Running Head: RACE, MATERNAL EDUCATION, AND BUS … · Race, maternal education and Bus Story...

41
Race, maternal education and Bus Story retells 1 Running Head: RACE, MATERNAL EDUCATION, AND BUS STORY RETELLS IN PRESS (2011): Journal of Speech, Language, and Hearing Research The effects of race and maternal education level on children‘s retells of the Renfrew Bus Story North American Edition Anne van Kleeck University of Texas at Dallas Alissa Lange State University of New York at Buffalo Amy Louise Schwarz University of Texas at Dallas

Transcript of Running Head: RACE, MATERNAL EDUCATION, AND BUS … · Race, maternal education and Bus Story...

Race, maternal education and Bus Story retells 1

Running Head: RACE, MATERNAL EDUCATION, AND BUS STORY RETELLS

IN PRESS (2011): Journal of Speech, Language, and Hearing Research

The effects of race and maternal education level on children‘s retells of the Renfrew Bus Story –

North American Edition

Anne van Kleeck

University of Texas at Dallas

Alissa Lange

State University of New York at Buffalo

Amy Louise Schwarz

University of Texas at Dallas

Race, maternal education and Bus Story retells 2

Abstract

Purpose: The Renfrew Bus Story North American edition (RBS-NA) is widely used clinically and

in research for determining children‘s language abilities, although possible influences of race and

maternal education on RBS-NA performance are unknown. The current study compared RBS-NA

retells of four groups of children: African American (AA) and European American (EA) whose

mothers had an education level of high school or less (<HS), or had an education level higher

than high school (>HS).

Method: Statistical tests were conducted on 163 kindergartners‘ story retells using raw scores for

all four RBS-NA measures: Information, Sentence Length, Complexity, and Independence.

Results: A 2 x 2 ANOVA indicated main effects for maternal education and race for the

Information score, with <HS and AA children scoring lower. For measures not meeting

ANOVA assumptions, 2 x 2 ANOVAs using ranked data indicated significant differences across

groups for maternal education on Sentence Length, Complexity, and Independence scores, with

<HS scoring lower. There were no additional effects of race.

Conclusion: There are systematic effects of maternal education and race on children‘s RBS-NA

performance, which is important for both researchers and clinicians to take into account when

using this instrument.

Race, maternal education and Bus Story retells 3

The effects of race and maternal education level on children‘s retells of the Renfrew Bus Story –

North American edition

Producing narratives requires the integration of a variety of linguistic and social-

cognitive skills. In the linguistic realm, producing a narrative requires coordinating semantic,

syntactic, morphologic, and phonological elements both within and across sentences. Social-

cognitive skills impact narrative production via the cultural conventions that shape the way

narratives are organized, shared, and adapted to a listener‘s level of background and shared

information on the topic. Due to this complexity, assessing young children‘s narrative skills is

considered to be both ―an ecologically valid and educationally relevant means of language

assessment‖ (e.g., Pankratz, Plante, & Insalaco, 2007, p. 390).

Narrative is important in its own right as a ―ubiquitous and universal construct used to

inform, socialize, entertain, and teach‖ (McGregor, 2000, p. 55). A number of researchers have

hypothesized that skill with narratives in preschool and the early elementary years would also be

a helpful component in later ability to comprehend connected text when reading (Anderson,

Anderson, Lynch, & Shapiro, 2003; Scarborough, 2001; Sénéchal & LeFevre, 2002; Storch &

Whitehurst, 2002; Tabors, Snow, & Dickinson, 2001; van Kleeck, 2007, 2008). Indeed, several

empirical studies have found that narrative abilities do predict later reading comprehension,

reading fluency, or written narrative skills in both children who have learning disabilities and

those who are typically developing (e.g., Feagans & Appelbaum, 1986; Griffin, Hemphill, Camp,

& Wolf, 2004; Reese, Suggtate, Long, & Schaughency, 2009; Snyder & Downey, 1991; Tabors,

et al., 2001). In a large study of over 1,500 Spanish/English bilingual children from kindergarten

to grade three, oral narratives (retells of a wordless picture book) were in fact the best predictor

of third grade reading comprehension (Miller, et al., 2006). Furthermore, narrative skills

Race, maternal education and Bus Story retells 4

uniquely contribute to reading fluency even after controlling for receptive vocabulary and

decoding skills (Reese, et al., 2009).

In the studies in which narrative ability has not predicted later reading ability (e.g.,

Menyuk, et al., 1991; O'Neill, Pearce, & Pick, 2004; Roth, Speece, & Cooper, 2002; Snow,

Tabors, Nicholson, & Kurland, 1995; Stein & Glenn, 1982), researchers have measured

children‘s reading outcomes in the second grade or earlier when word recognition still dominates

both the curriculum and tests of reading ability. Narrative ability would not be expected to

predict performance until children are required to begin engaging in independent comprehension

of longer and more complex connected text, which typically occurs in the third or fourth grade.

And indeed, using structural equation modeling, Storch and Whitehurst (2002) found that the

strong and direct link between oral language and literacy found during preschool disappeared

during the first two years of formal reading instruction, but reappeared in the third and fourth

grade. Others have also found that the links between oral language skills and reading

comprehension become stronger as children get older (e.g., Vellutino, Tunmer, Jaccard, & Chen,

2007). So, the importance of narrative ability during the preschool years appears to accrue when

the curriculum shifts from what is frequently referred to as ―learning to read‖ in kindergarten

through second grade to independently ―reading to learn‖ beginning in third grade.

When evaluating children‘s narrative productions, the complexity of the task is

acknowledged in that two levels of measurement are typically conducted, a micro- and a more

macro-level (Petersen, Gillam, & Gillam, 2008). The micro-level measures focus on such things

as word diversity and frequency and sentence level complexity. These measures are the same as

those typically used when analyzing spontaneous language samples of conversational dialogue.

So, for example, syntactic complexity is often assessed via measures such as mean length of

Race, maternal education and Bus Story retells 5

utterance in words or morphemes (MLU) and percent of complex sentences produced. The

lexical skills of a child are captured by determining the diversity of words used in the sample,

often using a measure such as the number of different words (NDW). When using these

measures, narratives are considered to be one type of language sample, one that can either be

spontaneously generated by the child or consist of a retell of a story told to the child. The

Renfrew Bus Story North American edition (RBS-NA) story retell used in the current study

captures the micro-level of narrative analysis at the sentence level with two measures. One is

called Sentence Length, and it indicates the length of the child‘s five longest sentences, the other

is called Complexity, and it indicates the amount of complex syntax produced. The RBS-NA does

not include micro-level measures at the word level.

To consider how children are able to organize their narratives at a more global level that

goes beyond the word and sentence level, macro-level measures are used. This level is typically

tapped by assessing children‘s ability to organize their narratives into the elements of story

grammar (e.g., Serpell, Baker, & Sonnenschein, 2005), or by assessing the amount of

information from the story that is included in story retell tasks. The RBS-NA contains one macro-

level measure called Information.

To date, The Bus Story Test (Renfrew, 1969) is the only norm-referenced screening

measure of young children‘s narrative abilities that spans the preschool and kindergarten age

range, a period of development during which it is important to be able to measure the language

skills foundational to later reading, and work to strengthen those that are weak. This assessment

tool was originally developed in England as the Bus Story Test, and now has a North American

version, the Renfrew Bus Story – North American edition (RBS-NA: Glasgow & Cowley, 1994)

that was used in the current study. This assessment includes a story and twelve accompanying

Race, maternal education and Bus Story retells 6

pictures, presented on four pages, about a bus that runs away from its driver. The story is read to

the child who then retells the story to the examiner. In addition to the micro- and macro- level

measures mentioned above, the child‘s retell is also analyzed for the amount of prompting

required for the child to discuss the story, a measure called Independence. Both versions of the

Bus Story have been shown to be predictors of persistent language impairment (Bishop &

Edmundson, 1987; Pankratz, et al., 2007; Stothard, Snowling, Bishop, Chipchase, & Kaplan,

1998), and the British version has also shown a strong relationship to later literacy (Stothard, et

al., 1998).

The Bus Story is widely used in research in the United States and the United Kingdom

that has focused on children who are developing their language in a typical fashion as well as

those with language impairments (e.g., Anderson, Morse, Catroppa, Haritou, & Rosenfeld, 2004;

Anderson, et al., 1997; Bishop & Edmundson, 1987; Botting, Conti-Ramsden, & Crutchley,

1997; Briscoe, Gathercole, & Marlow, 1998; Conti-Ramsden & Botting, 1999; Conti-Ramsden,

Crutchley, & Botting, 1997; Dockrell & Lindsay, 2001; Gallagher, Frith, & Snowling, 2000;

Girolmetto, Wiigs, Smyth, Weitzman, & Pearce, 2001; Gray, 2004; Hohen & Stevenson, 1999;

Hughs, Dunn, & White, 1998; Kennedy, et al., 2006; Kovas, et al., 2005; Mackie & Dockrell,

2004; Nathan, Stackhouse, Goulandris, & Snowling, 2004; Paul, 1996; Paul, Hernandez, Taylor,

& Johnson, 1996; Paul & Smith, 1993; Storch & Whitehurst, 2002; Stothard, et al., 1998). In the

United Kingdom, it is believed to be widely used clinically (Norbury & Bishop, 2003), being

―regularly employed in primary language units‖ (Botting, 2002, p. 2) that serve children with

specific language impairments (Conti-Ramsden & Botting, 1999). According to the distributor of

the North American version of the test, test sales are about 60% to Canada and 40% to the

Race, maternal education and Bus Story retells 7

U.S.A. Buyers include both speech-language pathologists and researchers in speech-language

pathology and education (Lange, 2010).

