Children and Youth Services Review 33 (2011) 732–740

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Children and Youth Services Review

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Does teacher educational training help the early math skills of English languagelearners in Head Start?

Sunha Kim a,⁎, Mido Chang a, Heejung Kim b

a Virginia Tech, United Statesb University of Virginia, United States

⁎ Corresponding author. Virginia Tech, 304 East E24061, United States.

E-mail address: sunkim@vt.edu (S. Kim).

0190-7409/$ – see front matter © 2010 Elsevier Ltd. Aldoi:10.1016/j.childyouth.2010.11.019

a b s t r a c t

a r t i c l e i n f o

Article history:Received 28 June 2010Received in revised form 12 November 2010Accepted 14 November 2010Available online 23 November 2010

Keywords:Head Start programTeachersImmigrant childrenHierarchical Linear Modeling (HLM) analysis

To address the early performance gap between native-born and immigrant children and to seek ways toreduce the disparity, the study explored the effect of teacher qualification on early math skills of preschoolers,with focused attention to immigrant children. Particularly, the study examined the effects of teachereducational levels, certification, and professional training, by employing Hierarchical Linear Modeling (HLM)analysis to a US nationally representative database from the Head Start Family and Child Experiences (FACES).The study found a significantly positive effect of teacher qualification for immigrant preschoolers in HeadStart, who displayed lower early math skills than their native-born counterparts. Among immigrantpreschoolers, those who were with teachers of lower educational levels showed significantly lower earlymath skills compared with their immigrant peers with teachers of higher educational levels. The study alsosupported teacher certification and professional training as potential mediators in promoting early mathcompetency of immigrant students, especially with teachers of lower educational levels.

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© 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Despite ongoing efforts by educators to promote mathematicalproficiency, American students' performance in mathematics rankslower than the international average (Baldi, Jin, Green, & Herget,2007). Moreover, there is a large gap between native-born andimmigrant students; immigrant students, especially those withlimited English proficiency (LEP) have lower performance inmathematics (Baldi et al., 2007; Haile & Nguyen, 2007). This gapappears in the early years of schooling and widens as studentsadvance through school (Liu, Anderson, & Thurlow, 2000; Perie, Grigg,& Dion, 2005). Preschool is a critical period to ensure academicsuccess during later schooling, and should be a time to reduce theeducational performance gap between mainstream and minoritychildren (National Research Council, 2000, 2009). As one way topromote high-quality preschool education, educational researchershave emphasized the importance of teacher educational andprofessional training. Indeed, many studies have provided evidencesof the importance of teachers' educational training in early childhoodeducation; teachers with higher educational backgrounds tend toelicit better learning outcomes, manage early childhood classroomsbetter, and demonstrate more positive interaction with children thanthose with lower educational training (Clarke-Stewart, Vandell,

Burchinal, O'Brien, & McCartney, 2002; Saracho & Spodek, 2007;Tout, Zaslow, & Berry, 2006).

On the other hand, a recent report from the National ResearchCouncil (NRC) stated that recent research results “call into thequestion the assumption that having a degree – especially an earlychildhood degree – must produce better developmental and learningoutcomes for children” (p.306, 2009). If this skepticism about theeducational background of preschool teachers is true, we shouldmakeefforts to improve other conditions of early childhood educationrather than spending our financial resources on teachers' education orcertification. Onwhat basis dowemake a sound educational policy forearly childhood education? This study was designed to examine thisunsettled but important educational issue and provide empiricalresults. The main goal of the study was to explore a relationshipbetween teacher professional development and the early mathemat-ical preparation of preschoolers from low-income families. As forteacher professional development, the study examined teachers'educational backgrounds, state-award certification, and professionaltraining. These issues are particularly important considering therelatively low educational backgrounds of teachers in Head Start(Saracho & Spodek, 2007). The study first explored whether the lowformal education of teachers functioned as a barrier to preschoolers inHead Start. Second, the study examined teacher certification andteacher training to see whether those two teacher factors would workas potential mediators to produce high quality preschool educationamong teachers with low educational backgrounds (associate degreeor lower). The study results are expected to contribute to theeducational policy for teacher professional support in Head Start.

733S. Kim et al. / Children and Youth Services Review 33 (2011) 732–740

This attempt would align with the efforts of Head Start concerning therelatively low educational preparation of Head Start teachers (Saracho& Spodek, 2007). Head Start programs have made an effort to raiseteacher qualifications by setting educational standards for all teachersto have at least an associate degree by 2003 and 50% of teachers toobtain a bachelor's degree by 2008 (Whitebook, 2003).

Another important attempt of the study is to pay special attentionto English Language Learner (ELL) children in Head Start. Statisticsshow an ever-increasing growth rate of ELL students. From 1996 to2006, the growth rate of ELL students was 57.17% in the total PK-12enrollment, having 3.66% of a growth rate of the total enrollment(NCELA, 2010). Approximately 10 million students in the US, ages 5to 17, speak a language other than English at home (NCES, 2005).Unfortunately, a large number of ELL students have displayed sig-nificantly low academic performance, including performance in themath subject area. The National Assessment of Educational Progress(NAEP) presented the math performance gap between ELL and nativestudents in fourth grade (217 for ELL vs. 242 for English-speaking)and in eighth grade (245 for ELL vs. 282 for English-speaking) (Lee,Grigg, & Dion, 2007).

