Language and disadvantage: a comparison of the language abilities of adolescents from two different socioeconomic areas

INT J LANG COMMUN DISORD, MAY–JUNE 2012,VOL. 47, NO. 3, 274–284

Research Report

Language and disadvantage: a comparison of the language abilities ofadolescents from two different socioeconomic areas

Sarah Spencer, Judy Clegg and Joy StackhouseDepartment of Human Communication Sciences, University of Sheffield, Sheffield, UK

(Received May 2009; accepted October 2011)

Background: It is recognized that children from areas associated with socioeconomic disadvantage are at an increasedrisk of delayed language development. However, so far research has focused mainly on young children and therehas been little investigation into language development in adolescence.Aims: To investigate the language abilities of adolescents from two different socioeconomic areas. The paper aimsto determine if a higher proportion of adolescents from an area of socioeconomic disadvantage have low languagescores when compared with adolescents from a relatively advantaged area.Methods & Procedures: Six standardized language assessments were used to measure expressive and receptive languageskills across vocabulary, syntax and narrative in two cohorts of 13 and 14 year olds: one cohort attending a schoolin an area of socioeconomic disadvantage (103 participants) and the other cohort attending a school in an area ofrelative socioeconomic advantage (48 participants).Outcomes & Results: The cohort from the area of disadvantage performed significantly lower than the assessments’normative mean on all measures of language ability. There were significant differences between the two cohortson four of the six language measures. More adolescents from the school in the area of socioeconomic disadvantagehad standardized assessment scores that suggest hitherto undetected language difficulties.Conclusions & Implications: Results suggest that socioeconomic background is associated with language ability inadolescence as measured by standardized tests. In particular, adolescents from an area of socioeconomic disadvantagewere at risk of low vocabulary scores. The advantages and disadvantages of using standardized language assessmentsare discussed and the implications for clinical and educational practice and for school level policies are highlighted.

Keywords: adolescence, social disadvantage, language assessment, identification.

What this paper addsSocioeconomic disadvantage has been linked to language delays in early childhood. Much less research has examinedthe associations between socioeconomic background and language in adolescence. The current paper addresses thisresearch gap and contributes further evidence that socioeconomic disadvantage is associated with lower scores onstandardized language assessments. Two cohorts of adolescents were compared: one from an area of socioeconomicdisadvantage; the other from an area of relative advantage. The biggest differences between the two cohorts wereon measures of vocabulary. Many participants had a profile of assessments consistent with undetected languagedifficulties. This paper adds to the debate on the functionality of standardized language assessments, particularlywhen used with adolescents from areas of socioeconomic disadvantage. This study has implications for: (1) the needfor whole-school support for language and vocabulary, given the large proportion of participants with low languagescores, particularly in areas of social disadvantage; (2) justifying increased language assessment and provision insecondary schools for individuals, as evidenced by the high numbers of participants who meet criteria for undetectedlanguage difficulties; and (3) working towards greater consistency for criteria used to identify adolescent languagedifficulties, to ensure equality in access to services across different settings.

Address correspondence to: Sarah Spencer, Department of Human Communication Sciences, University of Sheffield, Sheffield, UK; e-mail:[email protected]

International Journal of Language & Communication DisordersISSN 1368-2822 print/ISSN 1460-6984 online c© 2011 Royal College of Speech & Language Therapists

DOI: 10.1111/j.1460-6984.2011.00104.x

Language and disadvantage 275

Introduction

Inequality is a marked feature of the socioeconomicstructure of the UK. Four million children are livingin relative poverty, and this figure is rising (Brewer et al.2009). Inequality is a major concern for educationalattainment and is a current focus of government policy.

The links between socioeconomic disadvantage andearly language development are well documented withreports of up to 50% of young children from areasof socioeconomic disadvantage having language delay(Ginsborg 2006). Locke and Ginsborg (2003) assessed240 children aged from 3;0 to 4;8 on a standardizedlanguage test and found that 48% of participants scored> −1 standard deviation (SD); and 26% scored >−2 SD. Vocabulary in particular has been a focus forresearch, with findings suggesting that young childrenin areas of social disadvantage learn words at a differentpace to their more advantaged peers (Hart and Risley1995, Whitehurst and Fischel 2000).

Most of the research into language developmentand socioeconomic background has focused on youngchildren. However, Myers and Botting (2008) examinedthe literacy skills of 36 adolescents from an inner-cityschool in London, with high levels of entitlement to freeschool meals. Twenty-one participants scored > −1 SDbelow the mean on a reading measure, suggesting signifi-cant reading difficulties. Standard language measureswere also completed and group means were significantlybelow normative means. This study had a relatively smallnumber of participants due to the challenges of recruit-ment to the study. The study did not specifically examinethe incidence of language difficulties in the cohort butlooked at group trends.

