ERIC EJ1124381: Executive Function as a Mediator between SES and Academic Achievement throughout Childhood

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by  ERIC

InternationalJournalof
BehavioralDevelopment
Executive function as a mediator
2017,Vol.41(1)94–104
ªTheAuthor(s)2015
between SES and academic achievement Reprintsandpermissions:
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throughout childhood DOI:10.1177/0165025415603489
journals.sagepub.com/home/ijbd
Gwendolyn M. Lawson and Martha J. Farah
Abstract
Childhoodsocioeconomicstatus(SES),asmeasuredbyparentaleducationandfamilyincome,ishighlypredictiveofacademicachievement,
butlittleisknownabouthowspecificcognitivesystemsshapeSESdisparitiesinachievementoutcomes.Thisstudyinvestigatedtheextent
towhichexecutivefunction(EF)mediatedassociationsbetweenparentaleducationandfamilyincomeandchangesinreadingandmath
achievementinasampleof336childrenbetweentheagesof6and15yearsfromtheNIHMRIStudyofNormalBrainDevelopment.Verbal
memory was simultaneously modeled as a comparison candidate mediator. SES predicted significant changes in reading and math
achievement over a two-year time period. Furthermore, executive function, but not verbal memory, was found to partially mediate
therelationshipbetweenSESvariablesandchangeinmathachievement.Collectively,theseresultssuggestthatexecutivefunctionmay
beanimportantlinkbetweenchildhoodSESandacademicachievement
Keywords
academicachievement,executivefunctions,socioeconomicstatus
Childhood socioeconomic status (SES) is a powerful predictor of opposed to ‘hot’) EF, that is EF in emotionally neutral contexts
academic achievement throughout childhood and adolescence (Zelazo&Carlson, 2002),andfurthersubdivideEFinto working
(e.g., Bradley & Corwyn, 2002; Reardon, 2011). Research aimed memory, attention shifting, and response inhibition (Miyake
atunderstandingtheSESachievementgapintermsofchildcogni- et al., 2000). Such processes would impact student engagement
tive abilities has focused on executive function (EF), which both withlearninginthefaceofdistraction(e.g.,Blair,2002;Gathercole
predicts academic achievement (e.g., Best, Miller, & Naglieri, et al., 2008) and are taxed by some academic tasks, particularly
2011) and correlates with SES (see Lawson, Hook, Hackman, & math, which involve holding information in working memory,
Farah,inpress,forareview).EFhasbeenshowntopartiallymed- shifting attention, and inhibiting prepotent responses (Blair &
iatetheSES-achievementrelationinveryyoungstudents.Thecur- Razza,2007;Swanson&Beebe-Frankenberger,2004).
rentstudyexaminestheroleofEFinsocioeconomicdisparitiesin The prolonged development of EF (Best & Miller, 2010) may
readingandmathachievementamong6–15-year-oldchildren. makeitparticularlysusceptibletodifferencesinexperienceassoci-
Whilemuchattentionispaidtothedetrimentaleffectsofpov- ated with childhood SES (Hackman, Farah, & Meaney, 2010).
ertyonschoolachievement,evidencesuggeststhatschoolachieve- Indeed, SES differences have been identified in individual EF
mentvariesacrosstheentiresocioeconomicspectrum.Indeed,the task measures (Ardila, Rosselli, Matute, & Guajardo, 2005;
achievementgapbetweenchildrenwhosefamilieshaveincomesin Mezzacappa,2004;Sarsouretal.,2011),aswellaslatentexecutive
the 90th percentile and those with incomes in the 50th percentile function constructs (e.g., Blair et al., 2011; Rhoades, Greenberg,
(the 90/50 income gap) has widened over the past 60 years, such Lanza, & Blair, 2011; Wiebe et al., 2011), and EF is among the
thatitisnowlargerthanthegapbetweenthe50thpercentile and most pronounced cognitive correlates of SES (Farah et al., 2006;
the10thpercentile(the50/10gap;Reardon,2011). Noble, McCandliss, & Farah, 2007; Noble, Norman, & Farah,
ManyexplanationshavebeenproposedfortheseSESdisparities 2005).EFdoesnotsolelydifferbetweenpoorandnon-poor;SES
in academic performance, including parents’ investment in chil- gradients in EF performance have been observed across a wide
dren’scognitivedevelopment(e.g.,Bradley&Corwyn,2002),and rangeofSES(e.g.,Nobleetal.,2007;Sarsouretal.,2011).
children’s chronic stress exposure (e.g., G. W. Evans, 2004). Much of the evidence concerning EF and school achievement
Indeed,anumberoffactorsvaryalongSESgradientsandarelikely comes from studies of early childhood school readiness (e.g.,
to influence child development, including parenting practices, Alloway et al., 2005; Blair & Razza, 2007; Bull, Espy & Wiebe,
schoolquality,neighborhoodcharacteristics,andexposuretostres-
sors.Assuch,childhoodSEScanbeconceptualizedasaproxyfor
manyexperiencesthattendtodifferalongSESlines.
UniversityofPennsylvania,Philadelphia,PA,USA
Executive function would be expected to support school
achievement insofar as it encompasses cognitive processes that
Correspondingauthor:
enabletop-downcontrolofattentionandbehavior.Theseprocesses Gwendolyn M Lawson, University of Pennsylvania, 3720 Walnut Street,
havebeenclassifiedindifferentwaysintheliterature(seeJurado& Philadelphia,PA19104,USA.
