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Electronic Theses, Treatises and Dissertations
The Graduate School
2012
The Impact of Dialect Use, Executive
Functioning, and Metalinguistic Awareness
on Dialect Awareness
Lakeisha Johnson
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THE FLORIDA STATE UNIVERSITY
COLLEGE OF COMMUNICATION AND INFORMATION
THE IMPACT OF DIALECT USE, EXECUTIVE FUNCTIONING, AND METALINGUISTIC
AWARENESS ON DIALECT AWARENESS
By
LAKEISHA JOHNSON
A Dissertation submitted to the
School of Communication Science and Disorders
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Degree Awarded:
Summer Semester, 2012
Copyright © 2012
Lakeisha Johnson
All Rights Reserved
Lakeisha Johnson defended this dissertation on June 26, 2012.
The members of the supervisory committee were:
Kenn Apel
Professor Directing Dissertation
Richard Wagner
University Representative
Shurita Thomas-Tate
Committee Member
Carol McDonald Connor
Committee Member
Ramonda Horton-Ikard
Committee Member
The Graduate School has verified and approved the above-named committee members, and
certifies that the dissertation has been approved in accordance with university requirements.
ii
ACKNOWLEDGEMENTS
I would like to thank the members of my committee for all of their support, guidance,
encouragement and wisdom throughout this process. Without your continuous input, this project
would not have been possible. I would like to especially thank Dr. Kenn Apel and Dr. Shurita
Thomas-Tate for their roles as mentors. I appreciate all of the knowledge you freely gave and the
many emails and drafts back and forth. None of this could have been accomplished without both
of you.
Many thanks as well to Drs. Apel, Connor, Wagner, and LaPointe for the use of their
materials and tasks. I would also like to thank the following people who helped with data
collection and scoring: Danielle Brimo, Katie Califf, Sean Daley, Kaya Lawrence, Callie Little,
and Stephanie Lucas. Without your help, it would have been impossible to collect the data in
such an efficient manner. Many thanks as well to the teachers and school administrators for
allowing me to work with their students.
Finally, I would like to thank my husband Bradford, my family, and friends for their
constant support throughout this entire process. With God, your prayers, and words of
encouragement, I have been given the confidence and motivation to withstand and complete this
project.
This project was funded in part by the Florida State University Congress of Graduate
Students Dissertation Research Grant. Additionally, the research reported here was supported by
the Institute of Education Sciences, U.S. Department of Education, through Grant
R305F1000027 to Florida State University as part of the Reading for Understanding Research
Initiative. The opinions expressed are those of the authors and do not represent views of the
Institute or the U.S. Department of Education.
iii
TABLE OF CONTENTS
LIST OF TABLES .......................................................................................................................... v
LIST OF FIGURES ....................................................................................................................... vi
ABSTRACT .................................................................................................................................. vii
CHAPTER ONE: INTRODUCTION ............................................................................................. 1
CHAPTER TWO: METHOD ....................................................................................................... 12
CHAPTER THREE: RESULTS ................................................................................................... 17
CHAPTER FOUR: DISCUSSION ............................................................................................... 28
APPENDICES .............................................................................................................................. 35
REFERENCES ............................................................................................................................. 52
BIOGRAPHICAL SKETCH ........................................................................................................ 56
iv
LIST OF TABLES
Table 1. Mean Performance on all Measures by Grade, DELV-ST Category, and Overall. ........ 19
Table 2. Correlations Among Dialect Use, Dialect Awareness, Executive Functioning,
Metalinguistic Awareness and Language ..................................................................................... 23
Table 4. Hierarchal Multiple Regression Analyses Predicting Editing Task Performance Among
DVAR, Executive Functioning and Metalinguistic Awareness Variables, Controlling for
Language ....................................................................................................................................... 25
Table 5. Hierarchal Multiple Regression Analyses Predicting Editing Task Performance Among
DDMs, Executive Functioning and Metalinguistic Awareness Variables, Controlling for
Language ....................................................................................................................................... 25
Table 6. Hierarchal Multiple Regression Analyses Predicting Dialect Judgment Task
Performance Among DVAR, Executive Functioning and Metalinguistic Awareness Variables,
Controlling for Language .............................................................................................................. 26
Table 7. Hierarchal Multiple Regression Analyses Predicting Dialect Judgment Task
Performance Among DDMs, Executive Functioning and Metalinguistic Awareness Variables,
Controlling for Language .............................................................................................................. 27
v
LIST OF FIGURES
Figure 1. Frequency of NMAE features used in narrative writing samples for all participants. .. 20
Figure 2. Frequency of NMAE features used in narrative writing samples by grade level. ......... 20
vi
ABSTRACT
The purpose of this investigation was to examine the underlying factors related to dialect
awareness in second, third, and fourth grade students. Eighty-seven students were administered
measures of written and spoken dialect usage, dialect awareness, executive functioning,
metalinguistic awareness, and language ability. Performance on each measure was analyzed to
determine whether differences were seen by dialect usage group (strong, some, or no variation
from Mainstream American English). Students who used the least amounts of morphsyntactic
non-Mainstream American English features outperformed their peers on all measures.
Correlational analyses showed that increased performance on measures of executive functioning,
metalinguistic awareness, and language was significantly related to increased performance on
dialect awareness tasks. Hierarchal multiple regression analyses showed that language skills
accounted for most of the variation in dialect awareness performance, while cognitive shifting
and blending abilities accounted for an additional small amount of variance. While dialect
awareness is a complex task to measure, clinical implications of this investigation suggest that
strong overall language abilities are needed to successfully increase dialect awareness. Students
who are having difficulty with dialect awareness may benefit from a program that addresses
these skills explicitly, while also targeting overall language.
vii
CHAPTER ONE
INTRODUCTION
Many African American students begin school speaking various amounts of African
American English (AAE), which is a systematic, rule-governed dialect of English (Thompson,
Washington, & Craig, 2004). Despite gains that have been made and the growing research base
in the areas of dialect use and its impact on education, 52% of African American students in
fourth grade do not meet basic proficiency standards in reading (National Center for Education
Statistics, 2009). A potential area of interest to explore the relation between dialect usage and
educational outcomes is dialect shifting. Dialect shifting is the ability to switch between home
language and more formal language during appropriate contexts. For example, an African
American student who dialect shifts well is able to decrease his usage of contrastive AAE
features within the school environment. Although research has been completed on dialect
shifting and academic success (Connor & Craig, 2006; Craig & Washington, 2004; Craig,
Zhang, Hensel, & Quinn, 2009; Terry, Connor, Thomas-Tate, & Love, 2010), there is a lack of
information on the factors that contribute to shifting ability. One possible variable that may
contribute to a student’s ability to dialect shift is dialect awareness. Dialect awareness is the
conscious ability to think about and manipulate the grammatical features and structures that are
distinct to specific or various dialects. Therefore, the purpose of the current study is to gain
additional information on factors related to why some students have increased dialect awareness,
while others do not. It is hypothesized that executive functioning and metalinguistic awareness
may play a role in dialect awareness. Teaching these students to become more aware of the
differences in school and home language may have implications for creating appropriate
materials and differentiating instruction, which may lead to improved educational outcomes.
Teaching students to recognize the differences between home and school language and
the impact of this process on literacy has been an area of interest for some time (Cummings,
1997; LeMoine, 1999; Simpkins & Simpkins, 1981; Taylor, 1989; & Wheeler & Swords, 2004).
Several programs were created using the nonstandard dialect as the medium of instruction within
the classroom setting to acquire initial literacy skills, while the standard language was introduced
at a later stage of learning (Siegel, 1999). For example, the Bridge program created by Simpkins
and Simpkins (1981), contained similar stores in both AAE and Mainstream American English
1
(MAE). It was hypothesized that learning to read in the students’ home language would serve as
a bridge to reading MAE. Findings were that students in the Bridge program showed 6.2 months
of reading gains in a four month treatment period, compared to 1.6 months of gain by the control
group (Simpkins & Simpkins, 1981).
Taylor (1989) investigated the dialect use of students in a college writing course. Over an
eleven week treatment period, students were explicitly taught the differences between AAE and
MAE using contrastive analysis. Students in the treatment demonstrated a 59% decreased in
dialect usage in their writing. On the other hand, the control group, who was taught using regular
English conventions, increased their usage of dialect features by 8.5%.
There have been two programs implemented disctrict-wide that demonstrated positive
effects on teaching dialect speakers to use MAE within the school setting. Cummings (1997)
examined the effectiveness of a Bidialectal Communication course in Dekalb County, Georgia
schools using Title I funding. Fifth and sixth grade students were taught the differences between
home and school language and the appropriateness of both through various strategies including:
videotaping, role playing, peer critiques, and parent courses. Cummings (1997) found that all
students who took the course showed improvements in verbal scores and reading comprehension.
Additionally, the Los Angeles Unified school district’s Academic English Mastery Program
(AEMP) was targeted towards any students who were not proficient in MAE, and included
African Americans, Hispanics, and Native Americans (LeMoine, 1999). Findings showed that
students who participated in the AEMP outperformed those who did not.
Wheeler and Swords (2004) suggest that contrastive analysis techniques can help
students become bidialectal. Swords encouraged students in her own classroom to dialect shift
by providing them with a metaphor of the skill. Students were told to think of language as items
of clothing; different types of clothing are appropriate for different settings as is the same with
language. Explicitly contrasting features that differed in AAE and MAE and allowing students to
discover and construct the rules of patterns in both, helped to improve her students’ language
skills. After using the contrastive analysis approach for one year, African American and White
students performed equally well on year-end benchmarks (Wheeler & Swords, 2004).
Additionally, there is a building consensus in the current literature base that documents
the relationship between dialect shifting, the morphosyntactic and phonological features of AAE,
2
and academic outcomes – specifically, reading abilities (Connor & Craig, 2006; Craig &
Washington, 2004; Craig et al., 2009; Terry et al., 2010). Craig and Washington (2004) studied
the systematic variation of AAE production in 400 African American typically developing
students in preschool through fifth grade. Oral language samples were elicited from all
participants using three action pictures from the Bracken Concept Development Program
(Bracken, 1986) where students were asked to tell as much as they could about the pictures. The
authors found that there were no significant differences between preschoolers and kindergartners
and between first through fifth graders in the amount of dialect used in the language samples.
There was a significant downward shift in dialect usage at first grade though, which was
categorized as dialect shifting. The students who dialect shifted outperformed their peers who did
not shift on standardized measures of reading achievement and vocabulary knowledge (Craig &
Washington, 2004).
Connor and Craig (2006) examined the relations between the use of AAE and emergent
literacy skills in 63 African American preschoolers attending Head Start. AAE usage was
explored using two language contexts: the Sentence Imitation subtest of the Test of Language
Development – Second Edition: Primary (TOLD-2:P, Newcomer & Hammill, 1988) and an oral
narrative using a wordless storybook. Through hierarchical linear modeling (HLM), the authors
found that there was a significant U-shaped relationship between the frequency of AAE usage
and emergent literacy skills. Those who used AAE with a greater or lesser frequency had
stronger sentence imitation, letter-word recognition, and phonological awareness than moderate
AAE users. Fewer AAE features were used in the sentence imitation task, which suggested that
the explicit expectation for Mainstream American English (MAE) caused the preschoolers to
dialect shift and signaled the emergence of metalinguistic awareness (Connor & Craig, 2006).
