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Risk and Protective Factors Associated With Speech and Language Impairment in a Nationally Representative Sample of 4- to 5-Year-Old Children Purpose: To determine risk and protective factors for speech and language impairment in early childhood. Method: Data are presented for a nationally representative sample of 4,983 children participating in the Longitudinal Study of Australian Children (described in McLeod & Harrison, 2009). Thirty-one child, parent, family, and community factors previously reported as being predictors of speech and language impairment were tested as predictors of (a) parent-rated expressive speech/language concern and (b) receptive language concern, (c) use of speech-language pathology services, and (d) low receptive vocabulary. Results: Bivariate logistic regression analyses confirmed 29 of the identified factors. However, when tested concurrently with other predictors in multivariate analyses, only 19 remained significant: 9 for 24 outcomes and 10 for 1 outcome. Consistent risk factors were being male, having ongoing hearing problems, and having a more reactive temperament. Protective factors were having a more persistent and sociable temperament and higher levels of maternal well-being. Results differed by outcome for having an older sibling, parents speaking a language other than English, and parental support for childrens learning at home. Conclusion: Identification of children requiring speech and language assessment requires consideration of the context of family life as well as biological and psychosocial factors intrinsic to the child. KEY WORDS: risk factor, protective factor, epidemiology, speech, language, communication S peech and language acquisition in early childhood is a powerful indicator of the developmental and cognitive abilities that under- pin childrens successful transition to school (Nelson, Nygren, Walker, & Panoscha, 2006). Longitudinal results from the U.S. National Institute of Child Health and Human Development [NICHD] Study of Early Child Care and Youth Development have demonstrated that multiple path- ways all funnel through one final common pathway, namely the childs language skills, just before entering school I to define the childs read- inessfor school(NICHD, 2004, p. 28). These findings, particularly when viewed in combination with prevalence studies (e.g., King et al., 2005; Law, Boyle, Harris, Harkness, & Nye, 2000; McLeod & Harrison, 2009; McLeod & McKinnon, 2007) showing that a significant proportion of chil- dren do not successfully acquire speech and language prior to school, are compelling. They point to the need to identify and provide support for Linda J. Harrison Sharynne McLeod Charles Sturt University, Bathurst, Australia Journal of Speech, Language, and Hearing Research Vol. 53 508529 April 2010 D American Speech-Language-Hearing Association 508 on June 11, 2010 jslhr.asha.org Downloaded from

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Risk and Protective Factors AssociatedWith Speech and Language Impairmentin a Nationally Representative Sampleof 4- to 5-Year-Old Children

Purpose: To determine risk and protective factors for speech and languageimpairment in early childhood.Method: Data are presented for a nationally representative sample of 4,983 childrenparticipating in the Longitudinal Study of Australian Children (described in McLeod& Harrison, 2009). Thirty-one child, parent, family, and community factorspreviously reported as being predictors of speech and language impairment weretested as predictors of (a) parent-rated expressive speech/language concern and(b) receptive language concern, (c) use of speech-language pathology services, and(d) low receptive vocabulary.Results: Bivariate logistic regression analyses confirmed 29 of the identified factors.However, when tested concurrently with other predictors in multivariate analyses,only 19 remained significant: 9 for 2–4 outcomes and 10 for 1 outcome. Consistentrisk factors were being male, having ongoing hearing problems, and having amore reactive temperament. Protective factors were having a more persistent andsociable temperament and higher levels of maternal well-being. Results differedby outcome for having an older sibling, parents speaking a language other thanEnglish, and parental support for children’s learning at home.Conclusion: Identification of children requiring speech and language assessmentrequires consideration of the context of family life as well as biological andpsychosocial factors intrinsic to the child.

KEY WORDS: risk factor, protective factor, epidemiology, speech, language,communication

S peech and language acquisition in early childhood is a powerfulindicator of the developmental and cognitive abilities that under-pin children’s successful transition to school (Nelson, Nygren, Walker,

&Panoscha, 2006). Longitudinal results from theU.S. National Instituteof Child Health and Human Development [NICHD] Study of Early ChildCare and Youth Development have demonstrated that “multiple path-ways all funnel through one final common pathway, namely the child’slanguage skills, just before entering school I to define the child’s ‘read-iness’ for school” (NICHD, 2004, p. 28). These findings, particularly whenviewed in combination with prevalence studies (e.g., King et al., 2005;Law, Boyle, Harris, Harkness, & Nye, 2000; McLeod & Harrison, 2009;McLeod &McKinnon, 2007) showing that a significant proportion of chil-dren do not successfully acquire speech and language prior to school, arecompelling. They point to the need to identify and provide support for

Linda J. HarrisonSharynne McLeod

Charles Sturt University, Bathurst, Australia

Journal of Speech, Language, and Hearing Research • Vol. 53 • 508–529 • April 2010 • D American Speech-Language-Hearing Association508

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children at risk of speech and language impairmentin their early childhood years, which is when they aremost likely to benefit from early intervention (Almost &Rosenbaum, 1998; Gibbard, Coglan, & MacDonald, 2004).Early detection and early intervention can reduce theseverity and longevity of speech and language difficul-ties (Gibbard et al., 2004; Schwarz & Nippold, 2002);however, many children who are eventually identifiedas having speech and language impairment display noorganic basis and few obvious indicators in the first yearsof life (Dollaghan & Campbell, 2009; Roulstone, Miller,Wren,&Peters, 2009).Consequently, not all childrenwhomay benefit from and be eligible for speech-languagepathology services are identified or referred prior to com-mencing school.

Primary care professionals, such as doctors, nurses,and early childhood teachers, are often expected to iden-tify childrenwhomay be at risk andmay require a speechand language assessment to diagnose a speech andlanguage impairment. The methods that primary careprofessionals use to diagnose impairment tend to be(a) comparison with other children of a similar age,(b) acknowledgment of parental concern, and (c) comple-tion of checklists of speech and languagemilestones suchas having fewer than 50 words or not combining wordsat 24 months (e.g., Coplan, Gleason, Ryan, Bourke, &Williams, 1982; Luinge, Post, Wit, & Goorhuis-Brouwer,2006). Recognition and identification of known risk andprotective factors is anothermethod that can be employed,particularly when the above three screening methods areeither unavailable, inappropriate for a particular context,or lack sensitivity. Tomblin, Hardy, and Hein (1991) haverecommended that “programs of preschool identificationshould consider the inclusion of a registry of children whoare at risk for a communication disorder” (p. 1096). How-ever, as Nelson et al. (2006) concluded, “A list of spe-cific risk factors to guide primary care physicians inselective screening has not been developed or tested”(p. e302).

Current bioecological theories (Bronfenbrenner, 2005)provide a useful framework for addressing risk and pro-tective factors for children’s health and development.Bioecological models elucidate the interacting influ-ences of proximal social and psychological contexts (e.g.,parental and family characteristics) and distal social con-texts (e.g., community characteristics and supports) withthe inherited and biological characteristics of the indi-vidual. This approach accords with the InternationalClassification of Functioning, Disability, andHealth (WorldHealth Organization, 2007), which recognizes the com-plex interrelationships that exist betweenbiological, indi-vidual, and societal factors that influence child functioning.Research investigations of the predictors of speech andlanguage impairment that are consistent with these bio-ecologicalmodels seek to include awide range of variables

describing parental, family, and neighborhood attributes,along with documentation of child health and psycho-social characteristics (e.g., Reilly et al., 2007; Zubrick,Taylor, Rice, & Slegers, 2007). Such studies are ableto examine concurrently a large number of possible pre-dictors of speech and language delay or impairment anduse complex statistical analyses to identify the best setof predictors. Multivariable designs of this type, how-ever, are relatively rare in studies of speech and languageimpairment. A systematic review of risk and protectivefactors associatedwith screening for speechand languageimpairment in early childhood undertaken by the U.S.Preventative Services Task Force (Nelson et al., 2006;U.S. Preventative Services Task Force, 2006) showedthat no studies encompassed all the potential predictordomains (such as family history, child gender, socioeco-nomic status (SES), birth order, perinatal factors, paren-tal education,medical conditions, and other).Most of the16 studies reviewed had addressed only one or two ofthese domains. Nelson et al. (2006) concluded “Themostconsistently reported risk factors include a family historyof speech and language delay, male gender, and perina-tal factors; however, their role in screening is unclear”(p. e302).

For the present study, articles included in the U.S.Preventative Services Task Force study (2006) have beenreviewed again, alongwith additional literature, to sum-marize evidence that confirms or disconfirms an associa-tion between each risk factor and childhood speech andlanguage impairment. Risk/protective factors are re-viewed within the domains identified by Nelson et al.(2006) plus additional domains: child hearing status, oralsucking habits, temperament, parent language spoken athome, minority status/race, maternal mental health andmaternal age, family support for learning, family smok-ing habits, and neighborhood disadvantage (see Table 1).These domains are grouped within three broad categories:child, parent, and family/community. A brief summary ofthe literature for each domain is presented, followed byconsideration of design issues that may influence or ex-plain any differences in the reported findings.