Roselli,2007).Forthepresentpurposes,wewillfocuson‘cool’(as Email:[email protected]
Lawson and Farah 95
2008). However, emerging evidence suggests that EF may be Second, are mediation effects inflated by unmeasured ‘third
importantforschoolperformancethroughoutchildhoodandadoles- variables,’specificallyacademicachievementconcurrentwiththe
cence (Best, Miller, & Naglieri, 2011; St Clair-Thompson & assessment of EF? Several of the extant studies examining EF as
Gatherole,2006).Measuresofexecutivefunctionperformancedur- a mediator of socioeconomic disparities used a cross-sectional
ing childhood and adolescence correlate strongly with concurrent design,measuringEFsimultaneouslywithacademicachievement,
measuresofreadingandmathperformance,asmeasuredonstan- which cannot be used to determine causal relationships. Further-
dardized achievement tests (Best et al., 2011; St Clair-Thompson more, even when prospective longitudinal designs are employed,
&Gatherole,2006).Furthermore,longitudinalstudieshavefound Time1EFmaypredictTime2academicachievementexclusively
that executive function prospectively predicts academic achieve- because both variables are correlated with Time 1 academic
ment (e.g., Mazzocco & Kover, 2007), even after controlling achievement, thus inflating the estimate of the effect of EF per
for prior measures of academic achievement (Welsh, Nix, Blair, se.Extantstudieshavenotcontrolledforpriorlevelsofacademic
Bierman,&Nelson,2010). achievement,whichdoesnotallowthemtoruleoutthis‘thirdvari-
Because EF is associated both with socioeconomic status and able’(seeCole&Maxwell,2003).Itisthereforecriticaltoexamine
academic achievement, it is a likely candidate system to mediate theextenttowhichfindingsofEFasamediatorholdwhenexam-
the relationship between SES and academic achievement. For iningchangeinacademicachievement.
example,ifmoreadvantagedchildrenarebetterabletoshifttheir Third,doesEFmediationofSESdisparitiesinachievementper-
attentionandinhibitautomaticresponses,theymaybebetterable taintotherelativelycategoricaldifferencebetweenpoorandnon-
to learn new academic skills than their less advantaged counter- poorchildren,ordoesitoperateatalllevelsofSES,differentiating,
parts.However,withoutmeasuringSES,EFandacademicachieve- forexample,theachievementoflow-middlefromhigh-middleSES
mentinthesamechildren,onecannot concludethatEFmediates children?MostpriorstudiesincludedchildrenfromverylowSES
therelationshipbetweenSESandachievement.Severalrecentstud- backgrounds,contrastingthemwiththeirhigherSEScounterparts.
ieshavedoneso,mostlyinveryyoungchildren. ItisthereforeunknownwhetherEFmediatesSESachievementdis-
In the NICHD Study of Early Childcare, children’s sustained parities in general, or more specifically the achievement gap
attentionandimpulsivitywerefoundtopartiallymediatetheasso- betweenpoorandnon-poor.
ciation between home environment quality and achievement at The current study employs data from the National Institute of
54months(EarlyChildCareResearchNetwork,2003).Addition- Health (NIH) Magnetic Resonance Imaging (MRI) Study of Nor-
ally, in a sample of 54–66-month-old children, latent EF derived mal Brain Development, which includes demographic, cognitive,
frommeasuresofinhibitorycontrol,attentionalcontrol,andwork- and achievement measures for a sample of healthy children. The
ing memory partially mediated the association between SES and sampleforthecurrentstudyreflectsapredominantlymiddle-SES
math skill (Dilworth-Bart, 2012). Another study using a sample sample. Additionally, the current sample was subject to rigorous
ofpreschoolchildrenenrolledineitherneeds-basedorprivatepre- screening criteria (Waber et al., 2007) resulting in an unusually
schoolsfoundthatEFpartiallymediatedtherelationshipbetween healthyandhigh-performingsample.Thismakesthecurrentsam-
SES group and achievement (Fitzpatrick, McKinnon, Blair, & plesuitableforexaminingtheroleofEFinshapingSESgradients
Willoughby, 2014). A fourth study found that executive function inacademicachievementinhealthychildrenacrosstheentireSES
inkindergartenmediatedtherelationshipbetweenSES,acrossthe spectrum,ratherthanforchildrenlivingatornearthepovertyline
firstthreeyearsoflife,andmathandliteracyachievementinfirst orforchildrenwithphysicalormentalhealthproblems.
grade (Nesbitt, Baker-Ward, & Willoughby, 2013). Finally, in a We use structural equation modeling (SEM) of longitudinal
study of school-aged children from the NICHD Study of Early behavioraldatatotestthehypothesisthattaskmeasuresofexecu-
Childcare,EFasmeasuredbyperformanceontheTowerofHanoi tive function mediate the association between SES and change in
task in third grade, partially mediated the relationship between academicachievementovera2-yeartimeperiod.TheuseofSEM
early income-to-needs and academic achievement in fifth grade, allowed a latent measure of executive function derived from four
more strongly for math than for reading (Crook & Evans, 2014). cognitiveEFtaskstobeusedinourmodel.Latentmeasurescapture
Thesestudiesprovideinitialevidencethatexecutivefunctionmay sharedvariances betweenindicatorsofanunderlyingconstructto
mediatetherelationshipbetweenSESandacademicachievement, reduce measurement error (Kline, 2011), and latent measures of
butatleastthreeimportantquestionsremain. EFseemtobeapromisingapproachtoinvestigateindividualdif-
First, does EF mediate SES disparities in achievement among ferences in executive function (e.g., Willoughby, Blair, Wirth,
older school-aged children and secondary school students? Most Greenberg,&TheFamilyLifeProjectInvestigators,2010).There-
evidence so far is restricted to very young children. The study of fore, latent executive function was used as a mediator in models
EFanditsrelationtoschoolachievement isofimportanceacross where SES variables predicted change in two areas of academic
childhoodandadolescence(Best&Miller,2010).Furthermore,one achievement:passagecomprehensionandcalculationachievement.
