Citation
A Longitudinal Study Of Career Maturity And Career Decision-Making Self-Efficacy Of Rural Secondary School Students

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Title:
A Longitudinal Study Of Career Maturity And Career Decision-Making Self-Efficacy Of Rural Secondary School Students
Creator:
Emerson, Helen Charlotte
Place of Publication:
[Gainesville, Fla.]
Florida
Publisher:
University of Florida
Publication Date:
Language:
english
Physical Description:
1 online resource (109 p.)

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Agricultural Education and Communication
Committee Chair:
MYERS,BRIAN E
Committee Co-Chair:
BARRICK,R KIRBY
Committee Members:
OSBORNE,EDWARD WAYNE
TURNER,R ELAINE

Subjects

Subjects / Keywords:
career -- decision-making -- maturity -- secondary -- self-efficacy
Agricultural Education and Communication -- Dissertations, Academic -- UF
Genre:
bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Agricultural Education and Communication thesis, Ph.D.

Notes

Abstract:
The purpose of this study was to examine the relationships between career maturity and career decision-making self-efficacy and selected demographic characteristics. The study utilized three instruments for data collection: the Career Maturity Inventory (CMIR), the Career Decision-Making Self-Efficacy Short-Form (CDMSESF) and a demographic information survey, which was developed by the investigator. The population for this study was students attending a rural high school. All students in grades 9th-12th completed the Demographic Questionnaire, CMI, and the CDMSESF. Students selected for the study were chosen based on completion of the informed consent forms. This study was conducted as a longitudinal study over the course of three years. The population completed each of the instruments (CMI, CDSESF and Demographic Survey) in the fall of each academic year. Descriptive statistics were calculated for gender, ethnicity, extracurricular activities, athletic involvement, Advanced Placement courses and dual enrollment courses for 2013 and 2014. Correlation analysis and linear regression models were used to determine the relationship between career maturity and individual student demographic characteristics and the relationship between career decision-making self-efficacy and individual student demographics. Statistical analysis found students who did not take Advanced Placement (AP) courses had higher career maturity scores than those who took AP courses. Correspondingly, those students who took dual enrollment courses scored lower on the Career Maturity Inventory (CMIR) than those who did not. Additionally, participation in extracurricular activities was found to be significant. Students who participated in zero to one activity had a higher CMIR than those who participated in two or more extracurricular activities. Students who participated in two or three extracurricular activities and dual enrollment were found to have significantly higher CDMSESF means than others (p < .01). No other variables examined in this study were found to be significant. These findings suggested additional resources should be dedicated to career development by school administrators, counselors and teachers in order to enhance students career maturity and career decision-making self-efficacy before the end of their secondary school experience. Further investigation should be conducted to better understand and enhance the process of career development in secondary students. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
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.
Thesis:
Thesis (Ph.D.)--University of Florida, 2017.
Local:
Adviser: MYERS,BRIAN E.
Local:
Co-adviser: BARRICK,R KIRBY.
Statement of Responsibility:
by Helen Charlotte Emerson.

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
LD1780 2017 ( lcc )

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A LONGITUDIN AL STUDY OF CAREER MATURITY AND CAREER DECISION MA KING SELF EFFICACY OF RURAL SECO NDARY SCHOOL STUDENTS By HELEN CHARLOTTE EMERSON A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2017

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2017 Helen Charlotte Emerson

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To m y h usband, s on, and p arents

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4 ACKNOWLEDGMENTS First and foremost, I thank my Lord and Savior Jesus Christ for the promise of salvation and the gift of grace. It is because of this promise I have the strength to complete what I believed was impossible. It is because of this grace and mercy that I am stronger in my faith and my w alk with Christ and know without fail, this too shall pass To my husband, Duke Emerson, I am thankful for the love, support, wisdom humor and dedication you have shown me during this process. I am humbled by your ability to encourage and stay optimistic in the face of a challenge. You have carried the emotional weight of our family and for that I am forever grateful. I love you more than I will ever be able to express. To my one and only son, Case Emerson, I hope I have set an example of per severance and strength of mind and char acter for you to follow through out your life. I appreciate your forgiveness when I could not be wh ere you may have needed me I am thankful for your encouragement when I was in need and your love in moments of doubt. I pray you will always put God, faith, and family first in your life. To my parents, Bill and Loretta Westmoreland, I have never been more inspired by two individuals in my life. You are the embodiment of wisdom, the pinnacle of support and the definitio n of unco nditional love. I love you both. You are the reason I am the individual I am today. I am truly blessed. To my brother, Forrest Westmoreland and his family, thank you for supporting and loving me unconditionally. To my mother in law, Gail Emerson, thank you for feeding my family when I was working to complete this dissertation. I am thankful for you for many reasons, but mostly

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5 for raising a kind, hard working, supportive man. I pray I have done half as good a job with my son as you did with yours To the rest of my family near and far thank you for understanding when I could not attend every family function and event. They said this would be a sacrifice and they were right. To my committee chair, advisor, and most importantly my friend, Dr. Bri an Myers, a simple thank you does not seem to be adequate to express my gratitude. You have believed in me, supported me, scolded me and lifted me up on a multitude of occasions. This journey has been long and drawn out one that has included frustrations, revelations and certa inly a lot of tears. Your advice, patience and guidance have served as a beacon of light throughout this process. To my mentor and friend Dr. R. Elaine Turner, Dean of the College of Agricultural and Life Sciences, thank you for being my person. Your support of me is second to none and for this I am beyond grateful. Your academic and institutional knowledge amazes me on a daily basis. I will never be able to repay you for the pats o n the head, words of wisdom and your commitment to my success. To the remainder of my committee, Dr. R. Kirby Barr ick and Dr. Ed W. Osborne, you both are such an inspiration and an example of dedication an d academic excellence Both of you have been not only a part of this trip but you h ad i nvested in me professionally well before I started this process The respect and admiration I have for you both is b eyond description a extraordinary people. I am grateful you agreed to take this especially long walk with me.

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6 To the faculty and staff in the College of Agricultural office, each of you is a n irreplaceable part of my life I am blessed to have the opportunity to work with each of you and have you in my corner. A special thank you to Ms. Je was preoccupied with reading, writing, statistical analysis, or simply stressed about what was ahead. To my statistician friend James Colee, thank you for answering my emails and calls every single time. Your willingness to help me speaks volumes about the kind and compassionate person you are. To my colleag ues and friends, Cathy Carr, Natalie Co ers, and Jason Headrick thank you for being my cheerleaders and providers of cho colate, cupcakes or words of encouragement in the most critical of times To my district and school level partners in this project, Carlton Faulk, Mik e Ripplinger, Lanier Clyatt and the faculty, staff and students of the high school y our support of me go es well beyond the scope of this particular proj ect. I am forever thankful to have started my teaching career i n a school district that continues to embrace the idea of learning for the sake of learning and is dedicated to the success of students To my former teaching colleagues, Tom Williams, David Harris, and Scott Register astonishingly, I believe you molded me into the educator I am today. The foundation you helped me establish in the high school classroom will not soon be forgotten. I am tougher, m ore prepared and more resilient because of the three of you. You cannot make old friends, and I am glad you are mine!

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7 To the past and present College of Agricultural and Life Sciences Ambassadors, fo rmer and current graduate assistants the members of the University of Florida Collegiate Farm Bureau Chapter and the brothers of Alpha Gamma Rho, Alpha Gamma Chapter, thank you for being an inspiration to me. I a m blessed by each of you and will forever be proud to be affiliated with you. I pray each of you c o ntinue to grow academically, professionally and personally. Your contribut ion to the world is important do not take it lightly To Dick Wolf, you know what you did.

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8 TABLE OF CONTENTS P age ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ .......... 11 LIST OF FIGURE S ................................ ................................ ................................ ........ 13 LIST OF ABBREVIATIONS ................................ ................................ ........................... 14 ABSTRACT ................................ ................................ ................................ ................... 15 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 17 Statement of the Problem ................................ ................................ ....................... 19 Purp ose of the Study ................................ ................................ .............................. 19 Statement of Objectives ................................ ................................ .......................... 19 Significance of the Study ................................ ................................ ........................ 19 Definition of Terms ................................ ................................ ................................ .. 20 Limitati ons of the Study ................................ ................................ ........................... 22 Assumptions of the Study ................................ ................................ ....................... 23 Chapter Summary ................................ ................................ ................................ ... 23 2 REVIEW OF LITERATURE ................................ ................................ .................... 24 Constructivism ................................ ................................ ................................ ........ 24 Social Cogniti ve Theory ................................ ................................ .......................... 25 Theoretical Framework ................................ ................................ ........................... 27 Career Guidance ................................ ................................ ................................ .... 28 Career Development ................................ ................................ ............................... 28 Self Efficacy Theory ................................ ................................ ................................ 30 Career D ecision Making Self Efficacy ................................ ................................ ..... 32 Career Maturity ................................ ................................ ................................ ....... 33 Summary ................................ ................................ ................................ ................ 34 3 METHODOLOGY ................................ ................................ ................................ ... 37 Research Design ................................ ................................ ................................ .... 37 Procedures ................................ ................................ ................................ ............. 38 Population and Sample ................................ ................................ ........................... 39 Instrumentation ................................ ................................ ................................ ....... 42 Data Collection ................................ ................................ ................................ ....... 47 Confidentiality ................................ ................................ ................................ ......... 48 Analysis of Data ................................ ................................ ................................ ...... 48

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9 Summary ................................ ................................ ................................ ................ 49 4 RESULTS ................................ ................................ ................................ ............... 51 Sample ................................ ................................ ................................ .................... 52 Objective One: Describe the demographic characteristics of selected rural high school students. ................................ ................................ ................................ ... 52 Objective Two: Asses s the level of career maturity of students in a selected rural high school. ................................ ................................ ................................ 53 Objective Three: Assess the level of career decision making self efficacy of students in a selected rural high school. ................................ .............................. 55 Objective Four: Examine levels of career maturity ba sed on demographic characteristics. ................................ ................................ ................................ ..... 56 Objective Five: Examine levels of career decision making self efficacy based on demographi c characteristics. ................................ ................................ ............... 57 Summary ................................ ................................ ................................ ................ 57 5 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS ................................ .. 65 Objectives ................................ ................................ ................................ ............... 66 Methods ................................ ................................ ................................ .................. 66 Summary of Findings ................................ ................................ .............................. 67 Objective One ................................ ................................ ................................ ... 67 Objective Two ................................ ................................ ................................ ... 67 Objective Three ................................ ................................ ................................ 68 Objective Four ................................ ................................ ................................ .. 68 Objective Five ................................ ................................ ................................ ... 68 Conclusions ................................ ................................ ................................ ............ 69 Discussion and Implications ................................ ................................ .................... 69 Discussion and Implications of Research Methods ................................ ................. 73 Recommendations for Practitioners ................................ ................................ ........ 73 Recommendations for Future Research ................................ ................................ 74 Summary ................................ ................................ ................................ ................ 75 APPENDIX A STUDY INTRODUCTION LETTER ................................ ................................ ......... 76 B SCHOOL SUPPORT CORRESPONDENCE ................................ .......................... 79 C INFORMED CONSENT FORM ................................ ................................ ............... 82 D TEACHER ASSENT LETTER ................................ ................................ ................. 85 E STUDENT ASSENT LETTER ................................ ................................ ................. 88 F DEMOGRAPHIC QUESTIONNAIRE ................................ ................................ ...... 91

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10 G CAREER MATURITY INVENTORY QUESTIONNAIRE ................................ ......... 97 H CAREER DECISION MAKING SELF EFFICACY SHORT FORM QUESTIONNAIRE ................................ ................................ ................................ .. 99 LIST OF REFERENC ES ................................ ................................ ............................. 102 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 108

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11 LIST OF TABLES Table page 3 1 District and High School Student and Faculty Population ................................ ... 50 3 2 District and School Faculty ................................ ................................ ................. 50 3 3 District and High School Economically Disadvantaged ................................ ...... 50 3 4 High School Ethnic Groups ................................ ................................ ................ 50 3 5 District Ethnic Groups ................................ ................................ ......................... 50 3 6 District and High School Male Female Ratio ................................ ...................... 50 3 7 District and High School Grades ................................ ................................ ......... 50 4 1 Respondents by Year ................................ ................................ ......................... 58 4 2 Respondents With Multiple Surveys ................................ ................................ ... 58 4 3 Respondents by Gender Participation ................................ ................................ 58 4 4 Analysis of Self Reported Ethnicity Description ................................ .................. 58 4 6 Respondent Analysis of Extracurricular Activities ................................ ............... 59 4 7 Respondent Analysis of Sports Activities ................................ ........................... 59 4 8 Analysis of Dual Enrollment Courses ................................ ................................ 59 4 9 Analysis of Advanced Placement Courses ................................ ......................... 60 4 10 Analysis of Co Curricular Activities ................................ ................................ ..... 60 4 11 Analysis of Student Age in Years ................................ ................................ ....... 60 4 12 Means and Standard Deviations for Career Maturity Inventory .......................... 60 4 13 Means and Standard Deviations for Career Maturity by Grade Level ................. 61 4 14 Means and Standard Deviations of Career Maturity Scores by Gender ............. 61 4 15 Means and Standard Deviations for Career Decision Making Self Efficacy ....... 61 4 16 Mea ns and Standard Deviations for Career Decision Making Self Efficacy by Grade Level ................................ ................................ ................................ ........ 62