In 2009, Craig, Zhang, Hensel, and Quinn further investigated the contribution of dialect
shifting to reading achievement in 165 first through fifth graders who were typically developing.
Samples were analyzed using the dialect density measure (DDM), which is the ratio of dialect
features produced to total words used (Craig & Washington, 2000). Dialect shifting from AAE to
MAE was examined by comparing AAE usage frequency during oral and written narratives.
Through structural equation modeling, it was determined that AAE usage frequency was
inversely related to reading achievement. There was also a significant decrease of AAE usage
3
between the oral and written narratives as seen in other studies (e.g., Thompson et al., 2004).
Lower DDMs in writing predicted a substantial amount of the variance in standardized reading
scores, lending more support to the importance of dialect shifting and its impact on reading skills
(Craig et al., 2009).
Terry and colleagues (2010) investigated the relationship between the use of
nonmainstream American English dialects, literacy skills, and school environment among 617
African American (48%) and Caucasian (52%) typically developing first graders. Scores from
the Diagnostic Evaluation of Language Variation – Screening Test (DELV-ST, Seymour,
Roeper, & de Villiers, 2003) were used to calculate the percentage of dialect variation (DVAR)
by determining items that varied from MAE. Using HLM, the authors found that the relationship
of DVAR to vocabulary was negative and nonlinear, but varied by school socioeconomic status
(SES). Additionally, the more dialect participants used, the lower their phonological awareness
score, which was not moderated by school SES. As seen in other studies (e.g., Connor & Craig,
2006), a U-shaped relationship was present between word reading scores and DVAR; those who
used MAE or high levels of dialect had higher reading scores (Terry et al., 2010).
The aforementioned studies (Connor & Craig, 2006; Craig & Washington, 2004; Craig et
al., 2009; Terry et al., 2010), have demonstrated that students who are able to dialect shift
outperform peers who are not shifting on reading, phonological awareness, and vocabulary
measures. It is also suggested that students who use MAE and those who are high dialect
speakers have higher reading scores. These studies have provided a foundation for the
relationship among dialect usage, shifting ability, and reading skills, but do not explain the
process of dialect shifting. In an effort to understand why some students do not dialect shift
spontaneously, the current study is needed to explore the underlying factors.
To explain the relation between AAE use and reading difficulties, several major theories
have been proposed (Connor, 2008).The linguistic bias hypothesis (Goodman & Buck, 1973),
suggests that teachers perceive students who use AAE as less capable of completing work. This
leads to setting lower expectations for academic success and lower expectations may result in
fewer opportunities to learn. The second theory (Cecil, 1988), suggests that there is a mismatch
between the morphosyntactic and phonological structures of AAE and MAE which leads to an
increased difficulty in learning to read for African American children. This theory is supported
4
by several studies that have found that increased frequencies of AAE usage are negatively related
with literacy skills for elementary-aged school children (Charity et al., 2004; Craig &
Washington, 2004).
A third, more recent theory on the relation between AAE and difficulties in learning to
read is the linguistic flexibility hypothesis (Connor, 2008). This theory suggests that AAE
speakers who have strong linguistic flexibility and metalinguistic awareness are able to switch
between the morphosyntactic and phonological structures of AAE and MAE easily, and may be
demonstrated through dialect awareness. The linguistic flexibility hypothesis builds on the
linguistic bias hypothesis as the literacy learning abilities of children who have stronger
linguistic flexibility may not be impacted by the mismatch seen between AAE and MAE.
Children who have less linguistic flexibility may have more difficulty switching between the two
language systems, which impacts reading skills (Connor, 2008). Findings of the U-shaped
relation between dialect usage and literacy abilities draws more support to the limited linguistic
flexibility theory (Connor & Craig, 2006; Terry et al., 2010). Students who used limited and high
quantities of AAE features were found to have stronger, more sophisticated language and literacy
skills.
To date, no investigator has attempted to determine the underlying factors of dialect
awareness, which may be a key variable in a student’s ability to dialect shift. The linguistic
flexibility hypothesis would suggest that students’ metalinguistic skills, that is, there ability to
consciously think about and manipulate language, are required. It also may be that students must
have adequate executive functioning skills to increase their dialect awareness. Executive
functioning skills are the underlying processes involved in cognitive functioning (Booth &
Boyle, 2009). These two types of skills are detailed below.
Metalinguistic Skills
Metalinguistic awareness is the conscious ability to manipulate the elements of language
(Connor, 2008). As stated above in the linguistic flexibility theory, individuals with greater
metalinguistic awareness may be better dialect shifters. Metalinguistic awareness skills are
commonly measured by tasks of morphological and phonemic awareness. Both morphological
awareness (the ability to reflect on and manipulate morphological units within words) and
5
phonemic awareness (the ability to reflect on and manipulate phonemes within words) have been
shown to be positively related to literacy development (Apel & Thomas-Tate, 2009).
Apel and Thomas-Tate (2009) investigated the morphological awareness skills of thirty
fourth grade African American students and its impact on AAE use and literacy skills. Students
were administered the Derivational Suffix Test (DST; Green, 2004) to assess knowledge of
derivational morphology. The Word Attack, Word Identification, and Passage Comprehension
subtests of the WRMT-R (Woodcock, 1987) were used to assess word-level reading and reading
comprehension. The Test of Written Spelling – 4 (TWS-4; Larsen, Hammill, & Moats, 1999)
was used to assess spelling to dictation skills. Phonemic awareness was measured using the
Elision subtest of the Comprehensive Test of Phonological Processing (CTOPP; Wagner,
Torgesen, & Rashotte, 1999). Finally, receptive vocabulary was assessed using the Peabody
Picture Vocabulary Test – 3 (PPVT-3; Dunn & Dunn, 1997). Results showed that there were no
significant differences in the performance of low and high AAE users on the morphological
awareness tasks. Participants performed better on morphological awareness items that were
transparent (derivations that maintain phonological production and orthographic spelling of the
base word, e.g., farm-farmer). Significant relations were seen between word-level reading,
spelling, vocabulary, and morphological awareness (Apel & Thomas-Tate, 2009).
Kohler, Bahr, Silliman, Bryant, Apel, and Wilkinson (2007) evaluated the role of dialect
on phonemic awareness and non-word spelling tasks in low-income AAE speakers. Eighty
students, who were typically developing, in first and third grades, were asked to narrate events
that occurred in two wordless videos to determine dialect density. They were also administered
the CTOPP (Wagner et al., 1999) and a non-word spelling measure. Sixty age-appropriate nonwords were created that targeted nine specific AAE features of interest. The non-word was
presented alone and within a sentence and the participants were asked to spell each word.
Although first graders performed better than third graders on measures of phonemic awareness,
use of phonological AAE features explained few differences in phonemic awareness scores and
no differences were found between high and low dialect users. Participants with higher DDMs
produced more non-word spelling errors influenced by AAE, with greater effects seen in third
grade. Results indicated that after second grade, non-word spelling may be more sensitive to the
effects of dialect usage than in phonemic awareness tasks (Kohler et al., 2007).
6
Although the previous studies (Apel & Thomas-Tate, 2009; Kohler et al., 2007) provided
preliminary results on the importance of morphological awareness and phonemic awareness for
the literacy skills of African American students, further research in additional grades is
warranted to determine whether these skills impact dialect awareness. No differences were found
in the morphological and phonemic awareness skills of low and high dialect users, but both
groups performed differently on measures of literacy abilities. Therefore, it is important to
complete additional studies in this area to determine what contributes to these groups having
different literacy outcomes. The current study aims to fill this gap by exploring dialect
awareness.
Executive Functioning Skills
Another skill set that may contribute to dialect awareness is executive functioning (EF).
The relationships between EF and reading and writing skills have been researched for quite some
time. Executive functioning is an umbrella term for all of the skills that enable an individual to
self-regulate and engage in goal-directed behavior. Inhibition is considered the primary EF skill,
as it precedes and allows for development of all the other EF skills. Inhibition first appears in
children approximately at the ages of three to four, but continues to develop throughout
adolescence. Other skills included under the umbrella of EF are: self-regulation, shifting,
updating/monitoring, working memory, and planning. These skills follow inhibition in
development (Altemeir, Abbott, & Berninger, 2007).
To date, no studies have investigated the use of AAE and EF, but for the purpose of this
investigation, the three EF skills hypothesized to most likely contribute to dialect awareness are
inhibition, shifting, and working memory. These skills are thought to all be related, but separate
constructs (Altemeier et al., 2007; Best, Miller, & Jones, 2009). Best and colleagues (2009)
describe inhibition as the ability to suppress a dominant, automatic, or pre-potent response in
order to achieve a goal. Demands of inhibition vary based on whether working memory is
needed, the response modality, and the degree of prepotency. Significant improvements in
inhibition occur between the ages of five and eight (Best et al., 2009). A popular example of a
task of inhibition is the Stroop task. Participants are presented with a color word, where the ink is
printed in a different color (e.g., the word “red” printed in “blue” ink), and are asked to read the
7
words ignoring the color of the ink. Participants have to inhibit the automaticity of saying the
color of the ink and only read the printed words (Stroop, 1935).
Shifting is the ability to flexibly switch attention between mental states, operations, or
tasks (Altemeier et al., 2007). The ability to shift between more complex tasks improves with
age, typically until early adolescence. Successful shifting involves inhibition of previously
activated mental states, while poor shifting is demonstrated through perseverative errors of
continuing to respond according to previous rules. The Wisconsin Card Sorting Test (WCST,
Heaton, 1981) is considered a classic shifting task where participants are asked to sort cards
based on a specific dimension (shape, color, or quantity). At an undisclosed point, the sorting
rule is changed and the participant has to determine the new rule based on negative feedback
(Best et al., 2009).
Working memory is defined as the ability to maintain and manipulate information over
brief periods of time (Best et al., 2009). Task complexity impacts performance on working
memory tasks and development continues through adolescence where easier tasks are mastered
before those that are more complex (Best et al., 2009). The reading span task is an example of a
working memory measure where participants read several sentences and then have to recall the
last word of each sentence. This task grows in complexity as more sentences are held within the
working memory before the last words are recalled (Daneman & Carpenter, 1980).
Findings from several studies strongly support that inhibition, shifting, and working
memory play a role in reading skills, as reading comprehension difficulties are linked to
executive dysfunction. It appears that EF skills change over time and show different patterns of
growth. Findings also suggest that higher EF scores in all areas are related to better literacy
performance and language skills (Booth & Boyle, 2009; Locascio, Mahone, Eason, & Cutting,
2010; Sesma, Mahone, Levine, Eason, & Cutting, 2009). These findings are important to the
issues that AAE speakers face in regard to reading in several ways. Students have to be able to
hold and manipulate information when reading, to comprehend meaning and answer questions.
