A QUANTITATIVE STUDY ON CREDIT RECOVERY

A QUANTITATIVE STUDY ON CREDIT RECOVERY CURRICULA FOR IMPROVING HIGH SCHOOL GRADUATION RATES by Barbara A. Vaiana Copyright 2017

A Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Education in Educational Leadership with a Specialization in Educational Technology

University of Phoenix

ABSTRACT A quantitative, ex post facto causal comparative study methods was used to determine the impacts of implementing credit recovery into high school curricula on graduation rates. A thematic literature review described the particular areas of the literature and research purpose. The target sample was Illinois high school districts with and without credit recovery programs implemented and for years 2007-2010 pre-implementation and years 2011-2014 post-implementation. A multivariate analysis of variance (MANOVA) was conducted to test the multiple continuous dependent variables, high school graduation rates and the hypotheses regarding the effect of credit recovery programs on graduation rates. Results indicated there was no statistically significant difference between groups even when controlling for attendance. Recommendations and suggestions for future research were provided.

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DEDICATION I dedicate this dissertation to all the students in which I have encountered over the years who felt graduation attainment was beyond them. I have learned through my own journey and from this dissertation process nothing comes easy without knowledge, hard work, and perseverance. I further dedicate this dissertation to my mother, Theresa Monroe who has taught me that nothing in life is unattainable. You have taught me the importance of reaching my goals and the value of family as it pertains to staying strong especially during the times when I just wanted to give up. You helped me to be strong and realize that life can be meaningless if there were no obstacles to keep us challenging ourselves. I also dedicate this to my sons Anthony, Joey, and Michael as I hope that you now understand that I went through this long journey to pave the way for yours. I pray you follow my lead and find yourselves on a pathway to great success and continuous educational attainment. Finally, I dedicate this dissertation to my family and friends who have always provided me with the encouragement I needed to never give up. I especially want to dedicate this dissertation to Donna Marie Rouette, the best friend anyone can have when obstacles continuously entered my path, you have been there through all the ups and downs this dissertation journey has been for me. You have assured me when I felt defeated that better days are coming and giving up was never an option.

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ACKNOWLEDGEMENTS I would first like to express extreme gratitude to my dissertation chair, Dr. Marilyn Dickey, for providing the professional guidance and assistance needed for completing my dissertation. Her knowledge and support contributed greatly to my success. Dr. Dickey was always available for responding to questions, concerns, and solutions when I needed her. She is a person of great strength, and in which I felt extremely comfortable with sharing my disappointments in the processes for approval. She was always encouraging and resourceful. I cannot express my gratitude for having her on my side. I also would like to thank Dr. Scarbrenia Lockhart and Dr. Steele-Moses for serving on my committee and providing me with support and expertise toward helping me complete my dissertation and finish my doctoral journey. Thank you both for providing feedback and guidance. Thank you to Dr. Steele-Moses for taking on this task when I was struggling to find that third member to join my committee. I deeply appreciate all the hard work and dedication you have provided me as I struggled to complete the dissertation analysis. Finally, I would like to express my appreciation to my family, my sons, and husband who have had to bear through my struggles of long and late hours of writing, missed time together, dissertation obstacles, and disappointments. Thank you for your support and sacrifices to help me through this journey.

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TABLE OF CONTENTS Contents

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List of Tables ..................................................................................................................... ix List of Figures .................................................................................................................... xi Preface............................................................................................................................... xii Chapter 1: Introduction ........................................................................................................1 Background Of The Problem ...................................................................................2 Historical Overview .....................................................................................2 Current Overview.........................................................................................4 Statement Of The Problem.......................................................................................5 Purpose Of The Study ..............................................................................................8 Research Significance ..............................................................................................9 Nature Of The Study ..............................................................................................10 Research Questions ................................................................................................15 Theoretical Framework ..........................................................................................16 Scope, Limitation, And Delimitations ...................................................................22 Definition Of Terms ...............................................................................................23 Summary ................................................................................................................25 Chapter 2: Review Of Literature........................................................................................26 Title Searches, Articles, Research Documentation, And Journals.........................27 Dropouts, Risk Factors, And Concerns..................................................................27 Dropouts .....................................................................................................27 Risk Factors ...............................................................................................30 Economic Concerns ...................................................................................34 vi

Concerns For High School Districts ..........................................................37 Calculation Of High School Graduation Rates ......................................................39 The Status, Event, Cohort And Aggregate Graduation Rate .....................41 Dropout Prevention Strategies ...............................................................................43 Credit Recovery Programs .....................................................................................45 Credit Recovery Impacts On Graduation Rates .........................................48 Conclusion .............................................................................................................54 Chapter Summary ..................................................................................................56 Chapter 3: Methodology ....................................................................................................57 Research Design.....................................................................................................57 Sample Method ......................................................................................................58 Population ..............................................................................................................62 Data Collection ......................................................................................................63 Instrument ..............................................................................................................63 Reliability And Validity.........................................................................................64 Data Analysis .........................................................................................................65 Confidentiality .......................................................................................................67 Summary ................................................................................................................68 Chapter 4: Analysis Results ...............................................................................................69 Survey Of Data ......................................................................................................69 Sample Size................................................................................................69 Data Collection ......................................................................................................70 Data Analysis .........................................................................................................71