Child FactorsSex. Sex of child was examined in 14 studies, as

shown in Table 1. A significant association between be-ing male and having an increased risk for speech and/orlanguage impairment was found in 11 of these studies(Campbell et al., 2003; Chevrie-Muller, Watier, Arabia,Arabia,&Dellatolas, 2005;Choudhury&Benasich, 2003;Prior et al., 2008; Reilly et al., 2006, 2007; Stanton-Chapman, Chapman, Bainbridge, & Scott, 2002; Tomblinet al., 1991; Yliherva, Olsen, Maki-Torkko, Koiranen, &Jarvelin, 2001; Yoshinaga-Itano, Sedey, Coulter, &Mehl,1998; Zubrick et al., 2007).

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Perinatal factors. Perinatal factors were examinedin 11 studies, and a significant association with speechand language impairment was found in 5. Prenatal fac-tors were associated with risk for speech and languageimpairment in Fox, Dodd, andHoward (2002), who iden-tified significant effects for extreme stress, maternal in-fections, andmedications that could cause damage to thefetus during pregnancy, and in a population-based inves-tigation of birth risk factors for specific language impair-ment (SLI) by Stanton-Chapman et al. (2002), who notedthat late or no prenatal care was a significant predictorof SLI 6 years later, at school.

Less consistent results have been reported for peri-natal difficulties. For example, Fox et al. (2002) foundthat “forceps or ventouse delivery, induced delivery, com-plications such as umbilical cord prolapse, infections,preterm birth, and post-partum resuscitation” (p. 122)were significant predictors of speech impairment. Stud-ies byWeindrich, Jennen-Steinmetz, Laucht, Esser, andSchmidt (2000) andYliherva et al. (2001) have also linkedperinatal factors to speech and language problems. Incontrast, Peters, Grievink, van Bon, van den Bercken,and Schilder (1997); Reilly et al. (2006, 2007); Tomblinet al. (1991); andTomblin, Smith, and Zhang (1997) foundthat postnatal factors did not present significant risksfor speechand language impairment. For example, Tomblinet al. (1991) found that birth events such as “infections,low birth weight, breathing difficulty, ototoxic drugs, feed-ing problems, transfusions, and birth defects” (p. 1101)did not predict poor communication status. Tomblin et al.(1997) also found that birth events including type ofdelivery, induction of labor, duration of labor, and laborand birth complicationswere not significant risk factors.

Existing findings are also inconsistent for prema-turity and low birth weight. Reilly et al. (2006, 2007)found that being born preterm (< 36 weeks) was not asignificant risk factor for early language delay. Simi-larly, Tomblin et al. (1997), who used < 2,500 g as the cut-off, andReilly et al. (2007), who used a continuous scale ofbirth weight in kilograms, have reported that low birthweight was not a significant predictor. In contrast, Zubricket al. (2007) found that low birth weight and prematurebirth were independently significant for late languageemergence. Similar results were noted by Stanton-Chapman et al. (2002): low birth weight (< 2,500 g), verylow birthweight (< 1,500 g), and a low 5-minApgar scorewere significant risk factors for school-age specific lan-guage impairment.

Multiple birth. Two studies based on the same co-hort have examined the association between multiplebirth and the risk for language impairment. Reilly et al.(2006) found twin birth to be a significant risk factor forcommunication impairment in 8- and 12-month-old in-fants; however, by the time this cohort reached 2 years of

age, twin status was not a significant risk factor (Reillyet al., 2007).

Medical conditions. Mixed findings have been re-ported for the impact of child illness and infection onspeech and language impairment. Medical conditionswere examined in seven studies and a significant associ-ation was found in five. Singer et al. (2001) found thatpatent ductus arteriosus and bronchopulmonary dyspla-sia were significant risk factors associated with speechand language impairment. Choudhury andBenasich (2003)reported that autoimmune diseases presented a signif-icant risk, but asthma did not. The U.S. PreventativeServices Task Force review (2006) excluded studies ofotitis media (OME) as a risk factor because it is “a com-plex and controversial area” (Nelson et al., 2006, p. e302).Two studies have reported a significant association be-tween OME and speech impairment in bivariate anal-yses but a nonsignificant association when multivariateanalyses were applied (Campbell et al., 2003; Fox et al.,2002).Peters et al. (1997) indicated that “OMEevenwhencombined with a number of other risk factors producesonly minor effects on later language” (p. 31). In contrast,Shriberg, Friel-Patti, Flipsen Jnr, and Brown (2000),using structural equation modeling, reported a signif-icant relationship between otitis media and speech/language outcomes.

Hearing status. Impaired hearing was found to be asignificant risk factor for difficulties with speech, lan-guage, and learning in a large study of 8,370 Finnishchildren (Yliherva et al., 2001) but not a significant riskfactor in the study by Singer et al. (2001) of over 200 chil-dren. In a study of 150 childrenwithhearing impairment,Yoshinaga-Itano et al. (1998) reported that children hadgreater difficulties with speech and language develop-ment if they were identified with a hearing impairmentafter the age of 6months; identification prior to 6monthscoupled with subsequent early intervention was associ-ated with increased language scores.

Oral sucking habits. Oral sucking habits, includingbreast-feeding, have been found to be both a risk and aprotective factor in studies that have examined thisfactor. Fox et al. (2002) found that excessive sucking ofpacifiers, or thumb or bottle usage as a pacifier, was amoderate predictor of speech impairment. On the otherhand, Tomblin et al. (1997) demonstrated that breast-feeding for less than 9 months was associated with anincreased risk of speech and language impairment.

Temperament. Three studies have examined childtemperament characteristics as possible risk or protec-tive factors for speechand language impairment.Hauner,Shriberg, Kwiatkowski, and Allen (2005) considered theeffect of different aspects of child temperament as riskfactors for “increased severity of expression of speechdelay” (p. 635). Increased severity was related to negative

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Table 1. Summary of significant and nonsignificant risk factors for speech and language impairment for children based on the systematic review by the U.S. Preventative Task Force (2006) and additional studies.

Demographic information Child variables Parent variables Family and community variables

Study andcountry Number

Age inmonths











Minoritystatusor race




Maternalage atbirth

Familysize andbirth order


Smokingin the



Brookhouseret al. (1979)USA

24 cases 28–62 Language – – – y – – – n – – – – – – – – – – –

Campbell et al.(2003) USA

398 cases and241 controls

36 Speech y – – n – – – y – n y – – – – – – n –

Chevrie–Mulleret al. (2005)*France

2059 in cohort 42 Language y – – – – – – – y – y y – – – – – – –

Choudhury &Benasich(2003) USA

42 cases and92 controls

36 Language y n – y/n – – – y – – n n – y/n y – – n –

Felsenfeld &Plomin (1997)*USA

156 adoptedandnonadoptedchildren

84 Speech n – – – – – – y – – – – – – – n – – –

Fox et al. (2002)Germany

65 cases and48 controls

32–86 Speech n y – n – y – y – – – – – – – – – – –

Hauner et al.(2005)* USA

29 cases and87 controls

36–72 Speech – – – – – – y – – – – – – – – – – – –

Lyytinen et al.(2001) Finland

107 with risk ofdyslexia and93 without

0–54 Speech andlanguage

– – – – – – – y – – – – – – – – – – –

Peters et al. (1997)Netherlands

946 in cohort 84–96 Language n n – y – – – – y – y y – – – – – – –

Prior et al. (2008)*Australia

1,911 in cohort 12 and 24 Language y – – – – – y – – – – – y – – – – – –

Reilly et al. (2006)*Australia

1,911 in cohort 8 and 12 Language y n y – – – – y – – – – n – – – – – y

Reilly et al. (2007)*Australia

1,720 in cohort 24 Language y/n n n – – – – y y/n – y/n – n y/n n – – – n

Singer et al. (2001)USA

98 cases and70+95controls

36 Language – – – y n – – – – y n – – – – – – y

Stanton-Chapmanet al. (2002)USA

5,862 casesand201,834notidentified

72–84 Language y y – – – – – – n – y – – n y – n – –

Tallal et al. (1989)USA

76 cases and54 controls

48–59 Language – – – – – – – y – – y y – – – – – – –

Tomblin et al.(1991) USA

662 in cohort 30–60 Speech andlanguage

y/n n – – – – – y – – n y – – y – – – –

Tomblin et al.(1997) USA

177 cases and925 controls

kindergarten Speech andlanguage

– n – – – y – y/n – – y y – y – – y – –

(Continued on the following page)





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Table 1 Continued. Summary of significant and nonsignificant risk factors for speech and language impairment for children based on the systematic review by the U.S. Preventative Task Force (2006) and additional studies.

Demographic information Child variables Parent variables Family and community variables

Study andcountry Number

Age inmonths











Minoritystatusor race




Maternalage atbirth

Familysize and

birth order


Smokingin the



Weindrich et al.(2000)Germany

320 in cohort 54 and 96 Speech andlanguage

– y – – – – – – – – y y y – – – – – –

Whitehurst et al.(1991)

62 cases and55 controls

24–38 Language – – – – – – – n – – – – – – – – – – –

Yliherva et al.(2001) Finland

8,370 in cohort 96 Speech andlanguage

y y – y/n y – – – – – y – – y y – – – –

Yoshinaga–Itanoet al. (1998) USA

150 cases 13–36 Speech andlanguage

y – – – y – – – – n n – – – – – – n –

Zubrick et al.(2007)*Australia

1,766 in cohort 24 Language y y – – – – n y – y/n n n n n y – n n n

Note. y = yes, the variable was examined and there was a statistically significant association; n = no, the variable was examined and was not associated with speech and/or language delay; y/n = different findings for different outcomes; – = the variable was not examined.