This may also hold true for dialect awareness as students have to inhibit home language and
manipulate information that is written in MAE when completing school activities. As we know,
AAE use can impact literacy development if students are not dialect shifting appropriately in
school settings (Charity et al., 2004; Craig & Washington, 2004; Craig et al., 2010). For these
8
students, encouraging dialect awareness may be a viable option for educators. As the frequency
of dialect usage has been documented to occur less within writing (Thompson et al., 2004; Craig
et al., 2009), these researchers suggest that the written context may be optimal to encourage
increased dialect awareness and to improve metalinguistic awareness.
Given the purpose of the current study is to measure dialect usage in the written context,
it is important to examine the impact EF skills have on writing abilities. Singer and Bashir
(2006) state that writing is an intentional action that is under the control of executive and selfregulatory processes. For writing to be effective, it must be well-planned, organized, and
evidence a good use of language. Multiple, simultaneous processes involving cognitive,
linguistic, and motoric skills are occurring in written composition. Written language consists of
two components: writing processes and writing foundations. The process of writing includes
planning, organizing, generating, and revising. All of these processes are mitigated by executive
functioning and self-regulation to represent thoughts and ideas on paper. The foundation of
writing includes cognitive-linguistic skills such as working memory, processing speed,
conceptual ability, content knowledge, and meta-awareness. Additional foundations are
production (graphomotor skills and mode of output); social-rhetorical; and beliefs and attitudes.
If any of the foundations are underdeveloped, the writing process is weakened. In writers who
are typically developing, the composition process is recursive, not linear. Each step in the writing
process is fluid as thoughts are organized, generated, and revised several times within one text.
Executive functions allow the writer to control these processes as they shift among the four to
create a cohesive and effective writing. When executive functioning skills are compromised, the
writing process is not managed effectively or efficiently (Singer & Bashir, 2006).
Furthermore, to evaluate dialect usage in writing, it is important to understand how
typical writing and spelling develops to ensure that developmental errors are not classified as
dialect usage. It is difficult to characterize child AAE and distinguish its linguistic variations
from developmental sound production patterns (Craig & Washington, 2006). Seymour and
Seymour (1981) found that they while they were unable to identify unique patterns seen in 4- and
5-year old AAE and MAE speakers, quantitative differences were observed between the groups
with increased frequencies seen in the AAE speakers. Seymour, Bland-Stewart, and Green
(1998) suggest identifying noncontrastive features (those shared between AAE and MAE) when
9
looking at the language of AAE speakers and determining language disorders. Their results also
suggested that AAE speakers beyond five years old should not have difficulties with
noncontrastive features; difficulties with two or more of these features are highly suggestive of
language impairment. Therefore, it is important to look at the quantity and consistency of dialect
usage when evaluating students’ writing.
Taken as a whole, there may be a link among dialect awareness, EF abilities,
metalinguistic skills, the linguistic flexibility theory, and oral language skills. To increase dialect
awareness, students have to be able to contrast and think about language skills; therefore, good
shifting, inhibition, working memory and metalinguistic awareness skills may be required.
Further, adequate language skills are likely also required. Several current studies have found that
there is a relationship between the oral language skills of African American students, shifting
abilities, and literacy outcomes (Charity et al., 2004; Craig & Washington, 2004; Craig et al.,
2009). African American students with stronger reading skills also had better receptive
vocabulary knowledge (Craig et al., 2009) and those who used less dialect in early elementary
grades were found to perform better on reading measures in later grades (Craig & Washington,
2004). Students who were able to dialect shift demonstrated higher scores on the PPVT-3 (Dunn
& Dunn, 1997) than those who were not shifting, while also obtaining higher scores one year
later on standardized reading measures. In 2004, Charity and colleagues investigated the relation
between children’s familiarity with school English and their early reading achievement. Students
who were able to imitate more MAE sentences accurately had higher scores on the Woodcock
Reading Mastery Tests – Revised (WRMT-R, Woodcock, 1987) as well as on measures of
vocabulary. Therefore, when measuring dialect and the ability to shift, it is important to also
examine oral language skills and their contribution to this process.
To best help students to become more effective at increasing dialect awareness, it is
imperative to determine the underlying factors that contribute to this skill to best inform
instructional practice. If there are differences found in the EF skills based on the degree of
dialect use, specific training in EF may help to increase more automatic dialect shifting in school
settings. A similar approach would be true if metalinguistic skills are found to differ between
dialect groups. These types of findings could lead to the creation of effective educational
programs that will allow dialect speakers to shift more effortlessly, thereby leading to greater
10
academic success. Thus, foundational research is needed in the area of dialect awareness to
determine whether metalinguistic awareness and EF skills may be related to the ability of dialect
speakers to inhibit their home language and shift to school language appropriately. If such a
relationship is found, more support will be added to the linguistic flexibility theory. Therefore,
the purpose of this study is to determine the factors that contribute to dialect awareness to
provide more information on why some students have increased dialect awareness, while others
do not. The following research questions are posed:
1. Are there differences in dialect awareness, executive functioning, metalinguistic
awareness, and language skills based on DELV-ST category of dialect usage?
2. What are the relations between dialect use, dialect awareness, executive
functioning, metalinguistic awareness, and language?
3. Do dialect use, executive functioning, and metalinguistic awareness skills predict
dialect awareness, as measured by an Editing task and Dialect Judgment task,
above the contribution of language? Additionally, does the measure used to
categorize dialect use (DVAR or DDMs) impact the ability to predict dialect
awareness?
The first research question was answered through descriptive statistics, using means,
frequencies, ranges, and analysis of variance (ANOVA). It was hypothesized that students who
used fewer amounts of dialect would obtain higher scores on measures of executive functioning,
metalinguistic awareness, language performance, and dialect awareness. The second research
question was answered through correlational analyses. It was hypothesized that dialect speakers
with increased dialect awareness would have higher executive functioning, metalinguistic
awareness, and language performance. The final research question was answered through a series
of hierarchal multiple regression analyses. It was hypothesized that executive functioning and
metalinguistic awareness would account for a significant portion of variance in dialect
awareness, above that explained by language skills.
11
CHAPTER TWO
METHOD
Participants
Participants in the second through fourth grades were recruited from a low-SES
elementary school in Northeast Florida. All students were given parental consent forms to be
signed and returned. The sample consisted of 87 students (39 males and 48 females). There were
25 second graders, 40 third graders, and 22 fourth graders. Of the 87 students, 71 were African
American, seven White, four Hispanic, four Multiracial, and one Asian. Eight students were
receiving speech and/or language services at the time of the investigation (four speech, two
language, and two speech and language). All students who returned consent forms were included
in the sample. This investigation was approved by the local Institutional Review Board (IRB).
Measures
All participants were given several measures to assess their non-Mainstream American
English (NMAE) dialect usage and awareness, executive functioning skills, metalinguistic
awareness, and overall language abilities. The tasks for dialect usage consisted of a standardized
measure of language variation and a writing sample. Researcher-created Editing and Dialect
Judgment tasks were used to measure dialect shifting. Executive functioning skills were
measured through tasks of inhibition, shifting, and working memory. Metalinguistic awareness
was assessed using a morphological awareness task and two subtests of the Comprehensive Test
of Phonological Processing (CTOPP-2, Wagner et al., in press). The Recalling Sentences,
Formulated Sentences, and Word Classes subtests of the Clinical Evaluation of Language
Fundamentals – Fourth Edition (CELF-4, Semel, Wiig, & Secord, 2003) were used to control for
overall language skills independent of other executive functioning and metalinguistic awareness
skills.
Dialect usage. Two measures of dialect usage were administered in this investigation.
Students who were judged as using dialect on either task were considered NMAE speakers. First,
part one of the Diagnostic Evaluation of Language Variation – Screening Test (DELV-ST,
Seymour et al., 2003) was given as a standardized measure of whether the student’s language
varied from MAE. Performance on the DELV-ST served as a proxy for spoken dialect usage.
Students were asked to describe actions in pictures and to respond to questions based on pictures.
12
Responses were scored according to the manual for the frequency of MAE and non-mainstream
features produced, which allowed speakers to be classified as having strong, some, or no
variation from MAE. Scores from each item in Part I of the DELV-ST were further analyzed to
obtain the ratio of dialect variation of each student (DVAR, Terry et al., 2010). DVAR is
calculated by dividing the total score in column A (response varies from MAE) of the DELV-ST
by the sum of columns A and B (response is MAE). This number was then multiplied by 100 to
obtain the percentage of dialect variation. The author reported inter-examiner reliability of the
DELV-ST is .80 (Seymour et al., 2003).
The second task of dialect usage was a narrative sample, which was administered in a
group setting, to measure spontaneous language in the written context (see Appendix A). In this
task, students were shown a picture, provided with a prompt, and were instructed to write a story
about what they thought happened in the picture. A thirty-minute time frame was allotted for
students to both plan and write their narratives. Participants did not receive any assistance during
the writing sample. The written language samples were transcribed and analyzed using the
Systematic Analysis of Language Transcripts software (SALT, Miller & Chapman, 2008).
Morphosyntactic NMAE features used were characterized using established taxonomies
(Thompson et al., 2004; see Appendix B). Frequency counts were generated for NMAE features,
as well as dialect density calculated using the ratio of dialect features produced to total words
used (DDMs, Craig & Washington, 2000).
Dialect awareness. Two different researcher-created tasks were used to measure the
students’ dialect awareness. The Editing task was used as an expressive measure of dialect
awareness (see Appendix C). Participants identified and changed dialect forms used in sentences
to MAE in a small group setting. The target NMAE dialect forms used in the sentences were the
copula, plurals, past tense, preterite had, subject-verb agreement, and possessives. Students were
told that they needed to change each given sentence into one they would read in a book. They
read each sentence and then identified the part that was not correct. Students then rewrote the
sentences using the appropriate grammatical forms. Participants received one point for each item
correctly edited and the maximum score was 12. The Editing task was scored using a researchercreated rubric (see Appendix D). The calculated Cronbach alpha of this task was .78.
13
The second dialect awareness task was a measure of receptive ability. In the Dialect
Judgment task (see Appendix E), participants had three minutes to judge whether they thought a
sentence would be read in a book at school or not. The sentences were read aloud by the research
assistants, to avoid reading difficulties impacting performance. Participants received one point
for each item they got correct in the allotted time period and the maximum score was 30. The
calculated Cronbach alpha of this task was .73.
Executive functioning. The Wisconsin Card Sorting Test: Computer Version – Fourth
Edition (WCST: CV4, Heaton & Psychological Assessment Resources, 2003) was individually
administered to measure participants’ cognitive shifting ability in response to environmental
changes. Students were presented with 4 stimulus cards that varied by shape, color, and quantity.
They had to sort additional cards by one of those principals and were only given feedback on
whether they were correct or not. As the test continued, the sorting principal shifted and
participants had to determine the new sorting category based on feedback. The number of correct
shifts, trials, errors, and perseverative errors were reported. Standard scores between 85 and 115
are within the typically developing range. The author reported reliability of the WCST ranged
from .60 to .85 and averaged .74.
