Children’s knowledge of single- and multiple-letter grapheme-phoneme correspondences: An exploratory study

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EarlyChildhoodResearchQuarterly51(2020)379–391

ContentslistsavailableatScienceDirect

Early Childhood Research Quarterly

Children’s knowledge of single- and multiple-letter

grapheme-phoneme correspondences: An exploratory study

Linda Larsen

^a,b,c,∗

, Stefan Kilian Schauber

^d

, Saskia Kohnen

^b

, Lyndsey Nickels

^b

, Genevieve McArthur

^b

aTheDepartmentofSpecialNeedsEducation,FacultyofEducationalScience,UniversityofOslo,Oslo,Norway

bARCCentreofExcellenceinCognitionanditsDisorders,DepartmentofCognitiveScience,MacquarieUniversity,Sydney,Australia

cNorwegianInstituteofPublicHealth,Oslo,Norway

dCentreforHealthSciencesEducation,FacultyofMedicine,UniversityofOslo,Oslo,Norway

a r t i c l e i n f o

Articlehistory:

Received27July2018 Receivedinrevisedform 23September2019 Accepted2December2019 Availableonline21January2020

Keywords:

Grapheme-phonemecorrespondence knowledge

Grapheme-phonemecorrespondence complexity

Phonemestatus

Grapheme-phonemecorrespondence entropy

Grapheme-phonemecorrespondence frequency

Multilevelmodelling

a b s t r a c t

Inthisstudy,weexaminedAustralianchildren’sknowledgeofsingle-andmultiple-lettergrapheme- phonemecorrespondences(GPCs),andtheinﬂuenceofﬁvedifferentfactors–GPCcomplexity,phoneme status,thechild’sname,GPCentropy,andGPCfrequency–onGPCknowledge.Datafrom337Aus- tralianchildrenenrolledinKindergartentoGrade3wereincludedinthestudyandanalyseswere performedusingmixedeffectsmodels.ResultsindicatethatGPCknowledgevariedacrosschildrenand GPCs,childrenwerealmosttwiceaslikelytoaccuratelypronouncesingle-lettergraphemescompared tomultiple-lettergraphemes,andperformancewasbetterforGPCswhichoccurmorefrequentlyintext.

GPCswithhigherentropyvalues(lessconsistent)hadcloseto40%loweroddsofbeingknownbychil- dren.Thestudyhaspracticalimplicationsbyprovidinganevidence-basedguidefortheorderinwhich GPCsshouldbeintroducedtochildreninschools.

1. Introduction

Learning the namesof letters (letter-name knowledge) and the sounds that letters represent in written text (letter-sound knowledge) are two important skills for learning to read an alphabetic script. Letter-name knowledge in kindergarten is a strongpredictor of earlyreading skills (Bond & Dykstra, 1997;

Leppänen, Aunola, Niemi, & Nurmi, 2008; Scarborought, 1998;

Schatschneider, Fletcher, Francis, Carlson, & Foorman, 2004;

Torppa,Lyytinen,Erskine, Eklund,&Lyytinen,2010), becauseit appearstoassist children’sdevelopmentofletter-soundknowledge(forareview,seeFoulin,2005).Letter-soundknowledge,in turn,enableschildrentophonologicallydecodewrittenwordsthat theyhavenotyetlearned(Hulme,Bowyer-Crane,Carroll,Duff,&

Snowling,2012;Share,1999,2008).

∗ Correspondingauthorat:DepartmentofChildHealthandDevelopment,Nor- wegianInstituteofPublicHealth,0213Oslo,Norway.

E-mailaddress:[email protected](L.Larsen).

Atleasttwofactorshavebeensuggestedtoplayaroleinchil- dren’sdevelopmentofletter-nameknowledge:achild’snameand thephonologicalstructureofletter names.Research hasshown thatchildrenlearnthenamesofletters(andhowtowriteletters) intheirownnameearlierthanotherletters,becausetheyhave aparticularinterestinlearningtowritetheirownnameandare exposedtothewrittenformoftheirnamemoreoftenthanother writtenwords(containingotherletters;Bloodgood,1999;Justice, Pence,Bowles,&Wiggins,2006;Treiman&Broderick,1998).Chil- drenalsoappeartolearnthenamesoflettersmoreeasilywhen thosenameshaveaconsonant+vowel(CV)structure(e.g.,/b/+ /i:/fortheletterB;Treiman&Broderick,1998;Treiman,Tincoff,&

Richmond-Welty,1997),however,atleastonestudyhasnotfound thisresult(McBride-Chang,1999).

Whileitisnotyetclearifthephonologicalstructureofletter namesplaysacriticalroleinletter-nameknowledge,theresearch overwhelminglysupportsthatthephonologicalstructureofletter nameshasaroleinletter-soundknowledge(e.g.,Evans,Bell,Shaw, Moretti,&Page,2006;Justiceetal.,2006;McBride-Chang,1999;

Treiman,Tincoff,Rodriguez,Mouzaki,&Francis,1998; Treiman,

https://doi.org/10.1016/j.ecresq.2019.12.001

0/).

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Weatherston,&Berch,1994).Thishasbeenexplainedintermsof theiconicityofletters;thatis,mostletternamescontainthesound thattheletterrepresents(Treiman&Kessler,2003).Forexample, childrenﬁnditeasiertolearnthesoundsforletterswithnames thatcontainthecorrespondingsound(e.g.,Tispronounced/ti:/

whichcontainsthesound/t/)thanforlettersthatdonot(e.g.,Y ispronounced/waI/likethewordWHY,whichdoesnotcontain thesound/j/;Treimanetal.,1998).Further,childrenﬁnditeasier tolearnthesoundsforlettersthathaveaCVnamestructurethan aVCnamestructure(Treimanetal.,1998).Thatis,lettersounds areeasiertolearnforletterslikeBandK,wherethelettersound isatthebeginningofthelettername(i.e.,/bi:/and/keI/,respectively)thanletterslikeFandMwhere thesoundsisattheend ofthelettername(e.g.,/␧f/and/␧m/,respectively).Severalother factorshavebeenshowntoaffectthedevelopmentofletter-sound knowledgeincludingthemethodofliteracyinstruction(Piasta&

Wagner,2010),thevisualsimilarityofletters(e.g.,bandd)and similarityofsounds(e.g.,/m/and/n/;Carnine,1976,1980),andthe consistency(orambiguity)oflettersounds(e.g.,Dispronounced /d/versusCpronounced/k/or/s/;Huang,Tortorelli,&Invernizzi, 2014;Treiman,1993;Treimanetal.,1998).

Ofthesevariousfactors,itisthephonologicalstructureofletter namesthathasreceivedthemostattentionintheliterature.How- ever,theEnglishwritingsystemiscomplexandchildrenneedto learnmanyotherspellingunitsthanthe26letters,orgraphemes,of thealphabetandtheirassociatedsounds,orphonemes.Inparticular, childrenmustlearn manygrapheme-phonemecorrespondences (GPCs)wheremorethanonelettercorrespondstoasinglesound suchas EA-/i:/as inHEAT and SH-/ʃ/as inSHIP.However,the phonologicalstructureofletternamescannotaccountforhowchil- drenlearnthesemultiple-letterGPCs,sincethesegraphemes(i.e., EA,SH)arecompoundsofsingleletters(orgraphs)andtherefore donothavenamesperse.Single-letterGPCsmaybeusefulforchil- dren’searlyreadingdevelopmentandallowchildrentoreadsimple one-syllablewords,however,manywordscontainmultiple-letter GPCsthatchildrenmustlearninordertobecomeskilledreaders whomastercomplexwordsandtexts.Itisthereforeimportantto understandwhatfactorsareassociatedwithchildren’sknowledge ofsingle-letterandmultiple-letterGPCs.However,surprisinglyfew studieshavefocusedonmultiple-letterGPCs,andevenfewerhave investigatedfactorsassociatedwithchildren’sknowledgeofsingle- andmultiple-letterGPCsinthesamestudy.

Toaddressthisgapintheliterature,weexploredtheassoci- ationofseveralfactors(seebelow)withchildren’sknowledgeof bothsingle-andmultiple-letterGPCs.Basedonpreviousresearch, weselectedﬁvefactorsthatcouldbeappliedtobothsingle-and multiple-letterGPCs:(1)GPCcomplexity(i.e.,single-ormultiple- letter grapheme), (2) phoneme status (i.e.,consonant or vowel phoneme),(3)thechild’sownname(i.e.,own-nameadvantage),(4) GPCentropy(i.e.,measureofconsistency),and(5)GPCfrequency.