Many researchers suggest that gaining a bachelor's degree or ateaching certificate can be one way to improve the qualification ofteachers who serve many ELL children (Darling-Hammond, Berry, &Thoreson, 2001; Goldhaber & Brewer, 1996). Research also shows thatproviding professional training for those teachers is a critical way tohelp them to add knowledge and pedagogy in teaching ELL students(Dong, 2002; Janzen, 2008; Jiang, 2006; King, Shumow, & Lietz, 2001;Zientek & Thompson, 2008). Overall, researchers have suggested thepotential of teacher professional development to help ELL students'success in formal schooling. However, there is dearth of research thathas studied the professional development of Head Start teachers thatpromotes the performance of ELL children. We hope that the findingsof the study contribute to fill the research gap.

The study used a US nationally representative dataset from theHead Start Family and Child Experiences (FACES) Survey. Benefitingfrom advanced features of two-level Hierarchical Linear Modeling(HLM) analysis for multilevel data, this study examined how thevariables of teacher educational training at the higher level interactwith student variables at the lower level. The overarching researchquestions of this study are as follows:

• Are there significant relationships between the following threeteacher variables and the early mathematical skills of preschoolersin Head Start?o Teacher educational levelso Teacher preschool certificateso Teacher professional training• Are there significant differential effects of the teacher variables forELL preschoolers?

2. Literature review

2.1. Head Start and immigrant children

Head Start is a national program administered by Administrationfor Children and Family (ACF), U.S. Department of Health and HumanServices (HHS). Since it launched its first program in 1965 with themain goal of improving early reading and math skills of preschoolersfrom economically disadvantaged families, Head Start programs haveservedmore than 25 million children, reaching up to an enrollment of908,412 children in 2007. The age of children in the Head Startprogram ranges from three to five, with most children being eitherthree (36%) or four (51%) years old (Head Start, 2008a, 2008b).

Head Start serves many minority children, including immigrantchildren. Approximately 60% of Head Start children are racial mi-norities (Hispanic: 34.7%; Black: 30.1%; Asian: 1.7%; and Others: 28.5%)

and about 40% are Caucasian children (Head Start, 2008a, 2008b). HeadStart has also played a major role for immigrant families. According tothe study done by Turney and Kao (2009) using the Early ChildhoodLongitudinal Study-Kindergarten Cohort (ECLS-K), while only 4% ofnative-born Caucasian children were enrolled in Head Start programs,more than 10% of immigrant children from each racial group wereenrolled: 13% of Black immigrants; 14% of Hispanic immigrants; and10% of Asian immigrants. Unfortunately, these immigrant childrendisplay low cognitive development. According to the study usingnational data from the Early Head Start (EHS) program (Mistry, Biesanz,Chien, Howes, & Benner, 2008), preschoolers from immigrant parents,aged from birth to three, demonstrated significantly lower cognitivedevelopment as compared with their native peers. Many studiessupport the importance of highly qualified teachers to promote theshort-term and long-term outcomes of preschoolers, including im-migrant children, in cognitive, socio-emotional and physical areas(NRC, 2009). A study done by Gormley (2008) emphasized theparticular benefits of highly qualified teachers for immigrant childrenin Oklahoma Head Start programs. When teachers have college edu-cation and early childhood certification, Hispanic immigrant childrenin the program demonstrated significantly better cognitive improve-ments in early math, reading and writing skills as compared to HispanicEnglish-speaking peers in the program or Hispanic immigrant childrenwho were not in the program.

2.2. Teacher college degree

In analytical research using previous 40 studies on teachers forearly childhood education, Saracho and Spodek (2007) have high-lighted that the learning outcome of preschoolers is a function ofteacher's high qualification, which is often defined as a bachelor'sdegree in early childhood education. In a similar vein, several empir-ical studies have provided evidences of the positive effects of teachereducational backgrounds. Using the Study of Early Child Care from theNational Institute of Child Health and Human Development (NICHD),Clarke-Stewart et al. (2002) found that when children at child carecenters had teachers who attended college, they showed significantlybetter performance on cognitive tests at ages of 24 and 36 monthscompared with children with teachers who did not attend college.In conjunction with the importance of teachers' educational back-grounds, the low educational levels of Head Start teachers arenoteworthy. The qualification required of Head Start teachers ismuch lower than that of public pre-kindergarten programs. Althoughmany states stipulate the requirement of teachers in public pre-kindergarten programs to be a bachelor's degree in early childhoodeducation, child development, or related areas (Saracho & Spodek,2007), the reality is far below the specified regulation. According tosurvey results of Head Start (2008a, 2008b), among Head Startteachers who provide instruction (including administrative teachers),only 37% of classroom teachers and 23% of administrative teachershad 3–4 years of college education. On average, completed years ofschooling is 14.3 years (about 2.3 years of college education) amongclassroom teachers.

Not all research supports teacher educational background as acritical component for children's cognitive development. Early et al.(2007) could not find convincing evidence of an association betweenteacher education and academic outcomes of four year-old pre-schoolers, nor did they identify any pattern of the association in theirseven national data analysis studies. Most of their statistical analysesindicated insignificant associations, and even a small number ofsignificant results did not delineate a clear direction. Similarly, therecent report by the National Research Council (2009) cast doubt onthe validity of a teacher degree in childhood education as a strongpredictor of high quality teaching practice or preschoolers' develop-mental/learning outcomes.