The Stroop Color and Word Test – Children’s Version (Golden, Freshwater, & Golden,
2003) was individually administered to determine the participants’ ability to inhibit automatic
responses. The Stroop test consists of three parts and students are given 45 seconds to complete
each section as quickly as possible. Participants were first presented with a word sheet and had to
name each color (all color words were printed in black ink). In the second part of this task,
students were presented with a color sheet and had to identify the colors (XXXX were printed in
different colored ink). Finally, in the third part, students were shown the color-word sheet and
had to respond to the color, ignoring the written text (the word “red” written in blue ink). The
number of items named and interference scores were reported. The author reported reliability of
the individually administered Stroop test was .86, .82, and .73 for each part respectively.
The Working Memory Listening Span task (Wagner) was individually administered to
measure working memory (see Appendix F). Students had to listen to short easy questions and
respond yes or no. Then they had to say the last word in the question. The task gradually became
14
more challenging as more questions were added. Items were counted as correct if the student
recalled the last word accurately and the maximum score was 12.
Metalinguistic awareness. The Spoken Morphological Awareness task (Apel & Brimo,
2011) was used to determine the participants’ knowledge of how meaning affects the way that
words are written (see Appendix G). The research assistant provided a word and then a sentence
with a missing word (e.g., sock: Please put on your shoes and
). Participants had to
change the word provided to complete the sentence. Items were counted as correct if the sentence
was completed accurately and the maximum score was 40. The author reported reliability for this
task was .92 (Apel & Brimo, 2011).
The Blending and Phoneme Isolation subtests from the Comprehensive Test of
Phonological Processing - Second Edition (CTOPP-2; Wagner et al., in press) were used to
assess phonemic awareness. In the Blending subtest, participants blended separately presented
sounds together to form words (e.g., What word do these sounds make? s – u – n ). The
maximum score for the Blending subtest was 33. In the Phoneme Isolation subtest, participants
were required to identify specified sounds in words (e.g., What is the first sound in fan? /f/). The
maximum score for the Phoneme Isolation was 32.
Overall language. Three subtests of the CELF-4 (Semel, et al., 2003) were individually
administered to determine overall language abilities. The Recalling Sentences subtest required
students to repeat progressively longer and more complex sentences for verbatim. The
Formulated Sentences subtest required students to formulate a sentence based on a picture using
a given word(s). The Word Classes subtest measured receptive and expressive vocabulary
knowledge. Students were provided with three to four words and had to determine which two
words were related and then tell how they were related. All of the aforementioned subtests were
scored according to the CELF-4 manual. Participants were not penalized for use of dialect on the
Recalling Sentences and Formulated Sentences subtests based on the manual’s description for
dialectal differences. The total raw score across the three subtests was used in the analyses and
the total maximum score was 200. The author reported reliability of the CELF-4 ranges from .69
to .91 for individual subtests.
15
Data Collection
Students were administered all of the aforementioned measures by trained examiners,
with background experience in speech-language pathology, education, and psychology) in a
quiet classroom at the school. The writing samples and Editing task were completed in small
groups, while all other measures were administered individually. Measures were counterbalanced
to minimize test order effects. Assessments took place 1-3 sessions for each student. The
narrative writing samples were transcribed, coded, and checked for reliability by separate
investigators.
16
CHAPTER THREE
RESULTS
Data from all measures were inputted into PASW Statistics software. Descriptive
statistics were used to identify the means and ranges, and to characterize participants’
performance on all measures. Analysis of variance (ANOVA) was used to determine group
differences on all measures. Although no group differences were found on any measures by
gender or race (all p > .05), differences by DELV-ST category were found for each assessment
type. Correlational analyses were completed to determine the relations between dialect usage,
executive functioning, metalinguistic awareness, and language skills. Hierarchal multiple
regressions were conducted to determine whether dialect usage, executive functioning, and/or
metalinguistic awareness were predictors of dialect awareness ability, after controlling for
language performance.
Differences in Dialect Awareness, Executive Functioning, Metalinguistic Awareness, and
Language Based on DELV-ST Category of Dialect Usage
Descriptive statistics by DELV-ST category and grade are presented in Table 1 to
examine differences in performances in all areas. Due to small sample size, all additional data
analyses were collapsed across grade levels.
Dialect usage. The narrative writing samples and the DELV-ST were analyzed to
identify the characteristics and features of NMAE dialect that were used. The average number of
words used in the narrative writing sample was 108.46 (SD = 49.52); narrative samples ranged
from 24 to 228 words. An average of 3.01 (SD = 2.64) AAE morphosyntactic features were used
within the narratives, with a range of 0 to 13 features. The average DDM for the narratives was
3.09% (SD = 2.42), with a range of 0 to 9.8%. To further characterize participants using Part I of
the DELV-ST, 44.8% of the participants demonstrated strong variation from MAE (DVAR M =
60.51, SD =20.02); 13.8% demonstrated some variation from MAE (DVAR M = 33.26, SD =
14.35); while 41.4% demonstrated use of MAE (DVAR M = 10.89, SD = 8.27). Of the 46
students in the MAE group, six students did not use dialect at all on the DELV-ST and seven
students did not use any dialect features in the narrative writing sample. Therefore,
approximately 80% (29 students) still demonstrated some instances of AAE use, even though
they were classified as MAE speakers by the DELV-ST.
17
The most frequently occurring NMAE features in the narrative writings were zero past
tense, zero plural, subject-verb agreement, preiterite had, zero article, and zero possession (see
Figure 1). Although dialect features were more frequently used in the writings of fourth grade
students (M = 3.64, SD = 3.40), second grade students had higher average DDMs (M = 3.38%,
SD = 2.29) based on shorter samples. The frequency of NMAE features by grade level can be
found in Figure 2.
18
Table 1
Mean Performance on all Measures by Grade, DELV-ST Category, and Overall
2
n = 25
Mean
(SD)
31.44
(24.40)
Sample
Total
Words
77.96
(36.02)
3
n = 40
Mean
(SD)
37.15
(30.42)
110.43
(49.07)
3.00
(2.58)
3.09
(2.42)
57.34
(27.92)
20.55
(5.00)
3.15
(1.51)
13.45
(14.46)
92.48
(13.99)
29.00
(6.79)
23.78
(4.49)
18.20
(8.13)
107.75
(24.98)
4
n = 22
Mean
(SD)
39.95
(26.55)
139.55
(44.28)
3.64
(3.40)
2.74
(2.61)
55.60
(22.70)
21.23
(4.24)
3.91
(1.38)
17.59
(12.75)
94.64
(17.58)
29.82
(7.51)
24.36
(3.57)
21.41
(6.28)
118.00
(26.03)
MAE
n = 36
Mean
(SD)
10.89
(8.27)
105.53
(49.47)
1.89
(1.55)
2.21
(2.09)
74.56
(21.81)
22.92
(4.19)
3.44
(1.42)
17.25
(18.31)
97.94
(15.74)
32.22
(4.36)
25.19
(3.59)
21.50
(7.38)
117.81
(23.69)
Some
n = 12
Mean
(SD)
33.26
(14.35)
98.58
(57.52)
2.25
(2.09)
2.61
(2.11)
51.33
(15.26)
18.58
(3.92)
3.50
(1.73)
27.33
(14.08)
88.00
(12.27)
28.50
(4.85)
24.17
(5.29)
15.33
(4.54)
100.50
(17.74)
Strong
n = 39
Mean
(SD)
60.51
(20.02)
114.21
(47.60)
4.28
(3.03)
4.05
(2.49)
43.11
(22.90)
19.51
(4.42)
2.85
(1.44)
14.95
(11.58)
89.31
(14.73)
26.13
(7.37)
22.13
(4.42)
19.23
(7.49)
101.82
(24.66)
Total
n = 87
Mean
(SD)
36.22
(27.72)
108.46
(49.52)
3.01
(2.64)
3.09
(2.42)
57.26
(26.00)
20.79
(4.59)
3.18
(1.50)
17.61
(15.41)
92.70
(15.36)
28.98
(6.55)
23.68
(4.42)
19.63
(7.33)
108.25
(24.55)
DVAR
Total
AAE
DDM%
Editing%
Dialect
Judgment
Memory
Span
Stroop
Interference
WCST
Spoken
MA
CTOPP2
Blending
2.48
(1.81)
3.38
(2.29)
58.58
(26.48)
20.80
(4.33)
2.60
(1.32)
24.28
(17.15)
91.36
(15.84)
28.20
(5.30)
22.92
(5.01)
CTOPP2
Phoneme
Isolation
20.36
(6.61)
CELF
Total
100.48
(20.06)
Note. DVAR = Dialect Variation (Terry, Connor, Thomas-Tate, & Love, 2010), DDM = Dialect Density Measure (Craig &
Washington, 2000), WCST = Wisconsin Card Sorting Test (Heaton & Psychological Assessment Resources, 2003), Spoken MA =
Spoken Morphological Awareness (Apel & Brimo, 2011), CTOPP2 = Comprehensive Test of Phonological Processing-2 (Wagner,
Torgesen, & Rashotte, in press), CELF Total = Clinical Evaluation of Language Fundamentals – 4 (Sum of raw scores from Recalling
Sentences; Formulated Sentences; and Word Classes) (Semel, Wiig,& Secord, 2003).
19
Frequency of NMAE Features
60
Frequency
50
40
30
20
10
0
PRO DMK FSB HAD ART NEG SVA UPC ZAR COP ING
PST
ZPL
POS
ZPR
ZTO
NMAE Features
Figure 1. Frequency of NMAE features used in narrative writing samples for all participants.
Note. PRO = appositive pronoun, DMK = double marking, FSB = fitna/sposeta/bouta, HAD = preterite had, ART =
indefinite article, NEG = multiple negation, SVA = subject-verb agreement, UPC = undifferentiated pronoun case,
ZAR – zero article, COP = zero copula/auxiliary, ING = zero –ing, PST = zero past tense, ZPL = zero plural, POS =
zero possessive, ZPR = zero preposition, ZTO = zero to (Thompson, Craig, & Washington, 2004).
Frequency of NMAE Features by Grade
30
Frequency
25
20
2nd
15
3rd
10
4th
5
0
PRO DMK FSB HAD ART NEG SVA UPC ZAR COP ING PST ZPL POS ZPR ZTO
NMAE Features
Figure 2. Frequency of NMAE features used in narrative writing samples by grade level.
Note. PRO = appositive pronoun, DMK = double marking, FSB = fitna/sposeta/bouta, HAD = preterite had, ART =
indefinite article, NEG = multiple negation, SVA = subject-verb agreement, UPC = undifferentiated pronoun case,
ZAR – zero article, COP = zero copula/auxiliary, ING = zero –ing, PST = zero past tense, ZPL = zero plural, POS =
zero possessive, ZPR = zero preposition, ZTO = zero to (Thompson, Craig, & Washington, 2004).