cannot assume equivalent relations among EF, achievement and Finally,inordertoestablisharoleforEFperseinmediatingthe
SESatallages.Arecentstudysuggeststhat,whileEFandschool SES-achievement relation, as opposed to cognitive functioning
achievement are correlated for a sample of children between the moregenerally,weincludedasecondcandidatemediator,chosen
ages of 5 and 17 years, the magnitude of the relationship varies foritsrelationtoSESanditsapriorirelevancetoschoolachieve-
acrossage,withaparticularlystrongrelationshipbetweenEFand ment:verbalmemory.Verbalmemoryperformancehasbeenfound
achievementatage6andatage8–9years(Bestetal.,2011).Given tovarybySES(e.g.,Farahetal.,2006;Nobleetal.,2007),anditis
that the oldest age at which SES-EF-achievement relations have plausible that the recall of verbally delivered information is fre-
been examined is fifth grade, and this was one study using one quently required in the course of learning many different school
EF task, it is important to examine the role of EF as a mediator subjects.InordertoinvestigatetheextenttowhichEFisaspecific
between SES and achievement in primary and secondary school- cognitivemediator,verbalmemorywasincludedinthemodelasa
agedchildren. comparisoncandidatemediator.
96 International Journal of Behavioral Development 41(1)
Method converted to z scores for use in analysis. Of themothers, 3 (.9%)
had completed less than high school, 46 (13.7%) had completed
Study organization and design
highschool,102(30.4%)hadcompletedsomecollege,19(5.6%)
had completed some graduate school, and 58 (17.3%) had com-
DatausedforthisstudywereobtainedfromtheNIHPediatricMRI
pleted a graduate degree. Of the fathers, 8 (2.4%) had completed
Data Repository created bythe NIH MRI Study of Normal Brain
less than high school, 66 (19.6%) had completed high school,
Development(A.C.Evans,2006).Aspartofthisstudy,structural
90(26.8%)hadcompletedsomecollege,13(3.9%)hadcompleted
MRI,behavioralandclinicalmeasureswerecollectedatthreetime
some graduate school, and 59 (17.6%) had completed a graduate
points, approximately 2 years apart, for 431 healthy children and
degree.
adolescentsbetweentheagesof4.5and18.25years.Childrenwere
excludedfromparticipationinthestudybasedonrigorousdemo-
Executivefunction.EFismeasuredprimarilyusingbehavioraltasks
graphic,prenatalhistory,physical,behavioral,familyhistory,and
and, less frequently, self- and informant-report questionnaires
neurologicalcriteria(seeA.C.Evans,2006,forafulldescription
(Hughes,2011).Here,weusefourbehavioraltaskstoconstructthe
ofinclusionaryandexclusionarycriteria).Datacollectionoccurred
latent executive function variable. Three tasks—spatial memory
at six pediatric study centers in major urban areas (A. C. Evans,
span, spatial working memory, and intradimensional/extradimen-
2006).
sional(ID/ED)setshift—werefromtheCambridgeNeuropsycho-
logical Test Battery (CANTAB), a computerized, non-verbal
Participants neuropsychological test battery during which children perform
tasks using a computer touch-screen. The Wechsler Intelligence
Analysesused336childrenwithintheagerange(6to15years)eli- Scale for Children-III (WISC-III) digit span was also used as an
gibleforallexecutivefunctionandverbalmemorymeasuresatthe executivefunctiontaskmeasure.
firstwaveofdatacollection.Themeanageofthissampleatthefirst
time point was 10.13 years (SD ¼ 2.94 years), and 175 children CANTAB spatial memory span. ThistestismodeledontheCorsi
(52.1%) were females. Of the mothers, 27 (8.0%) were African- Block taping test (Milner, 1971), designed to test visual memory
American/Black,273(81.3%)wereWhite,five(1.5%)wereAsian, span. To perform this task, the child views an array of 10 white
seven(2.08%)werebi-ormulti-racial,and24(7.1%)didnotpro- boxesonthecomputerscreen.Theboxeschangecoloroneatatime
videinformationabouttheirrace. inasequence,andthechildisinstructedtorepeatthesequenceby
WJ-IIICalculationandPassageComprehensionmeasureswere tappingtheboxesinthesameordertheychangedcolor.Thespatial
administeredatthefirstandsecondtimepointsofdatacollection. spanisthelengthofthelongestsequencethechildcorrectlyrecalls.
Themeanlengthoftimebetweenthesetwotimepointsofdatacol-
lectionwas1.98years(SD¼.46years).Thelengthoftimebetween CANTAB spatial working memory. This is a computerized, self-
assessmentsdidnotsignificantlycorrelatewithage(r¼.08,p¼.19). ordered pointing task in which children must use information
Becausetherewassomevariabilityinthelengthoftimebetween in working memory to work toward a goal (Petrides & Milner,
timepoints,measurescollectedatTime2werecontrolledforthe 1982).Inthetask,childrenviewanarrayofcoloredboxesandpoint
interval(indays)betweentimepoints. totheboxesoneatatimetosearchforbluetokensthatarehidden
undersomeoftheboxes,withoutreturningtoaboxthathadbeen
previously searched. Between-trial return errors for children who
Measures
completed the task were used as the observed EF measure in the
SES. Two components of socioeconomic status were measured: presentstudy.
familyincomeandparentaleducation.Thefamilyincomeandpar-
entaleducationvariableswerestandardizedandaveragedtocreate CANTAB Intradimensional/Extradimensional shift. This task, an
anSEScompositemeasure. analogue of the Wisconsin Card Sort Task, measures set shifting
andreversallearningbasedonfeedback.Duringthetask,thechild
Family income. A self-report measure of family income was advances through nine stages, each of which requires him to use
obtained in 10 possible categories: 0–$5,000, $5,001–$10,000, feedbackfromthecomputerinordertorespondcorrectlytolines
$10,001–$15,000, $15,001–$25,000, $25,001–$35,000, $35,001– orshapes.Inordertosuccessfullycompletethetask,thechildmust
$50,000, $50,001–$75,000, $75,001–$100,000, $100,001–$150,000, shift his responses after feedback patterns from the computer
and(cid:2)$150,001.Familyincomewasestimatedasthemidpointofthe change.Healsomustusefeedbackfrompreviousstagestoshifthis
reportedincomerangeandwasadjustedforhouseholdsizebasedon response to new examples of lines or shapes. The child advances
adjustments used by the US Department of Housing and Urban fromonestagetothenextaftersixconsecutivecorrectresponses.