Concerns have been raised that the North American version of this assessment tool may

over-identify minority children as having poor narrative skills. In a review of this test, Haccoun

(2001) noted that African American (AA) children in the normative data appeared to obtain

lower scores, but unfortunately, information on SES is not provided in the norms. Data from

2008 shows that AA children are over three times more likely to be poor than their European

American (EA) counterparts (Federal Interagency Forum on Child and Family Statistics, 2010).

Furthermore, children living in households headed by single mothers were five times more likely

to be living in poverty than those living in households headed by two parents. For African

Americans, the poverty rate for households headed by single mothers was 50.2%, and the

number of female headed households in which there are children under 18 is approximately 2 ½

times higher for African Americans than for the total US population. Given the overall confound

of race and income, it is unclear if AA children‘s lower scores are related to race or SES.

Two key variables often used in determining SES are income and parental education. As

Dollaghan and her colleagues discuss (Dollaghan, Campbell, Paradise, Feldman, Janosky, Pitcair

& Kurs-Lasky, 1999), education level is considered to be the more stable (Huston, McLoyd, &

Garcia-Coll, 1994) and less controversial (Hauser, 1994) of the two. Furthermore, maternal

education level is the best predictor of intellectual attainment when family income, maternal

education level and paternal education level are compared (Mercy & Steelman, 1982). This may

be because, except for cases of extreme poverty, maternal education level is the component of

SES most strongly related to parenting measures (Bornstein, Hahn, Suwalsky, & Haynes, 2003;

Hoff, Laursen, & Tardif, 2002).

Race, maternal education and Bus Story retells 8

More directly tied to language ability, in a study of 701 African American preschoolers,

performance on the Preschool Language Scale-3 (Zimmerman, Steiner, & Pond, 1992) was

significantly different for different maternal education levels, but not for monthly income for

children of the 590 mothers who provided this information (Qi, Kaiser, Milan, Yzquierdo, &

Hancock, 2003). Furthermore, 81 percent of these mothers reported being single female heads of

household. Research from these various perspectives supports our use of maternal education

level as a marker for SES in the current study.

Previous research does not illuminate the potential impact of race on children‘s

performance on the RBS-NA. Although a study by Price and her colleagues (Price, Roberts, &

Jackson, 2006) used the Bus Story Test to study the structural development of AA children‘s

narratives, their data are not helpful for determining whether EA and AA children perform the

same or differently on the Renfrew Bus Story – North American edition because (a) no EA

children were studied, (b) the British version of the test was used (the North American version

was not yet available when their data were collected), and (c) these researchers developed their

own method of coding the narratives using a story grammar paradigm rather than following the

standardized analyses in the test manual.

Pankratz et al. (2007) obtained Renfrew Bus Story – North American edition retells from

racially and ethnically heterogeneous groups of preschoolers with specific language impairment

and those who were developing language in a typical fashion. When they grouped together

minority children in their sample (Hispanic, African American, and other), they found that,

compared to the EA preschoolers, the minority children scored significantly lower on the

Sentence Length score (number of words in the five longest utterances produced in the retells),

but not on the Information score (information provided by the child that corresponded to

Race, maternal education and Bus Story retells 9

information in the original story). This pattern also held when comparing just the Hispanic

children to the EA children, but there were not enough children in the other minority groups to

conduct similar analyses.

These studies just reviewed lead to potential concern regarding impact of race on

children‘s performance on the RBS-NA. We know of no research related to the impact of

maternal education level on the RBS-NA. However, there are two relevant studies that have used

other narrative measures.

Reese had her colleagues (Reese et al., 2009) explored the relationship between maternal

education level and three aspects of story book retell narratives produced by 61 six-year-old

(Study 1) and 39 seven-year-old (Study 2) children. In both studies, maternal education did not

significantly correlate with any of the oral narrative measures. It should be noted, however, that

there were no children of university graduates in this sample. This narrow range of maternal

education level may have obscured the impact of maternal education level on the children‘s

retells.

Kulkofsky and Klemfuss (2008) examined narratives of 112 preschoolers that focused on

a previously witnessed staged event. In Study 1 (n = 46), 89% of the mothers had at least a

college degree; in Study 2 (n = 66), 72% of the mothers had a college degree. There were no

main effects or interactions for maternal education, nor did maternal education correlate with

narrative quality. However, here again the range of maternal education in this study may have

been too narrow, although in this case containing too many mothers with higher education levels.

Given that previous research sheds little light on the topic, the goal of the current study

was to determine the effects, if any, of race and maternal education on kindergarten children‘s

Race, maternal education and Bus Story retells 10

retells of the Renfrew Bus Story – North American edition (RBS-NA) (Glasgow & Cowley,

1994).

Method

Participants

Data were gathered from a sample of the children participating in the large-scale research

project, Scaling-up TRIAD: Teaching Early Mathematics for Understanding with Trajectories

and Technologies, for which approval from the primary university‘s Institutional Review Board

was obtained following all local and federal guidelines for the protection of human subjects. This

study was a U.S. Department of Education-funded randomized control trial involving two urban

public school districts. District agreement to participate was obtained, and all eligible schools

within each district were then randomly assigned to Control or Treatment groups, including all

preschool classrooms in each school (the treatment took place the school year before the retell

data for the current study was collected when the children were in kindergarten). Thus, all

classrooms within each school were assigned to one condition. Children in the initial sample (n =

1305) were from 106 classrooms, in 42 schools in Buffalo, New York and Boston,

Massachusetts. On average, 81.6% of children in these schools were receiving free or reduced

school lunches, and the racial/ethnic distributions across the two school districts were the

following: 43% African American, 35% European American, 19% Hispanic or Latino, and 4%

Asian American).

Maternal education and race. The maternal education status was taken from a

question in the Parent Questionnaire of the TRIAD study. The questionnaire included

questions about parent education and employment, as well as questions about the child

relevant to the math study, such as parent-perceived child counting ability. The question

Race, maternal education and Bus Story retells 11

about maternal education from this measure asked the respondent to report the highest

level of education attained by the child’s mother by indicating one of seven categories (see

Table 1). The self-report Parent Questionnaire data were obtained by mail or by phone by a

trained interviewer during the child’s pre-kindergarten year.

Table 1 shows the number of African American (AA) and European American (EA)

children according to the seven maternal education categories used in the TRIAD study. The race

information was provided by the school districts involved. In the current study, we divided these

seven education categories to create two levels of maternal education, high school and less

(<HS), and higher than high school (>HS). However, to assure equivalence in maternal

education for the AA and EA children in the current study, we made sure that our sample had

equal numbers of children in each of the original seven maternal education categories. To do

this, we set the number of children in each of the seven maternal education categories as the

smallest number of either AA or EA children in that group in the original sample.

For example, the top section of Table 1 indicates that our search of the TRIAD database

yielded 26 AA and 13 EA children whose mothers had an eighth grade education. As shown in

the middle section of Table 1, we included all 13 of the EA children in our sample for this study.

We then randomly selected 13 out of the 26 AA children to participate in this study. We

followed this same procedure for each maternal education category until we had an equal number

of children by race in each maternal education category, as shown in the middle section of Table

1. We then collapsed the seven maternal education categories into two categories as shown in the

bottom section of Table 1, which assigned children to either the <HS or the >HS maternal

education level. Splitting the data into these two maternal education levels made sense

conceptually and, given the sample we were using, this particular split for maternal education

Race, maternal education and Bus Story retells 12

level also allowed us to have roughly equivalent sample sizes at each maternal education level.

Children excluded. We excluded children who were enrolled in special education

programs because they were not distributed proportionally in the original sample across race and

the seven maternal education categories. We also excluded control group children because there

were far fewer of them, and we did not want to have groups in which treatment and control

children were mixed, since the narrative-retell data was collected at the end of the math

intervention. This was the conservative approach to take given that in other research on a math

intervention with preschoolers (reported in Preschool Curriculum Evaluation Research

Consortium, 2008), very small effects (effect sizes ranged from d = 0.08 to 0.17) on language

abilities were found (immediately post treatment or one year later) as measured by the Peabody

Picture Vocabulary Test, Third Edition (Dunn & Dunn, 1997) or the Test of Language

Development Primary, Third Edition (Newcomer & Hammill, 1997). Our final sample for this

study included 172 children, with 86 AA and 86 EA children (see Table 2). A total of eighty-

two children fell within the <HS maternal education level, and 90 fell within the >HS maternal

education level.