20
Dialect awareness. As suspected, there were significant group differences by DELV
category on the Editing Task and Dialect Judgment Task (F2,84 = 20.403, p = .000, ηρ2 = .33 and
F2,84 = 7.843, p = .001, ηρ2 = .16 respectively). Students who were categorized as MAE speakers
performed significantly better than students in the strong and some variation from MAE groups
on both dialect awareness tasks. No significant differences were found between the strong and
some variation from MAE groups.
Executive functioning. No significant differences were found on the Memory Span task
based on DELV category (F2,84 = 1.857, p = .162, ηρ2 = .04), but significant differences were
present on the Stroop and WCST (F2,84 = 3.127, p = .049, ηρ2 = .07 and F2,84 = 3.854, p = .025,
ηρ2 = .08, respectively). Students who were categorized as having some variation from MAE
obtained higher scores on the Memory Span task than the other two groups (M =3.50, SD =
1.73), those with strong variation from MAE had less interference on the Stroop task than the
other two groups (M = 14.95, SD =11.58), and MAE speakers achieved higher scores on the
WCST than the other two groups (M = 97.94, SD = 15.74). No other group differences were
found.
Metalinguistic awareness. Significant differences were found by DELV category on the
Spoken Morphological Awareness task, CTOPP-2 Blending, and CTOPP-2 Phoneme Isolation
tasks (F2,84 = 9.811, p = .000, ηρ2 = .19, F2,84 = 5.016, p = .009, ηρ2 = .11, and F2,84 = 3.481, p =
.035 ηρ2 = .08, respectively). Those who were categorized as MAE speakers achieved higher
scores on all three tasks than those in the strong or some variation from MAE groups (M = 32.22,
SD = 4.36; M = 25.19, SD = 3.59; M = 21.50, SD =7.38, respectively). No significant differences
were found between the strong and some variation from MAE groups.
Overall language. The raw scores from each CELF-4 subtest (Recalling Sentences,
Formulated Sentences, and Word Classes) were totaled to obtain a composite language score. A
1 x 3 ANOVA showed significant differences by DELV category on CELF-4 performance (F2,84
= 5.108, p = .008, ηρ2 = .11). Mainstream speakers achieved significantly higher scores than
those in the strong or some variation from MAE groups on the CELF-4 (M = 117.81, SD =
23.69). No significant differences were found between the strong and some variation from MAE
groups.
21
Relations Between Dialect Usage, Dialect Awareness, Executive Functioning, Metalinguistic
Awareness, and Language
A correlational analysis was completed to determine the relation between dialect usage,
dialect awareness, executive functioning, metalinguistic awareness, and language skills (see
Table 2). Significant small to moderate correlations were found among all of the dialect usage
and dialect awareness measures. Small, inverse relations were seen among the Dialect Judgment
task and the measures of dialect usage (DELV-ST, DVAR, and DDMs), which may be explained
as higher levels of dialect usage resulted in decreased performance on the Dialect Judgment task.
Additionally, negative relations were found among both dialect usage measures (DVAR and
DDMs) and the executive functioning, metalinguistic awareness, and language measures.
Students who used more dialect had decreased scores in all other areas. Language performance
on the CELF was correlated significantly and positively with all of the executive functioning and
metalinguistic awareness measures except for the Stroop task.
22
Table 2
Correlations Among Dialect Use, Dialect Awareness, Executive Functioning, Metalinguistic Awareness and Language
1
2
3
4
5
6
7
8
9
10
11
1. DELV
Category
-
2. DVAR
.836**
-
3. DDM%
.356**
.499**
-
4. Editing%
-.563**
-.600**
-.471**
-
5. Dialect
Judgment
-.343**
-.315**
-.336**
.590**
-
6. Memory Span
-.189
-.211*
-.273*
.416**
.305**
-
7. Stroop
Interference
-.073
-.108
-.009
-.030
-.204
.050
-
8. WCST
-.261*
-.247*
-.261*
.476**
.410**
.344**
-.086
-
9. Spoken MA
-.434**
-.555**
-.540**
.663**
.515**
.404**
-.052
.460**
-
-.324**
-.415**
-.327**
.491**
.219*
.390**
.098
.152
.481**
-
-.141
-.189
-.249*
.381**
.314**
.260*
-.282**
.203
.355**
.180
-
-.302**
-.377**
-.423**
.661**
.530**
.597**
-.108
.392**
.731**
.388**
.341**
10. CTOPP2
Blending
11. CTOPP2
Phon Isolation
12. CELF Total
Note. **. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
23
12
-
Dialect Usage, Executive Functioning, Metalinguistic Awareness, Language and Their
Contribution to Dialect Awareness
Four multiple regression analyses were conducted to determine which dialect usage
(DVAR and DDMs), executive functioning (shifting, inhibition, or working memory) and
metalinguistic awareness (morphological awareness, blending, phoneme isolation) measures
were the greatest predictors of dialect awareness as measured by both the Editing and Dialect
Judgment tasks. The CELF-4 composite score was entered first into all four regression models to
control for language ability variation. Age was added into the model in the second step. In the
third step, either DVAR or DDMs was added as the dialect usage measure. The executive
functioning tasks were added in step four and the metalinguistic awareness tasks in step five.
In the first regression analysis (see Table 4), Editing task performance was used as the
dependent variable and DVAR as the measure of dialect usage. The overall model accounted for
68.4% of the variance in Editing task performance, while language accounted for 43.7% of the
variance alone. Age accounted for an additional 5.3% and DVAR significantly explained an
additional 11.7% of variance above language performance and age. In the last two steps, WCST
(additional 3.7 % of unique variance) and CTOPP2 Blending (additional 4% of unique variance)
performance were the only significant predictors.
In the second regression analysis (see Table 5), the Editing task remained as the
dependent variable, but DDMs was used as the measure of dialect usage. This model accounted
for 64.5% of the variance in Editing task performance, which is less than the previous model.
DDMs explained an additional 4.5% of variance above language ability and age (49%). These
findings suggest that DVAR is a better predictor of Editing task performance. As with the first
model, WCST and CTOPP2 Blending were the only significant predictors among the executive
functioning and metalinguistic awareness tasks.
24
Table 4
Hierarchal Multiple Regression Analyses Predicting Editing Task Performance Among DVAR,
Executive Functioning and Metalinguistic Awareness Variables, Controlling for Language
Variable
Step 1
CELF Total
Step 2
Age
Step 3
DVAR
Step 4
Memory Span
Stroop Interference
WCST
Step 5
Spoken MA
CTOPP2 Blending
CTOPP2 Phoneme
Isolation
ΔR2
.437
Β
.661
.490
-.231
.607
-.375
.644
.024
-.049
.198
.684
.018
.188
.121
Sig.
.000
.000
.000
.004
.000
.000
.000
.776
.492
.009
.000
.877
.019
.102
Table 5
Hierarchal Multiple Regression Analyses Predicting Editing Task Performance Among DDMs,
Executive Functioning and Metalinguistic Awareness Variables, Controlling for Language
Variable
Step 1
CELF Total
Step 2
Age
Step 3
DDMs
Step 4
Memory Span
Stroop Interference
WCST
Step 5
Spoken MA
CTOPP2 Blending
CTOPP2 Phoneme
Isolation
ΔR2
.437
Β
.661
.490
-.231
.535
-.233
.575
.003
-.016
.218
.645
.098
.236
.118
Sig.
.000
.000
.000
.004
.000
.006
.000
.973
.833
.009
.000
.412
.005
.130
25
In the last two hierarchal multiple regression models, the Dialect Judgment task was used
as the dependent variable to represent dialect awareness. In Table 6, DVAR was used as the
measure of dialect usage. The regression analysis accounted for only 41.3% of the variance in
Dialect Judgment performance, while language performance represented 28.1% of the entire
model and age accounted for another 3.2% of variance. DVAR accounted for an additional 0.9%
of variance above language performance and age. While steps 3 and 4 were significant, the only
significant individual predictors were the Stroop and WCST tasks.
In the final regression analysis (see Table 7), DDMs was used as the measure of dialect
usage and the overall model accounted for 41.6% of the variance in Dialect Judgment
performance. Similar to the previous model, DDMs accounted for only 1.5% of the variance
above language skills and age. Additionally, only the Stroop and WCST tasks were the only
significant individual predictors.
Table 6
Hierarchal Multiple Regression Analyses Predicting Dialect Judgment Task Performance Among
DVAR, Executive Functioning and Metalinguistic Awareness Variables, Controlling for
Language
Variable
Step 1
CELF Total
Step 2
Age
Step 3
DVAR
Step 4
Memory Span
Stroop Interference
WCST
Step 5
Spoken MA
CTOPP2 Blending
CTOPP2 Phoneme
Isolation
ΔR2
.281
Β
.530
.313
-.179
.322
-.102
.400
-.005
-.205
.204
.413
.159
-.012
.061
Sig.
.000
.000
.000
.051
.000
.310
.000
.965
.029
.037
.000
.307
.913
.543
26
Table 7
Hierarchal Multiple Regression Analyses Predicting Dialect Judgment Task Performance Among
DDMs, Executive Functioning and Metalinguistic Awareness Variables, Controlling for
Language
Variable
Step 1
CELF Total
Step 2
Age
Step 3
DDMs
Step 4
Memory Span
Stroop Interference
WCST
Step 5
Spoken MA
CTOPP2 Blending
CTOPP2 Phoneme
Isolation
ΔR2
.281
Β
.530
.313
-.179
.328
-.136
.404
-.011
-.199
.202
.416
.149
-.007
.056
Sig.
.000
.000
.000
.051
.000
.173
.000
.920
.032
.038
.000
.330
.949
.576
27
CHAPTER FOUR
DISCUSSION
The main purpose of this investigation was to determine the factors that contribute to
dialect awareness to provide more information on why some students have increased awareness,
while others do not. We were interested in whether any differences existed in dialect awareness,
executive functioning, metalinguistic awareness and language performance based on the
category of dialect usage. In addition, we examined the relations between dialect use, dialect
awareness, executive functioning, metalinguistic awareness, and language. Finally, it was of
interest to determine whether dialect use, executive functioning, and/or metalinguistic awareness
predicted performance on a dialect awareness measure (Editing and Dialect Judgment tasks)
above language skills. In line with the same question, we also wanted to know whether the
measure used to categorize dialect use (DVAR and DDMs) impacted the ability to predict dialect
awareness.
Differences in Dialect Awareness, Executive Functioning, Metalinguistic Awareness, and
Language Based on DELV-ST Category of Dialect Usage
Findings indicated that category of dialect usage affected performance on the measures of
executive functioning, metalinguistic awareness, and language. Students who used NMAE
features more frequently in their writing, and as determined by the DELV-ST, demonstrated
overall lower performance than their peers on measures executive functioning, metalinguistic
awareness, and language. This finding differs from previous studies where high dialect users and
mainstream speakers were found to have higher language and literacy related performance
(Connor & Craig, 2006; Terry et al., 2010).
There are several possible reasons for the differences between previous studies and the
current investigation. First, the way the dialect is measured has differed across studies. In the
current study, dialect was measured through a narrative writing sample and the DELV-ST.