Developmenttodefinethehighestincomelevelatwhichafamily The task is terminated when the child completes all stages, or if
qualifiesforpublicassistance(seeA.C.Evans,2006).Themean 50 trials pass in which he has not made six consecutive correct
adjustedfamilyincomeofthesamplewas$75,809(SD$33,676). responses (Luciana & Nelson, 1998). The total number of errors
Adjustedfamilyincomewasconvertedtozscoresforuseinanalysis. throughout all stages attempted was used as the observed EF
measure.
Parental education. Eachparent’seducation levelwasassigned a
valuefrom1to6(LessthanHighSchool¼1,HighSchool¼2, Wechsler Intelligence Scale for Children-III (WISC-III) Digit Span
SomeCollege¼3,College¼4,SomeGraduateLevel¼5,Grad- (Wechsler, 1991). The WISC-III was administered to children
uate Level ¼ 6).Maternal educationlevel and paternal education betweentheagesof6:0and16:11years(A.C.Evans,2006).This
level were summed for each child in order to create a parental isadigitspantask,inwhichparticipantsrepeatrandomdigitstrings
education index with possible values from 2 to 12, which was ofincreasinglength.Themeasureyieldsarawdigitspanscore,as
Lawson and Farah 97
Table1.DescriptivestatisticsforExecutiveFunctionandVerbalMemorymeasuresfortheentiresample.
Range
Taskanddependentmeasure N M SD Potential Actual Skewness Kurtosis
CambridgeNeuropsychologicalTestBatterySpatialWorkingMemoryerrors 291 39.05 20.98 0– 1–97 0.02 (cid:3)0.99
CambridgeNeuropsychologicalTestBatterySetShifterrors 331 24.45 14.78 0– 0–73 0.37 (cid:3)0.52
CambridgeNeuropsychologicalTestBatterySpatialSpan 331 5.53 1.63 0–9 0–9 0.25 (cid:3)0.24
WechslerIntelligenceScaleforChildren-IIIDigitSpan 333 13.35 3.61 0–30 3–26 0.48 (cid:3)0.03
CaliforniaVerbalLearningTasklong-delaycuedrecall 328 10.74 2.97 0–15 0–15 (cid:3)0.84 0.74
CaliforniaVerbalLearningTasklong-delayfreerecall 328 10.17 3.13 0–15 0–15 (cid:3)0.84 0.57
CaliforniaVerbalLearningTaskshort-delaycuedrecall 328 10.54 2.84 0–15 0–15 (cid:3)0.77 0.67
CaliforniaVerbalLearningTaskshort-delayfreerecall 328 9.88 3.14 0–15 0–15 (cid:3)0.64 (cid:3)0.003
wellasforwardandbackwardsubscores.Therawdigitspanscore theSocialSciences(SPSS)andallSEManalyseswereimplemen-
wasusedastheobservedEFmeasure. tedinMplusVersion7.
Verbal memory. Four observed memory measures from the
Results
California Verbal Test of Learning for Children (CVLT-C;
Delis, Kramer, Kaplan, & Ober, 1994) were used to derive the Descriptive statistics
latent verbal memory measure. In this task, children are asked
to learn a list of 15 concrete nouns (List A) that is presented ThedistributionalpropertiesofallobservedEFandverbalmemory
orally five times with immediate recall after each learning trial. measuresaredisplayedinTable1.Therewasnoevidencethatany
After the presentation of an interference list (List B), children task measures violated normality assumptions. Additionally, no
are asked to recall List A (short-delay free-recall) and are then taskmeasureshadameanwithinonestandarddeviationofthemax-
given semantic cues to recall List A a second time (short-delay imumorminimumscoreforanytasks,acriterionthathasbeenused
cued-recall). After a 20-minute delay, children are again asked toidentifyceilingorflooreffects(Nobleetal.,2007).
to recall List A (long-delay free-recall), and are given semantic
cues to recall List A (short-delay cued-recall). Short-delay free
Adjusting for age
recall, short-delay cued-recall, long-delay free-recall and long-
delay cued-recall were used as observed memory measures. All Passage comprehension and calculation achievement standard
memory measures were used from the first timepoint of data scoreswerebasedonagenorms,butthedatabaseincludedonlyraw
collection. scoresforexecutivefunctionandverbalmemorymeasures,which
werenotbasedonagenorms.Inordertoobtainage-adjustedexec-
Academicachievement.TwosubtestsoftheWoodcock-JohnsonIII utivefunctionandverbalmemoryscores,rawscoresonthesemea-
(WJ-III) academic achievement test (Woodcock, McGrew, & sures were controlled for age before entering the model. A prior
Mather, 2001) were used as observed measures of academic study using this data set has reported non-linear relationships
achievement.StandardscoresfortheWJ-IIIPassageComprehen- between age and the cognitive task measures examined (Waber
siontestwereusedasanobservedreadingmeasure.Inthistest,the etal.,2007).Therefore,hierarchicalregressionwasusedtopredict
child reads a brief passage and then performs a fill-in-the-blank EFtaskscoresfromalinearageterm(enteredinthefirststep),an
comprehensiontask.StandardscoresfortheWJ-IIICalculationtest age2term(enteredinthesecondstep),andanage3term(enteredin
wereusedasanobservedcalculationmeasure.Inthistest,thechild thethirdstep).Age2andage3termswereretainedwhentheysignif-
isaskedtosolveaseriesofcalculationproblemsofincreasingdif- icantlyimprovedmodelfit.ObservedEFandverbalmemorymea-
ficulty.Academicachievementmeasureswereusedfromboththe sures were residualized on the best-fitting age terms. Following
first and second time points of data collection, which occurred Wiebe, Espy, and Charak (2008), performance scores were con-
approximately2yearsapart. vertedtozscorestominimizetheimpactofdifferentvariablescal-
ingonmeasurementinvariance.
