6R

6:re

adin

g w

ords

and

non

wor

ds:m

illis

econ

ds1,

631.

011

,845

.03,

041.

31,

359.

63.

2316

.83

2,50

4.2

1,09

9.1

3.37

15.6

0R

7:ex

citin

g ad

vent

ure

text

:wor

ds/m

inut

e6.

113

5.3

55.0

24.9

0.37

–0.0

969

.225

.0–0

.24

–0.5

7R

8:Vi

nnit

täjä

Ten

koja

:non

wor

d te

xt:w

ords

/min

ute

6.1

63.3

26.5

10.3

0.37

–0.3

932

.712

.50.

37–0

.58

a. M

easu

red

at a

ge 5

.0 y

ears

.

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

360

Tabl

e 3

Pea

rson

Cor

rela

tion

s B

etw

een

Ear

ly P

redi

ctor

s an

d O

utco

me

Mea

sure

s in

the

At-

Ris

k an

d C

ontr

ol G

roup

s

12

34

56

78

910

1112

1314

1516

1718

1920

2122

2324

25

Age

3.5

yea

rs1.

Pho

nolo

gica

l aw

aren

ess

.42

–.36

.46

.34

.15

.57

.45

.44

.42

.28

.49

.34

–.18

.47

.45

.42

.29

.47

.29

.33

.17

–.08

.23

–.15

2. S

hort

-ter

m m

emor

y.1

8–.

34.4

1.3

2.2

6.4

2.5

3.3

6.3

9.4

0.3

9.2

6–.

13.4

7.3

9.3

6.1

6.3

8.1

9.2

7.1

8–.

18.2

4–.

093.

Rap

id n

amin

g–.

22–.

17–.

16–.

22–.

20–.

35–.

37–.

16–.

29–.

33–.

26–.

19–.

38–.

13–.

29–.

29–.

14–.

38–.

19–.

27–.

20–.

18–.

22–.

064.

Voc

abul

ary

.36

.31

–.14

.36

.11

.27

.48

.59

.33

.23

.34

.34

–.18

.74

.40

.31

.15

.30

.15

.19

.13

–.03

.17

.06

5. P

seud

owor

d re

petit

ion

.27

.16

–.12

.22

.34

.42

.39

.27

.44

.32

.44

.17

–.17

.42

.30

.30

.14

.30

.04

.16

.21

–.19

.27

.05

6. L

ette

r na

min

g.3

3.1

5–.

17.2

4.2

1.3

7.2

8.1

6.2

0.7

2.5

1.2

1–.

19.1

9.1

6.5

2.1

8.2

6.1

8.2

3.4

3–.

22.4

5.2

6A

ge 4

.5 y

ears

7. P

hono

logi

cal a

war

enes

s.5

0.2

9–.

20.3

2.1

2.3

8.5

2.4

6.5

2.5

2.7

2.3

9–.

26.4

0.4

5.5

4.3

0.4

9.1

5.3

1.3

3–.

19.4

8.0

98.

Sho

rt-t

erm

Mem

orya

.17

.29

–.05

.20

.27

.20

.04

.48

.41

.40

.48

.37

–.11

.44

.51

.40

.30

.42

.35

.24

.26

–.13

.29

.06

9. V

ocab

ular

ya.3

7.1

8–.

18.4

8.1

7.2

2.2

9.2

1.3

2.2

8.4

4.4

4–.

13.6

2.3

3.3

7.2

2.2

7.1

7.2

8.2

0–.

03.2

9.0

110

. Pse

udow

ord

repe

titio

n.3

1.1

0–.

08.1

2.3

5.1

8.3

8.2

3.1

3.2

9.4

2.1

8–.

14.3

9.5

1.2

8.2

0.3

8.0

7.1

7.2

1–.

19.2

7.0

511

. Let

ter

nam

ing

.37

.17

–.30

.27

.32

.63

.30

.36

.29

.29

.62

.25

–.28

.35

.27

.79

.29

.42

.29

.29

.46

–.27

.51

.26

Age

5.5

yea

rs12

. Pho

nolo

gica

l aw

aren

ess

.37

.24

–.21

.32

.37

.27

.46

.33

.30

.43

.48

.43

–.22

.49

.32

.66

.35

.42

.29

.37

.44

–.22

.55

.17

13. S

hort

-ter

m m

emor

y.2

3.2

0–.

17.1

6.1

1–.

01.1

3.0

6.1

2.0

4.0

9.1

7–.

02.4

2.3

5.2

9.2

0.3

2.2

4.3

2.3

0–.

16.3

9.1

114

. Rap

id n

amin

g–.

08–.

08–.

41–.

12–.

14–.

25–.

15–.

17–.

12–.

08–.

31–.

28–.

19–.

21–.

18–.

29–.

21–.

37–.

14–.

27–.

28.1

8–.

29–.

1915

. Voc

abul

ary

.34

.27

–.19

.59

.22

.20

.22

.40

.41

.12

.30

.27

.22

–.18

.42

.45

.16

.37

.16

.21

.22

–.05

.29

.06

16. P

seud

owor

d re

petit

ion

.40

.25

–.04

.21

.33

.30

.28

.42

.28

.52

.35

.30

.03

–.07

.31

.27

.21

.42

.18

.26

.23

–.11

.33

.03

17. L

ette

r na

min

g.3

4.2

4–.

36.2

7.3

4.4

5.3

4.3

9.3

5.3

2.7

7.6

0.1

9–.

39.3

3.3

2.3

2.4

8.2

8.3

3.4

9–.

20.5

3.2

2R

eadi

ng o

utco

mes

(m

ean

age

8.9

year

s)18

. Rea

ding

wor

ds a

nd

.08

.07

–.02

.16

.06

.16

.01

.11

.22

.13

.23

.06

.18

–.20

.15

.09

.31

.72

.54

.44

.65

–.59

.56

.52

nonw

ords

:acc

urac

y19

. Spe

lling

wor

ds a

nd

.20

.04

–.03

.22

.07

.26

.20

.07

.20

.17

.23

.13

.19

–.13

.12

.12

.34

.53

.50

.59

.64

–.55

.61

.38

nonw

ords

:acc

urac

y20

. Rea

ding

non

wor

d .1

6–.

04–.

13.1

6.2

4.1

2.1

4.1

2.1

3.1

2.1

1.2

1.1

8–.

33.1

3.1

3.2

4.4

1.3

4.6

2.4

9–.

