The Participation of Dropout Prevention Students in Career and Technical Education Programs

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Title:
The Participation of Dropout Prevention Students in Career and Technical Education Programs
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1 online resource (108 p.)
Language:
english
Creator:
Capriola, Patrick
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University of Florida
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Gainesville, Fla.
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Thesis/Dissertation Information

Degree:
Doctorate ( Ed.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Educational Leadership, Human Development and Organizational Studies in Education
Committee Chair:
OLIVER,BERNARD
Committee Co-Chair:
ELDRIDGE,LINDA BURNEY
Committee Members:
RITZHAUPT,ALBERT D
TOWNSEND,JANE SUSAN

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Subjects / Keywords:
at-risk -- cte -- dop -- dropout -- prevention
Human Development and Organizational Studies in Education -- Dissertations, Academic -- UF
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Educational Leadership thesis, Ed.D.
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theses   ( marcgt )
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Abstract:
This study investigated the availability of and participation in CTE programs by DOP students, based on poverty concentration in the community. The researcher sought to examine the impact that poverty in the community has on the availability of CTE programs and the degree to which DOP students in those communities participate in these programs. Eight different circumstances of this investigation were analyzed to ensure that the researchers exploration of the issue possessed both breadth and depth. They were; the availability of CTE high schools in the U.S. based on poverty concentration, and whether a relationship between them existed; the level of participation in CTE high schools by DOP students, and whether a relationship between them existed; the availability of CTE courses at regular high schools; and whether a statistically significant relationship between them existed; and the level of participation in CTE courses at regular high schools by DOP students, and whether a relationship between them exited. Data for the study was used from the NCES survey Dropout Prevention Services and Programs. It was comprised of data from 1,200 public schools across the United States. The data of specific interests to this study were questions 3A1, 3A2, 3B1, and 3B2. Descriptive statistics were used to analyze the data. Binomial and ordinal regression were used to determine relationships. Results indicated that there is a statistically significant relationship between the availability of CTE programs and poverty concentration. Results also showed that there is a statistically significant relationship between participation in CTE courses at regular high schools by DOP students based on poverty concentration. Although there was no relationship found between CTE high schools and the participation in them by DOP students based on poverty concentration. It is recommended that policy makers understand the levels of participation in CTE programs by DOP students. Most districts reported that some students do participate in CTE programs, however this is an ambiguous measure. Policymakers and researchers should seek to further quantify these levels and set out to develop initiatives to increase the level of access to and participation in CTE programs by DOP students.
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In the series University of Florida Digital Collections.
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Includes vita.
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Includes bibliographical references.
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Description based on online resource; title from PDF title page.
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This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Patrick Capriola.
Thesis:
Thesis (Ed.D.)--University of Florida, 2014.
Local:
Adviser: OLIVER,BERNARD.
Local:
Co-adviser: ELDRIDGE,LINDA BURNEY.

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lcc - LD1780 2014
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UFE0046457:00001


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1 THE PARTICIPATION OF DROPOUT PREVENTION STUDENTS IN CAREER AND TECHNICAL EDUCATION PROGRAMS By PATRICK A. CAPRIOLA A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFULLMENT OF TH E REQUIREMENTS FOR THE DEGREE OF DOCTOR OF EDUCATION THE UNIVERSITY OF FLORIDA 201 4

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2 201 4 Patrick A. Capriola

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3 To Stefanie and Nina. I will be forever grateful for your sacrifice. Our foundation is set.

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4 ACKNOWLEDGEMENTS There are several people whom I would like to thank for their contribution to this dissertation. First, I would like to thank my wife Stefanie for her unwavering support in what has been a very demanding doctoral program. Thank you for believing in me and in our vision for our future together with our family. To Nina, although you will not remember the sacrifices that you have made during this time I will. I will forever value the time that we have together and hope that sk ill centered, goal driven education will always be one of your priorities. To my mother, grandmother, and sister, thank you for instilling in me a belief in education as a basic value. It has served as a vehicle to improve the lives of our families in all the ways that we expected. In that vein, my gratitude is also extended to my uncle, brother, and brother in law for supporting and directing me towards a path guided by higher education. The cycle is broken and next year is now. My appreciation to Dr. Ber nard Oliver for his support and guidance throughout this program. He offered his ear and help in ways that I did not expect from a professor. The clich above and beyond is completely appropriate when thinking of his dedication and support. I am truly grat eful for his belief in me. This dissertation is not the end of how I will be able to use the lessons I learned from him. In fact, it is the beginning. I will spend the rest of my career working to serve the needs of students who are underserved. I would a lso like to thank Dr. Linda Eldridge for her lessons and support. It was a pleasure to learn from someone who could demonstrate poise and kindness in our classes no matter the circumstance. She was incredibly valuable in showing me how these traits contrib ute significantly to a n administrator those are the characteristics for which stakeholders in schools will remember you. I will continue to seek to develop these traits in my own character in both my personal and professional lives.

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5 Finally, to Dr. Ritzhaupt and Dr. Townsend. Thank you for the unbiased feedback from the qualifying exams through the dissertation process. I appreciate your objectivity and open mindedness as you learned about my experiences, goals, and research interests.

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6 TABLE OF CONTENTS page ACKNOWLED GEMENTS ................................ ................................ ................................ ............. 4 LIST OF TABLES ................................ ................................ ................................ ........................... 8 LIST OF FIGURES ................................ ................................ ................................ ....................... 10 DEFINITION OF TERM S ................................ ................................ ................................ ............ 11 ABSTRACT ................................ ................................ ................................ ................................ ... 12 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .................. 14 Statement of the Problem ................................ ................................ ................................ ........ 14 Purpose of the Study ................................ ................................ ................................ ............... 14 Evidence Justifying the Research Problem ................................ ................................ ............. 16 Deficiencies in Evidence ................................ ................................ ................................ ........ 18 Research Questions ................................ ................................ ................................ ................. 19 Significance of the Study ................................ ................................ ................................ ........ 20 2 REVIEW OF THE LITERATURE ................................ ................................ ........................ 21 Dropout Prevention Students ................................ ................................ ................................ .. 22 Attempted Policies ................................ ................................ ................................ .................. 32 Career and Technical Education ................................ ................................ ............................. 42 Conceptual Framework ................................ ................................ ................................ ........... 47 Summary ................................ ................................ ................................ ................................ 48 3 METHODOLOGY ................................ ................................ ................................ ................. 50 Purpose ................................ ................................ ................................ ................................ ... 50 Sample ................................ ................................ ................................ ................................ .... 50 Research Questions ................................ ................................ ................................ ................. 50 Research Question 1 ................................ ................................ ................................ ........ 50 Research Question 2 ................................ ................................ ................................ ........ 51 Research Questions 3 ................................ ................................ ................................ ...... 51 Research Questions 4 ................................ ................................ ................................ ...... 52 Hypotheses ................................ ................................ ................................ ...................... 52 Data Collection ................................ ................................ ................................ ................ 53 Data Analysis ................................ ................................ ................................ ................... 54 Summary ................................ ................................ ................................ ................................ 58

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7 4 RESUL TS ................................ ................................ ................................ ............................... 59 Descriptive Data ................................ ................................ ................................ ..................... 59 Research Question 1 ................................ ................................ ................................ ............... 62 Research Questi on 2 ................................ ................................ ................................ ............... 65 Research Question 3 ................................ ................................ ................................ ............... 69 Research Question 4 ................................ ................................ ................................ ............... 73 5 DISCUSSION AND CONCLUSIONS ................................ ................................ .................. 78 Research Question 1 ................................ ................................ ................................ ............... 80 Research Question 2 ................................ ................................ ................................ ............... 82 Research Question 3 ................................ ................................ ................................ ............... 83 Research Question 4 ................................ ................................ ................................ ............... 84 Limitations ................................ ................................ ................................ .............................. 86 I mplications for Future Research ................................ ................................ ............................ 87 Implications for Practice ................................ ................................ ................................ ......... 88 APPENDIX: SURVEY ................................ ................................ ................................ .................. 90 REFERENCES ................................ ................................ ................................ .............................. 95 BIOGRAPHICAL SKETCH ................................ ................................ ................................ ....... 108

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8 LIST OF TABLES Table page 2 1 Virtual School Enrollment Growth ................................ ................................ .................... 35 4 1 Poverty Concentration of Respondents ................................ ................................ .............. 6 0 4 2 Availability of Career and Technical High Schools ................................ .......................... 60 4 3 Participation in Career/Technical High School ................................ ................................ 61 4 4 Availability of Career/Technical Course at Regular High School ................................ ..... 61 4 5 Participation in Career/Technical Course at Regular High School ................................ ... 61 4 6 CTE High School Availability & Poverty Concentration ................................ .................. 63 4 7 Categorical Variable Codings Poverty Concentration and the Availability of CTE High Schools ................................ ................................ ................................ ...................... 63 4 8 Omnibus Test of Model C oefficients ................................ ................................ ................. 64 4 9 Variables in the Equation ................................ ................................ ................................ ... 64 4 10 Participation in CTE High Schools by DOP Students ................................ ....................... 66 4 11 Case Processing Summary ................................ ................................ ................................ 67 4 12 Model Fitting Information ................................ ................................ ................................ 68 4 13 Parameter Estim ates ................................ ................................ ................................ ........... 69 4 14 Test of Parallel Lines ................................ ................................ ................................ ......... 69 4 15 Availability of CTE Courses at a Regular High School ................................ .................... 70 4 16 Categorical Variables Codings ................................ ................................ .......................... 71 4 17 Omnibus Tests of Model Coefficients ................................ ................................ ............... 72 4 18 Varia bles in the Equation ................................ ................................ ................................ ... 73 4 19 Participation in CTE Courses at a Regular High School ................................ ................... 74 4 20 Case Processing Summary ................................ ................................ ................................ 75 4 21 Model Fitting Information ................................ ................................ ................................ 76

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9 4 22 Parameter Estimates ................................ ................................ ................................ ........... 77 4 23 Test of Parallel Lines ................................ ................................ ................................ ......... 77

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10 LIST OF FIGURES Figure page 1 1 National Dropout Rate ................................ ................................ ................................ ....... 15

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11 DEFINITION OF TERMS Career and Techni cal Education Programs or services that provide formal preparation for semi skilled, skilled, technical, or professional occupations. Career and Technical High Schools Schools that provide formal preparation for semi skilled, skilled, technical, or profe ssional occupations. Dropout Prevention Services or Programs Programs that are intended to increase the rate at which students are staying in school, progressing toward graduation, or earning a high school credential. Dropout Prevention Student A stud ent who is at risk of dropping out of school because they meet specified risk factors. National Center for Education Statistics Department of U.S. Government responsible for overseeing the implementation of the survey Poverty Concentration Indicates the percentage of children in the district between the ages of 5 17 living below the poverty line defined by three categories: Less than 10% 10% 19% 20% or more

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12 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Education THE PARTICIPATION OF DROPOUT PREVENTION STUDENTS IN CAREER AND TECHNICAL EDUCATION PROGRAMS By Patrick Capriola May 2014 Chair: Bernard Oliver Major: Educational Lead ership This study investigated the availability of career and technical education (CTE) programs in the United States and the participation in them by dropout p revention ( DOP ) students, based on poverty concentration in the community. The researcher sought to examine the impact that poverty in the community has on the availability of CTE programs and the degree to which DOP students in those communities participate in these programs. Four different circumstances were analyzed to ensure that the researchers exploration of the issue possessed breadth and depth. The y were (a) the availability of CTE high schools in the U.S and whether a statistically significant relationship with poverty concentration existed (b) the level of participation in CTE high scho ols by DOP students, and whether a statistically significant relationship between them existed based on poverty concentration (c) the availability of CTE courses at regular high schoo ls; and whether a statistically significant r elationship with poverty co ncentration existed (d) and the level of participation in CTE courses at regular high schools by DOP students, and whether a statistically significant relationship between them exited based on poverty concentration

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13 Data was used from the NCES survey Dro pout Prevention Services and Programs It was comprised of responses from 1,200 public schools across the United States. O f specific interest to this study were questions 3A1, 3A2, 3B1, and 3B2. Descriptive statistics were used to analyze the data. Binomia l and ordinal regressions were used to determine relationships. Results indicated that there is a statistically significant relationship between the availability of and participation in CTE hig h schools by DOP students based on poverty concentration in th e community; as well as the availability of and participation in CTE programs at regular high schools by DOP students based on poverty concentration in the community. It is recommen ded that policy makers understand the levels of participation in CTE progra ms by DOP students. Most districts reported that some students do participate in CTE programs ; however this is an ambiguous measure. Policymakers and researchers should seek to further quantify these levels and set out to develop initiatives that increase the level of access to and participation in CTE programs by DOP students.

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14 CHAPTER 1 INTRODUCTION Statement of the Problem A student drops out of school every nine seconds. Policymakers have sought to remedy this problem through laws, and educators have sought remedy through changes in professional practice. Yet, the dropout issue remains (Christie & Yell, 2008). Before a s tudent drops out of school indicators reveal their at risk status through both characteristics and behaviors. One of the most tellin g indicators of their likelihood to dropout is their socioeconomic status (SES). Students from families in the low SES category drop out at much higher rates than students in other socioeconomic groups. In 2000, 10% of low income students dropped out of hi gh school. That is double the amount of middle income students, and six times as many high income students (Englund, et al., 2008). There is no panacea to alleviate the dropout issue. However, access to and participation in enrichment programs can contrib se students from dropping out of school (Perry & Wallace, 2012). This study seeks to examine the availability of one of those enrichment programs, career and technical education (CTE) programs and the participation in them by dropout prevention (DOP) students. Purpose of the Study The purpose of this study is to examine the availability of CTE programs in the United States and the participation in them by dropout prevention (DOP) students based on poverty concentratio n within their communities The national discussion of the dropout epidemic has been heavily publicized, especially through the discussion of graduation rates (Thornburgh, 2006). On average, about 3/4 of students will graduate from high school (Bracey, 200 6; Tyler & Lofstrom, 2009).