A second study of adolescents from an areaof socioeconomic disadvantage who were at riskof permanent exclusion found that ten of the 15participants had undetected language difficulties, withfive scoring > −2 SD on two or more languagemeasures (Clegg et al. 2009). This study is in linewith other research that has suggested an associa-tion between adolescent behavioural difficulties andundetected language difficulties (e.g. Bryan et al.2007). Associations between adolescent language andbehaviour difficulties have often been examined in acontext of socioeconomic disadvantage. Research intoadolescent language and socioeconomic disadvantage inthe absence of behavioural difficulties may be useful incontextualizing such associations between language andbehaviour.

Assessing language skills in adolescence

A major review of services for children with speech,language and communication difficulties reported that

adolescents are very rarely assessed for previouslyundetected and unsupported language difficulties(Bercow 2008). Assessment of language skills inadolescence is difficult due to the lack of sensitive, well-standardized language instruments for this age group(Wiig 1995). There is also variation in criteria usedto detect language difficulties in adolescence. Whilesome longitudinal studies (Conti-Ramsden et al. 2001)and studies in contexts of social disadvantage (Myersand Botting 2008, Whitehurst and Fischel 2000) applyrelatively liberal criteria of > −1 SD from the tests’normative mean, other studies have more stringentcriteria of > −1.5 SD and the number of tests thisapplies to also varies (Stringer and Lozano 2007). Thispaper will discuss the issue of how to identify languagedifficulties in adolescence, examining the impact ofapplying different criteria.

The current study

There is currently a need for further research intothe language skills of adolescents attending mainstreamschool in areas of socioeconomic disadvantage. Thereis also a paucity of research into the incidence ofundetected language difficulties in adolescents fromdiffering socioeconomic backgrounds.

This study aims: (1) to compare the languageperformance of two groups of adolescents from differentsocioeconomic backgrounds; (2) to investigate the needfor universal targeting of language in a school inan area of social disadvantage; and (3) to examinethe number of undetected language difficulties inadolescence. Research with adolescents from areas ofsocial disadvantage have reported low proportions ofrecruitment to take part in research (Clegg et al.2009, Myers and Botting 2008), suggesting that thispopulation may be hard to reach. Therefore, this studyalso aimed to determine whether recruitment to partici-pate is representative, examining whether the sample isbiased.

The current study examines the language profilesof two cohorts of adolescents attending mainstreamschools: one from an area of socioeconomic disadvantageand one from an area of relative advantage. It addressesthree research questions:

• Is there a difference on language measuresbetween adolescents from different socioeconomicbackgrounds?

• Are there a higher proportion of individualadolescents with undetected language difficultiesin the cohort from the area of disadvantage thanin the cohort from an area of relative advantage?

• In addition, are participants in this study represen-tative of their peer group or is the sample biased?

276 Sarah Spencer et al.

Method

Design

Participants were recruited from two schools in a cityin the north of England. The head teacher of bothschools gave consent for the study to take place. Thestudy gained ethical approval through the Universityof Sheffield’s Department of Human CommunicationSciences ethics procedure.

The first school is situated in an area of socioeco-nomic disadvantage (SDis). The latest Indices ofDeprivation 2007 (Noble et al. 2008) makes use ofscores in seven domains (income, employment, health,education, crime, housing and services, environment)to rank the 32 482 super-ordinate areas of England,with 32 482 being the least deprived (table 1). Usingthese indices, the school’s catchment area is ranked inthe bottom 2% of England’s wards. Fewer than 20% ofstudents in this school leave with five A∗–C grades atGCSE including maths and English. Fewer than 40%of students leave with five A∗–C grades in any GCSEsubject.

The second school is situated in an area of relativesocioeconomic advantage (SAdv). Using the Indices ofDeprivation 2007 (Noble et al. 2008) the school issituated in an area ranked around the 50th percentileof England’s wards. This school was recruited to thestudy as it was more representative of the nationalaverage (although referred to as in an area of socioeco-nomic advantage, this advantage is in relation to thefirst school. It is not particularly advantaged as such,but is in line with national average). Approximately60% of students in this school leave with five A∗–Cgrades at GCSE including maths and English, threetimes more students than the school in the area of SDis.Around 70% leave with five A∗–C grades in any subject.The rate of absenteeism in this school was half that ofSDis.

Participants

In SDis, all students from Year 9 (aged 13–14) wereinvited to take part in the study, excluding those whowere born outside the UK or who did not considerEnglish as their strongest language. The total year group

(with the exclusions made) comprised 211 and 107returned positive consent forms from their parents orcarers. Three participants from these 107 were excludedbecause they had statements of special educationalneeds for learning difficulties. One further participantwas excluded because he did not consider English hisstrongest language. The remaining 103 students signeda consent form themselves and agreed to participate inthe study. Power calculation using Power and PrecisionVersion Two software (Biostat 2000) showed that acomparison cohort of 46 would allow sufficient powerto test a hypothesis that the two cohorts differed by 0.58SD. Therefore, a comparison between a cohort of 103and a cohort of 46 participants allowed moderate tostrong differences to be detected.