Asterisk indicates studies that were not included in the review by the U.S. Preventative Services Task Force (2006).








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affect associated with low approachability/sociability,negative mood, and low task persistence. Prior et al.(2008) found that having a shy temperament was nega-tively related to vocabulary production and communi-cation and symbolic development in a large cohort of 1- to2-year-old children. On the other hand, in Zubrick et al.(2007), only one of nine dimensions of child temperament(negative mood) occurred more frequently in 2-year-oldchildren with late language emergence.

Parent FactorsFamily history of speech and language problems.

Thirteen studies recorded family history of speech, lan-guage, and/or learning difficulties, with 11 identifyingthis as a risk factor for childhood speechand language im-pairment (Campbell et al., 2003; Choudhury & Benasich,2003; Felsenfeld & Plomin, 1997; Fox et al., 2002;Lyytinen et al., 2001; Reilly et al., 2006, 2007; Tallal,Ross, & Curtiss, 1989; Tomblin et al., 1991, 1997; Zubricket al., 2007). Of these, Tomblin et al. (1997) found thatpaternal family history was significant, but maternalhistory was not. Three other studies have reported thatfamilyhistoryof speech / language impairment (Brookhouser,Hixson, & Matkin, 1979; Whitehurst et al., 1991) andfamily history of hearing loss (Tomblin et al., 1991) werenot significantly associated with language impairmentor poor communication status in children. The impact offamily history may be due to genetic or environmentalinfluences or to a combination of both. This question hasbeen examined by Felsenfeld and Plomin (1997) in a studyof adopted and nonadopted children. Family history forbiological parents was a significant risk factor for speechimpairment, whereas for adoptive parents it was not.Their results support the view that the biological basis offamilyhistoryhas a stronger influence on children’s speechand language than the home learning environment.

Languages spoken. Risk for speech and languageimpairment in children with a nondominant languagebackground has been demonstrated in the case of non-English speakers in an English-dominant society (Reillyet al., 2007), non-French speakers in a French-dominantsociety (Chevrie-Muller et al., 2005), andnon-Dutch speak-ers in a Dutch-dominant society (Peters et al., 1997). Incontrast, Stanton-Chapman et al. (2002), who studiedan English-dominant U.S. state with a large Spanish-speaking population, reported that Spanish and othernon-English speakerswere “less likely to be placed in SLIclassrooms than native-English speakers” (p. 397).

Minority status or race. Risk for speech and lan-guage impairment has been studied in relation tominoritystatus or race. Singer et al. (2001) reported that childrenof a minority race were at greater risk than their peers;however, Campbell et al. (2003) found that identification

as African-American was not a significant risk factor.Minority status was not a significant factor in the studyconducted by Yoshinaga-Itano et al. (1998).

Educational level of mother and father. Of 14 studiesthat have examined the association between parents’educational level on children’s speech and language ac-quisition, 10 reported a risk for speech and languageimpairment at low parental educational level. Theseincluded studies of only mother ’s education (Campbellet al., 2003;Peters et al., 1997;Reilly et al., 2007; Stanton-Chapman et al., 2002; Yliherva et al., 2001), only father ’seducation (Tomblin et al., 1991), and both mother ’s andfather ’s education (Chevrie-Muller et al., 2005; Tallalet al., 1989; Tomblin et al., 1997; Weindrich et al., 2000).In contrast, 4 studies have shown that parental educa-tion level was not a significant risk factor (Choudhury& Benaisch, 2003; Singer et al., 2001; Yoshinaga-Itanoet al., 1998; Zubrick et al., 2007).

Parental mental health. Five studies that have ex-amined this domain reported mixed results. Three stud-ies found that indicators of parental mental health werenot associated with speech and language impairment in8- to 12-month-old infants (Reilly et al., 2006) or 2-year-olds (Reilly et al., 2007; Zubrick et al., 2007). In contrast,Prior et al. (2008) reported that maternal psychosocialindices, specifically mothers’ rate of coping and partnerrelationship satisfaction, were positively associated withlanguage development at 24months, andWeindrich et al.(2000) found that parental mental health was a risk fac-tor for speech, language, reading, and spelling in childrenaged 54 and 96 months.

Maternal age at birth of child. Studies that exam-inedmaternal age at the birth of the child have reportedmixed findings for speech and/or language impairment.Younger mothers have been identified in risk groupsfor childrenwith specific language impairment (Tomblinet al., 1997) and poor speech and language abilities(Yliherva et al., 2001). Choudhury and Benasich (2003)noted that youngermaternal agewas a characteristic offamilies with a history of speech language impairmentbut was not linked to children’s assessed receptive andexpressive language at age 3 years. Similarly, Stanton-Chapman et al. (2002) reported no relationship betweenmaternal age and school-identified specific language im-pairment, after accounting for the effects of other bio-logical and environmental risks. Reilly et al. (2007) foundthat older maternal age was a significant risk factor forcommunication and symbolic behavior at age 24 monthsbut not for vocabulary production.

Family and Community FactorsFamily size. Findings linking speech and language

acquisition with the number of siblings, the number of

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children in the household, and birth order have beenlargely consistent. Choudhury and Benasich (2003) dem-onstrated that an increased number of children in thehousehold was a significant risk factor for languageimpairment. Yliherva et al. (2001) found that havingmore than four children in the household increased riskof speech, language, and learning difficulties. Tomblinet al. (1991) demonstrated that children who held laterbirth positions in the family were more likely to havea poor communication status between 2.5 and 5 yearsof age, and, similarly, Stanton-Chapman et al. (2002)reported that children whose birth position was thirdor higher were more likely to be identified as havingspecific language impairment at age 6–7 years. Zubricket al. (2007) reported that the presence of two or moresiblings was a risk for language delay in a sample of2-year-olds, and Reilly et al. (2007), who studied thesame age group, reported that birth order was a risk forvocabulary production but not for communication andsymbolic behavior.

Home learning activities. In a study of adoptedand nonadopted children, Felsenfeld and Plomin (1997)used the HOME Scale of family environment (Caldwell& Bradley, 1984) and found that family environmentwas not significantly associatedwith speech outcome atage 7.

Smoking in the household. Tomblin et al. (1997)found thatmaternal smoking in the household increasedthe risk of speech and language difficulties but that thiswasmediated bymaternal education levels. Zubrick et al.(2007), on the other hand, reported no effect of maternalsmoking (current and during/before pregnancy) on latelanguage emergence, and Stanton-Chapman et al. (2002)found no effect of smoking during pregnancy on school-identified specific language impairment.

Socioeconomic factors. Of the five studies that con-sidered family SES (i.e., combined yearly income, occu-pational prestige, education levels, and qualification forMedicaid health insurance), only one found a significantrisk for language impairment (Singer et al., 2001). LowSESwas not identified as a risk factor by Campbell et al.(2003), Choudhury andBenasich (2003), Yoshinaga-Itanoet al. (1998), or Zubrick et al. (2007).

Neighborhood disadvantage. Two studies includedinformation provided by the census-based Socio-EconomicIndexes for Areas (SEIFA) from the Australian Bureauof Statistics (ABS, 2003) as a measure of neighborhooddisadvantage. Reilly et al. (2006, 2007) reported thatlower scores on the Index of Disadvantage (i.e., living ina more disadvantaged area) was a significant predictorfor language difficulties at age 8–12 months but not atage 24 months. Zubrick et al. (2007) noted no differencefor children with and without late language emergenceby SEIFA scores.

Methodological ConsiderationsWhen Examining Studiesto Determine Risk Factors

As can be seen from the collation of findings set outin Table 1, varying results have been reported as towhether or not each of the factors reviewed presents as asignificant risk for childhood speech and language im-pairment. These differences make it difficult to providea definitive list of specific risk factors to guide primarycare professionals (cf. Nelson et al., 2006). There arethree main reasons for the observed differences in studyresults: (a) differences in the size and nature of the sam-ples, (b) differences in the speech and language outcomemeasures that have been used to identify impairment,and (c) differences in the range and number of possiblepredictor variables included in the design and the anal-yses that have been applied to these variables.

Size and nature of the sample. The reviewed studieshave used two sampling techniques: clinical samplingwith or without a control group and cohort studies thatidentify children with and without speech and languageimpairment within the full population. Studies utilizingthe former approach tend to have relatively small sam-ples (e.g., 24 children aged 28–62months in Brookhouseret al., 1979; 63 cases and 48 controls in Fox et al., 2002)with some exceptions (e.g., 177 cases and 923 controls inTomblin et al., 1997), whereas the latter includes largersamples (e.g., 8,370Finnish children recruited byYlihervaet al., 2001; 1,911 Australian children reported on by Prioret al., 2008, andReilly et al., 2006; 207,693 children in theFlorida cohort surveyed by Stanton-Chapman et al.,2002). Larger population-based samples have greater var-iability across predictors, whichmaymask or blur differ-ences observed in clinical-control samples.