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15 Today about 500,000 students drop out of school annually (Dynarski, et al., 2008). Most of these students come from rural and urban areas (Stanley & Plucker, 2008). Concerns about education have been expressed by citizens with the belief that the world is rapidly changing, and technological revolution that will drastically change what it means to teach and learn. The critical point of c a wealth producing source. Without the knowledge that education produces students will be left behind, putting DOP students even more at risk (Cassel, 2003) National figures suppo rt these measurements in Figure 1 1 (National Center for Education Statistics. 2007; as cited in Tyler & Lofstrom, 2009): Figure 1 1. National Dropout Rate Many programs have been implemented to addres s the clear need to improve these statistic s Some o f these programs have produced positive results and have improved graduation rates among the groups which they served. Dodd & Bowen (2011) f ound that after school

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16 programs can improve family involvement at home. Franklin (2007) found students who receive t heir alte rnative schooling in an environment that emphasizes academics earn significantly more credits, enter college at higher rates, and have better attendance. Southwick et al., (2005) stated that resilience is enhanced through learning when a mentee im itates his or her more resilient mentor. Fairbanks et al., (2007) found that tiered instruction programs in the second grade were more effective when targeted efficiently, more individualized, and function based. The National High School Center (2010) stat learning and academic performance and to determine whether a specific intervention is effective for a particular student. Reynolds et al., (2011) found that participation in an early childhood pro gram such as the Child Parent Center Education Program serves as a preventative resource for pre school age children with multiple risk factors. As seen here, educational policy and professional practice have attempted an all of the above approach, with so me success. Unfortunately, there is no panacea, and a graduation rate gap still exists (Perry & Wallace, 2012). Evidence Justifying the Research Problem Policymakers and practitioners identify and target the needs of DOP students using their exhibited be haviors and characteristics as indicators. They include attendance patterns, achievement scores, intelligence scores, attitude towards school, and teacher input (Coleman, ping out (Carpenter & Ramirez, 2007). Whether or not a student has been arrested (Brownstein, 2010) is also a dropout indicator. Students most often attribute their attitude toward dropout to parent involvement and a feeling of connectedness to their schoo l (Haley, 2006; Myint U, A., et al., 2008), illustrating the impact of expectations and motivation on student perception and behavior (Montmarquette, et al., 2007). Interestingly, dropouts will admit that they had plans for how they

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17 would have used their d iploma (Lachman Fitzgerald, 1999). Research indicates that students with a higher number of risk factors are more likely to attend a non traditional school, such as an alternative school (Hemmer, 2009). Dropout rates differ across demographic areas. The d ropout rate for Latino, African American, and White students are 27.8%, 13.1%, and 6.9%, respectively (Brewster & Bowen, 2004). Approximately 11% of students with disabilities drop out of school. Between 50% 59% of students with emotional/behavioral disord ers drop out of school, and 32% 36% of students with learning disabilities drop out of school (Kemp, 2006). Students with emotional/behavioral disorders are known to have the lowest grade point average for any group of students with disabilities (Riccomini et al., 2005). Indicators are not limited to student characteristics. In fact, schools can assess whether operational characteristics may be negatively impacting their dropout rate. Some factors include overcrowding, high student teacher ratios, lack o f trained staff, limited opportunities in school activities, and infrequent interaction between adults and students (Christie & Yell, 2008). In March of 2006 the Gates Foundation released The Silent Epidemic, a study that examined the reasons why students drop out as indicators for research. The foundation conducted surveys soliciting responses directly from dropout students. The population of the study consisted of four focus groups. They totaled 467 ethnically and racially diverse 16 to 24 year olds who d id not complete high school. Participants lived in 25 different locations including large cities, small towns, and suburbs. Researchers found that the primary reasons for dropping out did not emphasize a belief that the work was too hard. The top five re asons participants revealed for dropping out of school were (Bridgeland, et al., p. 3): Classes were not interesting Missed too many days and could not catch up

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18 Spent time with people who were not interested in school Had too much freedom and not enough ru les in my life Was failing school Other reasons included lack of motivation, low expectations held by adults, the student had to get a job, and the student became a parent. Overall, the students who participated in the study accepted responsibility for no t graduating, with 51% of students responding that they were responsible, 26% responding that both the school and the student were responsible, and 22% responding that the school was responsible (Bridgeland, et al., 2006). It is important for the education system to prioritize these outcomes to improve well being as individuals and for the nation as a whole in an increasingly competitive global economy. (Tyler & Lofstrom, 2009). School factors known to positively impact the dropout rate i nclude the availability of a freshman academy, team teaching, interdisciplinary planning, teacher discussion, student mentoring, individualized instruction, and counseling (Zvoch, 2006). Further, academic performance and school engagement are strong indica success (Hupfield, 2011). Deficiencies in Evidence CTE programs facilitate positive outcomes for students, including DOP students (Advisory Committee, 2004). Participation in CTE programs can increase school attenda nce and improve labor market outcomes after graduation, whether or not the student attends post secondary education. Further, these programs deliver a better financial return than second chance programs aimed at high school dropouts adding value to the n otion of prevention (Bishop & Mane, 2003). When targeted specifically to decrease the risk of dropout, CTE programs can also be successful and can decrease the risk of dropout ( Bishop & Mane, 2003; Plank, DeLuca, & Estacion, 2008). However, the effectivene ss of a program can vary depending on its design level

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19 of continual monitoring, available technical assistance, and the school s ability to discontinue ineffective program s These factors can greatly impact the students who participate in the programs (St ern, et al., 1989). The way in which CTE programs connect to students varie s based on individual circumstances, many of which have been evaluated to better understand their impact. Some of these factors include the role of CTE programs in transitioning st udents to postsecondary education (Dare, 2006), the experiences of African American students in CTE programs (Fletcher, & Cox, 2012), the impact disability awareness can have on CTE instruction (Hall, 2007), and an examination of an oral reading fluency on CTE students (Mellard, Woods, & Desa, 2012). Although the significance of these studies should not be understated a problem remains. researched. School and district personnel are forced to make major programmatic decisions in the absence of replicating studies or, often, any process seeks to take a broader approach to the themes that impact DOP students in CTE programs by examining the topic of access to and participation in CTE programs by DOP students in the United States based on poverty concentration. This study seeks to examine those variables in a way that helps to create increased awareness. Research Questions The research in this study focused on understanding the issue of access to and participation in enriching education programs by DOP students in the United States based on poverty concentration The following four questions were designed to explore DOP students ability to access and participate in CTE programs in communities according to SES :

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20 Is there a statistically significant relationship between the availability of career and technical high schools in the United States and poverty concentration? Is there a statistically significant relationship between the participation in career and technical high schools in the United States by DOP students and poverty concentration? Is there a statistically significant relationship between the availability of career and t echnical education courses at regular high schools in the United States and poverty concentration? I s there a statistically significant relationship between the level of participation in career and technical education courses at regular high schools in the United States based on poverty concentration? Significance of the Study In order to promote a better understanding of the factors impacting the successes and failures of DOP students the author examin ed the access to and participation in CTE programs by DOP students based on poverty concentration using a quantitative approach Results of examining this research can contribute to a larger body of research on the contributions of enriching educational programs for DOP students, using the lessons learned her e for the benefit of students across socio economic levels to improve access and participation. Researchers can use the results of this study to stimulate their own research using the NCES survey Dropout Prevention Services and Programs to explore issues impact DOP students by isolating different variables to understand the impact that other sources can have on the issue. Federal, State, and Local school administrators can use this research to shape policies and programs that address the se issues. Further discussion of these implications will be conducted in chapter five.

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21 C HAPTER 2 : REVIEW OF THE LITERATURE The purpose of this study is to examine the availability of CTE programs in the United States based on poverty concentration in the community, and t he participation in them by DOP students This literature review will provide an overview of the issues and topics pertinent to the purpose of this study. The review of the literature will provide information surrounding the factors that impact this study ; DOP students, CTE Programs, and poverty. First, the literature on DOP students will be presented. This overview will frame the nature of the issues that impact DOP students. It will begin with the definition of the term at risk, to provide perspective fo r how the use of the concept has developed over time. Then, characteristics and circumstances of DOP students will be presented to contextualize their life experiences. Finally, an analysis of the poverty literature will be conducted, viewed through the l ens of the impact SES has on DOP students. Then, the policies and programs that have been attempted to alleviate the dropout problem will be presented. Each attempt produced some level of success. They positively impacted the lives of students to some ext ent and helped some DOP students to complete school, but none surfaced as a panacea to resolve the DOP problem. Productive elements of each program materialized as strategies which can be used in other settings to improve instruction and student retention. Their contribution to the success of DOP students will be reviewed to ensure a balanced review of available programs. Last this chapter will examine CTE programs to understand the context through which they can contribute to DOP efforts. A history of CT E programs will explain how they have developed over time. An analysis of these programs and their impact on DOP students will be conducted. The concept of CTE programs will be explored. Benefits of enriching educational

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22 programs will be examined. The repo rted experiences of both teacher and students will be variables. Student performance in the CTE setting will be analyzed to determine the factors that may inf luence academic growth. The potential for CTE programs as an enriching educational program to combat the dropout problem will be explored through an analysis of the CTE strategies that improve at risk student performance. The chapter will end by providing the conceptual framework of the study, then with a summary of the content. Dropout Prevention Students Students who are at risk of dropping out of school are referred to as dropout prevention mission on Excellence in Education to describe an economically and socially endangered society in the United States. Its continued use in subsequent reports from the Council of Chief State School Officers, Education Commission of the States, and the Nation al Coalition for Advocates of Students used the term to describe students by their high probability of school failure (Placier, 1993). The National Impact of Dropping Out. Those students who drop out of school have financial and social impact on society. T he United States would benefit from $45 billion more in tax revenues and reduced spending in healthcare, crime, and welfare if the number of high school dropouts among 20year olds were cut in half (Broom, 2010). Overall, dropouts cost the U.S. $240 billio n in lost wages and taxes. Dropouts face a higher risk for substance addiction, low socioeconomic status, welfare, and imprisonment (Doran, 2005). In some states high school dropouts make up 60% of the prison population (Young, 2008). Chapman, Laird, & Ke wal Ramani (2010) contend that the decision to drop out of high school is related to a multitude of negative outcomes, including; an average annual salary of $23,000 a figure $19,000 below the average salary of a high school graduate. Further, a lower

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23 pe rcentage of adults who dropped out are in the labor force and dropouts are more likely to suffer from poor health. On average, the individual dropout costs an additional $240,000 to taxpayers over their lifetimes as a result of higher reliance on Medicare/ Medicaid, higher rates of criminal activity, higher reliance on welfare, and lower overall tax contributions. Profile of Dropout Prevention Students. Dropout prevention students are characterized by their backgrounds and circumstances, the conditions they experience, and the risk indicators they demonstrate that increase the likelihood they will drop out of school. These factors include; course failure, grade retention, low test scores, school location, spending per pupil, student body composition, race, s ocioeconomic status, student mobility, resiliency, motivation, family characteristics, early adult responsibilities (Tyler & Lofstrom, 2009), aggressive behavior, and maternal education level (Ensminger, M. & Slusarcick, A., 1992) Students who become vic tims of these variables become handicapped as adults without high school diplomas. The act of dropping out of school may be just another event in a chain of events that are driven by these variables. Some research views the decision to drop out of school a s a long term process that encapsulates these issues and culminates with the act of dropping out (Finn, 1989). In a study of students surveyed on their participation of high risk behaviors about fifteen percent of students self identify as very high risk, 15 percent as high risk, 35 percent as medium risk, 20 percent as low risk, and 15 percent as no risk. Some of their characteristics of the very high risk category include having been arrested at least once, having access to guns, using alcohol, using ille gal drugs, being sexually active, being depressed, and attempting suicide. Students in the high risk category share characteristics such as alcohol use, marijuana use, behind in school, truant, and depressed. Students in the medium risk category, the large st risk category, are involved in at least two among these risk behaviors: being behind in school,

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24 truancy, alcohol use, marijuana use, and sexual activity. Low risk and no risk youth are less likely to drop out because of their behaviors which could inclu de cutting a class or taking a drink of alcohol. Although these students are categorized as low risk and no risk, they are surrounded by the students who are taking part in negative behaviors and face the possibility of being victimized by them (Dryfoos, 1 996). Low Socioeconomic Status. factor in the current study and one of the strongest indicators of DOP status will be analyzed first. While neighborhood characteristics influence educational attainment among young people, institutional factors also play a role. School quality is often higher in wealthier neighborhoods. The higher the quality of the neighborhood, as measured by wealth or socioeconomic status, the less likely young people are to drop out of high school and the more likely they are to attain a college degree (Santiago, et al., 2011). According to Vartarian & Gleason (2002), students in these neighborhoods benefit from more positive adult role models, peers with whom goals and experiences ca n be shared, and high quality local institutions. Likewise, as neighborhood conditions improve, they have a primary impact on high school dropout rates Living in socially isolated neighborhoods has a negative impact on educational attainment, due to the la ck of influence by positive adult role models. Specifically, young people are likely to model what those around them are doing. Socially isolated neighborhoods suffer from the lack of positive adult role models to impede the process of educational attainme nt. The most negative effects of living in socially isolated neighborhoods are the most severe among young people who do not have the family support, or positive adult presence, to support them as they attempt to overcome the challenges of such a setting ( Vartanian & Gleason, 2002).

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25 Urban area students are impacted by the challenges of their communities where there are high concentrations of poverty. Concentrations of depression are linked to these communities. Reasons for this link vary, and can range from higher level of stressors in the community, experiencing traumatic events to having low levels of social support and cohesion. Regardless of covariates, SES of the community remained a statistically significant indicator. Rural areas also suffer from many of these challenges (Galea, et al., 2007). school and contact with the legal system. Students who are arrested in ninth or tenth grade are six times as likely to make the decision to drop out of school as their counterparts (Hirschfield, 2009). Students who come from a background of low socioeconomic status (SES) feel the after effects of a community that does not have a good relationship with school systems or job markets (Ensminger & Slusarcick, 1992). They are more likely than other students to drop out of school (Bloom, 2010). The challenges that low SES students face may stem from events that occurred early in their lives. Black, et al. (2000) examined the Bayley Scales of Infant Development scores of infants from low incomes families to find that these infants developed at a lower rate than children from the normative sample. The study found that these students are less likely to explore objects in their en vironment, engage in tasks or with others, and have lower levels of enthusiasm, initiation, persistence, and emotional/dispositional quality. Students who progress through their early lives in a state of low SES are impacted by the risks of their circumst ances and negatively affected in the area of mental health. These circumstances include neighborhood disadvantage and poverty related stressors. They can result in delinquency, attention problems, aggression, somatic complaints, and anxiety/depression (San tiago, et al., 2011). They deal with daily challenges from their families, neighborhood, and

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26 school -all of whom are impacted and interconnected by the same challenges. As a result, the SES students experience emotional, cognitive, behavioral, spiritual and physiological reactions that occur during and after traumatic events. The level of risk for students of low SES to develop mental health disorders and impairment are higher than the risk levels for the general population (Kiser, L., 2006). Further, students in these circumstances are more likely to experience traumatic events that cause stress related issues. In a meta analysis of 25 potential risk factors for PTSD (post traumatic stress disorder) Trickey, et al. (2011) found that both the traumatic and post events factors experienced by the child play a major role in whether a child develops PTSD after the event. The criteria for the study considered children from 6 to18 years of age from 64 studies between 1980 and 2009. Variables examined include d age, race, gender, IQ, SES, pre and post trauma life events, bereavement, and severity. The results illustrated that children who experience low social support, social withdrawal, poor family functioning, and distractions have a higher likelihood for PTS D. It should be noted that a strong factor in the successful treatment of PTSD is early screening and prompt treatment. Improving the quality of education provided to students living in poverty would help to counter some of the adverse circumstances they e xperience on a daily basis. However, it appears that the opposite occurs in the United States. Students from high poverty districts are more likely to go to schools that have inadequate resources and poorly trained teachers. As a result these students leav e school without the skills needed to earn a living that would pull them out of the circumstances in which they grew up, thereby feeding the pattern of inequality of education, inequality of educational attainment, and inequality of labor marker earnings ( Murnane, 2007).