In order to recruit the participants from the SAdvschool, three classes (111 students) were invited toattend, rather than the whole of Year 9. These threeclasses were randomly selected, were not groupedaccording to ability and were not thought to differfrom the wider year group. Of these 111, 49 returneda positive consent form from their parent or carer. Ofthese, 48 students also signed a consent form themselves,only one decided not to participate. None of theseparticipants were born outside the UK and all consideredEnglish their strongest language.

Twenty-nine participants from SDis and sevenparticipants from SAdv spoke more than one languageand were members of ethnic minorities born in theUK. These participants reported that English was theirstrongest language. Therefore, they were not consideredto have English as an additional language, but weremultilingual. In each cohort, one male participant wasknown to speech and language therapy (SLT) services forspeech difficulties but neither had received any therapyor support in the previous year. No other participantswere currently known to SLT services or had previousSLT noted in their school record. Table 1 shows thecharacteristics of participants from both schools.

Materials

The number and length of assessments used in this studywere restricted, as the head teacher of the school in thearea of disadvantage had requested that participants were

Table 1. Summary of participant characteristics

Mean indices ofmultiple deprivation In receipt of free Gender (%) Language status (%)

score based school mealsCohort Age (months) (SD) on postcode (SD) (%) Female Male Bilingual Monolingual

Socioeconomic disadvantage 166.45 (4.42) 2466.21 (2995.62) 38.8 50.5 49.5 29.1 70.9Socioeconomic advantage 167.40 (3.48) 15686.63 (9645.26) 12.5 60.4 39.6 14.6 85.5


not absent from class for more than 1 h. The languageassessments were selected to investigate: receptive skillsat word, sentence and narrative level, and expressiveskills at sentence and narrative level. Non-verbal abilitywas also assessed, to allow comparison of verbal andnon-verbal skills.

Language measures

• The Test for Reception of Grammar, Version Two(TROG) (Bishop 2003) is a test of comprehensionof English grammar at sentence level and includesinflections, function words and word order. Thestimulus sentence is read by the tester and theparticipant is required to choose a picture whichcorresponds to the sentence from a choice of four.Standard scores are calculated with a mean of 100and SD of 15. It is recommended for use withchildren aged from 4;0 to 16;0, as well as adults.

• The Long Form of the British Picture VocabularyScale, 2nd edn (BPVS) (Dunn et al. 1997) assessesreceptive vocabulary at single-word level. Theparticipant is shown four pictures and requiredto select one that matches a word spoken by thetester. Standard scores are calculated with a meanof 100 and a SD of 15. It is recommended for usewith children aged from 3;0 to 16;0.

• The Expression, Reception, Recall of NarrativeInstrument (ERRNI) (Bishop 2004) assessesexpressive language and narrative skills. A seriesof 15 pictures are presented in sequence to elicit anarrative involving false belief. The ERRNI initialnarrative was used to give a measure of narrativeskills only. The story was recorded on minidisk andlater transcribed. The transcription is divided intoutterances and two scores were calculated: meanlength utterance score is an index of complex-ity of grammatical structure; and an informationindex indicates how much relevant story contentis mentioned. Standard scores and percentiles forboth measures can be calculated. Standard scoresare calculated with a mean of 100 and a SDof 15. It is recommended for use with childrenaged from 6;0 to 16;0 and can be used withadults.

• Clinical Evaluation of Language Fundamentals—3rd Edition Listening to Paragraphs subtest(CELF-3 Listening to Paragraphs; Semel et al.1995) was administered to assess receptive skills.The participant is read short paragraphs bythe examiner and five corresponding questionsare asked to test understanding of the mainidea, detail, sequence, inference and ability topredict. A practice paragraph is read (which is notincluded in calculation of the score), and two test

paragraphs with questions follow. Standard scoresare calculated with a mean of 10 and a SD of 3. Itis recommended for use with children aged from6;0 to 16;0.

• Wechsler Abbreviated Scale of IntelligenceVocabulary Subtest (WASI Vocab; Wechsler1999) was measured to give a measure ofexpressive vocabulary, verbal knowledge andverbal reasoning. The participant is presentedwith a word (written and orally) and askedto define it. Responses are given 0–2 pointsdepending on the thoroughness and saliency oftheir definition: example responses and guidelinesare given for each word in the manual. Standardscores are calculated with a mean of 50 and a SDof 10. This assessment was used as it is thought tobe a valid measure of expressive vocabulary anddefinitional skill. It is recommended for use withpeople aged from 6;0 to adulthood.