Statistical approach
Adjusting for prior academic achievement
Structuralequationmodelingwasusedfortheseanalysesbecause
itallowedtheconstructionofalatentexecutivefunctionmeasure Inordertoinvestigate change inacademicachievementacrossthe
andthepredictionofmultipleoutcomesandmediatorssimultane- 2-yearperiodbetweentimepoints,academicachievementscoresat
ously. SEM analyses used full information maximum likelihood Time2werecontrolledforscoresatTime1andtheintervalbetween
(FIML) estimation, which allows the retention of subjects with time-points. That is, WJ-III Calculation scores at Time 2 were
missingdataandhasbeenfoundinsimulationstudiestooutper- regressed on WJ-III Calculation scores at Time 1 and the interval
form classical methods for missing data, such as available case (indays)betweenTime1andTime2,andthestandardizedresiduals
methods and imputation (Enders & Bandalos, 2001; Peters & wereused asthe change incalculationvariable. Similarly,Time 2
Enders;2002).Ofthe336childreninthecurrentsample,107chil- WJ-III Passage Comprehension scores were adjusted for Time 1
dren (31.85%) were missing data for one or more measures. All WJ-IIIPassageComprehensionscoresandtheintervalbetweentime
regression analyses were implemented in Statistical Package for pointstocalculatethechangeinpassagecomprehensionvariable.
98 International Journal of Behavioral Development 41(1)
Figure1.TheoreticalmodelfortheFullModel.Dottedlinesareusedtoindicatepathspredictedtobeweakornonexistent.Sex(notshown)isusedasa
controlvariable.
Analysis strategy executive function were allowed to mediate the relationship
between SES and achievement scores. In the Executive Function
Inthehypothesizedmodel,SESpredictedthechangeincalculation Only model, mediating pathways to achievement change through
and change in passage comprehension variables directly and verbal memory were constrained to zero. In the Verbal Memory
throughindirectpathsmediatedbyTime1executivefunctionand Onlymodel,mediatingpathwaysthroughexecutivefunctionwere
verbalmemoryconstructs.Sexwasusedasacontrolvariableinthis constrained to zero. The use of nested models allows model fit
model,withpathsspecifiedfromsextoallexogenousandendogen- between the full and reduced models to be compared using chi-
ouslatentvariables.Thismodelallowedustoexaminetheextentto squaredifferencetests.
which SES predicts change in academic achievement, and the
extenttowhichthisrelationshipismediatedbyexecutivefunction.
Furthermore,theinclusionofverbalmemoryasanalternativecan- Correlation matrix
didate mediator allowed us to contrast mediating paths through
The correlation matrix for all observed variables after controlling
executive function with mediating paths through verbal memory.
forageand sexisshown inTable 2.Familyincome andparental
Wepredictedthatpathsthroughexecutivefunctionwouldbesig-
education were significantly correlated with each other (r ¼ .57,
nificant,butpathsthroughverbalmemorywouldbeweakornon-
p < .01). All correlations between SES measures and observed
existent. The theoretical model is shown in Figure 1 with dotted
EFmeasureswereintheexpecteddirection,suchthathigherSES
linesusedtoindicatepathspredictedtobeweakornonexistent.
wasassociatedwithbetterperformanceonthetask.Zero-ordercor-
Confirmatory factor analysis (CFA) was first used to examine
relations between SES measures and EF task measures ranged in
modelfitfortheEFandverbalmemorymeasurementmodels.The
magnitudefromr¼.10tor¼.17.Changeinpassagecomprehen-
hypothesizedstructuralmodelwasthenspecified,andpathcoeffi-
sionandchangeincalculationscoresweresignificantlycorrelated
cients and significance values were obtained. Bootstrapping with
witheachother(r¼.16,p<.05).SESmeasurescorrelatedsignif-
1000 samples was used to generate 90% and 95% bias-corrected
icantly with change in passage comprehension (family income:
confidence interval of specific indirect effects of SES on change
r¼.21,p<.01;parentaleducation:r¼.21,p<.01)andchange
in achievement mediated by EF and verbal memory. Bias-
incalculation(familyincome:r¼.22,p<.01;parentaleducation:
correctedbootstrapconfidenceintervalshavebeenfoundinsimu-
.15,p<.05)scores.