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27 Family Characteristics. behavior and attitude towards both school and life. The level of stress experienced by a family can have a significant impact on students in their acad emic and social lives. Stress impacts a stressful life events and the impact of parenting stress can create this result. Further, students lacking a stimulating ho me environment lack parental sensitivity. Students of low SES face the most intense challenges in this context, as they are most likely to face more than one, or all, of these factors (Oxford & Lee, 2011). Family processes such as structure, coping, and r elations differ from family to family and which they deal with problems (Kiser & Black, 2005). Positive family engagement can have a significant positive effect on student achievement (Smink & Reimer, 2005). The relationship between economic status and health is one that should also be noted. Generally, people outside of the low SES category tend to be healthier overall (Tipper, 2010). Some students who are in fo ster care face a significantly higher likelihood of participating in high risk behaviors. Particularly, students in this category who have low levels of caretaker support in their foster care setting are much less likely to experience positive outcomes. Re searchers in this study controlled for several factors, including; self competence, placement changes, poor self regulation, and caregiver support. Interestingly, girls who had high levels of caretaker support had consistently positive outcomes both academ ically and behaviorally. This illustrates that the students who were removed from negative circumstances and placed in positive ones benefitted from the change. Without facing so many of the risk factors they had

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28 experienced in their previous setting, the female foster students were able to thrive (Pears, et al., 2011). The manner in which children interact with their parents, specifically the manner in development of support them are more likely to reduce the risk the child will become depressed. In contrast, lihood for depression. Parents who react with anger and dysphoria increase the likelihood for depression. Parents who wish to reduce the likelihood of depression as an outcome for their child face challenges in systematically changing their behaviors over time, so they may increase the chances of a positive outcome for their children (Schwartz, et al. 2012). The significance of family characteristics and interaction was further highlighted by Kim Spoon, et al. (2011) who regulate. In a study that examined the behavior of parents with their toddlers, Whittaker (2010) found that mate rnal sensitivity is an indicator of socio emotional functioning among toddlers. Participants were mother child groups with the children aged 3 to 23 months. Of the 130 mothers aged 15 to 51 selected to participate, 114 participated in the first visit of th e study, and 95 participated in the follow up visit scheduled six months later. Participants were mostly minority women who had never been married. Measures of risk for a negative outcome in this study included parenting stress, parental depression, matern al sensitivity, and inadequacy of family

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29 Students benefit from high expectation in their families. High expectations from family high hopes for themselves. Students who have high hopes for themselves are five times as likely to have mothers that have high expectations of them. Female students who benefit from high expectations along with stricter rules regarding school from their m others perceive that their teachers are satisfied with their performance. Female students setting high expectations for themselves are more likely to graduate than their counterparts (Ensminger & Slusarcick, 1992). Drug Use. The use of drugs and the choic e to drop out of school may share the same precursors, which could explain their relationship. Students who make these decisions typically demonstrate less of a commitment to school and family, and are characterized by lower psychological well being. They experience poorer relationships with parents, stronger ties to their peers, worse grades, low self esteem, and more negative attitudes about school (Mensch & Kandel, 1988). Looking at this notion from an event history analysis may allow researchers to und erstand the characteristics that precede dropout and help them to control them in the future. Dropouts report a higher rate of drug use than their peers who choose to stay in school. Specifically, the use of cigarettes, marijuana, or other drugs at any age increases the likelihood that a student will drop out of school. As students become more frequent drug users and develop networks of friends who are also drug users, they become more likely to develop a lack of interest in academic issues. When they conti nue to associate with the same peers, they reinforce this belief system and lack of conformity to traditional institutional values. During this process their drug use may be further impairing their cognitive functioning and motivation, having a further neg ative impact on their commitment to school (Mensch & Kandel, 1988).

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30 Race. Minority students are more likely to leave school early than other students (Bloom, 2010). They are als o more likely to experience contact with the legal system (Hirschfield, 2009). Further, minority students are more likely to be exposed to high rates of crime and violence, compounding their circumstances, increasing the chances that they will become victi ms themselves (Black, et al., 1998). Student Mobility. High level s of student mobility vary among students and schools, with the highest degrees of prevalence being from large predominantly minority districts and students of low SES. It is related to stud ent misbehavior, youth violence, and can negatively impact student academic performance. Overall, transient students achieve on a lower level than their counterparts (Rumberger, 2003). There are many events that can be classified under the school mobility concept. These events include student being placed in special services then returning to his home school, expulsion, or involuntary transfer to another school then returning to the home school or another school attended in the past. (Osher, Morrison, & Bai ley, 2003). Exceptional Student Education Students. Exceptional Student Education (ESE) students complete high school at a lower rate than their counter parts. After school, they also are exposed to a higher possibility of negative adult outcomes (Korteri ng & Braziel, 2002). Learning Disabled (LD) students are especially susceptible because their intelligence and achievement levels suggest that academic success is not easy for them. Data supports the notion that there is an interrelationship between antiso cial behavior, academic failure, and school climate (Kortering & Braziel, p. 187). EBD Emotional & Behavioral Disabilities (EBD) students fit into this category with their data demonstrating a similar trend. Seventy three percent of EBD students

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31 who choose to drop out of school are arrested within three to five years (U.S. Department of Education, 1994; as cited in Osher, Morrison, & Bailey, 2003). School and teacher influence plays a role in this process. Students who experience social supports that devel op incentive and meaning, nurture personal skills, and provide adequate access to resources increase the likelihood that the student reacts positively to events in their lives. When those ties are weak, the student is more vulnerable and more likely to rea ct negatively to chance events. Specifically, these actions include student placement, method of special education delivery, teacher bias, the degree and access to opportunities and resources, student involvement, academic rigor, and vocational options (Ro jewski, 1999). Poor Academic Performance. Student performance on competency exams indicates a relationship between their performance and the decision to drop out of school. These exams tend to have adverse effects on disadvantaged, at risk students. Furth er, there is an inter relationship Relationship to School and its Culture. At risk students can feel alienated by the culture at their school. When teachers provide support to DOP students in a friendly way and can offer a cooperative and academically engaging classroom environment, they can positively impact the affective and cognitive outcomes of DOP s tudents, including their feeling of connectedness to the school (DelliCarpini, 2010). Even so, sadly, DOP students are more likely to demonstrate misbehavior in classrooms that would lead the development of negative relationships with teachers (Ladd & Burg ess, 1999; as cited in Osher, Morrison, & Bailey, 2003). Some of the school related challenges that DOP students face are represented through the research of Barker (2005), who linked student experience to the likelihood of school success

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32 using a flow cha on education, is connected to their school, and who uses school resources effectively benefits from a high probability that they will stay in school and graduate. Their counte rparts who have a low value of education, are not connected to their school, and demonstrate a problematic use of school resources suffers from a low probability that they will stay in school and graduate. Compounding the likely success or failure of the s tudent is the nature of their family support system, prior academic experience, and personal student characteristics. Adult Involvement. Interactions with parents and teachers are a significant determinant of school success. The relationships that the adu lts establish with children help them build the skills they need to develop positive interpersonal relationships with other students (Englund, Egeland, & Collins, 2008). A recent study of individuals who follow expected vs. unexpected educational pathways tracked participants from birth through age 23 (Englund, et al., 2008). Researchers identified behavior and academic standards as following the expected or unexpected educational pathways to reach graduation for those between the ages of 12 and 16. The st udy found that expected graduates had higher levels of parent involvement in middle childhood, more supportive parent child relationships in early adolescence, and higher levels of social competence with adults than dropouts. Low levels of parent involveme nt as an indicator of poor child behavior and academic performance also had a statistically significant relationship in a study by Morris et al. (2003). Attempted Policies Over time various policies have been attempted to increase accountability standards on both teachers and students to alleviate the dropout epidemic Examples include Title I of the 1965 Elementary and Secondary Education Act which encourages schools to improve student skills in reading writing and math (Karsten, 2006) and No Child Left B ehind (NCLB) which

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33 stated that all students must pass standardized tests at grade level by 2014 (U.S. Department of Education, 2001). Mandates implemented to ensure students benefit from more rigorous content, greater learning time, and higher levels of a chievement have resulted in tougher standards for DOP students (McDill & Natriello, 1986). Mandates impose unintended consequences on students who already suffer from negative risk factors such as neglect, physical abuse, sexual abuse, low socioeconomic st atus, low parent education levels, no biological parents, and single family household (Leiter & Johnson, 1994). Adding to these pressures are high student mobility rates, which can impact a student socially, psychologically, and academically. Ninth grade h as been identified as a critical year for most students who are pondering dropping out (McCallumore & Sparapani, 2010). As students enter high school, they react differently to the competitive and impersonal environment (Schiller, 1999). The challenges of making new friends, learning new norms, and fitting in, negatively impact DOP students and are linked to both misbehavior and violence (Rumberger, 2003). Although there are many factors that could help school personnel identify at risk students, no singl e factor will identify them because they are not homogenous. The process that leads a student to at risk or dropout status is unique and can begin before they enter public school (Jones, 2009). Policy makers and practitioners have given consideration to t he dynamic risk factors impacting DOP students when attempting to implement programs and strategies to alleviate the dropout epidemic. Some of those attempts will be discussed here. Early Childhood. Early childhood education programs have been identified as one method to combat the dropout problem. They produce positive educational outcomes for students that last into adulthood. Evidence indicates that early childhood programs create lasting gains for

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34 children and society as a whole (Rand, 2005). A study of the Chicago based Child Parent Center Education Program (CPC) revealed positive results for program participants. The study reported on the well being of participants at age twenty eight, in an attempt to measure the long term effects of early childhood education programs. In their evaluation researchers analyzed responses to three questions: 1) Is CPC participation beginning in preschool and continuing into school age associated with multiple domains of well being?, 2) Do estimated effects vary by child and family characteristics as well as dosage levels?, and 3) Are effects consistent across models for reducing bias in estimates? Results revealed that students who participated in preschool and school age programs benefitted with improved social skills, social adjustment, motivation, and family and school support behaviors (Reynolds, et al., 2011). Another significant head start program started twenty years ago when the Hidalgo Independent School District implemented a full day and free pre kindergarten program. The students in this district are 98% economically disadvantaged with over half of the students average (Texas House of Representatives, 2010). Virtual Schools. Since its initial implementation in the mid 1990s, virtual schooling has developed into the most common method of distance education. The most accepted definition of virtual school is an en tity approved by the state or governing body that offers courses through distance delivery most commonly through the internet (Barbour & Reeves, 2009). During the 2000 2001 school year virtual schools offered courses to about 4,000 students in 350 schoo ls in 30 states (Vrasidas, et al., 2003 as cited in Reid et al., 2009). By 2008 students attending the Dalat International School in Penang, Malaysia were participating in courses

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35 offered through Florida Virtual School (FLVS) (Wood & Baseman, 2005 as cited in Reid et al., 2009), and 28 states had established statewide virtual high schools (Christie, 2008 as cited in Reid et al., 2009). During the 2008 2009 school year FLVS had 84,000 students enrolled in 168,000 half credit courses. These figures represente d a tenfold increase since the 2002 2003 school year (Tucker, 2009). Over the past decade virtual schools became one of the most significant trends in education (Reid, et al., 2009). Liu & Cavanaugh (2012) offer the data highlighting the growth: Table 2 1. Virtual School Enrollment Growth Academic Year 2000/01 2001/02 2002/03 2004/05 2005/06 2007/08 Enrollment 40,000 180,000 300,000 500,000 700,000 1,000,000 Growth in the field has led to the customization of programs to meet the needs of specific demog raphics, such as dropout prevention students. The needs of the students in this group are as dynamic as the members of the group themselves. Programs that are successful in meeting the needs of these students share some qualities that contribute to their s uccess. They include (Roblyer, p. 33): Basic assumptions Example: It is a basic assumption that teachers are Web trained and that there is equitable access to necessary resources Curriculum and instruction Example: The content of high quality programs is systematically designed and clearly communicated, and activities are highly interactive and offer opportunities for critical thinking related to course objectives Management Example: high quality programs provide technical assistance and ensure that s tudent work is secure Evaluation and assessment High quality programs include assessment and have procedures in place for monitoring students during testing

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36 As delivery models evolve over time practitioners add components to online programs to meet the n eeds of their students. In the Virtual Education Academy, that includes a citizenship component to the program. It is made up of five levels (White, et al., p. 14): Level 1 Respecting the rights and feelings of others (self control) Level 2 Participati on and effort (self motivation) Level 3 Self Direction (developing on task independence and setting realistic goals) Level 4 Caring (interacting positively with other and understanding diverse perspectives) Level 5 Community (Service to others and acting as a positive role model) Response to Intervention (RtI). RtI is a three tiered system that uses assessment data to promote data driven instruction and intervention. The tiered system helps teachers to examine the context of learning, sample skills and curriculum, and uses periodic assessment tools that are sensitive to change (Ysseldyke & Scholin, 2010). RtI provides increasing levels of support to students who demonstrate a need as reflected in their data. Tier 1 seeks to prevent harm from occurrin g, Tier 2 seeks to reverse harm to students who did not respond to Tier 1 interventions by offering students additional support, and Tier Three seeks to reduce harm by supporting students exposed to multiple risk factors that were unresponsive to the inter ventions of Tiers 1 and 2 (Lane, et al., 2011). According to MacIver & MacIver (2010), Tier 1 can be described as a comprehensive approach to school reform, Tier 2 as the targeted interventions that are designed to help struggling students, and Tier 3 as intensive interventions that seek to remediate problem behaviors or negative academic outcomes on an individualized level. RtI is guided progress monitoring, a process that allows schools to evaluate students more frequently depending on their tier. The hi gher the tier the more intensive the intervention and monitoring (Zirkel & Thomas, 2010). Targeting outcomes permits educators to collect formative