Non-verbal measure

• The Wechsler Abbreviated Scale of IntelligenceBlock Design Subtest (WASI Block Design;Wechsler 1999) is a measure of non-verbalability which includes spatial visualization, visual-motor coordination, abstract conceptualizationand perceptual organization. Participants arepresented with 13 geographic patterns and areasked to replicate the patterns using their own setof two-colour cubes. The duration of participants’attempts is timed and their score is dependent onsuccessful replication of the target within one offour time bands. Standardized scores are calculatedwith a mean of 50 and a SD of 10.

Verbal reasoning (administered by the school)

• Scores from the Cognitive Abilities Test: 3 Verbalsubtest (CAT V) (NFER Nelson 2001) were alsoavailable via the schools. CAT V is used nationallyto assess pupils between the ages of 7 and 15years. Both schools in this study administeredthe CAT V upon entry into secondary school at11 years. It is used in secondary schools in additionto non-verbal and quantitative subtests as abaseline from which to predict pupil performancein national examinations including GCSE. It isalso widely used to inform target setting andlearning plans. The CAT V includes tests ofvocabulary, sentence completion, verbal classifica-tion, and verbal analogies, presented in a writtenform. Standardized scores are calculated with amean of 100 and SD of 15.


• CAT V was included in this study as it is anexternal measure used by educationalists and wasused for two purposes: (1) to compare the twocohorts and (2) to compare participating Year 9students to those in the year group who chosenot to participate in the study, as a measure ofrepresentativeness.

Procedure

The purpose of the study was explained to all potentialparticipants in year assemblies. Participants were alsoprovided with an information sheet and consent formto give to their parent/carer who returned the consentform to school where it was collected by the first author.The participants also completed a consent form.

Assessment took place individually in a quiet roomwithin the school and was carried out by the firstauthor, a qualified speech and language therapist. At thebeginning of this session, the purposes of the study wereexplained again and participants were given the opportu-nity to ask any questions and to withdraw from thestudy if they wished. No participants withdrew from thestudy. Assessments were administered in the followingorder: TROG, BPVS, ERRNI, CELF-3 Listening toParagraphs, WASI Block Design, WASI Vocabulary.

Results

Comparison of the two cohorts

Descriptive statistics are given in table 2 for bothschool cohorts. Two calculations were made: (1) onesample t-tests to see if the group means were signifi-cantly below the normative means of the standardizedassessments; and (2) Cohen’s d to show the differencebetween the cohort means and the assessments’standardized normative mean. This allowed compari-son of the difference between current cohorts and theexpected scores, allowing comparison of assessmentswith different standardization means. Given that therewere seven measures administered in this study (sixlanguage and one non-verbal), there is a seven-foldrisk that significance will have been reached by chance.Bonferroni corrections counters this, by setting alpha at0.0071 (0.05/7) instead of 0.05.

• SDis cohort: The WASI Vocabulary and BPVSscores were 1SD below the expected normativemean, with a Cohen’s d score of −1 and−1.1 indicating a large effect size. While thecohort’s scores on other language measures(TROG, ERRNI measures, CAT verbal, CELF-3 Listening to Paragraphs) were significantlybelow the normative mean, they were within the

average range with small or moderate Cohen’sd scores. In contrast, the WASI Block Designdid not differ significantly from the normativemean (see table 2). Cohen’s d calculationsuggests large differences between the cohort’s(1) BPVS mean and the BPVS normativemean; and (2) WASI Vocabulary mean and theWASI Vocabulary normative mean. Therefore, thebiggest differences between the scores of the SDiscohort and the assessment normative mean wereon assessments of vocabulary.

• SAdv cohort: The group mean scores of alllanguage measures and the non-verbal measurewere not significantly below expected levels.

• Comparison of SDis and SAdv cohorts: table 2 alsoshows the results of independent samples t-tests tocompare the assessment means of the two cohorts.SAdv had significantly higher scores on: measuresof vocabulary (WASI Vocabulary, BPVS andCAT verbal), and understanding of paragraphs(CELF-3 Listening to Paragraphs). Cohen’s dwas calculated to give an effect size of thesesignificant differences. There are large differencesbetween cohorts on measures of vocabulary (CATverbal, BPVS and WASI Vocabulary) and CELF-3Listening to Paragraphs scores.

Correlations between the results of different assessmentswere investigated using Pearson product-movementcoefficients (table 3). Strong correlations werepredicted between: (1) TROG, BPVS, ERRNI, CELF-3 and WASI VOCAB as they are all languagemeasures; and (2) BPVS, WASI VOCAB and CATVERBAL, given the common reliance on semanticskill.

The SAdv cohort had more correlations betweenassessment scores than the SDis cohort: the SAdv cohorthas 24 significant correlations, while the SDis cohort has13. The SAdv cohort also has stronger correlations: theSAdv cohort has 13 moderate correlations (e.g. 0.5 orabove), compared with just three moderate correlationsin the SDis cohort.