734 S. Kim et al. / Children and Youth Services Review 33 (2011) 732–740

2.3. Teacher certificates in early childhood education

Holding a state-granted teaching certificate is considered as aminimum qualification to become a teacher in public schools, in-cluding a teacher of early childhood education. Awarding a teachercertificate is an evaluation process of teachers' competence in subjectareas, teaching ability and classroom managing skills, and each statesets its own standards to evaluate the qualifications of the prospectiveteachers. Generally, a teaching certificate in many states is awardedto a prospective teacher who has at least a bachelor's degree andhas completed required training in education. However, due to theshortages of qualified teachers, teachers without a state certificate areoften allowed to serve the public schools with a provisional orprobationary certificate (NASDTEC, 2000).

There is also a heated debate on the importance of a teachercertificate. Darling-Hammond and Youngs (2002) concluded that thereis a significantly positive relationship between teachers' certificationstatus and the academic performance of students. They reviewed andanalyzed nine rigorous studies that used data from individual teachersas well as groups of teachers at state levels. Their findings from thoseempirical studies were: student achievement had significantly positiverelationships with certified teachers, schools with a high proportion ofcertified teachers, districts with teachers scoring high on certificationtests, and states with teachers holding full certification.

However, for teacherswho held at least a bachelor's degree, havinga certificate does not make any difference in children's learningoutcomes (Early et al., 2006). The authors studied a relationshipbetween state preschool certification and the learning outcomes offour-year-old preschoolers, particularly for teachers who hold at leasta bachelor's degree. The authors paid limited attention to teachingcertificates specialized for pre-kindergarteners, excluding teachingcertificates of general elementary education. The study results re-vealed no significant association between a certificate and any ofchildren's learning outcomes.

2.4. Teacher professional training

Considering the relatively low entry qualifications of preschoolteachers, the NRC has supported professional development opportu-nities for preschool teachers through formal or informal education(NRC, 2009). Aligned with the NRC support, various empirical studiesshowed positive effects of various educational opportunities forteachers. Honig and Hirallal (1998) showed the importance of teachertraining in early childhood education and child development. Com-pared with teachers who took less than five courses or workshopsin child education, teachers who took more than five courses tendedto provide their students with a richer learning experience. Thoseteachers used teaching methods stimulating the socio-emotional,language and cognitive development of preschoolers. Similarly,another study done by Clarke-Stewart et al. (2002) showed thatchildren with caregivers who received training in a child-care areaperformed better on cognitive and language assessments. Also, thesecaregivers provided quality care for children.

Training other than child education courses was also found ben-eficial. Horm, Caruso, and Golas (2003) explored the longitudinaleffects of participatory, hands-on-training designed for Head Startprogram staff. The training, supported by the Administration forChildren, Youth, and Families (ACYF) of the U.S. Department of HealthandHuman Services,was developed for 14Head Start teaching centersto promote fourmajor domains of Head Start: education, health, socialservices, and parent involvement. The training was customized forHead Start program staff to experience hands-on activities. Comparedwith Head Start staff in the control group, staff who received trainingdemonstrated visible improvement in knowledge, skills, and expertisethroughout the four-year training period.

3. Methods

3.1. Data sources

The study used a national and longitudinal dataset, the Head StartFamily and Child Experiences (FACES) 2003 Cohort Survey. The FACESis a well-designed database that contains a three-year, large-scaledata. It also provides information on the cognitive, social, andemotional development of Head Start children and parents who aremostly from low-income families. It also reports various facts onteachers and program staff through survey collections and classroomobservations. Thus, it allows researchers to pay focused investigationof the unique dynamics of learning environment of Head Start andfamily interaction. The FACES 2003 traces the developmental aspectsof 2457 three- and four-year-old children from 2003 when theyentered Head Start programs to 2005 or 2006 when they graduatedfrom kindergarten: three-year-old children completed their HeadStart programs in 2005 and kindergarten in 2006; and four-year-oldchildren graduated from Head Start programs in 2004 and kinder-garten in 2005.

The FACES 2003 used a four-stage sampling method. In the firststage, the FACES created Primary Sampling Units (PSUs) to include allHead start programs in the US and stratified 1669 programs based onthe size of the total enrollment in the program. The FACES selected 63participating Head Start programs. In the second stage, the FACESselected 110 center groups from 63 programs and gained participationagreement from 175 centers. In the third stage, 409 classes wereselected by sampling three classes from each of the participatingcenters. In the fourth stage, a total of 2457 childrenwere selected for theFACES 2003 cohort. Out of 2457 children, 2297 children participated inthe child assessments, demonstrating 93% of response rate.

The current study used one-year data of four-year-old childrenwho entered the Head Start program in the fall of 2003 and completedHead Start program in the spring of 2004. In addition to children'sdata, the study used the data from 326 teachers who wereinterviewed in the fall of 2003. The study accounted for the fourstage data sampling method of the FACES 2003 cohort by adoptingproper weights (see the Analysis section for detailed information onweights).

3.2. Variables

The main predictor variables are the three variables of Head Startteachers' qualifications: (1) teacher's educational level, (2) stateawarded certification, and (3) teacher's training enrollment status.Teacher educational level was measured by a survey item, “What isthe highest grade or year of school that you completed?” Althoughthere were 13 options for this item (1=up to 8th grade; 13=pro-fessional degrees after bachelor's degree), the study converted thedata values for a new variable (LowEdu) into two options (1=lowerthan bachelor degrees; 0=bachelor or higher degrees). This con-version was based on the goal of many Head Start programs as intro-duced in the literature review section.