Previous studies have used oral narratives and sentence imitation tasks, which do not represent
the same context or have the same language demands as a written task. Both oral and written
narratives are open-ended tasks; students are expected to formulate thoughts on a topic and
connect them in a narrative. These narratives may also vary in length by each participant, as they
are typically untimed. Sentence imitation tasks are more structured; students have to hold
28
information in their short-term memory before it is repeated back. The scoring of sentence
imitation tasks is more straightforward as students either repeat the item verbatim or not, while
oral and written narrative scoring is more subjective. The demands for written tasks are even
more complex as developmental errors in typical writing and spelling performance may occur.
Currently, there is no consensus in the existent research base for determining when an error is
developmental or not, other than its variable use. Given this, some of the errors seen in the
narrative writings of the participants may be developmental patterns.
Second, the manner in which NMAE category was assigned may further explain
differences. The categories assigned by the DELV-ST were used in the current study.
Interestingly, the DELV-ST categorized 41.4% of the sample as MAE speakers, although only
12.6% of the total sample did not use any dialect features in their writing. Though the DELV-ST
is the only current standardized measure for categorizing speakers, more information may be
needed to determine how the student is actually using dialect. The other method used to
categorize dialect speakers in the past was DDMs (Craig & Washington, 2000). Although DDMs
take longer to analyze and calculate, they may offer additional valuable information for those
who work with students beyond the category provided by the DELV-ST. As DDMs are
calculated based on a language sample, their utility is subject to the size of the sample (e.g.,
number of words used). Dialect users who have longer oral or written samples may have more
opportunities to use AAE features. At this time, there is no consensus in the field for how dialect
should be measured. Results may have differed if participants were divided into groups based on
their DDMs; however, there is no consensus on what percentage of DDM should be used as
cutoff scores.
Relations Between Dialect Usage, Dialect Awareness, Executive Functioning, Metalinguistic
Awareness, and Language
In the current study, increased performance on executive functioning, metalinguistic
awareness, and language tasks was related to increased dialect awareness performance. The
findings on the relation between language skills and dialect awareness are similar to those of
previous investigations (Charity et al., 2004; Craig & Washington, 2004; Craig et al., 2009).
Although there was an inverse relation between dialect usage and dialect awareness, it is
important to point out that there are two possible interpretations for this finding. The first
29
interpretation may simply be that strong dialect speakers have lower performance on dialect
awareness measures, as well as in the areas of executive functioning, metalinguistic awareness,
and language ability. The second interpretation may be that these students are unaware of the
appropriate times when they should switch to MAE. Evidence that may support this
interpretation is the performance of strong dialect users on the Editing, Dialect Judgment, and
Spoken Morphological Awareness tasks. Fourteen students who were categorized as strong
dialect users on the DELV-ST, achieved scores above the overall participant mean score of 57.26
on the Editing task. On the Dialect Judgment task, strong dialect users on average were able to
identify whether an item was one they would read in a book on more than half of the occasions
(19.51 of 30 items). These strong dialect speakers also could provide grammatical endings and
other morphemes necessary to complete sentences in the Spoken Morphological Awareness task
more than half of the time on average (26.13 of 40 items). This demonstrates that these students
do have knowledge of the same endings they tend to use variably within their writing.
Language was correlated positively with all of the executive functioning and
metalinguistic awareness measures except for the Stroop task, which may have been due to
Stroop interference scores being used in the analyses as opposed to raw scores for each section of
the task. The results suggest that as interference decreased on the Stroop, language performance
increased, which mirrors the strong relation between language and the other tasks. Our findings
extend those of other investigations as executive functioning has not been previously measured
in relation to dialect use and dialect awareness.
Dialect Usage, Executive Functioning, Metalinguistic Awareness, Language and Their
Contribution to Dialect Awareness
The measures used to categorize dialect usage, as well as the measures of dialect
awareness, impacted the relations between executive functioning, metalinguistic awareness,
language ability and dialect awareness. Overall language skills played a substantial role in dialect
awareness, while age, executive functioning and metalinguistic awareness played minor roles.
Executive functioning and metalinguistic awareness are language-based skills, so it was expected
that these tasks would share some variance with language performance. It is important to point
out that the skills measured by executive functioning and metalinguistic awareness did
significantly tap into a portion of the unique variance in dialect awareness performance above
30
what was explained by the CELF-4. This finding lends additional support to the importance of
having good language overall.
More variance in dialect awareness was accounted for in the models that used the Editing
task as the dependent variable. The results may have differed between dialect awareness tasks as
they measured ability in two different ways; the Editing task required an expressive response
while the Dialect Judgment task was a receptive measure. The Dialect Judgment task may have
been easier for students as only a yes/no response was required; whereas in the Editing task,
students had to not only identify the grammatical feature that needed to be changed, but also
make the necessary changes to reflect the language they would read in a book at school. These
different contextual demands may have allowed the Dialect Judgment task to be less sensitive at
predicting dialect awareness.
In the regression models where Editing task performance was predicted, DVAR
significantly accounted for more variance among the other variables, while DDMs did not. When
the Dialect Judgment task was used as the dependent variable to represent dialect awareness,
neither DVAR nor DDMs were significant variables. This may be due to dialect usage being
measured in two different contexts and the decreased sensitivity of the Dialect Judgment task.
In the models that used the Editing task as the measure of dialect awareness, performance
on the WCST and CTOPP-2 Blending subtest were the only significant executive functioning
and metalinguistic awareness variables that explained unique variance. These findings may be
due to several factors. First, the process of cognitive shifting on the WCST (the ability to switch
between mental states, operations, or tasks) is very similar to what dialect users must do on a
regular basis. Students must switch between the mental states of dialect and MAE dependent
upon the setting. Second, executive functioning is an umbrella term that is comprised of many
cognitive skills that are measured in multiple ways. Although the types of tasks used in this
investigation are typically used in studies related to cognition, other tasks (e.g., self-regulating,
updating/monitoring, and planning) may have led to different results. Finally, performance on
metalinguistic awareness tasks may be dependent on overall language performance as it is a
language-based skill. The CTOPP-2 Blending subtest may have been a significant variable in
predicting dialect usage above language because its design removes some of the linguistic
demands that are present in the other tasks. For example, in the Blending subtest students were
31
only required to blend phonemes (e.g. What word do these sounds make? s – u – n). On the other
hand, in the CTOPP-2 Phoneme Isolation task, students had to isolate phonemes based on
location (e.g., What is the fourth sound in the word trips?), which provided an added linguistic
demand.
Limitations
There were several limitations to this study that could have impacted the results and its
generalizability. First, the sample consisted of students in one geographical area; therefore,
results may differ in other regions where dialect usage varies. Second, the sample size was small.
A larger number of participants would have allowed for more sophisticated analyses to be
completed, including differences between grades. Third, DDMs were measured based on the
written context. As developmental writing and spelling errors may occur within this context, the
results may have differed if NMAE usage was measured in a different manner. Finally, there are
multiple ways to measure executive functioning and metalinguistic awareness. Other tasks may
be needed to document how well students perform on measures as complex as these (e.g.,
executive functioning - self-regulating, updating/monitoring, and planning; metalinguistic
awareness - elision, segmenting, relatedness, and non-word sentence completion).
Future Research and Implications
The phenomenon of dialect shifting is a complex task to measure. As NMAE is used
variably, it is difficult to measure and even more difficult to determine whether shifting is
occurring. Dialect shifting has been measured previously as the difference of dialect usage in
different contexts, such as writing, oral narratives, and oral reading (Craig et al., 2009;
Thompson et al., 2004). In this investigation, dialect awareness was measured as the ability to
edit sentences to reflect MAE and the ability to determine whether a sentence would be read in a
book at school. Once a consensus is reached on how to best measure this skill, we can move
towards determining how to most effectively help students. At that point, educators and speechlanguage pathologists can develop effective educational programs that will allow students to use
language based on appropriateness of the setting more spontaneously. It appears that strong
language skills are the first step in achieving this goal. Given the significant relation between
language skills and dialect awareness found in this study, it would seem that any dialect
awareness instruction should include a focus on language development.
32
The results showed that students who were categorized as MAE speakers were able to
outperform strong dialect speakers in all areas. Only seven of the 35 speakers in the MAE group
used no dialect at all, while the rest of the group used minimal instances of dialect on the DELVST and within their narrative writing samples. Thus, it is not a total absence of NMAE use that
relates to higher language, executive functioning, and metalinguistic awareness skills. Based on
the community and school environment, we would expect some level of dialect use from these
students. The students in the MAE category may have been using dialect upon school entry and
decreased these levels after several years of school instruction. Therefore, the dialect speakers
who are able to decrease their use of dialect in school settings will have a greater opportunity at
academic success.
Strong dialect speakers have knowledge of the grammatical endings they tend to leave off
as witnessed by their performance on the Spoken Morphological task. Therefore, the issue for
some dialect speakers may not be that they do not have a good understanding of MAE; rather,
they may not understand the appropriate time or situations when dialect usage should be
inhibited. These students may benefit from a dialect awareness program that focuses on the
grammatical features of both NMAE and MAE through contrastive analysis, the settings in
which they should be used, and targets executive functioning and metalinguistic awareness skills.
Preliminary evidence from Thomas-Tate, Connor, Johnson, and Underwood (in press),
demonstrate the effectiveness of a program of this sort. In other communities (e.g., GreekCyprus), bidialectal education has successfully been viewed as an opportunity to use home
language to teach the mainstream variation (Yiakoumetti, 2006).
Although executive functioning and metalinguistic awareness may not have explained the
majority of unique variance in dialect awareness performance, they did significantly explain
variance above the contribution of language skills. The findings of this investigation are a good
start for examining at other variables that may impact dialect awareness. In the future,
researchers should investigate other variables that may impact dialect awareness, including home
factors (e.g., parental education, household income) and school environment (e.g., whether
dialect is spoken by the teacher, teacher experience, quality of instruction, and school and
community diversity). Continuous investigation of dialect awareness will lead to further
33
understanding the relationship between this skill and literacy measures and determining whether
promoting dialect awareness will lead to improved academic success.
34
APPENDIX A
Writing Sample Prompt
Write a story about what you think happened before the cake was on the boy’s face.
