1. find your answers o the research topic o research problem o research question 2. What is the research aim? Hypothesis? if it is a hypothesis, if it is non-directional or directional 3
Nonmainstream Dialect Use and Specific Language ImpairmentJanna B. Oetting and Janet L. McDonald
Louisiana State University, Baton Rouge
Abstract Most work looking at specific language impairment (SLI) has been done in the context of
mainstream dialects. This paper extends the study of SLI to two nonmainstream dialects: a rural
version of Southern African American English (SAAE) and a rural version of Southern White
English (SWE). Data were language samples from 93 4- to 6-year-olds who lived in southeastern
Louisiana Forty were classified as speakers of SAAE, and 53 were classified as speakers of SWE.
A third were previously diagnosed σs SLI; the others served as either age-matched (6N) or
language-matched (4N) controls.
The two dialects differed in frequency of usage on 14 of the 35 coded morphosyntactic surface
patterns; speakers of these dialects could be successfully discriminated (94%) from each other in a
discriminant analysis using just four of these patterns. Across dialects, four patterns resulted in
main effects that were related to diagnostic condition (SLI vs. 6N), and a slightly different set of
four patterns showed effects that were related to developmental processes (4N vs. 6N). More
interestingly, the surface characteristics of SLI were found to manifest in the two dialects in
different ways. A discriminant function based solely on SAAE speakers tended to misclassify
SWE children with SLI as having normal language, and a discriminant function based on SWE
speakers tended to misclassify SAAE unaffected children as SLI. Patterns within the SLI profile
that cut across the two dialects included difficulties with tense marking and question formation.
The results provide important direction for future studies and argue for the inclusion of contrastive
as well as noncontrastive features of dialects within SLI research.
Keywords dialect; specific language impairment; morphosyntax
In a recent publication, Tager-Flusberg and Cooper (1999) summarize the comments of
participants from an NIH-sponsored workshop that focused on the study of specific
language impairment (SLI). The participants included experts in the fields of SLI and other
developmental disorders such as autism, learning disabilities, and dyslexia. As part of the
report, the authors highlight important study topics to help guide future research. One of the
recommendations listed in the report is the development of constructs that are important for
defining SLI in individuals who come from many language, cultural, and dialect
backgrounds.
The goal of the current work is to begin, at an exploratory level, to extend the study of SLI
to two nonmainstream dialects of English. Although the grammatical profile of SLI has been
explored in a wide range of languages, including Dutch, English, French, German, Greek,
Hebrew, Hungarian, Italian, Japanese, Spanish, Swedish, and even Inukitut (an Eskimo-
© 2001. All rights reserved
Contact author: Janna B. Oetting, PhD, Department of Communication Sciences & Disorders, 163 M&DA Bldg., Louisiana State
University, Baton Rouge, LA 70803. [email protected]. NIH Public Access
Author Manuscript
J Speech Lang Hear Res . Author manuscript; available in PMC 2012 June 25.
Published in final edited form as:
J Speech Lang Hear Res . 2001 February ; 44(1): 207–223.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Aleut language) (Leonard, 1998), the study of SLI within nonmainstream varieties of these
languages has been nonexistent. A primary reason for this has been limited knowledge on
the part of researchers about the developmental trajectories of nonmainstream dialects. In
fact, our limited understanding of dialect acquisition has led many to argue that
nonmainstream patterns should be ignored or excluded when diagnosing a child as language
impaired (Battle, 1996; Leonard & Weiss, 1983; McGregor, Williams, Hearst, & Johnson,
1997; Seymour, 1986; Seymour, Bland-Stewart, & Green, 1998; Stockman, 1996). The
rationale for excluding dialectal forms within analyses is to guard against viewing a
language difference (i.e., dialect difference) as a language impairment. As noted by
Seymour et al. (1998), evaluation of nonmainstream patterns establishes a “diagnostic
conundrum” because some patterns of nonmainstream dialect, on the surface, can look very
similar to those that characterize a language impairment.
For theoretical studies of SLI, however, a major limitation of excluding nonmainstream
features within one’s analysis is that decisions about what may or may not be important for
characterizing the impairment are made a priori. Moreover, after dialectal patterns are
excluded, the types of structures a researcher is left to examine are not always useful for
testing existing models of impairment. Data from Seymour et al. (1998) illustrate this point.
Within their study, they use the terms
contrastive and noncontrastive to differentiate
grammatical surface patterns that are unique to a particular dialect (i.e., contrastive) from
those that surface in both nonmainstream and standard English varieties (i.e.,
noncontrastive). Of the 11 structures that they considered to be noncontrastive, only 5 (i.e.,
complex sentences, conjunctions, locatives, modals, and verb particles) can be readily
applied to children who speak other nonmainstream English dialects, such as those spoken
in the rural South.
Moreover, only three of the noncontrastive patterns examined by Seymour et al. showed
significant group differences (i.e., use of prepositions, subjective marking of pronouns, and
demonstrative marking of pronouns). On these three, average use of the standard English
surface forms by the children with a language impairment also was quite high, at or above
89%. Thus, even if one were to develop a theoretical account for the co-existence of these
particular group differences, one would have to question their clinical relevance. To the
authors’ credit, they do acknowledge this point. Nevertheless, they maintain that until more
is known about the “deficit/difference” distinction within nonmainstream dialects, clinical
efforts should focus on the noncontrastive features of language development.
Unlike the clinical work cited above, children’s use of nonmainstream surface patterns is the
focal point of the current study. The primary goal of the project is to determine if the
morphosyntactic limitations of children with SLI can be studied within the context of
nonmainstream dialect use. Although this research objective resonates well with the
recommendations put forth by the participants of the NIH workshop, the impetus driving the
work has been our observations of nonmainstream dialect use among children who live in
one rural area of southeastern Louisiana. In particular, we have been struck by differences in
the children’s use of nonmainstream surface patterns that seem dependent upon both the
child’s dialect type and language ability.
As a preliminary study, we examined the grammatical features of SLI using language
samples from 31 children whom we perceived as using a variety of Southern White English
(SWE) (Oetting, Cantrell, & Horohov, 1999). Nine of the children were classified as
language impaired; the others served as either age-matched or language-matched controls.
Samples were elicited by an adult examiner within a small room at each child’s school.
Although no group differences were evident when either the types of nonmainstream
features spoken or the percentage of utterances with one or more nonmainstream dialect
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript pattern was evaluated, differences did emerge in the children’s use of some key grammatical
structures. These included regular third-person marking, contractible copula
be, auxiliary be,
and third-person marking of
do. In all cases, the children with SLI were found to be less
productive (i.e., either produced fewer marked forms or fewer obligatory contexts) with
these structures than their age- and language-matched controls.
In the current work, we continue this line of inquiry by expanding the data set to include
samples from children perceived to speak a rural variety of Southern African American
English (SAAE) and additional samples from children perceived to speak a variety of SWE.
Before outlining the research questions that guided the work, we review characteristics of
SAAE and SWE.
Characterstics of SAAE and SWE Nonmainstream dialects in the United States have been studied for at least five decades.
Like most areas of scientific inquiry, a number of issues remain unresolved and, thus,
continue to fuel rigorous debate and on-going inquiry. Opinion differs as to the origin,
current relationship, and direction of language change that is occurring within, and across
African American and White English varieties (for review see Baugh, 1983; Butters, 1989;
Montgomery & Bailey, 1986; Mufwene et al., 1998; Spears, 1992). Consensus has been
difficult because historical records are incomplete or lacking, and a number of within-
subject and between-subject factors can influence findings (Bailey & Bernstein, 1990;
Rickford, Ball, Blake, Jackson, & Martin, 1991; Wolfram, 1990). Within-subject variables
include age-graded phenomena, developmental processes, context effects, and code
switching/style shifting. Between-subject variables include race, age, sex, social class,
region, community, length of residency, and language contact. For the work here, a further
complication is the finding that much of the research on nonmainstream varieties of English
has been completed in urban settings located in the North, and study participants frequently
have been adolescents or adults who produce moderate to extreme versions of the
vernacular. Although these findings are interesting, they do not necessarily reflect the
patterns of all speakers in all discourse situations (Bailey & Bernstein, 1990).