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37 and summative data to drive their decision making (Nunn & McMahan, 2001). According to Ysseldyke & Scholin ( 2010), Tier 1 consists of general outcome measures analyzed 3 5 times per year based on group achievement data. An example would be the Florida Comprehensive Achievement Test (FCAT). Tier 2 consists of general outcome data and categorical sub skill mastery measures analyzed at least once every other week. An example would be curriculum based assessments supported in classroom instruction. Tier 3 consists of general outcome measures and sub skill mastery measures analyzed at least once per week. An example w ould be curriculum based measurements for fluency. Districts that adopt RtI desire to increase the likelihood that the academic competence of students will develop adequately (Fuchs et al., 2007), address disproportionality, promote overall student achiev ement, better integrate general and special education, and determine special education eligibility for students with disabilities (Sawyer et al., 2008). Freshman Academies. Ninth grade is a make or break year for completing high school. For many, 9 th grad e is the first year that students begin earning credits. Completion of these courses are required for graduation, and they are the most rigorous the students have ever taken. The increased influence of standardized testing also plays a great role in the pr essure placed on young people during high school (McCallumore & Sparapani, 2010). These transitional difficulties are only a few of the problems that incoming students face as they enter high school. Further challenges are presented by the very act of tra nsferring to a new school, which is often marked by poorer academic performance, higher absences, and increased disciplinary issues. Students do not understand the importance of their freshman year of high school. In fact, a survey of upper classmen reveal s that they wish they better understood

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38 the impact that ninth grade has on graduation when they got to high school (McCallumore & Sparapani, 2010). Schools are beginning to recognize the importance of the 9 th grade and are implementing innovative programs, like freshman academies. These programs function as separate wings of the school exclusively for 9 th graders, so they can become familiar with the challenges and rigor of high school before being integrated into the general population in the 10 th grade (M cCallumore & Sparapani, 2010). Alternative Schools. Gagnon & Bottge (2006) define an alternative school as a public elementary/secondary school that addresses needs of students that cannot typically be addressed in regular school, provides nontraditional e ducation, serves as an adjunct to a regular school, or falls outside the categories of regular, special education, or vocational education. Previous research documents the success of alternative schools in reducing dropout rates among students. At alterna tive schools, students benefit from smaller class sizes & increased differentiation. Students at these schools can catch up if they are behind and improve their GPA. Students who attend alternative schools that are academically oriented benefit from a DOP program that challenges them academically, and gives them the individual attention they need to be successful socially and academically (Franklin et al., 2007). An example of an academically centered alternative school is The Twilight Academy. An evaluatio n of the program identified the following components as key factors to its success Small teacher student ratio Several different sources of input to determine referral to the program Experienced teachers in all content areas wh o are creative

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39 All classes should be held in close proximity to limit movement and reduce opportunities for misbehavior Counseling services Link work experiences to success in the program Professional development specific to alternative education Developed broad based curriculum Set clear expectations and consequences for violations of the rules Eliminate unstructured time to minimize opportunity for misbehavior It should be noted that there are some indications that programs that share some of the qualitie s of alternative schools, such as shared environment of children who are participating in negative activities, may contribute to the further degradation of their behavior. Their increase in and continued exposure to fellow students who conduct themselves i n a different manner can create a greater risk by further choosing to participate in those activities. Iatrogenic effects can have a strong impact on peer to peer contagion and contribute to further negative behavior and activities (Cecile & Born, 2009). Middle College Alternative. The middle college alternative is being adopted in many areas as a bridge to college for students who would have otherwise failed or dropped out of high school. Many students who do not succeed in high school note classes that are not stimulating and a negative social environment. As a result, middle colleges work to familiarize students with the idea of higher education, its surroundings, and procedures. Middle colleges are often located on the campus of a community college. Th ey are usually funded by a local school district or a charter school. Students complete their high school coursework and begin their college coursework on campus at the same time. Students often find that being in a more mature

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40 environment is more conduciv e to their learning needs. Due to its recent development, research on middle college is limited, but available results are encouraging (Olsen, 2010). Counseling and Peer Tutoring. Counselors can instill motivation in students to help them become successful When a student knows someone in their school cares about him or her, s/he can use that as inspiration to be successful, forming an attachment to the school (Scheel et al., 2009). White & Kelly (2010) offer strategies that can assist counselors in serving DOP students. These strategies include; social supports such as a peer mentoring/buddy system, monitoring and mentoring via school personnel, developing personal and social skills, facilitating parental involvement, and providing academic support. Peer t utoring offers another avenue through which similar goals can be accomplished. Students who work together allow teachers a reduced workload during the intervention process, thereby providing individualized instruction to more students in the class. The ben efits of peer tutoring include increased time on task, increased opportunities to respond, enhanced student to student relationships, and improved classroom behavior (Menesses & Gresham, 2009). Mentoring. to support them, the adult Teachers can reframe for the student what it means to be in school and the benefits of student involvement in school. The teacher can a lso help to clarify the problems that are bothering the student (Center for Mental Health at Schools at UCLA, 2008). Mentors can enhance resilience in at risk students while providing a level of social support that is possibly life saving. Resilience is en hanced through learning, when the mentee imitates the mentor. If the imitation goes on long enough, the imitations can form habits within the mentee (Southwick, et al., 2005).

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41 Reliance and Self Determination. There are some individuals who face the challe nges of DOP students yet turn out to be resilient through the process and into adulthood. During this period in their lives, they acquire adaptive levels of functioning that contribute to their success (Dumont, et al., 2005). Six resiliency skills are link ed to academic success include building confidence, making connections, setting goals, managing stress, improving well being, and understanding motivation (Hupfiled, 2011). Students who make decisions for the benefit of their own future self determination demonstrate skills in problem solving, goal setting, decision making, choice making, goal attainment, self management, self efficacy, self awareness, and self knowledge (Zhang & Law, 2005). Schools can help students develop these skills by providing oppor tunities for success to build student confidence, communicate the relevance of education, help students build intrinsic motivators and problem solving skills, help students manage stress, and create a caring and supportive environment that emphasizes meani ngful teacher student relationships (Hupfield, 2011). Methods for schools to accomplish these goals are defined by Jones (2009). They include; increase pro social bonding, set clear and consistent boundaries, teach life skills, provide care and support, se t and communicate high expectations, and provide opportunities for meaningful participation. Schools should be careful not to inappropriately use the resilience construct. The research on resilience was initiated in the 1970s to document individuals who g rew up to be functioning, contributing members of society although they came from adverse circumstances. Resilience is a result. It should not be used specifically as a strategy, but instead be targeted as a skill to be developed in students (Bonanno, 2012 ).

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42 Career and Technical Education The notion of CTE programs in schools began in the early 1900s as the need for line workers grew. The Smith Hughes Act of 1917 funded the effort to create the first compliant and reliable workers in the United States to s erve the needs of American business. Over the next several decades a debate emerged over whether CTE courses reinforce class boundaries by creating a college preparation track and a vocational track (Perry & Wallace, 2012). In the 1970s CTE programs were refined to provide a more relevant education to students about to join an economy that was beginning to be impacted by globalization. In the early 1980s, after A Nation at Risk was published, education shifted again to academics and emphasized language, m ath, and science over CTE programs. In the early 1990s America s Choice: High Skills or Low Wages was published. It concluded that the U.S. was arriving at a point where it was going to have to choose between offering its workers low wages and little oppor tunity for advancement or give them an opportunity to develop high level skills as young people. At this point, the transition began to the modern expectation that local school districts, community colleges, and universities will begin to create school bas ed learning and work based learning initiatives (Perry & Wallace, 2012). Legislation promoting career and technical education has been presented in several states. Washington SB 6377 requires districts that implement CTE programs to meet specific performa to students leading to industry certification. California, Nevada, and Virginia implemented measures that require specific reporting of CTE performance data (Bush, 2009) Vocational programs are perceived to be an aid to districts seeking to enhance student skills and keep them from dropping out of school. Harvey (2001) notes the abundance of legislative initiatives which have been passed for this purpose. They include the Vocational

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43 Education Act of 1963, which established vocational education opportunities for students with academic, socioeconomic, or other challenges. The Carl D. Perkins Vocational Education Act of 1984 provided equal access to high quality vocational pr ograms to students with disabilities. This act was updated in 1998 to include a provision that ensured equal access for students with disabilities. As a result, efforts to recruit and enroll students with disabilities were improved. It was again reauthoriz ed in 2006 (DelliCarpini, 2010). A more academic approach to CTE is the career academy. Career academies typically serve students in grades nine through twelve, range in participation of 150 to 200 students, and consist of the following three features: (1 ) small learning communities to function as a personalized supportive learning environment, (2)combine academic, career, and technical curriculum in a way that integrates these concepts into a specific course, and (3) partner with local employers to ensure students have work based opportunities (Kemple, 2008; as cited in Kuo, 2010). Vocational education differs in its delivery method from traditional academic subjects. Classes are typically offered in separate buildings or separate sections of a campus, se gregated from the rest of the school community. It demands resources and infrastructure different from that of other classrooms and requires that most of the instruction take place away from the traditional student desk. CTE programs also benefit from addi tional funding initiatives at the state and federal levels which make districts responsible for the built in accountability measures that are associated with those funds (Cobb & Preskill, 1983). Career academies design vocational programs through a struct ure that provides real world skills and offers industry certifications. They operate within schools and offer career related curriculum based on both academic coursework and work experience. Such programs are found

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44 to help keep students in school, progress in school, and complete school (Kemple & Snipes, 2000). Academic Contribution. Whole school reform models that include some elements of career academies contribute to improving graduation rates (Kuo, 2010); CTE programs can also contribute to the academic development of DOP students (Smink & Reimer, 2005). Schools that have a specialized focus can help DOP students by playing to their interests and helping them overcome information and network deficits. Career academies have the potential to improve the ou tcomes for low achieving students when they help students develop skills that are valued in the market or make enough academic gains that surpass the threshold of poor academic performance (Cullen, et al., 2013). Schools that implement career academies wi th an academic focus on science, technology, engineering, and math (STEM) are among the programs that can contribute positively to the learning community within a school. STEM programs help schools achieve their desired outcomes of scho ol improvement. Thes e programs create personalized learning environments often with small enrollments, address academic content and pedagogy, and increase relevance to other content areas. Students in career academies outperform non academy students on measures of academic su ccess in high school. Further, young DOP males can increase the likelihood that they will have post high school labor market success by up to 18% by participating in a career academy (Fleischman, S. & Heppen, J., 2009). In a study of how career academies can impact students in urban districts the authors analyzed data on how these programs contribute to academic performance. They found that helping them focus. When C TE schools are selective they may create and foster a peer to peer

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45 effect that imposes a positive influence to achieve academically. When attendance at a CTE school is viewed as a privilege students are less likely to misbehave because they know they can be sent back to their home school (Neild, et al. 2013) In a study of oral reading fluency among DOP students in CTE programs Mellard, Woods, & Desa (2012) found that a two minute oral reading fluency assessment could be used by teachers to quickly measure student reading skills. Teachers in the study placed students in groups of four based on their median splits by total words read per min ute and their error rates. The structure allowed teachers to establish groups with a diverse range of reading skills so that they could quickly address the needs of students in class. In a study of math skills Stone et al., (2008) found that teachers who are trained to promote math skills in their CTE classroom can improve student performance on standardized tests. The mo del used to promote the professional development (PD) programs was called Enhanced Lesson lesson, explain it, and identify, discuss, point out, or pull out the math embedded in th e lesson. assessing, math vocabulary was introduced through the math example embedded in the CTE. The evaluation employed a variety of methods and techniques for assessing awareness of all students. In step 3 teachers worked through the math example embedded in the CTE class. The process included working through the steps of the embedded math example, and bridging the CTE and math language. Step 4 promoted work thr ough related, contextual math in CTE examples. This process included working through similar problems or examples in the same occupational context, using examples with varying difficulty, and continuing to bridge the math and CTE vocabulary. Step 5 facilit ated the work through traditional math examples. This process

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46 included working through traditional math examples in the way they could appear on a test, moving from basic to advanced examples, and checking for comprehension. Step 6 gave students an opportu nity to demonstrate their understanding. This progression included providing students with opportunities to demonstrate their understanding and tying the examples back into the CTE context. Step 7 was a formal assessment that incorporated math in tradition al CTE questions. By following this process teachers were able to develop more explicit instructional processes that contributed significantly to the improved math performance of their students. CTE programs have increased the rigor of their curriculum to adapt to the accountability movement, creating a demographic shift in the students who participate in the programs. Now more White and Asian students participate in career academies and fewer African American and Hispanic students participate (Fletcher & C ox, 2012). In a study that covered the experiences of 15 African American students in high school, the authors sought to identify the themes that emerged through their experiences in participation in CTE programs and outside the specialized setting. Four t hemes emerged: (a) preparation for the next level, (b) less time for school activities, (c) not just going through the motions, and (d) unrealized connection with core academic subjects. Overall, students felt like they were gaining skills that could help them in the future. However, they recognized the limitations of the program offered at their school (Fletcher & Cox, 2012). Academic Quality. The quality of the academic program and the level of reform the program has experienced have an impact on student outcomes. For example, female students who participate in traditionally female dominated CTE courses such as family and consumer sciences are actually less likely to enroll in two and four year colleges (Cashen, 2012). Further, Stern (2009) states that som e research indicates that students who take more CTE courses in high

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47 indicate that the research which indicates successful labor market outcomes for CTE stude nts may suffer from selection bias as a result of students self selecting into CTE programs and then pursuing careers in their intended field after school. This correlational data, he states, does not disaggregate between these students and others who did not self select in high school. These Conceptual Framework capital being social, economic, and cultural. This study examines the impact that socioeconomic status can have on the academic participation of a student and their access to academic resources. This framework will help to communicate the significance of the socioeconomic factors that can impact DOP studen ts so dramatically. These concepts illuminate the significance of each type of capital and their influence. resources which are linked to possession of a durable netw ork of more or less institutionalized relationships of mutual acquaintance and recognition, or in other words, to membership of a depends on the size of their network and how the individual can mobilize the capital within that network. Economic capital conjures a more direct explanation capital that is immediately and directly convertible into money and may be institutionalized i n Cultural capital can exist in three forms: in the embodied state akin to the dispositions of the mind and body; in the objectified state as in the form of cultural goods (pictures, books,

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48 dictionaries); and in the institutionalized state similar to educational qualifications. (Bordieu, 1986). Other traits, such as reading habits, can also be attributed to the existence of cultural capital (Johansson & Hojer, 2012). The framework of this study is based on th ese three forms of capital and how themselves, possess low level of capital in some or all of the areas described by Bordieu (1986) are disadvantaged and face more compl ex challenges than their peers This review of literature examine d the factors that impact DOP students and the policies and programs that have sought t o equalize their circumstances. As the factors were presented, a structure was put in place to convey th e manner in which those factors impact DOP students through the lenses of social, economic, and cultural capital. Students who experience the challenges presented by mobility, poor academic performance, poor relationship to school and its culture, drug use and poor levels of adult involvement (among other factors) are at a serious disadvantage in terms of likelihood of school success. Students who come from backgrounds of high poverty/ low SES are more likely to face one or more of these challenges The eff ects of these challenges are compounded by the effects that poverty has on people and communities impacted by these factor s (Finn, 1989). By viewing the challenges that DOP students face in relation to the poverty in their communities, and examining the re sources to which they have access, a better understanding of the challenges they face may be gained, allowing researchers and practitioners a stronger foundation to explore and propose solutions to the problem. Summary The benefits of CTE programs and the ir impact on DOP students are sound in the research base. Students who participate in these programs are more like ly to be academically successful. DOP students who participate in these programs are more likely to attend class and

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49 perform at a higher level academically. CTE programs address some of the specific drivers that fuel student dropout, namely a feeling of connectedness to the school (Kemple & Snipes, 2000) This background information provides a context for the present study in which the researche r seeks to examine the availability of CTE programs in the United States based on poverty concentration in the community and the participation in them by DOP students The relationships between the segments of this literature review provide a schema throug h which research can be conducted that addresses the needs of DOP students. As the qualities and characteristics of DOP students, CTE programs, and other attempted policies to meet the needs of DOP students are understood, the current study is better equip ped to add to the body of research on the availability of enriching educational programs and the participation in them by DOP students.