30.4

1.2

7te

xt:%

cor

rect

21. R

eadi

ng te

xt:%

cor

rect

.20

–.02

–.20

.14

.24

.15

.07

.23

.24

.11

.17

.25

.18

–.38

.13

.18

.30

.22

.30

.29

.56

–.38

.52

.31

22. L

uki-

Las

se:s

tand

ard

.13

.12

–.18

.23

.19

.34

.09

.24

.11

.20

.43

.20

.14

–.21

.20

.07

.49

.53

.46

.40

.41

–.73

.91

.80

scor

e23

. Rea

ding

wor

ds a

nd

.01

–.11

–.13

–.20

–.17

–.26

–.10

–.17

–.08

–.24

–.28

–.14

–.12

.28

–.08

–.07

–.42

–.56

–.45

–.39

–.32

–.75

–.65

–.60

nonw

ords

:tim

e24

. Exc

iting

adv

entu

re

.18

.19

–.28

.30

.24

.39

.14

.24

.19

.29

.47

.24

.17

–.33

.30

.16

.58

.43

.34

.35

.40

.88

–.70

.70

text

:wor

ds/m

inut

e25

. Vin

nitt

äjä

Tenk

oja:

.06

.16

–.21

.19

.06

.36

.07

.13

.02

.07

.37

.07

.11

–.29

.21

.05

.39

.42

.34

.23

.34

.82

–.63

.79

nonw

ord

text

:w

ords

/min

ute

Not

e:C

orre

latio

ns a

bove

the

diag

onal

are

for

the

at-r

isk

grou

p (n

=10

6) a

nd th

ose

belo

w th

e di

agon

al a

re f

or th

e co

ntro

l gro

up (

n=

92).

a. M

easu

red

at a

ge 5

.0 y

ears

.

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

Structure of the Models

Three separate models were built, one with data col-lected when children were 3.5 years of age, a second withdata from 4.5 years of age, and a third with data from 5.5years of age. In each phase, we expected to find one com-mon factor reflecting the core of the phonological and lan-guage abilities related to two latent outcome factors,reading and spelling accuracy and reading fluency at theage of 8.9 years. The phonological and language abilitieswere also expected to be related to the concurrent preread-ing skills assessed by letter-naming tasks. Letter-namingskills were further modeled to examine whether theymediated the association between the early phonologicaland language abilities and reading accuracy and fluency.Because of the known relevance of the familial risk as apredictor of reading and spelling skills, a categorical vari-able indicating the child’s familial background with regard

to dyslexia (at-risk vs. control group) was also added tothe model. Some error covariance was added to the modelon the basis of the modification indices (see Figure 1). Thefit of the models was evaluated using the following good-ness-of fit measures provided by Mplus (Muthén, 2004):root mean square error of approximation (RMSEA; valuesof .06 or less indicate good fit), standardized root meansquare residual (SRMR; values less than .08 indicate goodfit), Bentler’s comparative fit index (CFI; values of .95 orgreater indicate good fit), and the Tucker-Lewis index(TLI; values greater than .95 indicate good fit).

As shown by the fit indices, the models fit the data well:At age 3.5 years, χ2(79) = 95.42, p = .1007, RMSEA = .032,SRMR = .049, CFI = .987, and TLI = .983; at age 4.5years, χ2(66) = 102.68, p = .0026, RMSEA = .053,SRMR = .054, CFI = .972, and TLI = .962; and at age 5.5years, χ2(79) = 119.55, p = .0022, RMSEA = .051,SRMR = .057, CFI =.971, and TLI = .962.

Puolakanaho et al. / Language Skills 361

.98c

R1

R3

R4

R8

R7

R6

R5

R2

Familial risk for dyslexia

.

.72 a.73 b.69 c

.50 a .57 b .49 c

–.36a–.42c

.59a .55b.60c

.54a.57b

.48c

.34a

.66a

.63b

.69c

.29a

.23b

.27c

Reading&

SpellingAccuracy

ReadingFluency

–.33c

–.27c

.31c

1.1c

.78c

.67c

–.31c

.74c

1.0c

.97c

–.77c

.26c

–.20c

PA.72c

.47a .45b, .38c

PhonologicalAwareness

Short-TermMemory

Rapid Namingof Objects

ExpressiveVocabulary

PseudowordRepetition

LetterNaming

.29a

.34b

.34c

.38a

.58b

.67c

.14a

.16b

R2 = .46a/.46b/.53c

R2 = .23a/.27b/.32c

EarlyPhonological

AndLanguageProcessing

(EPLP)

.26b

Figure 1Three Age-Specific Models Representing Very Early Skills and Their Associations With

Second Grade Reading Outcomes Through Letter-Naming Skills (n == 198)

Note: PA = specific latent for phonological awareness (all significant paths are shown; however, the path between PA and letter naming emergedonly in the 5.5-year model). Second grade reading outcomes: R1 = word and nonword accuracy; R2 = spelling; R3 = nonword text accuracy;R4 = text accuracy; R5 = Luki-Lasse; R6 = word and nonword time; R7 = text time; R8 = nonword text time (only the links of 5.5 years areshown, because the difference of the magnitude of the connection was always less than r = .02 in the other age-specific models). The dotted linerepresents the nonsignificant path. The scores represent the standardized coefficients.a. Age 3.5 years.b. Age 4.5 years.c. Age 5.5 years.

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

Nearly identical models were found to describe the databest at each age. Figure 1 shows the three separate modelssimultaneously depicting the relationship between earlyskills and second grade reading and spelling skills. All thestandardized path coefficients shown in Figure 1 were sta-tistically significant, and no additional paths were required,nor were there any statistically significant residuals.

Associations Between the Predictive Skills

SEM indicated that the early skills shared a substantialamount of common variance and formed a single factor.We called this factor early phonological and languageprocessing (EPLP) because it included both phonologicalprocessing and vocabulary measures. The models alsoindicated that at early ages (3.5–5.5 years), phonologicalawareness was by far the strongest contributor to EPLP.On the other hand, RAN shared the least variance withEPLP in the two measured phases, indicating its relativeindependence from EPLP. Familial history of dyslexia(i.e., whether the child belonged to the at-risk or controlgroup) was significantly correlated with EPLP at eachage, indicating that the children belonging to the controlgroup reached a higher level in EPLP skills than thosechildren with familial histories of dyslexia. The slightvariation in the extent of the contribution of specific skillareas across age was likely due to the different psycho-metric properties of the measures.

Associations Between the Second Grade Outcome Measures

As expected, the reading and spelling measuresformed two latent factors: reading and spelling accuracyand reading fluency. The only task that significantlypredicted both factors was the text reading task (R7), inwhich scores were calculated by dividing the number ofall words read by the time spent on reading. As expected,the task was associated with reading fluency, but it wasalso negatively associated with accuracy. This probablyreflects strategies in reading (e.g., faster reading causesinaccuracy and vice versa).

Connections Between EPLP and Reading Outcomes

EPLP predicted 46%, 46%, and 53% of second gradereading and spelling accuracy when measured at ages3.5, 4.5, and 5.5 years, respectively. It is notable that nounique association between letter naming and accuracyemerged. However, letter naming and EPLP both predictedreading fluency. Together, they predicted 23%, 27%, and32% (at ages 3.5, 4.5, and 5.5 years, respectively) of

second grade reading fluency. Thus, the modelingshowed that EPLP measured at 3.5 years predicted abouthalf of second grade reading and spelling accuracy. Theassociation between EPLP and reading fluency wasmuch weaker: A maximum of 8.4% of the variance inreading fluency could be predicted by EPLP skills at anyage. Thus, the results suggest that second grade readingand spelling accuracy is well predicted by EPLP skills,but the variation in fluent reading skills is not wellexplained by the early skills.