Belowweprovideareviewoftheliteratureforeachoftheﬁve factorsinordertoexplainourpredictionsforthecurrentstudy.

1.1. GPCcomplexity

InEnglish,thereisamismatchbetweenthenumberofspeech sounds(phonemes)andthenumberoflettersinthealphabet.There aremanymorephonemes(approximately44 dependingonthe accent)thanthereareletters,buttheconcatenationofletters(e.g., CandHtoCH)compensatesforthis(Venezky,1999).Adistinction betweenGPCscanthusbemadeintermsofthecomplexityofthe grapheme,thatis,whetheragraphemeconsistsofasingleletter (e.g.,P,E)ormultipleletters(e.g.,PH,EA).Single-andmultiple- letterGPCsdifferin (atleast)one importantway.Asthename implies,a multiple-letterGPC contains atleast two lettersthat eachmapontoaphoneme(orphonemes).Thismayinterferewith

learningthephonemeassociatedwiththegrapheme.Forexample, learningtheOI-/ɔI/association,asinCOIN,maybehamperedby knowledgeoftheO-/ɒ/andI-/I/associationsasinHOTandHIT, respectively.Indeed,researchwithadultshasshownthatwhen presentedwithamultiple-lettergrapheme,thephonemesasso- ciatedwiththeindividuallettersofthemultiple-lettergrapheme areactivated(Peereman,Brand,&Rey,2006).Moreover,wehave observedthatchildrenintheMacquarieUniveristyReadingClinic oftenrespondwithtwosuccessivephonemesformultiple-letter graphemes,forexample,saying/ɒ/andthen/œ/forOA,andthis patternhasalsobeenreportedinacquireddyslexia(e.g.,/∧/and /I/for UIwhenreading theword SUIT;Newcombe&Marshall, 1985).Researchwithchildrendoesindeedsuggestthatmultiple- letterGPCsaremoredifficultthansingle-letterGPCsirrespective ofwhethertheGPCisaconsonantorvowel(Frederiksen&Kroll, 1976;Marinus &de Jong,2008; Olson,Forsberg, Wise,&Rack, 1994).While this maybethecase, it isimportant tonotethat literacyinstructionprogramsoften(ifnot always)teachsingle- letterGPCsbeforemultiple-letterGPCs,whichmeansthatthere maybeanorder-of-teachingconfound.Nonetheless,wepredict basedonpreviousresearchthatchildreninthepresentstudywill findmultiple-letterGPCsmoredifficultthansingle-letterGPCsand thusprovidefewercorrecttargetresponsestomultiple-letterthan single-letterGPCs.

1.2. Phonemestatus

AdistinctioncanbemadeintermsofwhetheraGPCisacon- sonantorvowel.SeveralstudieshavedemonstratedthataGPC’s phonemestatus(asconsonantorvowel)inﬂuenceschildren’sGPC knowledge.For example,StuartandColtheart(1988)foundthat youngchildrenwerelesslikelytoknowvowelsthanconsonants whenaskedtoprovidethesoundassociatedwitheachoftheletters ofthealphabet.Afurtheranalysisofchildren’serrorsshowedthat incorrectresponsestendedtobeletternames,whichforvowelsare oftenreferredtoasthe“longvowelsound”(e.g.,Apronounced/eI/

asinBABY).Itwasarguedthattherelativelypoorerperformance forvowelswasnotduetothe(low)consistencyofvowelsrela- tivetoconsonants(i.e.,generally,vowellettershavemorepossible pronunciationsthanconsonantletters)aschildrenalsomadeletter nameresponsesforconsonants.However,respondingwith/eI/to theletterAand/bi:/totheletterBisdifferent,becauseAdoesmake the/eI/-soundinwords(e.g.,BABY,TABLE),whileBdoesnotmake the/bi:/-soundinwords(itmakesthe/b/-soundasinBAG).Graham (1980)assessedprimary-schoolstudents’knowledgeofvoweland consonantsingle-andmultiple-letterGPCsusinganextensivenon- wordreadingtaskandalsofoundthatchildrenstruggledmorewith vowelsthanconsonants.Finally,amorerecentstudyshowsthat childrenhavemoredifﬁcultywithmultiple-letterGPCsforvowels thanconsonantsinnonwordreading(Gilbert,Compton,&Kearns, 2011).Basedonthisresearch,wepredictthatthechildreninthe presentstudywillhavemoredifﬁcultywithvowelsrelativetocon- sonants.

1.3. Own-nameadvantage

Childrenhavebeenfoundtobebetterabletolearnthoseletters ofthealphabetthatoccurintheirownname(Justiceetal.,2006;

Treiman&Broderick,1998;Villaume&Wilson,1989).Whetherthe own-nameadvantageextendstochildren’sGPCknowledgeisless clear.Forexample,TreimanandBroderick(1998)foundthat4-and 5-year-oldswerenomorelikelytoknowthephonemeassociated withasingle-lettergraphemeifthegraphemewasinthechild’s name,evenifthegraphemewastheinitialoftheirname.How- ever,amorerecentstudybyHuangetal.(2014)didﬁndevidence insupportoftheown-nameadvantageaschildrenintheirstudy

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L.Larsenetal./EarlyChildhoodResearchQuarterly51(2020)379–391 381

weremorelikelytoknowthephonemeassociatedwithindividu- allypresentedgraphemes,ifthegraphemeoccurredastheinitial ofthechild’sﬁrstname.WhydidHuangetal.(2014)ﬁndown- nameadvantageforGPCknowledge,whenTreimanandBroderick (1998)didnot?Onepossibilityrelatestohowtheinitialgrapheme ofchildren’snameswascategorised.Huangetal.(2014)“made distinctionsbetweenchildnamesinwhichtheletterrepresentsits mostcommonsound[i.e.,thetargetsound]andwhenitdoesnot”.

WhenaskedtoprovidethephonemeforthegraphemeC,children namedCathyorCarlwouldbeexpectedtoshowanown-name advantage,whereasCelineorCharliewouldnot,andthisiswhat wasfound.TreimanandBroderick(1998)didincludethisimpor- tantdistinction(inStudy2only),buttheydidnotfindasignificant own-nameadvantageforGPCknowledgewhenthiswasassessed usingeitherafreechoicetask(similartoHuangetal.;i.e.,what sounddoesthislettermake?)oratwo-choicetask(e.g.,isthisa /də/ora/pə/?).ThiscouldbeinpartduetoTreimanandBroderick’s smallsamplesize(n=47;Huangetal.:n=1197),restrictednum- berof(single-letter)graphemes(n=6)orastheauthorssuggest, becauseofaconfoundinthedesign.Namely,halfoftheconso- nantsincludedhavenameswithaVCstructure(i.e.,M,R,andS), whereastheotherhalfhavenameswithaCVstructure(i.e.,D,J,and K)andthechildrenintheirstudyperformedsignificantlybetteron graphemeswithaCVthanVCnamestructure.Thiseffectmayhave distortedtheown-nameadvantageasitwasnotfactoredintothe own-nameanalyses.

Butwhataboutmultiple-letterGPCsandtheown-nameadvantage–isCharliemorelikelytoknowtheCH-/tʃ/associationthan Chris, Cathy,or Danielle?Theonly studythat we areaware of reportingon this (Huang et al., 2014) didnot ﬁndevidenceto supportanown-nameadvantageonknowledgeofmultiple-letter GPCs.Inthepresentstudy,wewishtofurtherexplorethisissue.

Speciﬁcally,weinclude(1)single-andmultiple-letterGPCs,(2)a moreextensivelistofbothvowelandconsonantmultiple-letter GPCs(e.g.,AIforAidan,PHforPhillip),and(3)weusethename distinctionsimilartoHuangetal.(2014).Giventhemixedresearch ﬁndingsandrelativelackofresearchontheown-nameadvantage onknowledgeofGPCsandinparticularmultiple-letterGPCs,we donothaveanypredictionsregardingown-nameadvantage.