Measures: Renfrew Bus Story – North American edition

The RBS-NA is a standardized, norm-referenced screening measure of oral language

using a story retell narrative language sample. The assessment involves telling a child a

story, and then asking the child to retell the story using the pictures in a wordless

storybook as prompts. The stories that children retold while taking the RBS-NA were audio

and video recorded, transcribed and scored on the four measures described in the test

manual. For transcription, utterances were segments of language that consisted of main

clauses and their attached subordinate clauses or sentence fragments. As noted earlier,

Race, maternal education and Bus Story retells 13

there are three primary measures rating the content (macro-level) and form (micro- or

sentence-level) of the story retells, including Information, Complexity, and Sentence

Length. One additional subscale, Independence, taps the child’s self-initiative in generating

the story retell.

The Information subscale is a macro-level measure of how many of the 32 key pieces of

information (including both words and phrases) from the original story the children used in their

story retell. The emphasis is on how much of the information the child uses in the retell, so

children can get credit for a response that matches the ‗key‘ in meaning, even if not in identical

word use. Still, many of the ‗key‘ words or phrases are only given full credit if they do match

exactly. The total possible raw score, 52, includes some items worth 2 points. This subscale

represents the proficiency level of a set of integrated skills. In order to score well on this

measure, children must remember the names of the key words or concepts (memory), know the

meaning of the words well enough to use them appropriately in their retell (vocabulary), and

have a sufficient understanding of story structure to use the words in the right sequence

(book/story knowledge).

Sentence Length is a straightforward micro-level measure of morpho-syntactic

complexity. For this purpose, the RBS-NA uses the average number of words in the five longest

sentences a child generates in the retell. Complexity, also a micro-level narrative measure, looks

at the number of utterances containing a subordinate clause that children use in their story retells.

It is also an indicator of syntactic development.

The Independence score relates to neither macro- nor micro-level structure of the

narrative itself, but is focused instead on the amount of prompting the child needs from the

assessor in order to retell the story. The total prompt score is the summed prompt scores with a

Race, maternal education and Bus Story retells 14

maximum of 4 possible prompts available per picture, and prompts are reverse scored. For

example, ―no prompt‖ is given 4 points, while the 4th

prompt, ―The bus…,‖ is scored 0 points.

Children who retell the story without any need for prompting from the assessor will get the

highest Independence score, while children who need prompts will score lower on Independence.

The Independence measure was scored as directed in the manual.

Procedure

Assessors were trained prior to administering the RBS-NA. Assessors included research

assistants and retired school teachers. Training consisted of a lecture on the importance of strict

adherence to protocol, viewing practice assessments, and real-time test administration practice

with feedback.

Children were individually administered the RBS-NA in the fall semester of kindergarten;

the math treatment had occurred during the prior school year in pre-kindergarten. They were

assessed in an open space (e.g., library, hallway) in their school by a trained assessor.

Assessments were videotaped and audio taped to facilitate transcription. Audiotapes served as a

backup in case of videotape malfunctioned, and also aided in the transcription process when the

videotapes were difficult to hear.

Reliability. The reliability of the transcription and scoring procedures was calculated for

the full sample (n = 1027). Reliability of the transcription procedure was determined by

evaluating a one-way, single-measure interclass correlation (ICC) using a random 7% sample of

stories that were transcribed by two trained research assistants. The two sets of transcriptions

were then expert scored by the same researcher, and the ICCs between the pairs of scores on

three of the measures, which represented the reliability of the transcription process from the tape,

were 0.99 for Information, 0.97 for Sentence Length, and 0.94 for Complexity. Transcription

Race, maternal education and Bus Story retells 15

reliability for the Independence score was not determined, because this was an objective

recording of the type of prompt that was provided and recorded by the assessor during test

administration. Scorers/transcribers checked 100% of the transcriptions for the accuracy of the

Independence scores given by the assessors. Errors were made in less 1% of the time, and these

were corrected.

Inter-rater reliability for the scoring procedure was calculated using ICCs to determine

scoring agreement between two different scorers using the same transcriptions (a random 10% of

samples for Information, Sentence Length, and Complexity, and a random 8% for

Independence). A one-way ICC with single measures were used to account for error due to

having more than two raters, but not always the same two raters rating each target. Agreement

was 0.97 for Information, 0.94 for Sentence Length, 0.81 for Complexity, and 0.94 for

Independence.

Results

Box plots were used to identify outliers for each dependent measure. Specifically, data

points were considered outliers if they were beyond the first and third quartiles by 1.5 times the

interquartile range (Hinkle Wiersma, & Jurs, 2003). The box plots indicated that each dependent

measure included two to three outlier scores. A total of nine children produced the outlier scores

across all four dependent measures. These nine children were distributed relatively equally

across the four groups. For ease of reporting, as shown in Table 2, we removed these same nine

children from the analysis of each dependent measure; so our analysis is based on an n = 163

with 82 African American children and 81 European American children. Seventy-nine children

fell within the <HS category while 84 fell within the >HS category.

We first wanted to know whether our results were due to differences in gender and site of

Race, maternal education and Bus Story retells 16

testing (Buffalo vs. Boston). Only the Information score was normally distributed so we

conducted a two-tail independent t-test on the Information score variable and two-tail Mann

Whitney Tests on the remaining three measures. Table 3 contains the results of the gender

analysis and Table 4 contains the results of the site analysis. The sample contained 80 males and

83 females (eliminating any outliers as discussed above). There were no significant differences

for gender for any of the variables. The sample contained 133 children from Buffalo and 30

children from Boston. There were no significant differences for site for any of the variables. As

such, we proceeded in our planned analyses collapsing over both gender and site.

Two-factor, fixed-effect, analyses of variance (ANOVA) with two levels for the

independent variable race (AA or EA) and two levels for the independent variable maternal

education (<HS and >HS) were planned for four measures in the RBS-NA test manual

(Information, Sentence Length, Complexity, and Independence). A power analysis indicated that

an experimental effect could be detected only if the independent variables explained at least

3.86% of the variance of a given dependent measure (Abdi, Edelman, Valentine, & Dowling,

2009). For main effects, pooled variances to determine Cohen‘s d effect sizes were calculated

according to Cortina and Nouri (2000) and confidence intervals were calculated according to

Altman (2000). We followed Cortina and Nouri‘s (2000) recommendation to only report

Cohen‘s d effect sizes for main effects because there is disagreement on what procedure to use

when calculating Cohen‘s d values for interactions. We also followed their procedure to calculate

the pooled variances needed for the Cohen‘s d calculations. Since only two of the test manual

measures had standard scores, we chose to report findings for all measures using raw scores for

consistency and ease of reporting. Each dependent measure was tested for meeting the

assumptions of ANOVA using Chi Square for independence, the Lilliefors test (Lilliefors, 1969)

Race, maternal education and Bus Story retells 17

for normality, and the O‘Brien test of equal variance (O'Brien, 1979, 1981; Trujillo-Ortiz &

Hernandez-Walls, 2003). The means and standard deviations for the measures from the RBS-NA

are shown in Table 5. Only the Information score met the assumptions of ANOVA. The

Sentence Length data and the Complexity data had a right skewed distribution and equal

variance, while the Independence data had a left skewed distribution and unequal variance.

Attempts to transform these data to meet the assumptions of ANOVA were unsuccessful. When

no nonparametric test exists that fits the experimental design, Conover (1999) recommends

conducting an ANOVA on the raw data and on the rank transformed data. ―If the two procedures

give nearly identical results, the assumptions underlying the usual analysis of variance are likely

to be reasonable and the regular parametric analysis valid‖ (p. 419-420). Thompson (1991) states

that ANOVAs on ranked data are valid only when there is one significant main effect. In this

situation, the ranked data conform to a non-central X2 distribution. When the ANOVA on ranked

data indicates more than one significant main effect and a significant interaction, the approach of

using ranked data in ANOVA is invalid (Leys & Schumann, 2010; Thompson, 1991).

When we conducted ANOVAs on both the raw and ranked data for the three variables

not meeting the ANOVA assumptions (i.e., sentence length, complexity, independence) as

Conover (1999) recommends, we found that there was only one main effect in each case. Given

that our data conform to the situation in which ANOVA using ranked data is valid according to

Thompson (1991), we reported the results for sentence length, complexity, and independence as

ANOVAs based on ranked data. We set the alpha level at .05 and applied the Bonferonni

correction, which resulted in a correct alpha of .0125 (.05/4, because we had one inferential test

for each of the four dependent measures). The results of the inferential tests are shown in Table

6.