This study examined the association between state awardedpreschool certification (Certificate) and students' outcomes. The ques-tion asked, “Do you have a state-awarded preschool certificate?” Theresponses for the preschool certificate were coded 0 for no and 1 foryes.

The present study carefully examined professional developmentprograms for Head Start teachers (Training) along with certification.As we stated previously, we hypothesized training programs forteachers would improve teachers' skills and pedagogy, and in turnthe improved teacher practices will promote a quality of preschoollearning environment. The itemmeasuring training enrollment statusasked, “Are you currently enrolled in any additional teacher-relatedtraining or education, including post-secondary school degrees,

735S. Kim et al. / Children and Youth Services Review 33 (2011) 732–740

graduate degrees, etc.?”We coded 0 for teachers who did not enroll inany training programs and 1 for teachers who were in any programssuch as child development associate (CDA), special educationteaching degree, graduate degree, or other teacher related trainingor education.

In the study, we paid attention to teacher formal education, a state-awarded preschool certificate, and professional training by creating sixteacher groups as follows: 1. Teachers with high education (bachelor'sdegree or higher), no preschool certificate, no professional training; 2.Teachers with high education, no certificate, with training; 3. Teacherswith high education, with a certificate, no training; 4. Teachers with loweducation, no certificate, no training; 5. Teachers with low education,with a certificate, no training; and 6. Teachers with low education, nocertificate, with training. This grouping process was done by creatinginteraction variables of three teacher variables (education, certificate,and training). In the analytical model, the reference group was theteacher group with high education, no certificate, no training.

As a main outcome variable, the study focused on children's cog-nitive development in the mathematical domain. The study adoptedscores of the Applied Problems from Woodcock-Johnson Psycho-Educational Battery–Revised Edition (WJ-R: Woodcock & Johnson,1990), which assessed children's cognitive development using threesubscales of Letter–Word Identification, Spelling, and Applied Pro-blems. The standard score of the WJ-R Applied Problems assessedchildren's competency to analyze and solve early math problems atthe time the children completed the Head Start program in spring2004. The FACES 2003 standardized WJ-R subscales by adopting ItemResponse Theory (IRT) approaches. Its reliability coefficient is 0.91 forfour-year-old children (Woodcock & Johnson, 1990).

This study gave particular attention to ELL children. In this study, theELL children were defined as those who spoke other languages thanEnglish most frequently at home following the definition of the FACES2003.Descriptive statistics on theELL childrenof theFACES (N=31,224)showed that 93.9% of ELL children (N=28,730) have at least oneimmigrant parentwhile 6.1% of ELL children (N=1863) do not have anyimmigrantparents. Themajority of thoseELL children areHispanic (95%;N=29,674). The rest are Asian/Caucasian (3%;N=924) and other races(2%; N=625).

In addition to the main predictor variables, we included importantcovariates in the analytical model to control for the effects. The co-variates were children's persistence/attention level, children's race,gender, age, parents' immigrant status, family structure, the numberof children in the household, maternal depression, and parents'education. Children's persistence/attention level is known for havinga positive effect of preschoolers' persistence on intellectual achieve-ment tests (Palisin, 1986). The FACES created a persistence/attentionscore by equating and combining fifteen behavior items and having thevariable of 19-point scale. The study also converted racial groups ofchildren into two dummy variables (Hispanic, and Asian/Caucasian)reflecting the population ratio of ELL children in the group. Also, othercovariates include gender (1=male; 2=female), age in month (range48–59), parents' immigrant status (0=no immigrant parent; 1=atleast one immigrant parent), family structure (0=not living with bothmother and father; 1=livingwith bothmother and father), the numberof children in the household (range 1–9), maternal depression (0=nomaternal depression; 1=maternal depression), and parents' education(1=Less than 8th grade or 8th grade; 9=doctorate or professionaldegree). Lastly, as an attempt to control for the relation between familyincomeandchildren's cognitive outcome,which is an importantvariablefor children's academic competency (Carlson & Corcoran, 2001), thestudy included annual family income into the analytical model.

3.3. Analysis

As preliminary analyses, this study conducted descriptive statis-tics, correlation analysis and graphical presentations to investigate the

relationship of teacher variables with mathematical abilities of non-ELL and ELL preschoolers. The study applied weight variables forchildren (CHNRWT0) and teachers (CLNRWTA0) to have the studyresults represent the US population of children and teachers in HeadStart.

The study's main statistical analysis was two-level HierarchicalLinear Modeling (HLM) analysis using HLM software (Raudenbush,Byrk, & Congdon, 2005). HLM analysis reveals the statistical interac-tions between variables specified at different levels using a hierar-chical model (Hox, 2002). HLM deletes cases with missing data usinglistwise deletion. By employing HLM to the FACES data in whichstudentswere nested in Head Start classrooms, this study investigatedhow teacher variables served as moderators of the relationshipbetween children's ELL status and their early math skills. In search foranswers to research questions of the study, two HLM analyses wereconducted with two separate model specifications.

3.3.1. Model with all predictorsThe study conducted a series of two-level HLM analyses to examine

the significance of variables at each level and their interactionalassociation across the two levels. The Level 1 model adopted the ELL asthe main variable and included other individual variables as covariates.The covariates were children's gender, children's persistence, children'sage, children's race, parents' immigrant status, family structure, thenumber of children in the household, maternal depression, parents'educational level, and family's annual income. Level 2 was designed toshow the interactional relationship between teacher variables andstudent's ELL status on students' applied problem scores.