The samples also differ by the target age and the agerange of the study children. In some studies, childrenwereexamined at a specific age (e.g., 36 months in Campbellet al., 2003; 96 months in Yliherva et al., 2001), whereasother studies examined children over a wide age range(e.g., 30–60 months in Tomblin et al., 1991; birth to54months inLyytinen et al., 2001). Sampling differences,both in terms of the heterogeneity of the samples (cf.Nelson et al., 2006) and the likelihood of sampling con-founds (Zubrick et al., 2007), have constrained the eval-uation of risk factors for speech and language impairmentin early childhood. Furthermore, it is likely that “predic-tive relationships change over time” (Zubrick et al., 2007,p. 1588) such that certain risk factors become moresalient at younger or older ages (see Reilly et al., 2006,vs. Reilly et al., 2007; see Table 1).

Identification of speech and language impairment.Differences in results also reflect the type and specificityof the speech and language measures used to identify

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the target group. The majority of the reviewed studiesconsidered both speech and language outcomes, butsome considered speech outcomes only whereas othersconsidered language outcomes only. Reilly et al. (2006,2007) found that different outcome measures identifieddifferent risk and protective factors. The source of diag-nosis also varies, being based on parent or teacher reportor on direct assessment of speech and language skills. Al-though there is evidence of correspondence across thesesources (see McLeod & Harrison, 2009, for a discussion),there has not, as yet, been a systematic comparison ofrisk factors in relation to parent, teacher, and direct as-sessment sources of identification.

Range, number, and analysis of possible predictorvariables. Table 1 illustrates the range, number, andtype of predictors that were included and analyzed asrisk or protective factors in the reviewed studies. Somestudies focused on the predictive strength of a smallnumber of domains (e.g., two inBrookhouser et al., 1979,and Whitehurst et al., 1991); others have a broader cov-erage (e.g., five to seven domains in Campbell et al.,2003; Tomblin et al., 1991, 1997); and somehave includedmost of the identified domains (e.g., 10 in Reilly et al.,2007; 12 in Zubrick et al., 2007). The inclusion of mea-sures from a large number of domains enables the use ofmultivariate analysis techniques, which have been shownto negate previous findings using bivariate analyses. Forexample, usingmultivariate analyses, Reilly et al. (2007)and Zubrick et al. (2007) examined a large number ofknown risk factors for language delay in two samplesof Australian 2-year-olds. In both studies, results con-firmed only three or four predictors: male sex, perinatalfactors, family history, and presence of siblings in theZubrick et al. study; and family history, low maternaleducation, and non-English-speaking background in theReilly et al. study. Note, however, that a large samplesize is required when seeking to test predictors frommultiple domains. The discrepancies between the smallnumber of significant predictors identified in multivar-iate analyses versus themore diverse range of predictorsidentified in bivariate analyses warrant further inves-tigation. Suchwork is needed to investigate possible con-founding relationships between child biological factorsand parental or family factors.

Aim of the Present StudySeveral groups of researchers (Campbell et al., 2003;

Nelson et al., 2006; Reilly et al., 2006, 2007; Tomblinet al., 1991; Zubrick et al., 2007) have sought to identify aset of predictors that would provide a means of identi-fying children for speech and language assessment, us-ing different analytical techniques and with differinglevels of success. The present study sought to extend thiswork by assessing the unique and collective effects of a

comprehensive range of previously identified risk andprotective factors for speech/language acquisition. Italso sought to address some of the weaknesses of pre-vious research by focusing on a large population-basedsample of children of a similar age who were approach-ing the beginning of formal schooling and by includinga range of sources and types of information to identifyspeech/language impairment status. This article reportson the second of two studies examining speech and lan-guage impairment in a nationally representative popula-tion sampleofAustralian children.The first study (McLeod& Harrison, 2009) determined the prevalence of ex-pressive and receptive speech and language impairmentbased on four measures: two reported by parents, onedirectly assessed by trained interviewers, and periodprevalence of attendance at speech-language pathologyservices as reported by parents and teachers. Buildingon these findings, the present study used the same fourmeasures of speech and language impairment as bi-nomial outcomes to test the effects of a wide range ofpreviously identified risk/protective factors. Bivariateeffects for each of these factors were tested, followed bymultivariate analyses to identify the best set of predic-tors froma range of child, parent, family, and communityfactors.

MethodThe Study

As with the companion article (McLeod & Harrison,2009), the current study examined data collected fromthe kindergarten cohort of children in the first wave (age4–5 years) of Growing Up in Australia—The Longitudi-nal Study of Australian Children (LSAC; Sanson et al.,2002). LSAC is the first comprehensive national study ofAustralian children, funded by the Australian Govern-ment to examine children’s health anddevelopment overtime and within the social, economic, and cultural envi-ronments of the families and communities in which theyare growing up. Recruitment of the sample using themost comprehensive database of Australia’s populationwas facilitated by the Australian Government and theHealth InsuranceCommission. Anoverviewof LSACandadditional details about sample recruitment and dataweighting are provided in McLeod and Harrison (2009).LSAC data were weighted to allow for unequal probabil-ities of inclusion in the studyand to ensure that theLSACsample matched families in the Australian populationwith a 4- to 5-year-old child on a wide range of parentaland family characteristics, including parents’ ethnicity,country of birth, education, and income; family size andstructure; andwhether themother spoke a language otherthan English (LOTE) at home. Weighted sample datawere used in all the analyses reported in this article.

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ParticipantsA total of 4,983 children (2,537 boys; 2,446 girls) and

their parents participated in the kindergarten cohort ofthe LSAC study. Children ranged in age from 4;3 (years;months) to 5;7, but the majority (80%) were within a6-month age span: 4;6 to 5;0 with a mean age of 56.91months (SD = 2.64). Parents reported that over 96%of children were attending an early childhood servicesuch as a child care center, preschool, or school (Harrison& Ungerer, 2005; Harrison, Ungerer, et al., 2009). Withpermission from parents, each child’s teacher was ap-proached and asked to complete a brief questionnaire. Atotal of 3,276 children’s teachers participated.

MeasuresOutcome measures. Four outcome measures, devel-

oped and described in McLeod and Harrison (2009),were used to determine childhood risk status for speechand language impairment. Thesemeasures drew onmul-tiple informants, including the child’s parent and teacher,and direct assessment by a trained interviewer.

1. Parent report of expressive speech and languageconcern based on the Parents’ Evaluation of Devel-opmental Status (PEDS; Glascoe, 2000) question “Doyouhaveany concerns abouthowyour child talks andmakes speech sounds?” (25.2% of the sample wasidentified as impaired).

2. Parent report of receptive language concern basedon the PEDS question “Do you have any con-cerns about how your child understands what yousay to him/her?” (9.5% of the sample was identifiedas impaired).

3. Parent and teacher report of use of speech-languagepathology services in the past 12 months (14.5% ofthe sample was identified as impaired).

4. Assessed scores of vocabulary comprehension onthe Adapted Peabody Picture Vocabulary Test—III(PPVT–III; Rothman, 2003). Children were identi-fied as having difficulty if they scored more than orequal to 1 SD below the mean (14.7% of the samplewas identified as impaired).

There was a low-to-moderate overlap across these fourgroups. For example, of the childrenwhose parents wereconcerned about their expressive speech and language(Group 1), 27.2% had parents who were concerned aboutreceptive language, 43%were attending speech-languagepathology services, and 22.9%were in the low vocabularygroup. The use of four different outcome measures en-abled examination of distinct contributions of risk andprotective factors to explain the disparate findings sum-marized in Table 1.

Risk and protective factors. Risk and protective fac-tors identified by the U.S. Preventative Services TaskForce (Nelson et al., 2006; U.S. Preventative ServicesTask Force, 2006) and our review of these and additionalstudies (see Table 1) were matched to relevant items inthe LSAC dataset. All but the family history of speechand language impairment were available. Television view-ing was a risk factor that had not been considered inprevious studies but was added to the present study dueto its currency in public debate. A total of 31 potentialrisk/protective factorswere identified fromparent report.

Child factors. Postnatal factors were described byprematurity (defined as < 36 weeks of pregnancy), birthweight (low birth weight defined as < 2500 g), whetherthe child received neonatal intensive care, and the occur-rence of multiple birth versus single birth. Medical con-ditions identified by parents included ongoing problemsof asthma, bronchiolitis, and ear infections. Hearing sta-tus was identified by the occurrence of ongoing hearingproblems. Oral sucking habitswere described bywhetherthe child was breastfed for > 9 months. Child temper-ament was assessed using the 12-item Short Tempera-ment Scale for Children (STSC; Sanson, Prior, Garino,Oberklaid, & Sewell, 1987). The STSC provides ratingsfor three subscales: sociability (e.g., “This child is shywhen first meeting new children”), persistence (e.g.,“This child stays with an activity [e.g., puzzle, construc-tion kit, reading] for a long time”), and reactivity (e.g.,“When shopping together, if I do not buy what this childwants [e.g., sweets, clothing], he/she cries and yells”).Items are scored on a scale from 1 = almost never to 6 =almost always and combined to generate a mean scorefor each subscale. Children’s proficiency in an LOTEwas described by two factors: regularly spoken to in alanguage other thanEnglish and speaks a language otherthan English in the home.

