ABSTRACT LAMPRON, LAUREN ANNELIES. the Perceptions of Parents of Children Who Live in the Attendance Zone of a Title I Elementar

ABSTRACT

LAMPRON, LAUREN ANNELIES. The Perceptions of Parents of Children Who Live in the Attendance Zone of a Title I Elementary School Regarding the Implementation of Word Analysis Technology. (Under the direction of Dr. Bonnie C. Fusarelli and Dr. Michael E. Ward).

This study explored the willingness of parents of children, ages birth to three, who lived in the attendance zone of Title 1 elementary schools, to implement language environment analysis technology and to change behaviors in order to increase the literacy skills of their children. Interest in these activities was moderate to high.

I found limited statistically significant relationships among the willingness to engage in the various activities and parental demographic characteristics. There were 6 statistically significant results. Parental education levels demonstrated an effect as related to interest in word analysis technology components as follows: there was a decrease in interest for parents who attended high school but did not graduate and an increase in interest for parents with an associate’s degree and some college but no degree (p<.1). At the p<.01 level, parents with a master’s degree reported an elevated level of interest in word analysis components. In addition, there was a statistically significant increase in interest in word analysis technology among parents reporting the annual household income in the low income range of $6,000 to $15,000 range (p<.01).

Parent participants who attended high school but did not graduate were less willing to change behaviors to improve the literacy skills of their children than parents who graduated from high school; this difference was statistically significant (p<.01). Though not statistically significant, thematic trends were observed among participants with 3 children. These parents expressed lower interest across behaviors as compared to participants with 1 to 2 children.

Participants under 30 were more likely to be willing to engage in behaviors related to improving the literacy of their children. The generally high level of interest in language environment analysis technology and willingness to change daily behaviors that are evident in this study warrant the attention of policymakers and practitioners. Additional research is also in order to address some of this study’s limitations. It is important to determine how to effectively support low-income parents to encourage them to utilize different measures to support word-rich environments for their children.

by Lauren Annelies Lampron

A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Doctor of Education

Educational Administration and Supervision

Raleigh, North Carolina 2019

APPROVED BY:

______Dr. Bonnie C. Fusarelli Dr. Michael E. Ward Committee Co-Chair Committee Co-Chair

______Dr. William C. Harrison Dr. Colleen G. Paeplow

DEDICATION

I dedicate this finished work to our first born, Luca. You have provided me the motivation with which to finish. Now, may we enjoy each moment at play, uninterrupted.

BIOGRAPHY

After graduating from West Chester University with a Bachelor of Science in

Psychology, Lauren Annelies Lampron joined the eastern North Carolina Corps of Teach for

America. She earned her teaching credentials through the alternative licensure program at East

Carolina University. Subsequently, she earned a Master of School Administration degree at NC

State University as a Northeast Leadership Academy Fellow. Her educational background includes teaching 7th and 8th grade English and Social Studies, obtaining high school experience through a year-long principal internship, and being a middle school administrator. Sharing a passion for education reform, she and Phillip, her husband, reside in Edgecombe County, North

Carolina, where they are both administrators. Lauren currently serves as the principal of W.A. Pattillo Middle School in the city of Tarboro.

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ACKNOWLEDGMENTS

Giving all Glory to God, I am most thankful for the love He has gifted in my life. Sharing a life with my best friend, I am grateful for Phillip’s ongoing support and for always reminding me that “joy comes in the morning.”

I am thankful for the reassurance from family to finish this dissertation. Entertaining

Luca while I worked, my mother and Mike provided time and encouragement for me to complete my work. Their love, support, and homemade dinners made me confident that I could finish.

Offering support via our Three Musketeers group chat, my father and Aunt Gail provided reassurance that I would make it through to the end of this process and that it would be worth the time invested.

Always offering encouragement, I thank my W.A. Pattillo Middle School family for their support. God truly aligned our workplace to be able to engage in meaningful work as we live our motto of “learning for all…whatever it takes.” Our team’s willingness to share additional duties allowed me to be closer to graduation, each day.

What a blessing it has been to have the support of such talented, passionate individuals to serve on my committee: thank you to Dr. Bonnie Fusarelli, Dr. Bill Harrison, and Dr. Colleen

Paeplow. A continued thank you to Dr. Timothy Drake for his encouragement, willingness to meet virtually, and answering each email sent his way, each with the subject “final question.”

Overwhelmingly, I am grateful for co-chair Dr. Mike Ward’s ability to simultaneously inspire me to complete thousands of his recommended edits while remaining relentless in encouraging me to meet deadlines.

But they that wait upon the Lord shall renew their strength; they shall mount up with wings as

eagles; they shall run, and not be weary; and they shall walk, and not faint. Isaiah 40:31

TABLE OF CONTENTS

LIST OF TABLES ...... viii LIST OF FIGURES ...... x CHAPTER 1: INTRODUCTION ...... 1 Chapter Introduction ...... 1 Statement of the Problem ...... 3 Programmatic Attempts to Address Literacy Deficiencies ...... 5 Measuring Word Interactions ...... 9 Purpose of the Study ...... 10 Research Questions ...... 13 Definition of Terms ...... 14 Justification for the Study ...... 17 Organization of the Study ...... 18 Chapter Summary ...... 19 CHAPTER 2: LITERATURE REVIEW ...... 20 Chapter Introduction ...... 20 Background for the Study ...... 20 Literacy Gaps and Related Programmatic Responses over Time ...... 21 The Role of Word Interaction and the Impact of Poverty on Word Interaction ...... 27 Socioeconomic Status and Historic Patterns in Literacy Achievement ...... 29 Contemporary Policy Context ...... 33 Theoretical Framework ...... 36 Pertinent Research and Professional Perspectives ...... 39 Socioeconomic Correlates with Literacy ...... 40 Socioeconomic Status and Parenting ...... 41 Socioeconomic Status and Brain Development ...... 44 Effects of Socioeconomic Status on Proficiency Scores ...... 45 Early Literacy ...... 47 Parent Education to Close the Gaps in Literacy ...... 50 Literacy Education for Parents ...... 51 Improving Parent Capacity to Strengthen Children’s Literacy at Home ...... 55 Incorporating Language Analysis Technology ...... 56

Chapter Summary ...... 59 CHAPTER 3: METHODOLOGY ...... 60 Chapter Introduction ...... 60 Research Design ...... 60 Research Questions and Hypotheses ...... 61 Research Questions ...... 62 Hypotheses ...... 63 Study Participants ...... 63 Variables Addressed by the Study ...... 64 Quantitative Methods ...... 66 Data Collection ...... 66 Instrumentation for Collection of Quantitative Data ...... 66 Analysis of Quantitative Data ...... 69 Quasi-Qualitative Methods ...... 69 Data Collection ...... 70 Instrumentation for Collection of Qualitative Data ...... 70 Instrument Validity and Reliability ...... 71 Instrument Validity ...... 71 Study Procedures ...... 73 Limitations of the Study ...... 75 Assumptions of the Study ...... 76 Chapter Summary ...... 76 CHAPTER 4: FINDINGS ...... 77 Chapter Introduction ...... 77 Quantitative Results for Participant Demographics ...... 77 Results for Research Questions ...... 83 Research Question 1 ...... 84 Research Question 2 ...... 118 Chapter Summary ...... 157 CHAPTER 5: DISCUSSION ...... 159 Chapter Introduction ...... 159 Purpose of the Study ...... 159 Organization of the Study ...... 159

Summary of Findings ...... 160 Discussion of Study Findings ...... 166 Hypotheses ...... 171 Researcher Reflections ...... 172 Limitations of the Study ...... 174 Recommendations for Research, Practice, and Policy ...... 176 Recommendations for Future Research ...... 176 Recommendations for Practice and Policy ...... 178 Chapter Summary ...... 180 REFERENCES ...... 182 APPENDICES ...... 205 Appendix A: The Words Count Survey ...... 206 Appendix B: Expert Panel Validity Questionnaire ...... 211 Appendix C: Request to Superintendent for Permission to Conduct the Study ...... 215 Appendix D: Informed Consent for Research ...... 217

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LIST OF TABLES

Table 4.1: Number of Children and Number of Adults in Participant Households ...... 79 Table 4.2: Age of Parents Participating in the Survey ...... 80 Table 4.3: Yearly Income of Parents Participating in the Survey ...... 82 Table 4.4: Participants’ Highest Level of School Completed...... 83 Table 4.5: Qualitative Responses to Using Language ENvironment Analysis Technology ...... 86 Table 4.6: Participants’ Reported Interest Language Analysis Technology ...... 88 Table 4.7: Research1 and Research2 Variables Explained ...... 89 Table 4.8: Interest in Language Analysis Technology: Working Status and Home Visits ...... 90 Table 4.9: Interest in Language Analysis Technology – Working Status and Clip On ...... 90 Table 4.10: Interest in Language Analysis Technology – Working Status and Use App ...... 91 Table 4.11: Interest in Language Analysis Technology – Working Status and Plugging in Device ...... 92 Table 4.12: Regression among variables – Working Status and Interest in Language Analysis Technology Use ...... 93 Table 4.13: Interest in Language Analysis Technology – Number of Children and Home Visits ...... 94 Table 4.14: Interest in Language Analysis Technology – Number of Children and Device ...... 95 Table 4.15: Interest in Language Analysis Technology: Number of Children and App ...... 95 Table 4.16: Interest in Language Analysis Technology – Number of Children and Plugging in Device ...... 96 Table 4.17: Regression among Variables – Number of Children and Interest in Language Analysis Technology Use ...... 98 Table 4.18: Interest in Language Analysis Technology – Household Income and Home Visit ... 99 Table 4.19: Interest in Language Analysis Technology – Household Income and Clipping on Device ...... 100 Table 4.20: Interest in Language Analysis Technology – Household Income and the App ...... 100 Table 4.21: Interest in Language Analysis Technology – Household Income and Plugging in Device ...... 101 Table 4.22: Regression of Household Income and Interest in Language Analysis Technology Use ...... 103 Table 4.23: Interest in Language Analysis Technology – Age and Home Visits ...... 104 Table 4.24: Interest in Language Analysis Technology – Age and Clipping on Device ...... 105 Table 4.25: Interest in Language Analysis Technology – Age and Using the App ...... 105 Table 4.26: Interest in Language Analysis Technology – Age and Plugging in Device ...... 106 Table 4.27: Regression among Variables – Age of Parent and Interest in Language Analysis Technology Use ...... 107 Table 4.28: Interest in Language Analysis Technology – Education Level and Home Visit ..... 108 Table 4.29: Interest in Language Analysis Technology – Education Level and Clipping on .... 109 Table 4.30: Interest in Language Analysis Technology: Education Level and the App ...... 110 Table 4.31: Interest in Language Analysis Technology – Education Level and Plugging in Device ...... 110 Table 4.32: Regression among Variables: Education Level and Interest in Language Analysis Technology Use ...... 112

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Table 4.33: Regression of all Parental Demographic Characteristics and Interest in Language Analysis Technology Use ...... 114 Table 4.34: Willingness to Complete Actions that Foster Language Development ...... 120 Table 4.35: Willingness to Change: Working Status and Text Messages ...... 121 Table 4.36: Willingness to Change – Working Status and Support Group ...... 121 Table 4.37: Willingness to Change – Working Status and the Library ...... 122 Table 4.38: Willingness to Change – Working Status and Reading ...... 123 Table 4.39: Willingness to Change – Working Status and Talking ...... 123 Table 4.40: Willingness to Change – Working Status and Learning ...... 124 Table 4.41: Regression among Variables – Working Status and Willingness to Change Behaviors ...... 125 Table 4.42: Willingness to Change – Number of Children and Text Messages ...... 126 Table 4.43: Willingness to Change – Number of Children and Support Group ...... 126 Table 4.44: Willingness to Change – Number of Children and the Library ...... 128 Table 4.45: Willingness to Change – Number of Children and Reading ...... 128 Table 4.46: Willingness to Change – Number of Children and Talking ...... 129 Table 4.47: Willingness to Change – Number of Children and Words ...... 130 Table 4.48: Regression among Variables: Number of Children and Willingness to Changing Behaviors ...... 131 Table 4.49: Willingness to Change – Household Income and Text Messages ...... 132 Table 4.50: Willingness to Change – Household Income and Support Groups ...... 133 Table 4.51: Willingness to Change – Household Income and the Library ...... 133 Table 4.52: Willingness to Change – Household Income and Reading ...... 134 Table 4.53: Willingness to Change – Household Income and Talking ...... 135 Table 4.54: Willingness to Change – Household Income and Words ...... 136 Table 4.55: Regression among Variables – Household Income and Willingness to Change Behaviors ...... 137 Table 4.56: Willingness to Change – Age and Text Messages...... 138 Table 4.57: Willingness to Change – Age and Attending Support Group...... 139 Table 4.58: Willingness to Change – Age and the Library...... 139 Table 4.59: Willingness to Change: Age and Reading More ...... 140 Table 4.60: Willingness to Change – Age and Talking More ...... 141 Table 4.61: Willingness to Change – Age and Words ...... 142 Table 4.62: Regression among Variables – Age of Parent and Willingness to Change ...... 143 Table 4.63: Willingness to Change – Education Level and Text Messages ...... 144 Table 4.64: Willingness to Change – Education Level and Parent Support Group ...... 144 Table 4.65: Willingness to Change – Education Level and the Library ...... 145 Table 4.66: Willingness to Change – Education Level and Reading ...... 146 Table 4.67: Willingness to Change – Education Level and Talking ...... 147 Table 4.68: Willingness to Change – Education Level and Words ...... 147 Table 4.69: Regression among Variables: Education Level and Willingness to Change Behavior ...... 149 Table 4.70: Regression of all Parental Demographic Characteristics and Willingness to Change ...... 151 Table 4.71: Research1 and Research2 Percent of Interest ...... 158

LIST OF FIGURES

Figure 4.1: Participant Identification of Race ...... 80 Figure 4.2: Participant Identification of Employment Status ...... 81

CHAPTER 1: INTRODUCTION

Chapter Introduction

The National Commission on Excellence in Education’s landmark 1983 A Nation at Risk report prompted researchers to explore the field of education for areas of deficiencies. With language such as, “society being eroded as a rising tide of mediocrity” and “committing an act of unthinking, unilateral educational disarmament.” The repercussions of the 1983 report of “A

Nation at Risk” moved public education to the top of the national policy agenda. Early studies regarding achievement gaps between White and Black students concluded that Black students’

“stereotype threats” created barriers to educational success while testing as Black students have added concerns regarding how their test scores may confirm negative stereotypes associated with their race (Roach, 2000). However, these studies failed to elaborate on the fact that much of the gap was created prior to middle school and high school test-taking environments. Later studies found that the gaps in achievement (coined “achievement gap”) were rooted in gaps in language skills in elementary school (Snow, Burns, & Griffin, 1998). Useful research regarding the literacy gap in education is found within Hart and Risley’s (1995) findings regarding the 30

Million Word Gap. This gap occurs between the number of words heard by preschoolers in low and high-income households (Hart & Risley, 1995). The researchers recorded language interactions in 42 households across a continuum of low, middle, and high-income levels. The longitudinal study lasted over two and a half years and the researchers found the number of words with which children interact was related directly to income levels and contributed to the rate in which children’s vocabulary grow.

In order to eliminate stereotype threat, the achievement gap, and deficient literacy skills in low-income households, a number of strategies have been attempted. Among them is a

relatively new innovation. Language environment analysis technology provides insight into the number of words spoken in the home as a means to educate parents about ways that they can increase the interaction their children have with words (Hoff & Naigles, 2002; Gilkerson &

Richards, 2009). When the device is used with birth to pre-K children, the language environmental technology allows a child’s conversational talk turns with adults and the child’s babbling, to be converted into data. In turn, the feedback from the data allows parents or guardians to be more aware of the number of talk turns in which their child interacts (LENA

Research Foundation, 2016b). A recent study demonstrated that children’s language interactions, specifically child directed speech and talk turns, correlates with building brain language regions independent of poverty levels. By focusing on increasing exposure to words, parents can utilize the benefits of malleable neural plasticity (Romeo et al., 2018).

Early pioneers in language environment analysis technology include researchers who attempted to study how deaf and hard of hearing children acquired speech (VanDam, Ambrose,

& Moeller, 2012). In 1980, SALT (Systematic Analysis of Language Transcripts) was created to allow Madison School District’s speech language pathologists to use a computer to transcribe and interpret linguistic analyses efficiently. This advancement was needed as the technology allowed pathologists to receive data at a much greater rate than they could code by hand (Miller,

2017).

Similar to the progress made by the implementation of technology by SALT in the educational sphere, there are language environment analysis technologies being implemented for at-home use. The purpose of this study was to assess the degree to which parents in low-income eastern North Carolina communities perceive that language environment analysis technology would be beneficial to implement in their homes. Additionally, I assessed how willing parents

are to change their daily interactions with their child(ren) in order to read more and talk more with their child(ren). The willingness was matched with their parental demographic characteristics to determine if a correlation was present between parental demographic characteristics and willingness to change.

The following sections address the statement of the problem, which includes historically low proficiency scores in North Carolina as determined by the Early Childhood Longitudinal

Study, the National Assessment of Educational Progress, and North Carolina Department of

Public Instruction End of Grade tests. The tests also demonstrate a correlation between socioeconomic status and proficiency. The sections review a failed attempt to overcome the word gap with an initiative of parent training and measuring word interactions. Finally, the purpose of the study, definitions, and research questions are presented.

Statement of the Problem

The website of the first independent literacy organization charted in British Columbia

(http://projectliteracykelowna.org), opens with these sentences: “Helping someone to read and write effectively or acquire the basic math skills so many of us take for granted, improves the future of everyone in society. Literacy is critical to economic development as well as individual and community well-being” (Project Literacy, 2017, para. 1). The organization continues to explain the importance of literacy and the need to develop literate citizens in British Columbia.

They estimate that nearly 25% of the population of the province does not possess adequate literacy skills. The organization continues to explain, “there is a growing mismatch between the skills that employers need and the skills that workers have” (2017). Within the United Kingdom, similar literacy awareness initiatives are taking place. The Literacy Trust is one example of the

United Kingdom’s strategies to raising literacy levels. According to a 2014 issue of Literacy

Changes Lives by the National Literacy Trust, “in times of economic instability, low literacy makes individuals and communities more vulnerable to inequality, increasing the risk of social exclusion and undermining social mobility” (Dugdale & Clark, 2014, p. 5).

In a similar way to the efforts in the UK, researchers in the United States found comparable results regarding reading skills at the school entry level in Kindergarten. Reading skills have been used to predict future success (Duncan et al., 2007). The ability to read is a compounding process that occurs throughout a child’s educational experience. The Early

Childhood Longitudinal Study found that many students who scored in the lowest one third of

Kindergarteners in reading continued to score in the lowest one third in reading as third graders.

This suggests that an intentional intervention may need to be in place in order for the lowest

Kindergarten students to experience a level playing field at the time of entry in Kindergarten.

The same results were found for the highest one-third of participants. For example, 65% of the highest scoring third grade students (as identified by the end of grade reading tests) were the same students who scored the highest in their respective end of year Kindergarten reading tests

(Princiotta, Flanagan, & Hausken, 2006). It can be concluded that children who are proficient in early reading continue to remain proficient as their education continues.

In addition to the Early Child Longitudinal Study, students in the United States participate in the National Assessment of Educational Progress (NAEP). During the years in which No Child Left Behind was in effect, NAEP revealed that 4th, 8th, and 12th grade students across North Carolina have demonstrated low levels of proficiency (NCES, 2015b, 2016).

Furthermore, the tests disclose a positive, strong correlation between low proficiency and low income (NCES, 2016). According to the North Carolina Department of Public Instruction’s website, the designations for School Performance Grades in 2015 demonstrate a correlation

between schools with 50% of more poverty and being rated as a D or F on an A-F scale in which

A is the highest rank (NCDPI, 2016a). Using the rationale from the Early Childhood

Longitudinal Study, it is evident that intervention in high poverty communities needs to occur prior to children entering Kindergarten. This will help to ensure that students do not remain at the lower level of the state’s school rankings. The study assessed the perspectives of parents in low income communities and their receptiveness to language analysis technology to provide such intervention.

Programmatic Attempts to Address Literacy Deficiencies

Language development in the preschool period must be accelerated to disrupt the low proficiency levels across eastern North Carolina. The word gap, which has subsequent impact on children emerging into adolescence, is produced most markedly during the pre-school years.

Word deficiency not only affects vocabulary growth and acquisition as children mature, but also impacts the exposure to amount of words in which the child interacts. For this reason, the first two to three years of life are the most important determining predictor of IQ, future school success, and future reading and speaking ability (Hart & Risley, 1995). As stated in their research, “With few exceptions, the more parents talked to their children, the faster the children’s vocabularies were growing and the higher the children’s IQ test scores at age three and later”

(Hart & Risley, 1995, p. 144).

A vocabulary equity gap has been observed in the degree to which the exposure to words differs across income levels (Hart & Risley, 1995). The researchers found that:

 the average child in a professional family hears 2,153 words per hour; in four years,

the child will interact with 45 million words.

 the average working class child hears 1, 251 words per hour; in four years, the child

will interact with 26 million words.

 the average child on welfare hears 616 words per hour; in four years, the child will

interact with 13 million words.

These fundamental findings of the research completed by Hart and Risley in 1995 came to be known as the 30 Million Word Gap. The researchers coined the name from their conclusion about the difference in word interaction; the subtraction of the 13 million words heard in the welfare recipient’s household from the 45 million words heard in the professional family’s household produces a difference of just over 30 million words (Hart & Risley, 1995). Critics may not argue that the tests are flawed as numerous research studies have confirmed that exposure to words impacts a child’s access to vocabulary growth and language acquisition as they mature into adolescence (Bayliss, 2015; Clark, 2007; Hart & Risley, 1995). It is apparent that the vocabulary deficiencies among low-income children and the gaps between them and their higher income peers begin at home. Among the 42 children observed in Hart and Risley’s

(1995) study, the key findings included (a) a prediction of IQ based on the number of words a child hears from their parent, (b) a finding that advanced children had parents who talked more than those of the children who were determined to be almost as advanced, and (c) a positive correlation between the amount of talk heard from birth to three and success at age 10.

Criticism of the word-gap is that the gap blames low income parents; however, it is not just low-income parents that need to be more aware of how their values may affect their children.

According to research data from the 2001 Progress in International Reading Literacy Study

(PIRLS), a supportive home environment is crucial in fostering a child’s future reading success.

Internationally, it has been found that students whose parents indicated that they enjoyed reading

had significantly higher reading scores than students whose parents indicated that they did not enjoy reading (Mullis, Martin, Foy, & Drucker, 2012).

Programmatic reforms began to occur throughout the country to address pre-literacy deficits and the word gap. Such efforts resulted in the creation of Head Start and Early Head

Start at the federal level. State-specific innovations in North Carolina have included programs like Smart Start and More at Four.

In 1965, President Johnson created Head Start to foster the development of literacy skills

(Head Start, 2017). Using a focus on the whole child model, children who attended the program received regular access to dental and medical care along with the program’s specific framework to create a school-ready child. Eventually, the program developed a Home Start component that led to caretakers and employees being partners in the education of the enrolled child (Head Start,

2012). Throughout the years, Head Start added various programs such as Early Head Start and the federal Fatherhood Initiative (Head Start, 2017). In 2007, President Bush signed the

Improving Head Start for School Readiness Act of 2007 to increase components of a child’s approaches to learning including an increase in literacy skills (Improving Head Start for School

Readiness Act of 2007, 2007).

State initiatives have also targeted early literacy. Smart Start began in North Carolina in

1993 as a means for all children aged 0 to 5 to be school ready; unlike Head Start, no socioeconomic status ranges were targeted for the pre-Kindergarten intervention program

(McNeil, 2008). The Frank Porter Graham Child Development Institute at the University of

North Carolina at Chapel Hill has released numerous studies regarding the positive effects of

Smart Start on pre-literacy skills (Bryant et al., 2003; Maxwell, Bryant, & Miller-Johnson, 1999;

Taylor & Bryant, 2002).

Similar to the positive longitudinal results obtained by Smart Start through the Frank

Porter Graham Child Development Institute’s research, More at Four also had positive effects on third-grade reading proficiency (Ladd, Muschkin, & Dodge, 2014). North Carolina’s More at

Four Program targeted and served more than 49,000 at risk children between 2002 and 2006

(Peisner-Feinberg & Schaaf, 2007). Impact studies of More at Four resulted in a deeper understanding of at-risk children remaining behind their peers at the end of the intervention year

(Peisner-Feinberg & Schaaf, 2007).

The preceding studies suggest that, while programs such as the federal Head Start initiative, Smart Start, and More and Four have positively impacted pre-literacy skills, inadequate early reading deficits persist. In addition to making these programs more effective in this dimension of service delivery, it is arguable that additional innovations are needed. An option through which low-income parents might alter the trajectory of their children’s reading achievement is to capitalize on creating word rich environments in their own homes (Hart &

Risley, 1995; Gilkerson & Richards, 2009). Parents need to be trained on ways that will enable them to increase child-directed conversations. Such conversations with children from birth to age

3 impact the future success of the child (Hart & Risley, 1995). Within the text of policy priorities, the Children’s Defense Fund (2016) organization contends that the cognitive outcomes of the first five years of a child’s life are foundational for future cognitive development in the child’s rapidly developing brain.

The district in which the research study occurred borders the northeast and north central regions of North Carolina as determined by the State School Board District map (NCDPI,

2016a). The Northeast and North Central regions have the highest number of F schools and the lowest number of A schools under the state’s accountability system. This is contrasted with the

higher income, western region of the state (NCDPI, 2016a). A low-performing district has more than half of the schools in the district that are low performing. The northeast and north central regions contain three districts each that are low-performing; the other regions include one that contains two low-performing districts, and two with one low-performing district each. The remaining three regions have no low-performing districts (NCDPI, 2016b).

Measuring Word Interactions

There are varying language skills that should be measured to determine the progress of language in children. For example, Systematic Analysis of Language Transcripts (SALT) is technology that is used by speech and language pathologists to identify problems and deficits in the spoken language by technology codes a narrative from an audio tape or digital recording

(Nittrouer, Caldwell, & Holloman, 2012). The SALT Program stores language samples of over

6,000 speakers (Miller & Iglesias, 2008). The database has become a beneficial clinical tool for professionals assisting children with speaking impairments (Heilmann, Miller, & Nockerts,

2010).

The Language Environment Analysis (LENA) tool was developed to analyze language encountered by children from birth to age three. Its creators contend that it is revolutionizing the way in which speech and language data can be collected. Using a pocket LENA device and

LENA technology, one can ascertain the number of words with which a child interacts

(Gilkerson & Richards, 2008). Parents who utilize LENA receive real time results of average number of words heard by a child and average number of talk turns per hour. This feedback enables parents to monitor their behavior changes and progress toward their word and talk turn goals (Suskind & Leffel, 2013). It does not, however, record actual conversations.

Starling is another word analysis technology. Starling’s website claims that it is the first wearable word counter. Using a plastic word counter the size of a pacifier, VersaMe’s Starling synchs with a mobile application to count adult words and suggest age-specific activities for the adult to do with the child (VersaMe, 2017). An example of an age-specific activity, listed on the

Starling website, is for parents to place the Starling device on the child in the morning and to practice the names of the child’s body parts throughout the day, starting with the adult dressing the child, pointing to each body part, and saying the child’s body part as he/she wipes off the food on her/his face (VersaMe, 2017). As the parents continue throughout the day, they are able to log into the product’s online application and see immediate feedback regarding the number of words their child has heard that day.