Write Your Story Here:
35
APPENDIX B
Morphosyntactic types of child AAE used by participants with examples
Definition
Appositive pronoun –
Both a pronoun and a noun, or two pronouns, used to
signify the same referent
Double marking –
Multiple agreement markers for regular nouns and
verbs, and hypercorrection of irregulars
Fitna/sposeta/bouta Abbreviated forms coding imminent action
Preterite had –
Had appears before simple past verbs
Code
PRO
Example
“and the other people they wasn’t”
DMK
Indefinite article –
A is used regardless of the vowel context
Multiple negation –
Two or more negatives used in a clause
Subject-verb agreement –
Subjects and verbs differ in marking of number
Undifferentiated pronoun case –
Pronoun cases used interchangeably
Zero article –
Articles are variably included
Zero copula/auxiliary –
Copula and auxiliary forms of the verb to be are
variably included
ART
NEG
“he tries to kills him”
“they are taking the poor hitted boy to a
hospital”
“he fitna be ten”
“he bouta fall”
“he flew with a strong stick in his claws
while the turtle had held the stick fast in
her mouth”
“one day she met a eagle traveling to faraway lands across the sea”
“it not raining no more”
SVA
“Our cat Mimi like to sit on the roof”
UPC
“her fell”
ZAR
“this cake is (the) best present of all”
COP
Zero –ing –
Present progressive –ing is variably included
Zero past tense –
-ed markers are variably included on regular past
verbs and present forms of irregulars are used
Zero plural –
-s is variably included to mark number
Zero possessive –
Possession coded by word order so –s is deleted or
the case of possessive pronouns is changed
Zero preposition –
Prepositions are variably included
Zero to –
Infinitival to is variably included
ING
“but she always comes down when it (is)
time to eat”
“then you’(ll) have to wear the brown
ones instead”
“It was go(ing) to be a good birthday”
FSB
HAD
PST
“as soon as she open(ed) her mouth, she
fall straight into the ocean below”
ZPL
“Father went out to buy some pretty
flower ”
“The boy’(s) grandmother showed him
how to put worms on the hook so they
would not come off”
“she sits and looks (at) birds”
POS
ZPR
ZTO
“that man right there getting ready
on his one foot”
Adapted from “Variable Production of African American English across Oracy and Literacy
Contexts, by C.A. Thompson, H.K. Craig, and J.A. Washington, 2004, Language, Speech, and
Hearing Services in Schools, 35, 269-82. Copyright 2004 by the American Speech-LanguageHearing Association.
36
slip
APPENDIX C
Editing Task
Directions: Here are some sentences that you will have to change into sentences like you
would read in a book. Read each sentence carefully and identify the part that is not
correct. Rewrite the sentence on the line below.
Practice: The girl is ride her bike.
Practice: We going to the mall.
1. Kim made 2 cake for the party.
2. Mom bake chicken last night for dinner.
3. The shoes was too tight.
4. Shay had walked across the street.
5. Mark went to his friend house.
6. They watching TV in the back room.
37
7. I had cleaned up my room after playing.
8. All of the teacher were in a meeting.
9. She were late for the party.
10. He happy that it was finally spring break.
11. Please do not touch John shoes.
12. Last summer we plant flowers in the garden.
38
APPENDIX D
Scoring Rubric for the Editing Task
Student ID:
Grade:
Key:


1 if point assigned
X if any other category
Scorer 1:
Date:
Scorer 2:
Date:
*Points should only be assigned when student edits the sentence by changing the target dialect feature.
*Items where students correct the sentence, but not the dialect feature, will not be included in their total items
overall.
Feature
targeted
1
2
3
4
5
6
7
8
9
10
11
12
Correct
form used
(student
edited
target
form)
*add into
total*
Incorrect
Copula
included,
but not the
correct
form
*add into
total*
Changed the
sentence, but
still
grammatically
correct
Changed the
sentence, but
agrammatical
plural
(cakes)
past
(baked)
SVA
(were, are)
had
(had
walked)
possessive
friend’s
copula
(are, were)
had
(had
cleaned)
plural
(teachers)
SVA
(is, was)
copula
(is, was)
possessive
John’s
past
(planted)
Items Correct:
Total Items Overall:
Percentage Correct:
39
Additional
dialect
seen/or
other
comments
APPENDIX E
Dialect Judgment Task
Directions: Listen to each sentence below and decide whether you think it is something you
would read in a book at school or not. If you think it would be in a book, circle yes. Otherwise,
circle no. You will have 3 minutes to answer as many items as you can.
Practice: She plant the flowers last spring.
Examiner: Is this something you would read in a book at school?
If correct: You are right, you would not read this in a book at school because –ed is left off of
plant.
If incorrect or doesn’t understand: The words “last spring” in the sentence give us a clue that
“plant” should be past tense. The –ed is left off, so that means you would not read this in a book
at school.
Practice: Jenny had five pieces of gum.
Examiner: Is this something you would read in a book at school?
If correct: You are right. Jenny had five pieces of gum. This is a sentence we would read in a
book at school.
If incorrect or doesn’t understand: We would read this sentence in a book at school because
the grammatical ending –s is used on “pieces” to show that there is more than one.
1. Paul going to the store.
Yes
No
2. Kate look all over for the missing bear.
Yes
No
3. I would like to use one of Brooke’s pencils.
Yes
No
4. The girls shopped for hours yesterday.
Yes
No
5. They is running late for the meeting.
Yes
No
6. The team was so happy when they won the game.
Yes
No
7. This the best movie I have ever seen.
Yes
No
8. Please do not eat your sister’s cookie.
Yes
No
40
9. Jake wished that he could have a pet.
Yes
No
10. The two boy like to play basketball.
Yes
No
11. They going to the beach for spring break.
Yes
No
12. The cat had five beautiful kittens.
Yes
No
13. We were having fun at the park.
Yes
No
14. I is the best speller in my class.
Yes
No
15. The two sisters share a bedroom.
Yes
No
16. John wanted to borrow Mike shoes.
Yes
No
17. I cleaned my room before playing outside.
Yes
No
18. Dan like to be the first person in line.
Yes
No
19. Grandma is watching the news.
Yes
No
20. I want to go to my friend house after school.
Yes
No
21. Shay had finished her dinner first.
Yes
No
22. All of the boys got in trouble for running in the house.
Yes
No
23. Kelly walked the dog before dinner.
Yes
No
24. Dad had washed the car on Saturday.
Yes
No
25. Sarah wants to join the swim team.
Yes
No
26. The five duck walked in a row across the street.
Yes
No
27. Please put both game away.
Yes
No
28. Rebecca is going over to Michelle’s house to watch television.
Yes
No
29. Mom bake cookies for the bake sale last week.
Yes
No
30. Tonya read the book in four days.
Yes
No
41
APPENDIX F
Memory Span
Materials:
Basal:
Ceiling:
Note:
Prompt:
Sentences
Begin at Item 1
Stop after child misses 3 consecutive items (not including Yes/No errors).
An item is missed if one or more of the last words for an item are incorrect.
Last words do have to be recalled in the correct order for an item to be
considered correct. Record the last words given by the child in the order the child
stated them (we may give partial credit later).
Always say: Tell me the last words after the child answers yes or no to the last
sentence in a set.
If child takes longer than 2 seconds to Y or N urge them to respond.
DIRECTIONS:
I am going to say a short easy question that I can’t repeat so listen carefully.
I want you to answer it with Yes or No, then say the last word in the question.
Ready?
Practice Item A:
Are dogs blue? Y N ________ (after child responds Yes or No,
prompt for the last word by saying: Tell me the last word.)
If correct say: Good! (go to Item B)
If incorrect say: Listen carefully. I will say the question again
and you will tell me the last word in it.
Practice Item B:
Let’s do another one. First answer the question, then tell me
the last word in the question.
Do frogs jump? Y N (Tell me the last word) ________ (use
same correction procedure as above)
Practice Item C:
Now I’m going to say 2 questions in a row. I want you to
answer Yes or No after each question, then when you finish
answering the 2nd question tell me the last words in both
sentences in the order I gave them to you.
Do cats eat? Y N
Do birds fly? Y N (Tell me the last words) _______ /_______
If correct say: Good! (go to item D)
If incorrect say: Not quite. The questions were “do cats eat and
do birds fly’ so the last words were ‘eat’ and ‘fly’.
Practice Item D:
Let’s try this one.
42
Does water burn? Y N
Do people talk? Y N (Tell me the last words) ______ /______
If correct say: Good.
If incorrect say: Not quite. The last words in each question in
the correct order were ‘burn’ and ‘talk’.
TEST ITEMS: (give no feedback)
Stop after 3 consecutive errors, not including Y/N errors.
Record exact responses in blanks, circle Y or N, and + or – beside the item number.
____ 1. Are stoves hot?
Do lamps run?
Y N
Y N (Tell me the last words) ________ / ________
____ 2. Do spiders crawl?
Are men blue?
Y N
Y N (Tell me the last words) ________ /________
____ 3. Is ice cream cold?
Do tables cry?
Y N
Y N (Tell me the last words) ________ / ________
____ 4. Can balls bounce?
Can clocks swim?
Are bears green?
Y N
Y N
Y N (Tell …) _________ / _________ / _________
____ 5. Are forks sharp?
Are cups round?
Can pencils stop?
Y N
Y N
Y N (Tell …) _________ / _________ / _________
____ 6. Can glass cut?
Do ducks sing?
Do birds fly?
Y N
Y N (Tell …) _________ / _________ / _________
____ 7. Is summer hot?
Can chairs walk?
Do girls eat?
Are apples smart?
Y
Y
Y
Y
N
N
N
N (Tell …) ________ /________ /________ /________
____ 8. Are feathers heavy?
Can pigs read?
Is steel hard?
Do bells ring?
Y
Y
Y
Y
N
N
N
N (Tell …) ________ /________ /________ /________
____ 9. Is corn red?
Do fish hop?
Y N
Y N
Y N
43
Do lions roar?
Can cats speak?
Y N
Y N (Tell …) ________ /________ /________ /________
____ 10. Is milk white?
Are coats warm?
Are giants big?
Is rain dry?
Can boats sink?
Y
Y
Y
Y
Y
____ 11. Can trucks swim?
Are turtles fast?
Do boys play?
Are lemons sweet?
Can balloons pop?
Y N
Y N
____ 12. Can paper tear?
Do stars shine?
Are ants huge?
Can stones jump?
Is playing fun?
Y
Y
Y
Y
Y
N
N
N
N
N (Tell …) ______ /______ /______ /______ /______
Y N
Y N
Y N (Tell …) ______ /______ /______ /______ /______
N
N
N
N
N (Tell …) ______ /______ /______ /______ /______
44
APPENDIX G
Spoken MA Task
Directions: For this activity, I will say a word and then a sentence with a missing word. I want
you to think of what word is missing, using the first word that you heard. Let’s try one. “Party –
We went to three birthday ______________.” Now, use ‘party’ to help you think of what word
goes in the blank. Write child’s response below.
Practice: party
We went to three birthday ______________________________
If student gives correct response: Right, the word is ‘parties.’ Party helped you think of the
word parties for that missing word in the sentence.
If student gives incorrect response or doesn’t understand, then say: “We went to three
birthday________. Hmmmm, how can I use party to help me fill in the blank. I know…parties!
That is using party to fill in the blank, We went to three birthday parties.
Let’s try another one: “friend – The substitute teacher was very ____________.” Now, use
‘friend’ to help you think of what word goes in the blank. Write child’s response below.
Practice: friend
The substitute teacher was very ____________.
If student gives correct response: Right, the word is ‘friendly.’ Friend helped you think of the
word friendly for that missing word in the sentence.
If student gives incorrect response or doesn’t understand, then say: “The substitute teacher
was very ________. Hmmmm, how can I use friend to help me fill in the blank. I got
it…friendly! That is using friend to fill in the blank. The substitute teacher was very friendly.
Now let’s do more.
1. Sock
Please put on your shoes and
.