Parent factors.Demographic characteristics includedmother ’s age at child’s birth and mother ’s and father ’syears of education. Parental minority status or race wasrecorded if either parent self-identified as being ofAborig-inal or Torres Strait Islander background (indigenousstatus). Parents’ language statuswas also self-identifiedas “speaks a language other than English” (parents’LOTE status). There were 40 different home languagesspoken by the parents in this study. English (86%) wasthe main language spoken at home, followed by Arabic(1.6%), Cantonese (1.3%), Vietnamese (1.0%), Mandarin(0.8%), Greek (0.8%), Italian (0.7%), Samoan (0.5%),Spanish (0.5%), Hindi (0.4%), and other languages. Suchcultural diversity is typical of theAustralian population.Maternal mental health was measured using the 6-itemscreening version of the Kessler scale of nonspecific psy-chological distress (K6;Kessler et al., 2002). TheK6 is aneffective self-report measure for probing symptoms ofanxiety and depression and is a good predictor of mood

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or anxiety disorders measuring concurrent mental state(Furukawa, Kessler, Slade, & Andrews, 2003; Kessleret al., 2003). Items (e.g., “In the past 4 weeks, how oftendid you feel nervous?”) are rated on a 5-point Likert scale(1 = none of the time, 5 = all of the time) and combined toprovide an overall mean score.

Family factors. SESwas described by weekly house-hold income, which was defined as combined yearly in-come before tax. In addition, a specific index of familyfinancial hardship was determined by asking the pri-mary parent whether he or she had experienced anyof seven different types of financial hardship in the past12 months, such as “being unable to pay gas, electricity,or telephone bills on time” or “going without meals.”Summary categories of financial hardship were definedby the total number of indices endorsed by the parent(none, one, two, three, or more). Family size was describedby three related variables: the number of children in thehousehold and whether the LSAC child had older siblingsor had younger siblings. Home learning activities weredetermined by asking the primary parent whether heor someone in the household had provided any of sevendifferent types of learning support, such as reading tothe child, drawing or doing craft activities with the child,or playing with the child (Australian Institute of FamilyStudies, 2007). Aweekly score was recorded for each in-dex on a scale from0=none to 3 = every day andaveragedto generate an overall mean. Television watching werereported by the primary parent as the number of hourson typical weekdays and weekend days that the childwatches TV or videos. Scores ranged from 1 = does notwatch TVor videos to 5 = 5 hours ormore. Smoking in thehousehold was recorded if either parent reported thatthey smoked.

Community factors.Neighborhood disadvantagewasdescribed by SEIFA score (ABS, 2003), which provides ageneral indicator of neighborhood advantage or disadvan-tage based on information collected in the 2001 censusfor each postcode (zip code). A bivariate measure of com-munity disadvantage was computed, with disadvantagebeing equivalent to SEIFA scores > 1 SD below themean.

Analysis PlanOutcome measures were four binomial indicators of

speech/language risk: parent expressive language concern,parent receptive language concern, use of speech-languagepathology services, and low vocabulary comprehensionscore on the Adapted PPVT–III, each of which identifieda speech/language impairment group (impaired) andanon-speech/language impairment group (nonimpaired). De-scriptive analyses determined the frequency (n and% forbinomial variables) ormean scores (andSD for continuousvariables) for the 31 risk/protective factors for impairedand nonimpaired groups on each of the four outcomes.

Logistic regression analysis (Hosmer & Lemeshow,1989) was used to examine the unique contribution ofeach of these 31 risk/protective factors to the four mea-sures of speech/language impairment, in which impair-ment was coded as a binary variable (impaired = 1;nonimpaired = 0). Results report the odds ratio (OR) foreach equation.ORs that are above 1.00 indicate that anincrease in the predictor increases the odds of impair-ment, whereasORs that are below 1.00 indicate that anincrease in the predictor decreases the odds of impair-ment. The closer theOR is to 1.00, the smaller the effectof the predictor. The criteria for significance (p < .05) ofthe OR are based on the ORs for the 95% confidenceinterval (CI) not including 1.00; for example,OR = 1.24,CI [1.05, 1.47] is significant, whereas OR = 1.22, CI[0.83, 1.79] is not significant. A predictor with a sig-nificant OR > 1.00 is considered to be a risk factor forbeing in the impaired group, and a predictor with a sig-nificant OR < 1.00 is considered to be a protective fac-tor. See Tomblin et al. (1997) and Zubrick et al. (2007)for further description of the application of ORs to thefield of speech-language pathology.

Multivariate logistic regressionwas then used to testthe effects of individual predictor variables after adjust-ingmultivariately for the effects of all other child-related,parent-related, family, and community predictors. Thefull set of variables identified as being significant pre-dictors in bivariate analyses was entered in four sepa-rate regression equations, one for each of the outcomemeasures. These analyses assess the combined predic-tive effect of the full set of variables, as reported by theModel c2 and theNagelkerke pseudoR2. TheNagelkerkemeasure is an analog to theR2 produced bymultivariatelinear regression and provides an approximation of thepercent of variance explained (Tabachnick & Fidell, 2001).Effect size can be assessed by calculating the square-rootof theR2 andusingCohen’s (1988) guidelines: small effectsize, R = .1; medium, R = .3; large, R = .5. As noted pre-viously, the effect of each individual variable in themodelis indicated by the OR. Results of the analyses also pro-vide three additional statistics: the overall percent ofcases classified correctly; sensitivity or percent identifi-cation of true positives (impaired group), being the num-ber of true positives divided by true positives plus falsenegatives; and specificity or percent identification of truenegatives (nonimpaired group), being the number of truenegatives divided by true negatives plus false positives.

ResultsBivariate Analyses

Table 2 presents the results of descriptive and logis-tic regressionanalyses for eachof the31 riskandprotectivefactors for the four outcome measures: (a) parent-reported

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Table 2. Bivariate descriptive statistics and logistic regression results for child, parent, family, and community predictors of children being in the speech and language impaired (impaired) andnon-speech and language impaired (nonimpaired) groups.



Outcome 1:Parent-reported expressive

speech and language concernn = 4,980 to 3,197

Outcome 2:Parent-reported receptive

language concernn = 4,980 to 3,196

Outcome 3:Use of speech-language

pathology servicesn = 4,179 to 3,183

Outcome 4:Low receptive vocabularyPPVT–III score < 1 SDn = 4,375 to 2,916

Impairedn (%) orM (SD )

Nonimpairedn (%) orM (SD )

OR(95% CI)

Impairedn (%) orM (SD )

Nonimpairedn (%) orM (SD )

OR(95% CI)

Impairedn (%) orM (SD )

Nonimpairedn (%) orM (SD )

OR(95% CI)

Impairedn (%) orM (SD )

Nonimpairedn (%) orM (SD )

OR(95% CI)

Child factorsSex Male 808













Prematurity 77(6.4%)












Birth weight(< 2500 g)


























Single vs.multiplebirths

Multiple birth 39(3.1%)













Asthma 296(23.6%)












Bronchiolitis 217(17.5%)












Ear infections 145(11.6%)












Hearing status Ongoinghearingproblems













Oral suckinghabits/breastfeeding

Breastfed for> 9 months













Temperament Persistence 3.64(1.02)












Reactivity 2.89(1.00)












Social 3.77(1.20)












(Continued on the following page)








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Table 2 Continued. Bivariate descriptive statistics and logistic regression results for child, parent, family, and community predictors of children being in the speech and language impaired(impaired) and non-speech and language impaired (nonimpaired) groups.



Outcome 1:Parent-reported expressive

speech and language concernn = 4,980 to 3,197

Outcome 2:Parent-reported receptive

language concernn = 4,980 to 3,196

Outcome 3:Use of speech-language

pathology servicesn = 4,179 to 3,183

Outcome 4:Low receptive vocabularyPPVT–III score < 1 SDn = 4,375 to 2,916

Impairedn (%) orM (SD )

Nonimpairedn (%) orM (SD )

OR(95% CI)

Impairedn (%) orM (SD )

Nonimpairedn (%) orM (SD )

OR(95% CI)

Impairedn (%) orM (SD )

Nonimpairedn (%) orM (SD )

OR(95% CI)

Impairedn (%) orM (SD )

Nonimpairedn (%) orM (SD )

OR(95% CI)

Child factorsLanguages


spoken toin LOTE













Speaks anLOTE













Parent factorsMaternal age

at birthof child

Maternal ageat birthof child













Educational levelof parents

Mother ’s yearsof education













Father ’s yearsof education













Maternal mentalhealth














Minority statusor race














Languagesspoken andproficiencyin English

Parents’ LOTEstatus













(Continued on the following page)





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Table 2 Continued. Bivariate descriptive statistics and logistic regression results for child, parent, family, and community predictors of children being in the speech and language impaired(impaired) and non-speech and language impaired (nonimpaired) groups.



Outcome 1:Parent-reported expressive

speech and language concernn = 4,980 to 3,197

Outcome 2:Parent-reported receptive

language concernn = 4,980 to 3,196

Outcome 3:Use of speech-language

pathology servicesn = 4,179 to 3,183

Outcome 4:Low receptive vocabularyPPVT–III score < 1 SDn = 4,375 to 2,916

Impairedn (%) orM (SD )

Nonimpairedn (%) orM (SD )

OR(95% CI)

Impairedn (%) orM (SD )

Nonimpairedn (%) orM (SD )

OR(95% CI)

Impairedn (%) orM (SD )

Nonimpairedn (%) orM (SD )

OR(95% CI)

Impairedn (%) orM (SD )

Nonimpairedn (%) orM (SD )

OR(95% CI)

Family factorsSocioeconomic




























Family size Number ofchildrenin thehousehold













Older siblings 773(61.6%)

























Home learningactivities

Support forchildren’slearning














TV watching(weekdays)













TV watching(weekends)













Smoking in thehousehold

Smoking in thehousehold













Community factorsNeighborhood

disadvantageSEIFA > 1 SDbelow mean













Note. PPVT–III = Adapted Peabody Picture Vocabulary Test—III (Rothman, 2003); LSAC = The Longitudinal Study of Australian Children (Sanson et al., 2002); LOTE = language other than English;SEIFA = Socio-Economic Indexes for Areas (Australian Bureau of Statistics, 2003). Significant outcomes (p < .05) are indicated by bold type.