Creating a means for low-income parents to have real-time feedback on the number of words their children are hearing and with which they are interacting has been found to be a useful means of increasing pre-Kindergarten literacy skills (Gilkerson & Richards, 2009).

Researchers and developers contend that by employing language analysis technology, parents can play an integral part in closing the word gap (LENA Research Foundation, 2016b). The

LENA and Starling tools and methodology were the focus of this study.

Purpose of the Study

The purpose of this study was to assess the degree to which parents in low-income eastern North Carolina communities perceive that language environment analysis technology would be beneficial for use in their home. Based on information related to components of the language analysis technology, parents offered perspectives on whether the technology would be beneficial and how likely they would be to incorporate the technology in their own homes. The study examined archival data to determine which in-home parent/child models reduce pre-school

word interaction deficits. Additionally, I assessed how willing parents were to change their daily interactions in order to read more and talk more with their child; this willingness to change was correlated with parental demographic characteristics. The study incorporated quasi-qualitative case-study elements to achieve some of these purposes.

The methodology for the study encompassed a mixed methods research design. The central constructs included parents’ perspectives about potential implementation of language environment analysis technology. The study also examined variables that impacted the interest of parents in implementation of the technology’s components. Such variables included race, socioeconomic status, age, maternal education level, employment status, and number of children in the home.

The body of knowledge surrounding language analysis technology has evolved and is used in many fields. Language analysis technology has been used by researchers to understand the ways that cochlear implants allow deaf children to hear and experience language acquisition

(Leffel, Suskind, & Suskind, 2013). Language analysis technology has also allowed researchers to study adult word count at a school containing children on the autism spectrum and correlate the number of words spoken by adults and teacher burnout (Irvin, Hume, Boyd, Mcbee, &

Odom, 2013). Recently, language analysis technology has become available for at-home use as a way for parents to have meaningful feedback on how to improve their child’s access to hearing words (Suskind et al., 2015). As there is a gap in literature addressing using such technology in the home, the study may help to fill deficiencies in the past literature surrounding aspects of the language analysis technology that appeal to low-income parents.

The language analysis methodology of interest in this study is associated with the LENA and Starling technologies. Most parents believe that they have enough talk interaction with their

child since they do not have quantifiable data to measure. By evaluating talk turns, parents have worked to create enriched word environments which provide cognitive increases across many aspects of a child’s development (LENA Research Foundation, 2016a). The study addressed dimensions of language analysis technology and determined which dimensions of the program parents believed would yield potential benefits to children. The study further examined archival data from preschool programs such as Head Start, Early Head Start, Smart Start, and More at

Four programs. While examining archival data, I reviewed previous attempts to reduce preschool word interaction deficits. A survey instrument containing a Likert-type scale examined the perspectives of parents of minority, at-risk low-income students. These participants were asked to reflect upon the potential benefits of the four major components of the language environment analysis program.

The research inquired into early language home environments and parents’ willingness to change their daily routines. The first major component of the LENA and Starling systems is a recorder attached to the infant’s clothing. While the device does not record conversations, it counts word turns and allows parents track the quality and amount of talk time with infants.

Also, the Starling system sends notifications through the companion application to parents to remind parents to communicate throughout the day with their child. The third component is a structured curriculum available through the ASPIRE and Thirty Million Words Curriculum through which parents learn how to engage their children’s growing minds; the activities include brain-building tips and shared book-reading strategies (Suskind & Leffel, 2013). I determined how receptive parents were to using the recorder in everyday living, implementing the strategies received in the text messages, and following a parent curriculum.

Research Questions

The study’s purpose addressed multiple constructs related to the perceptions of parents regarding the potential benefits of language analysis technology. In order to carry out the intent of the study, the following research questions were addressed:

1. What are the perceptions of parents of children, ages birth to three, who live in the

attendance zone of Title 1 elementary schools in a northeastern North Carolina school

district regarding the potential implementation of language environment analysis

technology in their households?

a. Are the perceptions related to the different components of the language

analysis technology?

b. Are the perceptions of parents related to their working status (not employed,

part-time, full-time)?

c. Are the perceptions related to the reported number of children?

d. Are the perceptions related to the reported level of household income?

e. Are the perceptions related to the reported age of the parent?

f. Are the perceptions related to the reported education level of the parent?

2. To what extent are parents of children, ages birth to three, who live in the attendance

zone of Title 1 elementary schools in a northeastern North Carolina school district

willing to change daily behaviors in order to improve literacy skills of their children?

a. Is this willingness related to their working status (not employed, part-time,

full-time)?

b. Is this willingness related to the reported number of children?

c. Is this willingness related reported level of household income?

d. Is this willingness related to the reported age of the parent?

e. Is this willingness related to the reported education level of the parent?

Definition of Terms

The following terms are used throughout the chapters. Some are technical terms that are specific to either the technologies or variables that were the focus of the study. Some were specifically adapted for use in this study.

1. Application: A program or piece of technology designed and written to fulfill a

particular purpose of the user (Merriam Webster Online Dictionary, n.d.) For

example, LENA and Starling are applications designed for use in verbal interactions.

2. Approximate yearly income: A family’s annual inflow of money within income

level ranges ($0 to $5,000, $6,000 to $15,000, $16,000 to $25,000, $26,000 to

$35,000, $30,000 to $45,000, $46,000 to $60,000, $61,000 to $75,000, over $75,000)

3. At-risk student: Student who is at risk for dropping out of school early due to low

educational attainment (Chen & Kaufman, 1997).

4. Child-directed speech: Baby talk; adult use of high pitched speech with accented

facial expressions when communicating with young children (Weisleder & Fernald,

2013).

5. Common Core State Standards: Standard based curriculum adopted by forty two

states in the United States. While the number of formal adopters has dropped,

elements of the Common Core standards are infused in state curricula across the

country and in North Carolina (ASCD, 2013).

6. Early intervention: Providing assistance to at-risk children, birth to age three, as a

means of preventing or lessening word gaps (High, LaGasse, Becker, Ahlgreen, &

Gardner, 2000).

7. Education level: The highest level of school completed as defined by the following

ranges: no formal schooling, less than 9th grade, some high school but no diploma,

GED, high school diploma, vocational/technical program after high-school, some

college but no degree, AA, BA, MA, graduate or professional degree

8. Emergent literacy: The knowledge that a child possesses about reading and writing

before he/she knows how to read and write (Lonigan & Shanahan, 2009).

9. Growth: The degree to which schools exceeded, met, or did not meet cohort

achievement improvement expectations as defined and calculated in Education Value

Added Assessment System (EVAAS) (NCDPI, 2016a).

10. Language analysis technology: Technology used to analyze use of words and talk

turns (LENA Research Foundation, 2016a).

11. Language ENvironment Analysis (LENA): Proprietary hardware and technology

package designed by the LENA Research Foundation to be used to share timely

feedback with parents to increase number of words heard by child (LENA Research

Foundation, 2016b).

12. Parent: A person primarily responsible for raising a child. In most instances, this is

the mother and/or father, but may include another relative or appointed guardian.

13. Proficiency: Test rating that determines passing score; rating of 3, 4, 5 on

standardized, end of grade tests (NCDPI, 2016).

14. Receptive language: Ability to understand words and the meaning of what is said

(Bryant et al., 2003)

15. School readiness: Skills, attitudes, and knowledge needed to enter Kindergarten

successfully (Bayliss, 2015; Magnuson & Waldfogel, 2005)

16. Socioeconomic status (SES): Economic description used by the National School

Lunch Program (NSLP) to determine lunch status based on the current level of

income; students are eligible for free, reduced, or full price meals at school. The

NSLP was founded after the National School Lunch Act was signed by President

Truman (National School Lunch Program, 2017).

17. Starling: Wearable word counter with application that provides tracking and talk

suggestions of when and what to talk with the child about (VersaMe, 2017a).

18. Talk turns: The conversation between a child and an adult; could be through the use

of actual words or babbling (Hart & Risley, 1995; Suskind & Leffel, 2013)

19. Thirty Million Word Gap: The gap between words encountered by children in

professional families and welfare families (Hart & Risley, 1995).

20. Whole child: Reference to creating a well-rounded citizen and placing emphasis on

content area skills, as well as developing character traits such as empathy, creativity,

and determination (Head Start, 2017; Viadero, 2000).

21. Word analysis technology: A technology tool designed for use of calculating the

number of words spoken to a child. For example, LENA and Starling are word

analysis technologies designed for use in verbal interactions.

22. Working status: Number of hours an individual works; defined across a continuum

of not employed, part-time, or full-time (Muller, 1995).

Justification for the Study

The research was a direct result of the persistent debate over universal pre-K and the limited access to related funding for low-income communities. The debatable question for policymakers is not related to the effectiveness of universal pre-k, the question is whether it is financially possible (Kelleher, 2011; Reynolds, Temple, Robertson, & Mann, 2002). Programs such as Head Start and More at Four have addressed gaps in school readiness; however, gaps in school readiness continue to exist (Cannon & Karoly, 2007; Magnuson, Meyers, Ruhm, &

Waldfogel, 2004; Magnuson & Waldfogel, 2005). The results can be used to provide policymakers with research that discloses the interest in the use of language analysis technology in low-income households to better ensure the preparedness of preschool children.

A study of the perceived benefits and the perceived barriers to implementation of the language environment analysis tool may contribute to policy discussions around strategies for the elimination or narrowing of gaps in school readiness. The research may benefit practitioners as, depending upon the results, schools may choose to invest in pairing low performing pre-k through third grade students and their parents with LENA tools. In addition, the benefits can also extend to parents and their children. There is potential value in parents being receptive to language analysis technology as research has shown that toddlers who had increased access to child-directed speech were able to understand familiar words and comprehend novel words with greater ease (Weisleder & Fernald, 2013).

The body of knowledge relative to this topic is beginning to grow as research on language environment analysis technology has been implemented due to the creation of the

LENA and Starling technology. Currently, LENA’s website has several key research articles; however, the overall research on language environment analysis technology, particularly for this

application, is limited. This study adds to the relevant body of knowledge regarding the creation of word rich home environments. The potential value of this study to future researchers is multi- fold. It allows for further examination of whether policymakers should invest in language analysis tools. Additionally, future researchers have a research protocol to guide inquiry into other language analysis tools and in other contexts.

Organization of the Study

The following chapters address the degree to which parents in low income eastern North

Carolina communities perceive language analysis technology as potentially beneficial to their daily practices in child rearing. Chapter 2 includes a comprehensive literature review of language analysis technology as well as an understanding of the literacy gap and related programmatic responses over time. Literature related to the central research constructs was synthesized.

Pertinent research and the contemporary policy context were discussed.

Chapter 3 details the research design and procedures. It discusses the quantitative and qualitative methods used in the process of evaluating parent’s interest in using language analysis technology. The research questions and related hypotheses are provided. The study participants and the procedures to recruit them and to safeguard their anonymity are outlined. The study consisted of a quasi-qualitative case-study design. Instruments and the statistical tools to analyze data derived from them are discussed.

Chapter 4 elaborates on the results of the study. This chapter provides reports of the findings associated with each of the research questions and related hypotheses. Chapter 5 provides a discussion of the findings. Based on the conclusions, I discuss the implications of the research for policy, practice, and future research.

Chapter Summary

According to Weisleder and Fernald (2013), speech that is addressed directly to the child yields an increase in the child’s acquisition of vocabulary. This research into the existing body of knowledge asserts that children need more than simply overhearing adult conversations in order to learn to speak capably. Children need to be intentionally spoken to in order to foster language- decoding skills (Weisleder & Fernald, 2013). Additional research identifies the home environment as a catalyst for promoting language skills that are precursors to reading skills like comprehending narratives (Whitehurst & Lonigan, 1998).

Due to the cost of implementing high quality pre-k programs, there is a need for a feasible way in which to close the word gap. The developers of LENA technology contend that the tool allows parents of preschoolers to change the future of their children by intentionally teaching these parents how to create word rich environments in their own homes (LENA

Research Foundation, 2016b). Parents need to be trained in behaviors that will enable them to increase child-directed conversations.

The research continues to suggest that child-directed language increases cognitive ability later in life; therefore, it is necessary that parents have opportunity be empowered with this information (Majorano, Rainieri, & Corsano, 2012; Wasik & Jacobi-Vessels, 2016). The use of the LENA tool acknowledges the need for accountability and support to aid parents in the development of their children’s brains. Through the use of language analysis technology, researchers offer a potential solution to an ongoing problem; therefore, there was a need to study the degree to which parents of at-risk, low-income children are receptive to the major components of the LENA tool. Based on the responses of the parents, public policy may be impacted through the examination of investing in language analysis tools.

CHAPTER 2: LITERATURE REVIEW

Chapter Introduction

Chapter 2 provides a comprehensive review of the body of knowledge related to the study topic and purpose. In the following sections, I described and synthesized literature related to relevant background and contemporary public policies. The theoretical framework used to provide a foundation for the study is addressed. The study’s topic, research constructs, and research aims are explored within the context of pertinent research and expert perspectives from the field.

Previous studies articulate the pivotal role that parent interaction plays in a child’s school readiness (Bayliss, 2015; Bornstein, Tamis-LeMonda, Hahn, & Haynes, 2008; High et al., 2000).

Word analysis technology works to eliminate word gaps by educating parents about how to interact with their children to help them develop strong literacy skills (Foster-DeMers, 2012). It was therefore worthwhile for this study to examine parent perceptions of the potential benefits of interacting with word analysis technology.

Background for the Study

The following sections and sub-sections address historical developments related to concerns over deficiencies in pre-literacy skills among children, particularly children in poverty.

The concerns of policymakers and the public relative to reading achievement across decades are profiled, along with the impact of poverty on achievement. Policy-level and programmatic responses to literacy gaps, both at the federal and state level, are profiled. Included among these programmatic responses is a description of developments over time by researchers to establish the importance of word interactions in the life of a preschooler and early efforts to quantify word

interaction in the home and to apply technology to this process. The section closes with a description of the policy context in which the study occurred.

Literacy Gaps and Related Programmatic Responses over Time

Given the importance of literacy to a child’s prospects, and given persistent problems with literacy among children in poverty and children of color, federal and state initiatives have been implemented (Johnson & Lee, 2003; Miller, 1987; Peisner-Feinberg & Schaaf, 2007). The following sub-sections profile the three most prominent such programs in North Carolina over the past few decades.

Head Start. As the country anticipated desegregation in the 1960s, there were concerns about literacy gaps for African American and poor children. Education reformers attempted to alleviate pre-literacy deficits and engage parents in supporting the cognitive development of their children by creating Head Start programs. President Johnson was clear in his desire to use education to end the cycle of poverty and in 1965, he announced the creation of Project Head

Start (Miller, 1987). A major component of the literacy initiative was to encourage culturally responsive teaching. In its first summer in operation, 560,000 children throughout the United

States participated in an eight-week programs in Head Start Child Development (Smith &

Bissell, 1970). Using the whole child model, participants received in home assistance to the family to foster child’s development, nutrition assistance, and mental, dental and physical care.

A year later, due to the success of the summer program, the Head Start program was funded for another year (Head Start, 2017).

In 1967, through the Office of Economic Opportunity (OEO), the publication of the

Rainbow Series introduced specific frameworks for organizing a local Head Start program

(Branche & Overly, 1971). Although grantees were receptive to Rainbow Series publications, an

additional policy manual, 70.2, The Parents, led to staff members threatening to leave Head Start programs because the policy mandated a structure to allow parents transparent access to the decision-making process. It was mandated that parents would assist in classrooms and interact with staff (Head Start Bureau, 1970). In 1973, the trend to encourage parents to have a greater emphasis in their child’s life occurred through the presence of Home Start, which allowed Head

Start employees to provide services at the homes of children attending Head Start. The employees assisted in the continued development of parent-child relationships and offered connections to resources in the community (Head Start, 2012).

Over a period of 20 years, Head Start was reauthorized numerous times and continued to receive additional funding and expand the scope of services in order to adapt to changing needs.

Program enhancements included bilingual education, accommodations for students with disabilities, introducing nutrition services, and salary improvements for employees. In 1994 -

1995, Congress reauthorized Head Start to include Early Head Start for women who were pregnant, infants, and toddlers (Head Start, 2017).

Celebrating its 30th anniversary in 1996, the federal Fatherhood Initiative caused Head

Start to provide an intentional emphasis on including fathers into the mission (Head Start, 2017).

According to technical reports from Head Start, fathers and father figures from families that were involved in Head Start were more likely to participate in Head Start related child development activities than control families (Love et al., 2002). The activities included picking the child up from Head Start, participating in home visits, parenting classes, and parent-child group activities

(Love et. al, 2002).

Concerns about Head Start emerged over time as policymakers debated if individual states were effective in providing services or if the control should remain with the federal

government (Ripple, Gilliam, Chanana, & Zigler, 1999). Additionally, researchers began to question the positive long-term effects of Head Start (Currie & Thomas, 1995, 2000; Ludwig &

Miller, 2007).

In 2007, 42 years after the program was founded, President Bush proposed, and Congress enacted, a law titled Improving Head Start for School Readiness Act of 2007 to “promote the school readiness of low-income children by enhancing their cognitive, social, and emotional development” (Improving Head Start for School Readiness Act of 2007, para 1). An intended outcome of Head Start was to grow several components of a child’s approaches to learning, including acquisition of literacy skills. Head Start has specific approaches to strengthening the pre-literacy skills of children including a mandatory requirement that every Head Start location enabled ongoing training in language and emergent literacy for teachers. Additionally, families were provided with literacy services and workshops (Head Start, 2017).

The Head Start Child Outcomes Framework was created to ensure that “at children, at a minimum, progress in language, literacy, mathematics, science, cognitive abilities, approaches to learning, social and emotional development, creative arts, physical development and the acquisition of the English language” (Improving Head Start for School Readiness Act of 2007,

2007). Education reformers attempted to alleviate pre-literacy deficits and the word gap by creating Head Start programs, which currently serve 35% of low-income children at a cost of

$7,600 per child (Manley, 2015). Despite only investing in pre-Kindergarten experiences for less than half of low-income children, the cost to taxpayers for Head Start is $8 billion annually

(Manley, 2015).

Researchers Fryer, Levitt, and List (2015) created a research experiment to find a more cost-effective strategy than that provided by Head Start for educating parents to educate their

children. They wanted to see the outcome if parents were offered financial incentives to attend parent academies to learn early childhood cognitive development skills. Nearly $1 million was distributed across 257 families (Fryer, Levitt, & List, 2015). The research demonstrated the power that parents possess to change the trajectory of proficiency levels in their children.

According to the study, “We searched for existing curricula that would teach parents to help their children with both cognitive skills (such as spelling and counting) as well as non-cognitive skills

(such as memory and self- control)” (Fryer et al., 2015, p. 7). The data on the impact of increased parent involvement showed a statistically significant increase in non-cognitive scores, along with low levels of increase of cognitive scores. Related to the cost of Head Start, the program from

Fryer, Levitt, and List cost approximately $3,600 per child participant; therefore, nearly double the number of children could be served for about the same outlay of money (Fryer et al., 2015;

Manley, 2015).

Most recently, a study of Head Start found that the children of children who attended

Head Start had a decreased likelihood in teen pregnancy and reduced participation in a crime.

Additionally, the children of the children who attended Head Start had an increase in educational gains; specifically, the children were more likely to graduate from high school and go to college

(Barr & Gibbs, 2017).

North Carolina’s Smart Start program. North Carolina’s Smart Start initiative was created in 1994 for the purpose of ensuring that students were able to start school ready to succeed (Kroll & Rivest, 2000) Although the public costs of pre-literacy initiatives are often discussed, there has been a time in history in which North Carolina became recognized for the intentional partnership of private-public partnerships (Johnson & Lee, 2003). Through the Smart

Start program, $257 million was raised from private funds from 1993 - 2008. The money was

used as a significant supplement to the state of North Carolina’s $200 million annual contribution (McNeil, 2008). The Smart Start initiative was started in 1993 as a means for all children aged 0 to 5 to be school ready. Unlike Head Start, the Smart Start program did not target families within the low-income range; children of all socioeconomic status were targeted to receive services (Bryant et al., 2003).

Throughout the years, the Frank Porter Graham Child Development Institute at the

University of North Carolina at Chapel Hill has released numerous studies regarding the positive effects of Smart Start (Bryant et al., 2003; Maxwell et al., 1999; Taylor & Bryant, 2002). In a

2003 study surrounding child care quality, the researchers found that child care quality at Smart

Start increased over the years since Smart Start was established. It also found a positive correlation between classroom quality and children’s outcomes related to “receptive language, print awareness, book knowledge, applied math, and counting one-to-one” (Bryant et al., 2003, p. 12). In the evaluation, the number of high-quality classrooms had doubled since 1993; however, nearly 50% of all Smart Start facilities remained below the rating of “good” as measured by the researchers (Bryant et.al, 2003). Despite the Smart Start facilities receiving a good rating, a high percentage of North Carolina’s before-Kindergarten child care services remained below average in quality. These gaps created the need for More at Four to attempt to close learning gaps.

North Carolina’s More at Four program. The More at Four initiative was enacted in

2002 with the intent to provide opportunities to expose children to math and literacy skills to be school ready. An analysis of pre-k impact on third-grade reading through Smart Start and More at Four found positive effects in both reading and math (Ladd et al., 2014). The North Carolina

More at Four pre-Kindergarten program served more than 49,000 children within the program’s

first five years in 2002-2006 (Peisner-Feinberg & Schaaf, 2007). The State’s More at Four program targeted at-risk children who were unable to participate in a pre-k program.

Peisner- Feinberg and Schaaf (2007) studied the impacts of the program at the five-year anniversary of More at Four. The study assessed children’s language and literacy skills, including receptive language, rhyming, story concepts, letter naming. The children were categorized into risk groups by More at Four’s eligibility guidelines, including groups defined by income as determined by free lunch status, limited English proficiency, identified disability, and chronic health condition. The data demonstrated a notable difference in the children in the lowest-risk group. This group of children made statistically significant gains compared to their peers on rhyming skills. The researchers concluded this area of phonological awareness may require higher cognitive abilities. Children sorted into the higher-risk group demonstrated greater gains than their peers in receptive language (Peisner-Feinberg & Schaaf, 2007). In each subsection of areas of literacy, math, and general knowledge, the children at greater risk entering the More at Four program continued to be at the lower levels at the end of the year. For example, there were no significant differences noted among the cohorts of highest and lowest risk children from the tests in the fall; however, “the highest-risk group scored significantly lower than the other groups in the spring” (Peisner-Feinberg & Schaaf, 2007, p. 58).

Reading assessment data across the decades demonstrates the impact of poverty on achievement (Fernald, Marchman, & Weisleder, 2012; Hart & Risley, 1995; Kennedy, 1986;

Rowe, Raudenbush, & Goldin-Meadow, 2012). A key focus of school-ready initiatives like Head

Start, Smart Start, and More at Four has been to improve pre-literacy skills. Positive effects have been demonstrated throughout the years; however, gaps in literacy persist. Additional interventions are needed.

The Role of Word Interaction and the Impact of Poverty on Word Interaction

Word interaction among very young children has become better understood as a factor that is associated with pre-literacy literacy development (Roberts, Jurgens, & Burchinal, 2005).

In the 1970s, researchers Carey and Barlett studied how children acquire new words by the rate of exposure (Carey & Bartlettt, 1978). The study considered patterns as to how children learn a new, single word. The study was based on previous pilot studies of both the researchers to produce a deeper understanding of how children learn a new word (without explicitly teaching it) and observing whether the presence of fast mapping, or learning a new idea after seeing it one time, occurred (Carey et al., 1978). In the study, children interacted with a new word with their teacher to allow the researchers to record how word interaction caused an understanding of the new word. According to the Linguistic Society of America, based on research from Neil Smith

(1989), Jean Pecchi (1994), and Steven Pinker (1994), all children acquire language without direct instruction (Birner, n.d.). Children learn new words simply by interacting with words when adults talk with children. “Children who are never spoken to will not acquire language.

And the language must be used for interaction with the child; for example, a child who regularly hears language on the TV or radio but nowhere else will not learn to talk” (Birner, n.d., p. 2).

In 1995, researchers Betty Hart and Todd Risley found what they asserted was one of the fundamental causes of gaps among children in reading proficiency. They concluded, based upon intensive research, that the number of words with which a child interacts varies greatly across income levels. They further concluded that the development of pre-literacy skills was related to a child’s interaction with words. Since children in poverty tend to have significantly less interaction with word-rich environments during formative years, the vocabulary and reading proficiency of children in low-income homes is typically lower than those of middle-income

peers (Hart & Risley, 1995). Despite having access to education, they posited, children in poverty are prevented from being as school-ready as they might otherwise be by the lack of developmentally appropriate word interaction. This research illustrates the importance of intentionally developing vocabulary in children growing up in low-income households.

Contemporary research through Stanford University further illustrates a vocabulary gap, discernible by socioeconomic status, (SES) in children as young as 18 months old. Furthermore, the researchers found that by age 2, there is an observable 6-month gap in critical thinking and vocabulary building skills among children born into varying socioeconomic strata (Fernald &

Weisleder, 2013). Studies like this and that conducted previously by Hart and Risley (1995) provided compelling evidence that socioeconomic status is related to the level of word interaction and, hence, to language acquisition.

The role of cognitive neuroscience and learning a language is of great importance, as a child is cognitively predisposed to learn a language at a young age. Children are able to begin to build their vernacular at 30 months (Rowe et al., 2012). The brain has a distinct period of malleability and receptiveness to learning languages before a child reaches the age of 10 (Eliot,

2001). As children are exposed to new words and experiences, synapses continue to form bonds in their brains.

Children from high-income families, who are exposed to more words and experiences, have more synapses forming in their brains, creating more expansive vocabularies and word acquisitions (Eliot, 2001). Conversely, children from low-income families are exposed to significantly fewer words; therefore, there are significantly less synapses forming meaningful bonds. According to Eliot (2001), an excess of 20 billion synapses each day from childhood to

adolescence are lost if the brain connections are unused. This has implications for subsequent reading achievement.

Socioeconomic Status and Historic Patterns in Literacy Achievement

The National Assessment of Educational Progress (NAEP) is a key measure of subsequent reading achievement. The assessment is conducted in grades 4, 8, 12 and determines achievement trends in reading and math among American students. NAEP reading and math tests are completed once every two years to assess rates of student proficiency (NCES, 2015a). In

2015, only one third of fourth- and eighth-grade students performed at or above the Proficient level in reading. In fact, North Carolina was one of three states to see a statistically significantly decrease from 2013 to 2015 in both grade 8 NAEP scores for math and reading (NCES, 2015b).

While the data disclose relatively low levels of proficiency for North Carolina, the data also demonstrate the correlation between poverty and proficiency. For 8th grade students who took NAEP and were eligible for the National School Lunch Program (NSLP) the average reading scale score was 253; students who took NAEP and were not eligible for the National

School Lunch Program had an average reading scale score of 277. When reviewing the 4th grade reading scores, one notes that the scale score difference was more significant. Fourth grade students who participated in the NSLP had an average reading scale score of 209 on NAEP; on the other hand, 4th grade students who did not participate in the National School Lunch Program had an average reading scale score of 237 (NCES, 2016).

Trends in NAEP reading achievement data are mirrored in the results from North

Carolina’s reading and literacy assessments. The reading proficiency data for the North Carolina

Department of Public Instruction (DPI)’s End of Grade (EOG) tests are consistent with the scores of the NAEP test in both low levels of proficiency and the correlation between poverty

and student achievement. School Performance Grades are represented by the 80/20 model.