Race, maternal education and Bus Story retells 18

For the Information score, there was a main effect for both maternal education F(1,159) =

10.75, MS = 450.96, p = 0.001, Cohen‘s d = 0.49, 95% CI [0.47, 0.52] and race F(1,159) =

17.94, MS = 752.36, p < 0.001, Cohen‘s d = 0.65, 95% CI [0.63, 0.68], but no interaction

F(1,159) = 0.48, MS = 20, p = 0.491. Maternal education explained 6.33% of the variance, with

>HS children (M = 22.45) producing an Information raw score that was significantly higher than

the score achieved by the <HS children (M = 19.10). Race explained 9.51% of the variance, with

EA children (M = 23.01) producing an Information raw score that was significantly higher than

the score achieved by the AA children (M = 18.67). Recall that our power analysis indicated that

only 3.86% of the variance needed to be explained in order to detect an experimental effect. As

such, the power of our sample was more than adequate.

For the remaining three dependent measures, which did not meet the assumptions of

ANOVA, there were main effects for maternal education, no main effects for race, and no

significant interactions. The following results are reported as X2 values instead of as F values

because ANOVAs conducted on ranked data conform to a non-central X2 distribution

(Thompson, 1991). For Sentence Length, the main effect for maternal education X2(1,159) =

7.72, MS = 16,348.99, p = 0.006, Cohen‘s d = 0.47, 95% CI [0.45, 0.50] explained 4.63% of the

variance, with the five longest sentences of >HS children (M = 9.28) having greater average

length than the <HS children (M = 8.28). For Complexity, the main effect for maternal education

X2 (1,159) = 9.17, MS = 17,951.20, p = 0.029, Cohen‘s d = 0.48, 95% CI [0.46, 0.51] explained

5.45% of the variance, with >HS children (M = 2.73) producing more complex sentences than

the <HS children (M = 2.10). For the Independence score, the main effect for maternal X2 (1,159)

= 13.02, MS = 25,194.50, p = 0.004, Cohen‘s d = 0.66, 95% CI [0.64, 0.69] explained 7.31% of

the variance, with >HS children (M = 41.32) requiring less prompting than the <HS children (M

Race, maternal education and Bus Story retells 19

= 37.91).

Discussion

Maternal Education Effects

All four scores of the RBS-NA showed significant differences between children whose

mothers had a lower education level (<HS) as compared to a higher education level (>HS), with

the children in the higher maternal education group scoring higher in all instances. These

findings are supported by other research using spontaneous conversational language samples and

standardized, norm-referenced tests. Dollaghan and her colleagues (1999), for example, analyzed

the language samples and Peabody Picture Vocabulary Test –Revised (PPVT-R) (Dunn, 1980)

scores of 240 three-year old children. Comparing three levels of maternal education, they found

statistically significant linear trends across educational levels for their three spontaneous

language sample measures (mean length of utterance in morphemes, the number of different

words, and the total number of words) and on the standardized test of receptive vocabulary

(PPVT-R).

The mean length of utterance measure used by Dollaghan et al. has been found to be very

highly correlated with the mean length of a child‘s five longest utterances (e.g., Malakoff,

Mayes, Schottenfeld, & Howell, 1999), which was the Sentence Length measure used in the

RBS-NA. As such, these two findings are logically connected and support each other (although

the children were different ages and the language samples were collected differently). On a more

general level, the RBS-NA and the PPVT-R used by Dollaghan and her colleagues are both

standardized, norm-referenced language tests. At this level, the two sets of results are similar in

finding maternal education effects. Qi and her colleagues (Qi, Kaiser, Milan, & Hancock, 2006)

also found that among the 482 low-income AA preschoolers in their sample, maternal education

Race, maternal education and Bus Story retells 20

level had a significant impact on the children‘s PPVT-III performance, with children‘s whose

mothers had a high school degree or less scoring 11 points lower than children whose mothers

had a Bachelor‘s degree. However, the PPVT and the RBS-NA each focus on very different

aspects of language competence (oral narrative skill versus single word receptive vocabulary),

which weakens support that results on one test provide for the other.

A standardized, norm-referenced language test more closely aligned with the RBS-NA

(which requires the integration of many language skills in producing a narrative) is the Preschool

Language Scale-3, or PLS-3 (an omnibus test of language abilities). Qi et al., 2003, found

significant effects of maternal education level on preschoolers‘ performance on the PLS-3 within

low-income groups of both AA and EA preschoolers. The findings with the PLS-3 support those

obtained for the RBS-NA in the current study.

For research focused specifically on narratives, the findings of the current study are at

odds with previous research. Recall that Reese and her colleagues (Reese et al., 2009) found no

relationship between maternal education and three aspects of the storybook retell narratives in

six and seven year olds. However, also recall that this study contained no mothers who were

university graduates. This compressed range may have obscured any impact of education level

on the children‘s narrative language abilities. In the current study, 48 mothers (28% of our

sample) had Bachelor‘s or Master‘s degrees. The greater spread in maternal education levels in

our study may account for the differences in findings between our study and that of Reese et al.

(2009). Furthermore, different story retell tasks were used in each study, so it is also possible that

the different stories that were retold in these two studies might explain differences in the results.

In their study of preschoolers, Kulkofsky and Klemfuss (2008) found no main effects or

interactions for maternal education, nor did maternal education correlate with narrative quality.

Race, maternal education and Bus Story retells 21

However, this study may have had too large a proportion of college graduates in the sample.

Recall that in Study 1 (n = 46), 89% of the mothers had a college degree, and in Study 2 (n = 66),

72% did. Furthermore, both the type of narrative elicited and measures used in this study were

quite different than those used in the RBS-NA, so it is difficult to compare the results obtained

from them. The preschoolers‘ retells in the Lulkofsky and Kelmfyss study were of a previously

witnessed staged event, which was measured for overall quality and for the following four

components of narrative quality: volume (number of propositions), complexity (number of words

per proposition), descriptive texture (number of descriptive words), and cohesion (marking

temporal, causal, conditional, & optional states). These do not correspond well with any of the

RBS-NA measures.

In the Price et al. (2006) study of 65 African American children retelling the Bus Story at

age 4 and again at kindergarten entry, it was likewise found that their methods of scoring the Bus

Story (using a story grammar analysis yielding a maximum of 39 points) did not correlate with

maternal education, which ranged from 9 to 18 years (M = 13.0; SD = 2.02). This does not align

with the current findings, because we found a main effect for race, and maternal education level

on the RBS-NA, but there was no significant interaction. If there had been an impact for maternal

education level only for the EA children in the current study, we would have found a significant

interaction. However, the lack of similar findings in these two studies could possibly be due to

differences between the Information score as determined in the RBS-NA manual and the story

grammar analysis these authors conducted on the Bus Story narratives. Although both are macro-

level measures, they may be dissimilar enough to yield different findings. Furthermore, the

pictures are quite different for the British and North American editions, with the British edition

Race, maternal education and Bus Story retells 22

featuring a double-decker bus that most children in the U.S. would be unfamiliar with. It is

possible that this had an influence on the different findings of these two studies.

In summary, our findings that maternal education level impacted all measures of the RBS-

NA correspond well with research on spontaneous language samples produced by three-year olds

that were not narratives (Dollaghan et al., 1999) and performance on other standardized language

tests (Dollaghan et al., 1999; Qi et al., 2003; Qi et al., 2006), but they stand in contrast to

research that has specifically analyzed narratives produced by children ages 4 to 7 (Kulkofsky &

Klemfuss, 2008; Price et al, 2006; Reese et al., 2009).

Race Effects

Although there was a main effect for race for the Information score only, with AA

children performing more poorly than their EA peers, the Information score is a particularly

important one. When researchers report only one score from either version (British or American)

of the Bus Story, the Information score is almost always chosen (e.g., Bishop & Edmundson,

1987; Botting, et al., 1997; Conti-Ramsden & Botting, 1999; Conti-Ramsden, et al., 1997;

Kovas, et al., 2005; Mackie & Dockrell, 2004; Paul, 1996; Paul, et al., 1996; Paul & Smith,

1993; Storch & Whitehurst, 2002; Stothard, et al., 1998). When two scores are reported, they are

the Information score and the Sentence Length score, so here again, the Information score is of

considerable importance in how either version of the Renfrew Bus Story tends to be used in

research (e.g., Anderson, et al., 2004; Anderson, et al., 1997; Briscoe, et al., 1998; Dockrell &

Lindsay, 2001; Gallagher, et al., 2000; Girolametto, et al., 2001; Gray, 2004; Hohen &

Stevenson, 1999; Hughs, et al., 1998; Kennedy, et al., 2006; Nathan, et al., 2004). Indeed, few

studies report more than one or two of these measures. The current findings regarding the

relatively strong effect of children‘s race on their RBS-NA Information score is as such a very

Race, maternal education and Bus Story retells 23

important one, at least for use of this language assessment tool in the United States, from which

the AA sample in the current study was drawn.

These findings are not consistent with other data on the RBS-NA reported by Pankratz et

al. (2007), who found that the small subset of minority children (mostly Hispanic) in their

sample scored significantly lower on Sentence Length, but not on the Information score.

However, given that these children were mostly Hispanic, it is not possible to compare their

findings to those of the current study.