lation studies to provide the most accurate test of mediation in
structural equation modeling (Cheung & Lau, 2008). Confidence
intervalsforthecontrastbetweenspecificindirecteffectsthrough Measurement models
executive function and specific indirect effects through verbal
memorywerealsogenerated. We first used principal axis factoring with promax rotation to
Additionally,nestedmodelswereusedtocomparetherolesof explorethestructureofthemediatorvariables.Forthisanalysis,the
EFandverbalmemoryasmediatorsofallachievementoutcomes spatialworkingmemoryandID/EDsetshiftmeasureswerereverse
simultaneously. In the full model, both verbal memory and scored, so that higher scores indicated better performance on all
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d n
Memory,an 6 1 .117*[.01,.22].004[.11,.12](cid:3).105(cid:3)[.21,.01](cid:3).067[.05,.17](cid:3).071[.05,.19](cid:3).081[.04,.20](cid:3).120*[.01,.23] angescoresa
onsand95%confidenceintervalsforSES,ExecutiveFunction,Verbal 12345 1.570**1[.49,.66].136*.0921[.03,.24][.02,.20](cid:3).098.077.767**1[.01,20][.03,.18][.72,.81](cid:3)(cid:3).130*.093.862**.712**1[.02,.23][.02,.20][.83,.89][.66,.76](cid:3).129*.109*.673**.704**.690**[.02,.23][.01,.21][.61,.73][.65,.75][.63,.74].103.128*.155**.136*.168**[.01,.21][.02,.23][.05,.26][.03,.24][.06,.27](cid:3).140*.118*.057.053.040(cid:3)(cid:3)(cid:3)(cid:3)(cid:3)[.25,03][.23,.01][.17,.06][.17,.06][.15,.08](cid:3)(cid:3)(cid:3)(cid:3)(cid:3)(cid:3)(cid:3).118*.158**.140*.066.154**(cid:3)(cid:3)(cid:3)(cid:3)(cid:3)[.22,.01][.26,.05][.24,03][.17,.04][.26,.05](cid:3)(cid:3)(cid:3)(cid:3)(cid:3)(cid:3)(cid:3)(cid:3).174*.143**.051.033.049[.07,.28][.04,.25][.06,.16][.08,14][.06,.16](cid:3)(cid:3)(cid:3).206**.210**.055.132*.066[.09,.32][.09,.32][.07,.18][.01,.25][.06,.19](cid:3)(cid:3).217**.151*.073.099.065[.10,.33][.03,.27][.05,.19][.02,.22][.06,.18](cid:3)(cid:3)(cid:3).126*.090.127*.127*.136*[.02,.23][.02,.20][.02,.23][.02,.23][.03,.24](cid:3) Fmeasureshavebeencontrolledforage.WJ-IIIPassageComprehensionChments.
Table2.Correlationmatrixpresentingcorrelati Variables 1.Familyincome,Time12.Parentaleducation,Time1 3.CaliforniaVerbalLearningTasklong-delaycuedrecall,Time14.CaliforniaVerbalLearningTasklong-delayfreerecall,Time15.CaliforniaVerbalLearningTaskshort-delaycuedrecall,Time16.CaliforniaVerbalLearningTaskshort-delayfreerecall,Time17.WechslerIntelligenceScaleforChildren-IIIDigitSpan,Time18.CambridgeNeuropsychologicalTestBatterySpatialWorkingMemoryerrors,Time19.CambridgeNeuropsychologicalTestBatterySetShifterrors,Time110.CambridgeNeuropsychologicalTestBatterySpatialSpan,Time111.Woodcock-JohnsonIIIPassageComprehensionChange12.Woodcock-JohnsonIIICalculationChange 13.Sex Note.Nsrangefrom260to336.VerbalmemoryandEinterval(indays)betweenTime1andTime2assess*p<.05;**p<.01.
99
100 International Journal of Behavioral Development 41(1)
Figure2.Pathoutputwithstandardizedestimatesand95%confidenceintervalsfortheFullModel.Sex(notshown)isusedasacontrolvariable.
N¼336;þp<.1;*p<.05;**p<.01;***p<.001.
measures.Thefirsttwofactorshadinitialeigenvaluesabove1.The SEStoEFwassignificant(b¼.39,p<.001),andthepathfrom
firstfactorexplained40.77%ofthevariance,andthesecondfactor SEStoverbalmemorywassignificant(b¼.12,p¼.03).Thepath
explained 17.91% of the variance. Long delay cued recall, short from executive function to change in calculation was significant
delaycuedrecall,longdelayfreerecall,andshortdelayfreerecall (b¼.24,p¼.046),butthepathfromexecutivefunctiontochange
loadedonthefirstcomponent(allloadings>.7)anddidnotloadon inpassagecomprehensionwasnotsignificant(b¼.11,p¼.38).
thesecondcomponent(allloadings<.1).Spatialworkingmemory, Paths from verbal memory to change in calculation (b ¼ .006,
spatial span, and digit span loaded on the second component (all p ¼ .93) and to change in passage comprehension (b ¼ .04 p ¼
loadings>.35)anddidnotloadonthefirstcomponent(allloadings .61)werenotsignificant.Additionally,thereweresignificantdirect
<.1).ID/EDsetshifthadaloadingof.21onthesecondcomponent pathsfrom SES to changein calculation (b¼ .15,p ¼ .048) and
and.09onthefirstcomponent.Theseresultsareconsistentwithour fromSEStochangeinpassagecomprehension(b¼.17,p¼.02).
conceptualmodelofverbalmemoryandexecutivefunctionassep- Thepathfromsextoverbalmemorywassignificant(b¼.13,
aratefactors.WeretainedID/EDsetshiftasanEFmeasurebecause p¼.02),indicatingthatverbalmemoryperformancewassignifi-
ofitsclearconceptuallinktoEF. cantlyhigherforfemalesthanformales.However,thepathsfrom
Single-factor measurement models for the executive function sextoEF,changeincalculation,andchangeinpassagecomprehen-
and verbal memory constructs were specified, and confirmatory sionwerenotsignificant.
factor analysis (CFA) was used to evaluate model fit. The CFA
modelinwhichexecutivefunctionandverbalmemorywereallowed Mediationanalysis.InanSEMframework,asignificanttotaleffect
to correlate showed acceptable fit to the data, (cid:2)2(19) ¼ 49.00, and a significant indirect effect are needed toestablish mediation
p < .001; comparative fit index (CFI) ¼ .97; root-mean-square (Preacher & Hayes, 2004). The total effects of SES on change in
error of approximation (RMSEA) ¼ .069, 90% CI ¼ (.045, calculation(standardizedtotaleffect¼.243;p<.001)andchange
.093), pclose ¼ .09; standardized root-mean-square residual in passage comprehension (standardized total effect ¼ .217;
(SRMR) ¼ .040. All factor loadings for verbal memory, and p<.001)werebothsignificant.Weusedbootstrappingtocalculate
executive function weresignificantatp< .01.Standardizedfac- confidenceintervalsforspecificindirecteffectsthroughexecutive
tor loadings on EF were .38 (p < .001) for spatial span, (cid:3).24 functionandverbalmemory.Table3displays90%and95%bias-
(p<.01)forID/EDsetshift,(cid:3).51(p<.001)forspatialworking corrected confidence intervals for these specific indirect effects.