EPLP predicted letter-naming skills better with eachmeasurement point, accounting for 14%, 34%, and 40% atages 3.5, 4.5, and 5.5 years, respectively. A reciprocal pathemerged between letter naming and phonological aware-ness at the 5.5-year phase, but not before. Letter namingpredicted reading fluency (they shared 8.4%–10% of thevariance) significantly in each model, but no significantassociation to reading accuracy was found. Interestingly,no other unique connections from the early observed mea-sures (EPLP indicators) to the reading outcomes emergedbeyond those connecting to the common core factor.

Across-Age Model

Next, we examined the stability of the EPLP latentfactors and how much more information the subsequentmeasurement phases added to the prediction of readingstatus at 3.5 years. This question was analyzed using anapproach that was an application of fixed-order regressionin the context of latent variables. The previously presentedthree age-specific SEM models were combined in Mplususing the Cholesky decomposition method, detailed inde Jong (1999). The method separates the unique variancerelated to each age phase after taking into account theprevious ones. The three core latent EPLP factors fromthe age-specific structural equation models (at the agesof 3.5, 4.5, and 5.5 years), as well as the specific factorsfor letter naming, formed in the previous models, wereincluded into the Cholesky decomposition model. The3.5-year Cholesky components (of EPLP and letter nam-ing) were fixed to explain all the variance of the first agephase, the related variance of the second and third agephases, and the latent reading outcomes. The 4.5-yearCholesky components were set to explain all the remainingvariance of the second and third age phases and the latentreading outcomes that could not be explained by the3.5-year component. Finally, the 5.5-year Cholesky com-ponents were set to explain the residual variance of thethird age phase and the related variance of the readingoutcomes. In addition, associations between the compo-nents and familial background of dyslexia and 5-yearperformance IQ were examined.

362 Journal of Learning Disabilities

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

The across-age model is presented in Figure 2. Themodel fit the data well: χ2(298) = 380.803, p = .0008,RMSEA = .037, SRMR = .057, CFI = .969, and TLI =.963. The connection between early skills and readingoutcomes was manifested by the 3.5-year EPLPCholesky component and by the three age-specific let-ter-naming Cholesky components. In Figure 2, the sta-tistically significant connections are presented withtheir respective standardized β coefficients. The 3.5-year EPLP Cholesky component was almost per-fectly connected to 3.5-year (β = 1.0), 4.5-year (β =.98), and 5.5-year (β = 1.0) EPLP latent factors, indi-cating very high developmental stability. The 4.5- and5.5-year EPLP Cholesky components were not requiredin the model, indicating that all the predictive varianceof EPLP was already present in the 3.5-year compo-nent. The 3.5-year (β = .25), 4.5-year (β = .15), and

5.5-year (β = .17) letter knowledge (letter naming)Cholesky components shared some additional variancewith reading fluency. Thus, EPLP abilities at the age of3.5 years explained reading and spelling accuracy well,whereas fluency was explained by letter knowledgeskills from each age phase.

Some error covariances were found, mostly withincertain skill areas (e.g., in vocabulary and RAN across ages;not shown in Figure 2). The following pairs of residualcovariances were suggested by Mplus modificationindices and therefore were allowed to covariate: vocabu-lary at 5.5 years with vocabulary at 4.5 years, vocabularyat 5.5 years with vocabulary at 3.5 years, vocabulary at4.5 years with vocabulary at 3.5 years, RAN at 5.5 yearswith RAN at 3.5 years, pseudoword repetition at 5.5 yearswith pseudoword at 4.5 years, pseudoword repetition at 4.5 years with pseudoword repetition at 3.5 years,

Puolakanaho et al. / Language Skills 363

EPLP3.5 year

EPLP4.5 year

EPLP5.5 year

Reading&

SpellingAccuracy

ReadingFluency

EPLP-Cholesky

3.5

LN -3.5 year

LN -4.5 year

LN -5.5 year

LN-Cholesky

3.5

LN-Cholesky

4.5

LN-Cholesky

5.5

Familialriskfor

dyslexia

Perf.IQ

5.0 year

.69

.17 (ns.)

1.0

.15 .17

.44

.28 .56.52

R2 = .48

R2 = .28

.44

.45

1.0 .981.0

.25

.50

.76 .47

.21

.66

Figure 2Across-Age Model of the Dependence of the Consecutive Measurement Phases and Their Associations

With Letter-Naming Skills and Second Grade Reading Outcomes (n == 198)

Note: The dotted line represents the nonsignificant path. EPLP = early phonological and language processing; LN = letter naming; perf. =performance.

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

pseudoword repetition at 5.5 years with memory at 5.0years, pseudoword repetition at 5.5 years with phonolog-ical awareness at 5.5 years, memory at 5.0 years withmemory at 3.5 years, R4 with R1, R3 with R4, andphonological awareness at 5.5 years with R2.

To conclude, EPLP skills at the age of 3.5 yearsreflected EPLP skills at later ages (i.e., 4.5 and 5.5 years),almost completely indicating their high developmentalstability. However, the EPLP skills seemed to intertwinewith letter-naming skills increasingly by age (r = .44,r = .52, and r = .56 for ages 3.5, 4.5, and 5.5 years, respec-tively), and the letter-naming skills at each age phase hadunique connections to reading fluency. In the across-agemodel, EPLP skills explained 48% of the variance in thereading and spelling accuracy component and only 2.9%of the reading fluency component. The prereading skillsmeasured using the letter-naming task predicted a totalof 25.1% of the variance of reading fluency.

Additional Findings

To explore in more detail the role of IQ in predictingprereading and reading outcomes, a specific model wasbuilt for children at the age of 5.5 years because at thatage, reliable measures of all the key tasks were available.The significant paths from EPLP at the age of 5.5 yearsto the reading outcomes are shown in Figure 3.

EPLP was associated with IQ (WPPSI-R performancescale at 5 years), and they shared 11% of the variance.No special associations from IQ to letter naming, readingaccuracy, or reading fluency emerged. This indicates theimportance of EPLP in the prediction of letter namingand especially reading accuracy above the IQ level.Performance IQ (r = .45), measured at age 5.0 years, wasalso shown to be associated with the 3.5-year EPLPCholesky component in the across-age model (see Figure 2).By following the paths in the across-age model, it can alsobe concluded that of the explained 48% of accuracy vari-ance, about 9.6% was predicted by IQ. Interestingly, phono-logical awareness and IQ had an additional connectionthat was not explained by the EPLP and phonologicalawareness connection.

The familial background of dyslexia was connected tothe phonological and language skills (seen in all presentedmodels, r = 0.28–0.31). Additional analyses using t testsalso indicated that control group children significantlyoutperformed at-risk children on all predictive tasks. Thesefindings suggest that the genetic background already pre-disposes the development of these skills at the play age.The level of skills was significantly higher (about 8%)among control group children. It is also notable that nospecial associations between familial risk status andearly letter knowledge emerged.