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expressivespeechandlanguageconcern, (b)parent-reportedreceptive language concern, (c) use of speech-languagepathology services, and (d) low vocabulary comprehen-sion (score < 1 SD below the mean on the AdaptedPPVT–III). An examination of theORs and CIs for theseanalyses showed that all but 2 of the 31 predictor vari-ables were significant predictors of at least one measureof speech/language impairment. Twenty-two variableswere significant for three or four of the four outcomemea-sures. Risk factors for the child were being male, havingahistory of perinatal complications (prematurity, lowbirthweight, use of neonatal intensive care), having past andongoingmedical conditions (asthma, ear infections, hear-ing problems), having a more reactive temperament, andhaving older siblings. Higher levels of financial hardshipandmoreweekend andweekday television viewingwerefamily risk factors for all four outcomes.Protective factorsfor the child were being breastfed for > 9 months andhaving a more persistent temperament. Family protec-tive factors were mothers and fathers having completedmore years of education, higher levels of maternal psy-chologicalwell-being, andhigher household income.Pre-dictors thatwere identified as both a risk and a protectivefactor for different outcomes were the child speaking orbeing spoken to in LOTE, and parents’ being of indig-enous background or speaking LOTE. A further sevenvariables were identified as significant predictors forone or two of the outcomes: childhood bronchiolitis (risk),greater temperamental sociability (protective), olderma-ternal age at birth (protective), larger family size (risk),greater support for children’s learning in the home (pro-tective), smoking in the household (risk), and living in amore disadvantaged neighborhood (risk). Two predic-tors, being amultiple birth and having younger siblings,were not significant for any of the four outcomes.

Multivariate AnalysesMultivariate logistic regressions were used to test the

above predictor variables collectively. Variables that werefound to be nonsignificant in bivariate testswere excluded.Highly intercorrelated variables were either representedby a single variable (e.g., child speaking or being spoken toin LOTE was dropped and parents’ LOTE status was re-tained) or combined (e.g., weekday andweekend televisionviewing were summed). This process left 26 factors, whichwere included in four separate multivariate logistic re-gression analyses. Results are presented in Table 3.

Significant predictors forOutcome1:Expressive speechand language concern. Of the 26 putative risk/protectivefactors included in the model, 8 achieved significance asa predictor of parent-reported expressive speech andlanguage concern (impaired group). Increased odds forbeing in the impaired group were associated with thefollowing risk factors: being male (OR = 1.97), having

ongoing problems with ear infections (OR = 1.41) andhearing problems (OR = 3.18), having a more reactivetemperament (OR = 1.13), and having older siblings(OR = 1.26). Reduced odds for being in the impairedgroup were associated with the following protectivefactors: having a more persistent temperament (OR =0.75), mothers having higher ratings for psychologicalwell-being (OR = 0.74), and parents speaking an LOTE(OR = 0.52). The fullmodel accounted for 11.7% (pseudoR2 = .117) of the variance, which is equivalent to a me-dium effect size (R = .34; Cohen, 1988). In comparison,Reilly et al. (2007), usingmultivariate linear regressionand a smaller set of significant predictors (4), reporteda lower proportion of the explained variance (6.4% and7.0%) formothers’ ratings of their 2-year-olds’ speech andexpressed vocabulary. The proportion of cases classifiedcorrectly was relatively low (76.5%). Although the modelhad high specificity (97.7% of nonimpaired cases werecorrectly identified), it had poor sensitivity (11.3% of im-paired cases were correctly identified).

Significant predictors for Outcome 2: Receptive lan-guage concern. Seven variables were significant predic-tors for parental report of receptive language concern.Increased odds were evident for the following riskfactors: beingmale (OR = 1.39), having ongoing hearingproblems (OR = 4.43), and having a more reactive tem-perament (OR=1.47). Reduced oddswere evident for thefollowing protective factors: being breastfed for > 9months(OR = 0.70), having amore persistent temperament (OR =0.54), mothers having higher ratings for psychologicalwell-being (OR = 0.66), and the presence of older siblings(OR = 0.67). The full model accounted for 21.5% of thevariance. This is equivalent to amoderate-to-large effectsize (R2 = .215, R = .46; Cohen, 1988). The proportion ofcases classified correctly was relatively high (91.8%), butthis was due to the model’s high specificity (99.5% ofnonimpaired cases were correctly identified). The modelhad poor sensitivity (9.8% of impaired cases were cor-rectly identified).

Significant predictors for Outcome 3: Attendance atspeech-language pathology. Ten predictors were associ-ated with children’s attendance at speech-language pa-thology. Increased odds were noted for beingmale (OR =1.71), being premature (OR = 1.84), having asthma (OR =1.33), having ongoing hearing problems (OR = 2.95),having older siblings (OR = 1.39), and spending moretime watching television (OR = 1.10). Increased odds werealso seen for home support for learning (OR = 1.36), withmore support being associated with greater likelihood ofbeing in the impaired group. This finding is difficult toexplain in that it may reflect the use of speech-languagepathology services by parents who are also more sup-portive of their children’s learning, but equally it mayreflect the additional support that parents are provid-ing at home as a result of their children attending

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speech-language pathology. Reduced odds were seenfor the following protective factors: possessing a moresociable and more persistent temperament (ORs = 0.91and 0.79) and parents speaking an LOTE (OR = 0.55).The combined set of predictors accounted for 9.2% of thevariance for attendance at speech-language pathology(R2 = .092, R = .30). The lower figure for this outcome

(although still equivalent to amediumeffect size) is likelydue to additional factors, such as the availability andaffordability of speech-language pathology, which werenot assessed. The proportion of cases classified correctlywas 85.8%, with high specificity (99.7% of nonimpairedcases were correctly identified) but poor sensitivity (2.6%of impaired cases were correctly identified).

Table 3. Multivariate logistic regression: adjusted odds ratio and cumulative amount of variance (pseudo R2) for child, parent, family, andcommunity predictors of speech and language impairment.

LSAC identifier

Outcome 1:Parent-reportedexpressive speechand language


c2(26, N = 3,197) =256.22;

pseudo R2 = .117Adjusted OR (p)

Outcome 2:Parent-reported

receptive languageconcernModel

c2(26, N = 3,196) =314.05;

pseudo R2 = .215Adjusted OR (p)

Outcome 3:Use of speech-

language pathologyservicesModel

c2(26, N = 3,183) =164.93;

pseudo R2 = .092Adjusted OR (p)

Outcome 4:Low receptivevocabularyPPVT < 1 SDModel

c2(26, N = 2,916) =369.63;

pseudo R2 = .239Adjusted OR (p)

Child factorsMale 1.97 (<.001) 1.39 (.025) 1.71 (<.001) 1.29 (.055)Premature 1.17 (.549) 1.59 (.222) 1.84 (.035) .86 (.711)Birth weight (<2500 g) 1.04 (.863) .87 (.687) 1.14 (.612) 1.61 (.127)Neonatal intensive care 1.00 (.976) .96 (.824) .90 (.513) .94 (.767)Breastfed for > 9 months .87 (.147) .70 (.022) .83 (.101) .89 (.387)Asthma 1.01 (.930) 1.15 (.414) 1.33 (.024) 1.12 (.491)Bronchiolitis .97 (.818) 1.21 (.279) 1.06 (.680) .96 (.825)Ear infections 1.41 (.024) 1.38 (.136) 1.30 (.141) 1.27 (.319)Ongoing hearing problems 3.18 (<.001) 4.43 (<.001) 2.95 (<.001) 1.32 (.423)Temperament: Social .94 (.105) .98 (.676) .91 (.038) .86 (.007)Temperament: Persistence .75 (<.001) .54 (<.001) .79 (<.001) .82 (.005)Temperament: Reactivity 1.13 (.012) 1.47 (<.001) 1.11 (.084) 1.19 (.018)

Parent factorsMaternal age at birth of child 1.00 (.805) 1.00 (.852) 1.02 (.078) .96 (.004)Mother ’s years of education 1.00 (.979) .95 (.155) 1.00 (.935) .91 (.002)Father ’s years of education .99 (.559) 1.02 (.609) .98 (.320) .96 (.167)Maternal psychological distress/well-being .74 (<.001) .66 (<.001) .92 (.348) .78 (.014)Parents’ LOTE status .52 (<.001) 1.20 (.370) .55 (.002) 5.60 (<.001)Parents’ indigenous status .88 (.615) .99 (.987) .81 (.502) 1.14 (.711)

Family factorsHousehold income .99 (.614) .98 (.636) .97 (.321) .91 (.004)Financial hardship 1.03 (.670) 1.13 (.262) 1.06 (.547) 1.01 (.931)Number of children in the household 1.01 (.864) .94 (.469) .93 (.276) 1.43 (<.001)Older siblings 1.26 (.032) .67 (.017) 1.39 (.012) .88 (.411)Support for children’s learning at home 1.15 (.100) .99 (.946) 1.36 (.004) .68 (.002)TV watching (weekdays + weekends) 1.05 (.138) 1.02 (.719) 1.10 (.020) 1.03 (.516)Smoking in the household .92 (.555) 1.35 (.118) 1.04 (.801) 1.69 (.002)

Community factorsSEIFA > 1 SD below mean .88 (.284) 1.19 (.332) .83 (.209) 0.94 (.719)

Proportion of cases classified correctly 76.5% 91.8% 85.8% 89.6%Sensitivity 11.3% 9.8% 2.6% 15.8%Specificity 97.7% 99.5% 99.7% 99.0%

Note. Pseudo R2 is calculated according to Nagelkerk, as provided by the SPSS output. Significant outcomes (p ≤ .05) are indicated by bold type.