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12 4 17 Means and Standard Deviations for Career Decision Making Self Efficacy by Grade Level and Male and Female ................................ ................................ .... 62 4 18 Analysis of Dif ference in Career Maturity Inventory ................................ ............ 63 4 19 Means Scores for Ethnicity and Career Maturity Inventory ................................ 63 4 20 Analysis of Difference in Career Decision Making Self Efficacy ......................... 63 4 21 Means for Career Maturity and Career Decision Making Self Efficacy ............... 64

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13 LIST OF FIGURES Figure page 2 1 Conceptual Model ................................ ................................ ............................... 35 2 2 ................................ ......... 36 2 3 Efficacy Model (Bandura, 1977) ................................ ................. 36

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14 LIST OF ABBREVIATIONS AP Advanced Placement Courses CDSME Career Decision Making Self Efficacy CDSME SF Career Decision Making Self Efficacy Short Form CMI Career Maturity Inventory DE Dual Enrollment DQ Demographic Questionnaire

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15 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 Philosophy A LONGITUDI NAL STUDY OF CAREER MATURITY AND CAREER DECISION M AKING SELF EFFICACY OF RURAL SEC ONDARY SCHOOL STUDENTS By Helen Charlotte Emerson August 2017 Chair: Brian Myers Major: Agricultural Education and Communication The purpose of this study was to examine the relationships between career maturity and career decision making self efficacy and selected demo graphic characteristics. The study utilized three ins truments for data collection: the Career Maturity Inventory (CMI R), the Career Decision Making Self Efficacy Short Form (CDSE SF) and a demographic information survey, w hich was developed by the investigator The popula tion for this study was students att ending a rural high school. All s tudents in grades 9 th 12 th complete d the Dem ographic Questionnaire CMI and the CD M SE SF Students selected for the study were chosen based on completion of the informed consent forms. This study was conducted as a longitu dinal study over the course of three years. The population completed each of the instruments (CMI CDSE SF and Demographic Survey ) in the fall of each academic year Descriptive statistics were calculated for gender, ethnicity, extracurricular activities, athletic involvement, Advanced Placement course s and dual enrollment courses for 2013 and 2014. Correlation analysis and linear regression models were

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16 used to determine the relationship between career maturity and individual student demographic characteris tics and the relationship between career decision making self efficacy and individual student dem ographics Statistical analysis found students who did not take Advanced Placement (AP) courses had higher career maturity scores than those who took A P cours es. Correspondingly, those students who took dual enrollment courses scored lower on the Career Maturity Inventory (CMI R) than those who did not. Additionally, participation in extracurricular activities was found to be significant. Students who participa ted in zero to one activity had a higher CMI R than those who participated in two or more extracurricular activities. Students who participated in two or three extracurricular activities and dual enrollment were found to have significantly higher CDMSE SF means than others (p < .01). No other variables examined in this study were found to be significant. These findings suggest ed additional resources should be dedicated to car eer development by school administrators, counselors a nd teachers in order to enhan ce career maturity and career decision making self efficacy before the end of their secondary school experience. Further investigatio n should be conducted to further understand and enhance the process of career development i n secondary students.

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17 CHAPTER 1 INTRODUCTION understanding of yourself, aptitudes, abilities, interests, resources, limitations, and ot her qualities; second, a knowledge of the requirements and conditions of success, advantages and disadvantages, compensations, opportunities, and prospects in different lines of work and lastly, true reasoning of the relations of these two groups of (Parsons, 190 9 the time; however, as it relates to career development and career choices, his basic conceptual framework pro vided one of the first guides for career counseling. Parsons belie ved if people are actively involved in choosing their own career, based on their own attr ibutes, they will be more satisfied and content with their chosen career or vocation. physica l, cognitive and emotional factors (Seligman, 1980). According to Brown, Career development is an ever changing and metamorphic process that develops over a significant period of time rather than developing rapidly (2002) Additionally, c areer development requires an individual to understand certain personal traits, be able to identify self preconceptions to determine how each will influence personal career decision making (Brown, 2002). Career development can also be about how a n individual lives and how early life experiences, such as cur ricular and extra curricular experiences in school, interact to establish career choices. Two constructs that address career development include career maturity and career decision ma king self efficacy. Studies of c areer maturity and career decision making se lf efficacy have been numerous, particularly as they relate to post secondary

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18 students, students with disabilities in post secondary settings, career maturity and career d ecision m aking self efficacy in minorities, emotional intelligence and career maturity and career decision making self efficacy in millennials. According to Human Resources Executive Online, technology and globalization will also require more employees to engage in "lifelong learning," (Mcllvaine, 2004), leading to the belief that students who have an understanding of career development and have an appreciation of their own attributes will be led to a career that is suitable. Administrators as well as others invo lved with student success need an understanding of career trends in orde r to effectively guide students into appropriate career paths. Directing and facilitating students into a certain career path has been routine since the 1800 s. The goal has been to guide students from school to work in a way that supports interests and goals. Career guidance starts in elementary school and typically ends when an individual graduates from high school; however, research has indicated the need for better career developm ent services during post secondary education as well (Getzel et al., 2001). Students who pursue post secondary programs benefit from effective and continued career guidance not only while in elementary school and throughout their secondary education, but a lso on a continuum that lasts throughout their post secondary program of study. Maduakolam Ireh (2000) recommended career lifetime, and one of the most important aspects of career choice.

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19 Statement of the Problem high school graduates do not efficiently and effectively transition into careers that align with their abilities and interests in a way that support s a reasonable standard of living. Purpose of the Study The purpose of this study was to examine the relationship between career maturity and selected demographics and career decision making self efficacy and selected demographic characteristics of rural high school st udents. Demographic characteristics include age, gender, ethnicity extra curricular activitie s, co curricular activities, athletics Advanced P lacement courses taken and dual enrollment courses taken Statement of Objectives The objectives of this study include d the following: 1. Describe the demographic characteristics of selected rural high school students. 2. Assess the level of career maturity of students in a selected rural high school. 3. Assess the level of career decision making self efficacy of students i n a selected rural high school. 4. Examine le vels of career maturity based on demographic characteristics. 5. Examine levels of career decision making self efficacy based on demographic characteristics. Significance of the Study This caree r maturity and caree r decision making self efficacy longitudinal study of secondary students will be of interest to high school administrators, faculty and staff of high schools, and community and state colleges and universities who are concerned about and dedicated to helpin g students determine a career path and enhance the

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20 process of career development. The fundamental need for this study wa s to provide faculty and staff at high school s community and state college s and universit ies with relevant research that will be usefu l and relevant in advising students in high school and college course choices, providing career counseling and encouraging career exploration. T his study will give support to the population mentioned above in determining career paths that are suited to th e individual student based on the confidence level. Additionally, students will benefit in that guidance counselors and other administrators will be better equipped to aid in the career choice explorati on process. This longitudinal study collected data from s econdary students over a period of time, documenting change in Career Maturity Inventory (CMI) and Career Decision Making Self Efficacy ( CDMS E ) scores, and changes in key attributes. This longitudinal study will add to the body of knowledge regarding working with high school students and preparing them for making decisions regarding career possibilities Further this study will serve as a basis for changing how teachers and counselors work with students in grades 9 12 in ways that support their career maturity and career decision m aking self efficacy. The impact of this study will provide a reference for educational entities to better prepare students for the world of work based on various factors and key attributes. Definition of Terms For this study the following terms were defined operationally: 1. Administrators. An administrator oversees the daily operations of schools, colleges, universities, day care centers and preschools. A school ad ministrator's specific responsibilities differ between organizations, but often these administrators are an important link between students and local communities.

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21 2. Advanced Placement Courses. Rigorous, college level classes in a variety of subject areas tha t give students an opportunity to gain skills and experience. American colleges and universities may grant placement and course credit to students who obtain certain scores on AP examination s 3. Athletics. Athletic events participated in by student athletes ; organized by the school system. 4. Career Decision Making Self Efficacy. A to effectively engage in career decision making tasks and activities (Taylor & Betz, 1983). In this study, career decision making self efficacy was defined as the score on the Career Decision Making Self Efficacy Short Form (CDMSE SF). 5. Career Development. The enhancement or growth of a career, it is the enrichment of human potential in creating a pattern of relationships between life r oles, within the parameters of place, and over a lifetime (Peruniak, 2010). 6. Career Maturity. Career maturity is an important variable in the career developmental process (Burkhead & Cope, 1984). In this study career maturity was defined as a attitude toward his or her readiness to make career choices appropriate to age or developmental stage (Crites, 1976). 7. Co c urricular Activities. Activities considered intra curricular to courses offered on the high school campus. 8. De mographic Characteristics. F efficacy or career maturity while enrolled in high school Factors examined in this study include d Advanced Placement courses, dual enrollment, extracurricular activities, co curricular activities, and participation in athletics and sports. 9. Dual Enrollment. College courses available for eligible high school stude nts where the student can concurrently acquire high school and college credit. 10. Economically Disadvantaged. T he Florida Department of Education reports economically disadvantaged students to be those who qualify for free or reduced price lunches, which is the indicator used to calculate the percentages of students classified as economically disadvantaged in the state and school ( 2012). 11. E mployers. Employers are persons or organizations that employ others. 12. Extra c urricular Activities. Activities that do not fall with in the scope of the regular curriculum And are u sually approved by the school and affiliated with a school based student organ ization. 13. Gender. Identifying as male or female. 14. Honors Courses. Courses that are more intense and faster paced than typical high school classes

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22 15. Parents/Guardian. A person who is entrusted by law with the care of the person or property, or both, of another, as a minor or someone legally incapable of managing his or her own affairs, may be a father, mother or other designee ( Dictionary.com, 2017). 16. Secondary student. A student enrolled in grades 9 12. 17. Self efficacy. S ability, the barriers they might face the resources the school or college should provide them, and the opportunity they have regarding their actions related to the information they have received (Simpson et al., 1994). For the purpose of this study self score on the CDMSE SF questionnaire. 18. Skyward. Software for online school management and distribution for students, family, administrators, and faculty and staff. 19. Sports. Organized athletic events by an entity other tha n the school. 20. Stakeholders. According to Great Schools Partnership a stakeholder is a nyone who is interested in the welfare and success of a school, including students, parents, families, community members, local business leaders and elected officials su ch as school board members and other elected officials (2014) 21. High School. A school that typically compr ises grades 9 through 12 grades, attended after primary school or middle school. 22. Rural The U.S. Census Bureau defines rural as what is not urban that is, after defining individual urban areas, rural is what is left u rban areas represent densely developed territory, and encompass residential, commercial, and other non residential urban land uses (2012). Limitations of the Study The conclusions and impl ica tions drawn from this study were subject to certain limiting factors including: 1. The population for the study were students in a rural Florida high school in grades 9 12. 2. The findings of the student could not be generalized beyond the sample. 3. Due to the typical pattern of students entering and leaving high school, some attrition occurred in this longitudinal study. 4. The school community chosen for the study lacked economic, ethnic, and racial diversity.

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23 5. based survey may have been intimi dating to the participants who did not regularly use a computer. 6. An underrepresented percentage of minorities in the school population exists and therefore minority data are not comparable. 7. Technological difficulties may have occurred when students were completing the instruments. 8. The lack of privacy when taking the surveys may have been an issue for some students. 9. All information and data collected were self reported and may have given way to a social desirability response factor that could not be controlled in this study. Assumptions of the Study The following assumptions were made for the purposes of this study: 1. Participants involved in this study completed the demographic questionnaire, career maturity inventory and career decision making self efficacy inventory to the best of their ability. 2. Participants involved in this study responded to the survey instruments truthfully. 3. School administrators and t eachers facilitating the survey proces s allowed students ample time to complete the instrument s Chapter Summary The purpos e of this study was to examine the relationship between career maturity and student demographics, and career decision making self efficacy and student demographics. Stude nt demographics in secondary students were defined as extracurricular activities, co curricular activities, athletics, Advance d Placement courses, and dual enrollment. It was hypothesized that students who possess multiple positive demographic characteristics would have high career maturity scores and high career decision making self efficacy.