3.3.2. Final modelThe study chose the final HLM model after excluding individual

variables which did not indicate significant association with students'applied problem scores. Individual variables at the level 1 of the finalmodel were children's persistence, children's race, children's age, andparents' education. Themodel at the level 2 was not changed from theprevious model, keeping the same teacher variables (See Appendix Afor detailed Final Model specification).

4. Results

4.1. Preliminary analysis results

The study performed descriptive and correlation analyses as thefirst step of preliminary analysis. Head Start program demographics(Table 1) indicate that 41,480 teachers participated in national HeadStart programs. The average age of these teachers was about 42 yearsold with the average of 8 years' teaching experience at Head Startprograms. Their average annual salary was $23,770. Most teacherswere females (N=40,777; 98.3%) and either Caucasian or Afro-American (N=32,225; 77.7%). Less than 40% of Head Start teachersheld a bachelor's degree (N=15,690; 37.8%) and state-awardedpreschool certificates (N=12,393; 29.9%). About 70%were enrolled intraining education (N=28,926, 69.7%).

Approximately 144,130 four-year-old children attended HeadStart programs during 2003 and 2004. Most of these children wereHispanic (31.9%), Caucasian (31.1%), or Afro-American (29.5%). Thesechildren came from low SES families, having $16,778 as the averageyearly family income and the average educational level of parents ashigh school or General Education Development (GED). About 46% ofthese children lived with both their mother and father. About 71% ofchildren had at least one immigrant parent and about 12% ofchildren's mothers suffered from depression. On average, therewere about three children in a household. Out of 144,130 children,approximately 22% of children (N=31,224) were identified as ELLchildren. Most of these ELL children were Hispanic (95%) and have atleast one immigrant parent (93.9%).

Table 1Descriptives of Head Start program teachers and children.

Teachers

Weighted N Mean (SD) Minimum Maximum

Teacher's age in years 40,930 41.80 (10.54) 20.00 73.00Total annual salary 40,889 23,770.31 (9954.34) 1000.00 84,000.00Teaching experience 41,480 12.56 (8.18) 0 40HS experience 41,480 8.27 (7.44) 0 39

Frequency: weighted N (percent)

Race White Black Hispanic Asian Others21,233(51.2%)

10,992(26.5%)

6952(16.8%)

1143(2.8%)

1067(2.6%)

Gender Male Female Educationallevel

Low High702(1.7%)

40,777(98.3%)

25,790(62.2%)

15,690(37.8%)

Training Yes No Certificate Yes No28,926(69.7%)

12,554(30.3%)

12,393(29.9%)

29,087(70.1%)

Children

Weighted N Mean (SD) Minimum Maximum

Appliedproblemskills

122,460 87.733 (16.64) 18 132

Child's age inmonths

144,130 53.23 (3.44) 48 59

Persistence/attention

139,595 14.06 (3.89) 0.00 18.00

Annualfamilyincome

135,525 16,777.9 (12,828.51) 0 100,000

Parents'educationlevel

140,960 3.4 (1.44) 1 9

Children inhousehold

143,297 2.69 (1.34) 1 9

Frequency: weighted N (percent)

Race White Black Hispanic Asian Others43,847(31.1%)

41,575(29.5%)

45,014(31.9%)

1062(0.8%)

9527(6.8%)

Gender Male Female Parents'immigrantstatus

Yes No68,698(47.7%)

75,433(52.3%)

98,313(71.1%)

40,042(28.9%)

Maternaldepression

Yes No Familystructure

Yes No17,598(12.2%)

123,005(85.3%)

64,424(45.7%)

76,418(54.3%)

ELL status Yes No31,224(22.1%)

109,745(77.9%)

ELL children: race and immigrant status

Race Hispanic White/Asian Others Parents'immigrantstatus

Yes No29,674(95.0%)

924 (3.0%) 625(2.0%)

28,730(93.9%)

1863(6.1%)

736 S. Kim et al. / Children and Youth Services Review 33 (2011) 732–740

Children's early math ability was associated with teacher back-grounds and children's individual factors. The correlation resultsshowed that the applied problem scores of these children had asignificantly positive relationship with teachers' educational levels(r=0.020, pb0.01), teacher training (r=0.057, pb0.01), children'sfamily income (r=0.067, pb0.01), children's persistence levels(r=0.269, pb0.01), parents' education level (r=0.179, pb0.01).However, the applied problem scores had a significantly negativerelation with teachers' certificates (r=−0.022, pb0.01), children'sage (r=−0.110, pb0.01), the number of children in the house-hold (r=−0.070, pb0.01), parents' immigrant status (r=−0.139,pb0.01), maternal depression (r=−0.008, pb0.01), and family struc-ture (r=−0.016, pb0.01). The average applied problem score ofthese children was 87.733, with ELL children' score being lower than

that of non-ELL children (r=−0.166, pb0.01). The scores of boyswere lower than those of girls (r=0.069, pb0.01). Although wefound those significant correlations, we carefully interpreted theoutcomes considering the fact that when we used a large sample, asmall difference could make a statistical significance in the analysis.Moreover, we compared the correlation results with the results of theHLM analyses. Table 2 presents the results of bivariate correlationanalysis.

As the second set of preliminary analyses, the study conductedprofile analyses to understand overall patterns of early math per-formance levels of ELL and non-ELL children. Fig. 1 displays the per-formance gap in the problem solving scores between ELL (82) andnon-ELL children (89).