We used the model in Figure 3 to explore what happenswhen reading accuracy and fluency skills are allowed topredict each other in addition to EPLP, IQ, and letter-naming skills. When the early skills (EPLP, IQ, and letternaming) and fluency were taken into account, 76% of read-ing and spelling accuracy could be predicted. When theearly predictors’ contributions and accuracy were takeninto account, 65% of fluency could be predicted.

Discussion

The aim of the present study was to analyze the keybehavioral predictors of reading and dyslexia and theirassociations with reading and spelling accuracy and withreading fluency. SEM was used to study a battery ofclosely correlated predictors (phonological awareness,verbal short-term memory, rapid serial naming, expres-sive vocabulary, pseudoword repetition, performance IQ,and familial risk status), their developmental stability,and their associations, via letter knowledge, to secondgrade reading outcomes. The modeling confirmed sev-eral proposed hypotheses. First, a common core factorunderlying early phonological and language measurescould be identified at each age phase. This was termedEPLP. Second, EPLP was developmentally highly stable.Third, EPLP already predicted second grade reading

364 Journal of Learning Disabilities

EPLPat5.5

years

Familial risk for dyslexia

.30c

.70c

.33c

Reading&

SpellingAccuracy

ReadingFluency

PA

.37c

IQ

LetterNaming

.29c

.71c

.33c

.33c

R2 = 33

R2 = 55

.29c

Figure 3The Common Core Factor of the Age-Specific Modelat 5.5 Years and Its Associations With Second Grade

Reading Outcomes When IQ and Letter-NamingSkills Were Modeled as Mediators (n == 198)

Note: The scores beside the lines represent the standardized coeffi-cients. The dotted lines show nonsignificant connections. EPLP =early phonological and language processing; PA = specific latent forphonological awareness.

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

achievement when measured at the age of 3.5 years.Closer analyses indicated that individual differences insecond grade reading and spelling accuracy were wellpredicted (up to 55% of the variance) by early EPLP,with no additional contribution from letter-naming skills.EPLP predicted second grade reading fluency only to aminor extent but, together with early letter-naming skills,accounted for a moderate amount of the variance (at thehighest, 33%).

Connections Between the Predictors

The analyses showed that the measures entered into thestructural equation model (i.e., phonological awareness,short-term memory, RAN, expressive vocabulary, pseu-doword repetition, and familial risk for dyslexia) shareda large amount of variance and formed a latent factor,EPLP. Although significant correlations between theobserved measures at early ages have been found(e.g., Gallagher et al., 2000; Lyytinen, Aro, et al., 2004;Pennington & Lefly, 2001), or even several correlatedlatent factors (e.g., Wagner et al., 1994), structural analy-ses of the common core factor have been reported in onlya few studies. Lonigan et al. (2000) reported that phono-logical awareness, letter knowledge, and environmentalprint exposure were closely related, and in a similarmanner, Storch and Whitehurst (2002) showed that orallanguage, print principles, and phonological awarenesscould be modeled to form a single core factor. Carroll et al. (2003) reported findings of a 1-year follow up of 67children starting from the age of 3 years 10 months. Theyalso found significant correlations between vocabulary,mispronunciation detection, articulation accuracy, letterknowledge, and syllable and rhyming skills. In their SEManalyses, the skills also formed a common core latentfactor, not unlike in the present study, although in subse-quent analyses, Carroll et al. preferred to use a theoreti-cally derived model that drew a distinction between inputand output phonological processing.

Numerous studies have been published in which theseskills are seen as quite separate subskills. Our data-drivenmodeling, however, suggests that a wide range of skills(i.e., measures connected to the EPLP factor) contribute toreading achievement in a fashion resembling accounts byNation and Snowling (2004) and Dickinson, McCabe,Anastasopoulos, Peisner-Feinberg, and Poe (2003) sug-gesting broader language skills as preceding literacy skills.Nation and Snowling suggested that measures tappingvocabulary knowledge and listening comprehension, aswell as phonological skills at the age of 8.5 years, pre-dicted reading comprehension, word recognition, andexception word reading 4 years later. Dickinson et al.

argued that on the basis of studies of children aged 4 years9 months and their subsequent reading achievement, a vari-ety of oral language skills (including vocabulary, phono-logical awareness, and print knowledge) are connectedwith one another and are critical for emergent literacy skills.Our findings suggest that a broad constellation of earlylanguage and literacy skills are interrelated in a strongermanner than has been previously reported.

The results of SEM analyses also suggest that phono-logical and language processing skills are developmentallyhighly stable at an early age when no formal teaching isavailable. This was clearly indicated by the Choleskydecomposition analyses of EPLP. Although the increasein the mean scores indicated that children’s mastery of theskills increased with age, individuals tended to maintaintheir positions relative to their peers, and the differencesin level present at the age of 3.5 years remained. Althoughthe relative stability has been found in earlier studies withslightly older children (e.g., Wagner et al., 1993;Whitehurst & Lonigan, 2001), no such stability has beenobserved in the above-mentioned studies focusing onyounger children. Our finding of high stability prior tokindergarten and preschool age is a new finding that maybe due to our use of a large battery of measures that weresufficiently reliable to tap the skills early in development.

For decades, studies have consistently supported theidea of a phonological core deficit as underlying readingproblems (Snowling, 2000), and many theoretical modelssuggest that deficits in phonological representations andprocessing skills underlie causes of reading failure. Inaddition, phonological awareness tasks are consideredmost proficient at tapping these underlying skills (e.g.,Carroll et al., 2003; Leppänen et al., 2002; Metsala &Walley, 1998; Ramus, 2001; Swan & Goswami, 1997).In accordance with these accounts, phonological aware-ness was the task representing EPLP most powerfully atall age phases (with β values ranging from .69 to .73).Interestingly, phonological awareness and performanceIQ had a special connection in the modeling of 5-year-old children, probably indicating the shared cognitivecomponent of the tasks.

Although our measures of phonological awarenesswere naturally slightly different at the different ages,tapping the syllabic level more at the earlier ages and thephonemic level at the age of 5.5 years, phonologicalawareness nevertheless contributed to reading in a similarway at all age phases. This indicates the validity of themeasures used as age sensitive and indicates, similar toother studies (Anthony & Lonigan, 2004; Wagner et al.,1993, 1994), that not only phonemic awareness but alsothe awareness of larger phonological units is linked withreading development.

Puolakanaho et al. / Language Skills 365

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

The majority of other measures (e.g., memory, pseu-doword repetition, and vocabulary) contributed to EPLPin a relatively equal fashion (with β values ranging from.47 to .59). Vocabulary tasks are typically seen to drawon skills that are more connected to the lexical aspects ofvocabulary development and relatively independent ofthe other phonologically based skills (see Whitehurst &Lonigan, 2001). Our finding of a relatively strong linkbetween EPLP and expressive vocabulary is somewhatcontradictory to the prevailing view. The finding sug-gests that at an early age, these skill domains are notyet differentiated to the extent as at later ages, and the earlyexpressive productive vocabulary tasks may reveal bothdeveloping lexical and sublexical representation aspects(see Ramus, 2001; Szenkovits & Ramus, 2005).