Studies of SAAE and SWE do exist, however. Within these studies, participants are
typically selected from speech communities that are known to present the nonmainstream
dialect(s) of interest. Thus, the use of the terms SAAE and SWE are shorthand for
describing the patterns of specific linguistic groups—not all people who live in the South
nor all people of a particular race. Within comparative studies of these two dialects, effort
also is made to choose participants who present similar sociodemographic profiles. In some
studies, a full stratified sampling method has been employed; in others, participants have
been selected using a particular set of sociodemographic criteria, such as place of residence,
school enrollment, or SES (see edited works in Bernstein, Nunnally, & Sabino, 1997 and
Montgomery & Bailey, 1986).
The results of these studies and others indicate that SAAE and SWE are more similar, at
least on the surface, than northern dialect varieties. Nevertheless, both qualitative and
quantitative differences between the two have been documented. Qualitative differences
between SAAE and SWE primarily involve tense and aspect (see Bailey & Maynor, 1985,
1987; Green, 1993; Mufwene et al., 1998; Rickford, 1985). These include, but are not
limited to, grammatical constructions involving habitual or durative
be (often referred to as
be2 ), done, be done , and stressed been (referred to by some as BIN ). Many of these
structures are considered camouflaged forms because their surface forms (e.g., be, done)
appear in standard English and other White English varieties, but in African American
English varieties they express a wider range of meanings within the grammar. For example,
Oetting and McDonald Page 3
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Labov (1998) cites the following utterance—“So he went to where she was…and got the
nerve to lie to me…talking ’bout he
done went to work”—from Baugh (1983) to illustrate
the sense of moral indignation that the use of
done can carry in African American English.
Examination of the co-occurrence restrictions (i.e., what types of auxiliaries, verbs, and
adverbs can or cannot co-occur with these structures) and manipulation of these forms
within negative and past-tense constructions, tag questions, and yes/no questions also
indicate that their underlying grammatical structure in African American English varieties is
qualitatively different from other White English varieties.
Arguably, the greatest difference between SAAE and SWE is the frequency with which
different nonmainstream patterns are produced. This claim is based on the finding that many
of the same nonmainstream patterns surface in both dialects, but speakers of SAAE have
been found to produce these forms more frequently than speakers of SWE (Feagin, 1997;
Montgomery & Bailey, 1986; Mufwene et al., 1998; Wolfram & Schilling-Estes, 1998).
Zero-marking of verbal /s/ with third-person singular subjects (e.g., he walk) is commonly
used to illustrate this point. As discussed by Wolfram and Schilling-Estes (1998), this
particular nonmainstream pattern is present in both dialects, but within SAAE and other
African American English varieties, zero-marking can occur 85% of the time or more. In
SWE varieties, zero-marking can occur less then 5% of the time.
Traditionally, quantitative differences have been viewed as indicating similarities in the
deep structure of these dialects; however, there now is evidence that some quantitative
differences may reflect qualitative differences in rule construction as well. Work by Myhill
(1988) on postvocalic
is useful for illustrating this point. He examined
production in
two groups of speakers of African American English: those with frequent contact with
mainstream culture and those without. As expected, quantitative differences between the two
groups were observed, with the former producing
more frequently than the latter.
Important for the work here is the finding that the two groups’ use of
also was influenced
by the phonological characteristics of the preceding and following context in slightly
different ways. These differences can be considered qualitative in nature because they reflect
differences in the ways in which
production is constrained within the two linguistic
systems.
For researchers interested in extending the study of childhood SLI to nonmainstream
dialects, the status of dialectology research, the overlapping characteristics of SAAE and
SWE, and the overlapping characteristics of SLI and nonmainstream dialects may be viewed
as daunting. Clearly, a study of children’s use of nonmainstream patterns, especially if it
seeks only to identify surface differences between dialects and child language profiles,
should be viewed as a first step toward more empirical studies on the issue. With the above
discussion and qualifying statements in mind, we asked the following questions: (a) Can
children’s use of nonmainstream surface patterns be used to differentiate SWE from SAAE?
(b) Can effects of SLI be observed within the context of these nonmainstream patterns? and
(c) If effects of SLI are observed, do these effects differ as a function of the type of dialect
spoken?
Method
Participants The data were spontaneous language samples from 93 children who had previously
participated in one of two studies (Oetting, 1999; Oetting & Horohov, 1997). Across studies,
there were 40 children who were classified as speakers of SAAE and 53 who were classified
as speakers of SWE; 60% of the children were male. For the work here, the children were
classified as speakers of either SAAE or SWE using the extrinsic criterion of race. Grouping
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript the children in this way allows for linguistic description of the participants, and the analyses
to be conducted provide a direct test of the criterion that led to the grouping.
As discussed in the original papers, all of the children attended public schools, child
development centers, or Head Starts that were located in a rural area in southeastern
Louisiana. This area is situated on the Mississippi River and maintains a large port industry
involving natural and synthetic products. The social strata of the children’s families ranged
from skilled craft, clerical, and sales groups to small business, minor professional, and
technical groups. Estimates of the children’s social strata were based on the parent(s)’
highest level of education completed and occupation (Hollingshead, 1975). Data for this
index were collected through a voluntary questionnaire that was returned by 19 (48%) of the
SAAE and 41 (77%) of she SWE speakers. Responses from the questionnaires also
indicated that all of the children, who had returned the form had lived in southeastern
Louisiana since birth.
Thirty-one of the children were classified as SLI; 31 were identified as normally developing,
age-matched 6-year-olds (6N); and 31 were identified as normally developing, language-
matched 4-year-olds (4N). Classification of a child’s language status involved a number of
steps to guard against inaccurate diagnosis. These included soliciting children during the
second half of the school year so that any misdiagnosis that may have occurred at the
beginning of the year could be corrected, conducting teacher and SLP interviews to
document impressions of language status that were based on comparisons with classroom
peers, completing a battery of language tests, and collecting a language sample during an
informal play session. Although details related to participant selection are described
elsewhere, important demographic information and testing data are broken down by dialect
and diagnostic category in Table 1.
The highest level of education completed by the children’s mothers was available for the 60
participants who returned the parent questionnaire. Following Hollingshead (1975), a score
of 3 reflected completion of 8th grade, a 4 reflected completion of 12th, and a 5 reflected
completion of two years of college or additional vocational training. Although the mode
level of education was high school (4), a two-way analysis of variance showed a significant
interaction between dialect and group on this variable [
F(2, 59) = 3.41, p = .04]. Tukey post
hoc tests indicated that the interaction was related to higher scores for the SWE speakers
than for the SAAE speakers for the 4N group.
Differences related to diagnostic category but not dialect were significant for the Columbia
Mental Maturity Scale (CMMS; Burgmeister, Blum, & Lorge, 1972) [
F(2, 90) = 6.90, p < .
005], the Peabody Picture Vocabulary Test–Revised (PPVT-R; Dunn & Dunn, 1981) [
F(2,
92) = 83.90,
p < .001], the syntactic quotient score of the Test of Language Development–
Primary (TOLD-P; Newcomer & Hammill, 1988), [
F(2, 80) = 70.69, p < .001], and mean
length of utterance (MLU) regardless of whether MLU was calculated with words [
F(2, 92)
= 12.53,
p < .001] or with morphemes [ F(2, 92) = 13.56, p < .001]. Tukey tests indicated
that for the CMMS, PPVT-R, and TOLD-P, scores of the SLI group were lower than those
of the 6N and 4N group. For MLU-words and MLU-morphemes, scores of the SLI and 4N
groups were lower than those of the 6N group.