The current findings are also not consistent with a small study conducted by Serpell and

his colleagues (2005). These researchers read a published children‘s story, The Monkey and the

Crocodile (Galdone, 1969) to four groups of AA and EA children second graders, two from low-

income (n = 27 AA and 14 EA) and two from middle-income (n = 26 AA and 14 EA)

backgrounds. To assess their comprehension of the story, the children were asked a number of

literal and inferential questions tapping seven key features of the story. Finally, they were asked

to retell the story. None of the groups differed on two of the three measures used — answers to

the questions about the story, and the length of their retells in words. However, on story telling

quality, as measured by the number of story structure elements included in the retell, the low-

income AA children scored significantly higher than the middle-income AA children.

Furthermore, the low-income children‘s story telling quality scores were on average higher than,

but not significantly different from, both groups of EA children. Serpell et al. noted ―that low-

income African American children exhibited oral narrative skills on a par with the middle-

income European American children is a striking finding‖ (p. 163) and that these findings were

―strikingly different from those obtained for most of the other competency measures‖ (p. 163).

The findings on the effect of race on children‘s Information score in the current study do

Race, maternal education and Bus Story retells 24

dovetail with some, but not all, more general level with findings regarding other standardized,

norm-referenced language tests. The effect size in the current study was d = 0.65, which means

that the average information score performance of the AA children was 0.65 standard deviations

below that of the EA children. This number is similar to the discrepancy between the

performance of AA and EA children on other standardized, norm-referenced language tests

focusing on other aspects of language development besides narrative ability.

For example, Brooks-Gunn and her colleagues (2003) report findings from two large

scale studies on children‘s performance on the Peabody Picture Vocabulary Test –Revised,

PPVT-R (Dunn, 1980), a test of single-word receptive vocabulary. Data from 315 five year-olds

from the Infant Health and Development Program (IHDP) study showed a white-black gap of

1.63 standard deviations. When adjusted for family income, maternal education, and home

environment, the gap was reduced to 0.86 standard deviations. They reported additional data

from 1,354 five and six-year olds from the National Longitudinal Survey of Youth (NLSY),

which showed a white-black gap of 1.15 standard deviations. When adjusted for family income,

maternal education, and home environment, this gap was reduced to 0.73 standard deviations,

which is very close to the .65 standard deviations found in the current study.

Numerous subsequent studies of a new edition of this test, the PPVT –III (Dunn & Dunn,

1997), which some scholars believe may be less biased than the PPVT-R (Washington & Craig,

1999), also show that low-income AA children score on average substantially below the

expected mean based on national norms, ranging from -0.60 to -1.48 SD from the mean (e.g.,

Campbell, Bell, & Keith, 2001; Champion, Hyter, McCabe, & Bland-Stewart, 2003; Qi et al.,

2006; Restrepo, et al., 2006; Washington & Craig, 1999). And, there is evidence here again that

maternal education explains part, but not all, of this discrepancy in the scores of AA children

Race, maternal education and Bus Story retells 25

(Washington & Craig, 1999).

Although race differences have been found on various versions of the PPVT in some

studies, the study by Qi et al., 2006, found no significant race differences when low- income AA

children (n = 482) were compared to low-income EA preschoolers (n = 42) on the PPVT-III, the

Expressive Vocabulary Test (Williams, 1997), or the PLS-3. The authors note that caution

should be used in interpreting their results, since the sample sizes of AA and EA children were

substantially different. With this caution in mind, we do not find support for the race differences

found on the RBS-NA in the current study on these tests of receptive and receptive vocabulary

and an omnibus test of language abilities.

In sum, the race difference on the Information score of the RBS-NA dovetails with some,

but not all, previous research regarding the performance of AA children on standardized, norm-

referenced language tests.

Future Research

There is clearly a need to sort out possible reasons for the discrepancy in scores between

children of different races on the RBS-NA (and on other standardized language tests). One reason

often suggested for standardized test score discrepancies and/or achievement discrepancies (often

measured by standardized test scores) is that these children are so much more likely to live in

poverty (e.g., Duncan, Yeung, Brooks-Gunn, & Smith, 1998). We attempted to control for this

possibility in the current study by carefully equalizing the number of children from the two

different races that were in each of the seven specific categories of maternal education that

comprised our two levels of maternal education. Our results did indicate that children‘s

performance on all measures used in the RBS-NA, at least when using maternal education as a

measure of socio-economic status, was clearly influenced by socio-economic status. However,

Race, maternal education and Bus Story retells 26

there was also a performance gap on the Information score on the RBS-NA based on race alone,

and the effect size for it (d = 0.65) was larger than the effect size for maternal education (d =

0.49).

This general phenomenon of a test and/or achievement discrepancy between EA and AA

children has been referred to as the ―black-white achievement gap‖ (e.g., Craig & Washington,

2006), and other possible explanations for it in addition to poverty remain to be explored.

Scholars have suggested a variety of additional factors such as the potential cultural bias of tests

(e.g., Chamberlain & Madeiros-Landurand, 1991; Flowers, 2007; Thomas-Tate, Washington,

Craig, & Packard, 2006; Wyatt, 2002), discrepancies in teacher quality (e.g., Connor, Son,

Hindman, & Morrison, 2005; Talbert-Johnson, 2004; Wayne & Youngs, 2003), differences in

home literacy practices for EA and AA preschoolers (e.g., Anderson-Yockel & Haynes, 1994;

Vernon-Feagans, Hammer, Miccio, & Manlove, 2001), and mismatches for AA children between

the language of the classroom (and of standardized language tests) and the language of home

(see van Kleeck & Schwarz, under review).

Limitations of the Current Study

Before we are assured of maternal and racial difference in children‘s performance on the

Information score of the RBS-NA, it is essential that the current study be replicated. First of all,

the children in the current study all took place in a mathematics intervention during the previous

school year. It is possible, although we consider it unlikely, that the mathematics treatment

differentially impacted the narrative retell skills of these kindergartners as a function of either

race or maternal education.

An additional weakness stems from the fact that all of the children in this study were

from urban classrooms in two cities in the Northeastern United States, and most of them were

Race, maternal education and Bus Story retells 27

from just one of those cities. Greater geographic diversity would be preferable. Another

limitation was that there could be other unmeasured child, teacher, family, school, or even

neighborhood characteristics that impacted the results. Also, we had information only on

mothers‘ education levels as a measure of socio-economic status in the current study. Other

unmeasured aspects of a child‘s SES background were factors such as family income and

parental occupation. Further work that could tease out the independent contribution of these

factors would provide a more detailed explanation for our findings. And finally, the current study

compared only EA and AA children. It is essential to additionally determine how children from

other non-mainstream backgrounds perform on the RBS-NA.

Clinical Implications

The findings of the current study suggest that clinicians should use caution in using the

RBS-NA to determine the presence or absence of language impairment in AA children (at least

for the Information score) and children from low-SES backgrounds (for all RBS-NA scores).

Similar discrepancies in scores for African American children on other standardized language

tests have lead to suggestions that spontaneous language sampling, supplemented with other

informal language assessments, provide more valid methods for assessing these children‘s

language skills in an unbiased fashion (e.g., Craig & Washington, 2006).

Spontaneous language samples can be conversational, or they can be a narrative

generated by the child. As such, we might have expected that children‘s story retells, such as

those generated in the RBS-NA, would produce results similar to spontaneous language samples

and not show differences based on race alone. For the RBS-NA measures focused at the sentence

level, Sentence Length and Complexity, this was true, as there were no significant differences on

these measures based on a child‘s race.

Race, maternal education and Bus Story retells 28

The Information score, however, showed a medium large (d = 0.65) effect size for race,

and the importance of this score is highlighted by the fact that it is often the only RBS-NA score

used in many research studies, as noted earlier. The Information score requires more than being

able to generate language at the sentence level. As also noted earlier, it requires the integration of

other skills, such as memory (of key words and concepts), adequate knowledge of the specific

vocabulary in the story to be able to use it in a retell, and sufficient knowledge of story structure

to convey the information in the proper sequence. So, the language differences found on the

RBS-NA that were impacted by a child‘s race appear to reflect differences in language ability

beyond the sentence level. But this may not mean that AA children have a deficit in producing

language beyond the sentence level. It may instead reflect that the narrative a child is required to

produce in the RBS-NA is one that was not chosen by the child, and hence has no social

importance to him or her, unlike narratives the child may be quite adept at producing.

More specifically, it may be that the RBS-NA reflects what Bloome and his colleagues

(Bloome, Katz, & Champion, 2003) have referred to as ―narrative as text.‖ Narrative as text is

emphasized in school, and it tends to separate the existence of the storyteller from the

story. Narrative as performance, on the other hand, explicitly builds social relationships

and identities, but is of diminished importance in school. AA children are often skilled at

narrative as performance, but may lack experience with narrative as text that is important

in school.

The instructions for The Renfrew Bus Story reflect a more “school-like” task. The

examiner says, “I’m going to tell you a story about this bus. Listen very carefully because

when I’m finished, you’re going to tell me the story on this tape recorder. Are you ready?”