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Significant predictors for Outcome 4: Low score onAdapted PPVT–III. The largest number of significantpredictors (12) was identified for having a low assess-ment score (< 1 SD below the mean) on the AdaptedPPVT–III. Increased odds for being in the identifiedgroup were associated with five risk factors: being male(OR = 1.29), having a more reactive temperament (OR =1.19), parents speaking an LOTE (OR = 5.60), havingmore children in the household (OR = 1.43), and parentalsmoking in the household (OR = 1.69). Reduced odds wereassociated with the following protective factors: thechild having a more sociable and persistent tempera-ment (ORs = 0.86 and 0.82); mothers being older at thebirth of child (OR = 0.96), having more years of edu-cation (OR = 0.91), and reporting higher levels ofpsychological well-being (OR = 0.78); a higher house-hold income (OR = 0.91); and parents providing morehome support for learning (OR = 0.68). The full modelaccounted for almost one-quarter (23.9%) of the var-iance (R2 = .239, R = .49), which was equivalent toa moderate-to-large effect size. The proportion of casesclassified correctly was 89.6%, with high specificity(99.0% of nonimpaired cases were correctly identified)and low sensitivity (15.8% of impaired cases were cor-rectly identified).

The variance explainedwas similar to that noted forthe association for parent-reported receptive languageconcern (R2 = .215, R = .46); however, the pattern ofpredictors was notably different. Low scores on assessedreceptive vocabularywere strongly associatedwith envi-ronmental indicators (parent, family, and community);however, parent-rated concern was primarily associ-ated with child-related factors. In addition, the presenceof ongoing hearing problems was highly significant forparent-rated but not assessed poor receptive vocabulary(OR = 4.43, p < .001 vs. OR = 1.32, p = ns), and parents’LOTE status was highly significant for assessed butnot parent-rated low receptive vocabulary (OR = 5.60,p < .001 vs. OR = 1.20, p = ns).

The discrepancy in results for parents’ LOTE statusis likely due to the influence of the primary languagebeing assessed in each outcome measure. The AdaptedPPVT–III assessed competency in English, whereasparents were reporting on the child’s first and otherlanguages in the outcome measures of receptive andexpressive language concern. This wasmost notable forparent-reported expressive language concern, in whichLOTE status was a protective factor (OR = 0.52). Thediscrepancy in results for ongoing hearing problemsmay also be due to parents’ interpretation of the ques-tion “Do you have any concerns about how your childunderstands what you say to him?” which could be in-terpreted as “hearing what you say” or “understandingthe language you use.”

DiscussionRecent conceptualizations of child functioning

(World Health Organization, 2007) acknowledge thatbiological, psychosocial, and societal factors interactivelyinfluence development. Understanding and explicatingthe underlying contributors to childhood speech and lan-guage impairment, therefore, requires consideration ofmultiple attributes of the individual as well as of thefamily and social contexts, each of whichmay expose thechild to risk or protection. The present study has appliedthis model (Bronfenbrenner, 2005) to investigate child,parent, family, and community predictors of speech andlanguage impairment in a nationally representative sam-ple of 4- to 5-year-old children. Speech and languageimpaired versus nonimpaired status was described byfour distinct measures: parent-reported expressive andreceptive language concern, teacher- andparent-reporteduse of speech-language pathology services, and assessedreceptive vocabulary. Results for these four outcomesidentified a core set of significant predictors related tochild factors (being male; having ongoing hearing prob-lems; and having a less persistent, less sociable, andmore reactive temperament), parent factors (mothers witha lower sense of psychological well-being, parents speak-ing languages other than English), and family factors(support for children’s learning at home, the presence ofolder siblings). Table 4 provides a summary of these ef-fects and their direction (risk or protection). The first sixrows indicate the factors that were consistently associ-ated with all four indicators of speech and language im-pairment; the remaining rows showwhere discrepancieswere noted in the direction of the effects.

These and other results presented in this study addto existing knowledge that may be useful for primarycare professionals seeking to identify children at risk,who may require specialist assessment of their speechand language development or who would benefit fromearly intervention. The findings from this investigationconfirmed that childhood speech and language impair-ment is influenced by multiple factors. Each of these isdiscussed in turn.

Child FactorsBeing male was a significant risk factor for all four

outcome measures: parental concern regarding expres-sive as well as receptive speech and language difficulties,havinga lowscore for receptivevocabulary, andattendingspeech-language pathology services (ORs of 1.29–1.97).These findings were not surprising considering previousresearch (reviewed earlier in this article), but they doconfirman identifiable risk at an agewhenmost boys areattending an early childhood education program (Harrison& Ungerer, 2005). A message that boys are at risk for

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poorer speech and language should be strongly impartedto primary care professionals, including early childhoodteachers, who are in a position to recommend referral forspeech/language assessment.

Having ongoing hearing problems was also an im-portant risk factor, particularly for expressive and recep-tive language as reported by parents and for receipt ofspeech-language pathology support. Results showed thatchildrenwith ongoing hearing problemswere three timesmore likely to be identified with expressive speech andlanguagedifficulties and to be attending speech-languagepathology (ORs = 3.19 and 2.96) and four times morelikely to have difficulties with receptive language abilitythan their nonaffected peers (OR = 4.43). These resultsconfirm work by Yliherva et al. (2001) that included on-going hearing problems as a predictor for speech andlanguage impairment in a similarly large-scale studyof Finnish children. Both studies support the need forinfant hearing screening programs that can assist withearly identification of children with hearing problemsand have the potential to reduce the observed risk tochildhood speech and language.

Child medical conditions that predicted at least oneof the four speech/language outcomes were asthma, whichwas identified as increasing risk for attendance at speech-language pathology (OR = 1.33), and ongoing ear in-fections, which were a risk for expressive speech andlanguage concern (OR = 1.41).

A protective factor for receptive language was beingbreastfed for > 9 months (OR = 0.70). Although this wasincluded as an indicator of oral sucking habits, withbenefits for children’s oromuscular development, pro-longed breastfeeding is also likely to be influenced byand interact with parental and family factors, such asmaternal well-being and socioeconomic status.

Child temperament characteristics were an impor-tant and consistent predictor of speech and language

impairment, being associated with all four outcomes.Children with a more persistent temperament (i.e., ableto stay on task, keep on trying and do not give up easily)were less likely to have difficulties with speech and lan-guage (ORs of 0.54–0.82). Previous work by Hauner et al.(2005) has also shown that “decreased task persistence orattention” (p. 635)was a significant risk factor for speechimpairment. On the other hand, amore reactive temper-ament was associated with greater risk of speech andlanguage impairment (ORs of 1.13–1.47). Similar find-ings were noted by Hauner et al. (2005) who described“approach-related or withdrawal-related negative affect,negative emotionality or mood” (p. 635) as a significantrisk factor for increased severity of speech impairment.Reactivity in young children has been associated withinhibition and fearfulness in novel situations, impulsiv-ity, and behavioral dysregulation (see Sanson, Hemphill,& Smart, 2004), all of which could tend to impede speechand language acquisition. Temperamental sociability, onthe other hand, was associated with a reduced risk ofspeech and language impairment (ORs = 0.91 and 0.86).Sociable children tend to have more positive social rela-tions and be more popular with friends (Sanson et al.,2004), characteristics that are likely to enhance speechand language development. Prior et al. (2008) found thatshy children were at increased risk for communicationdifficulties; however, the authors stated it was unknownwhether shyness affected the child’s initiation and respon-siveness in conversation or whether poor communicationled to the child’s shyness.

Parent FactorsAmong the risks and protective factors associated

with the child’s home environment, maternal psycholog-ical well-being was consistently found to positively im-pact children’s expressive and receptive language (ORs =0.66–0.78). Conversely, children did less well when their

Table 4. Consistent risk and protective factors relating to speech and language impairment in a nationally representative sample of 4- to5-year-olds.

Risk/Protective factor

Outcome 1:Expressive speechand language


Outcome 2:Receptivelanguageconcern

Outcome 3:Attend


Outcome 4:Low scoreon AdaptedPPVT–III

Male Risk Risk Risk RiskOngoing hearing problems Risk Risk RiskTemperament: Social Protective ProtectiveTemperament: Persistence Protective Protective Protective ProtectiveTemperament: Reactivity Risk Risk RiskMaternal psychological well-being Protective Protective ProtectiveParents’ status regarding languages other than English spoken Protective Protective RiskOlder siblings Risk Protective RiskSupport for children’s learning at home Risk Protective

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mothers reported more symptoms of anxiety and de-pression. The sociofamilial environment, particularlylanguage stimulation and responsivity, is an importantcontext for the development of language skills (Desmarais,Sylvestre, Meyer, Bairati, & Rouleau, 2008). It has beenhypothesized thatmaternalwell-being facilitates languagestimulation in the home and conversely that maternalpsychological distress or depression reduces languagestimulation (Prior et al., 2008).