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50 CHAPTER 3 METHODOLOGY Purpose The purpose of this study is to examine the availability of CTE programs in the Unite d States based on poverty concentration in the community an d the participation in them by DOP students. Poverty concentration in the community is defined as the percentage of children between the ages of 5 17 living below the poverty line. The information in the survey was sorted into three categories so the respondents could indicate the extent of the prevalence of poverty in the community; less than 10%, 10% 19%, and 20% or more. The methodological approach is driven by the following concept on the us e of statistics from the former President of the Royal Statistical Society way that is vivid, simple, accurate, and integrate with subject matter considerations is a very 981; as cited in Gore, 1981). Sample The survey was delivered by the National Center for Education Statistics (NCES) within The study consisted of 1,200 public schools districts in all 50 states and the District of Columbia. Survey respondents were the Superintendent of the district, or their selected designee. The nationally representative sample was selected from the 2008 2009 NCES Common Core of Data, which wa s the most up to date version available at the time of the study. To identify the sample the sampling frame was stratified by instructional level and enrollment size (NCES, 2011). Research Questions Research Question 1 Is there a statistically significant relationship between the availability of career and technical high schools in the United States and poverty concentration?

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51 The purpose of this question is to determine the availability of career and technical education high schools in the United States bas ed on poverty concentration and the extent to which a relationship between them exists, if any. The dependent variable is the dichotomous answer that each responding Superintendent provided regarding the availability of a career and technical high school i n their district. Descriptive statistics were used to identify the frequency which each district reported the availability of career and technical high schools in their district, according to poverty concentration. Frequency distributions with percentages were completed to amalgamate this data. Then, a binomial logistic regression was performed to determine the relationship between the variables tested. Research Question 2 Is there a statistically significant relationship between the participation in career and technical high schools in the United States by DOP students and poverty concentration? The purpose of this question is to determine the level of participation by DOP students in CTE high schools based on poverty concentration, and the extent to which a relationship between these variables exists, if any. The dependent variable is the categorical response issued by respondents explaining the extent to which DOP students in their district participate in CTE high schools. Frequency distributions were perf ormed to determine the number of occurrences for each category, as well as the percentages. These figures are expressed in tables as well as bar charts. Then, an ordinal regression was performed to determine whether a relationship between the variables tes ted. Research Questions 3 Is there a statistically significant relationship between the availability of career and technical education courses at regular high schools in the United States and poverty concentration?

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52 This question is posed to determine the availability of career and technical courses at regular high schools in the United States based on poverty concentration and the extent to which a relationship between them exists, if any. The dependent variable is the dichotomous answer that each respondi ng Superintendent provided regarding the availability of career and technical courses at regular high schools in their school district. Descriptive statistics were used to identify the frequency which each district reported the availability of career and t echnical courses at regular high schools in their districts, according to poverty concentration. Frequency distributions with percentages were completed to compile the data. Then, a binomial regression was performed to understand the relationships between the variables. Research Questions 4 Is there a statistically significant relationship between the level of participation in career and technical education courses at regular high schools in the United States based on poverty concentration? The purpose of this question is to determine the level of participation by DOP students in CTE courses offered at regular high schools, based on poverty concentration, and the extent to which a relationship between these variables exists, if any. The dependent variable i s the categorical response issued by respondents explaining the extent to which DOP students in their district participate in CTE courses at regular high schools. Frequency distributions were performed to tabulate the responses for each category, as well a s the percentages. Then, an ordinal regression was run to determine the relationship between the variables. Hypotheses There will be no statistically significant relationship between the availability of career and technical high schools in the United Stat es and poverty concentration There will be no statist ically significant relationship between the level of participation in career and technical high schools by DOP students in the United S tates and poverty concentration.

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53 There will be no statistically sig nificant relationship between the availability of career and technical education courses at regular high school in the United States and poverty concentration. There will be no statistically si gnificant relationship between the level of participation in ca reer and technical education courses at regular high schools by DOP students in the United States based on poverty concentration. Data Collection Questionnaires were mailed to the Superintendent of each district in September of 2010. The accompanying cover letter requested that the information in the survey be completed by the person with the most knowledge about DOP services and programs with that district. Respondents were able to complete the survey online or by mail. Following the initial data collectio n period telephone follow up calls were made for those who did not respond to the survey and to clarify data from October 2010 to January 2011 (NCES, 2011). Of the 1,200 districts in the sample, five were deemed ineligible because it was determined that t hey were strictly administrative entities and did not actually participate in the operation of any schools. Of the remaining 1,195, 1,086 participated in the survey. This resulted in a 91 percent participation rate. Sixty one percent of the participants co mpleted the survey through the Internet, 27 % completed the survey by mail, 7 % completed the survey by fax or email, and 5% completed it b y telephone. Information regarding the validity of the survey was not available to the public, so the researcher cont acted the IES to learn more. In an email communication the following information was learned (J. Ralph, personal communication, March 14 th 2014) : The development work for this survey included a literature review on dropout prevention programs and servic es and four rounds of feasibility calls that informed the survey design. The first two rounds of feasibility calls were conducted to identify topics that could be addressed in a short questionnaire and they informed the initial draft of the survey. The th ird and fourth rounds were conducted to assess the clarity and relevance of the developed survey items, and to gauge whether respondents thought they could answer the questions without

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54 too much burden. In the third and fourth rounds of feasibility calls, respondents reviewed the draft survey and provided feedback over the phone for survey items. The questionnaire was then revised and the resulting draft of the survey was reviewed by the NCES Quality Review Board (QRB). Feedback was also obtained during s urvey development from other U.S. Department of Education offices and from an external reviewer who was an expert on dropout prevention programs (J. Ralph, personal communication, March 14 th 2014). Based on feedback from the QRB, the survey was revised and a pretest of the questionnaire was conducted to identify problems that respondents might have in providing the requested information. The purpose of the pretest was to verify that all questions and corresponding instructions were clear and unambiguou s, to determine if the information would be readily available to respondents, and to determine whether the burden on respondents could be further reduced. Responses and comments on the pretest questionnaire were collected by fax and telephone. Changes to the questionnaire were made based on the feedback received from the pretest (J. Ralph, personal communication, March 14 th 2014). The survey was then submitted to the federal Office of Management and Budget (OMB) for review. As part of this process, th e survey has a 30 day Federal Register notice that provides an opportunity for public comment on the questionnaire. No public comments were received, and no changes were requested by OMB. Information regarding the reliability of the study was available to the public. Non item response to the survey was l ow and the survey was designed to account for sampling error and minimize non sampling error However, estimates produced from the data collected are subject to both types of error. Accordingly, missing data were imputed for items with a response rate of less than 100%. There was a non response rate of less than 1% for each item (NCES, 2011). Data Analysis Quantitative research methods were used to describe the nature of the phenomena. Delivered surveys were cross sectional in nature. They asked questions on a specific topic to a subset population, allowing the results to be generalized to the larger population. (Bennett, Khangura, Brehaut, Graham, Moher, & Potter, 2011). The results were analyzed using d escriptive statistics, binomial regression, and ordinal regression.

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55 The public data set was analyzed using the Statistical Package for the Social Sciences (SPSS). The public coding sheet is readily available and has been tested for inaccuracies. Data will be analyzed using descriptive statistics to mine the broad scope data relevant to the study and frame the narrative of further data interpretation. The use of descriptive statistics is useful in communicating the different aspects of a population. This is particularly helpful when framing the narrative that a data set is revealing. The story can be told through various measures including mean, median, mode, standard deviation, interquartile range, skewness, kurtosis, and correlation (Bickel & Lehmann, 1975) between observation and experiment. For example, astronomy is usually regarded as observational, yet an as tronomer can design an experiment in which he plans in detail where to But descriptive statistics is not just concerned with presenting data to the eye, it is also concerned with the so called reduction of data, such a s when averages are computed. Perhaps the earliest example of data reduction in all civilizations and tribes was counting. Instead of describing all the animals on a farm separately, they could be described as say 17 swine and 43 kine. To put the matter so mewhat paradoxically, one of the aims of descriptive statistics is to suppress data. The uninformative features are suppressed so as to make the important feature stand out more clearly. We need insufficient statistics so to speak. An example mentioned by a referee relates to classical statistical mechanics. Even if one knew the position and momentum of each molecule in a gas, it would be necessary to discard nearly all of this information to make useful statements about the gas as a whole. Otherwise, one w ould not be able to see the gas for the molecules. This feature of descriptive statistics is a feature of all communication. We must suppress some truth to communicate the truth. We must tell the truth if it is not misleading, the whole truth that is of su fficient importance, and nothing but the truth apart from some figures of speech (Good, p p. 284 285). Binary regression tests will be run to determine the existence of a relationship between the poverty concentration in a community and the availability of CTE programs within them. By understanding the odds thresholds the researcher seeks to determine the likelihood of a student

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56 having access to CTE programs, based on the poverty concentration in the community. Ordinal regression tests will be run to determ ine the existence of a relationship between the poverty concentration within a community and the participation in CTE programs by DOP students. SES will be viewed as a continuous variable so that the researcher may understand the likelihood that DOP studen ts participate in either CTE high schools or CTE courses in regular high schools. By understanding the odds the researcher seeks to determine the likelihood that a DOP student would participate in CTE programs at their local schools, according to the pove rty concentration in their community. The demographic survey data relevant to this study is poverty concentration within the community. Survey items relevant to this study can be found under Question 3 of the survey, items 3a 1 3a2, 3b 1, and 3b2 Items 3a 1 and 3b1 are dichotomous questions where the respondent will indicate whether CTE programs are avail able in their district; items 3a2 and 3b2 are ordinal questions where the respondent will indicate to what extent DOP students participate in available CTE programs. Items 3a1 and 3b1 ask the respondent if CTE programs are available in their districts. For these questions, the dependent variable is the availability of CTE programs in the community, and the independent variable is the poverty concentration in those communities. Items 3a2 and 3b2 ask the respondents how many DOP students participate in CTE programs in their district, if they are available. For these questions the dependent variable is the participation of DOP student in CTE programs, and the in dependent variable is the poverty concentration in those communities. The questions were chosen for the study because of their direct correlation to enrichment programs that serve at risk students. CTE programs are one of the programs used to address the needs of at risk students (Wonacott, 2002) By concentrating on these questions, it enabled the

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57 researcher to explore the availability of CTE programs across the U.S. based on poverty concentration in the community and the participation in them by DOP stud ents, allowing for a focused analysis of the variables in the questions as well as the relevant research literature. Surveys are useful because they allow the researcher to understand the characteristics of a given population including their attitudes, op inions, beliefs, and practices (Bennett, et al., 2011). The survey approach allows the researcher to acquire responses from a diverse audience spanning the entire United States in an efficient and convenient manner. Using a cross sectional strategy the res earcher will be able to examine present availability of CTE programs and the participation in them by DOP students using a public data set based on the survey Dropout Prevention Services and Programs The cross sectional analysis of an existing data set wi ll expedite the data gathering process and allow for prompt data analysis. Surveys are a method of investigation used by researchers who seek to gather feedback from a small population and generalize the results to a larger population. Surveys are deemed particularly useful when attempting to collect data that address topics that are difficult to measure using alternative methods. Examples include studies that ask participants to report on their knowledge, beliefs, opinions, attitudes, or level of satisfac tion about a given topic (Bennett et al., 2011). Survey data are known to be subjective in nature, relying on participants to self report requested information based on their thoughts and opinions as opposed to their actions. Potential pitfalls to the re searcher typically include low response rates, which can impede the validity of the study and undermine its total value (Bennett et al., 2011). Quality of response also plays a role in determining the reliability of survey data. Datasets are likely to cont ain at least a few responses from respondents who were not completely attentive in their answer. Web based

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58 surveys pose a specific risk because respondents may be more focused on the content in other internet windows than the survey (Barge & Gehlbach, 2012 ). The data collection was carried out by the Institute of Education Sciences (IES). The elements of the survey of interest to this study seek to gauge the perceptions of Superintendents and their assignees in regards to the availability of CTE programs i n their community and the socioeconomic status impacts his perception of participation in DOP programs and what these programs can do to assist DOP students. Survey data will be evaluated through positivist notions recognizing the data as a measurement of real world events. The researcher will evaluate the findings through a through an objective prism where knowledge is viewed in the form of data that can be measured, a view that lends insight into the impact of CTE programs on DOP students from a macro policy developed his insights into the challenges of being a DOP student in the U.S. The same experiences also promoted his interest in DOP students and the issues that impact them. Using these experiences as a springboard, the researcher hopes to develop an understanding of how CTE programs can play a role addressing the dropo ut problem. S ummary The purpose of this chapter was to outline the methodology for the study, as well as the central theoretical tenets that drive the methods. The process for collecting and analyzing data was presented to outline the framework of how the information will be analyzed. The questions in the study were presented along with their purposes, and the specific methods of analysis for each respective question. In the next chapter the data will be analyzed according to the methods outlined in this ch apter.