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24 CHAPTER 2 REVIEW OF LITERATURE Chapter 1 provided the rationale influencing career maturity and career decision making self efficacy in rural secondar y students. The purpose of this study as outlined in Chapter 1 was to examine the relationship between career maturity and selected demographics and career decision making self efficacy and selected demographics in secondary students Chapter 1 provided o bjectives that framed this study, along with the hypothe ses K ey terms were defined and assumptions and limitations were stated. Chapter 2 describes the theoretical and conceptual frameworks and delineates research significant to this particular study. Ad ditionally, this chapter will present empirical literature related to the components of the conceptual model (Figure 2 1 ) developed by the researcher for this study. The review of literature foc used on textbooks and refereed and non refereed publications. Constructivism Constructivism, while not considered a theory as much as a philosophy is the major dynamic and foundation of this study. Constructivism is a learning philosophy established on the premise that reflecting on cons truct understanding of the world. Doolittle and Camp (1999) describe d constructivism previously stated constructivism is not viewed as a single theoretical position but as a continuum. The constructivism continuum is typically divided into three categories: cognitive constructivism, social constructivism and radical constructivism. Cognitive constructivism

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25 Camp, 1999, p. 9 ). Radical constructivism suggests that knowledge is subjective and is constructed internally by the learner (Doolittle & Camp, 1999). First and foremost, constructivism focused on cognitive development and understanding of the learners ; onstructivism c onstrues learning as an interpretive, recursive, nonlinear building process by active learners interacting with their believe learning is development, a state of imbalance facili tates learning, reflection drives learning, and social interactions provoke further thinking (Fosnot, 2005). The following are essential factors of a constructivist approach to pedagogy: Learning should take place in authentic and real world e Learning should involve so Content and skills should b Content and skill s should be understood within the framework of Students should be assessed formatively, se rving to info rm future learning Teachers serve primarily as guides and facilitator s of Teachers should provide for and encourage multiple perspectives and representations of content. (Doolittle & Camp, 1999, pp.9 13) Fosnot (1996) and Schunk (2004) concurred that constructivism has a theoretical foundation entrenched in Sociocultural Theory (Vygotsky, 1978) and the Theory of Cognitive Development (Piaget, 1972). According to Brooks and Brooks (1993) and Fosnot (1996), constructivism is a theory about knowledge, thinking, and learning, not a theory about teaching. Social Cognitive Theory Social Cognitive Theory is the underlying theory encompassing this study. According to Bandura (1986, 1997) the social cognitive theo ry emphasizes how

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26 cognitive, behavioral, individual and environmental factors interact to determine motiva tion and behavior. Further, social cognitive theory explains how peopl e acquire and maintain certain b ehavioral patterns, while also providing the bas is for intervention strategies (Bandura, 1997). Essentially, the principal focus of social cognition is to evaluate behavioral change as it relates to environment, people and behavior. bo th socially and physica lly. Socially, an individual exposed to family members, friends, teachers, co workers or colleagues; physically, the size of a room, temperature, or a vailable amenities may affect behavior. In the end environment and situation provi de the framework for understanding behavior (Parraga, 1990). Environmental factors, social factors, and behavior are persistently and continuously influencing one another. Behavior is not simply the result of the environment and the person, just as the en vironment is not simply the result of the person and behavior (Glanz et al, 2002). Models of behavior are provided b specifically what the individual observes, thus giving way to observational learning. Observational learning occurs when a person watches the actions of another person and the reinforcements that the person receives (Bandura, 1997). Behavior as a whole is defined in various ways, but the simplest definition is that if individual s perform certain behavior s, they must understand what the behavior is and have the skills and competencies to perform the particular behavior. The theory of social cognition includes four interrelated basic processes of goal attainment including; self observation, self evaluation, self r eaction and self efficacy (Redmo nd, 2010). Bandura (1986) stated that how a person functions is determined by

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27 the interactions of personal factors including cognitio n, affect and biological events; behavior factors; and environmental factors. Human perfo rmance is the result of the interaction of all thre e of the functions mentioned above which are referred to as Determinism (Figure 2 2 ). Pajares (2002) stated are viewed as self organizing, proactive, self reflecting and self regulating rather than as reactive organisms shaped and shepherded by environmental forces or driven by Theoretical Framework The theoretical framework (Figure 2 1 ) for this study was based on the overall work of Albert Bandura (Figure 2 2) Bandura set forth one of the most academic and realistic applications of concepts related to the general psychology of how self efficacy is defined. F urther, accordin g to Bandura (1977) the concept of self effi cacy includes our beliefs in and capabilities to successfully perform a given behavior or set of behaviors. Self efficacy is claimed to influence behavioral choices, performance and persistence. Bandura (1977) furthe r suggested that one of the primary roles of a counselor is to assi st the client in increasing his or her expectations of self efficacy with respect t o targeted behavioral domain(s) through interventions based on the sources of efficacy information such as performance accomplishments, vicarious learning, anxiety management, and verbal persuasion and encouragement. Fundamentally, self efficacy his or her capacity to perform a given assignment, behavior or job. B ed the foundation of this study. Additionally, Bandura describes self efficacy as being derived from four sources of influence : mastery experiences, modeled behavior, social persuasion, and physiological respons es to experiences (B andura, 1994 ). It is likely that students who have strong

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28 perceptions and confidence about career opportunities and development are better prepared and more c onfident when choosing a career, or will at least have a better perception of their own interest s and aspirations. Career Guidance Guiding students into a particu lar career path has been routin e since the early 19 th century, the idea is that students are fostered and guided from school to work in such a way that satisfies the independent interests and goals and their overall well being. Ideally, career guidance and direction starts in elementary school and traditionally ceases when the individual graduates from high scho ol; however, research has shown the need for better career development services during post secondary education (Getzel et al., 2001). Students who pursue a post secondary education would benefit from effective and continued career guidance not only while in elementary school and throughout their secondary education but on a continuu m lasting through their post secondary program of study. Maduakolam Ireh (2000) suggested career choice supervision should continue past high school because career concerns occur contentment is career choice. Career guidance as related to this study focused on self efficacy, caree r maturity, and career decision making self efficacy. Career Development Career development is a continuous process. There are numerous studies and theories associated with career d evelopment. Gies (1990) compared and contrasted two career theorists, David Tiedman and Don ald Super. Gies (1990) described definition of vocational development as a compromise between personal and social factors a nd self concepts and r a more meaningful

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29 career is chosen when the career choice is closer to self concept or the discernment or an u nderstanding of himself, then decisions about a career can be made Gies (1990) carefully outlined l ideas, and therefore, provided a foundation for his theory. Initially, it is important to take uniqueness into consideration in career develo pment; people have different capabilities, interests, and traits (Gies, 1990). that every individual has a range of abilities, personality characteristics, and traits. These traits help individuals become successful in a number of career s (Gies, 1990). Super continued each occupation requires different Theory is that career likes, desires, and abilities are not permanent. The developmental p rocess can be separated into life stages recognized as growth (self concept is formed when experiences provide knowledge of the work world), exploration (unrealistic desired occupation), establishment (deciding if career choice made in the exploration stag e is realistic), maintenance (adjustments and improvements to career), and decline (focus is has suggested that a career pattern is set out by the individu al parents socioeconomic level, hi s or her mental ability and personality characteristics and by the opportunities the individual is given. In order to make successful career choices, individuals should be encouraged to expand their abilities and interests. As a final point, Super recogn ized that work and life and beliefs (Gies, 1990).

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30 ory of career development stated that career development is the process of organizing and identifyi ng with work through the persona lity with society (Gies, 1990). Tiedman describe d two decision making periods: the period of anticipation and the period of implementatio n and adjustment (Gies, 1990). The career selections that individual s make influence the way they behave in certain si tuations. When people are confident in a decision, then they can further define the results of the choice and specification occurs (Gies, 1990). Tiedman designated the period of implementation an d adjustment in three sub stages: induction, transition, and maintenance. Induction starts when a person fits his or her goals into a grou p or society (Gies, 1990). When individual s begin to put their goals into action, group goals become part of personal goals as the interaction between the individual and group grows (Gies, 1990). Self Efficacy Theory ) explained the perception of a theoretical self efficacy f ramework. efficacy their form, serve as a means of creating and strengthening expectations of personal effic interacts in a complex manner with the environment as well as with other motivational and self regulatory mechanisms and with personal capabilities and performance accomplis expectancy and efficacy expectations as important components of self efficacy theory. given action will lead to specific

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31 results Efficacy expectation on the other hand is the assurance that an individual can effectively execute the behavior required to produce a result. Bandura identified distinct and clear associations between outcome expectations and efficacy expectations. Bandura (1977) divided self efficacy expectations into various dimensions, all having important performance implications. Self belief s concerning his or her ability to successfully perform a given task or behavior, and those expectat ions differ in magnitude, generality, and strength. According to Bandura experiences create circumscribed mastery expectations while others instill a more generalized sense easily extinguishable by disconfirming experience, whereas individuals who possess strong expectation s of mastery will persevere in their coping efforts despite four primary sources of efficacy expectations: performance accomplishments, vicarious experiences, verbal persu asion, and physiological states (Figure 2 3) These primary sources interrelate to influence accomplishments are possibly the most significant due to the relationship to person al experiences. Vicarious experiences are those gleaned from watching another individual experience an activity with little or no recourse. Witnessing others perform or partici pate in activities with success breeds the expectation of personal improvement a nd success

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32 suggesting to believing they can cope successfully with what has overwhelmed them in situations generally elicit emotional arousal and depending on the circumstances the Band care er maturity and career decision making self efficacy. With proper reinforcement and support secondary students can choose valuable post secondary experience s and long term career path. Career Decision Making Self Efficacy Career Decision Making Self Efficacy describes regarding her or his ability to perform the specific task and behaviors that are importa nt to effective career decision making ( Taylor & Betz, 1983). R esearchers have found a significant relationship between career decision making self efficacy and career decision making attitudes (Luzzo, 1993b), career decidedness (Robbins, 1985; Taylor & Pop ma, 1990) vocational identity (R obins, 1985), self esteem (Robbins, 1985), career exploration behavior (Bluestein, 1989) career indecision (Robbins, 1985; Taylor & Betz, 1983), and career locus of control (Taylor & Popma, 1990). Since the inception of efficacy theory in 1977, the re has been a great deal of attention in the efficacy theory were Hackett and Betz (1981) because of their belief that it could further explain traditional gender roles and male and female evaluations in relationship to career choice and certain behaviors. Taylor and Betz (1983) were the next to develop

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33 the construct of CDMSE and the inception of Social Cognitive Career Theory. Taylor and Betz developed a measurement too l for career decision making self efficacy. Hackett and Betz (1981) were the first to define the relationship between self efficacy and car eer related performance. Career related behavioral domains were determined by Crites (1971) as the following : (a) accurate self appraisal, (b) gathering occupational information, (c) goal selection, (d) making future plans, and (e) problem solving. Hackett and Betz (1981) id entified two additional domains assertion and the ability to take the initiative with th e intent of explaining the relationship between self efficacy and wom e Career Maturity Career maturity was originally refer Career continuum of of career maturity has been researched for more than 50 years and numerous measures have been created to evaluate this variable (Brown & Lent, 2005). Crit es (1976) divided career maturity into two separate dimensions : attitudinal and cognitive. vocational (career) choice and whether they continue to pursue their career ch oice as they enter the workforce. The c ognitive dime nsion refers to decision making skills; and the affective dimension includes attitudes toward the career decision making process (Patton & Creed 2001 ) Researchers have discovered that career maturity is significantly associated with a variety of other career development variables, such as self concept (Onivehn, 1991; Salami, 1999), career decision making (Wanberg & Muchinsky, 1992), career

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34 preference (Salami, 1997), career commitment (Lam, Poong, & Moo, 1995), career planning, career exploration and occupational information seeking behavior (Naidoo, 1998). Career maturity has been o ne of the most univers ally researched outcome measure s in career counseling and career development (Cook 1991; Luzzo, 1995; Spokane, 1991). Summary Chapter 2 provided a review of literature related to the problem of this study. The r esearch literature relating to care er maturity and career decision making self efficacy was examined to gain an enhanced understanding of previous studies and research. Past r esearch has supported the importance of care er maturity and career decision making self efficacy for secondary students. Chapter 3 will provide the methodology and procedures that were used in t his study. Further, Chapter 3 outlines the research design and perspective, research methods, research procedures, population and instrumentation, data collection, data analysis and potential threats to validity and reliability.

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35 Figure 2 1. Conceptual Model (Adapted from Lent Brown Cognitive Theory)

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36 Figure 2 2 (Bandura 1985) Figure 2 3 Efficacy Model (Bandura, 1977)

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37 CHAPTER 3 METHODOLOGY Chapter 1 provided a historical perspective on care er maturity and career decision making self efficacy among secondary school students. The purpose of this study was outlined, along with the objectives Key terms were defined, and assumptions and limitations were stated. Chapter 2 described the theoretical framework and the conceptual model used to guide this study on care er maturity and career decision m aking self efficacy. Chapter 2 additionally presented previous research findings related to components of the conceptual model em ployed in this study. In Chapter 3 methods used to address the research objective s are discussed. Specifically, Chapter 3 outlines the research design and perspective, research methods, research procedures, population and instrumentation, data collection, data analysis and potential threats to validity and reliability. Research Design This longitudinal study employed a descriptive correlational research design using a survey data collection methodology. Survey design is broadly used and reputable in the f ield of social sciences (Hackett, 1981). A cross sectional survey design was used in this study to gather data at se veral points in time and study changes in care er maturity and career decision making self efficacy over an extended period By u s ing questio nnaires data can be collected from a predetermined population allowing for comparisons to be made across two or more groups of participants ( Fraenkel & Wallen, 2003). Further this study described the group as a whole and the demographic characteristics r elated to care er maturity and career decision making self efficacy.