Language Sample Elicitation Language samples were elicited by having an examiner and child play together in a quiet
room within each child’s school. Samples were collected on the third day of meeting with
each child; standardized testing was completed during the first two sessions. Toys included a
gas station, picnic/park set, baby dolls, food set, Legos, Mardi Gras beads, and three pictures
from the Apricot I Picture Series (Arwood, 1985). All six examiners were White, and
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript although three were native to Louisiana and spoke a dialect that contained some Southern
characteristics, the language of all six during the study contained primarily standard English
grammatical forms. Visual inspection of the raw data did not reveal systematic patterning of
dialect use by the children that was dependent upon the native versus non-native resident
status of the examiners.1
A two-way analysis of variance was completed to determine whether the length of the
samples varied by dialect or diagnostic category. The average number of complete and
intelligible utterances per sample was the dependent measure. A main effect for dialect was
observed [
F(l, 92) = 6.20, p < .05], with samples elicited from the SAAE speakers shorter
than those elicited from the SWE speakers. It is important to note, however, that samples
from Oetting and Horohov (1997) were longer than those from Oetting (1999), and the
number of children who spoke each dialect was not consistent across studies. In the 1997
study, 94% of the children spoke SWE as compared to 35% in the 1999 study. Biases related
to dialect are not evident when the samples from the individual studies are examined
separately. For example, the average length of the samples from the SAAE speakers who
participated in Oetting (1999) was 202 (
SD = 65) and for the SWE speakers, it was 208 ( SD
= 59), p > .05. Therefore, the sampling bias observed here seems more a product of
differences in the time spent on language-sample collection during the original studies than
on the variable of dialect.2
Language Sample Transcription and Coding At the time of the original studies (1994–1995 and 1996–1997), the samples were
transcribed and frequently discussed nonmainstream patterns were coded. After extensive
literature review and consultation with native speakers, all tapes were replayed and
utterances were further coded for infrequent nonmainstream patterns (1998–1999). For both
phases, transcription and morphological coding followed the guidelines outlined by Miller
and Chapman (1992). Word processing Find/Replace commands and Systematic Analysis of
Language Transcripts software (SALT; Miller & Chapman, 1992) were used to facilitate and
check coding. Frequency counts of each dialect pattern were based on SALT printouts. At
least two examiners counted the patterns from the SALT printouts independently;
disagreements between examiners were resolved through consensus.
Thirty-five different types of nonmainstream patterns were identified in the 20,171
utterances that were transcribed as complete and intelligible. A pattern was considered
nonmainstream if it had been previously reported as a SWE or SAAE pattern in the literature
and if our native consultants felt that the pattern was characteristic of the dialects they heard
children and adults producing within this community.3
Although the criteria used to identify each nonmainstream pattern are listed in the Appendix,
at least three issues related to coding need to be highlighted. First, lists of dialect patterns
vary greatly across studies and are dependent upon the interests of the researcher(s) and the
characteristics of the participants under study. The patterns examined here reflect our
1 Although there were no obvious effects of examiner characteristics on outcomes, that possibility cannot be fully ruled out. To
adequately study the role of the examiner on dialect use, one would need equal numbers of African American and White examiners,
equal numbers of male and female examiners, and equal numbers of examiners whose use of nonmainstream patterns matched and did
not match those of the participants See Lucas and Borders (1994) for an ethnographic study of children’s use of nonmainstream
patterns across different partners and discourse contexts.
2 Differences across studies also played some role in the unequal numbers of parent questionnaires that were returned by the SAAE
and SWE speakers In Oetting (1999), when dialect use was more evenly distributed across the participants, the numbers of
questionnaires returned by the SAAE and SWE speakers were 18 and 19, respectively.
3 Patterns were considered nonmainstream regardless of whether they overlapped with patterns known to be characteristic of SLI and\
/
or with patterns known to be produced by younger, normally developing children.
Oetting and McDonald Page 6
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript interests in morphosyntax. Second, for some morphosyntactic categories, more than one
type of surface pattern was identified. For example, five different patterns (i.e., zero regular,
zero irregular, over-regularization, past as participle,
had + past) were coded for past tense,
and three (i.e., zero regular, zero irregular, subject-verb agreement with
don’t ) were coded
for third-person marking. Arguably some of these patterns or even patterns across
grammatical categories could be combined. Nevertheless as an exploratory study, these
individual patterns were counted separately to examine the independent variables of interest.
Finally, some scholars may disagree with particular coding decisions made for individual
patterns. For example, children’s variable use of historical present within narrative contexts
made it difficult to determine whether an unmarked form (e.g., she kick him) should be
coded as zero-marked for present third (e.g., she kicks him) or past tense (e.g., she kicked
Mm). As discussed by Wolfram (1990), nonmainstream dialects allow tense marking to
alternate not only across utterances but within utterances as well. Our decision here was to
accept all verbs that were marked for third person (e.g., he comes) as standard English uses,
even though some were clearly produced as overt uses of historical present. Also, unmarked
forms of the verb
say (e.g., she say stop that) were coded as involving zero-marking of third-
person present rather than past because (a) this verb is frequently produced in historical
present contexts, and (b) when produced in these contexts, this verb is typically unmarked
(Myhill & Harris, 1986). All other unmarked verbs were coded as present or past depending
upon the context and meaning of the utterance.
In Table 2, frequency counts of each nonmainstream pattern are presented. Although these
data are interesting, they should be viewed as crude indices of use because the number of
utterances in each sample was not controlled. To control for sample size differences, all
analyses were completed by dividing the total number of each nonmainstream pattern by the
total number of complete and intelligible utterances in each sample. In traditional studies of
SLI, percent of use often is calculated by dividing the total number of produced forms by the
total number of obligatory contexts for each structure. With some nonmainstream patterns,
however, the concept of an obligatory context is untenable. For example, there are no known
obligatory contexts for patterns that mark aspectual distinctions (e.g.,
be2 , done + verb)
(Seymour et al., 1998). Also, for past tense, third person, and others, more than one type of
nonmainstream surface pattern can be produced. Variable use of these different patterns
makes it difficult, if not impossible, to determine the exact number of obligatory contexts for
each. Finally, as described in the Appendix, some nonmainstream patterns (e.g., zero
be,
multiple negation, and undifferentiated pronoun) are much more likely to occur in certain
linguistic contexts than in others. Without a comprehensive study of how different contexts
influence nonmainstream pattern use, explicating the number of obligatory contexts for each
pattern remains elusive. Using the total number of utterances as the denominator when
calculating the rate at which each nonmainstream pattern occurred allowed us to (a) examine
each nonmainstream pattern in the same way, (b) avoid making a priori coding decisions
about obligatory contexts, and (c) control for sample size differences.
Reliability Transcription agreement was calculated at the utterance level because an error on a word,
morpheme, utterance boundary, or dialect code can affect the reliability of an entire
utterance. Also, reliability was calculated on the child utterances only, even though the adult
utterances were typed and checked. Nine (10%) of the samples were independently
transcribed and coded by a research assistant at the time of data collection. Across studies
and groups, intertranscriber agreement was 95% (2498 utterances in agreement/2641 total
child utterances). An additional 13 (14%) of the samples were checked by the principal
investigator after all nonmainstream patterns were coded; half of the samples were from
SAAE speakers, and a third were from each of the three child groups. Checking included
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript listening to the audiotapes while reading the transcripts and then proofing the coding.
Agreement was 93% (2602 utterances in agreement/2793 total child utterances; SAAE =
93.5%, SWE = 93.2%; SLI = 93.7%, 6N = 92.1%, 4N = 94.1%). An additional six (6%) of
the audiotapes/samples were independently checked by a teacher from one of the targeted
Head Start programs. This teacher was chosen because she had lived in the area her entire
life, her native dialect was SAAE, and as a bus driver for Head Start and mother of two she
interacted with children of this age range outside of the formal school setting. She disagreed
with the transcription of 19 (<1%) words from a total of 7159 total words checked.
Results Given that dialect use was not part of the participant selection process, several preliminary
steps were completed to confirm the children’s dialect status.4 First, the number of different
nonmainstream pattern types was counted for each child; all children were found to produce
at least 5 of the 35 different nonmainstream patterns. Next, the total number of
nonmainstream patterns produced by each child was divided by the total number of
complete and intelligible utterances in each sample (see Table 3). As can be seen, all of the
children produced a rate of nonmainstream dialect use that was .03 or greater. Both the type-
and token-based counts of pattern use verified to us that all of the children were
nonmainstream speakers.