Other demographic factors’ association withlanguage scores

As shown in table 1, there were some differencesbetween the cohorts regarding gender and multilin-gualism. Independent t-tests calculated the significanceof any gender differences. In SDis, there is only onesignificant difference between gender scores; femalesscored higher on the CELF Listening to Paragraphsthan males (t(100) = 3.956, p ≤ 0.001), though themagnitude of the difference in the means was large


Tabl

e2.

Ass

essm

ent

resu

lts

for

the

coho

rtin

the

area

ofso

cioe

cono

mic

disa

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tage

and

the

coho

rtin

the

area

ofre

lati

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cioe

cono

mic

adva

ntag

e

Com

pari

ngth

etw

oco

hort

sC

ompa

riso

nw

ith

norm

ativ

em

ean

t-te

stfo

rth

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ualit

yof

mea

nsM

inim

um–

Ass

essm

ent

Coh

ort

Num

ber

Mea

n(S

D)

max

imum

scor

eO

nesa

mpl

et-

test

Coh

en’s

dt

d.f.

Sign

ifica

nce

(tw

ota

iled)

Coh

en’s

d

TR

OG

Soci

oeco

nom

icdi

sadv

anta

ge10

393

.07

(9.5

2)71

–116

t=

−7.3

9,p

≤0.

001∗∗

−0.4

72.

556

149

0.01

2∗0.

44

Soci

oeco

nom

icad

vant

age

4897

.35

(9.7

5)67

–111

t=

−1.8

8,p

=0.

067

−0.2

CE

LF-3

List

enin

gto

Para

grap

hsSo

cioe

cono

mic

disa

dvan

tage

102

8.87

(2.2

7)3–

14t=

−5.0

0,p

≤0.

001∗∗

−0.3

35.

033

148

<0.

001∗∗

0.88

Soci

oeco

nom

icad

vant

age

4810

.98

(2.6

2)4–

16t=

2.59

,p

=0.

013∗

0.33

ER

RN

IM

ean

Leng

thU

tter

ance

Soci

oeco

nom

icdi

sadv

anta

ge10

194

.12

(14.

56)

65–1

35t=

−4.0

6,p

≤0.

001∗∗

−0.4

03.

202

147

0.00

2∗∗0.

56

Soci

oeco

nom

icad

vant

age

4810

2.38

(15.

01)

72–1

35t=

1.10

,p

=0.

279

0.13

ER

RN

IIn

form

atio

nsc

ore

Soci

oeco

nom

icdi

sadv

anta

ge10

192

.30

(14.

57)

64–1

25t=

−5.3

2,p

≤0.

001∗∗

−0.5

31.

330

147

0.18

60.

02

Soci

oeco

nom

icad

vant

age

4895

.54

(12.

42)

65–1

26t=

−2.4

9,p

=0.

017∗

−0.2

6

BPV

SSo

cioe

cono

mic

disa

dvan

tage

102

85.2

1(1

2.89

)61

–127

t=

−11.

60,

p≤

0.00

1∗∗−1

.04.

712

64.8

59<

0.00

1∗∗0.

96

Soci

oeco

nom

icad

vant

age

4810

0.46

(20.

61)

70–1

44t=

0.15

,p

=0.

878

0

WA

SIV

ocab

ular

ySo

cioe

cono

mic

disa

dvan

tage

103

39.2

9(8

.48)

21–6

5t=

−12.

82,

p≤

0.00

1∗∗−1

.15.

690

69.8

13<

0.00

1∗∗1.

12

Soci

oeco

nom

icad

vant

age

4850

.21

(11.

97)

25–7

8t=

0.12

,p

=0.

905

0

CAT

verb

alSo

cioe

cono

mic

disa

dvan

tage

9591

.53

(10.

10)

65–1

17t=

−7.9

6,p

≤0.

001∗∗

−0.5

34.

298

61.6

84<

0.00

1∗∗0.

90

Soci

oeco

nom

icad

vant

age

4710

3.34

(17.

45)

67–1

41t=

1.31

,p

=0.

196

0.2

WA

SIB

lock

desi

gnSo

cioe

cono

mic

disa

dvan

tage

103

49.2

1(8

.71)

30–6

3t=

−.92

,p

=0.

361

−0.1

1.99

571

.750

0.05

0∗0.

39

Soci

oeco

nom

icad

vant

age

4853

.02

(11.

81)

29–7

0t=

1.77

,p

=0.

083

0.3

Not

es:∗∗

Sign

ifica

ntat

the

0.01

leve

l(tw

o-ta

iled)

;∗ sign

ifica

ntat

the

0.05

leve

l(tw

o-ta

iled)

.C

ohen

’sd

of0.

2is

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alle

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t,0.

5is

am

oder

ate

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ctan

d0.

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ct(C

ohen

1988

).