Eighty percent of the grade is a performance grade composite based on the school’s proficiency scores; 20% of the performance grade composite is calculated by a school’s growth according to the Educational Value Added Assessment System (EVAAS) (NCDPI, 2015).

EVAAS provides a growth measure that uses data from current and previous student testing results to measure if students are increasing growth throughout their years in school.

Schools are given three possible ratings: exceeded expected growth, met expected growth, and did not meet expected growth (NCDPI, 2016a). According to DPI’s statistical summary report, in

2015, 27.5% of school exceeded growth, 46.1% of schools met expected growth, and 26.4% of schools did not meet expected growth (2016a). The designations for School Performance Grades in 2015 were a 15-point scale in which: A = 85–100, B = 70–84, C = 55–69, D = 40–54, F = 39 or Less (NCDPI, 2016a). Reading grades across North Carolina are correlated with School

Performance Grades; schools with 50% or more students living in poverty were a D or F school

(NCDPI, 2016a). There are 160 northeast North Carolina schools; 29.4% of these schools do not meet growth as established by EVAAS (NCDPI, 2016a).

A review of school reading grades among the eight state education districts (this is the term for the eight regions in the state education directory) reveals that the northeast district has

11 F-rated schools in reading grades; this equates to a rate of 8.9% of schools in the district. This is the highest percentage of F schools in any of the state education districts for reading grades.

Additionally, the percentage of D reading grades in schools within the northeast state education district is 44.4%. This is also the highest percentage of D reading grades schools in any of the state education districts. In 2015, there were no schools in the northeast state education district that earned the distinction of being an A school in reading. Neighboring state school district,

North Central, includes a number of schools that are also low-performing. These ratings are juxtaposed with those in the state’s Western state education district, which contains no F schools;

42% of the schools have a grade of B in reading (NCDPI, 2016a).

The northeast state education district, as outlined by the North Carolina State Board of

Education (2015), includes school systems in Beaufort, Bertie, Camden, Chowan, Currituck,

Dare, Gates, Halifax, Hertford, Hyde, Martin, Northampton, Pasquotank, Perquimans, Pitt,

Roanoke Rapids, Tyrrell, Washington, and Weldon. The North Central state education district includes the local school systems of Chapel Hill-Carrboro, Chatham, Durham, Edgecombe,

Franklin, Granville, Harnett, Johnston, Lee, Nash, Orange, Person, Vance, Wake, Warren, and

Wilson Counties. The Western state education district contains schools from the following districts: Asheville, Buncombe, Cherokee, Clay, Graham, Haywood, Henderson, Jackson,

Macon, Madison, Polk, Rutherford, Swain, Transylvania (North Carolina State Board of

Education, 2015).

A school is deemed as low-performing when it receives a School Performance Grade of

'D' or 'F' and a growth status of 'Met' or 'Not Met'. In 2015, there were 489 low performing schools in North Carolina (NCDPI, 2016a). More specifically, there were 109 of 544 schools

(20%) in the North Central state education district that were low-performing. There were 57 of

171 schools (33%) in the Northeast state education district that were low-performing.

A school system is considered low-performing if over 50% of its schools are low- performing. There were 10 low-performing districts in 2015 (Northeast: 3 districts, Northcentral:

3 districts, Southwest: 2 districts, Piedmont: 1 district, Sandhills: 1 district). In the 3 Northeast state education districts, the school systems had the highest percent of low-performing schools when compared to the percentages in the other 4 state education districts with the low-

performing school systems. Eighty-three percent of Northampton County’s schools are low- performing; this is the case with 80% of Washington County’s schools and 77.8% of Martin

County’s schools (NCDPI, 2016b). In the state education district that neighbors the Northeast, the North Central’s 3 school systems also had districts with high percentages of low-performing schools. Warren County had 62.5% low-performing schools, Nash-Rocky Mount had 58.3% of the schools as low-performing, and Wilson County had 54.2% of schools that were low- performing (NCDPI, 2016b).

This interaction between socioeconomic status and literacy attainment seems to be evident in third grade End of Grade (EOG) test scores in high-poverty schools across eastern

North Carolina (Burross, 2008). The EOG data disclose an observable distinction in the way in which schools perform on third grade tests in low-income areas in comparison with schools in higher income districts. The deficits in reading cause low-income elementary schools to offer more transitional classrooms than are offered by elementary schools in higher income districts.

According to the NCDPI, “The 3/4 Transition and Fourth Grade Accelerated Classes are intended to be classes where students receive the 4th grade standards and curriculum with an intense focus on reading to move the student to proficiency in reading” (NCDPI, 2016, p. 7).

The word gap and associated risk factors continue to be a concern throughout the United

States. There have been concerted efforts at the state and federal level to provide support for at risk children. Concerns over deficiencies in pre-literacy skills among children in poverty have prompted national and state policymakers to implement programmatic responses to the literacy gap; however, the literacy gap remains.

Contemporary Policy Context

Contemporary practices associated with the improvement of early literacy skills and reading achievement have evolved across multiple decades and multiple policy initiatives. The first of many laws surrounding educational opportunities for schools in low-income districts was signed into law by President Johnson in 1965. The Elementary and Secondary Education Act

(ESEA) was a civil rights law that offered grant opportunities for low-income districts to improve public educational facilities by funding textbooks and library books, offering scholarships for college students, and financial contributions towards special education centers

(Gamson, McDermott, & Reed, 2015). Through subsequent reauthorizations of ESEA, including

No Child Left Behind (NCLB) and the recently enacted Every Student Succeeds (ESSA), intentional literacy and reading skills improvement aims were implemented. Fiscal support has been provided through resources such as Title I and grants such as Reading First, Early Reading

First, and the William F. Goodling Even Start Family Literacy Programs (United States

Department of Education, 2017).

In April 1983, additional impetus for drastic educational improvement, including in reading, was created by the publication of A Nation at Risk (ANR). Among other data of concern were indicators of nationwide functional illiteracy rates (National Commission on Excellence in

Education, 1983). The report made a recommendation for stronger high school graduation requirements and increased admission requirements. One of the largest literacy initiatives related to the release of A Nation at Risk was an increase in standards-based reform. For example, in recent years, 46 states adopted Common Core State Standards (Association for Supervision and

Curriculum Development, 2013). While a number of states subsequently dropped formal

association with the Common Core, the standards continued to heavily influence their curricula

(Ujifusa, 2016).

The Improving America's Schools Act of 1994 (IASA) reauthorized ESEA and included a focus on four key initiates: increased “1) high standards for all students; 2) teachers better trained for teaching to high standards; 3) flexibility to stimulate local reform, coupled with accountability for results; and 4) close partnerships among families, communities, and schools”

(United States, 1995, p. 3). The IASA revisions of ESEA also included more accountability for schoolwide Title 1 programs to overcome school deficits based on factors of poverty (United

States, 1995). Initiatives to close literacy gaps by race and poverty have been implemented at the federal, state, and local levels. Such policies provide the relevant contextual backdrop in which the study will occur.

With respect to federal policy, mention has previously been made of the Elementary and

Secondary Education Act. Among the most recent reauthorizations of ESEA, No Child Left

Behind, which was enacted during the George W. Bush administration, created a requirement for universal proficiency in reading (Kim & Sunderman, 2005). The No Child Left Behind waiver process, undertaken by the Obama administration, maintained strong accountability provisions relative to reading proficiency. North Carolina has been operating under a No Child Left Behind waiver and will continue to do so until the accountability provisions of the newest iteration of

ESEA, the Every Student Succeeds Act accountability, are approved.

Students in many states, including North Carolina, are held to proficiency standards to be promoted to the next grade level. Under the Read to Achieve Act, students are expected to reach grade level proficiency in reading by the end of third grade (NCDPI, 2016c). The Act was ratified to ensure that all third graders were at or above third grade proficiency prior to entering

fourth grade. Research has shown that “children who do not demonstrate grade level proficiency in grade three are four times less likely to graduate from high school” (Workman, 2014).

Across the United States, an expansion of high-stakes testing and related accountability systems has caused teachers to focus instruction more heavily on assessment content and item types; i.e., to teach towards the test. This practice is more pronounced in schools that serve students of color and economically disadvantaged students (Kesler, 2013; McNeil, 2000). In reaction to this trend of teaching toward state and federal tests, there are teachers who are changing their pedagogy to incorporate less test preparation and a greater focus on effective teaching practices and culturally relevant pedagogy (Powell, Cantrell, & Rightmyer, 2013).

However, much of the student-centered teaching is replaced with test preparation due to the high stakes exams (Jones et al., 1999). High-stakes testing has also caused teachers and administrators to cheat by artificially augmenting student scores through erasures and corrections (McNeil,

2000). An example of the testing pressure prominent in schools is found in McNeil’s article, which includes an anecdote of a principal replacing a teacher’s lesson plans aligned with the state curriculum with the current Texas Assessment of Academic Skills (TAAS) preparatory books (McNeil, 2000). The article further asserts that the pressure is more pronounced in schools that serve students of color and economically disadvantaged students. In 2015, eleven teachers in

Atlanta were sentenced to prison after being found guilty of changing student answers on standardized tests (Catalano & Gatti, 2017). The unintended outcomes of the pressures of high stakes, standardized tests have led to testing scandals (Arnson, Murphy, & Saultz, 2016). Such scandals arise, it is argued, out of the intense focus on the outcomes of the students’ standardized test scores in place of the education of the whole child (Viadero, 2000).

North Carolina has invested relatively considerable resources into early childhood programming and K-2 initiatives. The initiatives include the previously discussed Head Start,

Early Head Start, Smart Start, and More at Four programs. As noted previously, these programs have produced increases in early literacy. Pre-k experiences have been found to correlate to positive social and emotional benefits as well as gains in achievement for the country’s most vulnerable children (Neuman, 2007; Puma et. al., 2010). According to the January 2015 Mayor’s

Report Card on Education for 33 cities around the United States, “the data show 94 percent of districts offer some level of pre-k services to students, and 52 percent deliver pre-k to all four- year-old students within district boundaries” (George W. Bush Insitute, 2015, p. 8).

In summary, historical developments, programmatic responses, and related research acknowledge the pivotal role of literacy in a child’s academic success, and the essential role of word interaction in creating literate children. Furthermore, the research connects circumstances in low-income households and patterns of suppressed reading achievement, which are compounded through adolescence. Although there have been programmatic responses to support students in poverty, including Head Start, Smart Start, and More at Four, the achievement gap continues to persist in low-income areas. Contemporary accountability policies have augmented the importance of closing literacy gaps. Deficiencies in reading proficiency have profound implications for the nation’s economic well-being and civic health, for perceptions about schools and educators, and, most importantly, for the well-being of individual students.

Theoretical Framework

The theoretical foundations used in this study included Bandura’s social learning theory and Joyce Epstein’s 1995 framework for involvement in programs of partnership. Social learning theory was cited by Hart and Risley in their book, Meaningful Differences. As observed by these

researchers, children’s experiences with word-rich environments differed across income levels; however, an exploration of everyday parenting led the researchers in longitudinal studies to acknowledge two factors that were important predictors of IQ scores. The logged hours of parenting per day and the degree of quality as related to verbal interaction of the parents were strongly correlated with IQ (Hart & Risley, 1995).

The present study focused on parents’ perceptions of the utility of changing their behavior towards using word analysis technology to create a more word-rich environment. Self- efficiency can be enhanced through performance accomplishments (Bandura, 1977). It can be assumed by educating parents on how to use word analysis technology as a benefit, parent’s self- efficacy would increase. Bandura’s (1982) writing of self-efficacy explains self-regulated behavior as the higher the level of reported self-efficacy, the greater chance of observed performance accomplishment and lower reported self-efficacy. As explained by a contemporary view of social learning theory, there are two things that need to occur to determine social learning change: 1) an observable, definable change occurs within an individual; 2) the change needs to continue through the individual and change societal norms (Reed et al., 2010). In this study, I extrapolated the social learning theory to the research context. The assumption is that a social change would occur. More specifically, the perceptions of low-income parents toward an intervention that would change the nature of the behaviors that influence pre-literacy skills at the individual family level and may expand.

The current study’s background information acknowledged that the language analysis device would be able to measure Hart and Risley’s (1995) variable of the quantity (and quality) of parent talk. Using Bandura’s (1977) social learning theory, I attempted to capitalize on an observed phenomenon in which individuals could establish changes in their social interactions

based on specific interventions. I assumed that the individual change of behavior would expand to broader society, creating a more literate society.

The study also considered the work of Epstein and the framework for the “six types of involvement for comprehensive programs of partnership” (p. 14). Epstein argues that sociologists of education should be more involved in research of school, family, and community partnerships, (Epstein, 2005). Although there was not an examination of an explicit partnership between school and home, the researcher framed the potential use of language analysis technology as a partnership between government and the home. There is a great parallel between

Epstein’s types of partnership between parents and schools for the child to be successful and the partnership needed with a provider of the language analysis technology tools, a governmental entity (such as school system), and the parent in order for the child to reach success. The six types of partnerships include: parenting, communicating, volunteering, learning at home, decision making, and collaborating with the community (Epstein, 1995).

The first involvement includes parenting and school staff members helping families to support their children at home by completing home visits, providing parent education, and workshops (Epstein, 1995). A focus on Epstein’s first type of partnership aligns well with the study. The second type, communicating, involves effective school to home communication regarding the progress of the child. The hypothetical scenario in the study that parent participants assessed, i.e., implementation of language analysis technology, allowed me to discern the nature of communication to parents that would impact their likelihood of engaging with technology that would provide insights regarding the progress of their children’s talk turns and heard words.

The third type, volunteering, surrounds recruiting and maintaining parent support

(Epstein, 1995). Although Epstein sees volunteering as a vehicle in which parents feel a part of

the child’s school experience, the language analysis technology will allow the parent to take on the active role of child’s first teacher. The fourth type, learning at home, asks schools to provide families with concrete activities and skill practice in which they can complete with their children

(Epstein, 1995). The language analysis tools enable the parent to create a word rich environment.

The fifth type, decision making, includes making parents advocates of their children.

While receiving training related to word analysis technology, parents will be empowered to advocate for their child to be school ready with their actions. Finally, the last type of partnership, collaborating with the community, asks schools to identify and integrate community resources

(Epstein, 1995). Should the language technology be beneficial to families, the fifth type relates directly to the district being persuaded to purchase additional language technology tools for the broader community.

The theories of Bandura’s Social Learning Theory (1977) and Epstein’s (1995) framework for partnerships are connected to the research question variables. The theories allowed me to acknowledge the perceptions of using the language analysis technology and parents’ willingness to change daily behaviors in order to read with and talk more to their child.

Pertinent Research and Professional Perspectives

The research questions for this study both explicitly and implicitly pointed to a number of constructs related to the issues of parents’ roles in children’s pre-literacy development and to the specific construct of parent participation in the use of language analysis technology. Research has demonstrated that parental involvement impacts literacy when parents create a rich literacy environment in their children’s lives during ages birth to three. The role of a parent who creates a word rich environment includes book sharing, levels of talk turns, word interactions, and early exposure to language. The research questions further examined the relationship of

socioeconomic factors in parents’ fulfillment of these roles. Finally, the questions also raised the issues of parent willingness to engage with language analysis technology and to modify parenting behaviors. The following sections address how socioeconomic status (SES) may correlate with achievement levels, SES and parenting, and the ways that SES can impact how a child’s brain develops. Additional sections address how socioeconomic factors also affect literacy, parenting, brain development, and proficiency.

Socioeconomic Correlates with Literacy

There are various dimensions of poverty and literacy. Past research demonstrates that the word gap occurs between homes of high income, middle income, and low-income households

(Hart & Risley, 1995). More specifically, when acquiring vocabulary, children in the 75th percentile of language acquisition and higher are typically children of high socioeconomic status

(Rowe et al., 2012). Researchers speculate that children of college-educated parents may have a greater advantage in accessing language as the parents of the children are intentionally and inadvertently modeling good reading skills (Clark, 2007).

Researchers have found language skill gaps in infancy through high school among children from low-socioeconomic homes. Children from low-income homes possess gaps in language processing, production, and comprehension as compared to their median-income and high-income peers (Fernald et al., 2012). Sean Reardon, professor of education and sociology at

Stanford, argues this divide between the children that are on opposite ends of the national income distribution. The highest 10 percent of children with family income levels higher than $165,000 and lowest 10 percent of children with family income levels lower than $15,000, demonstrate growing differences on the SAT. According to Reardon’s (2013b) research, in the 1980’s, the difference in SAT scores between the highest and lowest 10 percent of children was 90 points;

however, today it is 125 points. “This is almost twice as large as the 70-point test score gap between white and black children. Family income is now a better predictor of children’s success in school than race” (Reardon, 2013b, p. 13).

Socioeconomic Status and Parenting

Previous research in the field of child language acquisition has increased knowledge of child preparation to encourage school readiness. This section reviews existing research on the necessity of parent talk and how the level of parent education and socioeconomic status continues to be pervasive within the literature on school readiness. Across households stratified according to socioeconomic status, one can generalize about how often a child will interact with an adult verbally and about the type of conversation used to engage the child.

Existing research has demonstrated a word gap occurring between families of low-and high-income families, due to low levels of talk turns, minimal word interactions, and little early exposure to language in low-income households (Hart & Risley, 1995). The researchers found that in the same one hour, low-income children received half of the language interaction that was received by working-class children and the disparity with that received by high-income children was even more pronounced. This was due to the way in which parents across income levels interacted with their children. For example, in low-income households, children spent additional time in front of the television and spent little time interacting verbally with the adults in the homes they occupied (Hart & Risley, 1995). Researchers Guo and Harris (2000) further supported the correlation between low wealth and lower IQ. The researchers expounded upon the cognitive stimulation afforded to children by high-SES families, such as the increased number of reading interactions of parent and child and the increased number of books in the home, that helped to explain gaps between low income and high-income children (Guo & Harris, 2000).

At 36 months, children from families on welfare had accumulated 525 words, children from middle/lower SES had a cumulative vocabulary of 749 words, and higher SES children had a recorded vocabulary size of 1,116 words. Additionally, the vocabulary of the parents was measured; the size of the vocabulary increased respectively from adults on welfare to high SES

(Hart & Risley, 1995). Furthermore, it was found that imperative messages varied greatly across income levels. Children in low-income households heard one positive, affirming talk item for every two prohibitive imperatives (e.g., “Stop,” “Quit”). Children in high-income families were exposed to positive, affirming talk at a rate double that of middle-class children and five times that of the low-income children. Hart and Risley (1995) found that at the age of four, the average low-income child had 144,000 fewer interactions with encouraging, positive words and had experienced 84,000 more discouraging, negative words than his higher-income child peer. This has been in addition to the research of Farran and Haskins (1980) regarding socio-economic status on the language acquisition of children. The researchers found that low-income mothers were less likely to be involved with their children in sessions of mutual play. Ramey and Ramey documented an analysis from the American Time Use Survey to demonstrate an increase in the time in which college educated parents spent with their children. As stated, “Since the mid-

1990s, less educated mothers have reallocated over 4 hours per week to childcare, but college- educated mothers have reallocated more than 9 hours per week” (Ramey & Ramey, 2010, p.

170).

The 2006 data from the Program for International Student Assessment (PISA), which collects international data from 15 year-olds, illustrate that family wealth is correlated with academic performance. Specifically, an increase in academic performance was correlated with rate at which individuals reported the availability of a “dozen household items such as

computers, works of arts, dishwashers, and guest rooms” (Zhang & Lee, 2011, p. 468). One explanation offered by Reardon (2012) to explain the continuing income-related achievement gap is the access that high income families have to family and social resources. An example from the article articulates the ways in which high-income parents impact their children’s cognitive development. “Highly educated parents are more able and more likely than less- educated parents to provide resources and opportunities for their children to develop cognitive and academic skills in both the preschool years and the school-age years” (Reardon, 2012, p. 23).

Researchers Rowe et al. (2012) studied children from age 14-month to 46-month during a longitudinal study. Children with more expansive vocabularies gestured at the start of the 14 month study. The children with greater vocabulary also had parents who talked more with their children, were in a high SES status, and had more years of education. Earlier work from Rowe and Goldin-Meadow (2009) found that gesturing could be an early indicator of speech.

Gesture was defined by Rowe and Goldin-Meadow in two variations. Gesture vocabulary is defined as how a child may make meaning through the use a gesture. An example of gesture vocabulary would be a child pointing at a dog and shaking her/his head to indicate “no.” The second type of gesture, gesture plus speech combination, occurs when the child articulates an idea. An example of gesture plus speech combination is a child pointing at a cup and saying

“mommy” (Rowe & Goldin-Meadow, 2009). When studying vocabulary acquisition, Rowe et al.

(2012) found that children in the 75th percentile were children of high socioeconomic status who demonstrated high numbers of gesturing. Additionally, it was found that children within the 25th percentile were in low SES homes and had low rates of gesturing.

Child rearing practices differ across socio-economic levels (Yunus & Dahlan, 2013).

Children who are reared in low-socioeconomic homes often do not possess important school-

readiness skills, since lower socioeconomic parents have been found to talk less to their children.

The parents use more directive and less conversational language and use a less varied vocabulary with minimal range of difference grammatical structures (Hoff, 2013).

When studying nationally representative data samples in 1998 and 2010, researchers analyzed how Kindergarten’s early childhood experiences were represented through socioeconomic gaps. “The evidence suggests that over this period parents at all income levels increasingly structured their Kindergarteners’ lives to be more explicitly focused on engaging learning experiences” (Bassok, Finch, Lee, Reardon, & Waldfogel, 2016, p. 13). The researchers found that the increase in percentage of number of books in the home of low income families increased from 1988 to 2010; additionally, there was an increase in access to a computer two to three times a week and the use of a computer to learn reading or math skills (Bassok et. al, 2016).

Socioeconomic Status and Brain Development

The degree to which a child interacts with his/her parent is directly correlated with the way in which the brain continues to develop (Caskey, Stephens, Tucker, & Vohr, 2014).

Researchers studying preterm infants in neonatal intensive care units (NICU) observed a correlation between increased language use by the visiting parents and the future cognitive scores of the child. The researchers concluded that “language intervention can start in NICU”

(Caskey et al. 2014, p. 583). With research, one can discern the importance of parents and the ways in which their interactions allow a child to cognitively develop.

College-educated parents are at an advantage, as they may be unintentionally modeling for their child ways to be successful as children use repetition to process novel material and copy the adult’s use of the new words (Clark, 2007). Clark’s research reinforces the work of Hart and

Risley (1995) concerning the positive, affirming words that are more frequently used in home-

income homes. In high-income families, parents were observed teaching problem-solving skills to their children from infancy. This only occurred in one-third of middle class families.

Unfortunately, none of the low-income families were observed interacting with their child by encouraging problem solving, providing multiplicity in words used, or instilling confidence in ongoing determination of critical thinking. Such interactions were only found in high income and middle-income homes (Hart & Risley, 1995).

The higher rate in daily word count found among college-educated parents by Hart and

Risley (1995) was confirmed by researchers Gilkerson and Richards (2008). These researchers discovered, through language analysis technology, that the daily word count of a parent with a

“bachelor’s degree was significantly higher (Mean=14,926) than the average daily adult word count (Mean=12,024) for other parents” (Gilkerson & Richards, 2008, p. 20).

Effects of Socioeconomic Status on Proficiency Scores

The gap in literacy proficiency between high-income and low-income families has increased throughout recent years. Reardon’s income achievement gap analysis demonstrated that the gap in standardized tests scores between children from high-income families and low- income families increased significantly from .9 standard deviations from the 1950s through the

1970s to a statistically significant increase to 1.25 standard deviations in three decades (Reardon,

2013a).

A study of six longitudinal data sets allowed researchers to find that reading skills at the school entry level have strong predictive power across low and high SES backgrounds and gender over time (Duncan et al., 2007). The Progress in International Literacy Skills (PIRLS) also finds a significant positive correlation between children who attend a school where most

students enter with school ready reading skills and obtaining reading achievement at the fourth grade (Mullis et al., 2012).

A study of student performance on the Virginia Standards of Learning (SOL) tests demonstrated that the test scores are impacted by factors other than instructional methods at the school and classroom level. An inverse relationship was found between the income level of students, as operationalized through Free and Reduced Lunch (FRL) participation, and the proficiency rates on the state exams. It is evident that the student’s socioeconomic level is directly related to the level of student achievement (Cunningham & Sanzo, 2002).

By following socioeconomic factors, researchers Fernald et al. (2012) found deficits in vocabulary acquisition and language processing in children as early as 18 months. These differences indicate that intensive, intentional corrections to close the word gap are necessary.

For example, students who are school-ready may reach a score within the high range as it relates to decoding by the end of first grade; however, if students are within a lower readiness group at the start of school, it may take until third grade to obtain comparable scores (Foster & Miller,

2007).

Zill (2001) published data from the 1998-1999 Early Childhood Longitudinal Study, which illustrated that 46% of all entering Kindergarteners came from families with one or more risk factors as defined by:

 having a mother with less than a high school education;

 living in a family that received food stamps or cash payments;

 living in a single parent household; and

 having parents whose primary language is something other than English. (p. 17)

Farkas and Beron (2004) analyzed survey data from the 1979 data set and data from a survey given to children from the female respondents of the 2017 NLS79 survey called CNLSY

(U.S. Bureau of Labor Statistics, 2017). The researchers found that the major effect of socioeconomic status on vocabulary happens before three years of age in White children. After the 36 months, it appears that the White children grow their vocabulary at similar rates to all other races (Farkas & Beron, 2004). The principal effect of socioeconomic status (SES) for

African American children occurs at both intervals, including from birth to 3 and from age 3 to 4

(Farkas & Beron, 2004). By highlighting the start of differences in vocabulary for race and social class, the research demonstrated that social class affects vocabulary. Similar to Zill’s (2001) findings, Farkes and Beron (2003) found the SES effect on early oral language skill development to be correlated with verbal scores of the child’s mother.

Early Literacy

To understand the compounding effects of literacy acquisition and, conversely, failure to acquire strong literacy skills, researchers became interested in determining how early literacy affects outcomes as a child ages. Studies have demonstrated the importance of caregiver interaction, preschool programs, word interaction, and child play.

By examining the well cited High Scope’s Perry Preschool Study, which followed 123 low income children with a high at-risk status for several years, a better understanding of early literacy was reported. At ages 3 and 4, these children were divided into a group that attended

Perry Preschool during 1962 to 1967 and students who did not attend a preschool program.

Researchers surveyed 97% of original participants at age 40. The study found that the “adults at age 40 who had the preschool program had earned more advanced degrees, had engaged in fewer crimes, were more likely to maintain employment, and were more likely to successfully

completed high school than 40-year-old adults in the study who did not attend preschool

(Belfield et al., 2005).

Recently, the researchers surveyed 97% of the High Scope study’s original participants, now 40 years of age. The study found that the “adults at age 40 who had the preschool program had earned more advanced degrees, had engaged in fewer crimes, were more likely to maintain employment, and were more likely to successfully completed high school than 40-year-old adults in the study that did not attend preschool (Belfield et al., 2005).

Research illustrates how experiences with reading in childhood can affect reading achievement later in life (Adams, 1990; National Early Literacy Panel, 2008). This research was expanded to include identifiable early literacy and language skills, or called emergent literacy skills, such as letter name knowledge, letter sound knowledge, print knowledge, and phonological awareness (Whitehurst & Lonigan, 1998). The researchers of emergent literacy acknowledge that the emergent skills can predict future reading skills such as reading comprehension and decoding (Lonigan & Shanahan, 2009). Researchers Sénéchal and LeFevre

(2002) found that “children whose early literacy skills were relatively good at the beginning of

Kindergarten had better literacy skills early in grade 1 and were more likely to be decoding words at the end of grade 1 than their peers whose early literacy skills were weaker (p. 457).