General ranges of the rate at which the nonmainstream patterns were produced by the
children are also listed in Table 3. Note that there is very little overlap in the rates at which
the SAAE speakers produce the nonmainstream patterns as compared to those of the SWE
speakers, especially when one considers the three diagnostic categories (SLI, 6N, 4N)
separately. A difference in pattern frequency between the SAAE and SWE speakers is
consistent with our literature review, even though most of the previous work has been
completed with adults. However, as noted at the bottom of this table, 4 children did fall
outside the usage rates for their same-dialect child group. These particular rates of use raise
the possibility that these children are classified in the wrong dialect group. In order to check
this, a listener judgment task was preformed on the audiotaped samples of these four
children. When listeners blind to the identity of these children listened to 10 min of each
sample, the 3 African American children were identified as speakers of SAAE and the 1
White child was identified as a speaker of SWE.5 These findings, taken together, confirmed
our initial impressions that the data set contained two distinct dialects.
The data were then subjected to two sets of analyses. Analyses of variance were first
completed to examine whether nonmainstream pattern use varied as a function of dialect
type and/or child group. A series of discriminant analyses were then run to see how
discriminable the dialects and child groups were from each other and to ascertain the
minimal set of patterns that gave good discriminability. These analyses would further test
the validity of using race to determine the children’s dialect status and would allow us to
examine whether models developed on one subset of children could be used to classify
children in another subset. The dependent variable for all analyses was a percentage measure
(i.e., frequency of each pattern divided by the total number of complete and intelligible
utterances in each sample).
4 We also checked for, but did not find, dialect differences that were related to gender and SES Importantly, though, the limited
number of parent questionnaires that were returned, the restricted range of SES that was reported by those who returned the
questionnaires, and the unequal number of males and females in each group limited our ability to detect differences related to t\
hese
variables.
5 Initially, we completed the listener judgment tasks using three minutes of tape. When this was done, all three AA children were
identified as speakers of SAAE, but results were mixed for the one White child Exploration of listener judgment tasks as a way t\
o
classify a speaker’s dialect is on-going in our lab.
Oetting and McDonald Page 8
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Analyses of VarianceInitially, a three-way analysis of variance with dialect (SAAE vs. SWE) and child group
(SLI, 6N, 4N) as between-subjects factors and type of pattern (the 35 listed in Table 2) as a
within-subjects factor was run. All three main effects were significant. The main effect of
dialect reflected a higher percentage of coded patterns for the SAAE speakers (
M = 34.1%)
than for the SWE speakers (
M = 12.8%) [ F(l, 87) = 119.1, p < .001]. The main effect of
child group [
F(2, 87) = 5.7, p < .005] with subsequent Tukey post hoc tests showed a higher
percentage of coded patterns for the SLI group (
M = 27.6%) than either the 6N group ( M =
17.0%) or the 4N group (
M = 21.1%). Finally, the main effect of pattern type [ F(34, 2958) =
86.8,
p < .001] indicated that there was great variability in the rate at which the individual
patterns were produced. Across groups, the pattern with the highest percentage of use was
zero marking of
be (5.5%), and the pattern with the lowest percentage was I’ma (<.01%)
Both two-way interactions involving type of pattern also were significant: pattern type by
dialect [
F(34, 2958) = 30.6, p < .001] and pattern type by child group [ F(68, 2958) = 2.4, p
< .001]. These significant two-way interactions were further explored by running a 2
(dialect) × 3 (group) ANOVA on each nonmainstream pattern. Outcomes are reported in
Tables 4 and 5 as
F values, associated significance values, and eta squared values. Fourteen
of the patterns showed significant effects for dialect, with higher percentages of use
demonstrated by the SAAE children than by the SWE children (see Table 4). Note that these
patterns encompass verb agreement features with
be and don’t , zero-marking of many forms
(i.e., regular third-person present, irregular third-person present, copula and auxiliary
be
forms, regular past tense, plurals, possessives, of) and use of alternative or unique surface
expressions (i.e.,
be2 , had + past tense, multiple negation, use of a for an, and demonstrative
pronouns).
Six features showed significant group effects (see Table 5). Post hoc Tukey tests showed
that none of these features distinguished children with SLI from the 4N controls. Four
patterns, including zero marking of
be forms, zero marking of irregular past, omission of
auxiliary
do, and noninversion of Wh- questions, differentiated the SLI group from the 6N
group. For each of these patterns, rates of occurrence by the children with SLI were greater
than those of their age-matched peers. A slightly different set of four patterns (auxiliary
do
omission, zero marking of irregular past, zero marking of present progressive, and
appositive use) showed developmental trends between the 4N and 6N control groups. For
the first three patterns, rates of occurrence in the 4N group were greater than those in the 6N
group; for appositives, rate of use was higher for the 6N group than for the 4N group.
It is interesting to note that there was very little overlap in the patterns that differentiated the
two dialects from each other and those that differentiated the three child groups. In fact, only
zero-marking of
be was significant for both the dialect and group variables. Because there
was not a significant interaction between dialect and group for zero-marking of
be, the
effects of dialect and group status appear to be additive.
It also is important to note that in exploring the above interactions between pattern and
dialect and between pattern and group we performed multiple ANOVAs. One must always
be careful when interpreting the results of multiple tests to avoid finding spurious effects
that are actually due to inflated alpha error. However, we believe the effects that were
significant are of potential interest to other researchers who may wish to further explore
group differences on these features. The traditional way of adjusting for possible error is to
adopt a more restrictive alpha level for reporting significance, but this method carries the
risk of erroneously restricting the possible list of differentiating linguistic structures. A
supplemental source of information is eta, squared, a measure of variance accounted for. As
can be seen in Table 4, even with high within-group variability, two variables each account
Oetting and McDonald Page 9
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript for more than 40% of the variance across dialects. Also, as reported in Table 4, an alpha set
at .01 would identify SV agreement with
be (which accounted for 7% of the variance), but it
would not have identified multiple negation even though eta squared is 6%. Eta squared
values are lower in Table 5 (4%–10%), but recall that the group effects are being examined
with the dialects collapsed, so within-group variability is extremely high.
Discriminant Analyses Discriminant analysis is another way of determining which patterns allow for classification
of the children by dialect and which patterns allow for classification by group. In addition, if
patterns are intercorrelated, discriminant analyses will pick the best predictor and add
additional predictors only if they improve classification accuracy. The following
discriminant analyses were performed to see what patterns can be used to discriminate the
children by dialect and what patterns can be used to discriminate the children by group. Both
analyses considered only the SLI and 6N groups, following Bedore and Leonard (1998);
Dunn, Flax, Sliwinski, and Aram (1996); Fletcher and Peters (1984); and Gavin, Klee, and
Membrino (1993). According to Plante and Vance (1994), the ability to correctly classify
80% or more of the cases is considered fair, and the ability to correctly classify 90% or more
of the cases is considered good.
For both the analysis of dialect and the analysis of group, we started with a discriminant
function that included all 35 nonmainstream patterns to ascertain maximum group
discriminability. Thereafter, we ran stepwise discriminant analyses to get a model with a
reduced number of patterns that still gave reasonable discrimination. This type of analysis
adds, and deletes if appropriate, one pattern at a time until additional patterns do not
significantly improve discriminant performance. Weights are assigned to each pattern such
that the ability to distinguish between the groups is maximized. The entry and deletion
criterion for the current analyses was set to .10.
In order to test the generalizability of the discriminant functions, reduced models were
formed using only a subset of the participants and then these models were applied to the
remaining children to check the classification accuracy of members not used to set the
weights. For example, for the analyses seeking to classify children as speakers of either
SAAE or SWE, a discriminant function was set on the children with SLI and then this model
was applied to those in the 6N group, as well as vice versa. Similarly, for the analyses
seeking to classify children as either SLI or 6N, a discriminant function was set using SAAE
speakers and then this model was applied to SWE speakers and vice versa. Good
classification of new members indicates that a model is generalizable; poor classification
indicates that a model designed for a particular subgroup may not apply to others.