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59 CHAPTER 4 RESULTS The purpose of this study was to explore the availability of CTE programs in the United States and the participation in them by DOP students according to poverty concentration in the ir community This chapter will present the f indings of the study that resulted from the analysis of the public data set. It will answer four questions: 1 ) Is there a statistically significant relationship between the availability of career and technical high schools in the United States and poverty concentration? 2 ) Is there a statistically significant relationship between the participation in career and technical high schools by DOP students in the United States and poverty concentration? 3 ) Is there a statistically significant relationship between the availability of career and technical education courses at regular high schools in the United States and poverty concentration? 4 ) Is there a statistically s ignificant relationship between the level of participation by DOP students in career and technic al education courses at regular high schools in the United States based on poverty concentration? Descriptive Data Of the 1,195 surveys that were mailed, 1,086 surveys were returned, which results in a 91% response rate The distributions of responses acco rding to the categories relevant to this study will follow. Table 4 1 summarizes the poverty concentration levels of the respondents. School districts that experience poverty rates below 10% co mprised 30.8% ( n =335) of the responses School districts that experience poverty rates between 10% 19% compris ed 40.7% ( n =442) of the responses School districts that experience poverty rates above 20% comprised 28.5% ( n =309) of the responses

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60 Table 4 1 Poverty Concentration of Respondents Frequency Percent Valid Percent Cumulative Percent Valid LESS THAN 10% 335 30.8 30.8 30.8 10% 19% 442 40.7 40.7 71.5 20% AND MORE 309 28.5 28.5 100.0 Total 1086 100.0 100.0 Table 4 2 summarizes the availability of career and technical high schools of the respondents. S chool districts that offer career and technical high schools comprised 54.9% ( n =596) of the respondents School districts that do n o t offer career and technical high schools comprised 45.1% ( n =490) of the respondents Table 4 2. Availability of Career and Technical High Schools Frequency Percent Valid Percent Cumulative Percent Valid YES 596 54.9 54.9 54.9 NO 490 45.1 45.1 100.0 Total 1086 100.0 100.0 Table 4 3 summarizes the participation rates by at risk students in career and technical high sch ools, where they are available. Schools districts where few or no at risk students participate in CTE high schools compri sed 4.7% ( n =51) of the results School districts where some students participate in CTE high schools comprised 42.1% ( n =457) of the res ults. School districts where most at risk students participate in CTE high school comprised 8.1% ( n =88) of the results School districts that did not offer career and technical high schools comprised 45.1% ( n =490) of the results Table 4 4 summarizes the a vailability of career and technical courses at regular high schools among the respondents. School districts that offered career and technical courses in regular high schools comprised 76.2% ( n =828) of the responses. School districts that did not offer care er and technical courses at regular high schools comprised 23.8% ( n =258) of the respondents.

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61 Table 4 3. Participation in Career/Technical High School Frequency Percent Valid Percent Cumulative Percent Valid NOT APPLICABLE 490 45.1 45.1 45.1 NO OR FEW AT RISK STUDENTS PARTICIPATE 51 4.7 4.7 49.8 SOME AT RISK STUDENTS PARTICIPATE 457 42.1 42.1 91.9 MOST AT RISK STUDENTS PARTICIPATE 88 8.1 8.1 100.0 Total 1086 100.0 100.0 Table 4 4. Availability of Career/Technical Course at Regular High School Frequency Percent Valid Percent Cumulative Percent Valid YES 828 76.2 76.2 76.2 NO 258 23.8 23.8 100.0 Total 1086 100.0 100.0 Table 4 5. Participation in Career/Technical Course at R egular High School Frequency Percent Valid Percent Cumulative Percent Valid NOT APPLICABLE 258 23.8 23.8 23.8 NO OR FEW AT RISK STUDENTS PARTICIPATE 52 4.8 4.8 28.5 SOME AT RISK STUDENTS PARTICIPATE 590 54.3 54.3 82.9 MOST AT RISK STUDENTS PARTICIPATE 186 17.1 17.1 100.0 Total 1086 1 00.0 100.0 Table 4 5 summarizes the participation among at risk students in career and technical courses offered at regular high schools among respondents. School districts where few or no at risk students participated in CTE courses at regular high sch ools comprised 4.8% ( n =52) of the results. School districts where some at risk students participate in CTE courses at regular high schools comprised 54.3% ( n =590) of the results. School districts where most at risk students

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62 participate in CTE courses at re gular high schools comprised 17.1% ( n =186) of the results. School districts that did not offer CTE courses at regular high schools comprised 23.8% ( n =258) of the results. Research Question 1 Is there a statistically significant relationship between the ava ilability of career and technical high schools in the United States and poverty concentration? For this question the availability of CTE high schools in the community served as the dependent variable, and the concentration of poverty in the community serve d as the independent variable. Frequencies and cross tabulations were calculated to determine the availability of CTE programs based on poverty concentration. Frequencies communicate the numbers of responses collected for the question. The tabulation count s the frequency of the combination that the referenced variables occur. Table 4.6 summarizes the availability of career and technical high schools according to poverty concentration. Response percentages will be reported as their portion of the variable wi th in the category. Among the districts that reported they offer ed career and technical high schools 34.2 % ( n =204) had a poverty concentration of less than 10% 40.1 % ( n =239) had a poverty concentration between 10% 19% and 49.5 % ( n =156) had poverty a conc entration above 20% Among the districts that did not offer career and technical high schools 26.7 % ( n =131) had a poverty concentration of less than 10% 41.4 % ( n =203) had a p overty concentration of 10% 19% and 31.8 % ( n =156) had a poverty con centration o f 20% or more Further, 60.9% of schools with a poverty concentration of less than 10% offer ed career and technical high schools, whereas 39.1% d id not. Yet 54.1% of schools with a poverty concentration of 10% 19% offer ed career and technical high schools, whereas 45.9% d id not. Additionally, 49.5% of schools with poverty concentrations above 20% offer ed career and technical high schools, whereas 50.5% d id not. Table 4 6 illustrates this data.

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63 Table 4 6. CTE High School Availability & Poverty Concentrati on Poverty concentration T otal LESS THAN 10% 10% 19% 20% AND MORE Availability of career/technical HS YES Count 204 239 153 596 Expected Count 183.8 242.6 169.6 596.0 % within Availability of career/technical HS 34.2% 40.1% 25.7% 100.0% % wit hin Poverty concentration 60.9% 54.1% 49.5% 54.9% % of Total 18.8% 22.0% 14.1% 54.9% NO Count 131 203 156 490 Expected Count 151.2 199.4 139.4 490.0 % within Availability of career/technical HS 26.7% 41.4% 31.8% 100.0% % within Poverty concen tration 39.1% 45.9% 50.5% 45.1% % of Total 12.1% 18.7% 14.4% 45.1% Total Count 335 442 309 1086 Expected Count 335.0 442.0 309.0 1086.0 % within Availability of career/technical HS 30.8% 40.7% 28.5% 100.0% % within Poverty concentration 100.0% 10 0.0% 100.0% 100.0% % of Total 30.8% 40.7% 28.5% 100.0% Table 4 7 Categorical Variable Codings Poverty Concentration and the Availability of CTE High Schools Frequency Parameter coding (1) (2) Poverty concentration LESS THAN 10% 335 1.000 .000 10% 19% 442 .000 1.000 20% AND MORE 309 .000 .000 Binomial regression was performed to determine the extent of the relationship between poverty concentration and the availability of CTE programs. Table 4 7 represents the categorical coding, confirmin g adequate frequency counts for the binomial regression. It indicates that n =335 for responses tallied from districts with less than 10% poverty concentration, n =442 for

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64 responses that came from districts with 10% 19% poverty concentration, and n =309 for r esponses that came from districts with a poverty concentration of 20% or more. Table 4 8 reports two degrees of freedom for each predictor in the model, and the chi square statistics that calculate to 8.638. The statistical significance of the model is ou tlined, with the significance level being set at .05 and the results being reported as .013 in the model row The data reveal that the logistic regression model was statistically significant. Therefore, further data regarding this variable can be evaluate d knowing that the model is appropriate for exploring whether there is a statistically significant relationship between the availability of career and technical high schools in the United States and poverty concentration. Table 4 8 Omnibus Test of Model Coefficients Chi square D f Sig. Step 1 Step 8.638 2 .013 Block 8.638 2 .013 Model 8.638 2 .013 Table 4 9 Variables in the Equation B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B) Lower Upper Step 1 a POVST .232 .080 8.431 1 .004 1.261 1.0 78 1.474 Constant .655 .170 14.837 1 .00 1 .520 a. Variable(s) entered on step 1: POVST. Table 4 9 presents that statistical significance of the categorical independent variable of poverty concentration and the odds to predict the probability that c areer and technical high schools are offered in the community The statistical significance is p < 05; therefore the researcher can conclude that there is a statistically significant relationship between poverty concentration in the community and the avai lability of career and technical high schools. As a result, the researcher must reject the first null hypothesis: There will be no statistically significant relationship between the availability of career and technical high schools in the

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65 United States and poverty concentration The figure .232 represents the increase in the predicted log odds, holding other factors constant. The figure .080 represents the standard error, which are associated with the coefficients, to determine whether the parameter is sign ificantly different from zero. The figure 8.431 represents the Wald Chi Square, which is statistically significant as p < .05. The figure 1.261 represent s the odds that a CTE high school will not be offered as the responding districts SES class changes fro m that of a lower poverty concentration to that of a higher poverty concentration. Therefore, the data illustrate that districts with a higher poverty concentration are less likely to have a career and technical high school i n their district (UCLA, 2014 ) Research Question 2 Is there a statistically significant relationship between the participation in career and technical high schools in the United States by DOP students and poverty concentration? For this question the participation in CTE high schools b y DOP students served as the dependent variable, and the poverty concentration in the community served as the independent variable. Frequencies and cross tabulations were calculated to determine the participation in CTE high schools by DOP students, where available, based on poverty concentration. Frequencies communicate the numbers of responses collected for the question. The tabulation counts the frequency that the combination that the referenced variables occur. Table 4 10 summarizes the participation i n CTE high schools by DOP students, where available, based on poverty concentration. Among the districts that reported few or no at risk students participated in a career and technical high school in their district, 35.3% ( n =18) had a poverty concentration of less than 10%, 41.4% ( n =19) had a poverty concentration of 10% 19%, and 27.5% ( n =14) had a poverty concentration of more than 20%. Among the districts that reported some at risk students participated, 35.2% ( n =161) had a poverty concentration of less

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66 Table 4 10 Participation in CTE High Schools by DOP Students Poverty concentration Total LESS THAN 10% 10% 19% 20% AND MORE Participation in career/technical HS Participation in career/technical HS NOT APPLICABLE Count 131 203 156 490 Expec ted Count 151.2 199.4 139.4 490.0 % within Participation in career/technical HS 26.7% 41.4% 31.8% 100.0% % within Poverty concentration 39.1% 45.9% 50.5% 45.1% % of Total 12.1% 18.7% 14.4% 45.1% NO OR FEW AT RISK STUDENTS PARTICIPATE Count Expec ted Count % within Participation in career/technical HS 18 15.7 35.3% 19 20.8 37.3% 14 14.5 27.5% 51 51.0 100.0% % within Poverty concentration 5.4% 4.3% 4.5% 4.7% % of Total 1.7% 1.7% 1.3% 4.7% SOME AT RISK STUDENTS PARTICIPATE Coun t 161 183 113 457 Expected Count 141.0 186.0 130.0 457.0 % within Participation in career/technical HS 35.2% 40.0% 24.7% 100.0% % within Poverty concentration 48.1% 41.4% 36.6% 42.1% % of Total 14.8% 16.9% 10.4% 42.1% MOST AT RISK STUDENTS PA RTICIPATE Count 25 37 26 88 Expected Count 27.1 35.8 25.0 88.0 % within Participation in career/technical HS 28.4% 42.0% 29.5% 100.0% % within Poverty concentration 7.5% 8.4% 8.4% 8.1% % of Total 2.3% 3.4% 2.4% 8.1% Total Count 335 442 309 108 6 Expected Count 335.0 442.0 309.0 1086.0 % within Participation in career/technical HS 30.8% 40.7% 28.5% 100.0% % within Poverty concentration 100.0% 100.0% 100.0% 100.0% % of Total 30.8% 40.7% 28.5% 100.0% than 10%, 40% ( n =183) had a poverty co ncentration of 10% 19%, and 24.7% ( n =113) had a poverty concentration of over 20%. Among districts that reported most at risk students participated, 28.4% ( n =25) had a poverty concentration of less than 10%, 42% ( n =37) had a

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67 poverty concentration of 10% 19 %, and 29.5% ( n =26) had a poverty concentration of more than 20%. Ordinal regression was performed to determine the extent of the relationship between poverty concentration and the partic i pation in CTE programs. Table 4 1 1 represents the categorical codin g by tiers of both the independent and dependent variables confirming adequate frequency counts for the ordinal regression. This indicates that 45.1% ( n =490) of districts responded that career and technical high schools were not offered, 42.1% ( n =457) of responses came from districts where some at risk students participated in career a nd technical high schools, with and 8.1% ( n =88) of districts responded that most at risk students participate in their career a nd technical high schools Ta ble 4 1 1 Case Processing Summary N Marginal Percentage Participation in career/technical HS NOT APPLICABLE 490 45.1% NO OR FEW AT RISK STUDENTS PARTICIPATE 51 4.7% SOME AT RISK STUDENTS PARTICIPATE 457 42.1% MOST AT RISK STUDENTS PARTICIPATE 88 8.1% Poverty co ncentration LESS THAN 10% 335 30.8% 10% 19% 442 40.7% 20% AND MORE 309 28.5% Valid 1086 100.0% Missing 0 Total 1086 Table 4 12 offers model fitting information for the test. It indicates the 2 log likelihood values are 389.711 for the intercept only model and 301.800 for the final row, which includes the intercept and independent variables. The difference between the two models is represented by the

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68 Chi Square statistic, 5.946 distributed at 1 degree of freedom as defined by the number of predi ctors in the model In t his case the p < 0 5, as such the data reveal that the logistic regression model was statistically significant. Therefore, further data regarding this variable can be evaluated knowing that the model is appropriate for exploring whe ther there is a statistically significant relationship between the participation of DOP students in CTE high schools and poverty concentration Table 4 1 2 Model Fitting Information Model 2 Log Likelihood Chi Square df Sig. Intercept Only 58.385 Fi nal 52.440 5.946 1 .015 Link function: Logit. Table 4 1 3 represents the parameter estimates which serve as a measure of the variables in the question; the participation of DOP students in CTE high schools, and the poverty concentration in the community The standard error is reported at .075, which are associated with the coefficients, to determine whether the parameter is significantly different from zero. The Wald Chi Square statistic is 5.902 at one degree of freedom. The estimate column referenced b y POVST expresses the likelihood of an event occurring. In this instance, the data shows that as the response of socioeconomic class increases by a factor of 1, the likelihood that DOP students will participate in CTE high schools falls by a factor of .18 2. p < .05, with a statistical significance of .015 Therefore, the researcher rejects the null hypothesis: There will be no statist ically significant relationship between the level of participation in career and technical high schools by DOP students in t he United S tates and poverty concentration (UCLA, 2014) Table 4 14 expresses the results from the test of parallel lines. It tests the null hypothesis that the odds for each variable are the same throughout tiers. There is not a statistically