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38 Procedures Before the co mpilation of any data, U niversity Internal Review Board (IRB) authorization was sought and granted based on a brief description of this study ( Appendix A). The gov erni ng school board and school administration of the rural high school granted approval allowing student s to participate in this study ( Appendix B). Informed consent letters (Appendix C) explained the purpose of this study, provided information regarding confidentiality and anonymity of data collection, and incl uded the contact information for the researcher should guardians or parents have questions not addressed i n the informed consent letter. Informed consent letters, te acher introduction letters ( Appe ndix D), student assent letters ( Appendix E), and a copy of the university IRB approval were distributed to each student. Students were asked to return all forms with a guardian or parent signatures indicating permission to participate in the study. IRB c onsent forms were collected and securely filed for reference purposes. The data collection procedure for the study was determined by the researcher and was administered in cooperation with school administrators and classroom teach ers in the rural high sch ool. The predetermined day and period for each class level (freshman, sophomore, junior or senior) were established by the school administrator for the completion of the web based survey s Students were asked to complete the demographic questionnaire ( Appe ndix F), the Career Maturity Inve ntory Revised Form (CMI R) ( Appen dix G), and the Career Decision Making Self Effi cacy Short Form (CDMSE SF) ( Appendix H) u sing the web based instruments. As an incentive for students to complete and return all IRB forms and parent or guardian permission forms, students were informed that upon completion of the survey s they would qualify for a $25.00 gift card. Each participant who submitted forms and

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39 complete d survey s was entered into a drawing at the conclusion of the da ta collection phase. Four gift cards, one for each grade level, were given away in 2012, 2013 and 2014. In order to protect the anonymity of participants, four student numbers were drawn, one from each grade level, and returned to the school administrator along with the gift cards for distribution to the winning students. Population and Sample The population for this study included students in grades 9 12 attending a rural county high school in Florida a census of the entire population was ass essed In 2 012 t he high school population (Table 3 1) included 619 students and 45 adm inistrators, and teachers (Table 3 2 ). Approximately 51.9 % of students were classified as economically disadvantaged (Table 3 3 ) The percentage of ethnic groups of students in the high school was 78.5 % White, 14.5% Black or African American, 3.2% Hispanic or Latino and 2.3 % list more than o ne ethnicity (Table 3 4 ) The high school population was 53.2 % male and 46.8 % female (Table 3 6 ) In 2013 the high scho ol student population (Table 3 1) included 628 students and 44 administrators and teachers (T able 3 2 ) Approximately 51.9% of students were classified as economically disadvantaged (Table 3 3 ) The percentage of ethnic groups of students in the high school was 78.5% White, 13 .2% Black or African American, 3.3% Hispanic or Latino and 4 .3% list more than one ethnicity (Table 3 4 ) The high school student population was 50.7% male and 49.3 % female (T able 3 6 ) In 2014 t he high school population (Table 3 1) included 632 students a nd 44 adm inistrators and teachers (Table 3 2 ) Approximately 51.9 % of students were classified as economically disadvantaged (Table 3 3 ) The percentage of ethnic groups of students in the high school was 77.4 % White, 12.7% Black or African

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40 American, 4.7% Hispanic or Latino and 4.1 % list more than one ethnicity (Table 3 4 ) The high school population was 48.9% male and 51.1% female (Table 3 6 ) The o verall school district included three schools an elementary school, middle school, and high school. Student enrollment in the district was 2,257 students in 2012, 2,336 in 2013 and 2,342 in 2014 (Table 3 1 ) To serve these students the district had an average of 17 0 instructional and school administrators over the period of this study (Table 3 2 ) For the district, approximately 60.8% in 2012, and 62% in 2013 and 2014 of the stu dents were c lassified as economically disadvantaged ( Table 3 2) The percentages of ethnic groups in the district in 2012 were 79% white, 13.2% African American, 3.5% Hispanic and 3.4% list one or more ethnicity (Table 3 5 ) In 2013, 78.5% white, 13.2% African American, 3.3% Hispanic and 4.3% list one or more ethnicity in 2014, 77.4% white, 13.9% African American, 3.6% Hispanic, and 4.2% list one or more ethnicity (Table 3 5 ) The student population was stable for the three years of the study and transfer in and out of the district were minimal. The school was accredited by the Southern Association of Colleges and Schools (SACS) and offered programs in agricultural education, b usiness education, health and leadership education, unified arts programs, and honors courses in addition to the standard high school curriculum. The school offered five Advanced Placement courses. According to College Board AP archived data, an average o f 769 public and private schools in the state of Florida offer ed A dvanced Placement courses in 2012, 2013, and 2014 (2014) Further, the average number of courses offe red in schools nationally was 9.01 subjects per school and 214.24 exams were administered per school (College Board, 2014) According to school

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41 data, 50 students at the school selected for this study completed one or more Advanced Placement course s in 2012, 2013 and 2014 combined and 58 AP exams were taken over 2012 2013 and 201 4 The high school supported a plethora of athletics and e xtra curricular clubs and organizations. According to the Florida Department of Education state assigned district grades 2013 (2013) 2014 (2014) 2015 (2015) The high school received an overall grade of 2013 (2013) academic 2014 (2014) 2014 2015 (2015) school year (Table 3 7 ) District and school grades were calcula ted based on 11 components, including four achievement components, components for learning gains, learning gains of the lowest 25% of students, middle school acceleration, graduation rate, and college and career acceleration. The Florida Department of Educ ation added all 11 components together and divided that total by the total number of available points to determine a percentage. Further, the grade was based on the following percentages: A= 62% or greater of the total points available in the rating system B= 54% to 61% of total points available, C= 41% to 53% of total points available, D= 32% to 40% of total points available, and F= 31% or less of total points available. The selected high school offered five Advance d Pl acement (AP) courses, American h istory, world history, human geography, environmental science, and biology. Students had access to a multitude of d u al e nrollment (DE) courses offered at a nearby accredited state college. Extracurricular activities include d but were not limited to, FFA

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42 The school was located in a small county in the southeastern tier of the United States. The total county population was 15,535 people according to the United States Census Bureau (2010), and had an average household income of $45,645. The county consisted of 64.8% males and 35.2% females. In 2010, 76% of the adult population in the county had a high school diploma According to the 2010 census, county ethnicity percentages w ere divided as follows: 75.3%, w hite, 22.7% African American and 2% Hispanic, Latino, Native American or Asian combined. Primary employers in the county included the Department of Corrections, the school board and numerous industrial transportation companies. Instrumentation Three instruments were utilized during this study for data collec tion: the Career Maturity Inventory (CMI R), the Career Decision Making Self Efficacy Short Form assessment (CDMSE SF) and a demographic information survey (Demographic Questionnaire or DQ see Appendix F), which was developed by the investigator for this study. The following items were included in the demographic questionnaire: a ge, gender, grade, ethnicity extra curricular activities, athletics activities, current career goals, and Advanced Placement (AP) and dual enrollment courses. The Career Maturity Inventory (CMI) instrument developed by Crites (1978) was chosen for this study because the instrument has been well researched and used as an efficient and effective measure of career maturity (Busacca & Taber, 2002) The CMI evaluates the level of which participants are equipped to make practical decisions regarding their own career choices (Crites, 1978)

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43 The original assessment was called the Vocational Development Inventory (VDI), which included 50 true false attitude scale questions and 100 multiple choice competence questions (Crites, 1961) Later versions used in this study measured feelings and disposition of an individual toward making a career decision (Crites, 1978a, 1978b). Researchers have indicated the CMI is reliable and valid. H owever, a few inadequacies in the 1978 version have been found. These have included the length of questionnaire, length of time needed to administer the questionnaire, applicability to all demographics, use of the subscales, limited scoring options, and limited care er counseling (Crites and Savickas, 1996). Crites and Savickas (1996) revised the CMI as a result of the limitations mentioned above The Career Maturity Inventory Revised (CMI R) was intended to be more relevant and functional to counseling and career development programs (Busacca & Taber, 2002). Unlike the VDI, the CMI R has been accepted as appropriate for secondary school students, post secondary school students, the employed, the unemployed, and male and female individuals, as well as minority group s. Crites, as influenced by the work of Super (1955), proposed a structure for career development that included, (1) career choice attitudes, (2) career choice competencies, (3) realism of career choices, and (4) consistency of career choices. To test for career development co gnition the CMI consists of a competency test and an attitude test. The competence test addresses career choice attitudes and career choice competencies. Further, the competence test addresses characteristics of behavior including, (1 ) self appraisal, (2) occupational information, (3) goal selection, (4) planning, and (5) problem solving. The CMI attitude test references feelings and reactions that are subjective, the

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44 disposition the individual has toward making a career choice and ent ering the world of work (Crites, 1978b, p.3). Other differences between the 1978 CMI quest ionnaire and the 1995 CMI R include responses. According to Crites and Savickas (1996), this h as allowed for contrasting responses. For this study the CMI R attitude scale was used exclusively. The attitude scale of the career maturity inventory measured care er maturity (Savickas, 1984). The attitude scale scruti nizes attitudes toward decision mak ing including decisiveness, involvement, independence, orientation, and compromise (Busacca & Taber, 2002). The CMI attitude test is used as a screening tool t o address areas of concern when the clients may not have enough information about themselves or the world in order to make a realistic career decision (Powell & Luzzo, 1998). The CMI R attitude scale consisted of 25 varied statements. Individuals accrued a score from 1 25 for career maturity attitude, based on their responses to each statement. Stude nt responses for each item were compared to the scoring rubric and given a score of zero or one. The values for each question were then summed, and a score for career maturity was determined. A higher CMI R score, above 20, indicated advanced attitudes tow ards career decision s, career planning preparedness and career exploration. Individuals w ho scored in the 16 19 range were considered to be progressing at a normal pace, while individ uals who scored 15 or lower were determined not yet to be ready to make c areer choices and should be the target of career related interventions (Busacca & Taber, 2002). According to Crites (1978) mean scores increase monotonically across grades

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45 with grade 12 students being more career mature than those in grade 11 and so on d own to elementary school. Busacca and Taber (2002) were some of the first research ers to scrutinize the internal construct and criterion validity of the CMI Revised (CMI R). The original 1978 CMI scale was standardized with 1,648 students in grades 6 thro ugh 12 (Hansen, 1974). The K R20 internal consistency coefficients for the attitude scale of the 1978 version averaged .74, and the competence test c oefficients ranged from .63 to .86 mewhat lower for the reliability coefficients for the CMI R, they did find some corroboration evidence related to the CMI R, with higher scores being associated with greater readiness to make occupational decisions. Crites and Savickas (1996) reported that because the items in the 1995 CMI R were drawn from the 1978 CMI, the reliability and validity of the CMI R is equivalent to the previous version. Numerous researchers have concurred that the CMI R has suitable reliability and validity (Busacca & Taber, 2002; Dipeolu, 2007; Powell & Luzzo, 1998). However, investigators have been advised to interpret the results of the CMI R judiciously until future studies and supplementary data have been generated (Crites & Savickas, 1996; Dipeolu, 2007; McDivitt, 2001) According to Crites and Savickas (1996) attitude scales have been written on a fifth or sixth grade reading level CMI R limitations have include d a lack of usability of the questionnaire with individuals who are mentally challenged or have a visual or h earing impairm ent (Crites & Savickas, 1996). Those individuals were exempt from participating in this study.

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46 The Car eer Decision Making Self Efficacy instrument was the second q uestionn aire utilized in this study. This of confidence that he or she can productively complete tasks necessary for making career decisions (Taylor & Betz, 1983). This instrument was based on Crites (1978b) model of career maturity and included a five career choice competency scale that included ( 1) accurate self appraisal, ( 2) gathering occupational information, ( 3) goal selection, ( 4) establishing plans for the future, and ( 5) proble m solving. The Career Decision Making Self Efficacy Scale Short Form (CDMSE SF) was developed as a result of original concerns that the CDSME instrument was too long and required an unwarranted amount of time to administer and complete. The original instrument included 50 items relating to career decision making tasks and behaviors, while the shorter v ersion included only half the number of questions. The instrument developer of the CDMSE SF questionnaire narrowed the questions from 50 to 25 by discarding and eliminating five of the ten items from each of the five CDMSE scales (Betz, Klein, and Taylor, 1996). The researcher chose the CDMSE SF (Betz, Klein, and Taylor, 1996) as a mea ns of measuring career decision making self efficacy as it relates to career tasks. The CDMSE SF instrument allowed the researcher to evalu decision maki ng self efficacy on five scales about self appraisal, knowledge of occupational information, goal selection, future plans, and probl em solving. The Career Decision Making Self Efficacy (CDMSE SF) instrument used in this study included 25 questions with res ponses ranging from 1 (no confidence ) to 5 (complete confidence ). Each question was answered on a five point Likert type scale. Higher scores on the CDMSE SF indicated a higher level of career decision making self efficacy.