The Highscope Infant-Toddler Key Developmental Indicators related to word interactions include several components of communication, language, and literacy (Highscope, 2017):

 Listening and responding: Children listen and respond.

 Nonverbal communication: Children communicate nonverbally.

 Two-way communication: Children participate in two-way communication.

 Speaking: Children speak.

 Exploring print: Children explore picture books and magazines.

 Enjoying language: Children enjoy stories, rhymes, and songs. (p. 1)

Rush (1999) studied home environments to see how literacy skills were developed. This researcher studied four interactions surrounding caretaker interactions with children and materials in the home. The activity, caretaker involvement, child’s social behavior and literacy- related activities were the focus of the study. The degree to which caretakers participated in structured activities was related to the development of literacy skills (Rush, 1999). The simultaneous occurrence of low levels of structure and caregiver involvement resulted in an increased likelihood that a child would have lower scores on early literacy and vocabulary measures. Additionally, Rush (1999) observed that caregivers that had a lower level of interaction had children that were more likely to avoid interactive play and to watch television during the unstructured time. However, having a caregiver who provided structured play and higher participation in the child’s activity resulted in greater scores on literacy and vocabulary measures (Rush, 1999).

Additional research has shown that the mother’s age when her child was born and the mother’s level of education correlate with creating a literacy rich home environment. Birth to teenage mothers is correlated with low levels of literacy engagement. Increased maternal education typically yields a more robust literacy environment (Rodriguez et al, 2009). Further research completed by analyzing the American Heritage Time Use Study collected from the years 1965 through 2013 demonstrates that high and low-educated parents’ have an observable, widening, gap in the amount of reported time invested in participating in their child’s developmental activities (Altintas, 2016).

It is necessary to educate parents to create stronger literacy-enhancing environments, as research has demonstrated that a child’s contact with books is directly to early literacy skills.

“Children’s exposure to books at home played an important indirect role indirect role in the development of reading skills” (Sénéchal and LeFevre, 2002, p. 457). Additionally, Sénéchal and

LeFevre (2002) found that parental participation in explicitly educating children about reading and writing words fostered the presence of early literacy skills.

Parent Education to Close the Gaps in Literacy

Parents may not be able to change their educational experience or level of income at the point of conception; however, they are able to become more aware of issues confronting their children over time. A study of parent interaction and involvement indicated that parents are interested in learning ways to help their children in novel situations; applying this finding to the concept of language learning, i.e., providing education to parents, may assist children.

Gravesteijn, Diekstra, and Petterson (2013) found that parents wanted to increase their level of understanding when they learned that their children were being bullied. Parents wanted to support their children by learning strategies and skills to teach their children.

Research has demonstrated that the parents of preschool aged students who offer more intense levels of writing support have children with increased levels of fine motor and decoding skills (Bindman, Skibbe, Hindman, Aram, & Morrison, 2014). Longitudinal research demonstrates that Kindergarteners with higher literacy skills typically have mothers who have explicitly worked with their children to teach graphemes and phonemes by teaching letter teaching (Aram & Levin, 2004). Research results demonstrate that school and home efforts enhance outcomes of literacy attainment at age five (Dickinson & Tabors, 1991).

Literacy Education for Parents

Parents in most communities can access programs that provide support for assisting their children with early literacy development. There are many movements that encourage parents to read with their children (Dickinson & McCabe, 2001). According to research by Primavera

(2000) with one hundred parents of low-income preschoolers, workshops designed to assist parents in becoming more confident with book sharing practices yielded participant reports of increased time and interest in parent-child book sharing.

A study conducted by High et al. (2000) found that child literacy was promoted in pediatric care centers by educating parents and providing books for the home. There was a 40% increase in “child-centered literacy orientation” among the families who participated in the intervention.

Using this research, one can surmise that parents may be receptive to gaining knowledge on how to create a word-rich environment. The United States has many multigenerational reading education initiatives, including the Family Reading Partnership, The National Center for

Family Literacy, and The National Center for Families Learning. Although different in name, the literacy initiatives are similar in their approach to involve parents as intervention agents with comprehensive family literacy services.

The National Center for Families Learning (NCFL) developed four interdependent areas of language development: child education, parent education, a program called Parent Time, and another movement of Parent and Child Together Time (Darling, 2004). The focus on adult education involves increasing the amount of education afforded to the parent. Research by

Denton and Germino-Hausken (2000) found a positive correlation between a mother’s education and the Kindergarten math and reading scores of her child. Through initiatives like the NCFL

program, parents are exposed to educational opportunities resulting in improved English skills, obtaining better jobs, passing U.S. citizenship tests, and earning a GED certificate or high school diploma (NCFL, 2015). As the parent’s own education level increases, so does the likelihood of positive parental behaviors to support the early literacy of her/his child, making programs like these extremely valuable.

According to the article Theory to Outcome: NCFL’s Two-Generation Movement for

Families, the first theory of change in NCFL is “effective family-centered learning implemented through Parent and Child Together (PACT) Time® based in parent-focused capacity-building strategies” (Cramer, 2016, p. 2). PACT Time is the structured and intentional time for reading, working, learning, and playing between the parent and child. It is a means of prescribed interactions that can “lead to enhanced language, literacy, and emotional and cognitive development” (Darling, 2004, p. 19). The interactions are modeled by instructors to support a parent’s development of ways in which they may extend a child’s daily home learning.

Combining the four components into a structured system of experiences intentionally created to shape literacy may include a NCFL experience such as the one below:

During story hour, a kindergartner enjoys listening to her teacher read out loud to

the class, pointing out words that "sound the same," which the teacher calls rhyming words.

The kindergartner feels confident when the teacher calls her to the front of the room to pick

out rhyming words from the story.

In Parent Time, the kindergartner's father learns that repeating and copying down

rhyming words that he points out for his child can enhance her "ear for language" and her

"eye for words." The father learns that these are important steps in building phonological,

phonemic, and eventually phonic development. He jokes that it's a lot easier to remember

"eyes" and "ears" than all those "P" words!

When the kindergartner and her father are united in PACT Time, she proudly shows

him the chart and the rhyming words she correctly identified during story hour. Later, as

the teacher reads a familiar rhyming book out loud to the whole group, the father listens to

the teacher pause to let the children complete the sentences. The father notices how the

children are able to identify many of the rhyming words on their own.

Following PACT Time, the father practices word analysis in his adult education

class, identifying word families and creating real words by attaching different consonants

to the word families. Next, everyone in the class reads an article and highlights the word

patterns they are working on. The father realizes how closely related his own reading work

is to that of his daughter.

That night, father and daughter sit down to read a new rhyming book together. The

father points to the words as he reads them out loud so that his daughter can follow along.

When he comes to the end of a sentence, he pauses and asks his daughter what she thinks

the rhyming word is. She squeals with delight when her father tells her she's exactly right.

(Darling & Lee, 2004, p. 383-384)

The previous studies indicate a need for researchers to develop a way in which parents living in low-income environments can develop word-rich homes for their children. The increase in exposure to words will serve to foster increased brain synapses and longitudinal vocabulary development (Elliot, 2001; Rowe et al., 2012).

Research has shown that parents are willing to learn effective practices to increase the chances of their children’s success (Gravesteijn et al., 2013; Reedtz, Martinussen, Fredrik,

Handegård, & Willy-Tore, 2011). Therefore, it is arguable that parents of low-income households should be educated on ways to increase word talk at home. This education may reduce the word gap between low-income and high-income children. Due to the well- documented inconsistency in the levels of literacy environments within low income households, intervention may alter child outcomes significantly (Payne, Whitehurst, & Angell, 1994).

The literature points to multiple factors that may inhibit the participation of low-parents in parent education activities. For example, barriers created by employment may prevent parent participation. Muller (1995) found that full-time working mothers were less likely to join the

Parent Teacher Organization (PTO), less likely to know their child’s friends, had fewer restrictions on watching television during the week, and are more likely to have a child who is unsupervised after school. The study found that part-time employed mothers had the highest level of involvement in their adolescent’s educational experience (Muller, 1995).

Conversely, in a longitudinal study on literacy environments, a positive correlation was found between maternal employment and creating a literacy rich environment. The researchers hypothesize that working mothers may be able to financially support the necessary educational materials to create learning gains (Rodriguez et al., 2009).

A mother using book reading strategies yielded increased the child’s vocabulary scores on a longitudinal research assessment between the ages of 3 to Kindergarten enrollment (Roberts et al., 2005). Researchers found that shared book reading between preschoolers and their parents lead to greater language development; however, lower educated families used less varied reading strategies and spent less time reading the shared reading book with their child (Hindman, Skibbe,

& Foster, 2014).

Improving Parent Capacity to Strengthen Children’s Literacy at Home

A focus on parent-directed intervention is necessary as parent language is statistically significant in impacting a child’s language development and therefore the child’s school readiness (Connell & Prinz, 2002). When studying the first three years of child’s life, it has been found that children that have literacy rich environments perform within the normal range of the general population; however, children with literacy deprived-environments are susceptible to difficulties in learning (Rodriguez et. al, 2009).

Sénéchal and LeFevre (2014) used the Home Literacy Model to explain the connection between language and literacy rich environments. Using the Home Literacy Model definitions,

“informal literacy experiences are those where print is present but is not the focus of the parent– child interaction. In contrast, formal literacy activities are those where the attention is on the print itself” (Sénéchal & LeFevre, 2014, p. 1).

Parent-focused interventions to target the differences in children’s language environments due to low socioeconomic status were implemented by researchers in Chicago.

During the eight weekly, one-hour home visit interventions, the researchers documented the ability to increase the parent’s knowledge language development in children (Suskind et al.,

2015). The intervention included watching a video of best practices for parent-child interaction and completing goal-setting worksheets to plan for an increase of child literacy activities. The results demonstrated fluidity of parents’ education towards child language development. For example, parent to child conversation turns increased during the intervention, but did not after the intervention; however, the amount of parent knowledge of how children learn language continued to increase after the intervention (Suskind et al., 2015). Home Literacy Environment

(HLE) has been found to have a statistically significant impact on the preschool child’s early literacy skills (Burgess, Hecht, & Lonigan, 2002).

Out of New York, New York, Public Prep, United States’ oldest non-profit for tuition- free pre-K has partnered with Parent-Child Home Program (PCHP) to visit the homes of younger siblings in Public Prep. The visits will occur over a two-year span and occur two times a week from a trained community-based early learning specialist (Public Prep, 2017). The visits will be centered around an educational toy or book that is provided to the family to model parent-child interaction and literacy skills. Through the Parent-Child Home Program (PCHP), fourteen years of data demonstrated that children attended the preschool “scored developmentally above their age level and, on average, more than five months ahead of their counterparts who entered Title I elementary schools without participating in the Title I early education programs” (Ewen &

Matthews, 2007, p. 16).

Recent research confirms that preschoolers will have greater language development by parents who practice meaning-related activities. Such activities are defined by “behaviors related to highlighting new vocabulary, recalling or summarizing book content, relating the book to the child’s own experiences or to other familiar books, acting out the story, directing the child to examine the pictures, and expanding on the story” (Hindman et al., 2014, p. 296). In conclusion, informal literacy experiences, as defined by the Home Literacy Model, validate a strong correlation between informal literacy experiences and children’s language development

(Sénéchal & LeFevre, 2014).

Incorporating Language Analysis Technology

Building on the research for parent-directed intervention of teaching parents the importance of language environments and providing strategies for parents to enhance the

environment, researchers have added the concept of using language analysis technology to measure aspects of the language environment (Bayliss, 2015; Gilkerson et al., 2015; Oetting,

Hartfield, & Pruitt, 2009; Suskind et al., 2015; Wood, Diehm, & Callender, 2016). By using language analysis technology, the researchers are able to study the language environment of the home without being physically present.

LENA technology. The Language ENvironment Analysis (LENA) Technology foundation provides parents with instruments and technology for the electronic monitoring of quality and quantity of talk. The foundation has created a tool that can be inserted into the child’s clothing; the device then counts the talk turns of the child with an adult, the amount of adult words heard, and child vocalizations. The device also counts other sounds, such as the amount of television to which a child is exposed (Xu, Yapanel, & Gray, 2009). In third party research, independent of the LENA Foundation, support was garnered for the reliability of the device

(Canault, Le Normand, Foudil, Loundon, & Thai-Van, 2016). Additional independent research conducted through a pilot program found that parents using the device said it was easy to use

(Charron et al., 2016).

Once the device is inserted into a computer, it automatically begins analyzing the word talk for the respective day. Based on the monitoring and collection of data on verbal interactions,

LENA supports parents with parent education, useful tips about creating a word rich environment sent to their cell phones, and online data for parent reflection. The word analysis technology has been created as a means to measure the verbal communication of early-age children in an effort to close the word gap (LENA Research Foundation, 2016b).

Research has been commissioned on the LENA technology. One study concluded that

LENA has enriched the home language environment of participants after they had received one

parent education session and goal setting session. It was determined that conversational turn count increased 25% between the initial baseline received at the goal setting session and post intervention (Suskind et al., 2015). In China, researchers found the quantitative data of caregivers using the LENA device to be positive; participants reported that interactions between adult and child were affected. Additionally, it was found that caregivers below the word count baseline improved significantly (Zhang et al., 2015). As of 2013, an initiative in Providence,

Rhode Island, Providence Talks, was incorporating LENA as part of a city-wide approach to close the word gap and prepare Kindergarten children for school entry (Providence Talks, 2015).

Starling Technology. Similar to LENA, Starling is a device that attaches to a child’s clothing. Without recording actual conversations, Starling counts the number of words a child hears. According to Starling’s website, “The Starling uses a complicated, proprietary algorithm to instantly recognize your voice, filter out any speech that’s 4-6 feet away, get rid of background interference, evaluate word count and quality before calibrating itself to analyze the next words coming out of your mouth” (VersaMe, 2017c).

By connecting via Bluetooth to a smart phone, Starling provides the user with the daily word count and recommends activities to increase talk turns (VersaMe, 2017b). While the child is wearing the device, the adult is able to push the button on the Starling to engage a set of four flashing lights; each green light symbolizes 25% of the goal met (VersaMe, 2017b). For example, if a parent reaches 75 – 99% of the goal for the day, the user will see three green lights followed by one orange. According to VersaMe, setting a goal for the number of words is dependent on the home environment. The Starling device will track word interactions and prepare an average baseline. The parent then uses Starling as a frame of reference to increase the daily word count (VersaMe, 2017c).

Assistant director of the Lindenhurst, New York Memorial Library, Lisa Kropp, wrote in the School Library Journal how Starlings could be loaned out from local libraries. “The device is also a unique icebreaker for a librarian to engage in media and early learning mentoring with a family” (Kropp, 2017, p. 16). Kropp expressed interest in the “tip of the day” function and provided examples of Earth day activities, a YouTube link to songs, and ways to narrate what you are doing while changing a child’s diaper. “The Starlings in my library go out for three weeks at a time, allowing the user to gain plenty of feedback regarding the average number of words their child hears in a day” (Kropp, 2017, p. 16).

During conversation with Chris Boggiano, co-creator of Starling, he articulated of the different ways he has heard Starling being used in school settings such as using the device as professional development and awareness for preschool teachers to talk more with their students, sending the Starlings home as a means of parent education, and teachers using it with read aloud for other students to measure the increases in word count as students read more aloud over time

(personal communication, April 18, 2017).

Chapter Summary

The combination of public policies, pertinent research, and expert perspectives from the field articulate the role that parent interactions play in children’s school readiness. My study addressed, in part, the assumption that if parents were to acknowledge the existence of the impact of language interaction deficits and welcome the use of word analysis technology to provide instantaneous feedback, parents could close the word gap simply by providing word-rich environments in their homes.

CHAPTER 3: METHODOLOGY

Chapter Introduction

This study focused on research related to word analysis technology and other innovations designed to eliminate the achievement gap. Research has shown language analysis technology to been effective in tracking the number of words spoken in the home; a substantial word gap has been identified in the homes of low-income families. Thus, LENA helps by allowing individuals to study the richness of the word environment. The research suggests that there are many benefits to language analysis technology, as the work of the LENA foundation was noted in many current publications (Bayliss, 2015; Gilkerson et al., 2015; Oetting et al., 2009). Using the research associated with the 30 Million Word Gap, the LENA Start model was created for families to be more aware of their children’s language environments.

LENA Start focuses principally on families having access to regular feedback regarding the degree to which they interact verbally with their children. The LENA Start model uses early word analysis technology combined with parent groups to explicitly demonstrate how to create word-rich environments. Data support the assertion that the LENA Start program narrows the talk gap among children (Oetting et al., 2009). I conducted surveys with parents in low-income communities to determine their perceived openness to using the technology in their own homes.

Research Design

I used quantitative research as the dominant design, combined with quasi-qualitative elements. This research study examined parent perceptions of the potential benefits of implementing dimensions of LENA Start in their homes. Elements of a qualitative design were used to provide specific case studies of parents’ perceptions and I also used constructed-response items to study how receptive parents were to implementing the program. The research design

employed quantitative and qualitative methods of data collection and analyses. Quantitative methods were used for two purposes: 1) to fit a regression of parent perceptions and LENA Start by using a Likert Scale when administering a survey instrument to a broad sample of parents and

2) to examine the perception data based on participant subgroups. The survey instrument allowed me to be able to generalize the perceptions from parents in low-income eastern North Carolina homes from a sample size of nearly 100 parents (Creswell, 2014). I selected a convenience sample of parents of children, ages birth to three, who will attend Title 1 schools. Specifically, parents of children who live in the attendance zone of a Title I elementary school were surveyed.

Since the LENA Smart tool and the Starling tool are multifaceted and contain components that parents would need to be receptive to using, the demographics of the parents in the study influenced their perceptions of the usefulness of the tool. Thus, the impact of parent perceptions and the potential interest in implementation of language environment analysis technology across households was considered by the LENA and Starling components and the parent variables including: working status, number of children, level of household income, age, and education level. Additionally, parents were surveyed about the likelihood of changing their daily behaviors in order to increase time spent talking and reading with their children.

Research Questions and Hypotheses

The use of Social Learning Theory to focus on parent perceptions of cultivating a more word rich environment by changing daily behaviors and a focus on Joyce Epstein’s Program of

Partnership informed the development the research questions. The six types of partnerships include: parenting, communicating, volunteering, learning at home, decision making, and collaborating with the community (Epstein, 1995).

Research Questions

The constructs and the relationships discussed in the current study were addressed using the following research questions and related hypotheses:

1. What are the perceptions of parents of children, ages birth to three, who live in the

attendance zone of Title 1 elementary schools in a northeastern North Carolina school

district regarding the potential implementation of language environment analysis

technology in their households?

a. Are the perceptions related to the different components of the language

analysis technology?

b. Are the perceptions of parents related to their working status (not employed,

part-time, full-time)?

c. Are the perceptions related to the reported number of children?

d. Are the perceptions related reported to the level of household income?

e. Are the perceptions related to the reported age of the parent?

f. Are the perceptions related to the reported education level of the parent?

2. To what extent are parents of children, ages birth to three, who live in the attendance

zone of Title 1 elementary schools in a northeastern North Carolina school district

willing to change daily behaviors in order to improve literacy skills of their children?

a. Is this willingness related to their working status (not employed, part-time,

full-time)?

b. Is this willingness related to the reported number of children?

c. Is this willingness related reported level of household income?

d. Is this willingness related to the reported age of the parent?

e. Is this willingness related to the reported education level of the parent?

Hypotheses

I hypothesized that the perceptions of implementation would vary across parent factors of working status (not employed, part-time, full-time), number of children, level of household income, age of the parent, and education level of the parent. I hypothesized that the willingness to change daily behaviors in order to read with and talk more with their children were related to parent factors of working status (not employed, part-time, full-time), number of children, level of household income, age of the parent, and education level of the parent.

Study Participants

The participants in the current study were parents of children, ages birth to three, in low- income communities in eastern North Carolina. Low-income communities were identified through the SES profile of parents of children who live in the attendance zone of a Title I elementary school. If a school had over 50% free and reduced price lunch participation, the parents living in the attendance zone were eligible for inclusion in the study. When taking the survey, parents noted their family’s annual income. It was noted when participants were in a low-income community. Schools selected for the purpose of research were those in which the school had D or F grades under the state’s accountability system. Schools with D and F grades were chosen with the intent of identifying additional resources that may prompt policy changes in low-performing schools. I selected D or F schools so that if the study’s findings showed that parents in high-poverty, low-performing schools are interested in further education with word analysis technology, I could include a related policy recommendation specific to such schools.

I chose the parent participants because there is a gap in the research regarding parent perceptions of language analysis technology. Expanding the body of knowledge about the

resource can provide evidence regarding its viability as a tool for enhancing future success of readers, particularly in low-income homes. The tool would not be a useful investment if parents are not receptive to its use in their homes.

The participants were chosen through convenience sampling. This allowed me to utilize locations to hand out the survey in proximity to the Title I schools in the local school attendance zone. Such locations included neighborhood parent information nights, day care centers, and elementary schools. With the addition of the survey being accessible online, there were sufficient parent participants to include in the survey. I prompted low income parents to participate in the study by using snowball sampling; this allowed low income parents to recruit their acquaintances to take the survey by encouraging other parents to share the survey in their shared circles

(Streeton, Cooke, & Campbell, 2004).

My rationale for using a combination intermediary locations and directly contacting parents for participant contact allowed me to strengthen the likelihood of participants’ responses.

I was hopeful that the survey salience intrigued parents; if parents saw the survey in person, online, and at their child’s school, I believed the parent would view the survey as important. I assumed if parents believed the survey was important, they were more likely to take the survey.

Additionally, I assumed, if I use research suggestions from Dillman’s (1978) Total Design

Method, I would avoid a lower response rate. A few important findings from Dillman’s (1978)

Mail and Telephone Survey: The Total Design Method included moving demographic questions to the end and writing a hand signed cover letter.

Variables Addressed by the Study

There were a number of key variables addressed by the study as the variables related to parental demographic characteristics. The dependent variables were the perceptions of parents

regarding the potential implementation of language environment analysis technology in their homes and the parents’ willingness to change their daily behavior to read more and talk more with their child.

Perceptions of parents regarding the potential implementation of language environment analysis technology in their homes served as the dependent variable in Research Questions 1.a,

1.b, 1.c, 1.d, 1e, and 1.f. The independent variables in these questions included the parent’s working status, number of children, level of household income, age of the parent, and education level of the parent respectively. As was discussed in Chapter 2, working status was a variable that may define how much time a parent is able to commit to spending interacting with their child. To operationalize this variable, I asked parents to report the classification of their employment as full-time employment, part-time employment, or not employed. The second variable was the number of children below the age of 18 in their household. Although I was only interested in determining how receptive parents of children age birth through 3 are to the technology, I was interested in determining if the presence of additional children in the home made parents more or less likely to use the technology. The level of household income, age of the parent, and education level of the parent were all operationalized by the parent reporting his/her income, age, and level of completed education.

The second dependent variable was the extent to which parents of children (ages birth to three) who would attend Title I schools were willing to change daily behaviors in order to read with and talk more with their children. This variable was addressed in Research Questions 2.a,

2.b, 2.c, 2.d, and 2.e. The independent variables related to a parents’ likelihood to change are working status (2.a), number of children (2.b), level of household income (2.c), age of parent

(2.d), and a parent’s education level (2.e). These variables were operationalized as described in the previous paragraph.

Quantitative Methods

The analysis of the correlation of parent perceptions of language environment analysis technology with the different components of the language environment analysis technology and parent demographic characteristics allowed me to gauge openness towards language environment analysis technology across parent subgroups. Similarly, I was able to correlate the willingness to change daily behaviors with self-reported parental demographic characteristics.

Data Collection

The data instruments and data collection procedures are described in this sub-section. An instrument was needed for the collection of data. I did not find an existing survey through which to collect such data. I did, however, use the survey questions (number of children as a categorical variable, annual income ranges, and breakdown of parental education) generated through previous research of parent incentives and early childhood achievement (Fryer et al., 2015). The study instrument was a survey that I developed to generate data about parents’ perceptions of implementing language analysis technology in their homes and about parents’ willingness to change their daily behaviors in in order to read and talk more with their children. The survey instrument was created and then formally reviewed by a panel of experts. It is entitled The

Words Count Survey. The survey can be found in Appendix A. It was administered online and in a paper/pencil format.

Instrumentation for Collection of Quantitative Data

The items on the instrument are associated with the quantitative research question elements. Research Question 1 addressed the perceptions of parents regarding the potential

implementation of language environment analysis technology in their households and the relationship of these perceptions to selected demographic characteristics. The responses to Items

9 - 18 allowed participants to report their demographic characteristics (i.e., confirmation of having a child ages birth to 3, identification of school that elementary children in the household attend, working status (not employed, part-time, full-time), number of children in the home, number of adults in the home, level of household income, gender, race, and age of the parent, and education level of the parent).

Specific questions address the willingness of guardians to implement language environment technology in homes. The preamble to these items provided assurance to the participant that LENA, while counting the number of words the child hears or says each day, does not record conversations. The items that addressed the willingness to use the technology include Items 3.a (To what degree are you willing to have a visit to your home by a person that can teach you how to use devices such as Starling and LENA?), Item 3.b (To what degree are you willing to clip a device like LENA or Starling onto your child’s clothes each day that will count the number of words spoken to your child each day?), Item 3.c (To what degree are you willing to use an app on your phone each day to check the number of words spoken to your child?), and Item 3.d (To what degree are you willing to plug in a device like LENA or Starling each night in order for it to charge the battery for use the following day?). Response options for these items included: No. I do not want to do this; I probably would not do this; There is a chance I may do this; and Yes! I would do this. The survey items related to Research Question 1 concluded with a more global constructed-response item (Items 4 and 5) concerning interest in language environment analysis. These items provided qualitative data for further analysis of the first research question.

Research Question 2 assessed how likely parents are to change their daily behaviors to read with, and talk more with, their children in order to improve their literacy skills. When guardians were asked about changing habits, they were presented with questions about behaviors that are likely to increase school readiness in their child. Questions included Item 6.a (How likely are you willing to start to sign up for text messages with ideas to talk more with your child?). The second question that addressed changing habits was Item 6.b (How likely are you to attend a parent support group to teach you how to grow the amount of words spoken daily with your child?). The parent support group would be used to teach parents how to interact more with their child in order to increase the amount of words spoken daily with their child. The third question related to changing behaviors was Item 6.c (How likely are you to take your child to the public (or school) library more often than you’ve done before to pick out books to read?). The fourth question related to changing habits was Item 6.d (How likely are you willing to start to read more minutes each day with your child?). Item 6.e (How likely are you willing to start to talk more minutes each day with your child?) and Item 6.f (How likely are you willing to start to learn words that will help your child grow their vocabulary?) are the concluding items that address the parents’ willingness to change habits. Response options for these items include: No. I do not want to do this, I probably would not do this; There is a chance I may do this; and Yes! I would do this. The survey items related to Research Question 2 concluded with two constructed- response items (Items 7 and 8) concerning interest in changing habits. The two items provided qualitative data for further analysis of the second research question. The instrument concluded with a final constructed-response item. Item 19 provided participants with the opportunity to offer additional comments concerning any previous items in the survey.