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69 significant result expressed in the test, which means the odds for each variable throughout the tiers are consistent, with a statistically insignificant effect of .124. Table 4 1 3 Parameter Estimates Estimate Std. Error Wald df Sig. 95% Confidence Interval Lower Bound Upper Bound Threshold [Q3B2 = 8] .555 .161 11.912 1 .001 .870 .240 [Q3B2 = 1] .365 .160 5.191 1 .023 .679 .051 [Q3B2 = 2] 2.078 .182 130.998 1 .00 1 1.722 2.434 Location POVST .182 .075 5.902 1 .015 .330 .035 Link function: L ogit. a. This parameter is set to zero because it is redundant. Table 4 1 4 Test of Parallel Lines Model 2 Log Likelihood Chi Square df Sig. Null Hypothesis 52.440 General 48.263 4.177 2 .124 The null hypothesis states that the location paramete rs (slope coefficients) are the same across response categories. a. Link function: Logit. Research Question 3 Is there a statistically significant relationship between the availability of career and technical education courses at regular high schools in the United States and poverty concentration? For this question the availability of CTE courses at regular high schools served as the dependent variable, and the poverty concentration in the community served as the dependent variable. Frequencies and cross tabulations were calculated to determine the availability of CTE courses in regular high school based on poverty concentration. The availability of CTE course at regular high schools served as the dependent variable, and the poverty concentration in the co mmunity served as the dependent variable. Frequencies communicate the number of responses collected for the question. The tabulation counts the frequency that the combination of the reference d variable occurs. Response percentages will be reported as their portion of the variable

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70 with in the category. Frequencies communicate the number of responses collected for the question. Table 4 1 5 Availability of CTE C ourses at a Regular High School Poverty concentration Total LESS THAN 10% 10% 19% 20% AND MORE Availability of career/technical courses at a regular HS YES Count 231 354 243 828 Expected Count 255.4 337.0 235.6 828.0 % within Availability of career/technical courses at a regular HS 27.9% 42.8% 29.3% 100.0% % within Poverty concentration 6 9.0% 80.1% 78.6% 76.2% % of Total 21.3% 32.6% 22.4% 76.2% NO Count 104 88 66 258 Expected Count 79.6 105.0 73.4 258.0 % within Availability of career/technical courses at a regular HS 40.3% 34.1% 25.6% 100.0% % within Poverty concentration 31 .0% 19.9% 21.4% 23.8% % of Total 9.6% 8.1% 6.1% 23.8% Total Count 335 442 309 1086 Expected Count 335.0 442.0 309.0 1086.0 % within Availability of career/technical courses at a regular HS 30.8% 40.7% 28.5% 100.0% % within Poverty concentration 1 00.0% 100.0% 100.0% 100.0% % of Total 30.8% 40.7% 28.5% 100.0% Table 4 15 summarizes the availability of career and technical high schools according to poverty concentration. Response percentages are reported as their portion of the entire

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71 responding p opulation. Among the districts that reported they offered career and technical courses at regular high schools 27.9% ( n =231) had a poverty concentration of less than 10%, 42.8% ( n =354) had a poverty concentration of 10% 19%, and 29.3% ( n =243) had a poverty concentration of 20% or more. Among the districts that did not offer career and technical education courses at regular high schools 40.3% ( n =104) had a poverty concentration of less than 10%, 34.1% ( n =88) had a poverty concentration of 10% 19%, and 25.6% ( n =66) had a poverty concentration of 20% or more. Further, 69% of districts with a poverty concentration of less than 10% offered career and technical courses in regular high schools, while 39% did not. Additionally, 80.1% of districts with a poverty conc entration of 10% 19% offered career and technical high schools, while 19.9% did not. Finally, 78.6% of districts with a poverty concentration of 20% or more offered career and technical courses at regular high schools, while 21.4% did not. Binomial regres sion was performed to determine the extent of the relationship between poverty concentration and the availability of CTE classes i n regular high schools. Table 4 1 6 represents the categorical coding, confirming adequate frequency counts for the binomial re gression. It indicates that n = 335 responses came from districts with less than 10% poverty concentration, n = 442 responses came from districts with 10% 19% poverty concentration, and n =3 09 responses came from districts with a poverty concentration of 20% or more. Table 4 16 Categorical Variables Codings Frequency Parameter coding (1) (2) Poverty concentration LESS THAN 10% 335 1.000 .000 10% 19% 442 .000 1.000 20% AND MORE 309 .000 .000

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72 Tab le 4 1 7 reports two degrees of freedom for each predict or in the model, and a chi square statist ic that calculates to 14. The statistical significance of the model is outlined with the significance level being set at .05, and the results being reported as .001 in the model row. The data reveal that the logist ic regression model was statistically significant. Therefore, further data regarding this variable will be evaluated knowing that the model is appropriate for exploring whether there is a statistically significant relationship between the availability of c areer and technical courses in regular high schools in the United States and poverty concentration. Table 4 1 7 Omnibus Tests of Model Coefficients Chi square D f Sig. Step 1 Step 14.000 2 .001 Block 14.000 2 .001 Model 14.000 2 .001 Table 4 18 pre sents th e statistical significance of the categorical independent variable of poverty concentration and the odds to predict the probability that career and technical education courses are offered at regular high schools in the community. The statistical si gnificance in p < .05; therefore the researcher can conclude that there is a statistically significant relationship between poverty concentration in the community and the availability of CTE courses in regular high schools. As a result, the researcher must reject the third null hypothesis: There will be no statistically significant relationship between the availability of career and technical education courses at regular high school in the United States and poverty concentration. The figure .276 represents the decrease in the predicted log odds, holding other factors constant. The figure .094 represents the standard error, which is associated with the coefficients, to determine whether the parameter is significantly different from zero. The figure 8.627 rep resents the Wald Chi S quare, which is statistically significant as p < .05. The figure .753 represents the odds that CTE courses at regular high schools will not be offered as the responding districts SES class changes from

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73 that of a lower poverty concent ration to that of a higher poverty concentration This figure must be inverted for ease of interpretation, 1/.753 = 1.32. Since B is negative the interpretation is also inverted. Therefore, the data illustrate that districts with a higher poverty concentrat ion are less likely to have career and technical education courses offered at regular high school s in their district. Table 4 18 Variables in the Equation B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B) Lower Upper Step 1 a POVST .276 .094 8.62 7 1 .003 .759 .632 .912 Constant .633 .191 10.945 1 .001 .531 a. Variable(s) entered on step 1: POVST. Research Question 4 Is there a statistically significant relationship between the level of participation in career and technical education courses at regular high schools in the United States based on poverty concentration? For this question the participation by DOP students in CTE courses offered at regular high schools served as the dependent variable, and the poverty concentrati on in the community served as the independent variable. Frequencies and cross tabulations were calculated to determine the participation in CTE high schools by DOP students, where available, based on poverty concentration. They are communicate d in T able 4 19 Frequencies communicate the number of responses collected for the question. The cross tabulat ion counts the frequency of the combination that the cross referenced variables occur. Table 4 19 summarizes the participation in CTE courses at regular high schools by DOP students, where available, based on poverty concentration. Among the districts that reported few or no at risk students participated in a career and technical courses in regular high schools in their district, 34.6% ( n =18) had a poverty conc entration of less than 10%, 32.7% ( n =17) had a poverty concentration of 10% 19%, and 32.7% ( n =17) had a poverty concentration of more than

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74 Table 4 19 Participation in CTE Courses at a Regular High School Poverty concentration Total LESS THAN 10% 10% 19% 20% AND MORE Participation in career/technical courses at a regular HS NOT APPLICABLE Count 104 88 66 258 Expected Count 79.6 105.0 73.4 258.0 % within Participation in career/technical courses at a regular HS 40.3% 34.1% 25.6% 100.0% % within Poverty concentration 31.0% 19.9% 21.4% 23.8% % of Total 9.6% 8.1% 6.1% 23.8% NO OR FEW AT RISK STUDENTS PARTICIPATE Count 18 17 17 52 Expected Count 16.0 21.2 14.8 52.0 % within Participation in career/technical courses at a regular HS 3 4.6% 32.7% 32.7% 100.0% % within Poverty concentration 5.4% 3.8% 5.5% 4.8% % of Total 1.7% 1.6% 1.6% 4.8% SOME AT RISK STUDENTS PARTICIPATE Count 177 251 162 590 Expected Count 182.0 240.1 167.9 590.0 % within Participation in care er/technical courses at a regular HS 30.0% 42.5% 27.5% 100.0% % within Poverty concentration 52.8% 56.8% 52.4% 54.3% % of Total 16.3% 23.1% 14.9% 54.3% MOST AT RISK STUDENTS PARTICIPATE Count 36 86 64 186 Expected Count 57.4 75.7 52.9 186.0 % within Participation in career/technical courses at a regular HS 19.4% 46.2% 34.4% 100.0% % within Poverty concentration 10.7% 19.5% 20.7% 17.1% % of Total 3.3% 7.9% 5.9% 17.1% Total Count 335 442 309 1086 Expected Count 335.0 442.0 309.0 1086.0 % within Participation in career/technical courses at a regular HS 30.8% 40.7% 28.5% 100.0% % within Poverty concentration 100.0% 100.0% 100.0% 100.0% % of Total 30.8% 40.7% 28.5% 100.0% 20%. Among the districts that re ported some at risk students participated, 30% ( n =177) had a poverty concentration of less than 10%, 42.5% ( n =251) had a p overty concentration of 10%

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75 19%, and 27.5% ( n =162) had a poverty concentration of over 20%. Among districts that reported most at risk students participated, 19.4% ( n =36) had a poverty concentration of less than 10%, 46.2% ( n =86) had a poverty concentration of 10% 19%, and 34.4% ( n =64) had a poverty concentration of more than 20%. Ordinal regression was performed to determine the exte nt of the relationship between poverty concentration and the partic ipation of DOP students in CTE programs. Table 4 2 0 represents the categorical coding by tiers of both the independent and dependent variables, confirming adequate frequency counts for the ordinal re gression. It indicates that 23.8 % ( n =258) of districts responded that career and technical high schools are not offered, 4.8 % ( n =52) of responses came from districts where no or few at risk students participated in career and technical education classes at regular high schools, 54.3% ( n =590) of districts reported that some at risk students participated in c areer and technical courses at regular high schools, and 17.1 % ( n =186) of districts responded that most at risk students participate d in their career and technical high schools Table 4 2 0 Case Processing Summary N Marginal Percentage Participation in career/technical courses at a regular HS NOT APPLICABLE 258 23.8% NO OR FEW AT RISK STUDENTS PARTICIPATE 52 4.8% SOME AT RISK STUDENTS PA RTICIPATE 590 54.3% MOST AT RISK STUDENTS PARTICIPATE 186 17.1% Poverty concentration LESS THAN 10% 335 30.8% 10% 19% 442 40.7% 20% AND MORE 309 28.5% Valid 1086 100.0% Missing 0 Total 1086

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76 Table 4 2 1 offers the model fitting information for the test. It indicates the 2 log likelihood values are 74.274 for the intercept only model and 5 9 .431 for the final row, which includes the intercept and independent variables. The difference between the two models is represented by the Chi Square statist ics, 5.946 distributed at 1 degree of freedom, as defined by the number of predictors in the model. In this case the p < .05 ; thus, the data reveal that the ordinal regression model was statistically significant. Therefore, further data regarding this vari able can be evaluated as the model is shown to be appropriate for exploring whether there is a statistically significant relationship between the participation of DOP students in career and technical education classes at regular high schools in the United States and poverty concentration. Table 4 2 1 Model Fitting Information Model 2 Log Likelihood Chi Square df Sig. Intercept Only 74.274 Final 5 9 .431 1 4 .843 1 .00 1 Link function: Logit. Table 4 2 2 represents the parameter estimates, which serve as a measure of the variables in the question; the participation of DOP students in CTE courses at regular high schools, and the poverty concentration in the community. The standard error is reported at .075, which is associated with the coefficients, to det ermine whether the parameter is significantly different from zero. The Wald Chi Square statistic is 14.758at one degree of freedom. The estimate column referenced by POVST expresses the likelihood of an event occurring. In this instance, the data shows tha t as the response of socioeconomic class increases by a factor of 1, the likelihood that DOP students will participate in CTE courses at regular high school s increases by a factor of .292. p < .05, with a statistical significance of .015. Therefore, the re searcher rejects the null hypothesis: There will be no statistically si gnificant relationship between the level of

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77 participation in career and technical education courses at regular high schools by DOP students in the United States based on poverty concent ration (UCLA, 2014). Table 4 2 2 Parameter Estimates Estimate Std. Error Wald df Sig. 95% Confidence Interval Lower Bound Upper Bound Threshold [Q3B2 = 8] .603 .1 62 13.920 1 .00 1 .920 .286 [Q3B2 = 1] .353 .161 4.825 1 .028 .667 .038 [Q3B2 = 2] 2.168 .176 151.1001 1 .001 1.822 2.514 Location POVST .292 .076 14.758 1 .001 .143 .441 Link function: Logit. a. This parameter is set to zero because it is redundant. Table 4 2 3 expresses the results from the t est of parallel lines. It t ests the null hypothesis that the odds for each variable are the same throughout tiers. There is not a statistically significant result expressed in the test, which means the odds for each variable throughout the tiers are consistent with a statistically insignificant effect of .460. Table 4 2 3 Test of Parallel Lines Model 2 Log Likelihood Chi Square df Sig. Null Hypothesis 59.431 General 57.876 1.555 2 .460 The null hypothesis states that the location parameters (slope coefficients) are the same across response categories. a. Link function: Logit.

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78 CHAPTER 5 DISCUSSION AND CONCLUSIONS Based on the literature the researcher explored the availability of CTE programs in districts across the U.S. and the participation in them by DOP students Basic descriptive data were analyzed to gain an understanding of the dynamics of the districts that reported in the survey. Results from Table 4 1 showed districts with less than 10% poverty concentration comprised 30.8% ( n =335) of respondents, districts with 1 0% 19% poverty concentration comprised 40.7% ( n =442) of respondents, and districts with 20% or greater poverty concentration comprised 28.5% ( n =309) of respondents. T hese data offer the researcher insight into the number of schools from each category tha t responded. F urther, the initial data reveal that the sample from each category will be significant to move forward with the study (Barlett, et al., 2001) Results from Tab le 4 2 revealed that 54.9% ( n =596) of responding districts offered career and techn ical high schools, while 45.1% ( n =490) do not. With over 50% of the responding districts indicating that they offer career and technical high schools an opportunity for discussion opened for districts who offer career and technical high schools as well as career and technical courses at regu lar high schools, which will happen later in this section. The participation rates by DOP students in career and technical high schools were presented in Table 4 3 Districts where few or no at risk students were serve d by career and technical high schools comprised 4.7% ( n =51) of the responses. Districts where some at risk students were served by career and technical high schools comprised 42.1% ( n =457) of the responses. Districts where most at risk students were serve d by career and technical high schools comprised 8.1% ( n =88) of the responses. Data were un evenly distributed, which may be the result of the requirements of the career and technical high schools in the responding districts.