1.4. GPCentropy

Englishisoftendescribedasadeeporthography,whichmeans that the relationship between orthographic and corresponding phonologicalunits isnot one-to-one.Some graphemescanrep- resent more than one phoneme (orthography to phonology in reading;e.g.,THasinTHISversusTHIN,OOasinNOOKversus NOON) and some phonemes can be represented by different graphemes(phonologytoorthography inspelling;e.g.,/i:/:SEA andSEE;/␧ə/:STAREandSTAIR).Ofinterestinthisstudyisthe translationfromorthographytophonology;consequently,werefer specificallytoGPCconsistency(referredtoasambiguityinHuang etal.,2014).TheconsistencyofaGPCisdefinedastherelative frequencyofthatGPC(e.g.,EA-/i:/)asaproportionofalloccur- rencesofthatgrapheme(i.e.,EA).Previousresearchhasshownthat GPCconsistencyaffectschildren’sGPCknowledgewithinconsis- tentGPCsbeingmoredifficultforchildrentolearn.Thisisthecase bothwhenGPCknowledgeisassessedusingastandardGPCtask (i.e.,thechildseesagraphemeandisaskedtoprovidetheasso- ciatedphoneme)andanonwordreadingtask(Huangetal.,2014;

Siegel&Faux,1989).

Whilepreviousstudieshaveusedadichotomousmeasureof GPCconsistency(i.e.,consistentversusinconsistent),wesuggest thatthisisarathercrudemeasurethatdoesnotadequatelyreﬂect thenuancesofGPCconsistency.Instead,weuseamoresophis- ticated and sensitive measure, GPC entropy, which takes into

accountboththenumber ofdifferentpronunciationsassociated withagraphemeandtherelativeproportionsofthesepronuncia- tions(seeSection2andTable1fordetailsofhowentropyvalues are calculated andexample calculations).While entropy values forGPCshavebeencalculatedforseveraldifferentorthographies (Borgwaldt,Hellwig,&deGroot,2004;Protopapas&Vlahou,2009), wearenotawareofanyotherstudiesthathaveinvestigatedthe effectofGPCentropy(asanindexofGPCconsistency/ambiguity)on children’sGPCknowledge.Wepredictthatchildreninthepresent studywillshowmoredifﬁcultywithmoreinconsistentGPCs,that is,thosewithhigherentropyvalues.

1.5. GPCfrequency

Thefrequencywithwhichchildrenencountertheirownname hasbeensuggestedasapossibleexplanationfortheown-name advantage. However, there is another frequency index which mightbeimportantforchildren’sdevelopmentofGPCknowledge;

namely, thefrequencywithwhich GPCsoccurintext.Previous researchhasshownthatchildrenmayimplicitlylearnaboutGPCs inspelling(e.g.,Dispronounced/d/versusCpronounced/k/or/s/

Gingras&Sénéchal,2019;Treiman&Kessler,2011)andthegrapho- tacticandmorphologicalpatternsinspelling(Deacon,Conrad,&

Pacton,2008).Suchimplicitstatisticallearninghasalsobeenfound fordevelopingreadersintermsofGPCacquisition,wherevowel GPCfrequencyandtheconsonantalcontextofvowelsispredictive ofGPCknowledge(Steacyetal.,2019;Treiman,Kessler,&Bick, 2003;Treiman,Kessler,Zevin, Bick,&Davis,2006).While these studieshavefocusedspecificallyonthepronunciation ofalim- itedsetofvowelGPCsduringnonwordreading,iftheobserved frequencyeffectgeneralizesforvowelandconsonantGPCsmore broadly,thenchildrenshouldfinditeasiertolearnandtherefore knowagreaterproportionofmorefrequentGPCscomparedtothe proportionoflessfrequentGPCs.Ifchildrendonotmakeuseof thewrittenfrequencyofGPCs,thenwewouldnotexpecttofinda differenceinknowledgeacrossGPCswithdifferentfrequencies.

SeveralstudieshaveinvestigatedtheeffectoffrequencyonGPC knowledgeforvowelsandconsonants;however,critically,these studieshaveusedameasureofgraphemefrequency(andnotGPC frequency).Thatis,howoftenaparticulargraphemeoccursinwrit- tentext.Webelieveitisimportanttodifferentiatebetweenthe twofrequencyindicesastheymeasuresomethingquitedifferent, especiallywhenagraphemehasmultiplepossiblepronunciations (e.g.,Ahasatleastthreedifferentpronunciations;Coltheart,Rastle, Perry,Langdon,&Ziegler,2001).Whiletheresultsofpreviousstud- iesaremixedintermsoftheeffectofgraphemefrequencyonGPC knowledge(Ecalle,2004;Evansetal.,2006;Huangetal.,2014;

Treiman,Levin,&Kessler,2012),wesuggestthatGPCfrequency mightbeamoreappropriateindexforpredictingchildren’sGPC knowledge and hence, we include this measure in thepresent study.WepredictthatchildrenwillhavebetterknowledgeofGPCs thatoccurmorefrequentlyintextthanGPCsthatoccurlessfre- quently.

Insummary,thecurrentstudyinvestigatedchildren’sknowledge of the mappings between letters and sounds, GPCs. GPC knowledge is crucial for children’sreading developmentand it isthereforeimportanttounderstandwhatfactorsinfluencethis knowledge.Asfarasweareaware,thisisthefirststudytoinvesti- gatechildren’sGPCknowledgeusingacomprehensivelistofboth single-andmultiple-letterGPCs.Morespecifically,weexplorehow fivefactors–GPCcomplexity(single-ormultiple-lettergrapheme), phonemestatus(consonantorvowel),achild’sownname(own- nameadvantage),GPCentropy(anindexofconsistency),andGPC frequency(frequencyinwrittentext)–areassociatedwithchil- dren’sknowledgeofsingle-andmultiple-letterGPCs.

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Table1

Exampleentropycalculationsasindicesofgrapheme-phonemecorrespondenceconsistency.

Grapheme Phoneme Example Numberofoccurrences(tokenfrequency) Probability(tokenconsistency) Entropycomponents

B → /b/ BIN 509843 1 0.00

Entropyvalue:H(B)=0.00

EA → /eI/ STEAK 13340 0.1027 −0.337249396

→ /Iə/ HEAR 5085 0.0392 −0.183035783

→ /eə/ PEAR 366 0.0028 −0.023873174

→ /␧/ BREAKFAST 35564 0.2738 −0.511698234

→ /i:/ PEACH 75514 0.5815 −0.454844119

Entropyvalue:H(EA)=1.511

Table2

Studysampledemographicsbygrade(class),groupandage.

Gradelevel N Group Age(years)

Boys Girls Range Mean(SD)

Kindergarten 82 39 43 5.45–6.72 6.06(0.33)

Grade1 87 34 53 6.45–7.79 7.13(0.30)

Grade2 75 34 41 7.36–8.77 8.14(0.32)

Grade3 93 53 40 8.43–9.85 9.18(0.33)

2. Method

TheMacquarieUniversityHumanEthicsCommitteeapproved themethodsofthis research.Childrenparticipatedin thestudy withparentalconsent.Verbalassentwasalsoobtainedfromeach childatthebeginningofthetestingsession.

2.1. Participants

Weanalyseddatafromatotalof337children(177girls,160 boys)enrolledinKindergarten,Grade1,Grade2,andGrade3who participatedinthestudy.Childrenrangedinagefrom5years5 monthsto9years10months.SeeTable2forsampledemographics.

Childrenwererecruitedfromtwoindependentco-educational schools in the Sydney metropolitan area. Both schools were selectedonthebasisthattheyperformedwithintheaveragerange forAustraliaontheNationalAssessmentProgram–Literacyand Numeracy(NAPLAN),andwereinareasthatwereintheaverage rangeontwo socio-economicstatus(SES) measures(Australian BureauofStatistics,2006).TheschoolsweretypicalofAustralian schoolsinthattheytaughtreadingusingacombinationofdiffer- entmethods.Oneschoolusedamixofphonicsandsightwords.

Phonicsinstructionwasfocusedonteachinggrapheme-phoneme associationswherethepronunciationisthemostcommon(e.g.,A pronounced/æ/asinHATandCHispronounced/tʃ/asinCHECK) andsightwordsweretobememorisedviafrequentexposure(i.e., wordrecognition). Thisschool didnot usea particularreading instructionprogram,butratherusedamixofseveralprograms.