Analysis of Quantitative Data

Descriptive analyses of responses were conducted, and a regression model was applied to the investigation of parents’ perceptions of language analysis technology and willingness to change daily behavior (Loeb et al., 2017). Descriptive statistics were also used in reporting the demographic data of the parents who participated in the study. A regression model was used to determine which of the parent demographic characteristics were related to the likelihood of parents implementing language analysis technology. Additionally, the regression model was used to determine which parent demographic characteristics were related to a willingness to change daily behaviors in order to read and write more with their child.

My analyses in the current study included:

1. Descriptive statistics, including frequency, mean, standard deviation, and

percentages.

2. Regression model as an analysis of which parental demographic characteristics

influenced parents’ perceptions of implementing language environment analysis

technology.

3. Regression model as an analysis of the relationships among parental demographic

characteristics and willingness to make daily changes in order to increase time spent

with reading with and talking to their children.

Quasi-Qualitative Methods

In order to gain deeper insights into the degree to which parents were receptive to using language analysis technology, I incorporated open-ended items administered on the survey instrument. Responses to the constructed-response items were used to develop a greater depth of

understanding to which components of language analysis technology prompted parents to want to use the technology, and which made them hesitant to use the technology.

Data Collection

Language analysis technology represents a system that is divergent from the traditional approach to parenting pre-Kindergarten children. Qualitative research methods were used in this study to provide analysis of parents’ willingness to change their daily behaviors in order to read and write more with their children. The methods, including the data instruments and procedures for qualitative data, are described in this section.

Instrumentation for Collection of Qualitative Data

Data about parents’ perceptions of implementing language analysis technology in homes were generated. It was also important to generate data related to parents’ willingness to change their daily behaviors in order to read and talk more with their children. The survey instrument, found in Appendix A, was administered online and in a paper/pencil format. Both versions were used due to the fact that some participants might not have access to the internet.

The research question elements were addressed in each of the open-ended responses, including, “please describe why you would be willing to do the things listed above (text visit to your home, child wearing the device, using the app, charging the device, etc).” and “please describe anything that you think may make it hard for you to do what is listed above (text messaging, support group, library, reading, talking, etc.).” Responses to the constructed-response items were analyzed and coded based on emerging patterns and themes. The a priori codes utilized were:

1. Work schedule.

2. Uncomfortable about allowing recording in the home.

3. Apprehension about unfamiliar technologies.

4. Eager to learn new ways to help their child.

5. Already raised school ready children without the device.

6. Would not make time to use.

Emergent codes (interest in using an app, lack of time available, element of a stranger, already doing the activities, and lack of necessary materials) were added once themes emerged during the data analysis. As the data were analyzed using codes, I found major categories and associated concepts. For example, I was seeking to find whether there are key concepts to explain why an individual may accept or reject the use of word analysis technology. The information garnered allowed me to predict which households would be more or less likely to use the technology.

Instrument Validity and Reliability

As was noted previously, no instrument was available for the measurement of the constructs in the research questions. I created an instrument that I entitled The Words Count

Survey (Appendix A). Because this was an original instrument, I conducted processes to assess its validity and reliability.

Instrument Validity

The instrument was validated by a panel of experts. These individuals were recruited on the basis of their expertise with language analysis technology and their expertise with interacting with the parents of children whose students attend high-poverty schools. The panel of experts included a former state superintendent who coaches aspiring leaders in high poverty schools, a

former staff member of a non-profit organization specializing in healthy, school ready learners by the end of third grade, and a researcher specializing in conducting research in human centered design in high poverty areas related to adverse childhood experiences and teenage pregnancy.

The survey through which panel members provided feedback on the instrument can be found in

Appendix B.

Reliability of the instrument’s subscales related to the perceptions of parents regarding the potential implementation of language environment analysis technology and willingness to change daily behaviors in order to read with and talk more with their children was established through the use of Cronbach’s alpha. The following paragraphs describe the results of this analysis.

The study included two dependent variables. The first such construct was the perceptions of parents regarding the potential implementation of language environment analysis technology in their homes; participants addressed this variable in Item 3 and its sub-constructs, or components, in the survey. The second dependent variable was the parents’ willingness to change their daily behavior to read more and talk more with their children; participants addressed this construct in Item 6 and its sub-constructs, or components, in the survey. The sub-constructs were more detailed descriptions of components of the variables. In order to minimize random measurement errors, I created a scale for each of the dependent variables that combined the ratings by participants for each component. This process allowed me to see the parental demographic characteristics as a larger, combined variable.

I used Cronbach’s alpha to determine the reliability of these scales. The analysis of

Research Question 1 (What are the perceptions of parents of children, ages birth to three, who live in the attendance zone of Title 1 elementary schools in a northeastern North Carolina school

district regarding the potential implementation of language environment analysis technology in their households?), included four components of language environment analysis technology implementation: home visits, clipping on the device, using the app, and plugging in the device. I generated a scale in Stata called Research1 to combine these four components by using the alpha command. The average interitem covariance of the unstandardized items was .54. This produced an acceptable Cronbach’s alpha reliability coefficient of .88.

A second scale, titled Research2, was created in Stata to combine 6 items in a scale to answer Research Question 2 (To what extent are parents of children, ages birth to three, who live in the attendance zone of Title 1 elementary schools in a northeastern North Carolina school district willing to change daily behaviors in order to improve literacy skills of their children?).

The 6 components included signing up for text messages, attending a support group, visiting the library, reading more often, talking more often, and learning new words. The average interitem covariance was .3 with an acceptable Cronbach’s alpha coefficient of .88.

Study Procedures

After receiving approval from North Carolina State University’s Institutional Review

Board (IRB), I submitted a request to the superintendent of an eastern North Carolina county in which the study took place. I was given permission to work with the principals of public elementary schools and the respective feeder pattern middle school in order to gain permission to send home a survey and solicit feedback at various events. I also submitted requests to public and private institutions that parents of preschool age children frequent, such as Head Start locations, day care centers, and preschools.

Once my study was approved, I provided the paper/pencil surveys to local daycare centers and preschools. Unfortunately, I was not able to receive written consent to administer the

survey at local Head Start programs even after receiving verbal approval. It is for this reason that

Head Start centers were not included in the final study. With the blessing of the superintendent, the public-school system was a partner in the study; therefore, surveys were distributed to parents in backpacks with children at the end of the day. Additional survey distribution opportunities included Parent Teacher Organization (PTO) meetings, summer reading camp nightly parent meetings, and various school activities.

All distribution centers had envelopes in which to store and seal the submitted paper surveys until they were picked up. I was not present when parents completed the instruments and the instruments were not signed; this helped to ensure confidentiality of participants. I provided stamped, pre-addressed envelopes for the return of surveys that were sent in backpacks, PTO meetings, and other locations where there was not an envelope procedure. Although research demonstrates that the pre-addressed envelope significantly enhances the likelihood of response

(Dillman, 1978), no individuals from the survey elected to use mail service to return their surveys.

Online surveys were distributed to parent email address listservs via a survey link from respective elementary schools and institutions. Additionally, social media was used to advertise the survey’s online location. By utilizing the Internet to administer the instrument, I provided parents the means of entering data, confidentially, while at their own homes. Both the paper/pencil survey and the online survey remained confidential and did not identify individuals or families.

To secure informed consent, the survey contained an informed consent letter that explained to the participant what I was studying, why I believed the research was beneficial, and how I would safeguard participant information. The identity of participants was not known to

me; anonymity was protected at all times. In instances where the parent responded to the paper/pencil version of the survey, the survey instrument was attached to the informed consent letter. During the online survey, the survey appeared at the end of the informed consent letter.

The participants using both modes of the survey instrument were informed that completion of the survey signified their consent to participate.

Using the survey results, I was able to statistically analyze parents’ perceptions of word analysis technology. Through the use of a quasi-qualitative case-study design, themes were interpreted by categories and subcategories. The frequency and variation of scores on the Likert

Scale of perceptions of implementing language environment analysis technology for low-income households were analyzed.

Limitations of the Study

The delimitations of the study surround the constraints on generalizability and applications to practice.

1. The identified geographic region within the district was specifically chosen so that I

could analyze the receptiveness of parents in a district’s feeder pattern to

implementing word analysis technology. Generalizing these findings to other

geographic areas should be approached with caution.

2. Parents who opted to participate were the only individuals who were included in the

study; this could skew the trends in the perceptions of the parents toward those who

are more favorable to assisting their children, as they were more inclined to

participate in a survey related to parenting.

3. I relied on others to encourage potential participants to obtain and complete the

survey instrument. This is a limitation because the same sense of urgency that I had

for individuals to complete the survey may not have been communicated by those

distributing the survey.

Assumptions of the Study

The study encompassed the following assumptions:

1. I assumed that all individuals participating in the survey responded to the instrument

items honestly and without fear of repercussions for their responses.

2. I assumed that the population surveyed was representative of the population studied.

Chapter Summary

Language analysis technology has been effective in tracking the number of words spoken in the home (Gilkerson & Richards, 2008). A substantial word gap has been identified between the homes of low-income families and the homes of middle and upper income families. By capitalizing on the intersection of these conclusions from the literature and focusing on identified low-income areas of eastern North Carolina, where students are often not school-ready by

Kindergarten, parent perceptions of language analysis technology were analyzed. Although one may hypothesize that there will be potential gains to low-income areas with the implementation of this technology, further research is necessary prior to policy recommendations. As I studied parents’ perceptions about and willingness to use the language environment analysis technology, further understanding of parents’ impact on the word gap appeared. The results of the survey were reported in tables and elaborated upon in Chapters 4 and 5. Chapter 4 reports the findings and Chapter 5 addresses the implications of the results.

CHAPTER 4: FINDINGS

Chapter Introduction

The Language ENvironment Analysis (LENA) tool was developed to analyze language encountered by children from birth to age three. Its creators contend that it is revolutionizing the way in which speech and language data can be collected. Using a pocket LENA recorder and

LENA software, one can ascertain the number of words with which a child interacts (Gilkerson

& Richards, 2008). Similar in nature, Starling, another word analysis tool, provides data by giving a parent/caregiver the count of words encountered by a child and recommends activities to increase talk turns (VersaMe, 2017b). Parents who utilize devices such as LENA and Starling receive real time results of average number of words heard by a child and average number of talk turns per hour. This feedback enables parents to monitor their behavior changes and progress toward their word and talk turn goals (Suskind & Leffel, 2013).

The language environment analysis technology tools and methodology were the focus of this study, which examined the interest of low-income parents in having real-time feedback on the number of words that their children are hearing and with which they are interacting. These devices, developers contend, can play an integral part in closing the word gap.

This chapter articulates the findings of the study. The chapter reviews the quantitative method results, which include the sample’s parent demographic characteristics and the regression model results from the parent survey. Additionally, the chapter reports results from a qualitative analysis of six constructed-response items.

Quantitative Results for Participant Demographics

My first analyses focused upon quantitative data from participant responses to the demographic items in the study survey. Items 9 to 18 on the survey instrument addressed these

characteristics. I calculated descriptive statistics, including frequency, mean, standard deviation, and percentages. Using the online survey protocol Qualtrics to administer the questionnaire, I received a total of 139 recorded responses. Twenty-nine responses were blank and immediately removed from the final number of responses. Six respondents answered only the first two questions; therefore, they were removed from the survey as well. Of the remaining 104 respondents, consistent with the instrument header on page 1, 97 confirmed in item 9 that they were parents of a child ages birth to 3 and confirmed that their child would attend a Title I school

(Item 13).

Item 10 addressed the gender of respondents. The majority of respondents, 76 (78%) parents, were female. The balance of the respondents, 21 (22%) individuals, reported their gender as male. No participants identified as “Third gender/Non-binary,” which was the third response option in Item 10.

For Item 11, the 97 birth to age three parents reported the number of children present in the household. The response options ranged from 1 to 10, with an additional option to report

10+. The average number of children was 2.59 per family unit. There were no participants who reported having 7, 8, 9, or 10+ children in their home. Item 12 asked about the number of adults in the household (individuals over 18). There was an average of 2.23 adults per family unit.

There were no more than 6 individuals reported in the homes of the families surveyed. Table 4.1 depicts the data on number of children (birth through 3) and number of adults (individuals over

18) in the home of the participants.

Table 4.1: Number of Children and Number of Adults in Participant Households

Number of Reported Individuals Living in Home Classification of individual 1 2 3 4 5 6 7 8 9 10 Children, Under 18 25 (25%) 24 (24%) 28 (29%) 12 (12%) 6 (6%) 1 (1%) 0 0 0 1 (10%) Adults, 18+ 20 (21%) 55 (57%) 9 (9%) 8 (8%) 3 (3%) 2 (2%) 0 0 0 0 N= 97

Item 13 ensured that the respondents were families living in Title I attendance zones by asking, “Which school would your birth to age 3 child attend when entering Kindergarten?”

Parents had the option to choose from three elementary schools in a local school district or pick an option of other. If parents picked the option of other, they were required to write the name of their child’s school. If a parent was in the attendance zone of an elementary school that was not

Title I, they were removed from the study. In total, 6 surveys were removed from the data as they were invalid responses due to the combination of respondents that articulated attending private schools or not living in the attendance zone of a Title 1 school. As was noted earlier, among respondents, 97 represented households with birth to 3 age children who live in the attendance zones of Title 1 schools.

Item 14 in the survey queried respondents concerning their race. The participants were given the option of selecting all that applied among the following: Asian, Black or African

American, Hispanic, Native American, Pacific Islander, White. After reviewing the results, I organized the data on race in three categories as follows: White (45 participants, 46% of respondents), Black (43 participants, 45% of respondents), and Other Race (9 participants, 9%).

Figure 4.1 uses a pie-chart to profile these data.

RACE

white black other race

other race 9% white 46% black 45%

Figure 4.1: Participant Identification of Race

Item 15 addressed respondent age. The largest group of the participants chose the age range of 31-40; this range made up 39% of the parents who responded. This was follow by the age ranges of 21-25 and 26-30; both ranges made up 23% respectively. Table 4.2 provides these data.

Table 4.2: Age of Parents Participating in the Survey

Age of Parent Participants Number of Parents 18-20 21-25 26-30 31-40 41-50 Older than 50 N = 97 5 (5%) 22 (23%) 23 (24%) 38 (39%) 6 (6%) 3 (3%)

Item 16 addressed parental demographic characteristics related to work status, 65 (67%) of the parents reported that they were employed full-time, while 20 (21%) participants were employed only part-time. The remaining 12 (12%) identified themselves as being not employed. Figure 4.2 reports these data as a pie chart.

Figure 4.2: Participant Identification of Employment Status

The approximate yearly income of the families was addressed in Item 17. According to the United States Census Bureau (2017) QuickFacts, the median income of the population surveyed was $32,298. In the research survey, the annual income below $35,000 represented

51% of parents surveyed. It was interesting for to note the number of participants with households over $60,000 as I did not expect households surveyed to have such a high income. In fact, over a quarter of the incomes were reported as $61,000 and above. The frequencies and percentages for the income ranges in the item are reported in Table 4.3.

Table 4.3: Yearly Income of Parents Participating in the Survey

Income Frequency Percentage $0 to $5,000 12 12% $6,000 to $15,000 10 10% $16,000 to $25,000 17 18% $26,000 to $35,000 11 11% $36,000 to $45,000 11 11% $46,000 to $60,000 10 10% $61,000 to $75,000 11 11% over $75,000 15 15% Total 97 100%

The final query in the demographics portion of the survey was Item 18, which addressed highest level of school completed by the participants. All survey participants responded to this item. The frequencies and percentages for the education levels in the item are reported in Table

4.4. When comparing these data to the county’s statistics, I found that the survey participants’ education levels were higher than the education levels within the United States Census Bureau

(2017) QuickFacts. While only 10% of the county’s population held a Bachelor’s degree of higher according to the Census data, 32% of the individuals in the survey reported holding a bachelor’s or master’s degree (U.S. Census Bureau, 2017).

Table 4.4: Participants’ Highest Level of School Completed

Schooling Frequency Percentage No Schooling 0 0% Less than 9th grade 1 1% High school, but did not graduate 5 5% General Education Degree (GED 3 3% High school diploma 23 24% Some college but no degree 13 13% Vocational/technical program after high school 6 6% Associate's Degree 15 15% Bachelor's Degree 19 20% Master's Degree 12 12% Other degree (ie: PhD, EdD, etc) 0 0% Total 97 100%

Results for Research Questions

This sub-section examines the quantitative and qualitative data from the research questions that were developed to address the study purposes. Research Question 1 addressed the perceptions of parents regarding the potential implementation of language environment analysis technology in their households. Research Question 2 then explored the extent to which parents are willing to change daily behaviors in order to improve literacy skills of their children.

There is a great deal of detail about relationships among the various elements of language environment technology and parent demographic characteristics. This is also the case for relationship among willingness to change daily behaviors and parent demographic characteristics. The major regression analysis for Research Question 1 appears on p. 113. It is followed by the hypothesis results and qualitative analyses for this research question. The major

regression analysis for Research Question 2 appears on p. 150. It is followed by the hypothesis results and qualitative analyses for this research question.

Research Question 1

General interest in the use of language environment analysis technology. The survey began with information about language analysis devices such as LENA and Starling and described how they are used. The first item asked participants, “On a scale from 1 – 10; how interested are you in using a device like this?” Parents were able to note a numerical scale from 1

(not interested) to 10 (very interested) their degree of interest in using language analysis technology. The mean response for the 97 respondents was 7.41, with a standard deviation of

2.67. This indicates general interest in using a device like LENA or Starling.

Item 2 was a follow-up to the first item. This constructed-response item, phrased simply as “Why or why not?” offered participants an opportunity to explain the degree of interest in the technology that they had indicated in their response to Item 1. The qualitative tools used to analyze the responses to this item included open coding with constant comparative. The a priori codes utilized were:

1. Work schedule.

2. Uncomfortable about allowing recording in the home.

3. Apprehension about unfamiliar technologies.

4. Eager to learn new ways to help their child.

5. Already raised school ready children without the device.

6. Would not make time to use.

Emergent codes were added once themes emerged during the data analysis stage. The results from Item 2 analyses are as follows. Of the 97 parents who completed the survey

instrument, 73 provided responses to Item 2. The responses of the 73 parents who completed this item provided data for this analysis. Eleven parents responded that they were not interested in the device, seven parents articulated being unsure of the device and needing more information.

Finally, fifty-five parents wrote of their interest in using the device.

Thematic codes emerged from parents’ responses to Item 2: already talking enough with their child, not enough time to implement, and apprehension about unfamiliar technology.

Anecdotal descriptions of the parents who were not willing to use the language environment analysis technology included statements such as, “I already raised children who speak well” and

“it seems like a lot to do just for a word count.” A small group of parents expressed uncertainty towards the device as evident in their comments of, “is it safe to clip on a baby?” and “would have to know more information before making a decision.” The researcher found that parents who were willing to use language environment analysis shared anecdotal descriptions such as,

“I’m very interested in anything that can help my baby learn better” and “need to know if my child is being talked with at day care.”

In all, nine thematic codes emerged; three thematic codes the researcher predicted

(apprehension about unfamiliar technologies, eager to learn new ways to help child, and would not make time to use) and three codes that the researcher predicted that did not emerge (already raised school ready children without the device, uncomfortable about allowing recording in the home, and work schedule). Five emergent codes, as seen in Table 4.5, were already talking enough with their child, enjoy technology, never hearing of the product, requesting additional information, questions regarding safety, and wanting to monitor the amount of words heard.

Table 4.5: Qualitative Responses to Using Language ENvironment Analysis Technology

Parental Responses Description of Question Not willing to use Unsure if it would be used Willing to use Eager to learn new ways to help their Already talk enough with child Never heard of product child Why would or would you not be interested in Apprehension about unfamiliar Questions regarding safety Enjoy advances in technology using language environment analysis technologies technology? Request additioal Want to monitor the amount of words Concerns of time information heard

Parent willingness to use language environment analysis technology. Items 3, 4, and 5 in the survey were used to gather data to answer this research question. In Item 3, the participant was asked “to what degree are you willing to…” regarding four language analysis components, which were outlined in sub-items 3.a.) have a home visit by a person that can teach the parent how to use the language analysis device; 3.b.) clip the device to the child’s clothes each day to count the number of words spoken to your child each day; 3.c.) use an app on your phone each day to check the number of words spoken to your child; and, 3.d.) plug in the device each night in order for it to charge the battery for use the following day.

Parents were able to select from the following responses:

 No. I do not want to do this; this response was coded with a value of 1.

 I probably would not do this; this response was coded with a value of 2.

 There is a chance I may do this; this response was coded with a value of 3.

 Yes! I would do this; this response was coded with a value of 4.

My line of demarcation between an interest on the part of the respondent in using the technology and not using the technology fell at the numerical value of 2.5, or the halfway point between the responses “I probably would not do this” and “There is a chance that I may do this.” In Table

4.6, the data demonstrate that parents were most willing to use an app as described in Item 3.c.

(mean = 3.43). They gave the second highest rating to 3.d, plugging in the device (mean = 3.35).

The next highest rating went to item 3.b., clipping the device on (mean = 3.25). Parents, on the whole, were slightly more willing than not to have a home visit (mean = 2.80).

Table 4.6: Participants’ Reported Interest Language Analysis Technology

Descriptive Statistics Home vist Clip on Use app Plug in Mean 2.80 3.25 3.43 3.35 Median 3 3 4 4 Mode 3 4 4 4 Standard Deviation 0.99 0.91 0.89 0.85 N 97 97 97 97

To answer the research question, “What are the perceptions of parents of children, ages birth to three, who live in the attendance zone of Title 1 elementary schools in a northeastern

North Carolina school district regarding the potential implementation of language environment analysis technology in their households,” I analyzed correlations among the four components of the language environment analysis technology use (home visits, clipping on the device, checking an app, and plugging the device in nightly) with five parental demographics (working status, number of children, household income, age of parent, and education level of parent).

Research Question 1.a – Perceptions of language environment analysis technology and components of the technology. Parents were asked to share their level of interest when using components of language environmental analysis technology. When analyzing, I created an agreement/disagreement variable by combining the selected answers of “There is a chance I may do this” and “Yes! I would do this” on the survey to establish interest in the technology. Interest varied across parental demographic characteristics. The research question is further broken into the likelihood of parent participants allowing a home visit to occur in their home, using a clip-on device on their child, using an app on a smart phone, and plugging in the device nightly. The data are summarized below.

To run regressions, I created a variable of all components related to Research Question 1 and called it “Research1.” Using Stata, I used the code “alpha Homevisit_numeric

Clipon_numeric app_numeric Plugin_numeric, gen(Research1)” to generate the variable. The variable “Research1” uses the components of Research Question 1: interest to allow home visits, utilize a clip-on device, use an app, and plug in the device nightly. When analyzing the data, I refer to “Research1” as language analysis use (see Table 4.7 below). As displayed in the previous survey questions, a 1 – 4 scale was used.

Table 4.7: Research1 and Research2 Variables Explained

Research1 Research2 Language Analysis Use Willingness to Change Home visit to occur Sign up for text messages Use a clip-on device on child Attend a parent support group Use an app on smartphone Take child to the library more often Plug in the device nightly Read more minutes each day with child Talk more minutes each day with child Learn words to increase child's vocabulary

Research Question 1.b – Working status and home visits. When correlating the parental demographic of working status against home visits, I looked at the likelihood of parents having a visit to their home by a person who could teach the parent how to use language environment analysis technology device. In Table 4.8 it is observed that not working and full- time working participants were more likely to choose “There is a chance I may do this” or “Yes!

I would do this” on the survey.

Table 4.8: Interest in Language Analysis Technology: Working Status and Home Visits

Working Status Interest in Home Visit Not Working Part Time Full Time Total No. I do not want to do this. 3 4 7 14 I probably would not do this. 0 6 10 16 There is a chance I may do this. 6 8 28 42 Yes! I would do this! 3 2 20 25 Total 12 20 65 97

Research Question 1.b – Working status and clipping on device. Analyzing the parental demographic of working status against home visits, I looked at the likelihood of clipping on the device to the child in order for the parent to assess the number of words the child hears each day. When looking at the combined responses of “There is a chance I may do this” and

“Yes! I would this” as being receptive to clipping on the device, I found that the receptiveness of the parent varied without a particular pattern in relationship with working status: not working

(75%), part-time (70%), and full-time (88%). Table 4.9 displays these results.

Table 4.9: Interest in Language Analysis Technology – Working Status and Clip On

Working Status Clipping on Device Not Working Part Time Full Time Total No. I do not want to do this. 2 2 3 7 I probably would not do this. 1 4 5 10 There is a chance I may do this. 1 10 21 32 Yes! I would do this! 8 4 36 48 Total 12 20 65 97

Research Question 1.b – Working status and using the app. Parent’s working status had an effect on how receptive they were to using an app on their phone each day to check the number of words spoken to their child. Table 4.10 shows the interest of respective parents. I interpreted the responses of “There is a chance I may do this” and “Yes! I would this” were as parent receptiveness to using the daily app. I found that part-time and full-time working participants appeared somewhat more interested in using the app than those who were not working.

Table 4.10: Interest in Language Analysis Technology – Working Status and Use App

Working Status Using the App Not Working Part Time Full Time Total No. I do not want to do this. 2 0 4 6 I probably would not do this. 1 2 5 8 There is a chance I may do this. 0 9 12 21 Yes! I would do this! 9 9 44 62 Total 12 20 65 97

Research Question 1.b – Working status and plugging in the device. Across the participants, there was minimal difference in how working status affected parents’ interest in plugging in the device each night in order for it to charge the battery for use the following day.

As shown in Table 4.11, there was a slight increase in the likelihood of plugging in the device as the parents’ level of work increased. Using the combined responses of “There is a chance I may do this” and “Yes! I would this,” I found that 83% of not working parents were receptive, 85% of part-time parents were receptive, and 89 percent of full-time working parents were receptive.

Table 4.11: Interest in Language Analysis Technology – Working Status and Plugging in Device

Working Status Plugging in the Device Not Working Part Time Full Time Total No. I do not want to do this. 1 1 4 6 I probably would not do this. 1 2 3 6 There is a chance I may do this. 2 12 19 33 Yes! I would do this! 8 5 39 52 Total 12 20 65 97

Research Question 1.b – Regression of working status and interest in use. In order to further understand how parents’ working status changed the way in which they expressed interest in the word analysis technology, I created a variable of all components related to Research

Question 1 (see Table 4.7). Next, I compared working status of full-time and part-time work against the status of not working and Research1. The code used in Stata was “reg Research1

Work_1 Work_3.” Finally, I refer to Research1 as language analysis use; see Table 4.12 below for these data.

The level of interest in using language analysis for not working parents was represented by an average of 3.19 on a scale of 1-4. The response options and values included “No. I do not want to do this;” this response was coded with a value of 1, “I probably would not do this;” this response was coded with a value of 2, “There is a chance I may do this;” this response was coded with a value of 3 and “Yes! I would do this;” this response was coded with a value of 4. Parents working full-time were .12 units (3.31 of a scale from 1-4) more likely to use the language analysis components than not working parents. Part time working parents were .29 units (2.9 on

a scale from 1-4) less likely to use the language analysis components than not working parents.

No statistically significant measures of difference were found among the working status of the parents and their interest in language analysis technology. Table 4.12 reports the regression table.

Table 4.12: Regression among variables – Working Status and Interest in Language Analysis Technology Use

Language Work Status Analysis Use

Full Time 0.12 (0.24) Part Time -0.29 (0.28) Constant 3.19*** (0.22)

Observations 97 R-squared 0.04 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Research Question 1.c. – Number of children and home visits. No respondents reported having 7, 8, or 9 children; therefore, those columns were omitted from the table.