In our analyses, a unique connection emerged betweenphonological awareness and letter naming by the age of5.5 years. This finding is in accordance with the claimsof reciprocal development of these two skill areas duringthe preschool years (Burgess & Lonigan, 1998; Hoganet al., 2005; Holopainen, Ahonen, Tolvanen, & Lyytinen,2000; Wagner et al., 1994; Wimmer & Mayringer, 2002).The analyses further suggested that the associationsbetween EPLP and letter naming increased with age. Theincreasing association may be caused partly by the psycho-metric properties of the letter-naming task: In the earliestphases, the variation in letter knowledge is due mainly tothe letter knowledge of those children who know letterswell. Ziegler and Goswami (2005) suggested in theirpsycholinguistic grain size theory that learning aboutorthography also changes the development of the lexicalrepresentations and processing strategies. This kind ofmechanism may be behind the increasing associationsbetween the measures seen in our modeling.

Children with no family histories of dyslexia showeda significantly higher mastery of phonological and lan-guage skills at each measurement point. These findingsare in accordance with previous studies of the offspringof families at risk for reading disabilities (Elbro et al.,1998; de Jong and van der Leij, 2003; Lyytinen et al.,2001; Lyytinen, Aro, et al., 2004; Lyytinen, Eklund, et al.,2004; Pennington and Lefly, 2001; Scarborough, 1991;Snowling et al., 2003). It is notable, however, that noconnections from familial risk to reading outcomesemerged over and above the connections between thefamilial risk and EPLP, suggesting that these skills behindEPLP were able to capture the information about familialrisk for dyslexia.

Performance IQ, measured at the age of 5 years,shared roughly 10% of the variance with EPLP, in linewith other studies showing a moderate relationshipbetween IQ and phonological and language skills (e.g.,

Elbro et al., 1988; Lonigan et al., 2000; Pennington &Lefly, 2001). Our analyses showed that the common corefactor of phonological and language measures could pre-dict both letter-naming skills and reading accuracy muchbetter than performance IQ, and reading fluency slightlybetter than IQ.

Early Skills’ Connection to Reading Accuracy and Fluency

As hypothesized, early skills, especially phonologicalawareness, predicted reading accuracy better than readingfluency. The age-specific models showed that EPLP wasstrongly connected with the reading and spelling accu-racy component (predicting 46%–53% of the variance),but only to a minor extent with the reading fluency com-ponent (predicting 5.3%–8.4% of the variance). Thisconfirmed our expectations on the basis of previous studiesand their suggestions (e.g. Aro, 2006; Goswami, 2002;Hogan et al., 2005; Kirby et al., 2003; Parrila et al.,2004; van Orden & Kloos, 2005; Wimmer & Mayringer,2002). Reading fluency, but not reading accuracy, wasadditionally explained by letter knowledge at each agephase (8.4%–11.7%). This finding has not, to our knowl-edge, been shown previously in the literature. Thus, theresults suggests that second grade reading accuracy iswell predicted by early phonological and language skills,whereas fluent reading skills are not well predicted byearly skills alone.

Analyses of developmental continuity in across-agemodeling showed that EPLP was already present at 3.5years, and further age phases did not add explanatorypower to the EPLP factor. In the across-age analyses, nosignificant paths were observed from EPLP to readingfluency. EPLP skills at the age of 3.5 years were con-nected to the concurrent and future letter-naming skills,which in turn predicted reading fluency. The mecha-nisms responsible for the reading fluency developmentmostly remain open and clearly require further study.

Reading accuracy and fluency were moderately corre-lated with each other. When familial risk status, earlyskills (EPLP, IQ, and letter naming), and concurrently mea-sured reading fluency were included as predictors, 76%of the variance in reading accuracy could be accountedfor. Familial risk status, early skills, and concurrent readingaccuracy accounted for 65% of the variance in readingfluency. Our modeling highlighted the fact that becomingan accurate reader is not in itself sufficient for acquiringreading fluency (see also Share, 2004).

Although earlier findings have reported connectionsbetween RAN and reading fluency (for reviews, seeAllor, 2002; Georgiou et al., 2007; Hogan et al., 2005;

366 Journal of Learning Disabilities

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

Kirby et al., 2003; Parrila et al., 2004; van Orden &Kloos, 2005), contrary to our hypothesis, we did not finda unique connection between RAN and reading fluencybeyond that explained by the EPLP factor. At theyoungest age, it is difficult for some children to directtheir behavior according to the instructions of a task,although they understand the task and know the names ofthe objects. At the ages when RAN was included in themodel (3.5 and 5.5 years), it was always the measure thatcontributed least to EPLP. The findings fit with the theo-retical accounts arguing on behalf of RAN’s relative inde-pendence of other phonological and language processes(Swanson, Trainin, Necoechea, & Hammill, 2003; Wolf,Bowers, & Biddle, 2000).

The inspection of the correlations and modeled asso-ciations indicates that letter knowledge was associatedwith all other subskills (as well as the performance IQand familial history of dyslexia) through EPLP. The find-ings of Torppa et al. (2006) using the same sample showedthat letter knowledge, especially delayed knowledge ofletters at school entry, was associated not only with otherlanguage and cognitive skills but also environmental fac-tors, such as parental letter teaching and mother’s educa-tional level. Stephenson, Parrila, Georgiou, and Kirby(2008) suggested that parental attitudes and children’stask-focused behaviors may play a role in mediating thedevelopment of early reading-related skills. These find-ings provide guidelines for directing further study on theantecedents of letter knowledge and its predictive role indevelopment of skills needed in fluent reading.

Our results suggest that at least in regular languages,EPLP skills predict only a minor part of fluent readingskills in second grade. This finding might also explainthe results of the training studies suggesting that althoughthe initial gains can be reached by supporting phonologicaland letter knowledge skills, the positive outcomes are notnecessarily very stable and can vanish in the later grades(Scarborough, 2001; 2005). These findings are notrestricted to regular languages. Therefore, it seemsextremely important to study the mechanism behind flu-ent reading in different languages.

Only a few studies (see the meta-analysis by Kuhn &Stahl, 2003) have tried to analyze the mechanisms of theprogression from accurate to fluent reading. In the major-ity of studies, conducted almost exclusively in irregularlanguage contexts, the focus has been on analyzing theshift from decoding accuracy toward orthographic learning(e.g., Nation, Angell, & Castles, 2007). The theoreticalaccount of Share (2004) suggests that by using the “self-teaching device,” one can acquire more advanced knowl-edge about the orthography (i.e., orthographic learning).In regular orthographies, there is relatively little to be

learned about orthography after mastering basic decoding.The number of orthographic complexities is relativelylow, and decoding can be carried out reliably usinggrapheme- and phoneme-level mappings. It is clear thatdecoding skills are necessary for independent readingirrespective of orthography. Still, the mechanisms under-lying fluency development and their relation to orthogra-phy are largely unknown.