memory, and .42 (p < .001) for WISC digit span. The 95% confidence interval for the specific indirect effect from
SESthroughEFtochangeincalculationdidnotpassthroughzero,
Structural model indicating a significant specific indirect effect with a two-sided
alphaof.05.Theotherthreespecificindirecteffectswerenotsig-
Thefullmodelshowedevidenceofagoodfittothedata,(cid:2)2(43)¼ nificant.Additionally,wecontrastedthepathwaysthroughEFand
65.50,p¼.015;CFI¼.98;RMSEA¼.04,90%CI¼(.018,.058), verbalmemorytotheachievementchangevariables,andthe90and
pclose¼.81;SRMR¼.03.Standardized coefficientsforthefull 95%bias-correctedconfidenceintervalsforthesecontrastsaredis-
model are displayed in Figure 2. In thefull model, the path from playedinTable3.Forchangeincalculation,butnotforchangein
Lawson and Farah 101
Table3.Bias-correctedunstandardized90%and95%confidenceintervalsandforspecificindirecteffectsofSESonachievementscores(N¼336).
Mediatedpathsandcontrasts Lower2.5% Lower5% Estimate Upper5% Upper2.5%
Mediatedpaths
1)SES–ExecutiveFunction–CalculationChange .011 .020 .107 .269 .329
2)SES–VerbalMemory–CalculationChange (cid:3).020 (cid:3).013 .001 .020 .026
3)SES–ExecutiveFunction–PassageComprehensionChange (cid:3).053 (cid:3).031 .046 .207 .270
4)SES–VerbalMemory–PassageComprehensionChange (cid:3).015 (cid:3).011 .005 .026 .030
Contrasts
Contrastbetween1and2 .005 .014 .106 .280 .339
Contrastbetween3and4 (cid:3).071 (cid:3).048 .041 .197 .265
Table4.FitindicesfortheFullModelandnestedExecutiveFunctionOnlyandVerbalMemoryOnlymodels(N¼336).
ComparativeFit Root-Mean-SquareErrorof StandardizedRoot-MeanSquare AkaikeInformation
Model (cid:2)2 df p Index(CFI) Approximation(RMSEA) Residual(SRMR) Criterion(AIC)
FullModel 65.50 43 .02 .98 .04,90%CI(.02,.06),pclose¼.81 .03 9210.91
Executive 70.20 46 .01 .98 .04,90%CI(02,.06),pclose¼.82 .04 9209.61
Function
Only
VerbalMemory 89.36 46 <.001 .96 .05,90%CI(.04,.07),pclose¼.36 .05 9228.77
Only
Note.IntheExecutiveFunctionOnlymodel,allindirectpathstoAchievementChangethroughVerbalMemorywereconstrainedtozero.IntheVerbalMemoryOnly
model,allindirectpathstoAchievementChangethroughexecutivefunctionwereconstrainedtozero.
passagecomprehension,thecontrastwassignificant,indicatingthat Thecurrentstudyusedprospectiveacademicachievementmea-
EF was a significantly stronger mediator of this relationship than suresandcontrolled forbaseline achievement,demonstrating that
wasverbalmemory. executive function performance predicts change in math achieve-
ment over a 2-year time period. Because we examined change in
Model comparison. Model fit statistics for the Full Model, achievement,ratherthanachievementitself,priorachievementwas
Executive Function Only model, and Verbal Memory Only not serving as ‘third variable’ inflating estimates of the paths of
model are displayed in Table 4. Deleting the paths passing interest.
through verbal memory (Executive Function Only) resulted in Theseresultsalsoprovideinitialevidencethatexecutivefunc-
a non-significant change in model fit, (cid:2) (cid:2)2(3) ¼ 4.70, p ¼ .20. tion is unique as a mediator, as no support was found for verbal
Incontrast,deletingthepaths through executivefunction (Ver- memoryasanalternativecandidatemediator.Notably,asignificant
bal Memory Only) resulted in significantly worse model fit, relationship between SES and verbal memory was observed, but
(cid:2) (cid:2)2(3) ¼ 23.86, p < .001. verbal memory, unlike EF, did not predict change in reading or
math achievement. Furthermore, while many studies of EF use
tasksthatrelyheavilyonlanguageabilities(e.g.,Hughes&Ensor,
Discussion
2007;Wiebeetal.,2011),thecurrentstudyusedprimarilynonver-
Thesocioeconomicgradientinacademicachievementisasocietal balEFtasks,thusprovidingameasureofexecutivefunctionthatis
problemthathasyettobefullyunderstood.Onerecentapproachto lessconfoundedwithverbalability.However,futurestudiesshould
the problem has been to identify cognitive mediators of the SES- alsoinvestigateothercandidatecognitivesystems,includingverbal
achievementrelation.Executivefunction,whichisrelatedtoboth ability,asmediators.Nevertheless,thisstudyextendspastworkon
SES and achievement, has been identified as a mediator in executivefunctionbycomparingEFwithverbalmemory,another
preschool,kindergartenandfirstgradeusingmultipleorlatentmea- candidate mediator, and results are consistent with the claim that
suresofEF(Dilworth-Bart,2012;Fitzpatrick,McKinnon,Blair,& executivefunctionisaparticularlyimportantfactorexplainingthe
Willoughby,2014;Nesbitt,Baker-Ward,&Willoughby,2013).We relationshipbetweenSESandacademicachievement.