TheotherschoolpredominatelyusedtheTHRASSmethod(Davies&

Ritchie,2003).THRASSisacomprehensiveteachingmethodwhich covershandwriting,readingandspelling.TheTHRASSmethodhas astrongfocusonphonemicawareness(i.e.,segmentingwordsinto sounds),andteacheschildrenthe44phonemesinEnglishandhow thesemayberepresentedinwriting(e.g.,thephoneme/f/canbe spelledFasinFIONA,FFasinCOFFEE,orPHasinPHONE).Children alsolearna wordexamplefor each ofthesoundspellingasso- ciations(e.g.,BIRDforB, RABBITforBB),inadditiontolearning frequentlyoccurringotherwords(e.g.,THE, SAID,YOU).Teach- ers usea THRASS chartwith the44 phonemes, divided into a consonantandavowelsection,withthemostcommonspellings (graphemes)foreachphonemeshownwithawordandapicture example.Ifaphonemehasotherlesscommonspellings(e.g.,PB for/b/asinCUPBOARD)thesearerepresentedwithanasterisk onthechart.LesscommonspellingsarecalledGraphemeCatch-

Table3

Single-andmultiple-lettergraphemesusedintheLeST(orderedfromhighestto lowestwrittenfrequency).

Indexcard

1 t n s i l r a d c p e m o b

2 er g f u v k h j w y ar z th sh

3 ng ch ee x qu oo ph oi ai kn ay oa oy au

4 wr ea gn aw ir wh ou ur igh

Note.LeST=Letter-soundTest.

Alls(GCAs)andareusedtoteachfrequentlyoccurringwordsthat donothaveacommonspellingpattern(e.g.,SAIDwherethe/␧/- soundisspelledwithAI).The THRASSmethoddoesnothave a prescribedteachingsequenceforphonemegraphemeassociations andteacherscanteachtheseassociationsintheordertheywish (e.g.,tomatchtheirotherresources),andwordsareintroducedas theybecomeusefulforchildren(i.e.,dependsentirelyontheindi- vidualchildandclassroom).WhiletheTHRASSmethodteaches sound-to-spellingassociations (phoneme-to-grapheme) and not spelling-to-soundassociations(grapheme-to-phoneme),research suggeststhatneitherapproachissuperior,atleastduringbegin- ningliteracy instruction(Callinan& vander Zee,2010).In the present study,we exploredwhether there wereany difference acrossschoolsgiventhattheapproachestoliteracyinstructionvar- iedbetweentheschools,butwedidnotﬁndthistobethecase(see AnalyticApproachbelow).

2.2. Procedure

Children were tested individually on the Letter-Sound Test (LeST;Larsen,Kohnen,Nickels,&McArthur,2015)inaquietroomof theirschoolinthefinaltermoftheacademicyear(inAustraliathe academicyearhasfourtermsandthefinaltermrunsfromOctober toDecember).TheLeST,whichtook5−10mintoadminister,was administeredaspartofabatteryofreadingandspellingtestswhich formedalargenormingstudy.Thetestbatterywasadministeredin afixedordertoallstudents(Marinus,Kohnen,&McArthur,2013;

McArthuretal.,2013).

2.3. Measures 2.3.1. GPCknowledge

Children’sknowledgeofsingle-andmultiple-letterGPCswas assessedusingasingletask(noparallelforms),namely,theLeST (Larsenetal.,2015).TheLeSTcomprises51itemspresentedonA4 indexcards(9–14itemspercard)inArial24-pointfont.SeeTable3 forafulllistoftheGPCsincludedintheLeST.Childrenwereshown theﬁrstoftheindexcardsandaskedtoprovidethesoundthat eachofthesingle-ormultiple-letterGPCsmakes.Thescoringpro- cedurefortheLeSTissimilartopreviousstudiesinthatonlythe mostcommonphonemeassociatedwithagraphemeisaccepted– forsingle-lettervowelGPCstheshortvowelsoundwasaccepted, thehardsoundforCandG(i.e.,/k/and/g/,respectively)and/ks/

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forX.¹Werefertothisasthetargetresponsesortargetphonemes.

TheLeSThashightest-retestreliability(ICC=0.88)andmoderate tostrongcriterionvalidity(Pearson’srbetween0.49and0.70)with thenonwordlistfromtheCastlesandColtheart2(CC2;Castlesetal., 2009)anda39-itemnonwordslist(experimentalnonwordsvary- inginlengthandcomplexity).InternalconsistencyoftheLeSTin thepresentstudywasgood(r_KR-20=0.88).Forafulldescriptionof theLeSTincludinghowthetestwasconstructed(e.g.,itemselec- tion),normativedata,andfurtherinformationregardingreliability andvalidity,seeLarsenetal.(2015).

Forthepresentstudy,GPCsincludedintheLeSTwerecoded intermsofcomplexity,phonemestatus,entropyandfrequency.

Belowweoutlinetheclassiﬁcationsusedandanoverviewofthe codingofeachoftheGPCsispresentedinAppendixA.

2.3.2. GPCcomplexity

GPCswereclassiﬁedaccordingtowhetherthegraphemecon- taineda singleletterand coded 0orcontainedmultipleletters andcoded1.Therewasanequalnumber(25)ofsingle-letterand multiple-letterGPCs.

2.3.3. Phonemestatus

GPCswerecoded0iftheywereconsonantsand1iftheywere vowels.Therewere29consonantGPCsand21vowelGPCs.

2.3.4. Own-nameadvantage

To codethe data for theown-name advantageanalysis, we checkedifthepronunciationoftheinitialgraphemeofachild’s firstnamecorrespondedtoatargetresponseontheLeST.Ifthis wasthecaseitwascoded1,otherwiseitwascoded0.Forexample, Charliewascoded1asCHispronounced/tʃ/andCathywascoded 1asCispronounced/k/(i.e.,botharetargetresponsesontheLeST), butCharlotteandCelinewerecoded0asCHandCispronounced /ʃ/and/s/,respectively(i.e.,neitheraretargetresponsesonthe LeST).Firstnameswerescoredindependentlybythefirstauthor andasecondscorer(anativeEnglishspeaker)andagreementwas high(94%).Anydiscrepancieswereresolvedthroughdiscussion.A totalof86.9%ofchildrenhadafirstnamethatbeganwithatarget phoneme.

2.3.5. GPCentropy

Itispossibletocalculateentropyusingeithertypefrequency (i.e.,numberofdifferentwordtypes inwhicha GPCoccurs)or tokenfrequency(i.e.,thetotalnumberoftimesallofthewords, inwhichaGPCappears,occurinawrittencorpus)counts,butfol- lowingtherecommendationsofBalota,Yap,andCortese(2006)and ProtopapasandVlahou(2009),weusedtokenfrequencymeasures inthisstudy.Morespeciﬁcally,theentropy(H)valueforeachGPC ontheLeSTwascalculatedasthenegativesum,overthealternative mappings,oftheproductsofeachprobabilitytimesitslogarithm:

H= −

n

i=1

p_ilog₂p_i

GPCswithanentropyvalueofzerohaveaperfectone-to-one mapping(i.e.,consistentcorrespondence)betweengraphemeand phoneme(e.g.,B-/b/).However,asentropyincreases,theconsis- tencyoftheGPCdecreases.Forexample,thegraphemeEAhasﬁve possiblephonememappingsofwhich/i:/(asinTEA)isthemost frequent.Overall,thegraphemeEAoccurs129,869timesandon

1Therewasoneexceptiontothescoringprocedure.ForTHboththevoiced(e.g., THIS)andunvoiced(e.g.,THANK)pronunciationswereaccepted.Forthisreason,TH wasexcludedfromallanalyses.

75,514occurrencesitispronounced/i:/,amountingtoaprobabil- ity(p)of0.1027forthemappingEAto/i:/.Theprobabilitiesforthe othermappingsare0.1027for/eI/,0.092for/Iə/,0.0028for/eə/, and0.2738for/␧/, respectively(seeTable1).Byinsertingthese probabilitiesintotheentropyformula,wegettheentropyvalue H(EA)=1.1511,describingthe(in)consistencyofthepronunciation ofthegraphemeEA.

2.3.6. GPCfrequency

Thefrequencymeasureusedinthepresentstudyreferstothe frequencyofGPCsratherthangraphemesasdiscussedabove.That is, how often a particular GPC occurs in writtentext. GPC fre- quencycountsaregeneratedfromtheempiricalinvestigationsof largewordcorporaandtherehavebeenseveralsuchinvestigations (e.g.,Coltheartetal.,2001;Fry,2004;Hanna,Hanna,Hodges, &

Rudorf,1966).Inthepresentstudy,weusedthefrequencycounts byColtheartetal.(2001),whicharebasedonthemonosyllabic words(over7500differentwords)fromthewidelyusedCELEX database²(Baayen,Piepenbrock,&Gulikers,1995).Intheirlook- uptable, Coltheartand colleaguesprovideboth typeand token frequencycounts.WechosetouseColtheartandcolleagues’fre- quencycountsasitallowedustoexploreifGPCtypeandtoken frequenciesaredifferentlyassociatedwithchildren’sknowledge ofGPCs.