To the extent that The Renfrew Bus Story emphasizes narrative as text as opposed to

Race, maternal education and Bus Story retells 29

narrative as performance, AA children might be expected to perform more poorly on it than

their EA peers. Nonetheless, narrative as text is an important academic skill, so the RBS-NA

may be predicting future school difficulties for AA children, even if it does not reflect a

general problem with narrative generation. Future research will need to distinguish these

different types of narratives before the source of AA children’s difficulty with the

Information score on the RBS-NA can be discerned.

The clinical implications of the findings in the current study for children whose mothers

had lower education levels – they scored significantly lower on all RBS-NA measures – was

underscored by Dollaghan et al. (1999). These scholars concluded that there is a ―critical need to

examine the influences of maternal education and other sociodemographic variables on all norm-

referenced tests and measures of spontaneous language production and comprehension, as a

prerequisite to using such measures to compare children or to identify children with language

disorders‖ (p. 1441). Our results indicate that the RBS-NA will likely over-identify children from

low SES backgrounds as having language impairments.

Acknowledgments

The research reported here was supported by the Institute of Education Sciences, U.S.

Department of Education, through Grant No. R305K05157 to the University at Buffalo, State

University of New York, D. H. Clements and J. Sarama, ―Scaling Up TRIAD: Teaching Early

Mathematics for Understanding with Trajectories and Technologies.‖ The opinions expressed are

those of the authors and do not represent views of the U.S. Department of Education.

Race, maternal education and Bus Story retells 30

References

Abdi, H., Edelman, B., Valentine, D., & Dowling, W. J. (2009). Experimental Design & Analysis

for Psychology. New York: Oxford.

Altman, D. G. (2000). Appendix 1: Confidence intervals. In D. L. Sackett, S. E. Straus, W. S.

Richardson, W. Rosenberg & R. B. Haynes (Eds.), Evidence-based medicine: How to

practice and teach EBM. London: Churchill Livingstone.

Anderson, J., Anderson, A., Lynch, J., & Shapiro, J. (2003). Storybook reading in a multicultural

society: Critical perspectives. In A. van Kleeck, S. A. Stahl & E. Bauer (Eds.), On

reading to children: Parents and teachers (pp. 203-230). Mahwah, NJ: Lawrence

Erlbaum Associates.

Anderson, V. A., Morse, S. A., Catroppa, C., Haritou, F., & Rosenfeld, J. (2004). Thirty month

outcome from early childhood head injury: A prospective analysis of neurobehavioural

recovery. Brain, 127, 2608-2620.

Anderson, V. A., Morse, S. A., Klug, G., Catroppa, C., Haritou, F., Rosenfeld, J., et al. (1997).

Predicting recovery from head injury in young children: A prospective analysis. Journal

of International Neuropsychological Society, 3, 368-580.

Anderson-Yockel, J., & Haynes, W. O. (1994). Joint book-reading strategies in working-class

African American and white mother-toddler dyads. Journal of Speech and Hearing

Research, 37, 583-593.

Bishop, D. V. M., & Edmundson, A. (1987). Language-impaired 4-year-olds: Distinguishing

transient from persistent impairment. Journal of Speech and Hearing Research, 52, 156-

173.

Bloome, D., Katz, L. & Champion, T. (2003). Young children‘s narratives and ideologies of

language in classrooms. Reading & Writing Quarterly, 19, 205 – 223.

Bornstein, M. H., Hahn, C.-S., Suwalsky, J. T. D., & Haynes, O.M. (2003). Socioeconomic

status, parenting, and children development: The Hollingshead four-factor index of social

status and the socioeconomic index of occupations. In M. H. Bornstein & R. H. Bradley

(Eds.), Socioeconomic status, parenting, and child development (pp. 29 – 82). Mahwah,

NJ: Erlbaum.

Botting, N. (2002). Narrative as a tool for assessment of linguistic and pragmatic impairments.

Child Language Teaching & Therapy, 18(1), 1-22.

Botting, N., Conti-Ramsden, G., & Crutchley, A. (1997). Concordance between teacher/therapist

opinion and formal language assessment scores in children with language impairment.

European Journal of Disorders of Communication, 32, 317-327.

Briscoe, J., Gathercole, S. E., & Marlow, N. (1998). Short-term memory and language outcomes

after extreme prematurity at birth. Journal of Speech, Language, and Hearing Research,

41(41), 654-666.

Brooks-Gunn, J., Klebanov, P. K., Smith, J., Duncan, G. J., & Kyunghee, L. (2003). The black-

white test score gap in young children: Contributions of test and family characteristics.

Applied Developmental Science, 7(4), 239-252.

Campbell, M. J., Bell, S. K., & Keith, L. K. (2001). Concurrent validity of the Peabody Picture

Vocabulary Test – Third Edition as an intelligence and achievement screener for low SES

African American children. Assessment, 8(1), 85-94.

Race, maternal education and Bus Story retells 31

Chamberlain, C., & Madeiros-Landurand, P. (1991). Practical considerations for assessment of

LEP students with special needs. In E. V. Hamayan & J. S. Damico (Eds.), Limiting bias

in the assessment of bilingual students (pp. 111 - 156). Austin, TX: Pro-Ed.

Champion, T. B., Hyter, Y. D., McCabe, A., & Bland-Stewart, L. M. (2003). A matter of

vocabulary: Performances of low-income African American Head Start children on the

Peabody Picture Vocabulary Test-III. Communication Disorders Quarterly, 24, 121-127.

Connor, C. M., Son, S., Hindman, A., & Morrison, F. J. (2005). Teacher qualifications,

classroom practices, family characteristics and preschool experience" Complex effects on

first graders' vocabulary and early reading outcomes. Journal of School Psychology, 43,

343 - 375.

Conover, W. J. (1999). Some methods based on ranks. In Practical nonparametric statistics (pp.

269-427). New York: John Wiley & Sons, Inc.

Conti-Ramsden, G., & Botting, N. (1999). Characteristics of children attending language units in

England: A national study of 7-year-olds. International Journal of Language &

Communication Disorders, 34(4), 359-366.

Conti-Ramsden, G., Crutchley, A., & Botting, N. (1997). The extent to which psychometric tests

differentiate subgroups of children with SLI. Journal of Speech, Language, and Hearing

Research, 40, 765-777.

Cortina, J. M., & Nouri, H. (2000). Effect size for ANOVA designs. Thousand Oaks, CA: Sage.

Craig, H. K., & Washington, J. A. (2006). Malik goes to school: Examining the language skills

of African American students from preschool–5th

grade. Mahwah, NJ: Lawrence Erlbaum

Associates.

Dockrell, J. E., & Lindsay, G. (2001). Children with specific speech and language difficulties –

The teachers‘ perspective. Oxford Review of Education, 27(3), 369-394.

Dollaghan, C. A., Campbell, T. F., Paradise, J. L., Feldman, H. M., Janosky, J. E., Pitcairn, D.

N., et al. (1999). Maternal education and measures of early speech and language. Journal

of Speech, Language, and Hearing Research, 42, 1432-1443.

Duncan, L., Yeung, W. J., Brooks-Gunn, J., & Smith, J. R. (1998). How much does childhood

poverty affect the life chances of children? American Sociological REview, 63, 406 - 423.

Dunn, L. M. (1980). Peabody Picture Vocabulary Test-Revised (PPVT-R). Circle Pines, MN:

American Guidance Service.

Dunn, L. M., & Dunn, L. M. (1997). PPVT-III: Peabody Picture Vocabulary Test (3rd

ed.).

Circle Pines, Minnesota: American Guidance Service.

Feagans, L., & Appelbaum, M. I. (1986). Validation of language substyles in learning disabled

children. Journal of Educational Psychology, 78, 358-364.

Federal Interagency Forum on Child and Family Statistics (2009). America’s children: Key

national indicators of well-being, 2009. Washington, D.C.: U.S. Government Printing

Office.

Flowers, L. A. (2007). New directions in research recommendations for research to improve

reading achievement for African-American students. Reading Research Quarterly, 42,

424 - 428.

Galdone, P. (1969). The Monkey and the Crocodile. New York: Houghton Miflin.

Gallagher, A., Frith, U., & Snowling, M. J. (2000). Precursors of literacy delay among children

at genetic risk of dyslexia. Journal of Child Psychology and Psychiatry, 41, 203-213.

Race, maternal education and Bus Story retells 32

Girolametto, L., Wiigs, M., Smyth, R., Weitzman, E., & Pearce, P. S. (2001). Children with a

history of expressive vocabulary delay: Outcomes at 5 years of age. American Journal of

Speech-Language Pathology, 10, 358-369.

Glasgow, C., & Cowley, J. (1994). Renfrew Bus Story (North American Edition). Centreville,

DE: Centreville School.

Gray, S. (2004). Word learning by preschoolers with specific language impairment: Predictors

and poor learners. Journal of Speech, Language, and Hearing Research, 47, 1117-1132.

Griffin, T., Hemphill, L., Camp, L., & Wolf, D. (2004). Oral discourse in the preschool years and

later literacy skills. First Language, 24, 123-147.