Table 3. Correlations (Pearson) between assessment scores for both cohorts

Assessment TROG CELF-3 LP BPVS ERRNI MLU ERRNI Info WASI vocab WASI block design

SDis cohortCELF-3 LP 0.12BPVS 0.30

∗ ∗0.19

ERRNI MLU 0.14 0.20∗

0.05ERRNI info 0.12 0.32

∗ ∗0.06 00.02

WASI vocab 0.29∗ ∗

0.23∗

0.64∗ ∗ −0.02 0.09

WASI block design 0.40∗

0.08 0.19 0.09 0.19 0.38∗ ∗

CAT V 0.25∗

0.32∗ ∗

0.62∗ ∗

0.06 0.19 0.67∗ ∗

0.33∗ ∗

SAdv cohortCELF-3 LP 0.51

∗ ∗

BPVS 0.62∗ ∗

0.57∗ ∗

ERRNI MLU 0.25 0.49∗ ∗

0.45∗ ∗

ERRNI info 0.36∗

0.26 0.37∗ ∗

0.34∗

WASI vocab 0.64∗ ∗

0.61∗ ∗

0.78∗ ∗

0.61∗ ∗

0.41∗ ∗

WASI block design 0.49∗ ∗

0.30∗

0.62∗ ∗

0.27 0.41∗ ∗

0.68∗ ∗

CAT V 0.67∗ ∗

0.46∗ ∗

0.68∗ ∗

0.40∗ ∗

0.22 0.79∗ ∗

0.68∗ ∗

Note:∗ ∗

Correlation is significant at the 0.01 level (two-tailed);∗

correlation is significant at the 0.05 level (two-tailed).

(Cohen’s d = 0.78). On all other measures, there areno significant gender differences in either cohort.

Independent samples t-test were conducted tocompare assessment scores for multilingual andmonolingual participants. In SDis there were no statisti-cally significant differences between monolingual andmultilingual participants. In SAdv, monolingual partici-pants scored higher than multilingual participants onall language measures but findings are limited by thevery small numbers of participants (seven multilingualparticipants).

Proportion of individual adolescents withundetected language difficulties within each cohort

Table 4 shows the prevalence of language difficultiesusing differing criteria. Past research into the effect ofsocioeconomic inequality on language skills has usedvaried criteria for language difficulty. The most liberalcriteria was set as −1 SD on one or more languageassessment(s), and the most stringent criteria appliedwas −2 SD on two or more language assessments.The language assessment scores used for this analysisare: TROG, BPVS, both ERRNI measures, CELF-3Listening to Paragraphs, and WASI Vocabulary.

In SDis, between 9% and 84% of participants canbe classified as having a language difficulty, dependingon whether very stringent or very liberal criteria areadopted. Applying the same criteria to the SAdv group,the prevalence rates vary between 2% and 40%. Thechi-squared test for independence was used to determinewhether the frequency of ‘undetected language difficul-ties’ differed across the SDis and SAdv cohort. Resultssuggest that there were significant differences between

the numbers of participants with a ‘language difficulty’in the SDis and SAdv cohorts using all thresholds except:−1.5 SD on two or more language measures; and −2SD on two or more language measures. This indicatesthat using more stringent criteria (applied to two ormore language measures) reduced the difference in theprevalence of ‘undetected language difficulties’ betweenthe two cohorts.

Comparing participants to their peer group todetermine if the sample is representative

An independent t-test was used to compare participantsto a larger dataset of CAT V scores for the rest of theyear group. These results are summarized in table 5. InSDis, 97 of 103 participants completed CAT V whenaged 11 and there was a dataset of 87 other Year 9students who had completed CAT V. In SDis, studentswho participated in this study had significantly higherscores on the CAT V than those who did not participate.Effect sizes were moderate.

In the SAdv cohort, 47 of 48 participants completedthe CAT V and were compared with a larger dataset of157 Year 9 adolescents. In contrast to the SDis group,there were no significant differences between partici-pating students and the larger dataset on the verbalCAT.

Discussion

This study compared the language scores of twocohorts of adolescents from higher and lower socioeco-nomic backgrounds. The study addressed three researchquestions, which are discussed below.


Table 4. Number of participants with scores suggesting undetected language difficulties according to different criteria

Percentage of participants with a ‘language difficulty’ ineach cohort

Threshold used to define ‘language difficulty’ Socioeconomic disadvantage Socioeconomic advantage Chi-squared Significance

−1 SD One or more language measures 83.5 39.6 27.8 < 0.001∗ ∗

Two or more language measures 69.9 29.2 20.5 < 0.001∗ ∗

−1.25 SD One or more language measures 76.7 29.2 29.3 < 0.001∗ ∗

Two or more language measures 52.4 25.0 8.9 0.003∗

−1.5 SD One or more language measures 60.2 18.8 21.9 < 0.001∗ ∗

Two or more language measures 21.4 10.4 2.0 0.160−2 SD One or more language measures 31.1 8.3 8.1 0.004

∗

Two or more language measures 8.7 2.1 1.4 0.238

Note:∗ ∗

Significant at the 0.01 level (two-tailed);∗

significant at the 0.05 level (two-tailed).