2.4. Analyticapproach

All analyseswere performed using the R software environ- mentforstatisticalcomputingandgraphics(RCoreTeam,2017).

Thelme4package(Bates,Maechler,&Bolker,2012)wasusedto performgeneralizedlinearmixedmodels(GLMM)analyses.We employedatwo-stepapproach.Inthefirststep,weestimateda number ofdecreasinglycomplexmodelsincorporatingonly the interceptasafixedeffectand,successively,randomeffectsforstu- dents,items,classes,andschools.Thepurposewastodetermine theappropriaterandomeffectsstructureanddifferencesbetween modelswereassessedusinglikelihoodratiotests.Inthesecond step,weestimatedtherandomeffectsstructuredeterminedinStep OneandincludedfixedeffectsforGPCcomplexity,phonemestatus,GPCentropy,GPCfrequencyandtheinteractiontermbetween phonemestatusandGPCentropy.Wethenaddedfixedeffectsfor own-nameandage,³beforeestimatingamodeladdingtheinter- actiontermbetweenown-nameandage.Finally,weexploreda complexinteractionmodelinwhichweincludedfixedeffectsfor GPCcomplexity,phonemestatus,GPCentropy,andGPCfrequency, andtheinteractiontermsbetweenGPCcomplexity,phonemestatus,GPCentropy,GPCfrequencyandage,respectively.

3. Results

Descriptivedata(seeFig.1)showsthataccuracy–theabilityto providethetargetphonemeforsingleormultiple-lettergraphemes –variedacrossitemsandchildren.Morethan80%ofchildrencor- rectlyproducedthetargetphonemefor23outof50items.Dividing thesampleintoyoungerandolderchildrenbyamediansplit,we foundthatmorethan80%ofyoungerchildrencorrectlyproduced

2CELEXisbasedondictionarydatafromtheOxfordAdvancedLearner’sDictio- nary(1974,41,000entries)andtheLongmanDictionaryofContemporaryEnglish (1978,53,000entries),andtextcorpusdatafromtheCOBUILD/Birminghamcorpus (17.9millionwords),whichhasbeenspeciﬁcallyusedtodevelopCELEXfrequency measures.WhiletheCELEXfrequenciesarebasedontextsamplesfromadulttexts, evidencesuggeststhatthedistributionofGPCsinadultandchildren’sbooksis similar(Solity&Vousden,2009).

3ThankyoutotwoanonymousreviewersforsuggestingtheinclusionofAgeas apredictor.

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Fig.1.Percentageofyoungerandolderchildrenproducingthetargetresponse(phoneme)foreachgrapheme(thesolidlinerepresentsthetotalsampleofchildren).Items presentedinorderofdecreasingaccuracy.

Table4

Modelingrandomeffectsforstudents,items(GPCs),classes,andschools.Thenull modelswerenotsigniﬁcantlydifferentandthus,therandomeffectforschoolswas excludedwhenmodelingﬁxedeffects.

Nullmodel1 Nullmodel2

OR CI p OR CI p

Fixedeffects

(Intercept) 7.39 3.99–13.70 <0.001 7.39 3.98–13.70 <0.001

Variance ICC Variance ICC

Randomeffects

Students 0.858 0.110 0.858 0.110

Items 3.075 0.395 3.075 0.395

Classes 0.557 0.072 0.557 0.072

Schools 0.000 0.000

Note.OR=OddsRatio;CI=ConﬁdenceInterval;ICC=IntraClassCorrelation.

thetargetphonemefor25outof50items.Thiswas30outof50 itemsforolderchildren(seeAppendixB).

Asexpected,childrenproducedsingle-letterGPCs(e.g.,P-/p/, 98%)moreaccurately,buttwomultiple-letterGPCswerealsofairly accurate(i.e.,SH-/ʃ/,92%;CH-/tʃ/,89%).Aspredicted,childrenin generalwerelessaccurateonvowelsthanconsonants,andinpar- ticularmultiple-lettervowelGPCs.Onlytwosingle-lettervowel GPCswereproducedaccuratelybymorethan80%ofchildren(i.e., I-/I/andO-/ɒ/)andonlyonemultiple-lettervowelGPC(i.e.,EE-/i:/) wasknownbymorethan75%ofchildren.Theitemsthatchildren hadthemostdifﬁcultywithwereGN-/n/asinGNOME(39%cor- rect),OU-/aυ/asinSOUND(35%),andAU-/ɔ:/asinAUTOMATIC (25%).

Resultsfromthemixedeffects modelanalysesarepresented inTables4and5.In theﬁrststep,weperformedapreliminary multilevelanalysisofnullmodelswithfourlevels(i.e.,students,

items,classes,andschools)andthreelevels(i.e.,students,items, andclasses),respectively(seeTable4).Inthefirstmodel,theran- domeffectofschoolexplainedzerovariance.Excludingtherandom effectforschoolinthesecondmodeldidnotsignificantlyreducethe fitofthemodelandwethereforecollapsedthedataacrossschools forsubsequentanalyses.

Inthesecondstep,weexploredtheeffectofGPCcomplexity, phonemestatus, GPCentropy, GPCfrequency (token andtype), own-name,and ageonchildren’sGPCknowledge(see Table5).

Resultsarereportedasodds ratios(OR),whereasigniﬁcantOR greater(orless)than1indicatesapositive(ornegative)associa- tionwiththepredictorandanORof1indicatesnoassociationwith thepredictor.Forexample,anORof1.35indicatesa35%increasein theoddsofcorrectlyproducingthetargetphonemeassociatedwith aoneunitincreaseinthepredictorwhilecontrollingforallother variables.AnORof0.65indicatesa35%decreaseintheodds.In Model1weincludedGPCcomplexity,phonemestatus,GPCentropy andGPCfrequency(token).Theresultsindicatedthatallpredic- torswerestatisticallysigniﬁcantinthemodel,whichwasinaccord withourpredictions.Childrenwerelesslikelytoproducethetar- get phoneme for multiple-letter graphemes (OR=0.17), vowels (OR=0.45),andGPCsthathadhigherentropyvalues(i.e.,lessconsistent;OR=0.56).Further,morefrequentGPCswerealmosttwice aslikelytobeaccuratelypronouncedcomparedtolessfrequent GPCs(OR=1.81).

In Model 2 we ran the same analysis, but token frequency wasreplacedwithtypefrequencytofurtherexploreifthesetwo indices ofGPC frequencydifferentially predictedchildren’sGPC knowledge. This wasnot thecase: more frequent GPCs (using type frequency) still had 78% higher odds of being accurately pronouncedcomparedtolessfrequentGPCs(OR=1.78).GPCcom- plexityandGPCentropywerestillsigniﬁcantpredictorswithonly veryminorchangestotheoddsratios(i.e.,complexity:OR=0.16

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L.Larsenetal./EarlyChildhoodResearchQuarterly51(2020)379–391385 Table5

Resultsfromlinearmixedeffectsmodelsanalyses.

Nullmodel Model1:itemlevel

predictors

Model2:itemlevel predictors

Model3:itemlevel predictors

Model4:personlevel predictors

Model5:personlevel predictors

OR p OR p OR p OR p OR p OR p

Fixedeffects

(Intercept) 7.39 <0.001 30.98 <0.001 29.75 <0.001 29.32 <0.001 29.25 <0.001 29.52 <0.001

Graphemecomplexity 0.17 <0.001 0.16 <0.001 0.17 <0.001 0.17 <0.001 0.17 <0.001

Phonemestatus 0.45 0.013 0.56 0.079 0.52 0.090 0.52 0.091 0.52 0.091

GPCentropy^a 0.56 0.045 0.54 0.034 0.70 0.433 0.70 0.432 0.70 0.431

GPCfrequency(token)^b 1.81 <0.001 1.85 <0.001 1.84 <0.001 1.84 <0.001

GPCfrequency(type)^b 1.78 0.001

Phonemestatus×GPCentropy 0.69 0.515 0.69 0.510 0.69 0.511

Own-name 1.54 0.134 1.26 0.402

Age(inmonths)^b 1.46 0.042 1.47 0.038

Age×Ownname 0.39 <0.001

Randomeffects

Students 0.914 0.914 0.914 0.914 0.915 0.918

Items 3.150 0.789 0.820 0.783 0.780 0.785

Classes 0.526 0.496 0.497 0.496 0.198 0.199

Explainedvariance(percentage)inrelationtothenullmodel

Students 0.00 0.00 0.00 −0.11 −0.44

Items 74.95 73.97 75.14 75.24 75.08

Classes 5.70 5.51 5.70 62.36 62.17

Note.GPC=Grapheme-phonemecorrespondence;OR=OddsRatio.Boldvaluesindicatesigniﬁcanteffects.

aIndexofconsistencyusingtokencount.

b Log-scaled.