Haccoun, R. R. (2001). Review of The Renfrew Bus Story. In B. Plake & J. C. Impara (Eds.),

Fourteenth Mental Measurement Yearbook (pp. 1005-1008). Lincoln, NE: Buros Institute

of Mental Measurements.

Hauser, R. (1994). Measuring socioeconomic status in studies of child development. Child

Development, 65, 1541 – 1545.

Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics for the behavioral sciences

(5th ed.). New York: Houghton Mifflin Company.

Hoff, E., Laursen, B., & Tardif, T. (2002). Socioeconomic status and parenting. In M. H.

Bornstein (Ed.), Handbook of parenting (2nd

ed., pp. 231 – 252). Mahwah, NJ: Erlbaum.

Hohen, B., & Stevenson, J. (1999). The structure of genetic influences on general cognitive,

language, phonological, and reading abilities. Developmental Psychology, 35(2), 590-

603.

Hughs, C., Dunn, J., & White, A. (1998). Trick or Treat?: Uneven understanding of mind and

emotion and executive dysfunction in ―hard-to-manage‖ preschoolers. Journal of Child

Psychology & Psychiatry, 39(7), 981-994.

Huston, A. C., McLoyd, V.C., & Garcia Coll, C. (1994). Children and poverty: Issues in

contemporary research. Child Development, 65, 275 – 282.

Kennedy, C. R., McCann, D. C., Campbell, M. J., Law, C. M., Mullee, M., Petrou, S., et al.

(2006). Language ability after early detection of permanent childhood hearing

impairment. New England Journal of Medicine, 354, 2131-2141.

Kovas, Y., Hayiou-Thomas, E., Oliver, B., Bishop, D., Dale, P. S., & Plomin, R. (2005). Genetic

influences in different aspects of language development: The etiology of language skills

in 4.5-year old twins. Child Development, 76(3), 632-651.

Kulkofsky, S., & Klemfuss, J. Z. (2008). What the stories children tell can tell about their

memory: Narrative skill and young children‘s suggestibility. Developmental Psychology,

44(5), 1442-1456.

Lange, A. A. (2010). Sales Report for 2009 for Learning Tools LLC [Report]. Buffalo,

NY: Author. Leys, C., & Schumann, S. (2010). A nonparametric method to analyze interactions: The adjusted

rank transform test. Journal of Experimental Social Psychology, 46, 684-688.

Lilliefors, H. W. (1969). On the Kolmogorov-Smirnov test for the exponential distribution with

mean unknown. Journal of the American Statistical Association, 64, 387-389.

Mackie, C., & Dockrell, J. E. (2004). The nature of written language deficits in children with

SLI. Journal of Speech, Language, and Hearing Research, 47, 1469-1483.

Malakoff, M. E., Mayes, L. C., Schottenfeld, R., & Howell, S. (1999). Language production in

24-month-old inner-city children of cocaine-and-other-drug-using mothers. Journal of

Applied Developmental Psychology, 20(1), 159 - 180.

Race, maternal education and Bus Story retells 33

McGregor, K. K. (2000). The development and enhancement of narrative skills in a preschool

classroom: Towards a solution to clinician-client mismatch. American Journal of Speech-

Language Pathology, 9(1), 55-71.

Menyuk, P., Chesnick, M., Liebergott, J. W., Korngold, B., D‘Agostino, R., & Belanger, A.

(1991). Predicting reading problems in at-risk children. Journal of Speech and Hearing

Research, 34, 893-903.

Mercy, J. A., & Steelman, L. C. (1982). Familial influences on the intellectual attainment of

children. American Sociological Review, 47, 532 – 542.

Miller, J. F., Heilman, J., Nockerts, A., Iglesias, A., Fabiano, L., & Francis, D. J. (2006). Oral

language and reading in bilingual children. Learning Disabilities Research & Practice,

21(1), 30 – 43.

Nathan, L., Stackhouse, J., Goulandris, N., & Snowling, M. J. (2004). The development of early

literacy skills among children with speech difficulties: A test of the ―critical age

hypothesis‖. Journal of Speech, Language, and Hearing Research, 47, 377-391.

Newcomer, P., & Hammill, D. (1997). Test of Language Development-Primary. Austin, TX:

PRO-ED, Inc.

Norbury, C. F., & Bishop, D. V. M. (2003). Narrative skills of children with communication

impairments. International Journal of Language & Communication Disorders, 38(3),

287-313.

O‘Brien, R. G. (1979). A general ANOVA method for robust test of additive models for

variance. Journal of American Statistical Association, 74, 877-880.

O‘Brien, R. G. (1981). A simple test for variance effects in experimental designs. Psychological

Bulletin, 89, 570-574.

O‘Neill, D. K., Pearce, M. J., & Pick, J. L. (2004). Preschool children‘s narratives and

performance on the Peabody Individualized Achievement Test-Revised: Evidence of a

relation between early narrative and later mathematical ability. First Language, 24, 149-

183.

Pankratz, M. E., Plante, E., & Insalaco, D. M. (2007). The diagnostic and predictive validity of

The Renfrew Bus Story. Language, Speech, and Hearing Services in Schools, 38, 390-

399.

Paul, R. (1996). Clinical implications of the natural history of slow expressive language

development. American Journal of Speech-Language Pathology, 5, 5-21.

Paul, R., Hernandez, R., Taylor, L., & Johnson, K. (1996). Narrative development in late talkers:

Early school age. Journal of Speech and Hearing Research, 39, 1295-1303.

Paul, R., & Smith, R. (1993). Narrative skills in 4-year-olds with normal, impaired, and late-

developing language. Journal of Speech and Hearing Research, 36, 592-598.

Petersen, D. B., Gillam, S. L., & Gillam, R. B. (2008). Emerging procedures in narrative

assessment: The Index of Narrative Complexity. Topics in Language Disorders, 20, 111-

126.

Preschool Curriculum Evaluation Research Consortium. (2008). Effects of Preschool Curriculum

Programs on School Readiness (NCER 2008-2009). Washington, D.C.: National Center

for Education Research, Institute of Education Sciences, U.S. Department of Education.

Price, J. R., Roberts, J. E., & Jackson, S. C. (2006). Structural development of the fictional

narratives of African American preschoolers. Language, Speech, and Hearing Services in

Schools, 37, 178-190.

Race, maternal education and Bus Story retells 34

Qi, C. H., Kaiser, A. P., Milan, S., & Hancock, T. (2006). Language performance of low-income

African American and European American preschool children on the PPVT-III.

Language, Speech, and Hearing Services in Schools, 37(1), 5-16.

Qi, C. H., Kaiser, A. P., Milan, S., Yzquierdo, Z., & Hancock, T. B. (2003). The performance of low-income, African American children on the Preschool Language Scale-3. Journal of Speech, Language, and Hearing Research, 46(3), 576-590.

Reese, E., Suggtate, S., Long, J., & Schaughency, E. (2009). Children‘s oral narrative and

reading skills in the first 3 years of reading instruction. Reading and Writing,

Renfrew, C. E. (1969). The Bus Story: A test of continuous speech. North Place, Old Headington:

Oxford.

Restrepo, M. A., Schwanenflugel, P. J., Blake, J., Neuharth-Pritchett, S., Cramer, S. E., &

Ruston, H. P. (2006). Performance on the PPVT-III and the EVT: Applicability of the

measures with African American and European American preschool children. Language,

Speech, and Hearing Services in Schools, 31, 17-27.

Roth, F. P., Speece, D. L., & Cooper, D. H. (2002). A longitudinal analysis of the connection

between oral language and early reading. Journal of Educational Research, 95, 259-272.

Scarborough, H. S. (2001). Connecting early language and literacy to later reading (dis)abilities:

Evidence, theory, and practice. In S. B. Neuman & D. K. Dickinson (Eds.), Handbook of

Early Literacy Development (pp. 97-110). New York: Guilford Publications.

Sénéchal, M., & LeFevre, J. A. (2002). Parental involvement in the development of children‘s

reading skill: A five-year longitudinal study. Child Development, 73, 445-460.

Serpell, R., Baker, L., & Sonnenschein, S. (2005). Becoming literate in the city: The Baltimore

Early Childhood Project. Cambridge: Cambridge University Press.

Snow, C. E., Tabors, P. O., Nicholson, P. A., & Kurland, B. F. (1995). SHELL: Oral language

and early literacy skills in kindergarten and first-grade children. Journal of Research in

Childhood Education, 10, 37-48.

Snyder, L. S., & Downey, D. M. (1991). The language-reading relationship in normal and

reading-disabled children. Journal of Speech and Hearing Research, 34, 129-140.

Stein, N., & Glenn, C. (1982). Children‘s concept of time: The development of a story schema.

In W. Friedman (Ed.), The developmental psychology of time (pp. 255-282). New York:

Academic Press.

Storch, S. A., & Whitehurst, G. J. (2002). Oral language and code-related precursors to reading:

Evidence from a longitudinal structural model. Developmental Psychology, 38(6), 934-

947.

Stothard, S. E., Snowling, M. J., Bishop, D. V. M., Chipchase, B. B., & Kaplan, C. A. (1998).