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47 The original CDMSE scale was s tandardized using a sample of 346 students 0 .86 to 0 .89 for the subscales and generated an alpha of 0 .97 for the total scale score (Betz, Klein, & Taylor, 1983). Other researchers have described similar levels of reliability (Robbins, 1985; Taylor & Betz, 1983). Luzzo (1993a) reported a test retest reliability coefficient of .83 for the scale, while Bluestein (1989) and Taylor & Betz (1983) presented support for the cons truct, content and criterion validity of the measure. The CDMSE and CDMSE SF have both been found to be reliable. Reliability SF have ranged from .73 for self appraisal to .83 for goal selection for internal reliabil ity and have yielded an alpha score of .94 for the 25 item total score (Betz et al., 1996). Various researchers have also conveyed equivalent levels of internal consistencies; Nilsson et al. (2002) reported a .97 scale score (Betz & Voyten, 1997; Luzzo, 1996). The v alidity of the CDMSE short form (CDMSE SF) was established and showed scale scores that were linked to career indecision (Betz et al., 1996). Further, Betz et al. disclosed that the relationship of the CDMSE SF to career indecision ranged from .19 to .66 for indecision and from .03 to .76 for certainty. Data Collection The data collection period for this study was during the fall of 2012, 2013 and 2014. T he demographic questionnaire (DQ) added in 2013 and 2014, CMI R and CDMSE SF were administered to all students during the normal school day under the

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48 laboratory or classroom setting The demographic questionnaire, along with the two career instruments, took approximately 20 30 minutes to complete. The students completed each of the instruments in the f all of each academic year In order to address the attrition potential in t his study, students were added and deleted to the study each year as students entered and left the school. Students who did not complete the Institutional Review Board (IRB) form were excluded from the data set and were not used in data analysis. However, the majority of students completed the survey and all results were reported to the school for its interpretation and use. For the purpose of overall data collection it is important to report that collection is dependent on the cooperation of the administ ration and faculty of the school to administer and guarantee that students completed the surveys each year. Confidentiality For this study, no data from the Career Maturity Inventory, Career Decision Making Self Efficacy Short Form, and the student demogr aphic survey included recognizable information specific to any student. No identifying student characteristics were used in the analysis or reporting of this study. Further, all IRB consent forms were collected and securely stored. In order to award incen tives, participant identification numbers, assigned by grade level, were drawn from a manila envelope. The gift cards and student identification numbers, one for each grade level were delivered to the school administrator for distribution. This process was repeated in 2012, 2013 and 2014. Analysis of Data The primary dependent variables in this study were career maturity and career decision making self efficacy, as measured by the CMI R and CDMSE SF. The

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49 following student demographic characteristics were antecedent variables gender, ethnicity ethnicity, extracurricular activities, Advanced P lacement courses taken, and dual enrollment courses taken. Data were analyzed using JMP version 13 for Windows In order to determine the relationships and in teractions between selected demographic characteristics and care er maturity and career decision making self efficacy of secondary s tudents in a rural high school a linear mixed model was used Summary Chapter 3 described the methods, procedures, instrumentation, and research design procedures used to exami ne the relationships between career maturity, career decision making self efficacy and selected student demographics of students in a rural high school. Additionally, Chapter 3 addressed threats to validity and reliability. Chapter 4 will present the findings of the study.

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50 Table 3 1 District and High School Student and Faculty Population Year District Student School Student District Faculty School Faculty 2012 2013 2257 619 173 45 2013 2014 2336 628 168 44 2014 2015 2342 632 170 44 Table 3 2. District and School Faculty Year District School 2012 2013 123 4 5 2013 2014 168 4 4 2014 2015 170 44 Table 3 3 District and High School Economically Disadvantaged Year District School 2012 2013 60.8 % 5 1.9 % 2013 2014 62% 51.9 % 2014 2015 62% 51.9% Table 3 4 High School Ethnic Groups Year White African American Hispanic More than One 2012 2013 78.5% 14.5% 3.2% 2.3% 2013 2014 78.5% 13.2% 3.3% 4.3% 2014 2015 77.4% 12.7% 4.1% 4.1% Table 3 5 District Ethnic Groups Year White African American Hispanic More than One 2012 2013 79% 13.2% 3.5% 3.4% 2013 2014 78.5% 13.2% 3.3% 4.3% 2014 2015 77.4% 12.7% 3.6% 4.1% Table 3 6 District and High School Male Female Ratio Year District School 2012 2013 Male Female 52.1% 48.8% 53.2% 46.8% 2013 2014 Male Female 50.7% 49.3 50.7% 49.3% 2014 2015 Male Female 50.9% 49.10% 48.9% 50.1% Table 3 7 District and High School Grades Year District Grade School Grade 2012 2013 A A 2013 2014 B B 2014 2015 A A

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51 CHAPTER 4 RE SULTS The purpose of this study was to examine the relationship between career maturity and selected demographics, and career decision making self efficacy and selected demographic characteristics. Chapter 1 established the need for determining the relationship between career maturity and career decision making self efficacy in secondary students and the relationship of these scores and with various student demographic characteristic s. The purpose of Chapter 2 was to provide a conceptual and theoretical framework through which the study was developed and completed Substructures for this study included constructivism, social cognitive theory, and self efficacy theory A review of the literature included in Chapter 2 examined studies focusing on the processes of career maturity, career decision making, and career development. Chapter 3 detailed the methods and procedures through which the study was conducted. Further C hapter 3 outlined the research design a nd perspective, population and instrumentation, data collection, data analysis and potential threats to validity and reliability. Data were collected using three instrument s and were analyzed using descriptive statistics and regression Chapter 4 will present the findings of the study using the objectives o utlined in Chapter 1 Results concerning the relationships between career maturity and career decision making self efficacy and student demographics are presented in the f ollowing sections

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52 Sample The population for this study included students in grades 9 12 attending a rural county high school in Florida a census of the entire population was assessed Permission ration was granted (Appendix A ) for each of the three years or until the study was complete d The school administrator agreed to distribute and collect all teacher letters (Appendix B ), st udent assent letters (Appendix C ) and completed informed consent for ms (Appendix D) for the duration of the study. Table 4 1 exhibits the number of students who completed the comprehensive survey in comparison to the total student population. A total of 652 students completed the signed consent forms over the cou rse of 2012, 2013 and 2014 Table 4 2 illustrates the number of students who completed the survey in multiple years. Over the course of the longitudinal study 70 stu dents completed the survey over two years and 9 students completed the survey in 2012, 2013 and 20 14 To account for students be ing measured multiple times and the ir correlation responses over time students were treated as a random effect The results of the study were used to examine if there was a relationship between career maturity and selected demographics and career decision making self efficacy and selected d emographics and which variables were more impactful Objective One: Describe the demographic characteristics of selected rural high school students. Objective one was analyzed using descr iptive statistics for the selected demographic variables Students who completed the demographic questionnaire in 2013 and 2014 were mostly female (Table 4 3 ) ; white, non Hispa nic (Table 4 4); and in the 9 th grade (Table 4 5 ). The majority of the students in this sample participated in at

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53 least one ext r acurricular activity (Table 4 6 ) but less than 25% engaged in a thletic or sports activities (Table 4 7 ). Further, the vast majority of students did not participate in dual enrollment courses (T able 4 8 ) or c omplete an Advanced Placement course (Table 4 9 ). In 2013, 0.64% of students were enrolled in dual enrollment courses 0.32% of students were enrolled in Advanced Placement courses and no students were enrolled in both dual enrollment and Advanced Placemen t courses In 2014, 8.70% of students were enrolled in dual enrollment courses 9.02% students were enrolled in A dvanced Placement courses, and 2.69% were enrolled in both Approximately one third of students participated in one or more co c urricular activ ities (Table 4 10 ) The age of students 2012 was not collected in the demographic questionnaire. The mean age for participants i n 2013 was 16 years with a range in age from 14 years to 17 years. In 2014, the mean age was 16 years and a n age range of 14 ye ars to 20 years (Table 4 11 ). Objective Two : Assess the level of career maturity of students in a selected rural high school. Career maturity of the participants was assessed using the Career Maturity Inventory Revised (CMI R) A higher CMI R score, above 20, indicates advanced attitudes towards career decisions, career planning preparedness and career exploration. Individuals who score in the 16 19 range are considered to be progressing at normal pace, while individuals who score 15 or lower are determined to not yet be ready to make career choices and should be the target of career related interventions (Busacca & Taber, 2002) Participants in the 2013 data year showed th e highest mean CMI R score ( M = 16.21 ) with the 2014 cohort s howing the lowest ( M = 1 4.24 ). However, it should be

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54 noted that all three years (2012, 2013, and 2014 ) had relatively similar mean scores of 14.54, 16.21 and 14.24 respectively (Table 4 12 ) and the 2013 sample size was very small. Further investigation of career maturity and grade level reveals a higher mean score for 9 th graders in 2013 of 19.2 0 compared to the mean scores of 10 th 11 th and 12 th graders of 18 .00 16.32 and 12.58 respectively (Table 4 13 ) It should be noted the number of students participating in 2013 was extremely low, with only 5 students in the 9 th gr ade 1 in the 10 th grade 32 students in the 11 th grade and 3 in the 12 th grade (Table 4 13 ). However, in 2 014 participants were more representative of the student population with 9th grade participation at 125 students, 10 th grade participation at 103, and both 11 th grade and 12 th grade participation at 78 (Table 4 13 ). The 2014 mean scores showed 9 th graders with a higher career maturity score of 15.16, followed by 11 th graders with a mean score of 14.43, 10 th graders a 14.24 and 12 th graders a 13.01 (Table 4 13 ). Career Maturity scores appear to be declining as a student progresses through high school, further, career maturity scores appear to be declining among female students as well (Table 4 14). Further, when ma king male versus female comparisons by grade levels for 2013 and 2014 combined, career maturity mean score differences were the greatest between males and females in the 1 0 th grade 15.44 and 13.55 respective ly ( Table 4 14 ) Career maturity scores for males in the remaining grade levels were higher than those of females, but the difference in male and female career scores was small (Table 4 14).

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55 Objective Three : Assess the level of career decision making self efficacy of students in a selected rural high school. The career decision making self efficacy of students in this sample was assessed using the Career Decision Making Self Efficacy Short Form ( CDMSE SF ). Participants in the 2012 stu dy showed the highest mean CDMSE SF score ( M = 94.17 ) with the 2013 cohort showing the lowest (87.90 ). However, it should be noted that all three years (2012, 2013, and 2014) had similar m ean scores of 94.17, 87.90, and 91.76 respectively (Table 4 15 ) wit h a standard deviations of 0.56, 0.56, and 0.43 respectively. Career decision making self efficacy scores range from 1 to 25 no confidence at all, 26 to 50 very little confidence, 51 to 75 moderately confident, 76 to 100 much confidence, and 101 to 125 com plete confidence based on the computation of highest possible scores. Further investigation of career decision making self efficacy and grade level revealed a higher mean score in the 2013 data set for 9 th graders (94.73), compared to 10 th 11 th and 12 th g raders (88.93, 93.88 and 87.85, respectively) (Table 4 16). Again, it should be noted the number of students participating in 2013 was extremely low. The 2014 mean scores showed 12 th grade rs have a higher career decision making self efficacy mean score of 96.38 followed by 10 th gr aders with a mean score of 94.93, 9 th graders with a mean score of 92.65 and 11 th graders with a mean score of 87.22 (Table 4 16 ). Further, when making male versus female comparisons by grade levels for 2013 an d 2014 combined, career decision making mean score differences were the greatest between males and females in the 10 th grade, 88.93 versus 94.73 respe ctively (Table 4 17 ). Career decision making self efficacy mean scores for males in the re maining

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56 grade levels w ere also l ower than those of females except for 9 th graders where males had a higher score than females (Table 4 17). CDMSE scores for 10 th grade males and females were 88.93 and 94.73, respectively. Students in the 11 th grade had mean scores of 87.85 for males and 93.88 for females and 12th grade male and female scores were 94.93 and 96.38, respectively (Table 14 17). Objective Four : Examine levels of career maturity based on demographic characteristics. A number of demographic characteristics were found to be si gnificant ly related to career maturity Student s who had not completed Advanced Placement courses were found to have significantly lower CMI R scores ( p = 0.01). Students who did complete Advanced Placement courses had a least squares mean of 13.57. Those students who did not comp lete an AP cour se had a least squares mean of 15.44. Similarly, students who completed dual enrollment courses also had a signifi cantly higher CMI R score s than those who did not ( p <.01). Those who did complete at least one dual enrollment course had a mean score of 11.59, while those who did not complete a dual enrollment course had a mean score of 15.81. ( Table 4 18 ). Participating in extracurricular activities was also found to be significant in comparing CMI R scores ( p = 0.03 ). Students with zero or one extracurricular activity had higher mean scores than those who participated in two or more activities Student self reported ethnicity also was found to be significant ( p <.01) (Table 4 19 ) Students who self reported as Hispanic or Latino were found to have the highest levels of career maturity followed by Asian or Asian American, Black or African American, American Indian, Other, Hawaiian or Other Pacific Islander, Other and White Non Hispanic or Latino (T able 4 19 ) The variables of age, participation in sports, career interest and gender were not found to be significant.