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andentropy:OR=0.54).However,whiletheoddsratioforphoneme statusremainedalmostthesame,theassociatedp-valueindicated thatitwasnolongerasigniﬁcantpredictorinthemodel(OR=0.56).

Thisresult maybe due toa lackof statistical power basedon the(relativelysmall)samplesize(asdiscussedintheSection4.1, below).

InModel3weincludedthephonemestatusbyGPCentropy interactiontermto(tentatively)explorewhetherGPCentropyacts differentlyacrossvowelsandconsonants,andhelpunderstandwhy childrenseemtobelessaccurateonvowelscomparedtoconso- nants.However,neithertheinteractionnorphonemestatusnor GPCentropyweresigniﬁcantinthemodel.

InModels4and5weaddedfixedeffectsforown-nameandage, andtheown-namebyageinteractionterm,respectively.InModel 4wefoundthatage,butnotown-name,wasasignificantpredic- torinthemodel(age:OR=1.46andown-name:OR=1.54).GPC complexityandtokenfrequencyremainedsignificantinthemodel, whilephoneme status,GPC entropy, andthe phonemetype by GPCentropyinteractionremainednon-significant.InModel5we foundasignificantown-namebyageinteractioneffect(OR=0.39), indicatingthatolderchildrenbenefittedlessfromtheown-name advantage.Finally,inModel6(identicaltoModel1,butwiththe inclusion of higher order interaction terms betweenGPC complexity,phoneme status, GPC entropy, and GPC frequency, and age,respectively),wefoundthatGPCcomplexity,phonemestatus,GPCentropy,andGPCfrequencyweresignificantpredictorsin themodelwithonlyminorchangestooddsratioscomparedtothe othermodels.Wealsofoundsignificantinteractioneffects,except foragebyphonemestatus.Forexample,thelargestoddsratiowas fortheagebyGPC complexityinteraction(OR=2.51, p<0.001), indicatingthatolderchildrenweretwoandahalftimesmorelikely toknowcomplexGPCs.However,weinterprettheseresultswith cautionasModel6failedtoconverge(seeAppendixCformodel results).

When comparing the overall fit of the models (fit indices Model 1: AIC=12,846 and BIC=12,908; Model 2: AIC=12,848 andBIC=12,909;Model3:AIC=12,848and BIC=12,917; Model 4: AIC=12,846 and BIC=12,931; Model 5: AIC=12,835 and BIC=12,928),wedidnotfindasignificantdifferencebetweenmod- elsand,ascanbeseen,fitindicesareverysimilar.About75%ofthe varianceattheitem(GPC)levelwasaccountedforacrossthefive fittedmodels.

4. Discussion

Learningthesoundsthatlettersrepresentisacrucialskillfor children’sreading development in analphabeticlanguage. It is therefore importantto understandhow this skill develops and whatfactorsareassociatedwithit.Thepurposeofthisresearch wastoinvestigatechildren’sknowledgeofsingle-andmultiple- letter GPCs, and the association between GPC knowledge and ﬁvedifferentfactors,namely:(1)GPCcomplexity(i.e.,single-or multiple-lettergrapheme),(2)phonemestatus(i.e.,consonantor vowelphoneme),(3) the child’sown name(initial GPC in ﬁrst name),(4)GPCentropy(i.e.,measureofconsistency),and(5)GPC frequency.

Fromourdescriptiveanalysis,itwasevidentthattherewasvari- ationinchildren’sknowledgeofGPCs,both acrosschildren and acrossGPCs.Themajority(morethan80%)ofyoungerchildrencor- rectlyprovidedthetargetphonemeforhalfofthegraphemes,while olderchildrenunsurprisinglywerebetterandprovidedthetarget phonemefor30outof50graphemes.Furtherandaspredicted,chil- drenperformedbetteronsingle-letterGPCs,withtheexceptionof twomultiple-letterconsonantGPCs(i.e.,SH-/ʃ/andCH-/tʃ/).Apos- sibleexplanationastowhychildrenperformedsowellonthesetwo

itemsisthatbotharehighlyconsistent(lowentropyvalues)and occurfrequentlyinwrittentext(seeAppendixAforentropyand frequencyvalues).ThismayalsoaccountforwhyPH-/f/(i.e.,PHas inPHONE)wasrelativelymoredifﬁcult,as,whileconsistent,itis lessfrequent.

Fromthedescriptivestatisticschildrenalsoappearedtoexhibit greater difficulty with vowels and in particular multiple-letter vowelGPCscomparedtoconsonants,althoughthisfindingmaynot beasrobustasphonemestatus(consonantorvowelGPC)wasnot asignificantpredictoracrossthefivemodelswheretheotherpre- dictorsarecontrolledfor.Onlytwosingle-lettervowelGPCswere knownbymorethan80%ofchildrenandonlyonemultiple-letter vowelGPCwasknownbymorethan75%ofchildren.

ItisinterestingtonotethatA-/æ/(asinCAP)wasnotamong theeasiestvowels,eventhoughitisgenerallyrecommendedthat childrenbeintroducedtothisvowelveryearlyinteaching(e.g., JollyPhonics;Lloyd,1992,LettersandSounds;PrimaryNational Strategy,2007).Instead,I-/I/asinPITandO-/ɒ/asinPOTwerethe vowelsonwhichaccuracywasthehighest.Ifeasierlettersounds shouldbetaught beforeharderlettersounds, ourdatasuggests thatI-/I/andO-/ɒ/shouldbetaughtfirst,followedbyE-/␧/,A-/æ/, andfinallyU-/∧/.However,ourfindingscontrastwithHuangetal.

(2014),whofoundA-/æ/tobetheeasiestvowel,followedbyO- /ɒ/,E-/␧/,I-/I/,andU-/∧/.Whilewedonothaveaclearexplanation forthecontrastingresults,itispossiblethattheeasilydistinguish- ableletter-formofOmayhaveaffectedtheacquisitionoftheO-/ɒ/ associationandhence,makingthisitemeasierforchildreninour study.AnotherpossibilityisthedifferenceinEnglishaccent,with thepresentstudyusingAustralian-EnglishandHuangetal.(2014) usedAmerican-English.Thus,orthographicfeaturesofgraphemes anddifferentaccentsaffectingvowelsmayplayaroleinchildren’s lettersoundacquisition.

Formultiple-lettervowelGPCs,twowereparticularlydifﬁcult forchildren,namely,OU-/aυ/asinSOUNDandAU-/ɔ:/asinAUTO- MATIC.Only35%and25%ofchildrenprovidedthetargetphoneme forthese,respectively.Manyteachersusetherule“whentwovow- elsgowalkingtheﬁrstdoesthetalking”toteachmultiple-letter vowelGPCs.WhatthismeansisthatinthewordBOATtheOsays itsname/əυ/andtheAissilent.Whilethisruleisusefulforsome multiple-lettervowelGPCs(e.g.,OAasinBOAT,EAasinTEA,andIE asinPIE),thisisnothelpfulforlearningOU-/aυ/orAU-/ɔ:/.Itmay beconfusingforchildrentolearnthisrulethatappliessometimes andmayhamperlearningofsomemultiple-letterGPCs.