Dialect As mentioned earlier, children were independently classified as speakers of SAAE or SWE
on the basis of race. Use of discriminant analysis allows us to further examine whether this
grouping variable led to a differentiation of speakers by dialect. Indeed, a discriminant
function analysis that included all 35 nonmainstream patterns classified 97% of the speakers
into the correct dialect category. This full model could be substantially reduced without
much sacrifice in accuracy; a stepwise discriminant analysis yielded four features that
correctly classified 94% of all speakers. Table 6(a) provides details of the classification
accuracy of the full and reduced models. The four patterns in the reduced model, in order of
entry, were zero-marking of regular third, zero-marking of
be, subject-verb agreement with
be, and zero-marking of irregular past. These four patterns accounted for 72% of the
variance between the dialects. All four occurred more frequently in the SAAE dialect than in
the SWE dialect.
Oetting and McDonald Page 10
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Next, stepwise discriminant analyses to distinguish the two dialects were run separately over
the SLI group and 6N controls. The reduced model discriminating dialects based solely on
the SLI group yielded four patterns: zero-marking of
be, zero-marking of regular third,
subject-verb agreement with
be, and zero-marking of irregular past. Note that these features
were the same as those derived when both the SLI and 6N groups were considered together.
These patterns accounted for 62% of the variance between dialects in the SLI group. The
reduced model based solely on children with SLI correctly classified 81% of the children by
dialect type; see Table 6(b). When tested on the age-matched controls, this model correctly
classified everyone (100%), showing the stability of the model. The poorer performance on
the SLI group as compared to the 6N group, despite the fact that the weights were set on the
SLI group, indicates that the data from the SLI group were somewhat noisier than those of
their age-matched controls.
The reduced model based on the 6N controls alone also yielded four patterns: zero-marking
of regular third, zero-marking of
be, subject-verb agreement with be, and over-regularization
(e.g.,
breaked for broke ). Note that the first three patterns were in common with the model
based on the SLI group alone. This model accounted for 88% of the variance between
dialects in the 6N group. All features were more prevalent in SAAE than in SWE speakers.
This model correctly classified 97% of the age-matched controls and 74% of the children
with SLI; see Table 6(c). This model showed poorer transfer to the children with SLI
because of the over-regularization pattern. Indeed, when this pattern was eliminated from
the discriminant function, and weights reset over the 6N group, all age-matched controls
were correctly classified, and classification of the children with SLI climbed to 90%. Thus,
when discriminant function weights are set over one diagnostic group and then used to
classify members from the other, there is good discrimination of SAAE and SWE speakers.
This result indicates that the same patterns that distinguished the two dialects from each
other for normally developing children also apply to children with SLI.
Groups Children were independently classified into the SLI or 6N groups on the basis of criteria set
out in the Method section. A discriminant function considering all 35 nonmainstream
patterns was able to successfully mirror this classification at a 90% accuracy rate. A
stepwise discriminant analysis reduced the 35 patterns down to 4, while accuracy remained
fair at 82%. The 4 features were zero-marking of irregular past, auxiliary
do omission,
noninversion of Wh- questions, and over-regularization. For all four patterns, rates of use
were higher for the SLI group than for the 6N group. This model accounted for 50% of the
variance between the groups. This model had more trouble classifying SLI children correctly
(sensitivity of 74%) than age-matched controls correctly (specificity of 90%). Details of the
model’s performance are given in Table 7(a).
Next we examined whether the patterns that distinguish SLI from 6N for speakers of SAAE
also distinguish these diagnostic groups for speakers of SWE and vice versa. Good
classification across dialects would show the stability of the discriminant function and
would indicate that SLI manifests itself similarly in the two dialects. Poor classification in
one dialect with weights set from the other would indicate that the surface patterns of SLI
vary as a function of dialect.
The model to distinguish normal children from affected children that was fit solely on
speakers of SAAE had three patterns: zero-marking of irregular past, non-inversion of Wh-
questions, and zero-marking of irregular third. This model accounted for 45% of the
variance between the SAAE groups. SAAE speakers who were SLI were more likely to
zero-mark irregular past and to produce noninverted Wh- questions than their age-matched
peers, but they were less likely to produce zero-marked irregular third-person present forms.
Oetting and McDonald Page 11
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Although this model had fair accuracy (82%) when applied to SAAE speakers, the accuracy
rate fell to 71% when it was applied to SWE speakers. An examination of the performance
in Table 7(b) shows that the SAAE model had poor sensitivity (47%) and fair specificity
(89%). That is, applying the SAAE model to SWE speakers resulted in misclassifying many
SWE children with SLI as having normal language.
The model to distinguish normal children from impaired children that was fit solely on the
SWE speakers had five patterns; zero-marking of irregular past, auxiliary
do omission, zero-
marking of irregular third, omission of infinitive
to, and subject-verb agreement with don’t .
This model accounted for 72% of the variance between the SWE groups. For all five
patterns, SWE speakers with SLI had higher percentages of occurrence than SWE age-
matched controls. Although this model had good accuracy (91%) when applied to SWE
speakers, the accuracy rate fell to 61% when applied to SAAE speakers. As shown in Table
7(c), the sensitivity of the model was not bad (88%), but the specificity was extremely poor
(25%). That is, the SWE model tended to incorrectly classify normally developing SAAE
speakers as language impaired.
Discussion The SAAE and SWE speakers differed in the rate at which certain nonmainstream patterns
were used. This was evidenced by an interaction between dialect and pattern in the ANOVA
outcomes. Patterns involving subject-verb agreement features, zero-marking of many forms,
and the use of alternative or unique expressions occurred at a greater rate in SAAE than in
SWE, The discriminant analyses showed that four features, all verb-based, were sufficient to
give good discrimination between the dialects. Discriminant functions formed over one
subset of children, such as those in either the SLI or 6N group, also succeeded in classifying
children in the other subset. This pattern of results indicates that differences across the two
dialects are relatively stable, regardless of child profile. For models of impairment, this
finding provides evidence that children with SLI are extremely good at learning the
distributional properties of the dialect to which they are exposed.
The three child groups were also distinguished by the rate at which certain nonmainstream
patterns were used. An exploration of the interaction between group and pattern showed that
diagnostic category (SLI vs. 6N) differed on four patterns, whereas developmental
differences (4N vs. 6N) were present on four slightly different patterns. Interestingly, there
was very little overlap between patterns that differentiated the groups and those that
differentiated the dialects. This is an important finding because it shows that the SLI group
effect was not a result of these children’s being heavier dialect users overall than the
controls. Instead, on only a few specific nonmainstream patterns did the children with SLI
produce rates of use that were greater than their age-matched peers.
Another important finding came from the stepwise discriminant analyses that were run to
distinguish children with SLI from those developing language normally. Recall that
classification accuracies were fair to good when the dialects were examined separately.
Transfer of the reduced models across dialects, however, was very poor. This finding
indicates that the surface characteristics of SLI are influenced by the type of dialect spoken.
Thus, it is imperative that models of SLI be tested not only with a wide range of languages,
but also within the context of dialect diversity.
Even though differences in the SLI profile were observed, it is interesting that some patterns
related to tense marking showed effects for SLI in both dialects. Across analyses, these
included zero
be, zero irregular past, and zero irregular third. At least two other dialect
studies have reported tense-related problems for children with SLI. For example, when
Oetting and McDonald Page 12
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript calculating use by dividing the number of marked forms by total number of obligatory
contexts, Seymour et al. (1998) report a 41% difference (normal = 91% vs. impaired = 50%)
in overt past-tense marking between the normal and affected AAE-speaking children they
studied. In addition, using the same index of use as Seymour et al., overt marking of regular
third-person present was identified as difficult for SWE-speaking children with SLI in our
earlier work (normal = 95% vs. impaired = 72%; Oetting et al., 1999). Tense-related
weaknesses in children with SLI are consistent with standard English studies of SLI as well
as those that have examined SLI in other languages (Bedore & Leonard, 1998; Leonard,
Miller, & Gerber, 1999; Rice, Noll, & Grimm, 1997; Rice, Wexler, & Hersberger, 1998).
Thus, one important avenue for future work seems to be in the area of tense marking. Given
the different tense-marking patterns that have surfaced across the studies just reviewed, it
seems important to explore the effects of different indices of use on results as well as to
consider the full range of tense-marking options that are available in a given dialect(s).
Difficulties with question formation among the children with SLI also cut across the two
dialects. Recall that group effects (SLI vs. 6N) were observed for Wh- noninversion and
omission of auxiliary
do; the former pattern surfaced in the SLI reduced discriminant
function involving SAAE, and the latter surfaced in the reduced SLI model involving SWE.