Recent theoretical assumptions suggest that the effec-tiveness of the buildup process of associations with phono-logical and orthographical representations may be a criticalfactor behind fluent reading (Snowling, 2000; Wolf et al.,2000). Studies have shown that in addition to preliterature,phonological, and RAN measures, dynamic behavioralassessment procedures (see Byrne, Fielding-Barnsley, &Ashley, 2000; Compton, Fuchs, Fuchs, & Bryant, 2006;Mayringer & Wimmer, 2000; Swanson & Howard, 2005)in which grapheme–phoneme learning gains are mea-sured, may tap into the association process and thereforebe the most obvious candidates for early precursors of flu-ent reading. Studies, including the present one, have con-centrated on cognitive precursors of fluency. It is alsoobvious that reading experience and the frequency of prac-tice are important factors (see Cunningham & Stanovich,2001) in shaping fluent reading skills.

Limitations of the Study

The present study provides support for the differentpredictive relationships of early phonological and lan-guage skills and their connections to prereading skillsand thus to reading accuracy and fluency in second gradeFinnish children. However, the outcome measuresincluded only one assessment point, and it might be impor-tant to follow the developmental paths of the reading out-comes also to obtain a more complete picture.

The analyses were completed using SEM. We presentedthe most parsimonious models that were found to fit.Other models with different factorial presentations couldbe also found (such as closely correlated first-order fac-tors), but this was not the aim of the study. Our goal wasto simultaneously explore the common core factor andunique associations to reading outcomes. The presentedmodels were large and included over 20 observed mea-sures and several latent factors. Therefore, all thechildren were combined in the analyses, which led to theoverrepresentation of children with familial risk statuscompared to the normal population. The multigroupanalyses were applied to the across-age model to findsigns of significant group differences. Although no indi-cation of such emerged, the results need to be interpretedwith some caution.

Puolakanaho et al. / Language Skills 367

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

Conclusions

Our findings suggest a stronger intertwining of veryearly phonological and language skills and a broaderconstellation of interrelated skills than previously sug-gested. Using behavioral measures, the skills can bereliably measured as early as 3.5 years of age (i.e., 4years prior to the start of formal schooling). Early abil-ities and prereading skills showed associations withreading accuracy and fluency. Early abilities stronglypredicted second grade accuracy and with increasingage also predicted letter knowledge, even after control-ling for IQ. However, early abilities were poor at pre-dicting variation in fluency, and only together withdeveloping letter-naming skills could a moderate pre-diction be achieved. The findings support the view thatreading accuracy is built on very early core phonologi-cal skills, whereas reading fluency is only partly derivedfrom the same origins. Future research and model build-ing are needed to explain the mechanisms behind thedevelopment of reading fluency.

Notes

1. Phonological awareness is used here as a general term. Thesubtasks used to elicit phonological awareness vary: The easiest tasksassess the earliest phases of phonological awareness skills develop-ment (i.e., phonological sensitivity to larger sound units), while themost demanding tasks assess phonemic awareness. In preliminaryanalyses, the tasks loaded on a single factor in each age phase.Summing the subtasks, however, increased the reliability of the age-specific measures.

2. We also conducted multigroup analyses for the across-agemodel to explore the reliability of the results within the at-risk andcontrol groups. The model remained the same, although the magni-tude of the coefficients changed slightly. Typically, the associationsbetween the observed variables and the factors were somewhat higherand the associations between the different factors somewhat lower orequal in the at-risk group than the control group. No significant groupdifferences were found.

References

Allor, J. H. (2002). The relationships of phonemic awareness andrapid naming to reading development. Learning DisabilityQuarterly, 25, 47–57.

Anthony, J. L., & Lonigan, C. J. (2004). The nature of phonologicalawareness: Converging evidence from four studies of preschooland early grade school children. Journal of Educational Psychology,96, 43–55.

Aro, M. (2006). Learning to read: The effect of orthography. InR. M. Joshi & P. G. Aaron (Eds.), Handbook of orthography andliteracy (pp. 531–550). Hillsdale, NJ: Lawrence Erlbaum.

Bryant, P. E., MacLean, M., Bradley, L., & Crossland, J. (1990).Rhyme and alliteration, phoneme detection, and learning to read.Developmental Psychology, 26, 429–438.

Burgess, S. R., & Lonigan, C. J. (1998). Bidirectional relations of phono-logical sensitivity and prereading abilities: Evidence from a preschoolsample. Journal of Experimental Child Psychology, 70, 117–141.

Byrne, B., Fielding-Barnsley, R., Ashley, L. (2000). Effects ofpreschool phoneme identity training after six years: Outcomelevel distinguished from rate of response. Journal of EducationalPsychology, 92, 659–667.

Carroll, J. M., & Snowling, M. J. (2004). Language and phonologicalskills in children at high risk of reading difficulties. Journal ofChild Psychology and Psychiatry, 45, 631–640.

Carroll, J. M., Snowling, M. J., Stevenson, J., & Hulme, C. (2003). Thedevelopment of phonological awareness in preschool children.Developmental Psychology, 39(5), 913–923.

Catts, H. W., Fey, M. E., Zhang, X., & Tomblin, J. B. (2001).Estimating the risk of future reading difficulties in kindergartenchildren: A research-based model and its clinical implementation.Language, Speech, and Hearing Services in Schools, 32, 38–50.

Chaney, C. (1994). Language development, metalinguistic awareness,and emergent literacy skills of 3-yr-old children in relation tosocial class. Applied Psycholinguistics, 15(3), 371–394.

Compton, D. L., DeFries, J. C., & Olson, R. K. (2001). Are RAN- andphonological awareness deficits additive in children with readingdisabilities? Dyslexia, 7, 125–149.

Compton, D. L., Fuchs, D., Fuchs, L. S., & Bryant, J. D. (2006).Selecting at-risk readers in first grade for early intervention: A two-year longitudinal study of decision rules and procedures. Journalof Educational Psychology, 98, 394–409.

Cunningham, A. E., & Stanovich, K. E. (2001). What reading doesfor the mind. Journal of Direct Instruction, 2, 137–149.

de Jong, P. F. (1999). Hierarchical regression analysis in structuralequation modeling. Structural Equation Modeling, 6, 198–211.

de Jong, P. F., & Olson, R. K. (2004). Early predictors of letter knowl-edge. Journal of Experimental Child Psychology, 88, 254–273.

de Jong, P. F., & van der Leij, A. (1999). Specific contributions ofphonological abilities to early reading acquisition: Results form aDutch latent variable longitudinal study. Journal of EducationalPsychology, 91, 450–476.

de Jong, P. F., & van der Leij, A. (2003). Developmental changes inthe manifestation of a phonological deficit in dyslexic childrenlearning to read a regular orthography. Journal of EducationalPsychology, 95, 22–40.

Denckla, M. B., & Rudel, R. G. (1976). Rapid “automatized” naming(R.A.N.): Dyslexia differentiated from other learning disabilities.Neuropsychologia, 14, 471–479.

Dickinson, D. K., McCabe, A., Anastasopoulos, L., Peisner-Feinberg,E. S., & Poe, M. D. (2003). The comprehensive language approachto early literacy: The interrelationships among vocabulary, phono-logical sensitivity, and print knowledge among preschool-agedchildren. Journal of Educational Psychology, 95(3), 465–481.