4.2. HLM results

4.2.1. Model with all predictorsThe results of Model I, which included all covariates in the analysis,

were found in Table 3. This HLM analysis revealed ELL childrendisplayed significantly lower early math scores in classrooms taughtby teachers who had lower than a bachelor's degree (γ11=−12.467,pb0.05). Importantly, teacher professional training was significant-ly associated with the high math score of ELL children with teachersof low educational level (γ13=18.060, pb0.01). However, the pre-school certificates of a teacher did not demonstrate a significant re-lationship with math score of ELL children.

Among the children's variables, the study found children'spersistence significant, having a positive association with children'searly math performance (γ30=1.225, pb0.01). Results of this studyconfirm previous findings of the positive effect of preschoolers'persistence on cognitive outcomes (Palisin, 1986). Compared withchildren of the other racial group, children of Caucasian and Asiangroups demonstrated significantly higher math scores (γ50=10.955,pb0.01). Also, the educational level of parents indicated a significantpositive relationship with the math scores of children (γ80=1.298,pb0.01). However, the age had a significantly negative relationshipwith children's math scores (γ60=−0.853, pb0.01). This negativerelationshipmight be caused by the fact that all these childrenwere ofsimilar ages. Other variables did not reveal significant relationshipswith the math scores of children in this study. These variables werechild's family income, child's gender, Hispanic background, parents'immigrant status, maternal depression, the number of children in thehousehold and family structure.

4.2.1. Final modelAnother HLM analysis was conducted by excluding insignificant

covariates from the previous analytical model. The results of finalmodel were presented in Table 3. The results revealed that theperformance gap between non-ELL and ELL depends on teacher ed-ucational level. Based on children's linguistic status and teachereducational level, the study reported the results of the average earlymath scores of four groups: Non-ELL preschoolers with teachers ofhigh education (Math Score: 89.145), Non-ELL students with teachersof low education (Math Score: 89.447), ELL preschoolers withteachers of high education (Math Score: 85.535) and ELL preschoolerswith teachers of low education (Math Score: 79.872). Fig. 2summarizes the performance patterns of the four groups.

The HLM results revealed that there was no significant differencein the average early math scores of three groups: non-ELL preschoo-lers with teachers of high education and low education, and ELLpreschoolers with teachers of high education. However, ELL pre-schoolers with teachers of low education demonstrated significantlylower math achievement compared with those three groups (γ11=−14.570, pb0.01).

Furthermore, the study explored teacher professional training.There was a significant positive relationship between higher teacher

Table 2Inter-correlations of variables.

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1. Children's appliedproblem score

__ 0.020** 0.057** −0.022** −0.166** 0.067** 0.069** 0.269** −0.110 ** 0.179** −0.070** −0.139** −0.008** −0.016**

2. Teacher education __ −0.263 ** 0.080** −0.065** 0.024** 0.015** −0.030** 0.003 0.015** −0.030** 0.012** 0.008 −0.023**3. Teacher training __ −0.035** 0.140** −0.073** −0.024** −0.002 0.042** −0.007** −0.037** 0.157** −0.048** 0.064**4. Teacher certificate __ 0.118** 0.008** 0.006* −0.067** −0.110** 0.035** 0.006* 0.106** 0.039** 0.038**5. Children ELL status __ 0.000 −0.029** 0.035** 0.058** 0.149** 0.023** 0.763** −0.080** 0.289**6. Children's familyincome

__ 0.006* 0.081** −0.036** 0.233** −0.011** 0.001 −0.104** 0.291**

7. Children's gender __ 0.264** 0.057** 0.031** −0.056** 0.027** −0.014** −0.023**8. Children'spersistence

__ 0.089** 0.083** 0.035** 0.061** −0.053** 0.058**

9. Children's age inmonths

__ −0.050** 0.001 0.053** −0.014** 0.017**

10. Parents'education level

__ −0.117** −0.144** −0.055** 0.040**

11. Children in thehousehold

__ −0.006* 0.010** 0.059**

12. Parents'immigrant status

__ −0.089** 0.290**

13. Maternaldepression

__ −0.195**

14. Family structure __

**Correlation is significant at the 0.01 level.*Correlation is significant at the 0.05 level.

737S. Kim et al. / Children and Youth Services Review 33 (2011) 732–740

professional training and ELL preschoolers' math scores when theirteachers had lower education (γ13=18.304, pb0.01). The four groupsof preschoolers above, depending on preschoolers' linguistic statusand teachers' educational level, were further divided into eight groupsby adding teacher professional training. As shown in Table 3, teacherprofessional training had a significant positive relationship with earlymath performance of ELL preschoolers with teachers of low education,after controlling for teacher certificates, children's persistence level,race, age, and parental education. ELL preschoolers with teachers oflow education who had professional training demonstrated compar-atively highmath performance (Score: 83.059), making a conspicuouscontrast with ELL preschoolers with teachers of low educationwithout professional training (Score: 76.187). Fig. 3 summarizes theperformance patterns of the eight groups.

An important finding from this model is that both teacher cer-tification and professional training had a positive association withthe early math skills of ELL children. When teachers held a state-awarded certificate, although they had a lower than bachelor's degree,their ELL children in Head Start programs displayed significantlyhigher early math skills than those of teachers without certificates(γ15=13.810, pb0.01). This positive role of the teacher certificatemirrored the findings by Darling-Hammond and Youngs (2002). ELLpreschoolers with teachers of low education without professionaltraining demonstrated low math skills (Score: 77.396). In contrast,ELL preschoolers with teachers of low education who had certificatedemonstrated comparatively high math performance (Score: 83.980).Fig. 4 presents the performance patterns of the eight groups.