Interestingly, work by Rice, Wexler, and Cleave (1995) with standard English speakers
found use of
be and do forma in questions to differentiate children with SLI from same-age
peers who were developing language normally. Craig and Washington (2000) also report
that African American English-speaking children with SLI perform lower than their age-
matched peers on tasks involving question comprehension. Thus, future work may want to
include question formation as a variable within studies of SLI. Like tense marking, however,
it is critical that one consider the full range of options children have when questions are
posed.
Findings related to tense marking and question formation suggest that, in at least some
cases, the basic mechanism underlying SLI may be the same across these two dialects, even
though the surface manifestations of the impairment differ. Findings related to zero marking
of irregular third person also warrant additional comment, because with this pattern surface
similarities of the SLI profile across these two dialects seem to be masking important
underlying differences. Recall that this pattern was found to discriminate children with SLI
from controls in both dialects, but distributions of use differed as a function of dialect type.
For SWE, children with SLI produced higher rates of zero marking than the controls; for
SAAE, this pattern was reversed.
Interestingly, when the number of obligatory contexts for irregular third person marking is
controlled (i.e., use is divided by number of obligatory contexts rather than number of
utterances) and rates of zero-marking are recalculated, findings for the SWE speakers do not
change; those with SLI are still found to zero-mark these forms at a greater rate (54%) than
their peers (4%). Findings for the SAAE speakers change, however, with the direction of the
group difference reversing (SAAE SLI = 73% zero-marked vs. SAAE 6N = 69%;
p > .05).
Inspection of the data indicate that those in the 6N group produced twice as many irregular
third contexts per sample (average = 4) than those with SLI (average = 2). Thus, unlike the
SWE speakers, the most pronounced difference between these two groups of SAAE
speakers is not in their rates of zero marking but in the frequency at which obligatory
contexts for this structure are produced. We speculate that this finding is related to the
normally developing SAAE speakers’ superior ability to use narrative discourse genre and
their use of historical present tense within these narrative contexts. If this is the case, then
future studies of SLI that are conducted within the context of dialect diversity will need to
carefully consider the role discourse plays in children’s use of morphosyntax.
Oetting and McDonald Page 13
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript In closing, it is noteworthy that children with SLI were distinguishable from those
developing language normally even though the analyses were completed with the contrastive
patterns of the target dialects. Recall from the literature review that contrastive patterns (i.e.,
those that differ from standard English) are often viewed as problematic within assessment
because they may be mistaken for a language deficit rather than a language difference. The
current findings do not suggest that current practice is necessarily misguided. In fact, data
presented in Table 2 can be used to reinforce the claim that surface characteristics of
nonmainstream English dialects often overlap with those reported as standard English
characteristics of SLI. What the current findings do suggest is that this overlap is at the level
of individual utterances and individual patterns. When children’s use of a full range of
nonmainstream patterns are considered and pattern use is treated as a continuous rather than
categorical variable, it is possible to distinguish different dialects and different child profiles
from each other. What, this means is that future studies of SLI can, and should, include the
contrastive as well as noncontrastive features of dialects.
The findings also indicate that future research is needed to improve the rate at which we are
able to correctly classify children as either SLI or normal. This work seems particularly
important for children who speak SAAE. Recall that only an 82% accuracy rate was found
for discriminating SAAE speakers with SLI from those developing normally. Although this
level of discrimination is considered fair from a statistical standpoint, it is lower than the
91% rate observed for the SWE speakers. One approach to improving classification
accuracy is to examine the profiles of the 5 SAAE speakers who were misclassified. Four of
the 5 children came from the SLI group, and 1 came from the 6N group.
Unfortunately, inspection of the data suggests nothing unusual about these five cases; their
testing data and rates of nonmainstream pattern use were consistent with those of their same-
dialect peer group. When individual patterns were examined, zero-marked
be was the only
one in which these 5 children seemed, to differ from their peers. The 6N child produced this
pattern at a rate of 12%, and this rate was slightly higher than the average (7.8%) of her
respective 6N group; the 4 misclassified children with SLI produced this pattern at rates of
6%–8%, and these rates were slightly lower than the average (10%) obtained by the SAAE
speakers with SLI. Unfortunately, zero-marking of
be did not surface within the reduced
discriminant function. Nevertheless, perhaps additional analyses along the lines of Wyatt
(1991) that take into account the effects of the preceding and following linguistic context of
each
be form will be useful for improving the classification accuracy of these children and
others in the future.
Another way to improve classification accuracy may be to consider adding noncontrastive
patterns to the discriminant function. Adding other language measures, such as MLU or use
of complex syntax, also may improve classification rates. Recently, both of these indices
have been found to differentiate AAE-speaking children with language impairments from
controls (Craig & Washington, 2000). Finally, there is some evidence that nonword
repetition tasks may be useful for identifying a language impairment, regardless of whether
a child is from a majority or minority culture (Dollaghan & Campbell, 1998; Oetting, Lynch,
Habans, Eyles, & Hall, 1999).
Research on SLI in the context of different dialects is sorely needed. One hopes that the
current findings will motivate others to test the replication and generalization of these
findings in other linguistically diverse, English-speaking communities. Work needs to
include participants from rural and urban settings, and dialects from different regions of the
country need to be examined. It also is important to examine alternative ways of classifying
children as dialect speakers, especially in communities where the overlap between the
variables of race and dialect is unclear.
Oetting and McDonald Page 14
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Of course, it is important to reiterate the preliminary and exploratory nature of the current
analyses and to restate the inherent limitations of using an existent data set to study
nonmainstream pattern use. In particular, effects of the examiner characteristics on the
children’s use of dialect could not be rigorously examined here. Also, small and unequal
numbers of sample sizes made it difficult to detect possible differences in dialect use that
were related to the participants’ gender and/or the educational level of the participants’
mothers. In fact, the current data set says very little about the complex relationship that
exists between dialect use and multiple sociodemographic variables because a stratified
sampling method was not used to select participants and information about whether the
children were part of open or closed social networks was not collected.
Finally, the current findings do not speak to the underlying grammatical representation of
SAAE and SWE. Our focus on surface pattern use, although helpful for thinking about the
ways in which morphosyntactic manifestations of a linguistic impairment may differ from
patterns of normal language variation, falls extremely short of providing a comprehensive
description of the two dialects under study. Qualitative studies and other sociolinguistic
research paradigms are needed to fully explore the unique and shared characteristics of these
two English varieties.
Acknowledgments The project was made possible by a grant from the National Institute on Deafness and Other Communication
Disorders (R03 DCO3609) that was awarded to the first author and an Interdisciplinary Research Summer Stipend
from Louisiana State University that was awarded to both authors Appreciation is extended to the families and staff
of the Ascension Parish school system who made the research possible, Jeannie Breaux who served as our SAAE
consultant, Lisa Green who shared with us her expertise of nonmainstream dialects of Louisiana, and Karen Pollock
who provided valuable comments regarding the design of the study and dissemination of the findings. Thanks also
are extended to the many students who have helped with various aspects of data collection and coding. These
include, but are not limited to, Amy Brock, Julie Cantrell, Cathy Cavell, Amy Costanza, Jennifer Depew, Lesley
Ellison, Lesley Eyles, Lesli Habans, Anita Hall, Janice Horohov, Dia McGowen, Myra Redlich, and Christy Wynn.
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Appendix The criteria used to code the 35 patterns are listed below, examples are taken from the
normally developing children in this study Descriptions of 13 other nonmainstream patterns
that were considered but not included are available from the authors. Unless noted, all
patterns are described in the literature as possible in both dialects; however, most of the data
on these forms come from studies of AAE varieties.
Zero be: Zero-marking of copula and auxiliary structures regardless of contractibility,
persor, or number was counted (e. g., Oscar in the can). Although zero-marking of
be is rare
or infrequent in some contexts (e.g., with first-person pronouns, in finite contexts, clause
find positions, and in contexts with emphatic stress) and there is thought to be differences in
SAAE and SWE regarding the effect of these contexts on
be marking, all contexts were
coded here to examine the effects of the independent variables of interest.