Overall, 70% of respondents reported having an interest in having a home visit from an individual that would teach parents how to use language analysis technology by selecting “There is a chance I may do this” or “Yes! I would do this” in the Likert scale survey. The category of

home visits, however, was the component in which parents expressed the lowest level of interest compared to their willingness to use the app, clip on the device, or plug in the device. Eighty- four percent of participants with one child reported that they were interested in the home visit; the interest decreased to 75% when there were two children in the home. The interest further decreased to 57% with the presence of three children in the home and even further with 4 and 5 children respectively. Table 4.13 depicts these data.

Table 4.13: Interest in Language Analysis Technology – Number of Children and Home Visits

Number of Children Interest in Home Visit 1 2 3 4 5 6 10 Total No. I do not want to do this. 1 2 7 1 2 0 1 14 I probably would not do this. 3 4 5 3 0 1 0 16 There is a chance I may do this. 12 10 12 5 3 0 0 42 Yes! I would do this! 9 8 4 3 1 0 0 25 Total 25 24 28 12 6 1 1 97

Research Question 1.c – Number of children and clipping on device. Ninety-seven respondents participated in responding to the question of whether they would participate in clipping the language analysis device onto their child. Using “There is a chance I may do this” and “Yes! I would do this!” as response options that denoted interest, I found that 92% of participants with one and two child(ren) in the home reported they would clip on the device. As displayed in Table 4.14, this number went down to 68% of participants with three or more children in the home.

Table 4.14: Interest in Language Analysis Technology – Number of Children and Device

Number of Children Clipping on Device 1 2 3 4 5 6 10 Total No. I do not want to do this. 0 1 5 1 0 0 0 7 I probably would not do this. 2 1 4 1 2 0 0 10 There is a chance I may do this. 10 6 8 4 3 1 0 32 Yes! I would do this! 13 16 11 6 1 0 1 48 Total 25 24 28 12 6 1 1 97

Research Question 1.c – Number of children and using the app. Throughout the response options for number of children in the home, most participants stated that they would use the language analysis app. When parents reported having one child (100% of respondents) to two children (92% of respondents), they were most likely to be willing to use the app. As previously noted, and as seen in Table 4.15, interest in using the app decreased when participants reported having three children (75% of respondents) in the household.

Table 4.15: Interest in Language Analysis Technology: Number of Children and App

Number of Children Using the App 1 2 3 4 5 6 10 Total No. I do not want to do this. 0 1 3 1 0 0 1 6 I probably would not do this. 0 1 4 3 0 0 0 8 There is a chance I may do this. 6 4 5 1 4 1 0 21 Yes! I would do this! 19 18 16 7 2 0 0 62 Total 25 24 28 12 6 1 1 97

Research Question 1.c – Number of children and plugging in the device. Respondents addressed their willingness to plug in the language analysis technology device for recharging.

Ninety-two percent of respondents with one child at home articulated interest in the device, while 95% of participants with two children in the home displayed interest. Following a pattern seen in previous correlations related to number of children and interest in some element of the technology’s use, a decrease in interest in plugging in the device was found in participants with three children in the home. This reduction is displayed in Table 4.16 as 72% of participants with

3 children reported interest.

Table 4.16: Interest in Language Analysis Technology – Number of Children and Plugging in Device

Number of Children Plugging in the Device 1 2 3 4 5 6 10 Total No. I do not want to do this. 0 1 4 1 0 0 0 6 I probably would not do this. 2 0 2 1 1 0 0 6 There is a chance I may do this. 8 8 9 2 4 1 1 33 Yes! I would do this! 15 15 13 8 1 0 0 52 Total 25 24 28 12 6 1 1 97

Research Question 1.c – Regression of number of children and interest in use. The name “Research1” was created from variables of all components related to Research Question 1.

As stated previously, in Stata, I used the code “alpha Homevisit_numeric Clipon_numeric app_numeric Plugin_numeric, gen(Research1)” to generate this variable. The variable

“Research1” uses the components of research question 1: willingness to allow home visits, utilize a clip-on device, use an app, and plug in the device nightly. When interested in seeing

how the number of children affected a parents’ willingness to use the technology, I compared parents with more than one child against having one child and Research1. The code used in Stata was “reg Research1 Children_2 Children_3 Children_4 Children_5 Children_6 Children_7.”

When analyzing the data, I refer to “Research1” as language analysis use (see Table 4.7).

Regarding the level of interest in using language analysis, parents with one child were represented by an average of 3.47 on a scale of 1-4. The response options and values included

“No. I do not want to do this;” this response was coded with a value of 1, “I probably would not do this;” this response was coded with a value of 2, “There is a chance I may do this;” this response was coded with a value of 3 and “Yes! I would do this;” this response was coded with a value of 4.

When I ran the regression, the estimate was related to having one child. The average score was 3.47 on a scale of 1-4. The pattern shows a decrease of interest in various dimensions of the technology’s use as the number of children increases. The coefficients are all negative.

With the exception of having three children, none of the estimates were statistically significant.

The results of the regression including number of children indicated having three children explained 12% of the variance (R2=.12, F(6,90)=.01, p<.05).

As seen in Table 4.17, increases in the number of children related negatively to interest in language analysis use. Parents with three children, for example, were .55 units less likely to use the language analysis components than parents with one child (2.92). The decreased interest in use of the language analysis technology by parents with three children was statistically significant at the p<0.05 level.

Table 4.17: Regression among Variables – Number of Children and Interest in Language Analysis Technology Use

Language Number of Analysis Children Use

Two Children -0.04 (0.22) Three Children -0.55** (0.21) Four Children -0.3 (0.27) Five Children -0.55 (0.35) Six Children -0.72 (0.78) Seven Children -1.22 (0.78) Constant 3.47*** (0.15)

Observations 97 R-squared 0.12 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Research Question 1.d – Level of household income and home visits. Of the four components of language analysis technology all participants, from across household income ranges, reported home visits as the component they were least likely to do. That said, two-thirds

(69%) of participants were interested in participating in a home visit. The three other components of the language analysis technology were rated at 83% interest and above.

As seen in Table 4.18, parents within the annual household income ranges of $26,000 -

$35,000 and $36,000 - $45,000 were least receptive to home visits (55% and 45%, respectively).

As income increased to the ranges of $61,000 - $75,000 and over $75,000, the percentage of parents willing to have a home visit was 91% and 87%, respectively.

Table 4.18: Interest in Language Analysis Technology – Household Income and Home Visit

Annual Salary $6,000 to $16,000 to $26,000 to $36,000 to $46,000 to $61,000 to over Interested in Home Visit $0 to $5,000 Total $15,000 $25,000 $35,000 $45,000 $60,000 $75,000 $75,000 No. I do not want to do this. 3 1 3 1 2 2 0 2 14 I probably would not do this. 1 2 3 4 4 1 1 0 16 There is a chance I may do this. 6 6 7 4 0 3 7 9 42 Yes! I would do this! 2 1 4 2 5 4 3 4 25 Total 12 10 17 11 11 10 11 15 97

Research Question 1.d – Level of household income and clipping on the device.

When looking at the participants’ overall interest in clipping the language analysis device to their child, I found that 83% of participants reported a willingness by selecting “There is a chance I may do this” or “Yes! I would do this!” The trend of an increase in interest as the income increased was also observed in this component. Of the 61 participants who reported making less than $46,000, 40 or 66% of these participants displayed interest in clipping on the device. An increase in participants’ willingness to clip on the device increased as their annual household income increased. Participants who made more than $46,000 displayed a combined interest level of 86% (31 of 36). These data are listed in Table 4.19.

Table 4.19: Interest in Language Analysis Technology – Household Income and Clipping on Device

Annual Salary $6,000 to $16,000 to $26,000 to $36,000 to $46,000 to $61,000 to over Clipping on Device $0 to $5,000 Total $15,000 $25,000 $35,000 $45,000 $60,000 $75,000 $75,000 No. I do not want to do this. 2 0 1 1 0 2 0 1 7 I probably would not do this. 2 3 1 0 2 0 1 1 10 There is a chance I may do this. 3 2 4 7 4 3 3 6 32 Yes! I would do this! 5 5 11 3 5 5 7 7 48 Total 12 10 17 11 11 10 11 15 97

Research Question 1.d – Level of household income and using the app. When participants were asked of their willingness to use the language analysis technology app, 86% of participants reported they were interested (combining survey responses “There is a chance I may do this” and “Yes! I would do this!”). No observable trends were noted in Table 4.20, as the level of interest appeared to be similar across the income levels below and above 46,000 (85%,

86% respectively).

Table 4.20: Interest in Language Analysis Technology – Household Income and the App

Annual Salary $6,000 to $16,000 to $26,000 to $36,000 to $46,000 to $61,000 to over Using the App $0 to $5,000 Total $15,000 $25,000 $35,000 $45,000 $60,000 $75,000 $75,000 No. I do not want to do this. 0 1 2 0 0 2 0 1 6 I probably would not do this. 2 2 0 0 2 0 1 1 8 There is a chance I may do this. 2 2 3 5 2 3 2 2 21 Yes! I would do this! 8 5 12 6 7 5 8 11 62 Total 12 10 17 11 11 10 11 15 97

Research Question 1.d – Level of household income and plugging in the device.

According to the survey results, 88% of all participants would be willing to plug in the language

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analysis technology nightly in order for it to charge. The 88% represents participants who selected “There is a chance I may do this” and “Yes! I would do this” from the Likert scale survey responses. Table 4.21 reports these findings. There was not an observable difference between participants with annual incomes below or above $46,000 as the percentage of interest was 53 of 61 (87%) and 32 of 36 (89%), respectively.

Table 4.21: Interest in Language Analysis Technology – Household Income and Plugging in Device

Annual Salary $6,000 to $16,000 to $26,000 to $36,000 to $46,000 to $61,000 to over Plugging in the Device $0 to $5,000 Total $15,000 $25,000 $35,000 $45,000 $60,000 $75,000 $75,000 No. I do not want to do this. 1 0 2 0 0 2 0 1 6 I probably would not do this. 2 0 0 1 2 0 0 1 6 There is a chance I may do this. 3 5 4 5 2 4 3 7 33 Yes! I would do this! 6 5 11 5 7 4 8 6 52 Total 12 10 17 11 11 10 11 15 97

Research Question 1.d – Regression of household income and interest in use. To have a better understanding of how annual income may alter how parents see language analysis technology, I ran a regression to determine how household income fit with the components of

Research Question 1 (see Table 4.7). More specifically, in Stata, I used the code, “reg Research1

Income_7 Income_6 Income_5 Income_4 Income_3 Income_2 Income_1” to compare the income ranges of $0 - $75,000 against annual household income over $75,000 and Research1. I found that household income only accounts for 3% of the variance. As seen in Table 4.22,

Research1 has been renamed to language analysis use for convenience when viewing the table.

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When I controlled for covariates, I found that no results were statistically significant when comparing all components related to the word analysis technology and making over $75,000.

I chose to compare interest among income levels as compared to annual income of over

$75,000 because I was interested to see the degree to which wealth would affect one’s interest in use of the technology. The mean for parents with an income over $75,000 was 3.25 on a scale of

1-4. Most coefficients were negative; income groups reported scoring lower on the willingness to use components of language analysis.

Parents with an annual household income of $0 to $5,000 were represented by an average of 3.04 on a scale of 1-4 (“No. I do not want to do this;” this response was coded with a value of

1, “I probably would not do this;” this response was coded with a value of 2, “There is a chance I may do this;” this response was coded with a value of 3 and “Yes! I would do this;” this response was coded with a value of 4. No statistically significant measures of difference were found among the household income of the parents and their interest in language analysis.

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Table 4.22: Regression of Household Income and Interest in Language Analysis Technology Use

Language Annual Income Analysis Use

$61,000 to $75,000 0.27 (0.32) $46,000 to $60,000 -0.22 (0.33) $36,000 to $45,000 -0.02 (0.32) $26,000 to $35,000 -0.09 (0.32) $16,000 to $25,000 0.01 (0.28) $6,000 to $15,000 -0.12 (0.33) $0 to $5,000 -0.21 (0.31) Constant 3.25*** (0.21)

Observations 97 R-squared 0.03 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Research Question 1.e – Age of parent and home visits. Participants reported their ages in one of 6 ranges provided in the survey. Most participants (86%) were in three categories:

21 – 25, 26 – 30, and 31 – 40. There were 22 participants (23%) between the ages of 21-25, 23 participants (24%) between the ages of 26 – 30, and 38 participants (39%) between the ages of

31 – 40. Using the three groups as comparison points, due the fact the other categories had less than 10 participants each, I found no apparent significant difference. Using inferential statistics,

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one may assume age does not have an effect on the likelihood of engaging in home visits.

Overall, nearly 1/3 of participants were not interested in having a home visit. Table 4.23 reports these data.

Table 4.23: Interest in Language Analysis Technology – Age and Home Visits

Highest Education Completed Less than High school but High school Vocational Some college Associate's Bachelor's Master's GED Total Interested in Home Visit 9th grade did not graduate diploma program but no degree Degree Degree Degree No. I do not want to do this. 0 1 0 2 2 0 1 1 0 7 I probably would not do this. 0 2 0 5 1 1 0 1 0 10 There is a chance I may do this. 0 0 0 9 0 5 7 11 0 32 Yes! I would do this! 1 2 3 7 3 7 7 6 12 48 Total 1 5 3 23 6 13 15 19 12 97

Research Question 1.e – Age of parent and clipping on device. Overall, participants appear to be more receptive to clipping on the language analysis technology device than participating in home visits. While 70% of participants were interested in home visits, 82% of individuals reported that they were likely to clip on the device. There does appear to be a slight increase in willingness to clip on the device as age increased. As seen in Table 4.24, 18 of 22

(82%) participants between the ranges of 21 and 25 report interest in clipping on the device. As the range of age increases to 26 – 30-year-old participants, there is also an increase of interest on the part of 20 of 23 participants (87%) in this age span. When the age increases to 31 – 40, 32 of the 38 participants are interested. The interest rate of participants in the 31 – 40 age group is

84%.

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Table 4.24: Interest in Language Analysis Technology – Age and Clipping on Device

Age Clipping on Device 18-20 21-25 26-30 31-40 41-50 Older than 50 Total No. I do not want to do this. 1 0 2 2 1 1 7 I probably would not do this. 1 4 1 4 0 0 10 There is a chance I may do this. 2 5 8 14 3 0 32 Yes! I would do this! 1 13 12 18 2 2 48 Total 5 22 23 38 6 3 97

Research Question 1.e – Age of parent and using the app. As seen in Table 4.25, age does not appear to significantly influence interest in use of the app. The 31- to 40-year-olds are least likely to use the app when compared with the cohorts of 21 to 25 and 26- to 30-year-olds.

Ninety-five percent of participants between the ages of 21 and 25 report they are likely to use the app, while 87% of 26- to 30-year-old participants are willing. The percentage decreases to 80% among 31- to 40-year-olds.

Table 4.25: Interest in Language Analysis Technology – Age and Using the App

Age Using the App 18-20 21-25 26-30 31-40 41-50 Older than 50 Total No. I do not want to do this. 0 0 1 3 1 1 6 I probably would not do this. 1 1 2 4 0 0 8 There is a chance I may do this. 1 5 6 6 3 0 21 Yes! I would do this! 3 16 14 25 2 2 62 Total 5 22 23 38 6 3 97

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Research Question 1.e – Age of parent and plugging in the device. The trend of 31- to

40-year-olds being less interested in components of language analysis technology continued to the fourth component: plugging in the device. While 95% of 21- to 25-year-olds and 96% of 26- to 30-year-old participants express interest in plugging in the device each night, a lower percentage (84%) of 31- to 40-year-olds expressed interest. Table 4.26 reports these data.

Table 4.26: Interest in Language Analysis Technology – Age and Plugging in Device

Age Plugging in the Device 18-20 21-25 26-30 31-40 41-50 Older than 50 Total No. I do not want to do this. 1 0 1 2 1 1 6 I probably would not do this. 1 1 0 4 0 0 6 There is a chance I may do this. 2 8 6 13 4 0 33 Yes! I would do this! 1 13 16 19 1 2 52 Total 5 22 23 38 6 3 97

Research Question 1.e – Regression of age of parent and interest in use. It was necessary for me to create a variable of all the components of research question one called

Research1 (see Table 4.7). Next, in order for me to see how parent ages impacted the perceived use of the components, I ran a regression to compare parents ages 21 and older against 18-20 year-old parents and Research. In Stata, I used the code, “reg Research1 Age_2 Age_3 Age_4

Age_5 Age_6.”

Using the Likert Scale labeled 1- 4 for interest in use, parents aged 18 - 20 had a mean response of 2.7 on a scale of 1-4. The parents between the ages of 21 - 25 were most likely to use the components of language analysis. With a mean of 3.37, the response of the 21 – 25-year-old parents is statistically significant as it is .67 unit increases higher than the parent cohort of 18 –

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20 year-olds (p<.05). The pattern of interest in use increases slightly increases as the ages of the parents increase.

An observable drop in the pattern is seen from parents who are 41 – 50. While the 41- to

50-year-old parents had an increase of .13 units (2.83 on a scale of 1-4) more than the 18- to 20- year-old parents to use (2.70 on a scale of 1-4), the 41- to 50-year-old age range represents the lowest average. In the oldest age range, above 50 years old, the average is 3.0 on a scale of 1-4,

.30 units above the 18- to 20-year-old parents being regressed against. Table 4.27 reports the regression data.

Table 4.27: Regression among Variables – Age of Parent and Interest in Language Analysis Technology Use Language Age of Parent Analysis Use

21-25 0.67* (0.39) 26-30 0.59 (0.39) 31-40 0.50 (0.37) 41-50 0.13 (0.48) Older than 50 0.30 (0.57) Constant 2.70*** (0.35)

Observations 97 R-squared 0.05 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Research Question 1.f – Education level of parent and home visits. As seen in Table

4.28, two survey response options were not used in the table as they were not selected by any of the participants’ as their education levels. The 2 survey responses eliminated from the chart were no school and other degrees, such as doctorate.

In Table 4.28, one sees a slight increase in interest in home visits as the level of education increases. Interest was expressed by 100% of participants with a master’s degree, 84% of participants with a bachelor’s degree, 53% of participants with an associate degree, 62% of participants with some college but no degree, 70% of high school diploma participants, and 50% of vocational school participants. When looking at individuals with college degrees (associate’s degree, bachelor’s degree, and master’s degree), I observed that 36 of the 46 (78%) participants were interested in home visits. Of the 51 participants who did not report holding a postsecondary degree, 31 parent participants (61%) were interested in home visits.

Table 4.28: Interest in Language Analysis Technology – Education Level and Home Visit

Highest Education Completed Less than High school but High school Vocational Some college Associate's Bachelor's Master's GED Total Interested in Home Visit 9th grade did not graduate diploma program but no degree Degree Degree Degree No. I do not want to do this. 1 2 0 3 2 2 3 1 0 14 I probably would not do this. 0 1 1 4 1 3 4 2 0 16 There is a chance I may do this. 0 2 1 12 2 4 5 13 3 42 Yes! I would do this! 0 0 1 4 1 4 3 3 9 25 Total 1 5 3 23 6 13 15 19 12 97

Research Question 1.f – Education level of parent and clipping on the device. When comparing participants with and without postsecondary degrees relative to their interest in clipping the language analysis device on their child, I observed a slight difference. While 72%

(37 of 51) of participants without degrees were interested in clipping on the device, the percent

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increased to 93% (43 of 46) for participants with degrees. This increase is observed in Table 4.29 below.

Table 4.29: Interest in Language Analysis Technology – Education Level and Clipping on

Highest Education Completed Less than High school but High school Vocational Some college Associate's Bachelor's Master's GED Total Clipping on Device 9th grade did not graduate diploma program but no degree Degree Degree Degree No. I do not want to do this. 0 1 0 2 2 0 1 1 0 7 I probably would not do this. 0 2 0 5 1 1 0 1 0 10 There is a chance I may do this. 0 0 0 9 0 5 7 11 0 32 Yes! I would do this! 1 2 3 7 3 7 7 6 12 48 Total 1 5 3 23 6 13 15 19 12 97

Research Question 1.f – Education level of parent and using the app. On the third component of language analysis technology (using a phone app to check the number of words each day) and comparing parents with and without degrees, one observes that the previously noted trend continued. Parents with postsecondary degrees reported a greater level of interest in using the app than parents without degrees. In this specific component, 40 of 51 (78%) participants without degrees expressed interest while 42 of 46 (93%) participants with degrees expressed interest. One hundred percent of individuals with a master’s degree reported being interested in using the app. These data are reported in Table 4.30.

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Table 4.30: Interest in Language Analysis Technology: Education Level and the App

Highest Education Completed Less than High school but High school Vocational Some college Associate's Bachelor's Master's GED Total Using the App 9th grade did not graduate diploma program but no degree Degree Degree Degree No. I do not want to do this. 1 1 0 1 1 0 1 1 0 6 I probably would not do this. 0 1 1 3 1 1 0 1 0 8 There is a chance I may do this. 0 1 0 6 1 3 4 6 0 21 Yes! I would do this! 0 2 2 13 3 9 10 11 12 62 Total 1 5 3 23 6 13 15 19 12 97

Research Question 1.f – Education level of parent and plugging in the device.

Examination of results related to the fourth component of word analysis technology, plugging in the device, discloses the now-familiar trend of greater interest among participants with postsecondary degrees. Forty-two of fifty-one (83%) participants without degrees chose “There is a chance I may do this” or “Yes! I would do this” on the Likert survey, while, 43 of 46 (93%) participants with degrees chose the same survey options. These results are reported in Table

4.31.

Table 4.31: Interest in Language Analysis Technology – Education Level and Plugging in Device

Highest Education Completed Less than High school but High school Vocational Some college Associate's Bachelor's Master's GED Total Plugging in the Device 9th grade did not graduate diploma program but no degree Degree Degree Degree No. I do not want to do this. 0 1 0 2 1 0 1 1 0 6 I probably would not do this. 0 1 0 2 2 0 0 0 1 6 There is a chance I may do this. 1 1 0 11 0 5 6 9 0 33 Yes! I would do this! 0 2 3 8 3 8 8 9 11 52 Total 1 5 3 23 6 13 15 19 12 97

Research Question 1.f – Regression of education level and interest in use. When attempting to discover what role higher education may play in a parent’s perception of word

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analysis technology, I used a regression. Since I created a variable called Research1(see Table

4.7), containing all components of the first research question, I was able to compare the parent’s level of education against parents who had completed high school as their highest education and

Research1. In Stata, I used the code, “reg Research1 AL_1 AL_2 AL_3 AL_5 AL_6 AL_7 AL_8

AL_9.”

The mean level of interest in use was 3.02 on a scale of 1-4 for parents who graduated from high school. A pattern is observed in that a decrease in education predicted a lower score on the interest in use scale; while an increase in education level was related to an increase of interest in use. The latter pattern was seen in every increase of levels of education, with the exception of individuals that completed a vocational program after high school. The parents who graduated with a master's degree reported interest in use with a mean of 3.89 on a scale of 1-4. The increase of means at the master’s level was statistically relevant at the .001 range. Table 4.32 reports the regression data.

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Table 4.32: Regression among Variables: Education Level and Interest in Language Analysis Technology Use

Language Education Level of Parents Analysis Use

Less than 9th grade -0.77 (0.75) High school, but did not graduate -0.47 (0.36) General Education Degree 0.56 (0.45) Vocational Program -0.31 (0.34) Some college but no degree 0.34 (0.25) Associate's Degree 0.18 (0.24) Bachelor's Degree 0.2 (0.23) Master's Degree 0.87*** (0.26) Constant 3.02*** (0.15)

Observations 97 R-squared 0.2 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Research Question 1 – Regression of all parental demographic characteristics and interest in use. To understand how all parental demographic characteristics contribute to a parent’s likelihood of using language analysis technology, I used the variable, Research1 to run a regression. When I controlled for covariates related to parental demographic characteristics of a parent with one child, not working, who is 18 -20, makes over $75,000, and the highest education completed was high school. In Stata, I ran the code, “reg Research1 Age_2 Age_3

Age_4 Age_5 Age_6 Work_1 Work_3 Income_7 Income_6 Income_5 Income_4 Income_3

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Income_2 Income_1 AL_1 AL_2 AL_3 AL_5 AL_6 AL_7 AL_8 AL_9 Children_2 Children_3

Children_4 Children_5 Children_6 Children_7.”

The constant determined by Stata was 2.43. Within the range of p<.1, there was a difference observed as an increase of .84 units when I examined parents with annual income within the $6,000 - $15,000 range. This increase was demonstrated by an average of 3.27 on a scale of 1-4.

Additionally, a .75 decrease in units was observed among parents who went to high school but did not graduate (1.68 on a scale of 1-4). This observed decrease was statistically significant at the p<.1 range. With respect to parent educational level, an increase in participation was seen among parents with some college but no degree (3.05 on a scale of 1-4) and parents that have an associate's degree (3.01 on a scale of 1-4). Both of the increase in interest of parents with some college and no degree and the interest of parents with an associate’s degree were at the p<.1 level; however, the parents with a master's degree reported a higher score on the scale with a 1.21 unit increase (3.64 on a scale of 1-4) at the .01 level. The most statistically significant difference was at the master’s degree. Table 4.33 reports these regression data.

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Table 4.33: Regression of all Parental Demographic Characteristics and Interest in Language Analysis Technology Use

Language Parental Demographic Analysis Characteristics Use 21-25 0.52 (0.48) 26-30 0.55 (0.51) 31-40 0.53 (0.52) 41-50 0.32 (0.68) Older than 50 0.16 (0.68) Full Time -0.3 (0.36) Part Time -0.5 (0.37) $61,000 to $75,000 0.21 (0.31) $46,000 to $60,000 -0.08 (0.37) $36,000 to $45,000 0.19 (0.35) $26,000 to $35,000 0.25 (0.35) $16,000 to $25,000 0.48 (0.35) $6,000 to $15,000 0.84* (0.48) $0 to $5,000 0.67 (0.51) Less than 9th grade -1.55 (0.98) High school, but did not -0.75* graduate (0.43) General Education Degree 0.42 (0.51) Vocational Program -0.02 (0.41) Some college but no degree 0.62* (0.33) Associate's Degree 0.58* (0.33) Bachelor's Degree 0.57 (0.38) Master's Degree 1.21*** (0.43) Two Children -0.01 (0.24) Three Children -0.35 (0.27) Four Children -0.06 (0.35) Five Children 0.07 (0.40) Six Children -0.32 (0.93) Seven Children -

Constant 2.43*** (0.74)

Observations 97 R-squared 0.36 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Research Question 1 – Hypothesis. I hypothesized that the perceptions of implementation would vary across parent factors of working status (not employed, part-time, full-time), number of children, level of household income, age of the parent, and education level of the parent. As was reported in the results for the regression of all parent demographic characteristics and interest in using language technology, there were 5 statistically significant differences out of the 28 parental demographics characteristics. At the p<.1 level, a .75 unit decrease in interest for parents who attended high school but did not graduate was observed.

Additionally, at the p<.1 level, there was an increase in interest for parents with an associate’s degree (.58 unit increase), some college but no degree (.62 unit increase), and between the annual household income of the $6,000 to $15,000 range (.84 unit increase). One statistically significant increase in interest at the p<.01 level was observed – parents with a master’s degree had a mean unit that was 1.21 units higher than parents who reported an annual household income over $75,000. In light of the results, this hypothesis was not accepted.