Language-impaired preschoolers: A follow-up into adolescence. Journal of Speech and

Hearing Research, 41, 407-418.

Tabors, P. O., Snow, C. E., & Dickinson, D. K. (2001). Homes and schools together: Supporting

language and literacy development. In D. K. Dickinson & P. O. Tabors (Eds.), Beginning

literacy with language (pp. 313-334). Baltimore: Paul H. Brookes.

Talbert-Johnson, C. (2004). Structural inequities and the achievement gap in urban schools.

Education and Urban Society 37(1), 22 - 36.Thomas-Tate, S., Washington, J., Craig, H.,

& Packard, M. (2006). Performance of African American preschool and kindergarten

students on the expressive vocabulary test. Language, Speech, and Hearing Services in

Schools, 37, 143 - 149.

Race, maternal education and Bus Story retells 35

Thompson, G. L. (1991). A note on the rank transform for interactions. Biometrika Trust, 697-

701.

Trujillo-Ortiz, A., & Hernandez-Walls, R. (2003). Obrientest: O‘Brien‘s test for homogeneity of

variances. A MATLAB file. [WWW document], from

http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=3335&obje

ctType=FILE

van Kleeck, A. (2007). SLPs‘ foundational role in reading comprehension: A response to Kamhi.

The ASHA Leader, 12 (10), 32 – 33.

van Kleeck, A. (2008). Providing preschool foundations for later reading comprehension: The

importance of and ideas for targeting inferencing in book-sharing interventions.

Psychology in the Schools, 45(6), 627-643.

van Kleeck, A. & Schwarz, A. L. (under review). Making ―school talk‖ explicit: A critical

preschool foundation for later reading comprehension and general academic success.

Swiss Journal for Educational Research.

Vellutino, F. R., Tunmer, W. E., Jaccard, J. J., & Chen, R. (2007). Components of reading

ability: Multivariate evidence for a convergent skills model of reading development.

Scientific Studies of Reading, 11, 3-32.

Vernon-Feagans, L., Hammer, C., Miccio, A., & Manlove, E. (2001). Early language literacy

skills in low-income African American and Hispanic children. In S. B. Neuman & D. K.

Dickinson (Eds.), Handbook of early literacy development (pp. 192-210). New York:

Guilford Publications.

Washington, J. A., & Craig, H. K. (1999). Performance of at-risk, African American

preschoolers on the Peabody Picture Vocabulary Test-III. Language, Speech, and

Hearing Services in Schools, 30, 75-82.

Wayne, A. J., & Youngs, P. (2003). Teacher characteristics and student achievement gains: A

review. Review of Educational Research, 73(1), 89 - 122.

Williams, K. T. (1997). Expressive Vocabulary Test. Circle Pines, MN: American Guidance Service.

Wyatt, T. (2002). Assessing the communicative abilities of clients from diverse cultural and

language backgrounds. In D. E. Battle (Ed.), Communication disorders in multicultural

populations (3rd ed.) (pp. 415 - 458). Boston: Butterworth-Heinemann.

Zimmerman, I., Steiner, V., & Pond, R. (1992). Preschool Language Scale - 3. San Antonio, TX: The Psychological Corporation, Harcourt, Brace, Jovanovich.

Race, maternal education and Bus Story retells 36

Table 1

Frequency Counts of Children Who Received the TRIAD Treatment by Race and Maternal Education Categories and Sample

Selection Procedure for the Current Study

TRIAD Maternal

Education Categories 8

th Grade

High School

(GED)

Vocational /

Technical

1 to 2 years

College

Associate‘s

Degree

Bachelor‘s

Degree

Master‘s

Degree Total

African American (AA) 26 96 8 77 28 27 5 267

European American (EA) 13 28 1 14 6 19 15 96

Total 39 124 9 91 34 46 20 363

Quasi-Random Sample Selection for the Current Study

African American (AA) 13 28 1 14 6 19 5 86

European American (EA) 13 28 1 14 6 19 5 86

Total 26 56 2 28 12 38 10 172

Re-categorization of Maternal Education Categories for the Current Study

High School or Less (≤ HS) More than High School (> HS) Total

African American (AA) 41 45 86

European American (EA) 41 45 86

Total 82 90 172

Race, maternal education and Bus Story retells 37

Table 2

Sample characteristics by group

Classification Initial N N Less Outliers Boys Bostona

AA ≤ HS 41 40 21 7

AA > HS 45 42 21 9

EA ≤ HS 41 39 18 5

EA > HS 45 42 20 9

TOTAL 172 163 80 30

a = Data meeting our race and maternal education search criteria were collected at a site in

Buffalo, New York and in Boston, Massachusetts

Race, maternal education and Bus Story retells 38

Table 3

Analysis of gender differences on each dependent measure

Variable

Female

(n = 83)

Male

(N = 80)

Test

Statistic

Cohen’s

d

Confidence

Intervals

Mean SD Mean SD

Two-Tailed t-test Results (t statistic) df = 161

Information

Score

21.11 7.74 20.54 6.15 0.57 0.08

-0.04, 0.20

Two-Tailed Mann Whitney Test Results (U statistic)

Sentence

Length

8.95 2.32 8.63 1.99 3,063.50 0.15

0.07, 0.23

Complexity 2.46 1.35 2.39 1.31 3,225.00 0.05

-0.02, 0.13

Independence 39.48 4.98 39.86 5.85 3,579.00 -0.07

-0.18, 0.03

* p < .0125, **p <.001

Race, maternal education and Bus Story retells 39

Table 4

Analysis of site differences on each dependent measure

Variable

Buffalo

(n=133)

Boston

(n=30)

t-test

values

Cohen’s

d

Confidence

Intervals

Mean SD Mean SD

Two-Tailed t-test Results (t statistic) df = 161

Information

Score

20.77 6.81 21.07 7.83 -0.21 0.04

-0.12, 0.20

Two-Tail Mann Whitney Test Results (U statistic)

Sentence

Length

8.83 2.19 8.64 2.05 2,043.50 -0.09

-0.21, 0.04

Complexity 2.45 1.33 2.30 1.29 2,123.50 -0.11

-0.23, 0.00

Independence 39.52 5.53 40.33 4.89 1,813.50 0.15

0.00, 0.30

* p < .0125, **p <.001

Race, maternal education and Bus Story retells 40

Table 5

Renfrew Bus Story – North American Edition Means, Standard Deviations, and Ranges for Measures from the Test Manual

Information

Raw Score

Sentence

Length

Complexity

Independence

Raw Score

Race Maternal

Education

Mean

(SD)

Range Mean

(SD)

Range Mean

(SD)

Range Mean

(SD)

Range

AA High school or less 17.33

(6.10)

7.00 to

32.00

8.00

(2.14)

5.40 to

12.40

1.85

(1.05)

1.00 to

4.00

36.90

(7.52)

17.00 to

44.00

More than high school 19.95

(5.97)

10.00 to

32.00

9.26

(2.61)

5.60 to

16.40

2.64

(1.50)

1.00 to

7.00

42.17

(2.57)

36.00 to

44.00

EA High school or less 20.92

(6.13)

10.00 to

34.00

8.56

(1.51)

6.00 to

12.20

2.36

(1.22)

1.00 to

6.00

38.95

(5.11)

30.00 to

44.00

More than high school 24.95

(7.54)

11.00 to

39.00

9.29

(2.02)

6.00 to

15.60

2.81

(1.31)

1.00 to

6.00

40.48

(4.00)

29.00 to

44.00

Note: SD = standard deviations; Information = total score for amount of key information used in the story retell. Sentence length =

Average length in words of 5 longest utterances; Complexity = number of utterances with one or more subordinate clause;

Independence = inverse of number of prompts needed to complete retell. AA = African American; EA = European American

Race, maternal education and Bus Story retells 41

Table 6

Two-way fixed effect analysis of variance results

ANOVA Results using Raw Data

MS F

(1,159) Cohen‘s d

Confidence

Intervals

Information Score

Maternal Education 450.96 10.75* 0.49 0.47, 0.52

Race 752.36 17.94* 0.65 0.63, 0.68

Maternal Education x Race 20.00 0.48

ANOVA Results using Ranked Data

MS X

2

(1,159) a

Cohen‘s d

Confidence

Intervals

Sentence Length

Maternal Education 16,348.99 7.72* 0.47 0.45, 0.50

Race 5,974.36 2.82 0.14 0.11, 0.16

Maternal Education x Race 1,388.69 0.66

Complexity

Maternal Education 17,951.20 9.17* 0.48 0.46, 0.51

Race 8,246.40 4.21 0.26 0.24, 0.29

Maternal Education x Race 1,229.10 0.63

Independence Score

Maternal Education 25,194.50 13.02* 0.66 0.64, 0.69

Race 579.30 0.30 0.03 0.00, 0.05

Maternal Education x Race 10,594.40 5.47

Note: * = significant at the corrected alpha (Bonferroni) of 0.0125, a = the results are reported as

X2 because ANOVAs conducted on ranked data conform to a non-central X

2 distribution

(Thompson, 1991).