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57 Objective F ive: Examine levels of career decision making self efficacy based on demographic characteristics. All demographic characteristics were co mpared to participant career decision making self efficacy scores. There were two demographic characteristics with significantly higher CDMSE SF means, participants who took dual enrollment courses and those participants with two or three extracurricular a ctivities (Table 4 20) Participants who took dual enrollment courses were found to have a mean score of (p < .01) (Table 4 20 ) for career decision making self efficacy Participation in extra curricular activities was also found to be significant. Student s who participated in two or three extracurricular activities were found to have significantly higher CDMSE SF mean scores than others (p < .01 ) (Table 4 20 ) No other variables examined in this study were found to be significant. Summary Chapter 4 presented results of the study as dictated by the objectives and hypothesis. Objectives include d: (1 ) to describe the demographic characteristics of selected rural high school students, ( 2 ) to assess the level of career maturity of students in a selected rural high school, (3 ) to assess the level of career decision making self efficacy of students in a selected rural high school, (4 ) to examine levels of career maturity based on demographic characteristics and (5) to examine levels of career decis ion making self efficacy based on demographic characteristics. Chapter 5 will summarize these findings, offer recommendations and conclusions as related to the aforementioned results. Chapter 5 will further detail how these f indings can strengthen career maturity and career decision making self efficacy in high school students.

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58 Table 4 1. Respondents by Year Note. To account for students being measured multiple times and their correlation responses over time, students were treated as a random effect. Table 4 3 Respondents by Gender Participation Year Male Female Total 2013 Number Percentage 24 57.14 18 42.86 42 2014 Number Percentage 172 44.68 213 55.32 385 Note Includes data from 2013 and 2014 Table 4 4. Analysis of Self Reported Ethnicity Description Self Description 2013 2014 Total Asian or Asian American Number Percentage 0 0 4 1.04 4 Black or African American Number Percentage 3 7.14 43 11.23 46 Hawaiian or Pacific Islander Number Percentage 0 0 1 0.26 1 Hispanic or Latino Number Percentage 5 11.90 13 3.39 18 Native American Number Percentage 0 0 11 2.87 11 White, Non Hispanic or Latino Number Percentage 34 80.95 294 76.76 328 Total 42 383 425 Note Includes data from 2013 and 2014 Year n Percent of School Population 2012 Survey 225 36.4 0 2013 Survey 43 6.84 2014 Survey 385 60.9 0 Table 4 2 Respondents With Multiple Survey s Years n One 652 Two 70 Three 9

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59 Table 4 5. Respondent Analysis of Grade Level Year 9 th Grade 10 th Grade 11 th Grade 12 th Grade Total 2013 Number Percentage 5 12.20 1 2.44 32 78.05 3 7.32 41 2014 Number Percentage 125 32.55 103 26.82 78 20.31 78 20.31 384 Total 130 104 110 81 425 Note Includes data from 2013 and 2014 Table 4 6 Respondent Analysis of Extracurricular Activities Year 0 1 2 Total 2013 Number Percentage 12 28.57 22 52.38 8 19.05 42 2014 Number Percentage 116 30.13 171 44.42 98 25.45 385 Note Includes data from 2013 and 2014 Extracurricular activities coded as 0= none; 1= 1 2; 2=3 or more. Table 4 7 Respondent Analysis of Sports Activities Year 0 1 2 Total 2013 Number Percentage 26 61.90 14 33.33 2 4.76 42 2014 Number Percentage 292 75.84 83 21.56 10 2.60 385 Total 318 97 12 427 Note Includes data from 2013 and 2014 Table 4 8. Analysis of Dual Enrollment Courses Year Not Enrolled Enrolled Total 2013 38 90.48 4 9.52 42 2014 330 85.71 55 14.29 385 Total 368 59 427 Note Includes data from 2013 and 2014

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60 Table 4 9. Analysis of Advanced Placement Courses Year Not Enrolled Enrolled Total 2013 40 95.24 2 4.76 42 2014 328 85.19 57 14.81 385 Total 368 59 427 Note Includes data from 2013 and 2014 Table 4 10 Analysis of Co Curricular Activities Year 0 1 2 Total 2013 Number Percentage 28 66.67 14 33.33 0 0.00 42 2014 Number Percentage 223 57.92 150 38.96 12 3.12 385 Total 251 164 12 427 Note Includes data from 2013 and 2014 Co curricular activities coded as 0= none; 1= 1 2; 2=3 or more. Table 4 11 Analysis of Student Age in Years Year Mean Min. Age Max. Age SD 2013 16.14 14 17 .04 2014 16.00 14 20 .02 Note. Information not available for 2012 data collection. Table 4 12. Means and Standard Deviations for Career Maturity Inventory Year Mean SD SE Min Max 2012 14.54 0.57 0.01 0 1 2013 16.21 0.56 0.03 0 1 2014 14.24 0.43 0.01 0 1 Note. CMI R scores above 20, indicates advanced attitudes towards career decisions, scores 16 19 are considered to be progressing at normal pace, scores 15 or lower are determined to not yet be ready to make career choices.

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61 Table 4 13 Means and Standard Deviations for Career Maturity by Grade Level Year n Mean SD SE 2013 9 th Grade 10 th Grade 11 th Grade 12 th Grade 5 1 32 3 19.20 18.00 16.32 12.58 5.40 6.05 5.55 2.42 1.07 3.20 2014 9 th Grade 10 th Grade 11 th Grade 12 th Grade 125 103 78 78 15.16 14.24 14. 45 13.01 4.85 5.33 5.6 3 4.72 0.43 0.53 0.64 0.53 Note. Includes data from 2013 and 2014. CMI R scores above 20, indicates advanced attitudes towards career decisions, scores 1 6 19 are considered to be progressing at normal pace, scores 15 or lower are determined to not yet be ready to make career choices. Table 4 14 Mean s and Standard Deviation s of Career Maturity Scores by Gender Year N Mean SD SE 9 th Grade Male Female 63 67 15.48 15.16 5.06 4.80 0.64 0.59 10 th Grade Male Female 40 64 15.44 13.55 4.87 5.48 0.77 0.69 11 th Grade Male Female 52 58 15.91 14.14 6.12 5.40 0.85 0.71 12 th Grade Male Female 40 41 13.06 12.92 4.62 4.86 0.73 0.76 Note. Includes data from 2013 and 2014 CMI R scores above 20, indicates a dvanced attitudes towards career decisions, scores 16 19 are considered to be progressing at normal pace, scores 15 or lower are determined to not yet be ready to make career choices. Table 4 15. Mean s and Standard Devia tions for Career Decision Making Self Efficacy Year Mean SD SE Min Max 2012 94.17 17.00 3.25 1 5 2013 87.90 18.25 1.00 1 5 2014 91.76 17.25 0.25 1 5 Note Career decision making self efficacy scores range from 1 25 no confidence at all, 26 50 very little confidence, 51 75 moderately confident, 76 100 much confidence, and 101 125 complete confidence, the higher the CDMSE SF score the more career mature. In 2012 respondents totaled 245, 43 in 2013, and 385 in 2014.

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62 Table 4 16 Means and Standard Deviations for Career Decision Making Self Efficacy by Grade Level Year n Mean SD SE 2013 9 th Grade 10 th Grade 11 th Grade 12 th Grade 5 1 32 3 84.98 119 89.27 86.65 19.75 19.50 13.50 2.50 2.25 2.25 2.50 2014 9 th Grade 10 th Grade 11 th Grade 12 th Grade 125 103 78 78 90.50 92.24 91.75 96.01 16.50 18.00 17.50 17.75 1.50 1.75 2.00 2.00 Note Includes data from 2013 and 2014 Career decision making self efficacy scores range from 1 25 no confidence at all, 26 50 very little confidence, 51 75 moderately confident, 76 1 00 much confidence, and 101 125 complete confidence the higher the CDMSE SF score the more career mature. Table 4 17 Means and Standard Deviations for Career Decision Making Self Efficacy by Gra de Level and Male and Female Year n Mean SD SE 9 th Grade Male Female 63 67 92.65 87.22 17.50 15.00 2.25 1.75 10 th Grade Male Female 40 64 88.93 94.73 14.25 19.75 2.25 2.50 11 th Grade Male Female 52 58 87.85 93.88 18.25 17.25 2.50 2.25 12 th Grade Male Female 40 41 94.93 96.38 15.50 19.75 2.25 3.00 Note Includes data from 2013 and 2014 Career decision making self efficacy scores range from 1 25 no confidence at all, 26 50 very little confidence, 51 75 moderately confident, 76 1 00 much confidence, and 101 125 complete confidence the higher the CDMSE SF score the more career mature.

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63 Note. Advanced Placement courses coded as 1= yes and 0= no; Dual Enrollment courses coded as 1= yes and 0= no. Extracurricular activities, co curricular activities, athletics coded as 0= none; 1= 1 2; 2=3 or more. In 2012 respondents totaled 245, 43 in 2013, and 385 in 2014. T able 4 19 Means Scores for Ethnicity and Career Maturity Inventory Self Description LSM SE Asian or Asian American 3.19 0.23 Black or African American 3.05 0.10 Hawaiian or Pacific Islander 2.38 0.44 Hispanic or Latino 3.21 0.12 American Indian 3.04 0.18 White, Non Hispanic or Latino 2.80 0.07 Note. In 2012 respondents totaled 245, 43 in 2013, and 385 in 2014. Tabl e 4 20 Analysis of Difference in Career Decision Making Self Efficacy Construct dF F p AP Course 1 0.24 0.62 Dual Enrollment 1 23.59 < 0 .01 Co Curricular Clubs 2 1.80 0.17 Extracurricular Clubs 3 5.77 < 0 .01 Athletics Participation 2 0.32 0.73 Number of Athletic Activities 2 2.26 0.11 Number of Career Areas of Interest 6 1.79 0.10 Number of Extracurricular Activities 2 4.98 0 .01 Note. Advanced Placement courses coded as 1= yes and 0= no; Dual Enrollment courses coded as 1= yes and 0= no. Extra curricular activities, co curricular activities athletics coded as 0= none ; 1= 1 2 ; 2=3 or more In 2012 respondents totaled 245, 43 in 2013, and 385 in 2014. In 2012 respondents totaled 245, 43 in 2013, and 385 in 2014. Table 4 18. Analysis of Difference in Career Maturity Inventory Construct dF F p AP Course 1 0.01 0.01 Dual Enrollment 1 39.54 < 0 .01 Co Curricular Clubs 5 1.39 0.23 Extracurricular Clubs 7 2.32 0.03 Athletics Participation 5 1.99 0.08 Number of Athletic Activities 5 1.99 0.08 Number of Career Areas of Interest 6 0.99 0.43 Number of Extracurricular Activities 3 3.00 0.03

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64 Tabl e 4 21 Means for Career Maturity and Career Decision Making Self Efficacy Construct Yes SE No SE Career Maturity Advanced Placement Dual Enrollment 13.57 11.59 1.23 1.21 15.44 18.81 1.03 1.05 Career Decision Making Advanced Placement Dual Enrollment 92.85 101.94 10.0 0 3.50 91.61 90.26 2.50 2.50 Note. In 2012 respondents totaled 245, 43 in 2013, and 385 in 2014.