Wenowturntotheresultsfromthemixedeffectsmodelsand thefactorsassociated withchildren’sGPC knowledge.First, we lookedatGPCcomplexity,whichisconcernedwithwhetherthe graphemeconsists ofa singleletter (e.g.,P, E)or multiplelet- ters(e.g.,PH,EA).GPCcomplexitywasasigniﬁcantpredictorin allourmodels.Single-lettergraphemeshadaroundan83%higher oddsofbeingaccuratelypronouncedcomparedtomultiple-letter graphemes(e.g.,Model1:OR=0.17).Thisisinlinewithourpre- dictionandaddsfurthersupporttopreviousresearch(Frederiksen

&Kroll,1976;Marinus&deJong,2008;Olsonetal.,1994).Itis notsurprisingthatmultiple-letterGPCsshouldbemoredifﬁcult forchildrenandapossibleexplanationisthatmultiple-letterGPCs containtwo(ormore)singlelettersthateachmapontotheirown associated phonemeor phonemes.This maycauseinterference orinhibitaccesstothephonemewithwhichthemultiple-letter graphemeisassociated.Anotherpossibleexplanationisrelatedto theageatwhichchildrenaretaughtdifferentGPCs.Whenchil- drenﬁrstbeginreceivingliteracyinstructiontheyaremostoften(if notalways)taughtsingle-letterGPCsbeforebeingtaughtmultiple- letter GPCsthat allowthem toread more complex text.While childreninthepresentstudyweresampledfromschoolsthatuse amixedapproachtoliteracyinstructionandhence,minimisedthe possibleconfoundoftypeofliteracyinstruction,itispossiblethat

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ourresultscouldbeconfoundedbyageofacquisitioneffects.That is,thatchildrenhadbeenintroducedtoandtaughtsingle-letter GPCsatanearlierageandonlylateronhadbeentaughtmultiple- letterGPCs.TentativeevidencefromModel6lendsindirectsupport forthisastheGPCcomplexitybyageinteractionwasfoundtobe signiﬁcant.

Second,wefoundevidencetosuggestthatchildrenmayfind vowelsmoredifficultthanconsonants.Infact,foreveryonevowel achildknew,theyknewtwoconsonants.However,theeffectof phonemestatus(i.e.,consonantorvowel)onGPCknowledgewas onlystatisticallysupportedinModel1,despitetheoddsratio(and associatedconfidence interval)beingstable acrossmodels.This indicatesthattheeffectofphonemestatusmaylackrobustness.

Nonetheless,consideringtheresultsof thedescriptive dataand resultsfromthemixedeffectsmodels,wecautiouslysuggestthat ourresultsareinaccordwithourpredictionandpreviousresearch (Gilbertetal.,2011;Graham,1980;Stuart&Coltheart,1988).Also, toourknowledge,thisisthefirsttimethatthedegreeofdifficulty ofvowelsrelativetoconsonantshasbeenquantifiedusingboth single-andmultiple-letterGPCsofwhich29wereconsonantsand 21werevowels.

Wemight thenask,what itis thatmake vowelsGPCsmore difﬁcult than consonants GPCs for children? Undoubtedly, the acquisitionofvowelGPCsisparticularlyinterestingandcomplex asthenameofvowelletters(e.g.,nameofEis/i:/)isalsothesound whichtheselettersandothermultiple-letterunitsareusedtorep- resent(i.e.,E-/i:/asinME,EE-/i:/asinTREE,andEA-/i:/asinTEA).

Further,therearephoneticsimilaritiesbetweenvowelsounds(e.g., /␧/-/I/,/æ/-/␧/)andtherearemanymorevowelsoundsthanthere arelettersinthealphabettorepresentthese.It seemsunlikely thatthedifficultywithvowelsshouldberelatedtospeechsound acquisition,asvowelsareacquiredearlierthanconsonantsinlan- guagedevelopment(Priester,Post,&Goorhuis-Brouwer,2011)and vowelspresentlessdifficultyforspeechinterpretationandintel- ligibilitythandoconsonants(e.g.,Cole,Yan,Mak,Fanty,&Bailey, 1996;Fogerty&Humes,2012).Further,wenotethattheacquisition orderofvowelGPCsinthepresentstudydoesnotfitclearlywiththe acquisitionorderofvowelphonemesinspeech(McLeod,2009).We arenotawareofanystudiesthathaveusedspeechsoundacqui- sitionordertopredictGPCknowledge.However,onestudyhas focusedonconsonantspeechsoundacquisitionandletternaming ofconsonants(Justiceetal.,2006).Thisstudyfoundthatchildren werebetterabletonamelettersthatcorrespondedtoconsonant speechsoundsacquiredearlierratherthanlaterindevelopment.⁴ Returningtovowels,evidencefromneuropsychologicalsingle- casestudiesofdysgraphia,aphasiaanddyslexiadoessuggestthat (phonologicallyandorthographically)consonantsandvowelsare distinctcategoricalrepresentations(Khentov-Kraus&Friedmann, 2018;Miceli,Capasso,Benvegnù,&Caramazza,2004).Inparticu- lar,thestudybyKhentov-KrausandFriedmann(2018)isrelevant forourstudyasitreports23singlecasesofvowelletterdyslexia (i.e.,selectiveimpairment inreadingvowels inwordsandnon- words).Whilethismaylendsupporttothepossibilitythatthere issomethingspecialaboutvowelsthatmakethesemoredifficult forchildren therecouldbea morestraightforwardexplanation.

Namely,thespokensimilarityofvowels(i.e.,thereismuchgreater spoken similarity between vowels than there is among consonants). The more limited discriminabilitybetweenvowels may resultinlessdistinctmemoriesfor vowels,whichinturncould leavemoreroomforerrorduringvowelGPCacquisition.Taken together,theremay beacomplex interactionofseveralfactors

4Inasupplementaryanalysis,weexploredwhethertheconsonant-orderadvan- tageextendedtolettersounding,butwedidnotﬁndevidenceofthis.

whenlearningvowelGPCs,andmoredirectstudiesarerequired tobetterunderstandthis.

Third,weinvestigatedtheown-nameadvantage,whichstates that children should bemore likely to know those lettersthat occur in their name. In this study, we were specifically inter- ested in whetherchildren showedan advantage for the initial grapheme(single-ormultiple-letter)oftheirnamewhen asked toprovideitsassociatedphoneme.Toourknowledge,onlyone studyhasexploredtheown-nameadvantageon(alimitedsetof) knowledgeofmultiple-letterGPCsinapreliminaryanalysis.Inthe presentstudy,weincludedacomprehensivelistofbothsingle-and multiple-letterGPCs.InlinewithHuangetal.(2014)andTreiman andBroderick(1998),ourresultsdonotsupporttheown-name advantage.Forexample,achildnamedCharliewasnomorelikely toknow the CH-/tʃ/ association than a child whose name was Chris,CathyorDanielle.Despitethelackofasignificanteffectof own-nameonGPCknowledge,wedidfindasignificantinteraction withage,witholderchildrenbenefittinglessfromtheown-name advantagethanyoungerchildren.Itispossiblethattheown-name advantagecouldbesimilartoa(specific)frequencyeffect(i.e.,fre- quencyofownname),whichwashesoutovertime,possiblywith increasedexposuretotext.Thatis,olderchildrenareexposedto manymorewordsthanjusttheirname,whereasyoungerchildren areexposedtofewerwordsoverall,thusincreasingtheimpactof seeingtheirname.

Fourth,inthepresentstudy,weusedarelativelysophisticated measuretoindexconsistency,namelyentropy,whichtakesinto accountallthephonememappingsforagivengraphemeandtheir relativeproportion(e.g.,Protopapas&Vlahou,2009).Wefoundthat GPCentropywasasignificantpredictorofchildren’sGPCknowl- edge.Thisisinlinewithourpredictionandprovidesstronger(more conclusive)evidencefortheeffectofconsistencyonchildren’sGPC knowledgecomparedtopreviousstudiesthathaveusedcoarser measuresofGPCconsistency(Huangetal.,2014;Siegel&Faux, 1989).Thisresulthaspracticalimplicationsforclassroomteachers whenplanningliteracyinstruction–specificallyGPCinstruction– asitsuggeststhatGPCswithlowerentropyvalues(i.e.,thatareless ambiguous)shouldbeintroducedbeforeGPCswithhigherentropy values(moreambiguous).Todothis,teachersmayfindtheGPC entropyvalueslistedinAppendixAausefulresource.

Finally,thisstudyfound,ashypothesized,thatGPCfrequency wasasigniﬁcantpredictorofchildren’sGPCknowledge.Children hadaroundan80%higheroddsofknowingmorefrequentGPCs relativetolessfrequentGPCs.GPCknowledgewasnotdifferen- tially predictedby the number of word typesa particular GPC occursin(typefrequency)comparedtothesumofthefrequency ofthosewordtypes(tokenfrequency).Asfarasweareaware,the presentstudyistheﬁrsttouseGPCfrequency(ratherthanletter(or grapheme)frequency)andinvestigateiftypeandtokenfrequency havedifferentialeffectsonGPCknowledge.