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79 CTE high schools are as varied and diverse as any program in education. They serve students of all ability and behavior levels (Kemple & Snipes, 2000) For the districts that serve few at risk students it is possible that thos e CTE high schools are not being used to serve students who are facing behavioral or academic challenges. Whereas, the districts that reported most at risk students participate d in their CTE high schools may have programs that are being used to serve students who are facing behavioral or academic challenges. It is not reasonable to assume that the courses offered in these high schools are less academically rigorous because there is significant research which says that DOP students who participate in academically rigorous programs can be successful (Hammond, et al., 2007) In Table 4 4 the frequency distributions for the availability of career and technical courses at regular high schools were presented. Data revealed that 76.2% ( n =828) of districts offered CTE courses in regular high schools and 23.8% ( n =258 %) did no t offer CTE courses in regular high schools. It is important to revisit the data regarding the availability of career and technical high schools at this point. Those data revealed that 54.9% ( n =596) of districts offered CTE high schools. This indicates an overlap where multiple CTE programs could be offered in these districts to serve students of differing ability and need levels. The limitations and implications of this notion will be discussed later in this chapter. Table 4 5 reveals that participation by DOP students in CTE courses offered at regular high schools. 23.8% ( n =258) of districts did not offer programs and therefore answered not applicable. Additionally, 4.8% ( n =52) of districts answered few or no at risk students participate. Further, 54.3% ( n =590) answered that some at risk students participate d 17.1% ( n =186) answered that most at risks students participate d These data could help the districts who responded to better understand the extent to which they are helping DOP students. In learning

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80 about the extent to which other districts were supporting DOP students the data may assist them in further promoting the availability of CTE programs to DOP students. Further, o f the districts that offer neither CTE high schools nor CTE courses in regula r high schools benefits could be realized from knowledge of the criteria and structures that allowed other districts to establish their programs. Research Question 1 Research Question one examined the statistical significance of the availability of CTE high schools in the United States based on poverty concentration. The data revealed that of the 54.9% ( n =596) that did offer CTE high schools 18.8% ( n = 204) had a poverty concentration of less than 10%, 22% ( n =239) had a poverty concentration of 10% 19%, a nd 14.1% ( n =153) had a poverty concentration of 20% and more. 45.1% ( n =490) of respondents did not offer CTE high schools to their students. 12.1% ( n =131) had a poverty concentration of less than 10%, 18.7% ( n =203) had a poverty concentration of 10% 19%, a nd 14.4% ( n =156) had a poverty concentration of 20% or more. These data outline the distribution of availability of CTE high school in responding districts in a manner closely aligned with the purpose of the study, which helps the researcher to understand the distributions at a surface level. However, to understand if there was a statistical significance between the variables further test s needed to be performed. Results of the binomial regression indicated that there was a statistically significant relat ionship between the variables. Therefore, further results produced by SPSS were analyzed. The Omnibus Test of Model Coefficients revealed a significance of .013. In this situation significance levels were set at .05, meaning that there was a statistically significant relationship between the availability of CTE high schools in the United States and poverty concentration. The data reveal that as poverty concentration increased by a factor of 1 district s faced increased odds of .232 of not offering CTE high schools. The reasons for this could be many. CTE are

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81 schools are expensive endeavors that require a substantial commitment from districts to establish and operate. Districts with fewer resources may be less likely to devote a high sum of funds to what is commonly known as elective style courses, as opposed to the content area courses which are viewed to be directly im pacted by standardized testing (Pazera, 2011) The results here support the notion of inequality of access to education according to SES. Whe n the SES of a community falls, the likelihood that CTE high school will be available in the community decreases as well, creating another societal structure that places students fr om poor families at a disadvantage. ational programs decreases, the likelihood that they will fill that time with productive activities falls, especially in a low SES community where they will face so many other challenges (Alexander, et. al., 1997 ) Th e disadvantage for students in this sce of capital; social, economic, and cultural, students face challenges that compound the effects of build on any existing social capital base in their community that could enhance their inte rconnectedness to the community, which would also allow them to build on the cultural capital base of the residents of their neighborhoods This reduces their opportu nity to learn job related knowledge and skills from other members of their communities, also reducing the chances that they develop deepening i nterests in professional areas being taught at their local school. In communities of diverse backgrounds, a stude factors, because they are have less access to the diverse capital bases in t heir communities and the forms of capital they can offer, reducing the chances that they can build off of the capital and benefit from the many forms of diversity in their communities (Van Dorn, et. al, 2006).

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82 Research Question 2 The second research question explored whether there was a statistically significant relationship between the participation of D OP students in CTE high schools based on poverty concentration Data used to answer this question emphasized descriptive data to develop for the researcher a broad understanding of the participation in CTE high schools by DOP students. Data illustrated 45. 1% ( n =490) of responding districts did not offer CTE high schools. Of these districts 12.1% ( n =131) had a poverty concentration of less than 10%, 18.7% ( n =203) had a poverty concentration of 10% 19%, and 14.4% ( n =156) had a poverty concentration of 10% or more. Further, 4.7 % ( n =51) of districts reported that few or no at risk students participate d in their CTE high schools. Of these districts 1.7% ( n =18) had a poverty concentration of less than 10%, 1.7% ( n =19) had a poverty concentration of 10% 19%, and 1.3% ( n =14) had a poverty concentration of 20% or more. Additionally, 42.1% ( n =457) reported that some at risk students participate d in their CTE high schools. Of these districts 14.8% ( n =161) had a poverty concentration of less than 10%, 16.9% ( n =183) had a poverty concentration of 10% 19%, and 10.4% ( n =113) had a poverty concentration of 20% or more. 8.1% ( n =88) of districts reported that most at risk students participate in CTE high schools. Of these districts 2.3% ( n =25) had a poverty concentration of l ess than 10%, 3.4% ( n =37) had a poverty concentration of 10% 19%, and 2.4% ( n =26) had a poverty concentration of 20% or more. Overall, these data reveal to the researcher that most districts responded that some at risk students participate d The words few some and most were not defined to the respondents in the study, nor were they quantified. This may have created a level of ambiguity in the question where different respondents understood those words in different ways. The results of the ordinal r egression revealed that there is a statistically significant relationship between the variables, as the significance of test under model fitting information was

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83 at .01 5 and the significance level was set to .05. As socioeconomic class increases by a facto r of 1, the likelihood DOP students participate in CTE high schools decreases by a factor of .182, indicating that the wealth within a community impacts the likelihood that a DOP student will participate in CTE high schools when available. The reason for decreasing participation rates could be related to the resources available to actively seek to identify potential students and then enroll them in the CTE program. This topic was not within the scope of the intended research, but could be an element of a f uture study. The results here support the concept of school nt behaviors have a direct impact on their decision to drop out of school. It should be pointed out engagement process. Students who have less access to resources, su ch a CTE courses in high schools, are further disadvantaged because they will not get the opportunity to become engaged by new topics presented in these schools. Research Question 3 The third question explored whether there was a statistically significan t relationship between the availability of CTE courses at regular high schools in the United States based on poverty concentration. purpose was to explore the descriptive statistics that help to inform the analytical process as well as the binomial regression. The data revealed that 76.2% ( n =828) of districts offered CTE courses at regular high schools. Of those districts 21.3% ( n =231) had a poverty concentration of less than 10%, 32.6% ( n =354) had a poverty concentration of 10% 19%, and 22 .4% ( n =243) had a poverty co ncentration of 20% or more. These data outline the distribution of districts where CTE courses are offered at regular high schools by responding districts, data essential to the purpose of this study. These data help the

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84 researc her to understand the data at a descriptive level. However, to better understand the significance of the data it was necessary for further tests to be performed. Results of the binomial regression indicated that there was a statistically significant relat ionship between the variables. Therefore, further results produced by SPSS were analyzed. The Omnibus Test of Model Coefficients revealed a significance of .0 01 In this scenario significance levels were set at .05, meaning that there was a statistically s ignificant relationship between the availability of CTE courses at regular high schools in the United States and poverty concentration. Further analysis revealed that as poverty concentration in the community increased the likelihood that CTE courses in re gular high schools would be available fell by a factor of 1.32. Financial reasons drive the possibilities for this, as CTE programs are expensive to operate at school sites (Pazera, 2011). For districts that must make difficult decisions about where to all ocate scarce resources, CTE programs may be less of a priority, further promoting the concept of inequality in access to educational programs as a systemic challenge that negatively impact students of low SES disproportionately. Raudenbush, et. al., (1998) point out that students of socially advantaged backgrounds have greater access to educational resources than students of socially disadvantaged backgrounds, which contribute to proficiency deficiencies according to SES group. When compo unded with the ineq uality produced as a result of their home and neighborhood environments, students of low SES backgrounds are less likely to experience educational success. Research Question 4 Research Question four explored whether there was a statistically significant r elationship between the participation of DOP students in CTE courses offered at regular high schools based on poverty concentration. Descriptive data were used to inform the narrative question of four This indicates that 23.8% ( n = 258) of responding distr icts did not offer CTE courses at regular

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85 high schools. Of these districts, 9.6% ( n =104) had a poverty concentration of less than 10%, 8.1% ( n =88) had a poverty concentration of 10% 19%, and 6.1% ( n =66) had a poverty concentration of less 20% or more. Furt her, 4.8% of districts that offered CTE courses at regular high schools said that no or few at risk students participate d Of these districts 1.7% ( n =18) had a poverty concentration of less than 10%, 1.6% ( n =17) had a poverty concentration of 10% 19%, and 1.6% ( n =17) had a poverty concentration of 20% or more. Additionally, 54.3% ( n =590) reported that some at risk students participate d in CTE courses offered at regular high schools. Of these districts 16.3% ( n =177) were from districts with a poverty concent ration of less than 10%, 23.1% ( n = 251) were from districts with a poverty concentration of 10% 19%, and 14.9% ( n =162) were from districts with a poverty concentration of 20% or more. Further, 17.1% of districts reported that most at risk students participa te in CTE courses offered at regular high schools. Of these districts 3.3% ( n =36) had a poverty concentration of less than 10%, 7.9% ( n =86) had a poverty concentration of 10% 19%, and 5.9% ( n =64) had a poverty concentration of 20% or more. When compared with similar data on CTE high schools these data reveal that CTE courses are offered at regular high schools more frequently than CTE high schools. It is possible that the costs associated with CTE high schools make offering CTE courses at regular high sch ools the more financially prudent choice. The results of the ordinal regression revealed that there is a statistically significant relationship between the variables, as the significance of test under model fitting information was below .000, and the sign ificance level was s et to .05. The parameter estimates reveal that there was a 292 ratio for the odds for lower to higher participation rates of responding districts based on poverty lev el. This indicates that students in districts where poverty concentra tions are higher are more likely to participate in CTE classes in regular high schools This could be the result of

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86 former CTE models continuing to prevail in surveyed high schools, as many were established to create work skills for lower income students i n the US (Perry & Wallace, 2012). Students with limited school experiences are less likely to express a feeling of school connectedness, a key contributor to schoo l dropout school (Haley, 2006; Myint U, A., et al., 2008). School experiences have a direct i (Shernoff, et. al., 2003) As students take part in less engaging experiences, their opportunities for success decrease, and along with them their likelihood for success. As shown through the results produced by th is question of the study, students in communities of low SES are less likely to participate in CTE courses at regular high schools. These students, who are not taking part in the enriching academic experience that can improve their feelings of connectednes s to their schools, are further disadvantaged as a result of this phenomenon. When this variable is compounded with the neighborhood and home related factors that are known to place students of low SES at a disadvantage, clear viable evidence is produced t o demonstrate a problem in the educational system that needs to be addressed to create equity and fairness for all students. Limitations The limitations of this study are bound by the parameters of the questions examined in the survey, as well as the perc eptions of the concepts in it, as determined by the respondents. The lack of a definition for the words few some and most in the questions serve as a prime example of this limitation. Further, the interpretation of the results may be limited by the crossover that could exist between the districts that offered CTE high schools as well as CTE courses in regular schools. Of those districts that offer both questions could arise about what the purposes of those programs are, and to what extent they are designed to serve DOP students. These reasons could impact the availability and participation rates in the district. Thus, the reason the districts that reported low participation rates in their CTE high schools could be

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87 because they have CTE courses offer ed in their regular high schools designed to serve DOP students This study examined important variables to better understand the relationship between the availability of and participation in CTE programs by DOP students based on poverty concentration. In examining those variables the impact of other relevant data became apparent. The dynamics of each individual district could h ave created circumstances which would reveal nuances that may explain the existence, or lack thereof, of CTE programs, with the pr eceding example illustrating this point. Therefore, opportunities for future research exist in examining the impact that other variables in the survey, such as region on community type, have on the variables examined in this study. For example, in some geo graphic regions, students from low SES backgrounds are more likely to be successful in school than students from other geographic regions (Lamb, 2011 ) programs or work based learnin g programs. By analyzing other enrichment programs, researchers could further strengthen the link between DOP students and their access to and participation in enrichment programs. Researchers could also seek to determine whether the use of strategies to i dentify DOP students, as explored in the survey used for this study, have a relationship with their access to and participation in enrichment programs. Examining other variables such as the one provided in the preceding example s could allow researchers to gain further insight into the nuances of the survey, and the factors that impact the access to and participation in CTE programs by DOP students. Implications for Future Research This study examined important variables to bet ter understand CTE programs and their relevance to DOP students. The information in this study adds to the body of literature on DOP students and the factors that impact them, specifically the likelihood that they will have access to

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88 and participate in CTE programs. Because relationship s between the studied variables were already established, this study reinforces the notion that DOP students are disadvantaged at school, and the problem compounds according to the poverty concentration in the community. Researchers can use this informatio n to inform future studies exam ining the survey that was used i n this study as well as other studies that examine the factors that impact DOP students and students from low SES backgrounds This study can inform those studies through its analysis of DOP s participation in those programs by DOP students according to poverty concentration, and using those factors to construct a relationship to graduation rates. For example, as DOP s tudents of low SES face the compounded challenges posed by not having access to enriching educational programs, like CTE programs, they are less likely to have the opportunity to participate in engaging programs that increase their connectedness to their s chools. In turn, they are less likely to graduate from high school (Plank, et. al., 2005). So as researchers examine the factors that impact DOP students, future research could build on the results of this study to explore possible remedies to the phenomen a, and their likely effects. Implications for Practice The conditions that impact DOP students are bound only by the dynamics of the individual situations of which they are a part. Practitioners who work with them on a daily basis face this reality in th eir work day. A multitude of factors could impact the daily success of a DOP student. As such, the more supports that are in place for these students the more likely it would be that they are successful. CTE programs are just one tool designed to help th i s population of students. Practitioners, from teachers to superintendents, can use the research from this study as a springboard to examine the extent to which CTE and other programs are available in their district. With this knowledge they will be empower ed to further develop relevant

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89 programs that will promote the success of their DOP students. By using the data examined from this study to inform their practice, they can have an improved understanding of the challenges the students in their school face, w ith the data on the access to and participation in CTE programs by DOP students based on poverty concentration serving as a reminder of the disadvantages that DOP students face in terms of inequality and the likelihood that enriching educational programs w ill be available to increase their feelings of connectedness to their school.

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108 BIOGRAPHICAL SKETCH Patrick Capriola is a Senior Professional Development Special ist with The University of Florida, Lastinger Center for Learning. He currently works with administrators and teachers to improve classroom instruction in Abu Dhabi, UAE, where he resides with his family.