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65 CHAPTER 5 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS The purpos e of this study was to examine the connection between career maturity and career decision making self efficacy and specific student demographics includin g age, gender, ethnicity, extra curricular activities, co cur ricular activities, athletics, Advanced P lacement courses taken, and dual enrollment courses taken Chapter 1 established the need for determining the relat ionship between career maturity and various student demographic characteristics and the relationship between career decision making self efficacy and student demographic characteristics in secondary students. T he study aimed to determine the demographic ch aracteristics that affect career maturity and career decision making self efficacy. The purpose of Chapter 2 was to provide a conceptual and theo retical framework along with previous r esearch relevant to the study. Theories and principles guiding this stud y include d constructivism, social cognitive theory, self efficacy theory and principles of career decision making A review of the literature supporting the framework focused on career guidance, career d evelopment, and career maturity. Chapter 3 detailed t he methods and procedures used to conduct the study, including research design, procedures, treatment, the population and the sample. Chapter 3 discussed data collection, procedures and techniques used to analyze data. Chapter 4 presents the results associated with the objectives as well as the hypotheses. Objectives included, describing the demographic characteristics of selected rural high school students, assessing the level of career maturity of students in a selected rural high school, assessing the level of career decision making self efficacy of students in a select ed rural high school, examining the levels of career maturity based

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66 on demogra phic characteristics and examining levels of career decision making self efficacy based on demographic c haracteristics. Chapter 5 offers a summary of the study and provides conclusions based on findings. In addition, recommendations will be presented for future research. Objectives The following objectives were the guiding factors for this study: 1. Describe the demographic characteristics of selected rural high school students. 2. Assess the level of career maturity of students in a selected rural high school. 3. Assess the level of career decision making self efficacy of st udents in a selected rural high school. 4. Examine levels of career maturity based on demographic characteristics. 5. Examine levels of career decision making self efficacy based on demographic characteristics. Methods This study employed an online combined set of survey s consisting of three componen ts; a demographic survey, the Career Maturity Inventory Revised and the Career Decision Making Self Efficacy Short Form questionnaire. The instruments were found to be valid and reliable (Crites and Savickas, 1996) students in grades 9 12 in a rural h igh school in Florida with a disproportionate number of males versus females (46% males, 54% females). Additionally, more than 75% of the study participants were White, Non Hispanic or Latino. An online survey was administered at the beginning of each school year from 2012 through 2014 under the direction of the high school administrator For 2012, the survey assessed career maturity and career decision making self efficacy. In 2013 and

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67 2014, a de mographic information survey was added, as the original qu estionnaire did not include the collection of this information. The addition of the demographic survey required Internal Review Board approval. T he school used its online school management system student portal Skyward, to provide students with the Qualtrics link to the online survey. Students were permitted to complete the survey during a designated class period on a designated day which varied each year as determined by the school level adminis trator Data were analyzed using JMP version 13 for Windows Summary of Findings Findings are summarized using the stu s presented in previous chapters While the school population was 618 in 2012, 628 in 2013, and 632 in 2014 the number of study participants w as much less due to failure to c omplete and return the necessary institutional review board consent forms. Final student participation numbers were 225 in 2012, 43 in 2013, and 385 in 2014. Objective One Objective one s ought to de scribe the demographic characteristics of selected rural high school students. Study participants at this rural school were reported to be majority female, white non Hispanic and in the 9 th grade. The majority of students par ticipated in at leas t one extra curricular activity and did not take Advanced Placement courses or dual enrollment courses. Objective Two Objective two sought to a ssess the level of career maturity of students in a sel ected rural high school through the Career Maturity Inventory Revised (CMI R) questionnaire. awareness of the need to choose an occupation and the factors which enter into this decision (Crites, 1973) The results

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68 presented in C hapter 4 showed mean scores for 2012, 2013, and 2014 of 14.54, 16.21 and 14.24 respectively Objective Three Ob jective three a ssess ed the level of career decision making self efficacy of students in a selected rural high school. Career decision making self efficacy is an level of his or her own aptitude to complete and perform certain tasks as they relate to careers (Bandura, 1977) M ean scores on the CDMSE SF for 2012, 2013 and 2014 were 94.17, 87.90, and 91.76 respectively. Objective F our The fourth objective was to examine levels of career maturity based on demographic characteristics. Students who did not take Advance d Placement courses had higher career maturity scores than those who took Advanced Placement courses. Correspondingly, those st udents who took dual enrollment courses scored lower on the Career Maturity Inventory (CMI R) than those who did not. Additionally, participation in extracurricular activities was found to be significant. Students who participated in zero to one activity h a d a higher CMI R than those who participated in two or more extracurricular activities. Analysis of ethnicity range d from a least means squared of 3.21 for Hispanic or Latino to a least means square of 2.38 Hawaiian or Pacific Islander The variables age participation in athletics or sports, and gender were not related to CMI score. Objective F ive The fifth objective was to examine levels of career decision making self efficacy based on demographic characteristics. Career Decision Making Self Efficacy assessment examines an

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69 the specific task and behaviors that are importa nt to effective career decision making (Taylor & Betz, 1983). Students who participated in two or three extracurricular activities and dual enrollment were found to have significantly higher CDMSE SF means than others (p < .01). No other variables examined in this study were found to be significant. Conclusion s various interpretations can be drawn. 1. The vast majority of p articipants i n this study were white. The male to female overall ratio for this study was 45.90% male to 54.09% female. 2. A small percentage of participants took Adv anced Placement or dual enrollment p articipated in athletics, extra curricular, or co curricular activities. 3. A small percentage of respondents participate d in athletic activities or sports outside the school. 4. Students who completed at least one Advanced Pl acement course or at least one d u al e nro llment course h ave l ower Career Maturity Inventory scores 5. Student s who participated in zero extracurricular activities h ad a tendency to have higher Career Maturity Inventory scores than those students who participated in two or more extracurricular activities. 6. Student s who c ompleted at least one d ua l e nrollment course had a tendency to have higher Career Decision Making Self E fficacy scores. 7. Students who participated in two or three extracurricular activities tended to have higher Career Decision Making Self E fficacy scores. Discussion and Imp lications This particular rural high school was not representative of high sc hools across the state The high school revealed a balanced male to female ratio yet lacks significant ethnic diversity. Additionally, the high school offers wel l below the average number of A dvanced Placement courses and honors courses as compared to schools in the state While the school offers a plethora of athletic sports and activities, the variability is

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70 nonetheless lesser in comparison to larger urban schoo ls in the area and in the state of Florida Da ta in this study revealed that most high school students reported a CMI SF score of 15 or lower indicating a lack of readiness to make a career decision. This was consistent with previous literature on student s in this high school age group A higher CMI R score, above 20, indicates advanced attitudes towards career decisions, career planning preparedness and career exp loration. Individuals who score in the 16 19 range are considered to be progressing at normal pa ce, while individuals who score 15 or lower are determined to not yet be ready to make career choices and should be the target of career related interventions (Busacca & Taber, 2002) According to Crites (197 8), the means scores increased monotonically across grades, with the grade 12 students being more career mature than those in grade 11 and so on down to elementary school students. In this study, younger students tended to report higher CMI scores than older students and male s tended to have higher scores than females. Although these difference s were not found to be statistically significant, further study on these areas is warranted. As stated in Chapter 2 that a career pattern is set and personality characteristics, and by the opportunities the individu al is given. Further, Super states that c areer development is a lifelong process and self concept is constantly being shaped, wo rk and life satisfaction is dependent upon extent of adequate outlets for abilities, interests, personality, and values (1957). C areer adaptability depends on a person's ability to face, pursue, or accept career change

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71 ( Super, 1957). In order to make succe ssful career choices, individuals should be encouraged to expand their abilities and interests. Completion of dual e nrollment courses was found to lead to higher CDMSE SF scores. student s i n the age range of this study, f urther investigation is needed to understand how completion of these types of courses affect CDMSE SF scores. The same is true with participation in extracurricular activities. The question is raised if participati on in these activities affects the CDMSE SF scores or if students with higher CDMSE scores tended to be drawn to these types of activities. Factors outside the scope of this study, such as the need or perceived need or desire of the student to engage in activities outside of the school setting, such as after school jobs, organizations outside of school, and family commi tments may have been a factor. These outside of school activities most likely include components that would affect career decisio n making. ced Placement and dual e nrollment courses. The finding that dua l enrollment tended to increase CDMSE SF scores and lower CMI R i s worth further investigation. Does this imply that students who complete these courses feel they are able to make a career decision, but were unable or did not have enough information ge campus Further, the finding that participation in extracurricular activities was associated with difference s in CMI and CDMSE SF score suggests a need to seek a deeper understanding abou t how and

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72 why participation in these activities w as found to make a difference. Are there characteristics of the different extracurricular activities that influence these variables? Is extra curricular participation or lack thereof related to socio economi c factors? The convenience sample used in this study lack of demographic data for all three years and low participation rate in 2013 limited the generalizability of the findings. Findings are generalizable to schools that are similar in demographics an d provide practical value to the school. Moreover, t he results are nonetheless useful in guiding a and the development of future studies similar in nature. Providing district and making and career maturity growth. T he original questionnaire, including career maturity and career decision m aking self efficacy, did not include a demographic questionnaire and therefore did not allow for comparisons to be made between 2012 scores and relationships between the career maturity and career decision making self efficacy demographic characteristics I t was not possible to follow prog scores over time. Because of this omission, in 2013, the IRB was revised to allow for collection and analysis of demographic information. Student participation numbers var ied significantly from year to year Responses in 2012 and 2014 accounted for 36.4% of the school population and 60.9% of the school population respectively. In 2013, a dismal 6.84% of the school student population successfully completed the survey. Conve rsations with the school level administrator revealed a possible complication with the school based online student e

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73 learning system. Nonetheless, the results of the 43 students who completed the survey were included in results. Discussion and Implications of Research Methods The study offers findings that indicate that career maturity or career decision making self efficacy s. Students in the rural secondary school displayed similar mean scores for 2012, 2013 and 2014, and s tudent career decision making self efficacy for this rural high school was rela tively consistent from year one to year three. The finding that students en rolled in academically challenging courses and a multitude of activities compared to those who were did not participate in these activities had a lowe r career maturity inventory but higher career decision making self efficacy scores shows the need for futu re study. R ecommendations for Practitioners Based on the findings of this study, the following recommendations were made for stakeholder s in the school system: 1. School stakeholders (a dministrators, counselors, faculty and staff) must be prepared to help st udents increase their perceptions of career development, career matu rity and career decision making through curriculum and instruction as students matriculate through the high school years Professional development should be designed and delivered to schoo l officials on this topic. 2. District stakeholders (superintendent, associate superintendent, curriculu m and instruction dir ec tors, reading coaches, career and technical education directors etc. ) must be prepared to seek out and implement a career developm ent and preparation curriculum to prepare and provide faculty with the instruments and tools necessary to expose students to career growth. 3. D istrict and school stakeholders must invest in faculty professional development to support awareness and implementation of career de velopment strateg ies in secondary schools and their students. A ssessment of career maturity and career decision making should become a regular event throughout a high school c areer in order to p rovide targeted assistance.

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74 4. Requirements for students to select a distinct career pathway early in their high sch ool career should be reconsidered Data in this study questioned a high school ake informed career decisions and choices Educational programs should focus on helping students gain the skills and information needed to make career decisions rather than forcing a n early decision. Recommendations for Future Research This study served as a continuation of previous studies relat ed to career matur ity and career decision making and the relationship of these constructs to gender, ethnicity, students with disabilities, college students, college athletes, and career and technical education students. Future studies should include the f ollowing. 1. This study should be repeated with all surveys in place from the beginning of the study to ensure complete data collection for each year and longitudinal comparisons by student. 2. This study should be conducted in a school where there is a more diverse minority population given the lack of representation in this study. 3. An investigation, using the methods of this study, should be repeated in various size schools and geographical areas to determine if school setting and environment are indeed a f actor in career maturity and career decision making 4. An investigation is suggested to compare co curricular courses and clubs in a school such as business, agriscience, technology and health occupations for example. 5. A n investigation is suggested that inc ludes the demographics included in this study, and the addition of socio economic status, societal influence and family demographic disclosure 6. An investigation is suggested to compare traditional academic programs and accelerated academic programs. 7. C onsideration should be given to a qualitative method s component A qualitative design including, but not limited to, focus group s or interviews, would allow for further exploration into behavior, beliefs, opinions, emotions and relationships about career matur ity and career decision making. 8. Consideration should be given to a study that includes the elements of this study with the addition of a career occupational survey.

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75 Additional follow up studies may perhaps help school level administrators, guidance c ounselors, and classroom teachers gai n a better understanding of the factors that making abilities. The aforementioned recommendations for further research would be successful if district administrator s, schoo l administrators and fa culty take ownership in the design, methods, variables and objectives of future studies Further, a partnership between school administrators, faculty, students and the researcher would better foster an understanding of the c areer development benefits of the study. Summary Chapter 5 presented the results associated with the detailed inspection of the relationships between career maturity and student demographics and career decision making self efficacy and student demographics Chapter 5 also provided recommendations for district and school administrators, stakeholders, faculty and staff. Finally, the chapter offered recommendations for future research to enhance the body of knowledge as it relates to career maturity and career decision making self efficacy. The teachers and administrators, the possibility of future research can impact student career development. vious research, provided recommendations for teachers and administrators, the possibility of future research can impact student career development.