Fig. 1. The average early math scores of ELL and non-ELL children.

5. Discussion

This study responds to the call to address the achievement dis-parity between mainstream and minority students early enough —

prior to the start of formal schooling (National Research Council,2009). This study paid focused attention to economically disadvan-taged preschoolers with focused attention on ELL preschoolersenrolled in the Head Start program. The study found a significantdisparity in early math skills between ELL and non-ELL preschoolersin Head Start, which will be a comparatively new piece of dataregarding the disparity existing in early math skills among four-yearold minority children from low income families. At the same time,this result is consistent with the previous studies that have shown asignificant gap between ELL and native English-speaking students inmath skills throughout the formal school years (Albus, Thurlow, & Liu,2002; Lee et al., 2007).

Researchers (Saracho & Spodek, 2007) have shown concern aboutthe low educational preparation of teachers in Head Start. The resultsof the study revealed that teacher's educational levels for nativeEnglish-speaking children did not indicate significant association. Thisresult for native English-speaking children confirms the studies ofEarly et al. (2007) and the NRC (2009), in which researchers did nothave convincing evidence of the relation between teachers' formaleducation and the learning outcomes of preschoolers.

However, the study found significant differential associationbetween teacher educational preparation and ELL preschoolers. Com-pared with their peers with teachers of lower educational back-ground, ELL preschoolers with teachers of higher educational levelsdemonstrated significantly higher early math competency. Thisresult is expected to add a new empirical result to the research field,exhorting researchers to add a new paradigm in exploring teachereducation considering various characteristics of preschoolers includ-ing their linguistic background. With regard to teacher qualification,the study's results show that teacher's state-awarded preschool cer-tificates were significant only for the teachers with a low educationalbackground. Importantly, teacher certificates had a significant associ-ation with the early math performance of ELL preschoolers, providinga new insight.

As another important teacher variable, this study explored arelationship between teacher professional training and Head Startpreschoolers. The positive associations of the study results on teacher

Table 3Results of HLM analysis.

Fixed effects

Model with allpredictors

Final model

β SE β SE

For initial status (β00): non-ELL childrenIntercept (γ00): Teachers with HighEdu,no Training, no Certificate

84.239** 1.943 83.252** 1.557

LowEdu (γ01): Teachers with LowEdu,no Training, no Certificate

0.811 1.998 2.198 1.908

Training (γ02): Teachers with HighEdu,Training, no Certificate

6.578* 2.904 9.029** 3.149

LowEdu Training (γ03): Teachers withLowEdu, Training, no Certificate

−7.643* 3.632 −9.822* 3.812

Certificate (γ04): Teachers withHighEdu, no Training, a Certificate

1.763 1.959 3.628 1.954

LowEdu Certificate (γ05): Teacherswith LowEdu, no Training, a Certificate

−4.396 2.863 −5.038 2.660

For ELL (β10): ELL childrenIntercept (γ10): Teachers with HighEdu,no Training, no Certificate

−0.696 4.645 1.979 4.097

LowEdu (γ11): Teachers with LowEdu,no Training, no Certificate

−12.467* 5.493 −14.570** 5.157

Training (γ12): Teachers with HighEdu,Training, no Certificate

−9.240 5.721 −9.251 5.687

LowEdu Training (γ13): Teachers withLowEdu, Training, no Certificate

18.060** 6.854 18.304** 6.808

Certificate (γ14): Teachers withHighEdu, no Training, a Certificate

2.101 6.164 −2.216 5.799

LowEdu Certificate (γ15): Teacherswith LowEdu, no Training, a Certificate

11.264 7.339 13.810** 6.842

For sex (β20)Intercept (γ20) 0.605 1.427

For persistence (β30)Intercept (γ30) 1.255** 0.166 1.332** 0.158

For Hispanic (β40)Intercept (γ40) −0.867 2.471 0.397 2.382

For Asian/Caucasian (β50)Intercept (γ50) 10.955** 1.726 10.229** 1.679

For age (β60)Intercept (γ60) −0.853** 0.149 −0.725** 0.147

For family income (β70)Intercept (γ70) 0.000 0.000

For parental education (β80)Intercept (γ80) 1.298** 0.447 1.467** 0.421

For immigrant status (β90)Intercept (γ90) 2.318 2.316

For maternal depression (β100)Intercept (γ100) 2.698 2.086

For children in family (β110)Intercept (γ110) −0.613 0.663

For family structure(β120)Intercept (γ120) −0.594 0.672

Random components and deviance statistics

Model with all predictors Final model

Component df χ2 Component df χ2

Level 2interceptvariance

7.883 50 63.848 9.015 51 60.154

Level 2 slope(Asian_Caucasian)variance

36.881 55 72.857 42.447 56 76.128*

Level 1 196.113 201.165Deviance 5183.999 5544.468Estimatedparameter

4 4

*pb0.05, **pb0.01.

Fig. 2. The average early math scores of ELL and non-ELL children of teachers with highand low educational levels.

Fig. 3. The average early math scores of eight groups that were classified by children'slinguistic status, teachers' educational level, and teachers' training status.