Is there a difference on language measures betweenadolescents from different socioeconomicbackgrounds?

The cohort of adolescents who attended a schoolin an area of socioeconomic disadvantage (SDis)performed less well than the cohort of adolescentsfrom an area of relative socioeconomic advantage(SAdv) on standardized assessments of comprehensionof spoken paragraphs, sentence length, comprehen-sion of vocabulary and ability to form definitions forwords. There were no significant differences betweenthe cohorts on a measure of information containedin a narrative or comprehension of grammar. Thebiggest differences between the two cohorts were onassessments of vocabulary. The differences between thecohorts could not be accounted for by multilingualismor gender. Despite these trends, there were large ranges ofperformance and individual variation leading to overlapbetween the two cohorts.

When comparing the SDis cohort to publishedlanguage assessment norms, the cohort scored statisti-cally significantly below the normative mean on alllanguage measures, including measures of grammaticalunderstanding, receptive and expressive narrative, andvocabulary. The SDis cohort did not perform belowaverage on a measure of non-verbal ability. The findingthat cohort language scores are below expected levelsis in line with previous research which reported that asmaller cohort of 11 year olds attending school in an

area of socioeconomic disadvantage scored below thenormative mean on language assessments (Myers andBotting 2008). The finding that vocabulary scores werelow is also supported by research with younger childrenin the United States (Hart and Risley 1995, Whitehurstand Fischel 2000).

Are there a higher proportion of individualadolescents with undetected language difficulties inthe cohort from the area of disadvantage than in thecohort from an area of relative advantage?

This paper has highlighted that hitherto undetected andtherefore unsupported language difficulties can remainuntil adolescence, particularly in a context of socialdisadvantage. Using criteria of −1.5 SD on one ormore assessment, 60% of participants have undetectedlanguage difficulties. Therefore, as well as justifyingwhole-school approaches to vocabulary teaching, thispaper also identifies the need for screening, assessmentand intervention for individual adolescents. This is inthe context of concern that adolescents do not getdiagnosed with language difficulties and that those withdiagnoses do not get appropriate support (Bercow 2008;Stringer and Lozano 2007).

Identification of language difficulties may beespecially needed in areas of social disadvantage, giventhat there were significant differences in the numbersof undetected language difficulties using the criteria

Table 5. Comparison of CAT scores of participating and non-participating students from both schools

Group mean (SD) t-test for equality of means

Participating Non-participating SignificanceAssessment Cohort students students t d.f. (two-tailed) Cohen’s d

CAT verbal Socioeconomic disadvantage 91.03 85.59 2.61 142.68 0.010∗ ∗

0.039Socioeconomic advantage 103.36 99.39 1.44 205 0.153 0.23

Note:∗ ∗

Significant at the 0.01 level (two-tailed);∗

significant at the 0.05 level (two-tailed).


of: −1 SD (on both at least one test and at least twotests); −1.25 SD (on both at least one test and at leasttwo tests); −1.5 SD (on at least on test); and −2 SD(on at least two tests). Using more stringent criteriareduced the differences between the two cohorts. Whencomparing participants who scored less than −1.5 or −2SD on two or more language measures, there were nosignificant differences between SDis and SAdv cohorts(although the SDis cohort still had double the numberof participants who met the criteria). Therefore, whileSDis participants were more at risk of having lowlanguage scores, they are not at increased risk of severeand more pervasive language difficulties when comparedwith SAdv participants.

Inevitably, there was variation in the numbers ofundetected language difficulties as well as the severity ofdifficulties, according to the criteria applied (from liberalto stringent). This highlights the need for agreementand consistency in the application of criteria in bothresearch and practice, in order to ensure equality ofaccess to services across different settings. There is a needto balance criteria used to identify language difficulties:stringent criteria may be useful for identifying childrenat most in need of specialist support, but may alsorisk missing a proportion of children with significantlanguage difficulties.

Are participants in this study representative of theirpeer group?

Recruitment to this study was potentially biased towardsadolescents with higher verbal reasoning scores in theSDis cohort, as measured by the school-administeredverbal cognitive abilities test. It is therefore possible thata more representative sample would find bigger discrep-ancies between cohorts and potentially higher levels ofundetected language difficulties.