Elbro, C., Borstrom, I., & Petersen, D. K. (1998). Predicting dyslexia fromkindergarten: The importance of distinctness of phonological represen-tations of lexical items. Reading Research Quarterly, 33, 36–60.

Gallagher, A., Frith, U., & Snowling, M. J. (2000). Precursors of lit-eracy delay among children at genetic risk of dyslexia. Journal ofChild Psychology & Psychiatry & Allied Disciplines, 41, 202–213.

Gathercole, S. E., & Adams, A. (1994). Children’s phonologicalworking memory: Contributions of long-term knowledge andrehearsal. Journal of Memory and Language, 33, 672–688.

Georgiou, G. K., Parrila, R., & Papadopoulos, T.C. (in press). Predictorsof word decoding and reading fluency across languages varying inorthographic consistency. Journal of Educational Psychology.

368 Journal of Learning Disabilities

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

Goswami, U. (2002). Phonology, reading development, and dyslexia:A cross-linguistic perspective. Annals of Dyslexia, 52, 141–163.

Hogan, T. P., Catts, H. W., & Little, T. D. (2005). The relationshipbetween phonological awareness and reading: Implications for theassessment of phonological awareness. Language, Speech, andHearing Services in Schools, 36, 285–293.

Holopainen, L., Ahonen, T., & Lyytinen, H. (2001). Predicting delayin reading achievement in a highly transparent language. Journalof Learning Disabilities, 34, 401–413.

Holopainen, L., Ahonen, T., Tolvanen, A., & Lyytinen, H. (2000).Two alternative ways to model the relation between reading accuracyand phonological awareness at preschool age. Scientific Studies ofReading, 4, 77–100.

Kaplan, E., Goodglass, H., & Weintraub, S. (1983). The Boston NamingTest (2nd ed.). Philadelphia: Lea & Febiger.

Kirby, J. R., Parrila, R. K., & Pfeiffer, S. L. (2003). Naming speed andphonological awareness as predictors of reading development.Journal of Educational Psychology, 95, 453–464.

Korkman, M., Kirk, U., & Kemp, S. (1998). NEPSY: A developmentalneuropsychological assessment. San Antonio: TX: PsychologicalCorporation.

Kuhn, M. R., & Stahl, S. A. (2003). Fluency: A review of develop-mental and remedial practices. Journal of Educational Psychology,95(1), 3–21.

Leinonen, S., Müller, K., Leppänen, P.H.T., Aro, M., Ahonen, T., &Lyytinen, H. (2001). Heterogeneity in adult dyslexic readers:Relating processing skills to the speed and accuracy of oral textreading. Reading and Writing, 14, 265–296.

Leppänen, P.H.T., Richardson, U., Pihko, E., Eklund, K. M., Guttorm,T. K., & Aro, M., et al. (2002). Brain responses to changes in speechsound durations differ between infants with and without familialrisk for dyslexia. Developmental Neuropsychology, 22, 407–422.

Lonigan, C. J., Burgess, S. R., & Anthony, J. L. (2000). Developmentof emergent literacy and early reading skills in preschool children:Evidence from a latent-variable longitudinal study. DevelopmentalPsychology, 36, 596–613.

Lyytinen, H., Ahonen, T., Eklund, K., Guttorm, T. K., Laakso, M.,Leinonen, S., et al. (2001). Developmental pathways of childrenwith and without familial risk for dyslexia during the first years oflife. Developmental Neuropsychology, 20, 535–554.

Lyytinen, H., Aro, M., Eklund, K., Erskine, J., Guttorm, T.K., Laakso,M.-L., et al. (2004). The development of children at familial riskfor dyslexia: Birth to school age. Annals of Dyslexia, 54, 184–220.

Lyytinen, H., Eklund, K., Erskine, J., Guttorm, T., Laakso, M.-L.,Leppänen, P., et al. (2004). Development of children at familialrisk for dyslexia before school age. Enfance, 56, 289–309.

Lyytinen, H., Erskine, J., Tolvanen, A., Torppa, M., Poikkeus, A.-M., &Lyytinen, P. (2006). Trajectories of reading development: A follow-up from birth to school age of children with and without risk fordyslexia. Merrill-Palmer Quarterly, 52, 514–546.

Mayringer, H., & Wimmer, H. (2000). Pseudoname learning byGerman-speaking children with dyslexia: Evidence for a phono-logical learning deficit. Journal of Experimental Child Psychology,75, 116–133.

Metsala, J. L., & Walley, A. C. (1998). Spoken vocabulary growth andthe segmental restructuring of lexical representations: Precursors tophonemic awareness and early reading ability. In J. L. Metsala &L. C. Ehri (Eds.), Word recognition in beginning literacy (pp. 89–120).Mahwah, NJ: Lawrence Erlbaum.

Muter, V., Hulme, C., Snowling, M. J., & Stevenson, J. (2004).Phonemes, rimes, vocabulary, and grammatical skills as founda-

tions of early reading development: Evidence from a longitudinalstudy. Developmental Psychology, 40, 665–681.

Muthén, B. O. (2004). Appendix 5: Model tests of fit and model modi-fication. In Mplus: Statistical Analysis With Latent Variables:Technical appendices. Available at: http://www.statmodel.com/download/techappen.pdf

Nation, K., Angell, P., & Castles, A. (2007). Orthographic learningvia self-teaching in children learning to read English: Effects ofexposure, durability, and context. Journal of Experimental ChildPsychology, 96, 71–84.

Nation, K., & Snowling, M. J. (2004). Beyond phonological skills:Broader language skills contribute to the development of reading.Journal of Research in Reading, 27, 342–356.

Parrila, R., Kirby, J. R., & McQuarrie, L. (2004). Articulation rate,naming speed, verbal short-term memory, and phonologicalawareness: Longitudinal predictors of early reading development?Scientific Studies of Reading, 8, 3–26.

Pennington, B. F., & Lefly, D. L. (2001). Early reading development inchildren at family risk for dyslexia. Child Development, 72, 816–833.

Puolakanaho, A., Poikkeus, A.-M., Ahonen, T., Tolvanen, A., &Lyytinen, H. (2003). Assessment of three-and-a-half-year-oldchildren’s emerging phonological awareness in a computer anima-tion context. Journal of Learning Disabilities, 36, 416–423.

Ramus, F. (2001). Outstanding questions about phonological processingin dyslexia. Dyslexia, 7, 197–216.

Savage, R., & Frederickson, N. (2005). Evidence of highly specificrelationship between rapid automatic naming of digits and text-reading speed. Brain and Language, 93, 152–159.

Scarborough, H. S. (1990). Very early language deficits in dyslexicchildren. Child Development, 61(6), 1728–1743.

Scarborough, H. S. (1998). Early identification of children at risk forreading disabilities: Phonological awareness and some other promis-ing predictors. In B. K. Shapiro, P. J. Accardo, & A. J. Capute (Eds.),Specific reading disability—A view of the spectrum (pp. 75–119).Timonium, MD: York.