Be 2: Instances where
be was produced to signify an event or activity distributed
intermittently over time or space, including auxiliary and copula contexts that refer to
durative or habitual meaning (e.g., It be on the outside). Utterances with omitted
will and
other standard English uses (e.g., I’m going to be a dalmation) were not included.
I'ma : Instances where
I’ma was produced instead of the standard English I’m going to (e.g.,
I’ma go peek and see if my class gone out that way). This pattern is mentioned in
discussions of reduced
gonna forms and is thought to occur in AAE varieties.
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Subject-verb agreement with be forms : Instances where the person and number of the be
form differed from its subject (e.g., When we was about to go to church).
Omission of auxiliary do: Instances where auxiliary
do was not produced, but in standard
English its presence is obligatory. Many of these instances involved question inversion (e.g.,
How you get up here? and What you did?). Questions with an omitted
do in the initial
position of the utterance (e.g., You know what? and You got a baby?) were not counted. See
discussion below about the coding of noninverted indirect requests/questions.
Omission of auxiliary have: Instances where auxiliary
have, has , and had was not
produced, but in standard English its presence is obilgatory (e.g., I only been there a few
times). As demonstrated by the example, many of these utterances involved the verb
been .
Zero regular third present : Instances where regular third-person marking on the verb was
zero-marked (e.g, But when she poo on herself I don’t change her). Decisions as to whether
present or past tense was implied by the child were based on context.
Zero irregular third present : Instances where the subject of the verbs
say, have , and do
required says, has , and does in standard English but the child produced the unmarked form
(e.g., She just do it herself). Utterances involving
don’t were not included because they were
counted elsewhere. For the verb say, all zero-marked forms were coded as third present
irregular. For some of these utterances, the child’s meaning may have been past rather than
present. The decision to include all of the say examples as present was based on the
children’s frequent use of historical present with the verb say (e.g, So she says stop it!).
Within the sociolinguistic literature, a distinction between regular versus irregular verb
forms is not always made, although some (like Myhill & Harris, 1986) exclude the verb say
in analyses because it is irregular and typically zero-marked.
Subject-verb agreement with don't: Instances where the subject of the verb required
doesn’t in standard English, but the child produced don’t (e.g., And he don’t go to school).
Zero regular past : Instances where unmarked verbs were produced and in standard English
simple past marking is obligatory (e.g., I dress them before). Adjectival readings also were
included because they are included in sociolinguistic research (e.g., It’s finish).
Zero irregular past : Instances where an irregular verb was zero-marked for past tense (e.g.,
fall for fell ) or a different past-tense form was used instead of a standard English form (e.g.,
Course I brung him up real fast). In some cases, the different verb form was the participle
(e.g., I seen it).
Had + past : Instances where
had + a past-tense verb was produced and the standard English
gloss would be the simple past or the past participle (e.g., One day I had went on the back of
the levee to the beach). This pattern has been reported for SAAE.
Over-regularization : Instances where regular past-tense marking was used with an
irregular verb form (e.g., She drinked it all). This pattern is thought to occur infrequently in
both dialects.
Past as participle : Instances where the simple past-tense form was produced and in
standard English a participle form is required (e.g., But her whole head got broke).
BIN and been: Stressed
BIN and unstressed been contexts were included. BIN contexts
were those where the event was thought to be on-going or the completive activity is in the
remote past (e.g., Because I
BIN having them for a bunch of times. And I BIN had shots).
Oetting and McDonald Page 18
J Speech Lang Hear Res . Author manuscript; available in PMC 2012 June 25.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Seven of the utterances reflect clear examples of BIN as confirmed by Green (personal
communication). The other 8 are less clear, two may reflect
BIN , at least four can be glossed
with was, one may be a past-tense form of
be2 , and two may reflect omission of have . Been
uses involving clear cases of zero-marked have were not included in this category but were
included as instances of zero
have (see above). BIN is thought to be an AAE feature.
Ain't : Instances where
ain’t was used and in standard English negative forms involving be,
do
, or have are obligatory (e.g., We ain’t got none).
Multiple negation : Instances where negation was marked more than once in the utterance
(e.g., Cause she don’t want no people on the rocks). This pattern often occurs with
don’t and
ain’t .
Indefinite article : Instances where indefinite article
a was used and the following context
involves a vowel (e g., It’s a animal story). This pattern is thought to occur in SAAE.
Zero present progressive : Instances where present progressive inflection was zero-marked
and in standard English overt marking is obligatory (e.g., Yep I’m build one of those).
Zero plural : Instances where the regular plural inflection was zero-marked and in standard
English overt marking is obligatory (e.g., Six dollar and fifty-five). This pattern is thought to
occur most frequently with nouns of weights and measures or with nouns preceded by
quantification.
Zero possessive : Instances where the possessive inflection was zero-marked and in standard
English overt marking is obligatory (e.g, We’ll probably need everybody plates).
Omission of infinitive to: Instances where infinitive
to was omitted. Omission of to as a
preposition was not included (e.g., “My sister asked me if I wanted her bake some cookies
with the sugar”).
For to/to : Instances where
for to was produced and in standard English infinitive to is
produced. Only two instances of this pattern were found in the data, and both may be
considered questionable (e.g., I mean for to take a walk. For to go to store and pay).
Zero of : Instances where the preposition of was omitted (e.g., I can’t tell too much the story
yet).
What for that or zero that : Instances where the relative pronoun what was produced (e.g.,
Anything what my momma brings) or the relative pronoun was omitted (e.g., And they had
that thing you gotta shift your money in). Relative pronouns in the subject and object
position were included even though absence of that occurs in some standard English object
clauses.
Done + verb : Instances where
done + verb indicated a completive action or event (e.g. He’s
looking for his cat but it done went down the garbage can).
Fixing + verb : Instances where
fixing and fitna were used as a main verb and followed by
an infinitive (e.g., He was fixing to go off of the roof like that). One instance of
might gotta
(e.g., I might gotta take you somewhere) also was included in this category.
Undifferentiated pronoun : Instances where the unmarked pronoun form was used instead
of standard English nominative (e.g., Me and him do it sometimes), use of nominative
Oetting and McDonald Page 19
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript marking instead of genative (e.g., they cat), and use of masculine forms for feminine (e.g.,
he do it).
Reflexive : Instances where a different reflexive pronoun form was produced instead of a
standard English form (e.g., My daddy once went by hisself because he didn’t want to be
worried about us).
Demonstrative : Instances where the objective pronoun form was produced instead of the
demonstrative (e.g., He wrecked them back tires).
Dative : Instances where a personal dative was produced (e.g., I take me a shot).
Y'all varieties : Instances where a variant of a second-person plural form was produced
instead of a standard English pronoun (e.g., Y’all take turns).
Appositive : Instances where both a pronoun and noun were used to refer to the same
person(s) or object (s) (e.g., But my friend, he have a gate). This pattern occurs in standard
English but is thought to be more frequent in SAAE and SWE varieties.
Existential it and they: Instances where
it or they was used instead of there (e.g., My dad
grabs it with a paddle whenever it’s only men).
Wh- noninversion : Instances where a Wh- question form began the utterance or clause, but
the auxiliary was not inverted (e.g., Why this one won’t sit).