Research Question 1 – Qualitative analyses of responses to constructed response items related to interest in using language analysis technology. Item 4 on the survey asked parent participants why they would be willing to engage in using language word analysis technology activities such as participating in a home visit, clipping a device to their child, using an app to track word use, and plugging in a device each night to charge. In total, there were 75 constructed responses to this item.

The fifth item in the survey was open-ended. Parent participants were asked to respond to the following prompt. “Please describe anything that you think may make it hard for you to do what is listed above (visit to your home, child wearing the device, using the app, charging the device, etc.).” Fifty-five parent participants responded to this constructed-response item.

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Responses to Item 4 about willingness to use. Overall, parents stated a variety of reasons for their willingness to use language environmental analysis technology. When answering the open response question, “please describe why you would be willing to do the things listed above (text visit to your home, child wearing the device, using the app, charging the device, etc.),” parents articulated various statements regarding the technology. Among the 75 comments, there were four major themes: ease of adding the device to the family’s routine, interest in using an app on the phone, wanting to help their child learn more, and a desire to monitor their child’s learning.

Parents articulated the fact that the device had the ability to be easily incorporated into their routine. Examples included statements such as, “this wouldn’t change much of our routine” and “I would be willing to do these things because they are things I do daily now, like plugging in my cell phone at night and using apps on my phone.” Parents appeared most willing to incorporate the app and daily text messages due to the ability to use their cell phone. One interested parented wrote, “I always have my phone on me so that app would be a good idea.” It appeared that some parents believed that the use of the cell phone app was the most noninvasive.

Examples of support for using an app on a cell phone included, “The app would be easy. The others require more commitment.”

More pervasive than the idea of the device being easily adapted into the parents’ routines was the desire of parents to help their child learn at a greater rate. There were many examples of how parents wanted to assist their child with learning. These examples are best illustrated through quotes from the responses:

 “I want my son to be successful in learning and reading so I am willing to do

whatever to enhance the process.”

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 “I want to help my child in every way possible.”

 “I want to know what else I need to teach them.”

 “I would be willing to do these things because I feel like the device would be useful

and helpful.”

 “I would do all of these to be sure my child is hearing the number of words he should

be hearing.”

 “I would will [sic] to do this, cause I would like to know will this help me understand

and better my child in learning.”

 “Information is power.”

 “To help him with his speech.”

 “Want my son to learn more.”

 “Willing to do anything to improve my daughter’s future success.”

The final reoccurring theme was the desire for a parent to monitor and track the progress of their child’s learning. Parents were interested in “knowing how many words my kids heard that day.” Examples included: “I would be willing to do it so I could monitor my child’s vocabulary” and “learn the number of words spoken to my child daily so I know if I need to further help them with speech.” Although some parents stated their interest in what their child was hearing at day care, many parents seemed to want to use the device as confirmation of their own parenting abilities, such as, “I think knowing the number of words spoken to my child is interesting; knowing if I’m doing enough to help develop my child.”

Responses to Item 5 about anything that would make use difficult. When participants answered the constructed-response item, “Please describe anything that you think may make it hard for you to do what is listed above (text visit to your home, child wearing the device, using

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the app, charging the device, etc.),” various answers emerged as themes. The major themes observed were time, the device, stranger, and technology (use/hardware).

The word time appeared 20 times within the 55 entries. Specifically, it appears that survey participants were concerned about having time available for an individual to enter the participant’s home due to a work schedule. “Crazy,” “hectic,” “challenging,” and “busy” were adjectives used to describe the participants’ schedules. One solution listed was to have online sessions to eliminate the need to schedule something else into the participant’s daily routine.

The device itself arose as a theme relative to what might make it difficult to use the technology. Of the 55 entries, 13 indicated concerns with the device. Most of these participants

(8 of 13) articulated concerns that the child would move their device at day care or when being active and causing it to break or be eaten by the child. Two parents articulated concerns that clipping the device on would hurt the baby.

Having a stranger enter the participants’ home was a concern for seven of the parent participants. Five participant responses fell into the theme of electronics. While two parents stated that they do not use electronics, three stated that they do not have access to a phone and therefore would not be able to use an app.

Research Question 2

Survey Items 6-8 were used to gather data to answer this Research Question 2. Question

6 was a quantitative question which used a Likert scale. Questions 7 and 8 were open ended qualitative questions. In Item 6, the participant was asked “how likely are you willing to start doing the following” regarding six action items to foster language development: sign up for text messages with ideas to talk more with your child, attend a parent support group to teach you how to grow the amount of words spoken daily with your child, take your child to the public (or

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school) library more often than you’ve done before to pick out books to read, read more minutes each day with your child, talk more minutes each day with your child, and learn words that will help your child grow their vocabulary.

Parents were able to select from the following responses:

 No. I do not want to do this; this response was coded with a value of 1.

 I probably would not do this; this response was coded with a value of 2.

 There is a chance I may do this; this response was coded with a value of 3.

 Yes! I would do this; this response was coded with a value of 4.

My line of demarcation for gauging willingness to engage in an activity outlined in Sub-

Items a – f was between the responses “I probably would not do this” and “There is a chance I may do this.” For purposes of analysis, I decided that respondents who selected either “There is a chance I may do this” or “Yes! I would do this” were willing to start an activity.

Ninety-six of the 97 parents in the sample answered this set of questions. When I looked at the means across the samples, I found that the means ranked as follows: reading more minutes

(mean=3.77) and talking more minutes with their children (mean=3.77), learning words to help the child grow her/his vocabulary (mean=3.72), visiting the library more often (mean=3.56), receiving texts with ideas (mean=3.49), and attending a parent support group (mean=2.85). Table

4.34 reports these data.

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Table 4.34: Willingness to Complete Actions that Foster Language Development

Descriptive Statistics Text Parent Support Group Visit Library More Read More Talk More Learn New Words Mean 3.49 2.85 3.56 3.77 3.77 3.72 Median 4 3 4 4 4 4 Mode 4 3 4 4 4 4 Standard Deviation 0.88 1.03 0.66 0.55 0.53 0.59 N 96 96 96 96 96 96

To run regressions, I created a variable of all components related to Research Question 2 and called it “Research2.” Using Stata, I used the code “alpha Text_numeric

SupportGroup_numeric Library_numeric Read_numeric Talk_numeric Words_numeric, gen(Research2)” to generate this variable. The variable “Research2” used the six components of

Research Question 2: signing up for text messages, attending a parent support group, taking child to library more often, reading and talking more minutes each day with child, and learning words that will help grow the child’s vocabulary. When analyzing the data, I refer to “Research2” as willingness to change (see Table 4.7).

Research Question 2.a – Working status of parent and signing up for text messages.

Most individuals willing to sign up for text messages to encourage parents to talk more with their child were participants that were employed part-time (90% or 18 of 20 participants). This was followed by full-time working participants (88% or 57 of 65 participants), and participants who were not working (73% or 8 of 11 participants). Table 4.35 displays these results.

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Table 4.35: Willingness to Change: Working Status and Text Messages

Working Status Willingness to receive texts Not Working Part Time Full Time Total No. I do not want to do this. 2 1 3 6 I probably would not do this. 1 1 5 7 There is a chance I may do this. 0 5 12 17 Yes! I would do this! 8 13 45 66 Total 11 20 65 96

Research Question 2.a – Working status of parent and attending a parent support group. The rate at which parents indicated a willingness to engage in the component of attending a parent support group was moderate. Overall, the reported likelihood was 67% (64 of 96) among all participants. As shown in Table 4.36, the group that was least willing was parents who worked part-time (55% or 11 of 20). This increased to 63% (7 of 11) for participants that are not currently working. Seventy-one percent of full-time working participants indicated willingness to attend support groups.

Table 4.36: Willingness to Change – Working Status and Support Group

Working Status Willingness to attend group Not Working Part Time Full Time Total No. I do not want to do this. 1 7 5 13 I probably would not do this. 3 2 14 19 There is a chance I may do this. 3 6 24 33 Yes! I would do this! 4 5 22 31 Total 11 20 65 96

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Research Question 2.a – Working status of parent and taking child to the public (or school) library. I found that all not-working participants were willing to take their child to the library more often. While 94% of participants who were employed full-time were willing to go to the library more, 85% of part-time employed participants expressed willingness. Table 4.37, below, displays these percentages.

Table 4.37: Willingness to Change – Working Status and the Library

Working Status Willingness to visit library Not Working Part Time Full Time Total No. I do not want to do this. 0 0 1 1 I probably would not do this. 0 3 3 6 There is a chance I may do this. 3 5 19 27 Yes! I would do this! 8 12 42 62 Total 11 20 65 96

Research Question 2.a – Working status of parent and reading more minutes each day. Willingness to read more with children was high across the span of the participants, regardless of working status. As seen in Table 4.38, 100% of participants who were not working expressed willingness to read more with their child. Close in comparison, I found that 97% of participants who were employed full-time were willing. Ninety percent of the part-time working participants expressed willingness to read more minutes with their child each day.

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Table 4.38: Willingness to Change – Working Status and Reading

Working Status Willingness to read Not Working Part Time Full Time Total No. I do not want to do this. 0 0 1 1 I probably would not do this. 0 2 1 3 There is a chance I may do this. 2 4 7 13 Yes! I would do this! 9 14 56 79 Total 11 20 65 96

Research Question 2.a – Working status of parent and talking more each day. In all,

97% of participants were willing to change their daily routines in order to talk more with their child. Every working status group reflected willingness to engage in this activity. Ninety-five percent of part-time parents were willing to talk more each day with their child and 97% of full- time participants were willing. One hundred percent of not-working participants were willing to talk more with their child. Table 4.39 depicts these data.

Table 4.39: Willingness to Change – Working Status and Talking

Working Status Willingness to talk Not Working Part Time Full Time Total No. I do not want to do this. 0 0 1 1 I probably would not do this. 0 1 1 2 There is a chance I may do this. 2 5 8 15 Yes! I would do this! 9 14 55 78 Total 11 20 65 96

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Research Question 2.a – Working status of parent and learning words to help child grow vocabulary. I found that the participants were receptive to changing their daily practice to include learning words to help their child’s vocabulary continue to grow. The lowest level of willingness was indicated by the part-time employed parents (90%). An increase was observed as

95% of parents who were employed full-time reported a willingness to learn vocabulary. As seen in Table 4.40, 100% of not-working parents expressed willingness to learn more words.

Table 4.40: Willingness to Change – Working Status and Learning

Working Status Willingness to learn Not Working Part Time Full Time Total No. I do not want to do this. 0 0 1 1 I probably would not do this. 0 2 2 4 There is a chance I may do this. 2 4 10 16 Yes! I would do this! 9 14 52 75 Total 11 20 65 96

Research Question 2.a – Regression of working status and willingness to change. As

I did to analyze the impact of working status on likelihood of using word analysis technology, I also analyzed how working status may relate to a parent’s willingness to change daily behaviors.

I created a variable of all components related to Research Question 2 (see Table 4.7). In order to determine the specific effect of working status, I ran a code in Stata (reg Research2 Work_1

Work_3). This regression allowed me to compare the working status of full-time and part-time working parents against not working and Research2. The results are displayed in Table 4.41.

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Compared to not-working parents (mean of 3.56 on a scale of 1-4), full-time parents were

.01 points more willing to change their behavior (mean of3.57 on a scale of 1-4). Parents who worked part-time were .19 points less likely to change their daily behaviors (mean of 3.37 on a scale of 1-4). In all, I found that work status does not predict differences in willingness to change as there were no statistically significant differences reported.

Table 4.41: Regression among Variables – Working Status and Willingness to Change Behaviors

Willingness Work Status to Change

Full Time 0.01 (0.19) Part Time -0.19 (0.22) Constant 3.56*** (0.18)

Observations 96 R-squared 0.02 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Research Question 2.b – Number of children and signing up for text messages. No respondents reported having 7, 8, or 9 children; thus, those columns were not used in the table. A decrease in willingness to sign up for text messages was noted as the number of children increased from one to four. Starting with parents with one child in the home, 96% were willing to receive messages. The percent who were willing decreased to 88% when two children were present. The willingness further decreased with three children (81%) and four children (75%) in the home. Interestingly, 83% of parents in households with 5 children were willing to sign up for text messages. Table 4.42 depicts these data.

Table 4.42: Willingness to Change – Number of Children and Text Messages

Number of Children Willingness to receive texts 1 2 3 4 5 6 10 Total No. I do not want to do this. 0 2 2 1 1 0 0 6 I probably would not do this. 1 1 3 2 0 0 0 7 There is a chance I may do this. 3 5 5 2 1 1 0 17 Yes! I would do this! 21 16 17 7 4 0 1 66 Total 25 24 27 12 6 1 1 96

Research Question 2.b – Number of children and attending a parent support group.

While 72% of participants with one child and 79% of participants with two children reported a willingness to attend a support group, the number drops significantly to 48% and 58%, respectively, for participants with three and four children. As depicted in Table 4.43, I found that

83% of participants with five children in the household were willing to sign up for text messages.

Table 4.43: Willingness to Change – Number of Children and Support Group

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Number of Children Willingness to attend group 1 2 3 4 5 6 10 Total No. I do not want to do this. 1 2 7 2 1 0 0 13 I probably would not do this. 6 3 7 3 0 0 0 19 There is a chance I may do this. 10 8 8 2 3 1 1 33 Yes! I would do this! 8 11 5 5 2 0 0 31 Total 25 24 27 12 6 1 1 96

Research Question 2.b – Number of children and taking child to the public (or school) library. Respondents addressed their willingness to take their child to the library more often. Table 4.44 shows a slight decrease in willingness of participants to take their child to the public or school library as the number of children increased from 1 or 2 to 3. Ninety-six percent

(47 of 49 participants) of parents having one to two children reported willingness to visit the library more often. The percent who were willing decreased to 89% (24 out of 27 participants) among parents with three children. However, this trend reversed when the number of children increased to 4; 92% (11 of 12 participants) of parents with four children were willing to visit the library more often. Finally, 83% (5 of 6 participants) of parents with 5 children articulated a willingness to change their behavior to include visiting the library more often with their child.

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Table 4.44: Willingness to Change – Number of Children and the Library

Number of Children Willingness to visit library 1 2 3 4 5 6 10 Total No. I do not want to do this. 0 1 0 0 0 0 0 1 I probably would not do this. 1 0 3 1 1 0 0 6 There is a chance I may do this. 7 6 7 4 1 1 1 27 Yes! I would do this! 17 17 17 7 4 0 0 62 Total 25 24 27 12 6 1 1 96

Research Question 2.b – Number of children and reading more minutes each day.

When viewing the component of willingness to change to include more reading, I found that an increase of children in the home relates to a slight decrease in willingness of parents to read more with their child. As seen in Table 4.45, 100% of participants with one child reported being willing to read more with their child. Ninety-six percent of participants with two and three children were willing more among parents. As the number of children increased to four and five in the home, the percent willing decreased slightly to 11 of 12 participants (92%) and 5 of 6 participants (83%), respectively.

Table 4.45: Willingness to Change – Number of Children and Reading

Number of Children Willingness to read 1 2 3 4 5 6 10 Total No. I do not want to do this. 0 1 0 0 0 0 0 1 I probably would not do this. 0 0 1 1 1 0 0 3 There is a chance I may do this. 2 2 6 1 2 0 0 13 Yes! I would do this! 23 21 20 10 3 1 1 79 Total 25 24 27 12 6 1 1 96

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Research Question 2.b – Number of children and talking more each day. Willingness to talk more with children was high across the participants. I observed that one participant in the survey chose, “No. I do not want to do this” when asked if he/she would be willing to change daily behavior to include talking to the child more each day. Two participants chose “I probably would not do this.” The other 93 of 96 participants (97%) expressed a willingness in talking more with their child. As seen in Table 4.46, a slight decrease in willingness was noted as the number of children in the house increased from one to four (100%, 96%, 96%, and 92% respectively).

Table 4.46: Willingness to Change – Number of Children and Talking

Number of Children Willingness to talk 1 2 3 4 5 6 10 Total No. I do not want to do this. 0 1 0 0 0 0 0 1 I probably would not do this. 0 0 1 1 0 0 0 2 There is a chance I may do this. 3 2 6 1 3 0 0 15 Yes! I would do this! 22 21 20 10 3 1 1 78 Total 25 24 27 12 6 1 1 96

Research Question 2.b – Number of children and learning words to help child grow vocabulary. When participants were asked their level of willingness to learn words in order to help their child grow her/his vocabulary, a slight decrease in willingness was observed as the number of children increased. One hundred percent of parents with one child were willing to learn vocabulary words. The percentage of willing individuals decreased to 96% (23 of 24 participants) among parents with two children. The number dropped further to 25 of 27

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participants (93%) for parents of three children. Ninety-two percent (11 of 12 participants) of parents with four children were willing to learn. Finally, 5 of 6 (83%) of parents with five children were willing. Table 4.47 depicts these data.

Table 4.47: Willingness to Change – Number of Children and Words

Number of Children Willingness to learn 1 2 3 4 5 6 10 Total No. I do not want to do this. 0 1 0 0 0 0 0 1 I probably would not do this. 0 0 2 1 1 0 0 4 There is a chance I may do this. 3 3 6 2 2 0 0 16 Yes! I would do this! 22 20 19 9 3 1 1 75 Total 25 24 27 12 6 1 1 96

Research Question 2.b – Regression of number of children and willingness to change behaviors. The variable Research2 was created in Stata to combine the six components of willingness to changing daily behaviors. To see how the number of children may have affected the parents’ willingness to change, I compared having two or more children against having one child and Research2. In Stata, I used the code, “reg Research2 Children_2 Children_3

Children_4 Children_5 Children_6 Children_7” to determine the outcome. As shown in Table

4.48, the Willingness to change (Research2) and the coefficients (of having multiple children) are all negative.

As parents reported having more than one child, lower means on the scale present themselves. This illustrates that parents with more than one child in the home displayed an observable pattern of decrease of willingness to change daily behaviors. Statistically significant

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within the p>.1 range, parents with three children were .30 units less likely to change their daily behavior (3.39 on a scale of 1-4) when regressed against one child in the home (3.69 on a scale of 1-4).

Table 4.48: Regression among Variables: Number of Children and Willingness to Changi

Behaviors

Number of Willingness Children to Change

Two Children -0.09 (0.17) Three Children -0.30* (0.16) Four Children -0.23 (0.21) Five Children -0.35 (0.27) Six Children -0.19 (0.60) Seven Children -0.02 (0.60) Constant 3.69*** (0.12)

Observations 96 R-squared 0.05 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Research Question 2.c – Level of household income and signing up for text messages. As profiled in Table 4.49, the response to asking parent participants if they were

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willing to sign up for text messages with ideas for talking more with their children varied by income levels. While 100% of parents who reported an income of $0 - $5,000 expressed willingness to receive such text messages, I found that 80% of participants in the $6,000 to

$15,000 range expressed willingness. No patterns were inferred.

Table 4.49: Willingness to Change – Household Income and Text Messages

Annual Salary $6,000 to $16,000 to $26,000 to $36,000 to $46,000 to $61,000 to over Willingness to receive texts $0 to $5,000 Total $15,000 $25,000 $35,000 $45,000 $60,000 $75,000 $75,000 No. I do not want to do this. 0 0 3 0 0 2 0 1 6 I probably would not do this. 0 2 0 1 2 0 2 0 7 There is a chance I may do this. 3 1 5 4 2 0 1 1 17 Yes! I would do this! 8 7 9 6 7 8 8 13 66 Total 11 10 17 11 11 10 11 15 96

Research Question 2.c – Level of household income and attending a parent support group. Parents across all household income ranges displayed low levels of willingness to attend a parent support group. Even at the highest levels, less than three quarters of the parent participants reported willingness to attend a parent support group. Seventy-three percent of participants with a household income of $0 to $5,000 were willing to attend a support group. The same is true for dependents whose salary fell between $36,000 to $45,000 and $61,000 to

$75,000 household income ranges. I found that the lowest area of willingness was 53% of participants with an annual salary of over $75,000. These data are profiled in Table 4.50 below.

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Table 4.50: Willingness to Change – Household Income and Support Groups

Annual Salary $6,000 to $16,000 to $26,000 to $36,000 to $46,000 to $61,000 to over Willingness to attend group $0 to $5,000 Total $15,000 $25,000 $35,000 $45,000 $60,000 $75,000 $75,000 No. I do not want to do this. 2 2 2 2 0 3 1 1 13 I probably would not do this. 1 1 4 2 3 0 2 6 19 There is a chance I may do this. 4 5 6 3 4 3 5 3 33 Yes! I would do this! 4 2 5 4 4 4 3 5 31 Total 11 10 17 11 11 10 11 15 96

Research Question 2.c – Level of household income and taking child to the public

(or school) library. When I looked at the participants’ overall willingness to take their child to the library, I found that seven participants displayed a low level of willingness. One hundred percent of participants from four income ranges reported their willingness to visit the library more often: $0 to $5,000, $6,000 to $15,000, $61,000 to $75,000, and over $75,000. Table 4.51 demonstrates that participant willingness to take their child to the library varied across household income levels.

Table 4.51: Willingness to Change – Household Income and the Library

Annual Salary $6,000 to $16,000 to $26,000 to $36,000 to $46,000 to $61,000 to over Willingness to visit library $0 to $5,000 Total $15,000 $25,000 $35,000 $45,000 $60,000 $75,000 $75,000 No. I do not want to do this. 0 0 0 0 0 1 0 0 1 I probably would not do this. 0 0 2 1 3 0 0 0 6 There is a chance I may do this. 3 3 4 4 2 2 4 5 27 Yes! I would do this! 8 7 11 6 6 7 7 10 62 Total 11 10 17 11 11 10 11 15 96

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Research Question 2.c – Level of household income and reading more minutes each day. Participants reported a willingness to read more with their child. This was reflected in all household income ranges; 96% of participants responded that they were willing to read more minutes each day. The same ranges that had 100% of participants willing to attend the library more often with their child were willing to read more with their child as well: these included the ranges of $0 to $5,000, $6,000 to $15,000, $61,000 to $75,000, and over $75,000. A specific increase in willingness was observed for parents earning over $75,000. Ten of 15 participants selected “Yes! I would do this” when surveyed about taking their child to the library. Fourteen of

15 parents indicated that they would be willing to read more with their child.

Table 4.52: Willingness to Change – Household Income and Reading

Annual Salary $6,000 to $16,000 to $26,000 to $36,000 to $46,000 to $61,000 to over Willingness to read $0 to $5,000 Total $15,000 $25,000 $35,000 $45,000 $60,000 $75,000 $75,000 No. I do not want to do this. 0 0 0 0 0 1 0 0 1 I probably would not do this. 0 0 1 1 1 0 0 0 3 There is a chance I may do this. 3 2 2 1 1 2 1 1 13 Yes! I would do this! 8 8 14 9 9 7 10 14 79 Total 11 10 17 11 11 10 11 15 96

Research Question 2.c – Level of household income and talking more each day.

Participants reported their annual household income ranges in one of 8 ranges provided in the survey. Ninety-seven percent of participants were willing to talk more each day with their child.

In five of the annual salary ranges, 100% of participants reported that they were willing to talk more: $0 to $5,000, $6,000 to $15,000, $16,000 to $25,000, $61,000 to $75,000, and over

$75,000. The lowest rate of willingness was 90%; this occurred in the $46,000 to $60,000 annual

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salary range. One of 10 participants in the range selected the response, “No. I do not want to do this.” These data are reported in Table 4.53.

Table 4.53: Willingness to Change – Household Income and Talking

Annual Salary $6,000 to $16,000 to $26,000 to $36,000 to $46,000 to $61,000 to over Willingness to talk $0 to $5,000 Total $15,000 $25,000 $35,000 $45,000 $60,000 $75,000 $75,000 No. I do not want to do this. 0 0 0 0 0 1 0 0 1 I probably would not do this. 0 0 0 1 1 0 0 0 2 There is a chance I may do this. 3 2 3 1 1 2 2 1 15 Yes! I would do this! 8 8 14 9 9 7 9 14 78 Total 11 10 17 11 11 10 11 15 96

Research Question 2.c. – Level of household income and learning words to help child grow vocabulary. As seen in Table 4.54, there are four ranges of annual income in which

100% of participants reported willingness to learn new words in order to help grow their child’s vocabulary. These were the income ranges in which 100% of participants also indicated willingness to attend the library, talk more with their child, and read more with their child. These annual income ranges include $0 to $5,000, $6,000 to $15,000, $61,000 to $75,000, and over

$75,000. In the participants within the over $75,000 annual salary, 14 of 15 participants selected the response, “Yes! I would do this!”

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Table 4.54: Willingness to Change – Household Income and Words

Annual Salary $6,000 to $16,000 to $26,000 to $36,000 to $46,000 to $61,000 to over Willingness to learn $0 to $5,000 Total $15,000 $25,000 $35,000 $45,000 $60,000 $75,000 $75,000 No. I do not want to do this. 0 0 0 0 0 1 0 0 1 I probably would not do this. 0 0 1 1 2 0 0 0 4 There is a chance I may do this. 3 2 2 2 2 2 2 1 16 Yes! I would do this! 8 8 14 8 7 7 9 14 75 Total 11 10 17 11 11 10 11 15 96

Research Question 2.c – Regression of level of household income and willingness to change behaviors. To establish the degree to which willingness to change daily behaviors was influenced by household income, I compared parents who make $75,000 or less against those who make over $75,000 and the variable Research2. I utilized the code, “reg Research2

Income_7 Income_6 Income_5 Income_4 Income_3 Income_2 Income_1” in Stata to run this regression.

The average willingness to change score for household income over $75,000 was 3.67 on a scale of 1-4. When controlling for covariates, I found that no results below the household income range were statistically significant. There was, however, a trend in which parents’ willingness to change behaviors decreased as parents’ income levels decreased from over

$75,000 annually. Table 4.55 reports the regression data.

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Table 4.55: Regression among Variables – Household Income and Willingness to Change Behaviors

Willingness Annual Income to Change

$61,000 to $75,000 -0.06 (0.24) $46,000 to $60,000 -0.3 (0.24) $36,000 to $45,000 -0.21 (0.24) $26,000 to $35,000 -0.2 (0.24) $16,000 to $25,000 -0.19 (0.21) $6,000 to $15,000 -0.12 (0.24) $0 to $5,000 -0.08 (0.24) Constant 3.67*** (0.15)

Observations 96 R-squared 0.03 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Research Question 2.d – Reported age of parent and signing up for text messages.

Parent participants were given 6 age ranges from which to choose when completing the survey.

Parent participants also indicated the degree to which they were willing to sign up for text messages with ideas for talking more with their children. When I compared the percent of participants in each of the 6 age ranges, I found no significant differences. While 88% of participants under 30 were willing to sign up for text messages, the rate decreased slightly to

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85% for participants over 30. As seen in Table 4.56, the lowest rate of willingness was seen in ages 26 – 30; 82% (18 of 22) of these participants were willing to sign up for text messages.

Table 4.56: Willingness to Change – Age and Text Messages

Age Willingness to receive texts 18-20 21-25 26-30 31-40 41-50 Older than 50 Total No. I do not want to do this. 0 1 2 2 1 0 6 I probably would not do this. 0 1 2 4 0 0 7 There is a chance I may do this. 2 4 5 4 1 1 17 Yes! I would do this! 3 16 13 28 4 2 66 Total 5 22 22 38 6 3 96

Research Question 2.d – Reported age of parent and attending a parent support group. As was seen with Research Question 1, attending a parental support group was the component rated the lowest by participants in Research Question 2 as well. When reviewing the data by age, I found a slight decrease in willingness was observed as the age of the participant increased. In the 18 – 20 age range, 80% of participants reported willingness to attend a parent support group; in the 21 – 25 age range, 73% were willing. Sixty-four percent in the 26 – 30 range and 63% in the 31 – 40 range were willing. Half of the 41 – 50-year-old participants were willing. A change in the pattern is observed within the 3 participants who disclosed being older than 50. One hundred percent of these participants expressed their willingness to attend a support group. Table 4.57 reports these data.