The need for intervention targeting adolescentlanguage development and difficulties

The current findings suggest that adolescents fromareas of socioeconomic disadvantage have low scores onmeasures of vocabulary when compared with normativemeans and a cohort from an area of relative advantage.This suggests that in areas of socioeconomic disadvan-tage in particular, it may be necessary to focus on specificvocabulary teaching. This study therefore adds supportto programmes such as Word Generation, which worksat a whole-school level to develop new word learning(Fusaro 2009). Word Generation is a project in an areaof social disadvantage in the USA, working with 11–14 year olds at a whole-school level to raise vocabularyknowledge (Fusaro 2009). Schools take part in a 24-

week programme, and each week focus on a topicalissue such as immigration laws, censorship in music,or euthanasia. Each topic has five target words whichare embedded in activities across the school throughoutthe week. Words are taught directly and indirectly, withinstruction on both specific meanings and word learningstrategies, such as how to use morphemes within theword to infer meaning.

Programmes in the UK are currently beingdeveloped to work in collaboration with schools: workdone on teaching vocabulary in the classroom (Wilsonet al. 2010); collaboration with teaching support workersto teach vocabulary as well as narrative (Joffe et al.2009); and school-wide imbedding of language andcommunication in the classroom (Secondary Talk; ICAN 2010). These programmes are based on inclusivemodels of intervention through multidisciplinary work,rather than individual sessions focusing on childrendiagnosed with impairments. Such interventions havepractical advantages in the secondary setting, such asavoiding stigma attached to individual support andallowing greater links with the curriculum (Wilsonet al. 2010). The results presented in this paper couldbe used by commissioners and education managers tojustify further development and implementation of suchprogrammes.

The paper also highlighted a proportion ofadolescents who may have unsupported languagedifficulties (e.g. 21% of the SDis cohort and 10% ofthe SAdv cohort had scores that were −1.5 SD ontwo or more language measures). There is currentlylimited support for individuals with language difficul-ties in many secondary schools. A survey of speech andlanguage therapy managers in the UK found that halfhad no secondary provision at all (Lindsay et al. 2002)and a national review highlighted the lack of supportfor school-aged children beyond primary age (Bercow2008). This paper provides evidence that there is a needfor targeted, specialist support for identified secondary-aged pupils.

Evaluation of methods used

This study makes an important contribution as it beginsto address the paucity of research into the language skillsof adolescents attending mainstream school in areas ofsocioeconomic disadvantage, as well as the incidenceof undetected language difficulties in adolescents fromdiffering socioeconomic backgrounds. However, thefindings need to be replicated on further cohorts ofadolescents across a larger number of schools, examininglanguage skills in more detail.

The battery of standardized assessments used in thisstudy had a number of advantages: it allowed adolescentlanguage abilities to be compared with a normative


mean, and examined a range of language skills in a shortamount of time. The battery highlighted a range oflanguage abilities in each cohort and identified individ-uals who have potential language difficulties.

However, the nature of standardization means thattests should be used with caution when working withadolescents from areas of socioeconomic disadvan-tage. Vocabulary assessment is most open to itembias on standardized testing because vocabulary reflectsindividual experiences, the language of the home andfamiliarity with school curricula (Stockman 2000). Itis unlikely that assessment bias entirely accounts forthe differences between the SDis and SAdv cohorts,given that within the SDis cohort there was variationin individual scores. However, assessment bias maybe a relevant factor in explaining some of the ‘belowaverage’ scores in the SDis cohort (e.g. 84% scoring−1 SD on one or more language assessment). Futureresearch is needed to develop supplementary approachesto adolescent language assessment such as dynamicassessment (Camilleri and Law 2007) and short, self-evaluation interviews (Spencer et al. 2010). Also in termsof assessment, the CAT V had significant correlationswith vocabulary measures, and therefore may also havepotential use in identifying adolescents who may needsupport to develop their vocabulary skills.

Although there were differences on vocabularyscores, on assessments of understanding sentences andnarrative skills there were relatively smaller differencesbetween cohorts and between the SDis cohort mean andnormative means. The functional relevance of the lowerlanguage performance of the SDis cohort is unknown.Examining language scores in relation to academic scoresand absenteeism would allow deeper understanding of(1) the extent to which language performance relatesto such functional outcomes, (2) if they are associatedcan these language skills be improved and (3) is thereevidence that improved language performance impactson such functional outcomes.

Conclusions

Adolescents from areas of socioeconomic disadvantageperform less well than peers from areas of relativeadvantage on standardized language assessments, inparticular on vocabulary tests. The SDis cohort’s lowscores demonstrate that whole-school approaches tolanguage development and new word learning arenecessary. However, this may not be the whole answeras some individuals are at risk of undetected languagedifficulties which justify more specialist support. Usingcriteria of −2 SD on at least one test, 31% of partici-pants in the SDis cohort had previously undetectedlanguage difficulties. Bercow (2008) found that inequal-ity of provision is an area of concern within SLT. This

relates to the use of criteria for language difficulties acrossdifferent socioeconomic settings to allow equal access tolanguage intervention and support services.

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Language and disadvantage: a comparison of the language abilities of adolescents from two different socioeconomic areas