Scarborough, H. S. (2001). Connecting early language and literacyto later reading (dis)abilities: Evidence, theory, and practice. InS. Neuman & D. Dickinson (Eds.), Handbook for research inearly literacy (pp. 97–110). New York: Guilford.

Scarborough, H. S. (2005). Developmental relationships between lan-guage and reading: Reconciling a beautiful hypothesis with someugly facts. In H. W. Catts & A. G. Kamhi (Eds.), The connectionsbetween language and reading disabilities (pp. 3–24). Mahwah,NJ: Lawrence Erlbaum.

Seymour, P.H.K. (2005). Early reading development in Europeanorthographies. In M. J. Snowling & C. Hulme (Eds.), The scienceof reading: A handbook (pp. 296–315). Malden, MA: Blackwell.

Seymour, P. H., Aro, M., & Erskine, J. M. (2003). Foundation literacyacquisition in European orthographies. British Journal ofPsychology, 94, 143–174.

Share, D. L. (2004) Orthographic learning at a glance: On the timecourse and developmental onset of self-teaching. Journal ofExperimental Child Psychology, 87, 267–298.

Snowling, M. (2000). Dyslexia (2nd ed.). Oxford, UK: Blackwell.Snowling, M. J., Gallagher, A., & Frith, U. (2003). Family risk of

dyslexia is continuous: Individual differences in the precursors ofreading skill. Child Development, 74, 358–373.

Stephenson, T. J., Parrila, R. Georgiou, G. K., & Kirby, J. R. (2008).Effects of home literacy, parents’beliefs, and children’s task-focusedbehaviour on emergent literacy and word reading skills. ScientificStudies of Reading, 12, 24-50.

Puolakanaho et al. / Language Skills 369

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from

Storch, S. A., & Whitehurst, G. J. (2002). Oral language and code-related precursors to reading: Evidence from a longitudinal struc-tural model. Developmental Psychology, 38, 934–947.

Swan, D., & Goswami, U. (1997). Phonological awareness deficits indevelopmental dyslexia and the phonological representationshypothesis. Journal of Experimental Child Psychology, 66, 18–41.

Swanson, H. L., & Howard, C. B. (2005). Children with reading dis-abilities: Does dynamic assessment help in the classification?Learning Disability Quarterly, 28, 17–34.

Swanson, H. L., Trainin, G., Necoechea, D. M., & Hammill, D. D.(2003). Rapid naming, phonological awareness, and reading: Ameta-analysis of the correlation evidence. Review of EducationalResearch, 73, 407–440.

Szenkovits, G., & Ramus, F. (2005). Exploring dyslexics’ phonologicaldeficit I: Lexical vs sub-lexical and input vs output processes. Dyslexia:An International Journal of Research and Practice, 11, 253–268.

Torgesen, J., Rashotte, C., & Alexander, A. W. (2001). Principles offluency instruction in reading: Relationships with establishedempirical outcomes. In M. Wolf (Ed.), Dyslexia, fluency, and thebrain (pp. 333–355). Timonium, MD: York.

Torppa, M., Poikkeus, A.-M., Laakso, M.-L., Eklund, K., & Lyytinen,H. (2006). Predicting delayed letter name knowledge and its relationto Grade 1 reading achievement in children with and without famil-ial risk for dyslexia. Developmental Psychology, 42, 1128–1142.

van Orden, G. C., & Kloos, H. (2005). The question of phonology andreading. In M. J. Snowling & C. Hulme (Eds.), The science ofreading: A handbook (pp. 61–78). Malden, MA: Blackwell.

Wagner, R. K., Torgesen, J. K., Laughon, P., & Simmons, K. (1993).Development of young readers’ phonological processing abilities.Journal of Educational Psychology, 85, 83–103.

Wagner, R. K., Torgesen, J. K., & Rashotte, C. A. (1994).Development of reading-related phonological processing abilities:New evidence of bidirectional causality from a latent variablelongitudinal study. Developmental Psychology, 30, 73–87.

Wechsler, D. (1989). Wechsler Preschool and Primary Scale ofIntelligence–Revised. San Antonio, TX: Psychological Corporation.

Wechsler, D. (1991). Wechsler Intelligence Scale for Children (3rded.). San Antonio, TX: Psychological Corporation.

Whitehurst, G. J., & Lonigan, C. J. (2001). Emergent literacy:Development from prereaders to readers. In S. B. Neuman &D. K. Dickinson (Eds.), Handbook of early literacy research(pp. 11–29). New York: Guilford.

Wimmer, H., & Mayringer, H. (2002). Dysfluent reading in theabsence of spelling difficulties: A specific disability in regularorthographies. Journal of Educational Psychology, 94, 272–277.

Wimmer, H., Mayringer, H., & Landerl, K. (2000). The double deficithypothesis and difficulties learning to read a regular orthography.Journal of Educational Psychology, 92, 668–680.

Wolf, M., Bowers, P. G., & Biddle, K. (2000). Naming-speed processes,timing, and reading: A conceptual review. Journal of LearningDisabilities, 33, 387–407.

Ziegler, J. C., & Goswami, U. (2005). Reading acquisition, develop-mental dyslexia and skilled reading across languages: A psycholin-guistic grain-size theory. Psychological Bulletin, 131(1), 3–29.

Anne Puolakanaho, PhD, is a psychologist, and works as aresearcher at the University of Jyväskylä and Niilo MäkiInstitute in Jyväskylä. Her current interests include early pre-dictors of reading, especially phonological awareness, skilldevelopment, and reading disability.

Timo Ahonen, PhD, is a professor of developmental psychology at the University of Jyväskylä. His currentinterests include learning disability and developmentalneuropsychology.

Mikko Aro, PhD, is a researcher at Niilo Mäki Institute inJyväskylä. His current interests include reading acquisitionand developmental dyslexia.

Kenneth Eklund, MSc, is a research coordinator in theJyväskylä Longitudinal Study of Dyslexia

Paavo H. T. Leppänen, Docent, PhD, is an academy researchfellow in the Finnish Center of Excellence for Learning andMotivation, Department of Psychology, University ofJyväskylä. His current interests include developmental brainresearch, learning disabilities, dyslexia, and auditory andspeech processing.

Anna-Maija Poikkeus, PhD, is a professor of early childhoodand primary education in the Department of TeacherEducation at the University of Jyväskylä. Her current interestsinclude language, cognitive, and social skills of children atschool entry.

Asko Tolvanen, PhD, is an expert in statistics at theUniversity of Jyväskylä and works as a statistician in theDepartment of Psychology at the University of Jyväskylä.His current interests include statistical methods in longitudi-nal data (e.g., simulation study relating to growth mixturemodeling).

Minna Torppa, PhD, MPs, is a psychology researcher at theUniversity of Jyväskylä. Her current interests include earlypredictors of reading, especially environmental risks and supportive factors and psychometrics.

Heikki Lyytinen, PhD, is a professor of developmentalneuropsychology at the University of Jyväskylä. His currentinterests include learning disabilities, neuropsychology, andpsychophysiology.

370 Journal of Learning Disabilities

at Jyvaskylan Yliopisto on June 2, 2014ldx.sagepub.comDownloaded from