investigatedneurocogntivemediationoftheSES-achievementlater TheseresultsaddtoalargebodyofevidencedemonstratingSES
inschooling,whenchildren’sEFismoredevelopedandthenature disparities in executive function performance. While much of the
ofacademicachievementisalsodifferent. literatureonSESandexecutivefunctiondevelopmenthasfocused
Ina sampleof 6-through 15-year-oldchildren, SES predicted primarily on the detrimental effects of poverty (e.g., Blair et al.,
change in academic achievement over a 2-year time-period. We 2011;Rhoadesetal.,2011),thecurrentstudyobservesSESdiffer-
modeled SES variables prediction of change in reading and math ences in a predominantly middle-class sample. Furthermore,
achievement directly and through two candidate mediators, latent observed SES differences in executive function performance are
EF and verbal memory measures. Our results support executive particularlystrikinginlightoftherigorousscreeningcriteriaused
function,butnotverbalmemory,asapartialmediatoroftherela- to obtain this sample of children. During population-based sam-
tionshipbetweenSESandchangeinmathachievement.Thesame pling forthe study, theearly screening interview, which included
wasnottrueofreadingachievement. questionsaboutchildandfamilyhistoryofmedicalandpsychiatric
102 International Journal of Behavioral Development 41(1)
disorders,andchildprenatalsubstanceexposure,ledtotheexclu- Theprospectivedesignofthecurrentstudy,inwhichacademic
sion of 37.9% of low-SES children, as compared to 21.8% of achievement was modeled from two time points, is an important
high-SES children. Additionally, 19.4% of low-SES children, as strength,butcausalinferenceisneverthelesslimitedinthisstudy.
compared to 8.7% of high-SES children were excluded due a Interventionstudiesusingpreschoolpopulationshaveprovidedevi-
Tscoreabove70onanysubscaleoftheChildBehaviorCheckList, dencethatinterventionscanimproveexecutivefunction(Diamond
indicating elevated behavior problems (Waber et al., 2007). The &Lee,2011;Raveretal.,2011)andthatimprovementsinexecu-
fact that SES disparities in executive function exist even in this tive function partially mediate gains in school readiness (Raver
healthy population, where they cannot be explained by elevated etal.,2011),suggestingthattherelationshipbetweenEFandaca-
ratesofphysicalormentalillnessorprenatalsubstanceexposure, demicachievementamongpreschoolersis,atleastinpart,causal.
suggests that these mechanisms do not fully explain SES differ- Intervention studies using school-aged populations will provide
encesinexecutivefunction. importantevidenceaboutcausalrelationshipsinthesepopulations.
Our finding that executive function statistically mediates SES
disparitiesinschoolachievementsuggeststhatinterventionstarget-
ingexecutivefunctioncouldhelpclosetheSES-achievementgap. Funding
Thisisapromisinghypothesisinourview,butonethatshouldbe The author(s) disclosed receipt of the following financial support
tested furtherwith theappropriate designs ratherthanassumed at fortheresearch,authorship,and/orpublicationofthisarticle:This
present. Indeed, preschool interventions targeting executive func- researchwassupportedbyNIHgrantHD055689.Datausedinthe
tion as part of a larger set of targets have been associated with preparation of this article were obtained from the NIH Pediatric
improved academic readiness among disadvantaged preschoolers MRI Data Repository created by the NIH MRI Study of Normal
(e.g., Head Start REDI; Bierman et al., 2008; Chicago School Brain Development release 4.0. This is a multisite, longitudinal
Readiness Project; Raver et al., 2011), although studies with the studyoftypicallydevelopingchildrenfromagesnewbornthrough
appropriate design to test a causal relationship between EF and youngadulthoodconductedbytheBrainDevelopmentCooperative
achievementarestillneeded(seeJacob&Parkinson,inpress,for GroupandsupportedbytheNationalInstituteofChildHealthand
areview).Thecurrentresultssuggestthatsimilarapproachesmay Human Development, the National Institute on Drug Abuse, the
alsobepromisingforimprovingachievementamongolderyouths. National Institute of Mental Health, and the National Institute of
Furthermore, computerized training interventions have attracted Neurological Disordersand Stroke(Contract #sN01-HD02-3343,
recent attention as potential approach to improve EF, particularly N01MH9-0002, and N01-NS-9-2314, -2315, -2316, -2317, -2319
amongat-riskpopulations,althoughtheevidenceabouttheeffec- and -2321). A listing of the participating centers and a complete
tiveness of these interventions is mixed (Rabipour & Raz, 2012). listing ofthestudyinvestigatorscanbefoundat:https://nihpd.crbs.
Experimental studies that include measurements of academic ucsd.edu/nihpd/info/participating_centers.html. This manuscript
achievement following EF training are needed to further evalute reflectstheviewsoftheauthorsandmaynotreflecttheopinions
thisapproach,aswellastogatherstrongerevidenceaboutthecau- orviewsoftheNIH.
salroleofEF.Finally,itisimportanttonotethatthecurrentsample Theresearchreportedherewassupported[inwholeorinpart]
includedchildrenfromawiderangeofsocioeconomicstatus,rather bytheInstituteofEducationSciences,U.S.DepartmentofEduca-
thanfromprimarily disadvantaged backgrounds. ThefactthatEF tion,throughGrant#R305B090015totheUniversityofPennsylvania.
emerged as a mediator in this sample suggests that targeting EF Theopinionsexpressedarethoseoftheauthorsanddonotrepresent
maybeaneffectiveapproachtoreducingSESdisparitiesacrossthe theviewsoftheInstituteortheU.S.DepartmentofEducation.
entiregradientofSES.
Therewereseverallimitationstothisstudy,andresultsmustbe
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