The home language environment was a further pre-dictor of speech and language impairment; however, asdiscussed in the previous section, the direction of re-sults differedby the outcomebeingmeasured. Parentswhospoke an LOTE reported less concern about their child’sexpressive speech and language ability (OR = 0.52), buton the other hand, these children were more likely to bein the low scoring group forEnglish receptive vocabulary(OR = 5.60), a discrepancy likely due to the parentreporting on all of the languages spoken by the child, notjust English. Children of parents who spoke an LOTEwere also less likely to be attending speech-languagepathology (OR = 0.55), but the reduced use of speech-language pathology could be due to cross-cultural dif-ferences in definitions of disability and differences inaccessing professional services (cf.Hwa-Froelich&Westby,2003). When comparing these results to previous findings,it is important to note that several studies that haveexamined risk factors have specifically excluded childrenwith bilingualism (e.g., Fox et al., 2002; Tallal et al.,1989). Three studies found increased risk for speech/language impairment in nondominant language speakerswithin examined societies that were English-dominant(Reilly et al., 2007), French-dominant (Chevrie-Mulleret al., 2005), and Dutch-dominant (Peters et al., 1997).However, other research (summarized in Table 1) hasshown mixed results for the impact of proficiency inhome language and minority status or race. In the pre-sent study, the inclusion of four distinct outcome mea-sures has enabled the current research to examine someof the discrepancies in the results of these studies.

Family FactorsThe presence of siblings is a further feature of the

home environment that was expected to impact child-hood speech and language impairment. In the presentstudy, only having an older sibling had a significanteffect, being a risk for expressive speech and languageconcern and for attending speech-language pathology(ORs = 1.26 and 1.39) and a protective factor for recep-tive language concern (OR = 0.67). These findings pro-vide some evidence to support the suspicion, often notedin clinical folklore, that older siblings are more verbal(i.e., use more “air time”) than younger siblings andspeak on behalf of their younger siblings. Relatedly,

Barr, McLeod, and Daniel (2008) found that typicallydeveloping children who are a sibling of a child with acommunication impairment can be worried, protective,and act as an interpreter for their sibling. The benefitis that younger siblings hear rich language from theirolder siblings as well as their parents and therefore donot have receptive language difficulties. The finding thathaving younger siblings was neither a significant risknor a protective factor is further evidence to support thesespeculations.

Children’s access to a supportive home-learning en-vironment was a protective factor for assessed receptivevocabulary (OR = 0.68). This result, alongwithmaternalage and years of education, family income, and house-hold size, suggest that children from more advantagedfamilies with parents who were more involved in theirchildren’s learning were less likely to be classified ashaving poor receptive vocabulary (on the AdaptedPPVT–III). The result is consistent with other findingsfor the LSAC children linking sociodemographic vari-ables (education, income, occupation, SEIFA) and sup-port for home learningwith enhanced learning outcomes(Wake et al., 2008) and with recent findings from theUnited States linking higher SES backgrounds withgreater vocabulary at school entry (Rowe & Goldin-Meadow, 2009).

Smoking in the household was a significant risk fac-tor for low scores on the Adapted PPVT–III (OR = 1.68).A similar finding was noted for kindergarten childrenstudied by Tomblin et al. (1997) but not for 2-year-olds(Zubrick et al., 2007).

Television watching is a variable that had not beenassessed in previous studies. Bivariate analyses identi-fied more time spent in television watching as a signif-icant risk for all four outcomes; however, it was onlyretained for the use of speech-language pathology ser-vices (OR = 1.10) in the multivariate analyses. It is notclear whether the amount of TV watching affected thechild’s ability to engage in communication or whetherpoor communication skills led to the increase in TVwatching.

StrengthsTo summarize, the strengths of the current study

include the size of the Longitudinal Study of AustralianChildren (LSAC) sample, the use of population samplingto represent all 4- to 5-year-olds in Australia, the in-clusion of a broad range of potential risk and protectivefactors, and the concurrent testing of the effects of thesefactors using a multivariate design. The current studyalso included new and rarely studied variables such aschild temperament and features of the home environ-ment such as television watching and smoking in thehousehold. Furthermore, by using and comparing findings

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for four speech and language outcome measures, thisstudy has enabled a deeper consideration of some of thecomplexities involved in the processes influencing speechand language acquisition.

This study adds to the growing number of large-scale studies that are examining theways that biologicaland environmental factors explain or influence speechand language development in early childhood (Reilly et al.,2006, 2007; Zubrick et al., 2007). The results of recentresearch with children under 2 years of age have sug-gested that biological trajectories are the primary driversof early communication and vocabulary development(Zubrick et al., 2007) and that social and environmentalfactors have a minimal effect (e.g., explaining less than7%of the variance;Reilly et al., 2009).Findingspresentedhere are consistent with this work, in that factors intrin-sic to the child (sex, temperament) or related to neuro-biological mechanisms (prematurity, low birth weight,perinatal difficulties) were significant predictors of speechand language impairment. However, ongoingmedical con-ditions, which are likely to be affected by social and envi-ronmental conditions, were also important and consistentpredictors, as were other factors related to the family en-vironment. The overall impression from the present studyis that psychosocial and socioeconomic factors do con-tribute to childhood speech and language development,at least at age 4–5 years. The variance explained forexpressive and receptive language (11.7%, 21.5%, and23.9%) was considerably larger than reported by Reillyet al. (2009) for 2-year-olds. This suggests that screeningfor child and family risk factors in combination with ob-servations or informal testing of children’s speech andlanguage ability and vocabulary growth in relation to de-velopmental norms is likely to provide the best means ofidentifying childrenwho need referral to speech-languagepathology or specialist early intervention.

LimitationsThe limitations of the current study relate to the

measures that were possible to collect in a large multi-dimensional study such as LSAC. Only one of the out-comes was based on direct assessment of the children(Adapted PPVT–III). Two of the outcomes were basedon parent report of concern, and although the validityof two outcomes has been confirmed (Harrison, McLeod,Berthelsen, &Walker, 2009; McLeod & Harrison, 2009),these should be considered a screening measure ratherthan representing a comprehensive assessment. A furtherlimitation is the lack of information pertaining to fam-ily history of speech and language impairment. This isan important predictor, which would likely add to thevariance explained. The large amount of unexplainedvariance is acknowledged as a limitation; however, wenote that other multivariate analyses of the LSAC data

set (predicting other child outcomes) have reported sim-ilar figures (10%–25%, Harrison, Ungerer, et al., 2009;7%–20%, Wake et al., 2008). The multivariate logisticregression results for proportion of cases classified cor-rectly, sensitivity, and specificity also point to limita-tions in the predictive power of this approach. The set ofpredictor variables did not correctly identify impairedcases (low sensitivity), although there was a high levelof specificity (correctly identified nonimpaired cases).

ConclusionThis study has extended previous work on identifi-

cation of risk and protective factors for childhood speechand language impairment by using a large, nationallyrepresentative sample of 4- to 5-year-old children. Acomprehensive range of predictor variables was used,including previously unexplored or underexplored vari-ables. The multivariate design enabled testing of thecollective contribution of these predictors. When the fullset of child, parent, family, and community variables wasincluded in themodel, many of the variables identified assignificant predictors in bivariate analyses were elimi-nated due to overlapping of underlying constructs. Ninefactorswere consistently identified inmultivariate anal-yses as having a unique effect on speech and languageimpairment: being male (risk), having ongoing hearingproblems (risk), having a more reactive temperament(risk), having a more persistent temperament (protec-tive), having amore social temperament (protective), in-creasedmaternal well-being (protective), having an oldersibling (risk/protective), parental LOTE status (risk/protective), and support for children’s learning in thehome (risk/protective). These early risk and protectivefactors alongwith observations of children’s speech andlanguage milestones can be a useful guide for primarycare professionals seeking to identify children who ben-efit from early intervention communication programs.

AcknowledgmentsThis research was supported by the following sources:

Australian Research Council Discovery Grant DP0773978and the Charles Sturt University Research Institute forProfessional Practice, Learning, and Education. An earlierversion of a portion of this article was presented at the 2008Conference of the International Clinical Linguistics andPhonetics Association in Istanbul, Turkey. The authors wouldlike to acknowledge the contribution of the Australian RotaryHealth Fund and Foundation for Children Research Grantand the members of the Longitudinal Study of AustralianChildrenResearchConsortium: JohnAinley, DonnaBerthelsen,Michael Bittman, Linda Harrison, Ilan Katz, Jan Nicholson,Bryan Rodgers, Ann Sanson, Michael Sawyer, Sven Silburn,Lyndall Strazdins, Judy Ungerer, Graham Vimpani, MelissaWake, and Stephen Zubrick.

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Received April 24, 2008

Revision received April 3, 2009

Accepted July 30, 2009

DOI: 10.1044/1092-4388(2009/08-0086)

Contact author: Linda Harrison, School of Teacher Education,Charles Sturt University, Panorama Avenue, Bathurst,NSW, 2795, Australia. E-mail: [email protected].

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