4.1. Limitations

Thepresent studyhasa numberof limitationswhich future studiesmaywishtoaddress.Oneisthatthesamplewascollapsed acrossfourdifferentgradesinordertoincreasethesamplesize (andhence,statisticalpower)whenperformingthemixedeffects analyses.Weacknowledgethatoursamplesizeofmorethan330 childrenmayseemrelativelysmallcomparedtosomeotherstudies investigatingGPC(orletter-name)knowledge(Huangetal.,2014;

Justiceetal.,2006;Phillips,Piasta,Anthony,Lonigan,&Francis, 2012).Nonetheless,given that thisis theﬁrst studytoinvesti- gatechildren’sknowledgeofacomprehensivelistofsingle-and multiple-letter GPCsand factorsassociated withchildren’sGPC knowledge,itrepresentsasolidstartingpoint.

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Asecondlimitationrelatestothefactthatchildrenwhopartic- ipatedinthepresentstudyweredrawn fromonlytwo schools.

Itcouldbearguedthat, forexample,theliteracy instructionor method used at each school may have inﬂuenced the results.

However, both schools were typical of Australian schools and usedacombinationofphonicsandsightwords(wholewords)to teachreading.OneoftheschoolsusedtheTHRASSmethodpre- dominately. Critically, however,the main ﬁndings of thestudy wererobustevenwheneffectsofschoolonaccuracyandinterac- tionsbetweenschoolandgraphemecomplexity,phonemestatus, GPCentropy,GPCfrequency,and own-name,respectively,were exploredinsupplementaryanalyses.Nevertheless,wesuggestthat futurestudieswouldbeneﬁtfromusingalargersampleofchildren drawnformawidervarietyofschools.

WeencouragefuturestudiestouseGPC(ratherthanletteror grapheme)frequency(typeandtoken)andGPCentropyasanindex ofconsistency,andtoinvestigatespeechsoundacquisitionorder ortheorthographicstructureofletters/graphemesandhowthis relatestochildren’sGPC acquisition.Otherchild-relatedfactors mayalsobeincludedsuchaslanguagebackgroundandspeciﬁc measuresofchildren’sspeechororallanguage.Finally,westrongly encouragefuturestudiestoexploreliteracyinstructionpractices withinschoolsandtheirrelationshipwithchildren’sGPCacquisi- tion.

4.2. Implicationsforinstruction

Letter-sound (GPC) knowledge directly influences children’s reading development by allowing children to phonologically decodewordstheyhavenotyetlearned(Share,1999).Whilechil- drenneedtolearnbothsingle-andmultiple-letterGPCstobecome proficientreaders, many studies todate have only focused on children’sknowledgeofsingle-letterGPCs.Thepresentstudyis thefirsttoexplorechildren’sknowledgeofacomprehensivelist ofsingle-andmultiple-letterGPCsandseveralfactorsassociated withchildren’sGPCknowledge.Itthereforehasdirectimplications forliteracyinstruction.First,thestudyprovidesnewinformation regardingthesequenceofacquisitionofacomprehensivelistof GPCsthatincludesbothsingle-andmultiple-letterGPCs,andcon- sonantsandvowels.Asitseemsnaturaltointroducechildrento easierGPCsbeforemoredifficultGPCs,Fig.1(earlier)canbeused toguidetheorderinwhichteachersandliteracyinstructorsintro- duce GPCsto beginning readers.Assuggested earlier, it seems thattheorderinwhichmanyliteracyinstructionprogramsintro- ducesingle-letterGPCsdoesnotcorrespondwithwhatthecurrent researchshowsabouttheacquisitionorderofGPCs.Second,the acquisitionorderofGPCsisalsohelpfulforteachersasaguidefor howmuchtimeshouldbedevotedtoteachingdifferentGPCsand theamountofrepetitionfordifferentGPCs.Thatis,theintroduc- tionofearlieracquiredGPCs(i.e.,easierGPCs)mayprogressquicker thantheintroductionoflateracquiredGPCs(i.e.,harderGPCs),and easierGPCsmayalsorequirelesstimededicatedtorepetitionthan harderGPCs.

Third,thepresentstudyprovidesnewinformation regarding whysomeGPCsareeasyforchildren toacquireandwhysome arehardertoacquire.Forexample,wefoundthatchildrenstrug- gledthemostwithGN-/n/(asinGNOME),OU-/aυ/(asinSOUND), and AU-/ɔ:/ (as in AUTOMATIC). The two last items are both multiple-lettervowelGPCsthathaverelativelylowconsistencyand frequency.Inotherwords,thesetwoitemshavemultiplecharacter- isticsthatmakeGPCsharderforchildrentolearn.Itisnoteworthy thatGN-/n/–alsoamultiple-letterconsonantlowinfrequency– wassurprisinglydifficultforchildren.Whycouldthisbe?Onepos- sibilityisthelow frequencyalone.However,anotherpossiblyis thatithastodowiththewordpositionspecificsoftheitem.That is,GNmostoftenappearsinwordfinalposition(e.g.,SIGN,ALIGN)

andonlyrarelyinwordinitialormedial position(e.g.,GNOME, POIGNANT). While wedidnot investigateword positioninthe presentstudy,itispossiblethatthiscouldsomehowhavenega- tivelyinﬂuencedperformanceontheLeSTwheretestitemswere presentedinisolation(andinasenseinwordinitialposition).

Inconclusion,thepresentstudyextendspreviousresearchon children’sGPCknowledge byinvestigatinga comprehensiveset ofbothsingle-andmultiple-letterGPCsinthesamestudy.Our ﬁndingssuggestthatchildren’sGPCknowledgeisassociatedwith GPC complexity (i.e., single- versus multiple-letter grapheme), phoneme status (i.e., consonant or vowel), GPC entropy, and GPCfrequency.Further,theeffectofa child’sownname(initial grapheme)onGPCknowledgeisstrongerforyoungerthanolder children.Thepresentstudyprovidesnewinsight intotheorder inwhich single-andmultiple-letter GPCsshouldbeintroduced tochildrenduringbeginningliteracyinstructionwheretheaimis generallytointroduceeasierGPCsbeforemoredifﬁcultGPCs.

5. Declarationofinterest None.

Acknowledgments

We would like to thankthe teachers and children for their timeandeffortdonatedtothisresearch.WethankEvaMarinus andThusharaAnandakumarforcoordinatingthetestingandthe researchassistantsfortestingthemanyparticipants.Wealsothank AthanasiosProtopapasandtwoanonymousreviewersforveryuse- fulcommentsonthemanuscript.Thispaperwaspreparedwhile LindaLarsenwasfunded bya PostdoctoralFellowship fromthe DepartmentofSpecialNeedsEducationatUniversityofOlso,Saskia KohnenwasfundedbyaMacquarieUniversityResearchFellow- ship(MQRF),GenevieveMcArthurwasfundedbyanARCAustralian ResearchFellowship(0879556),andLyndseyNickelswasfunded byanARCFutureFellowship(120100102).

AppendixA

GPC GPCcom-

plexity

Phoneme status

GPC entropy

Typefre- quency

Token frequency

t-/t/ 0 0 0.000 1641 2081414

n-/n/ 0 0 0.000 1169 1836402

s-/s/ 0 0 1.285 2411 918372

i-/I/ 0 1 0.578 818 1359101

l-/l/ 0 0 0.000 1639 562756

r-/r/ 0 0 0.000 1364 443701

a-/æ/ 0 1 1.636 729 1329249

d-/d/ 0 0 0.340 1334 1232701

c-/k/ 0 0 0.735 724 213457

p-/p/ 0 0 0.000 1277 315754

e-/␧/ 0 1 1.068 506 396199

m-/m/ 0 0 0.000 833 746321

o-/ɒ/ 0 1 1.605 483 901467

b-/b/ 0 0 0.000 794 508525

er-/∧r/ 1 1 0.061 58 80649

g-/g/ 0 0 0.986 513 165363

f-/f/ 0 0 0.999 629 523968

u-/∧/ 0 1 0.801 620 283584

v-/v/ 0 0 0.000 286 155171

k-/k/ 0 0 0.000 637 257224

h-/h/ 0 0 0.000 307 740592

j-/ / 0 0 0.003 122 42149

w-/w/ 0 0 0.000 396 641173

y-/j/ 0 0 0.969 67 77988

ar-/a:/ 1 1 0.622 134 63256

z-/z/ 0 0 0.128 83 6507

sh-/ʃ/ 1 0 0.000 293 137319