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Table 4.57: Willingness to Change – Age and Attending Support Group

Age Willingness to attend group 18-20 21-25 26-30 31-40 41-50 Older than 50 Total No. I do not want to do this. 1 2 3 5 2 0 13 I probably would not do this. 0 4 5 9 1 0 19 There is a chance I may do this. 4 8 8 9 3 1 33 Yes! I would do this! 0 8 6 15 0 2 31 Total 5 22 22 38 6 3 96

Research Question 2.d – Reported age of parent and taking child to the public (or school) library. When parent participants rated their level of willingness to take their child to the public or school library more often, 100% of participants within the ranges of 18-20, 41-50, and older than 50 were willing. Ninety-five percent of participants within the ranges of 21 – 25 and

26-30 reported a willingness to visit the library. A slight decrease in the rate of willingness was observed for individuals within the age range of 31 to 40 years old. Eighty-seven percent of individuals from this age range rated being willing to visit the library more often. These data are reported in Table 4.58.

Table 4.58: Willingness to Change – Age and the Library

Age Willingness to visit library 18-20 21-25 26-30 31-40 41-50 Older than 50 Total No. I do not want to do this. 0 0 0 1 0 0 1 I probably would not do this. 0 1 1 4 0 0 6 There is a chance I may do this. 2 4 6 11 3 1 27 Yes! I would do this! 3 17 15 22 3 2 62 Total 5 22 22 38 6 3 96

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Research Question 2.d – Reported age of parent and reading more minutes each day. Examination of the results of the fourth component of behavior changes, reading more each day, disclosed that 98% of participants under the age of 30 and 94% of participants over the age of 30 were willing to read more minutes each day with their child. One hundred percent of participants within the ranges of 18-20, 21-25, 41-50, and older than 50 were willing to increase the number of minutes spent reading with their child. The lowest rate of willingness was reported by participants within the age range of 31-40. Thirty-five of 38 participants (92%) indicated willingness to read more. These results are reported in Table 4.59.

Table 4.59: Willingness to Change: Age and Reading More

Age Willingness to read 18-20 21-25 26-30 31-40 41-50 Older than 50 Total No. I do not want to do this. 0 0 0 1 0 0 1 I probably would not do this. 0 0 1 2 0 0 3 There is a chance I may do this. 1 4 2 4 2 0 13 Yes! I would do this! 4 18 19 31 4 3 79 Total 5 22 22 38 6 3 96

Research Question 2.d – Reported age of parent and talking more each day.

Participants displayed elevated levels of interest across age ranges related to the fifth component of changing daily behaviors, talking more minutes each day with children. Participants in five of the six age range groupings reported being 100% willing same issue as before to talk more each day with their child. The age ranges in which 100% of participants were willing to talk more included 18-20, 21-25, 26-30, 41-50, and older than 50. Ninety- two percent of participants from

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31-40 displayed a willingness to talk more with his/her child with 35 of 38 participants displaying interest. These data are profiled in Table 4.60.

Table 4.60: Willingness to Change – Age and Talking More

Age Willingness to talk 18-20 21-25 26-30 31-40 41-50 Older than 50 Total No. I do not want to do this. 0 0 0 1 0 0 1 I probably would not do this. 0 0 0 2 0 0 2 There is a chance I may do this. 1 4 3 5 2 0 15 Yes! I would do this! 4 18 19 30 4 3 78 Total 5 22 22 38 6 3 96

Research Question 2.d – Reported age of parent and learning words to help child grow vocabulary. When looking at the parents’ willingness to learn more words, I observed a slight decrease in the willingness of participants as the age ranges of the participants increased.

While 98% of participants under 30 were willing to change their daily habits to include talking more with their child, 91% of participants over 30 expressed willingness. The age range that indicated the lowest willingness was the 31 – 40 age group. Eighty-nine percent of participants

(34 of 38 participants) in said age group displayed a willing to learn words. These data are reported in Table 4.61.

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Table 4.61: Willingness to Change – Age and Words

Age Willingness to learn 18-20 21-25 26-30 31-40 41-50 Older than 50 Total No. I do not want to do this. 0 0 0 1 0 0 1 I probably would not do this. 0 0 1 3 0 0 4 There is a chance I may do this. 1 5 3 5 2 0 16 Yes! I would do this! 4 17 18 29 4 3 75 Total 5 22 22 38 6 3 96

Research Question 2.d – Regression of age of parent and willingness to change. In order to determine if the age of the parent changed the way in which parents interacted with the

Likert scale for willingness to change their daily behaviors, I analyzed a regression. Using the variable, Research2 which includes six language acquisition behavioral changes, I compared parents over 20 years of age against parents that were 18 – 20 and Research2. The code used in

Stata was “reg Research2 Age_2 Age_3 Age_4 Age_5 Age_6.” In Table 4.62, Research2 is represented by the column heading ‘Willingness to Change.”

Willingness to change was regressed against parents that were ages 18 - 20. While parents in this age range expressed a mean interest score of 3.53 on a scale of 1-4, only minor differences were noted in other age ranges. When controlling for covariates, no results were statistically significant.

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Table 4.62: Regression among Variables – Age of Parent and Willingness to Change

Willingness Age of Parent to Change

21-25 0.09 (0.29) 26-30 0 (0.29) 31-40 -0.06 (0.28) 41-50 -0.2 (0.36) Older than 50 0.3 (0.43) Constant 3.53*** (0.26)

Observations 96 R-squared 0.02 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Research Question 2.e – Education level of parent and signing up for text messages.

Education level appeared to have little influence on a participant’s tendency to sign up for text messages. I observed that 86% of the participants were willing to sign up for text messages. The

86% is an average of the 86% of participants without degrees who were willing to receiving text messages and the 87% of participants with degrees who were willing to sign up. Table 4.63 reports these data.

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Table 4.63: Willingness to Change – Education Level and Text Messages

Highest Education Completed Less than High school but High school Vocational Some college Associate's Bachelor's Master's GED Total Willing to receive texts 9th grade did not graduate diploma program but no degree Degree Degree Degree No. I do not want to do this. 0 1 0 1 1 0 1 2 0 6 I probably would not do this. 0 1 0 2 1 0 0 3 0 7 There is a chance I may do this. 0 0 1 5 2 3 4 1 1 17 Yes! I would do this! 1 2 2 15 2 10 10 13 11 66 Total 1 4 3 23 6 13 15 19 12 96

Research Question 2.e – Education level of parent and attending a parent support group. Attending a support group is the only component of both the language technology analysis components (Research Question 1) and word acquisition behaviors (Research Question

2) in which the interest of the participants without degrees was higher than that of the participants with degrees. Seventy percent (35 of 50) of participants without degrees were willing to attend a parent support group; by contrast, 65% (29 of 46) of participants with degrees were willing. These results are seen in Table 4.64.

Table 4.64: Willingness to Change – Education Level and Parent Support Group

Highest Education Completed Less than High school but High school Vocational Some college Associate's Bachelor's Master's GED Total Willingness to attend group 9th grade did not graduate diploma program but no degree Degree Degree Degree No. I do not want to do this. 0 2 1 2 2 0 2 4 0 13 I probably would not do this. 0 1 0 4 2 1 4 5 2 19 There is a chance I may do this. 1 1 0 11 1 6 5 5 3 33 Yes! I would do this! 0 0 2 6 1 6 4 5 7 31 Total 1 4 3 23 6 13 15 19 12 96

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Research Question 2.e – Education level of parent and taking child to the public (or school) library. As seen in Table 4.65, only a small number of individuals indicated that they were not willing to take their child to library more often. In total, 6 individuals were not receptive to the increase in library time; however, 93% of the total participants reported being willing. One participant, with a bachelor’s degree, reported that he/she did not want to increase his/her time at the library. Additionally, two participants each in the educational attainment categories of associate’s degree and vocational program responded that they probably would not go to the library more. Finally, a participant who attended high school but did not graduate and a participant with a high school diploma both articulated that they would probably not attend the library more. In all, 92% of participants without degrees and 93% of participants with degrees were interested in increased library participation with their children.

Table 4.65: Willingness to Change – Education Level and the Library

Highest Education Completed Less than High school but High school Vocational Some college Associate's Bachelor's Master's GED Total Willingnes to visit library 9th grade did not graduate diploma program but no degree Degree Degree Degree No. I do not want to do this. 0 0 0 0 0 0 0 1 0 1 I probably would not do this. 0 1 0 1 2 0 2 0 0 6 There is a chance I may do this. 1 1 0 6 1 3 5 6 4 27 Yes! I would do this! 0 2 3 16 3 10 8 12 8 62 Total 1 4 3 23 6 13 15 19 12 96

Research Question 2.e – Education level of parent and reading more minutes each day. Nearly all participants expressed a willingness to change their daily behavior to include reading more with their child. One participant in each of the categories of high school but did not graduate, high school diploma, vocational program, and bachelor’s degree reported that she/he

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was not willing to read more with their child. One hundred percent of participants in the reported education levels of less than 9th grade, GED, some college but no degree, associate’s degree, and a master’s degree indicated a willingness to read more with their child. When looking only at the

“Yes! I would do this” response, I found that 100% of participants with less than a 9th grade education (1 of 1), GEDs (3 of 3) and master’s degrees (12 of 12) indicated their willingness to change. These data are reports in Table 4.66.

Table 4.66: Willingness to Change – Education Level and Reading

Highest Education Completed Less than High school but High school Vocational Some college Associate's Bachelor's Master's GED Total Willingness to read 9th grade did not graduate diploma program but no degree Degree Degree Degree No. I do not want to do this. 0 0 0 0 0 0 0 1 0 1 I probably would not do this. 0 1 0 1 1 0 0 0 0 3 There is a chance I may do this. 0 1 0 5 0 1 4 2 0 13 Yes! I would do this! 1 2 3 17 5 12 11 16 12 79 Total 1 4 3 23 6 13 15 19 12 96

Research Question 2.e – Education level of parent and talking more each day. Of the

96 participants who answered the willingness to read more with their child Likert scale item, only 3 individuals stated that they would not be interested. Overall, I found that people were most willing to change their daily behavior to talk more with their child than any of the other components of word acquisition behavioral changes. No noticeable change in the parents’ interest in talking more each day was observed between parents with or without degrees. In fact,

96% (48 of 50) of participants without degrees and 98% (45 of 46) of participants with degrees were willing to talk more. These data are profiled in Table 4.67.

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Table 4.67: Willingness to Change – Education Level and Talking

Highest Education Completed Associate's Bachelor's High school High school but Less than Master's Some college Vocational program Willingness to talk GED Total Degree Degree diploma did not graduate 9th grade Degree but no degree after high school No. I do not want to do this. 0 1 0 0 0 0 0 0 0 1 I probably would not do this. 0 0 0 1 0 0 0 0 1 2 There is a chance I may do this. 4 2 0 5 2 0 1 1 0 15 Yes! I would do this! 11 16 3 17 2 1 11 12 5 78 Total 15 19 3 23 4 1 12 13 6 96

Research Question 2.e – Education level of parent and learning words to help child grow vocabulary. I observed a small difference in the data in Table 4.68 when comparing participants without college degrees (92% of participants willing to learn words) to participants with college degrees (98% of participants willing to learning words). When I viewed the way in which education levels affect a participant’s willingness to change his or her daily behavior, I observed that 100% of master’s degree participants indicated that they were willing to learn new words.

Table 4.68: Willingness to Change – Education Level and Words

Highest Education Completed Associate's Bachelor's High school High school but Less than Master's Some college Vocational program Willingness to learn GED Total Degree Degree diploma did not graduate 9th grade Degree but no degree after high school No. I do not want to do this. 0 1 0 0 0 0 0 0 0 1 I probably would not do this. 0 0 0 1 1 0 0 0 2 4 There is a chance I may do this. 6 2 0 5 1 0 1 1 0 16 Yes! I would do this! 9 16 3 17 2 1 11 12 4 75 Total 15 19 3 23 4 1 12 13 6 96

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Research Question 2.e – Regression of education level of parent and willingness to change behavior. As discussed previously, the presence of variable Research2, allowed me to see the impact of parental demographic characteristics interacting with the willingness to change daily behaviors. By running the regression (reg Research2 AL_1 AL_2 AL_3 AL_5 AL_6 AL_7

AL_8 AL_9) in Stata, I compared parents from various education levels (see Table 4.7) against parents with a high school diploma as their highest education level and Research2. As seen in

Table 4.69 below, Research2 is referred to as Willingness to Change.

The constant, a high school diploma, was compared against the other education levels of parents. The average scale score for a parent with the highest education as a high school diploma was a mean scale of 3.52 on a scale of 1-4. Parents who attended high school but did not graduate had a mean level of willingness of 2.96 on a scale of 1-4 at the p<0.1. With the exception of parents who attended high school but did not graduate, no statistically significant results were found.

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Table 4.69: Regression among Variables: Education Level and Willingness to Change Behavior

Willingness Education Level of Parents to Change

Less than 9th grade 0.14 (0.57) High school, but did not graduate -0.56* (0.30) General Education Degree 0.26 (0.35) Vocational Program -0.38 (0.26) Some college but no degree 0.26 (0.20) Associate's Degree -0.07 (0.19) Bachelor's Degree -0.08 (0.17) Master's Degree 0.28 (0.20) Constant 3.52*** (0.12)

Observations 96 R-squared 0.14 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Research Question 2 – Regression of all parental demographic characteristics and willingness to change. To understand how all parental demographic characteristics contribute to a parent’s willingness to change behaviors, I ran a regression. Overall, when I generated the variable Research2, it was used against individual parental demographic characteristics; however, I also combined the characteristics to see if there were any specific parental demographic characteristics that were statistically significant. I ran the regression, “reg

Research2 Age_2 Age_3 Age_4 Age_5 Age_6 Work_1 Work_3 Income_7 Income_6 Income_5

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Income_4 Income_3 Income_2 Income_1 AL_1 AL_2 AL_3 AL_5 AL_6 AL_7 AL_8 AL_9

Children_2 Children_3 Children_4 Children_5 Children_6 Children_7” in Stata. The regression allowed me to compare parents over 20, who worked full-time and part-time, with an income less than $75,000, across all education levels, and more than one child against parents ages 18 –

20, not working, with the highest educational experience of high school diploma, over an annual income of $75,000 with one child and Research2. These data are displayed in Table 4.70.

The constant derived from the regression was 3.87 on a scale of 1-4. At the p<0.1 level, the only statistically significant result was a negative coefficient of 0.67 units. The decrease in willingness was among parents who went to high school but did not graduate; their score was 3.2 on a scale of 1-4. When I controlled for covariates, no other results were statistically significant.

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Table 4.70: Regression of all Parental Demographic Characteristics and Willingness to Change Parental Demographic Willingness Characteristics to Change

21-25 0.04 (0.39) 26-30 0.11 (0.42) 31-40 -0.06 (0.42) 41-50 -0.2 (0.56) Older than 50 0.29 (0.56) Full Time -0.12 (0.30) Part Time -0.24 (0.31) $61,000 to $75,000 -0.07 (0.25) $46,000 to $60,000 -0.2 (0.30) $36,000 to $45,000 -0.26 (0.29) $26,000 to $35,000 -0.27 (0.29) $16,000 to $25,000 -0.19 (0.28) $6,000 to $15,000 0.08 (0.39) $0 to $5,000 -0.04 (0.42) Less than 9th grade -0.22 (0.80) High school, but did not -0.67* graduate (0.37) General Education Degree 0.16 (0.42) Vocational Program -0.37 (0.33) Some college but no degree 0.35 (0.27) Associate's Degree 0.08 (0.27) Bachelor's Degree -0.11 (0.31) Master's Degree 0.2 (0.35) Two Children -0.13 (0.20) Three Children -0.21 (0.22) Four Children -0.14 (0.29) Five Children -0.16 (0.33) Six Children -0.03 (0.75) Seven Children -

Constant 3.87*** (0.60)

Observations 96 R-squared 0.22 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Research Question 2 – Hypothesis. I hypothesized that the willingness to change daily behaviors in order to read with and talk more with their children were related to parent factors of working status (not employed, part-time, full-time), number of children, level of household income, age of the parent, and education level of the parent. As was reported in the results for the regression of all parent demographic characteristics, only one statistically different parent demographic characteristic relative to changing daily behaviors was observed. Parent participants who attended high school but did not graduate were .67 units less willing to change behaviors. In light of the results, the hypothesis was not accepted.

Research Question 2 – Qualitative analyses of responses to constructed-response items related to willingness to change behaviors to improve child’s word acquisition. Item 7 on the survey asked parent participants why they would be willing to engage in daily behaviors such as receiving text messages, attending a support group, visiting the library more often, reading more, or talking more with their child. In total, there were 82 constructed responses to this item.

The eighth item in the survey was also open-ended. Parent participants were asked to respond to the following prompt. “Please describe anything that you think may make it hard for you to do what is listed above (text messaging, support group, library, reading, talking, etc.).”

Sixty-six parent participants responded to this constructed-response item; however, fifteen responses were removed, as these participants answered by saying, “I would do all of the items listed above.” The decision to remove the 15 responses was due to the fact that the question was intended for parent participants to address barriers that would make implementing items difficult.

When the participants wrote that they would complete all of the items, no useful information was

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available to be gathered. In light of this, I confined the analysis for this item to the other fifty-one parent participant answers to this constructed-response item.

Responses to Item 7 about willingness to change. Overall, parents stated a variety of reasons for their willingness to change their behavior. When answering the open response question, “please describe why you would be willing to do the things listed above (text messaging, support group, library, reading, talking, etc.),” parents articulated various statements regarding changing their daily activities. I used thematic coding in the analysis of the responses to this item. Among the 82 comments, there were two major themes: wanting to help their children learn and the fact that parents were already doing the indicated activities.

It was evident that parents reflected upon their current practices when answering the open-ended item. For example, one parent articulated, “Whenever I'm not at work I am with my child so it would be easier for me to talk and read with them more. And I think that maybe we could work on going to the library more to get books instead of reading the same ones over and over again.” Other parent participants wrote, “As with most parents I desire to further my child’s education with any resources available” and “I would be willing to do the things listed because as a parent, one could always improve in those areas.”

Of the 82 constructed responses, 21 used the word “help.” Forty-seven parent participants stated that they would be willing to engage in the various behaviors in order to assist their child(ren) in learning. Specific responses included:

 “Anything to help my child grow I am willing to do.”

 “I love intelligence and my kids deserve the best start I can give them.”

 “I want my child to succeed and that starts at home. Anything I can do to get him

ahead academically, I am willing to do it.”

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 “I will do anything to contribute to my kids’ success in life.”

 “I'm always searching for ways to encourage my child’s language skills.”

 “These things would be beneficial to my child’s development so I would be willing to

do them.”

 “To get an early start on reading.”

Ten of the 82 parent participants who answered Item 7 articulated that they were already incorporating many of the items from the survey in their daily lives. For these 10 individuals, adding more time to the time already dedicated to reading, talking, visiting the library, etc. was described as an easier commitment. Sample comments from the theme of already doing behaviors included, “The world is so advance now, I think it’s important for him to learn word every day. This want be out of the norm for him because we go to library often and read every night,” “we love to read books after dinner; going to library is fun for us,” and “I enjoy talking with my children. We read 1-3 books each night before bed, I could always do more.”

Four participants explicitly expressed interest in receiving text messages. The four individuals articulated having small children and the parents described texts as very convenient to receive. For example, one parent stated, “We love library and books. I would be willing to get texts as they are easy communication when I have small children.” Additionally, three parent participants shared their love of reading and their desire for the children to love reading as well

(“I love to read and would love if my children had that same love!”). One parent participant stated that since she/he was currently unemployed, time would be available to commit to the changes associated with word acquisition.

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Responses to Item 8 about anything that would make change difficult. As with Item

7, I used thematic coding in the analysis of the responses to Item 8. Of the fifty-one participants with valid responses, 34 parents stated that their current schedules or lack of available time would make it difficult for them to participate in the change-of-behavior activities associated with Item 6. Themes of scheduling/time were able to be broken down further into work schedule, time with family or family responsibilities, and not having access to related technology components. A specific concern relative to attending a support group was also explored.

Responses related to the theme of work schedule included, “depending on when parent groups are scheduled it may conflict with my work or church schedule” and “I have 2 jobs so I work a lot. It is hard for me to commit to going to group meetings since my schedule changes.”

An additional parent stated, “I can go to parent meeting if children are in school.” Further statements related to time concerns include responses such as, “parent support group may not fit schedule,” “support groups could conflict with my regular, daily schedule,” and “time would be the hardest obstacle.”

Thematic coding yielded the topic of time with family or family responsibilities. Parents seemed to be concerned at the lack of time they would have available to commit to change behaviors to improve their child’s word acquisition. Illustrative quotes included, “working full- time, other children that are school age to care for, school events, commitments, etc.,” “time on weekends is for family. Weeknights we have sports,” and “time as single mom.” Parent participants also articulated a specific need for child care, “Going to a support group might be hard as I have a nine-month-old and a three-year-old while my husband works 60 plus hours a week and is a full-time college student, so having someone to watch the kids is a little tough.”

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An additional reason that parent participants articulated regarding the difficulty of changing their behavior included lack of necessary materials. For example, two parent participants shared the lack of transportation as a barrier for them to attend parent meetings or go to the library more often. Another parent wrote that she/he did not have access to a cell phone, thus making it impossible to receive regular text messages.

While parents, overall, were moderately receptive to the idea of attending a support group, this was the behavioral change with the lowest ratings. One parent articulated the presence of strangers as a barrier to attending the support group and observed that “attending a support group would be challenging because I don't like taking advice from people I don't know or trust.” A similar explanation for not trusting individuals was seen in another parent participant’s rationale for not participating: “I wouldn’t support the support group only because I wouldn’t want to monitor or compare my child rate of learning to another’s.” Another parent participant explained her/his unwillingness to participate in support groups with the statement, “I do what I need to, but that doesn’t mean I need support groups for things like this.” A fourth parent participant wrote, “I would do all accept (sic) meeting in a support group.”

Qualitative: Other comments. The final question of the survey, Item 19, was open- ended and read, “Are there any other comments you’d like to share related to items you have read in the survey?” I initially assumed that responses to this item would provide additional insights into Research Questions 1 and 2. This did not occur, as respondents provided little to no further information. Many parents opted to write, “N/A” or skip the question altogether.

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Chapter Summary

I found differences within the subgroup items, such as that fact that when parental household income increased, interest in language analysis technology and willingness to change daily behaviors to include more literacy enriched activities also increased. I also saw a slight increase in the interest in the language technology (Research Question 1) and behavioral change

(Research Question 2) components as level of education increased.

There were 6 statistically significant differences observed; 5 in analyzing Research

Question 1 and 1 in analyzing Research Question 2. At the p<.1 level, a .75 unit decrease in interest for parents who attended high school but did not graduate was observed. Additionally, at the p<.1 level, there was an increase in interest for parents with an associate’s degree (.58 unit increase), some college but no degree (.62 unit increase), and between the annual household income of the $6,000 to $15,000 range (.84 unit increase). One statistically significant increase in interest at the p<.01 level was observed – parents with a master’s degree had a mean unit that was 1.21 units higher than parents who reported an annual household income over $75,000.

Parent participants who attended high school but did not graduate were .67 units less willing to change behaviors than parents who graduated from high school, which was statistically significant difference (p<.01). Though not statistically significant, thematic trends were observed in participants with 3 children. Said parents expressed lower interest across components as compared to participants with 1 to 2 children while participants under 30 were more likely to be willing to complete tasks related to literacy.

When looking at the quantitative elements, I found that parent participants were interested in helping their children learn. Participants articulated of their interest in trying to implement word analysis technology into their lives (Research Question 1). They, did not,

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however, express as strong an interest in home visits as in other components. Parents expressed a willingness to change daily behaviors that would lead to language acquisition (Research

Question 2). In this area, more than half of the qualitative responses relate, thematically, to helping their child learn. Qualitative data demonstrated that parents were less interested in attending parent support groups and, overall, were concerned with lack of time to implement daily behavior changes.

With respect to the results of my analyses, parental demographic characteristics displayed small differences observed throughout Research Questions 1 and 2. Parents, in general, appeared interested in using technology and changing daily behavior as evidenced by their responses.

These data are presented in Table 4.71. As demonstrated, when rounding to the nearest zero, 10 of 10 parents were interested in three behavior components. Nine of 10 parents were interested in two use components and two behavior components. Eight of 10 are interest in 1 use component.

Seven of 10 are interested in one use components and one behavior component.

Table 4.71: Research1 and Research2 Percent of Interest

Research1 Research2 Language Analysis Use Willingness to Change Home visit to occur 69% Sign up for text messages 86% Use a clip-on device on child 82% Attend a parent support group 67% Use an app on smartphone 86% Take child to the library more often 93% Plug in the device nightly 88% Read more minutes each day with child 96% Talk more minutes each day with child 97% Learn words to increase child's vocabulary 95% Total 81% 89%

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CHAPTER 5: DISCUSSION

Chapter Introduction

The purpose of Chapter 5 is to summarize the findings from Chapter 4 and discuss the conclusions that I reached regarding these findings. Additionally, I include limitations and recommendations for policy, practice, and future research, and for discussion.

Purpose of the Study

The purpose of this study was to assess the degree to which parents in low-income eastern North Carolina communities perceive that language environment analysis technology would be beneficial to implement in their homes. Additionally, I assessed the willingness of parents to change their daily interactions with their child(ren) in order to read more and talk more with their child(ren). I further assessed the relationship of parents’ demographic characteristics to their interest in implementing language environment analysis technology and willingness to change behaviors related in order to improve the literacy skills of their children.

Organization of the Study

Beginning with an introduction to The National Commission on Excellence in

Education’s landmark 1983 A Nation at Risk report, Chapter 1 outlines why researchers were prompted to explore the field of education for areas of deficiencies. Chapter 1 further explores the historically low proficiency scores on standardized assessments in North Carolina and a demonstrated correlation between socioeconomic status and grade level proficiency. The chapter raises the questions of whether language environment technology might be a tool for improving student performance and outlines the study of parent perceptions of such technology. Chapter 1 previews the study with an outline of the issues that give rise to the inquiry, a statement of purpose, and a preview of the research protocol.

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Chapter 2 provides further explanation of language environment analysis technology as well as an understanding of the literacy gap and related programmatic responses over time. The theoretical framework that undergirds this study is described. A thorough literature review including the central research constructs, pertinent research, and contemporary policies were presented.

The research design and procedures were discussed in Chapter 3. Two research questions and related hypotheses were presented. The quasi-qualitative case-study design evaluated parents’ interests in using language analysis technology and their willingness to change daily behaviors to create a more linguistically rich environment for their child.

Chapter 4 elaborates the results from the analysis of data associated with the study, including the findings associated with each of the research questions and related hypotheses.

This chapter, Chapter 5, provides a summary and discussion of the findings, limitations of the research, and implications for policy, practice, and future research.

Summary of Findings

The findings of the study were related to two research questions that guided the current investigation:

1. What are the perceptions of parents of children, ages birth to three, who live in the

attendance zone of Title 1 elementary schools in a northeastern North Carolina school

district regarding the potential implementation of language environment analysis

technology in their households?

a. Are the perceptions related to the different components of the language

analysis technology?

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