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76 APPENDIX A STUDY INTRODUCTION LETTER

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79 APPENDIX B SCHOOL SUPPORT CORRESPONDENCE

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82 APPENDIX C INFORMED CONSENT FORM

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85 APPENDIX D TEACHER ASSENT LETTER

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88 APPENDIX E STUDENT ASSENT LETTER

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91 APPENDIX F DEMOGRAPHIC QUESTIONNAIRE

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97 APPENDIX G CAREER MATURITY INVENTORY QUESTIONNAIRE

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99 A PPENDIX H CAREER DECISION MAKING SELF EFFICACY SHORT FORM QUESTIONNAIRE

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102 LIST OF REFERENCES Ary, D., Jacobs, L. C., Razavieh, A., & Sorensen, C. (2010). Introduction to research in education Cengage Learning. Bandura, A. (1977). Social learning theories. Englewood Cliffs, New Jersey: Practice Hall B andura, A. (1977). Self efficacy: toward a unifying theory of behavioral change. Psychological review 84 (2), 25 Bandura, A. (1986). The explanatory and predictive scope of self efficacy theory. Journal of social and clinical psychology Bandura, A. (1985). Model of causality in social learning theory. In Cognition and psychotherapy (pp. 81 99). Springer US. Bandura A. (1994). Self efficacy. In V. S. Ramachaudran (Ed.), Encyclopedia of human behavior (Vol. 4, pp. 71 81). New York: Academic Press. (Reprinted in H. Friedman [Ed.], Encyclopedia of mental health San Diego: Academic Press, 1998). Bandura, A. (1997). Sel f efficacy: The exercise of control. Macmillan. 23. Betz, N. E., & Hackett, G. (1981). The relationship of career related self efficacy expectations to perceived career options in college women and men. Journal of counseling psychology 28 (5), 399 410. B etz N. E., Klein, K. L., & Taylor, K. M. (1996). Evaluation of a short form of the career decision making self efficacy scale. Journal of Career Assessment, 4 (1), 47 57. Betz, N. E., & Voyten, K. K. (1997). Efficacy and outcome expectations influence career exploration and decidedness. The Career Development Quarterly, 46 (2), 179 189. Blustein, D. L. (1989). The role of goal instability and career self efficacy in the career exploration process. Journal of Vocational Behavior, 35 (2), 194 203. Brooks, J. G. & Brooks, M. G. (1993). Becoming a constructivist teacher. Search of understanding: the case for constructivist classrooms 101 118. Brown, D. (2002). Career choice and development (4th Ed .). San Francisco, CA: Jossey Bass. Brown, S. D., & Lent, R. W. (Eds.). (2004). Career development and counseling: Putting theory and research to work John Wiley & Sons.

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103 Burkhead, E.J., Cope, C.S., (1984). Career maturity and physically disabled college students. Rehabilitation Counseling Bulletin 27, 14 2 150. Busacca, L. A., & Taber, B. J. (2002). The career maturity inventory revised: A preliminary psychometric investigation. Journal of Career Assessment, 10 (4), 441 455. College Board. (n.d.). AP Data Archived Data 2014 Research. Retrieved May 25, 2017, from https://research.collegeboard.org/programs/ap/data/archived/2014 Cook, E. P. (1991) Annual Review: Practice and research in career counseling and development, 1990. Career Development Quarterly 99 131. Crites, J. (1971). The maturity of vocational attitudes in adolescence. The Career Development Quarterly 20 (1), 30 31 Crites, J. O. (1973). Career Maturity. NCME Measurement in Education 4 (2). C rites, J. O. (1976). A comprehensive model of career develo pment in early adulthood. Journal of Vocational Behavior 9 (1), 105 118. Crites, J. O. (1978 a ). Career maturity inventory: Administration & use manual CTB/McGraw Hill. Crites, J.O. (1978b). Career Maturity Inventory: Theory and research handbook Monterey CA: CTB/McGraw Hill. Crites, J. O., & Savickas, M. L. (1995). Career maturity inventory sourcebook www.Bridges.com. Crites, J. O., & Savickas, M. L. (1996). Revision of the career maturity inventory. Journal of Career Assessment, 4 (2), 131 138. Dictionar y.com. (n.d.). Retrieved July 20 2017, from http://www.dictionary.com/browse/guardian Dictionar y.com. (n.d.). Retrieved July 20 2017, from http://ww w.dictionary.com/browse/parent Dipeolu, A. O. (2007). Career instruments and high school students with learning disabilities support for the utility of three vocational measures. Journal of Career Development, 34 (1), 59 78. Doolittle, P. E., & Camp, W. G. (1999). Constructivism: The career and technical education perspective. Journal of Career and Te chnical Education 16 (1). 22. Florida Department of Education (2013 ). School Publi c Accountability Reports. Retrieved July 22, 2017, from http://www.f ldoe.org.

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104 Florida Department of Education (2014 ). School Public Accountability Reports. Retrieved July 22, 2017, from http://www.f ldoe.org. Florida Department of Education (2015 ). School Public Accountability Reports. Retrieved July 22, 2017, from http://www.f ldoe.org. Fosnot, C. T. (2005). Constructivism revisited: Implications and reflections. The Constructi vist 16 (1), 22 23 Fraenkel, J., & Wallen, N. (2003). How to design and e valuate research in education. McGraw Hill. Fuqua, D. R., & Newman, J. L. (1989). An examination of the relations among career subscales. Journal of Counseling Psychology, 36 (4), 48 7. Getzel, E.E., Stodden, R.A., & Briel, R.W. (2001) Pursuing postsecondary education opportunities for individuals with disabilities. In P. Wehman (Ed.), Transition strategies for young people with disabilities (3 rd ed., 247 259). Baltimore Gies, V. (19 90). Developing a personal career counselling theory: An overview of the theories of Donald Super and David Tiedman. Guidance & Counseling, 6(1), 54 61. Glanz, K., Rimer, B.K. & Lewis, F.M. (2002). Health Behavior and Health Education. Theory, Research and Practice. San Francisco: Wiley & Sons Great Schools Partnership (2014). In S. Abbott (Ed.), The glossary of education reform. Retrieved July 22, 2017 from http://edglossary.org/hidden curriculum Hackett, G. (1981). Survey research methods. The Personnel and Guidance Journal, 59 (9), 599 604. Hackett, G., & Betz, N. E. (1981). A self efficacy approach to the career development of women. Journal of vocational behavior 18 (3), 31 Hansen, J. C. (1974). Test review: J.O. C rites, career maturity inventory. Jo urnal of Counseling Psychology, 21 (2), 168. Ireh, M. (2000). Career development theories and their implications for high school career guidance and counseling. The High School Journal 83, 28 40. Lam, P., Poong, Y. Y., & Moo, S.N. (1995). Work life, career commitment, and job satisfaction as antecedents of career withdrawal cognition among teacher int erns. Journal of Research in Education 28, 32. Luzzo, D. A. (1993). Reliability and validity testing of the career decisi on making self efficacy scale. Measurement and Evaluatio n in Counseling and Development.

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105 Luzzo, D. A. (1993). Value of career decision making self efficacy in predicting career decision making attitudes and skills. Journal of Counseling Psychology 40 (2), 194. Luzzo, D. A. (1995). Gender differences in college students' career maturity and perceived barriers in career development. Journal of Counseling & Development 73 (3), 32. McDivitt, P. (2001). Career maturity inventory (CMI). reer Assessment Instruments, 4 336 342. Mcllvaine, A. (2004, May 2). Human Resources Executive Online. Rand Predicts Future Workforce Trends. Retrieved June 2, 2013, from www.hreonline.com Nilsson, J. E., Schmidt C. K., & Meek, W. D. (2002). Reliability generalization: An examination of the career decision making self efficacy scale. Educational and Psychological Measurement, 62 (4), 647 658. Onivehn, A. O. (1991). The relative influence of sex and self concept o n career maturity of Nigerian adolescents. The Nigerian Journal of Guidance Counseling 4, 45 52. Pajares, F. (2002). Overview of social cognitive theory and of self efficacy. Parraga, I. M. (1990). Determinants of food consumption. Journal of the American Dietetic Association 90 (5), 23 24. Parsons, F. (1909). Choosing a vocation Houghton Mifflin. 5 Patton, W., & Creed, P. A. (2001). Developmental issues in career maturity and career decision status. The Career Development Quarterly, 49(4), 336 351. Patt on, M. Q. (2005). Qualitative research Wiley Online Library. Peruniak, G. S. (2010). A quality of life approach to career development. Toronto: University of Toronto Press. Piaget, J. (1972). Intellectual evolution from adolescence to adulthood. Human development 15 (1), 23 24. Powell, D. F., & Luzzo, D. A. (1998). Evaluating factors associated with the career maturity of high school students. The Career Development Quarterly, 47 (2), 145 158. Redmond, B. F. (2010). Self Efficacy Theory: Do I think that I can succeed in my work. Work Attitudes and Motivation Robbins, S. B. (1985). Validity estimates for the career decision making self efficacy scale. Measurement and Evaluation in Counseling and Development

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106 Salami, S. O. (1997). Birth order, gender, fa mily type and vocational preferences among Journal of Science Teaching and Learning 3 32 42. Savickas, M. L. (1984). Career maturity: The construct and its measurement. Vocational Guidance Quarterly, 32 (4), 222 231. Sch unk, D. H. (2004). Learning theories: An educational perspective. (4th Ed .). Upper Saddle River, NJ: Prentice Hall. Schunk, D. H., & Pajares, F. (2004). Self efficacy in education revisited. Big theories revisited 4 115. Seligman, L. (1980). Assessment in Developmental Career Counseling. Cranston: The Carroll Pres. Super, D. E (1955) Dimensions and Measurement of Vocational Maturity. Teachers College Record, Volume 57, 1955, pp. 151 163 Super, D. E. (1955). The dimensions and measurement of vocational m aturity. Teachers College Record, 57 151 163. Simpson. R.D., Koballa Jr., T., Oliver, J., @ Crawley, III, F. (1994) Research on the affective dimension of science learning. In D Gabel (Ed.), Handbook of research on science teaching and learning, 211 234. New York: MacMilliam Publishing Company Spokane, A. R. (1991). Career intervention Prentice Hall, Inc. 32. S tahkovic, A.D., & Luthans, F. (1996) Social cognitive theory and self efficacy: Going beyond traditional motivational and behavioral approaches. Organizational Dynamics 26, 62 74 Super, D. E., Crites, J. O., Hummel, R. C., Moser, H. P., Overstreet, P. L., & Warnath, C. F. (1957). Vocational development; a framework for research. Super, D. E. (1955). Dimensions and measurement of vocational maturi ty. Teachers College Record Taylor, K. M., & Betz, N. E. (1983). Applications of self efficacy theory to the understanding and treatment of career indecision. Journal of Vocational Behavior, 22 (1), 63 81. Taylor, K. M., & Popma, J. (1990). An examination of the relationships among career decision making self efficacy, career salience, locus of control, and vocational indecision. Journal of vocational behavior 37 (1), 30 31. The United States Census Bureau: Urban and Rural. (2012, September 01). Retrieved July 22, 2017, from https://www.census.gov/geo/reference/urban rural.html

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107 Wanberg, C. R., & Muchinsky, P. M. (1992). A typology of career decision status: Validity extension of the vocational decision status model. Journal of Counseling Psychology 39 (1), 32. Zimmerman, B. J., & Schunk, D. H. (2004). Self regulating intellectual processes and outcomes: A social cognitive perspective. Motivation, emotion, and cognition: Integrative perspectives on intellectual functioning and development 323 349.

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108 BIOGRAPHICAL SKETCH Helen Charlotte Emerson was born in Douglas, Georgia and moved to the Gainesville, Florida area in 1975. Her interest in agriculture began when she was a little girl following in the footsteps of her grandfat her on the farm. Once in middle school she enrolled in agricultural education courses, which continued into high school where she became an active member of the intracurricular National FFA Organization (formerly, Future Farmers of America Organization) After graduating with honors from Santa Fe High School (Alachua, Florida) in 1988 she became one of seven Florida State FFA Officers and traveled the state sharing the opportunities for students in the FFA and agriculture. Additionally, she and her teammates were able to meet with legislators and industry representatives to garner supp ort for agricultural education and the FFA. Upon completion of her year of service to FFA, she completed her Associate of Arts degree at Santa Fe College and transferred to the University of Florida and co e ducati on and c ommunication. In 1994 Charlotte accepted a teaching positi on at Union County High School, where she was the first female agriculture teacher in the schools 50 plus year history. During her 12 year tenu r e she taught courses in animal sciences, horti culture, agriculture foundations, and was responsible for writing the state student performance standards for agricultural communication and later one of the first to teach the curriculum. Additionally, she served on multiple school and district level comm ittees, served as the president of the Florida Association of Agricultur al Educators and a regional secretary for the National Association of Agricultural Educators. She was recognized a s the district Teacher of the Year by Union County in 1997 and the

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109 Agr iscience Teacher of the Year by the Florida FFA Association in 2006 Her true successes came through the efforts of her students. She coached a multitude of FFA Career Development Events (CDE) where her students became state champions on seven different oc University in curriculum and instruction in 2003 and became a Nationally Board Certified Teacher in career and technical education in 2002 In 2006, she became the Director of Recruitment and Alumni Services for the College of Agricultural and Life Sciences (CALS) at the University of Florida In this students in to one of the 23 majors in CALS. Additionally, she was responsible for the day to day operations of the CALS Alumni and Friends group. It was not until August of 2009 that she made the decision to pursue a Ph.D. in agricultural education and communication part time. To date, Charlotte continues to serve CALS in the capacity of Director of Student Development and Recruitment. In this capacity, Charlotte is responsible for student organizations, groups, events and initiatives including the CALS Ambassadors, t he CALS Leadership Institute and the Florida Youth Institute. Charlotte is the academic advise r of Alpha Gamma Rho Fraternity, Alpha Gamma Chapter, the advise r of Collegiate Farm Bureau and a newly inducted member of the University of Florida Blue Key Lead ership Honorary