2. Mile
To get home, we must drive 150 more
.
3. Cry
The baby bumped his head so he
.
4. Size
At the store there were shirts of all
.
5. Call
No one answered the phone when I
.
6. Roll
When I touched the bug, it
.
7. Ask
The woman said I could have the toy if I
.
8. Foot
The lady put her slippers on her
.
9. Tooth
The boy had braces on his
.
10. Eat
The man was hungry so he
.
45
11. Lose
The little girl was scared because she was
.
12. Take
I didn’t like the apple I
.
13. Begin
We kept walking until we got back to where we
.
14. Mouse
The man screamed when he saw three
.
15. Hear
The woman spoke up so she could be
.
16. Pay
The workers were happy when they got
.
17. Freeze
It was so cold that the plants outside
.
18. Happy
When the student did not get an A, he was very
.
19. Usual
The children thought the pink and purple cat was very
.
20. Appear
The magician put the rabbit in his hat and made it
.
21. Cycle
Instead of throwing cans away, you should
.
22. Test
Before learning something new, the teacher gave the students a short
23. Dirt
Put your clothes in the laundry basket when they are
.
24. Help
The old man thanked the little girl for being so
.
25. Slow
Of all the animals, the turtle was the
.
26. Take
The man couldn’t sit next to his friend because the seat was
.
27. Danger
Walking in the middle of the road is very
.
28. Drive
For the long car trip, she asked if she could be the
.
29. Direct
The man was lost so he asked the policeman for
.
30. Beauty
The teacher’s painting was very
.
31. Noise
The marching band was very
.
32. Write
The man’s hands shook, so it was difficult to read what he had
.
33. Cheer
Even though his tooth hurt, the boy remained
.
34. Differ
The twins looked so much alike I couldn’t see a
.
35. Measure
Only one student had the ruler, so she was responsible for making the
36. Nerve
Walking on the high bridge made the man
.
37. Educate
My sister went to college to get a good
.
38. Invite
The boy wanted to go to the party but he did not receive an
.
39. Attend
The teacher waved her hand to get the children’s
.
40. Energy
Exercising in the morning made my brother very
.
46
.
.
APPENDIX H
IRB Approval Letter
APPROVAL MEMORANDUM (for change in research protocol)
Date: 9/15/2011
To: Christopher Lonigan
Address: 4301
Dept.: PSYCHOLOGY DEPARTMENT
From: Thomas L. Jacobson, Chair
Re: Use of Human Subjects in Research (Approval for Change in Protocol)
Project entitled: Florida State University
Research and Development Center for Pre-K to 5th Grade Student Comprehension: Examining Effective
Intervention Targets, Longitudinal Intensity, and Scaling Factor
The form that you submitted to this office in regard to the requested change/amendment to your research
protocol for the above-referenced project has been reviewed and approved.
If the project has not been completed by 5/9/2012, you must request a renewal of approval for continuation of the
project. As a courtesy, a renewal notice will be sent to you prior to your expiration date; however, it is your
responsibility as the Principal Investigator to timely request renewal of your approval from the Committee.
By copy of this memorandum, the chairman of your department and/or your major professor is reminded that
he/she is responsible for being informed concerning research projects involving human subjects in the
department, and should review protocols as often as needed to insure that the project is being conducted in
compliance with our institution and with DHHS regulations.
This institution has an Assurance on file with the Office for Human Research Protection. The Assurance Number is
FWA00000168/IRB number IRB00000446.
Cc:
HSC No. 2011.6972
47
Appendix I
Christopher J. Lonigan, Ph.D.
Phone: 850-644-7241
850-645-4816
[email protected]
The Florida State University
Tallahassee, Florida 32306-4301
Department of Psychology
850/644-2040
850/644-7739 (fax)
Informed Consent/Participation Form
Title: DEVELOPING READING FOR UNDERSTANDING (PARENT CONSENT)
Investigators: CHRISTOPHER J. LONIGAN, PH.D.
Telephone: (850) 644-7241
CAROL CONNOR, PH.D.
Telephone: (850) 228-7006
Dear Parent:
How can we be more effective teaching our children how to understand what they read?
For the past several years, this question has been at the center of our research. We are writing to invite you and
your child to participate in a study of how to teach students to better understand what they are reading. To do this,
we will be developing assessments and interventions that will help us understand and improve the strategies your
child uses to comprehend. Some of these activities are on the computer and others are paper and pencil. Still others
are interventions. Our goal is to design interventions that will improve students’ reading comprehension skills,
which,
as you know, are very important. If you decide to participate, your child may participate in one or more of the
following educational or assessment activities:
•
Read short paragraphs and sentences on a computer that shows where your child is looking at any moment
•
Listen to or read sentences and then choose pictures on the computer
•
Participate in activities designed to improve sensitivity to differences between conversational
speaking and the more formal grammar used when reading or writing
•
Participate in activities designed to improve vocabulary and grammar when talking, reading or writing
•
Participate in activities designed to increase understanding of science and social studies concepts,
including understanding books about science and social studies.
•
Participate in activities designed to improve your child’s understanding about how different kinds of text are
organized, called text structure.
•
Participate in assessments designed to determine whether your child’s skills and understanding
improved after participating in the activities.
•
Each assessment for this project will be conducted in 25 to 40 minute sessions (based on your child’s age).
We will arrange sessions with your child’s classroom teacher to limit disruption of ongoing activities.
•
We may use assessment information about your child that is collected by school district teachers and staff
(e.g., scores on FAIR, DIBELS, PPVT, & SAT10, which are measures of
•
Participate in activities designed to improve understanding of fiction and non-fiction text
FSU Human Subjects Committee Approved on 5/12/11. Void after 5/09/12. HSC# 2011.6393
48
language, reading, and mathematics). Some of this information will be accessed through the Progress
Monitoring Reporting Network (PMRN) and through district records using your child’s identification
numbers; we will continue to monitor your child’s progress on these measures.
In each of the coming years, we will send you a reminder of these follow-up assessments, at which time you
may decline continued participation.
You may be asked to complete questionnaires seeking information about your child and family. You do not need to
answer any questions that you do not want to answer. Parents who complete the questionnaires for this study will
receive a gift card to a local retailer (e.g., Publix, Target) worth between 10 and 20 dollars (depending on the
number and length of questionnaires completed).
We know of no risks associated with your or your child's participation in this project.
Your participation is completely voluntary. You do not have to participate if you do not want to. Your decision
whether to participate or not will have no effects on any other treatment or services for which you are eligible from
Florida State University or your child’s school. You may change your mind and withdraw from this project at any
time without penalty. There are no risks associated with withdrawal from this project.
All information collected for this project will be kept confidential to the extent allowed by law. Confidentiality will
be ensured in the following ways: In public reports of the results of this project, we will only report results that
have been averaged over large numbers of children. No child or family will ever be identified publicly.
Assessments of your child's skills are solely for research purposes. These assessments and other information
gathered on your family and child will be kept in a locked file storage area in project offices at the Department of
Psychology at Florida State University, identified only by a code, and will not be available to your child's school or
to any other person or institution unless you ask us in writing to do so. Identifying information will be retained for
a period of up to 2 years following completion of this project. It is expected that the project will end by 07/30/15.
Data from this project, with all identifying information removed, will be retained indefinitely.
If at any time you have questions about this project, please contact Dr. Christopher Lonigan (phone: 850-644-7241;
email: [email protected]) or Dr. Carol Connor (phone: 850-228-7006; email: [email protected]) at the
Department of Psychology, Florida State University, (850) 644-7241. A description of the group results of this
project will be sent to you upon request. If you have questions about your rights as a participant in this project, or if
you feel you have been placed at risk, you can contact the Chair of the Human Subjects Committee, Institutional
Review Board, through the Office of the Vice President for Research, at (850) 644-8633.
Reading for Understanding Consent
Page 2
FSU Human Subjects Committee Approved on 5/12/11. Void after 5/09/12. HSC# 2011.6393
49
We hope you will become involved. This study will help us develop better ways to teach reading for understanding;
how to help children learn; and discover what we can do to make sure all children succeed. If you agree to
participate in this research project, please sign and print your name and the name of your child below. Your
signature indicates that you have read the information provided above, or have had it read to you, and that you have
decided to participate. The extra copy is for you. Thank you so much.
Signature and printed name of parent or legal guardian
Today’s Date
Printed name of child
Child's Date of Birth
Please include the following information so that we can contact you regarding project results.
Street Address
Home Phone:
City
Work Phone:
Email Address:
Child’s School
Child’s Teacher and Grade in School
Reading for Understanding Consent
Page 3
FSU Human Subjects Committee Approved on 5/12/11. Void after 5/09/12. HSC# 2011.6393
50
State
Zip
Christopher J. Lonigan, Ph.D.
Phone: 850-644-7241
850-645-4816
[email protected]
The Florida State University
Tallahassee, Florida 32306-4301
Department of Psychology
850/644-2040
850/644-7739 (fax)
Release of
Information Form
Title: DEVELOPING READING FOR UNDERSTANDING
Investigators: CHRISTOPHER J. LONIGAN, PH.D.
CAROL CONNOR, PH.D.
Telephone: (850) 644-7241
Telephone: (850) 228-7006
Dear Parent:
Thank you for agreeing to participate in this project being conducted during the 2010-2011 school year
in your child’s classroom. As a part of your child’s involvement, he or she will be administered a brief
set of measures related to language, listening comprehension, or reading comprehension at least once
during the school year. Many classroom teachers like to have the results of these assessments to help
them with their instructional planning.
This form asks you whether or not you give the FSU project personnel permission to share your child’s
results with his or her classroom teacher. We would provide the results in a secure format to your child’s
teacher. No one outside of the school personnel and the FSU project personnel would have access to your
child’s assessment results.
You are not required to release this information to your child’s teacher. If you signed the consent form but
do not also agree to release the results, your child will still be able to participate in the research project
activities. If you have any questions about this Release of Information Form, or about what would be
shared with the classroom teacher, you can contact the project coordinator, Dr. Elizabeth Crowe at (850)
770-2236 (email:[email protected]).
Please check the box below indicating your decision regarding release of assessment results to your
child’s teacher. Please also complete the signature lines below.

I agree to have project personnel share my child’s assessment results with his or her
teacher.

I do not agree to have project personnel share my child’s assessment results with his or her teacher.
Signature and printed name of parent or legal guardian
Today’s Date
Printed name of child
Child's Date of Birth
FSU Human Subjects Committee Approved on 5/12/11. Void after 5/09/12. HSC# 2011.6393
51
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BIOGRAPHICAL SKETCH
Lakeisha Johnson is a native of Sumter, SC. She received her Bachelor of Arts degree in
Speech Pathology and Audiology from South Carolina State University on May 12, 2006 and her
Master of Science degree in Communication Science and Disorders from Florida State
University on August 9, 2008. Her Doctor of Philosophy degree from Florida State University is
expected in August 2012. Lakeisha’s areas of interest include African American English,
language and literacy skills in at-risk students, and childhood language disorders. Post
graduation, she will pursue a tenure-track faculty position in the university setting.
56