738 S. Kim et al. / Children and Youth Services Review 33 (2011) 732–740

training are consistent with the previous studies (Clarke-Stewartet al., 2002; Honig & Hirallal, 1998; Horm et al., 2003). Also, the studyresults indicated teacher training could work as potential mediators

for ELL preschoolers with teachers of lower educational levels. Whenteachers who had less than a bachelor's degree participated inprofessional training, their ELL preschoolers displayed better earlymath skills than ELL preschoolers with teachers without training.

In this paper, teacher training programs such as the Child Devel-opment Associate (CDA) degree program, special education teachingdegrees and graduate degrees were included to examine those effects,resulting in finding a significant combined effect particularly forteachers with low educational backgrounds. This attempt and findingcan be the most important contribution of this study to the researchfield that does not reach a consensus regarding the contents of thetraining programs for preschool teachers (National Research Council,2009). Based on findings, this study lends support for scholarshipprograms including Teacher Education Assistance for College andHigher Education Grant Program, Early Childhood Educator Profes-sional Development Program, CDA degree programs and other degreeprograms (National Research Council, 2009).

While this study revealed significant results using a sound meth-odology, this study is based on the survey data. Future experimentalstudy is needed to address the causal relation of the teacher variablesin this study for clearer understanding.

6. Conclusion

While featuring methodological rigor by adopting HLM analysis to aUS nationally representative data with proper weights, thisstudy demonstrated the differential association of teacher educa-tional levels, training, and certificates for ELL children who do notspeak English as a primary language at home. When teachers had loweducational background, their ELL children displayed significantly

Fig. 4. The average early math scores of eight groups that were classified by children'slinguistic status, teachers' educational level, and teachers' certificate status.

739S. Kim et al. / Children and Youth Services Review 33 (2011) 732–740

low early math skills; when the teachers of low educational backgroundhad professional training, their ELL children had significantly high earlymath skills; andwhen teachers held a state-awarded certificate, althoughthey had low educational background, their ELL children displayedsignificantly high early math skills. These findings suggest an importantsolution to promote the academic success of language minority childrenwho are from economically and ethnically/culturally diverse back-grounds. Considering theever-increasinggrowth rateof ELL students andthewidening achievementdisparity (Lee et al., 2007;NCES, 2005;NCELA,2010), this finding is relevant and crucial for policy-makers who aresearching for solutions for the at-risk student population.

Appendix A

A.1. Final model

Level 1

Yij = β0j + β1jðELLÞij + β2jðParental EducationÞij + β3jðAgeÞij+ β4jðPersistenceÞij + β5jðHispanicÞij + β6jðCaucasian=AsianÞij+ rij

where Yij is dependent variable;β0j is the intercept in class j whichis the average applied problem score of non-ELL students aftercontrolling for parental education, age, race and persistence; β1j isthe regression slope of ELL children in class j which is the differencein average applied problem score between ELL and non-ELL childrenafter controlling for parental education, age, race and persistence;β2j is the regression slope of Parental Education in class j; β3j is theregression slope of children's age in class j;β4j is the regression slope ofchildren's persistence in class j; β5j is the regression slope of children'srace (Hispanic) in class j; β6j is the regression slope of children's race(Caucasian/Asian) in class j; and rij is a random component (error/variation among individual children).

Level 2

β0j = γ00 + γ01ðLowEduÞj + γ02ðTrainingÞj+ γ03ðLowEdu TrainingÞj + γ04ðCertificateÞj+ γ05ðLowEdu CerficiateÞj + u0j

β1j = γ10 + γ11ðLowEduÞj + γ12ðTrainingÞj+ γ13ðLowEdu TrainingÞj + γ14ðCertificateÞj+ γ15ðLowEdu CertificateÞj

β2j = γ20β3j = γ30β4j = γ40β5j = γ50β6j = γ60 + u6j

where γ00 is the average of the class means on applied problem scorewith teachers of high qualification and without training or certificateacross the populations of classes; γ01 is the difference in the classmeans on applied problem score with teachers of low qualificationcompared with those of high qualification after controlling for othervariables; γ02 is the difference in applied problem mean scorebetween teachers of high qualification with training and withouttraining; γ03 is the applied problem score difference in the classmeans on applied problem score with teachers of low qualificationand enrolled in training compared with teachers of low qualificationwithout training; γ04 is the difference in the class means on appliedproblem score with teachers of high qualification and with andwithout certificate; γ05 is the difference in the class means on appliedproblem score with teachers of low qualification and with andwithout certificate;γ10 is the difference of the class means on appliedproblem score between non-ELL and ELL children with teachers ofhigh qualification without certificate or training;γ11 is the effect of theteachers of low qualification with ELL children on class means ofapplied problem score;γ12 is the effect of teacher training amongteachers of high qualification with ELL children on class means ofapplied problem score;γ13 is the effect of training among teachers oflow qualification with ELL children on class means of applied problemscore;γ14 is the effect of teacher certificate among teachers of highqualification with ELL children on class means of applied problemscore;γ15 is the effect of certificate among teachers of low qualifica-tion with ELL children on class means of applied problem score; u0j isthe unique increment to the intercept associated with class j; γ20 isthe effect of parental education on class means of applied problemscore; γ30 is the effect of children's age on class means of appliedproblem score; γ40 is the effect of children's persistence on classmeans of applied problem score; γ50 is the effect of children's race(Hispanic) on class means of applied problem score; γ60 is the effect ofchildren's race (Caucasian/Asian) on class means of applied problemscore; and u6j is the unique increment to the Caucasian/Asian slopeassociated with class j.

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