Oetting and McDonald Page 20
J Speech Lang Hear Res . Author manuscript; available in PMC 2012 June 25.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Oetting and McDonaldPage 21 Table 1
Participant characteristics. SLI6N4N SAAESWESAAESWESAAESWEN 161512191219 Males 912513512 Age in months77.1 (6)76.3 (8)74.5 (4)76.1 (6)56.83 (3)48.3 (5)Average level of education a 3.62 (1.40)4.67 (.50)5.00 (0.00)4.60 (.99)3.63 (.74)5.11 (.99) 89315817 CMMS b 95.8 (5)98.6 (6)101.3 (4)104.3 (9)102.4 (7)104.7 (10)PPVT c 71.4 (10)73.9 (10)102.2 (13)104.9 (12)97.8 (8)102.2 (7)TOLD III d 5.0 (2)6.3 (2)8.8 (3)8.7 (2)10.0 (2)9.0 (2)TOLD IV6.1 (1)5.7 (2)12.1 (2)11.4 (3)9.4 (2)9.6 (1)TOLD V6.1 (1)6.7 (2)9.9 (3)12.2 (3)9.3 (2)9.7 (1)GFTA e 73.8 (21)66.6 (29)95.6 (6)92.8 (13)80.1 (15)88.8 (8)MLU-w f 4.43 (.9)4.41 (.7)5.49 (1.5)5.27 (.8)4.65 (.5)4.41 (.48)MLU-m g 4.75 (.9)4.83 (.7)5.90 (1.6)5.80 (.8)4.98 (.6)4.85 (.6)Mean C&I utterances h 188 (52)248 (87)221 (79)242 (47)192 (63)204 (38)Total C&I utterances i 300337252652461323023876
aScores based on Hollingshead (1975), numbers below standard deviations reflect the number of children who returned a questionn\
aire.
bStandard scores from the Columbia Mental Maturity Scale (Burgmeister, Blum, & Lorge, 1972): M = 100, SD = 15.
cStandard scores from the Peabody Picture Vocabulary Test–Revised (Dunn & Dunn, 1981): M = 100, SD = 15.
dStandard subtest scores from the Test of Language Development–Primary (Newcomer & Hammill, 1988), Subtest III = Grammatical Co\
mprehension, IV = sentence Repetition, V = Sentence Completion:M = 10, SD = 3.
ePercentiles from the Goldman-Fristoe Test of Articulation (Goldman & Fristoe, 1986),
fMean length of utterance calculated by words.
gMean length of utterances calculated by morphemes.
J Speech Lang Hear Res . Author manuscript; available in PMC 2012 June 25. NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Oetting and McDonaldPage 22 hAverage number of complete and intelligible utterances per sample.
iTotal number of complete and intelligible utterances per group. J Speech Lang Hear Res . Author manuscript; available in PMC 2012 June 25. NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Oetting and McDonaldPage 23 Table 2
Frequency of nonmainstream dialect patterns. SLI6N4N SAAESWESAAESWESAAESWEzero be3091612116324689be2513373341 I’ma for I’m going to 002000 SV agreement with be 392935252325omission of auxiliary do 525614224260omission of auxiliary have 2941214 zero regular third13542112258519zero irregular third 2927327268 SV agreement with don’t 382523233633zero regular past 46312853310zero irregular past 40401563134had + past22053080 over-regularization 12201171134participle as past 1113243 ain’t 271315231015multiple negation 422425302935indefinite article 6662152 zero present progressive 118471116zero plural 39171515215 zero possessive 239279146 zero infinitive to 14181271213for to/to 000110 zero of 231112121212what/that or zero that 445255 been and BIN 213225 done + verb000101 fixing + verb204211
J Speech Lang Hear Res . Author manuscript; available in PMC 2012 June 25. NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Oetting and McDonaldPage 24 SLI6N4N SAAESWESAAESWESAAESWEundifferentiated pronoun 614116141925reflexive 612253 demonstrative 1000230 dative 2642113 y’all varieties624227 appositive 252937491316existential it and they 010200 Wh- noninversion 262673712
J Speech Lang Hear Res . Author manuscript; available in PMC 2012 June 25. NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Oetting and McDonaldPage 25 Table 3
Rate of nonmainstream pattern use.
a
SAAESWE SLI6N4NSLI6N4NM .37.31.34.18.09.13SD .13.09.13.08.04.06General range b .24–.60.19–.47.22–.67.06–.22.03–.18.05–.25Outliers c male .17nonefemale .11male .42nonenonefemale .17
aSum of nonmainstream pattern use divided by total number of complete and intelligible utterances.
bThe general ranges are based on 89 of the 93 children. Excluded are the 4 children who are presented as outliers.
cFour children are presented as outliers because their rates of nonmainstream dialect fell outside those of their same-dialect ch\
ild group; the gender of each outlier is provided for descriptive purposes.
J Speech Lang Hear Res . Author manuscript; available in PMC 2012 June 25. NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Oetting and McDonaldPage 26 Table 4
Patterns that differ by dialect. PatternSignificanceEta SquaredSAAE MSWE Mzero be F(1, 87) = 74.6, p < .001 .429.42.5 be2F(1, 87) = 21.6, p < .001 .191.6.1 SV agreement with be F(1, 87) = 7.1, p < .01 .071.3.6 zero regular thirdF(1, 87) = 69.0, p < .001 .434.6.7 zero irregular thirdF(1, 87) = 19.3, p < .001 .171.1.3 SV agreement with don't F(1, 87) = 8.4, p < .005 .081.3.7 zero regular pastF(1, 87) = 23.0, p < .001 .191.5.4 had + past F(1, 87) = 8.7, p < .005 .091.20.0 multiple negationF(1, 87) = 5.6, p < .05 .061.2.7 indefinite articleF(1, 87) = 13.3, p < .001 .12.4.2 zero pluralF(1, 87) = 9.1, p < .005 .091.0.3 zero possessiveF(1, 87) = 17.5, p < .001 .16.8.2 zero of F(1, 87) = 11.0, p < .005 .11.6.3 demonstrativeF(1, 87) = 61, p < .05 .06.2.01
J Speech Lang Hear Res . Author manuscript; available in PMC 2012 June 25. NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Oetting and McDonaldPage 27 Table 5
Patterns that differ by group. Mean PatternSignificanceEta SquaredSLI6N4Nomission of auxiliary do F(2, 87) = 4.8, p < .05 .10 1.4a.4 b1.6a zero irregular pastF(2, 87) = 7.1, p < .005 .14 1.4a.3b1.1a zero be F(2, 87) = 3.7, p < .05 .04 7.2a3.9 b5.4ab Wh- noninversionF(2, 87) = 4.3, p < .05 .09 .7a.1b.3ab zero present progressiveF(2, 87) = 3.5, p < .05 .07 .3ab.2a.4bappositiveF(2, 87) = 3.3, p < .05 .07 .8ab1.2a.4b
aMeans with different superscripts are significantly different by Tukey post hoc tests.
J Speech Lang Hear Res . Author manuscript; available in PMC 2012 June 25. NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Oetting and McDonaldPage 28 Table 6
Discriminant analysis models distinguishing between dialects. Overall
accuracy in
classificationAccurate
classification of SAAEAccurate
classification SWE (a) Models with weight set over SLI and 6N groups combined Full model 60/6228/28 32/34 Reduced model a 58/6227/28 31/34 (b) Reduced model set on SLI only b Applied to SLI 25/3113/16 12/15 Applied to 6N 31/3112/12 19/19 (c) Reduced model set on 6N only c Applied to 6N 30/3111/12 19/19 Applied to SLI 23/3114/16 9/15
aReduced model included zero regular third, zero be, subject-verb agreement with be, zero irregular past (72% of variance explained).
bReduced model included zero be, zero regular third, subject-verb agreement with be, zero irregular past (62% of variance explained).
cReduced model included zero regular third, zero be, subject-verb agreement with be, overregularization (88% of variance explained).
J Speech Lang Hear Res . Author manuscript; available in PMC 2012 June 25. NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript Oetting and McDonaldPage 29 Table 7
Discriminant analysis models distinguishing between SLI and 6N groups. Overall
accuracy in
classificationAccurate
classification
of SLI groupAccurate
classification of 6N group (a) Models with weights set over SAAE and SWE dialects combined Full model 56/62 27/3129/31 Reduced model a 51/62 23/3128/31 (b) Reduced model set on SAAE only b Applied to SAAE 23/28 12/1611/12 Applied to SWE 24/34 7/1517/19 (c) Reduced model set on SWE only c Applied to SWE 31/34 13/1518/19 Applied to SAAE 17/28 14/163/12
aReduced model included zero irregular past, auxiliary do omission, Wh- noninversion, overregularization (50% of variance explained).
bReduced model included zero irregular past, Wh- noninversion, zero irregular third (45% of variance explained).
cReduced model included zero irregular past, auxiliary do omission, zero irregular third, omission of infinitive to, subject-verb agreement with don’t (72% of variance explained).
J Speech Lang Hear Res . Author manuscript; available in PMC 2012 June 25.