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Descriptive Statistics ..................................................................................72 The Repeated Measures Analysis Of Variance .........................................74 Assumptions...............................................................................................74 Multivariate Analysis Of Variance ............................................................79 Assumptions Of MANOVA ......................................................................80 Multivariate Analysis Of Covariance ........................................................84 Chapter Summary ..................................................................................................85 Chapter 5: Discussion ........................................................................................................87 Interpretations of Data Analsis ..............................................................................89 Research Questions And Hypothesis .........................................................90 Conclusions ............................................................................................................91 Implications................................................................................................95 Recommendations For Future Research ................................................................96 Chapter Summary ..................................................................................................99 References ........................................................................................................................101 Footnotes ..........................................................................................................................117 Appendix A: Sample Data Excel Spreadsheet .................................................................118 Author Biography………………………………………………………………………119

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LIST OF TABLES Table 1: Descriptive Statistics for Numeric Values: Number of Schools with/without Credit Recovery, Graduation Percentage, and Attendance Percentage Data…….....73 Table 2: Summary of Categorical Values: Schools with (Yes Credit Recovery) and Schools without (No Credit Recovery) Statistics…………………………………..73 Table 3: Shapiro-Wilk Tests for Univariate Normality and Sphericity of Pre-Years 20072010 Graduation Rates .............................................................................................. 75 Table 4: Means and Standard Deviation for Graduation rates 2007-2010 for Schools with and without Credit Recovery .................................................................................... 75 Table 5: Repeated Measures ANOVA for Schools with and without Credit Recovery PreImplementation Years 2007-2010............................................................................. 76 Table 6: One-Within One-Between ANOVA for Schools with and without Credit Recovery ................................................................................................................... 77 Table 7: Test Within-Subjects Effects Graduation Rates for Years 2007-2014 ............... 78 Table 8: Graduation Rate Means for Schools with and without Credit Recovery, and for all years 2007-2014...………………….……………………………………………79 Table 9: Test of Equality of Covariance Matrices for Schools with and without Credit Recovery Graduation Rates ...................................................................................... 82 Table 10: Laverne’s Test for Assumptions of Homegeneity of Variance of Graduation Rates for Years 2007-2014 ....................................................................................... 82 Table 11: MANOVA Test for Differences in Graduation Rates for Years 2007-2014 and for Schools with and without Credit Recovery ......................................................... 83

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Table 12: Schools with and without Credit Recovery Graduation Rates Controlling for Attendance Years 2007-2014 .................................................................................... 85

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LIST OF FIGURES Figure 1: High School Dropout Rate Calculation Formula.. ............................................ 43 Figure 2: Q-Q Scatter Plot for Mahalanobis Distances .................................................... 74 Figure 3: Within-Subject Variable Means for Graduation Rates Pre-Implementation Years 2007-2010 ....................................................................................................... 76 Figure 4: Scatter Plot of Schools Without Credit Recovery Graduation Rates. ............... 81 Figure 5: Scatter Plot of Schools With Credit Recovery Graduation Rates ..................... 81

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PREFACE The purpose of this dissertation was to introduce and demonstrate a new approach to supporting research on the implementation of credit recovery programs and the impact on high school graduation rates. The methodology uses a multivariate analysis of variance (MANOVA) in which results indicated credit recovery programs do not increase graduation rates even when controlling for attendance. The unique contribution of this work is the exploration of impacts on graduation rates when implementing credit recovery programs into school curricula and in support of students at risk of dropping out. This dissertation should be of interest to educational administrators seeking to implement effective dropout prevention programs and for increasing graduation rates within their districts. It should also be of interest to scholars of decision analysis and operations research, and practitioners of educational policy.

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Chapter 1 Introduction Over the years’ educational attainment in the United States has become an important determinant of personal success and well-being in the labor market, social, and family life, civic participation, mental health, and personal life satisfaction. Those who fail to complete high school face enormous obstacles in achieving employment. The cost of dropping out has increased over time for both the dropouts and society (Sum, Khatiwada, McLaughlin, & Palmer, 2011). According to research by the Alliance for Excellent Education (2011) since the economic recession which began December of 2007, the national unemployment rate has increased from 5% to 9.1%. The 2007 recession impacted unemployment rates for individuals of all education levels. However, high school dropouts faced the most difficulty in obtaining employment. High school dropouts have significantly fewer job prospects, make lower salaries, and are more often unemployed than those who stay in school and graduate (Cardon & Christensen, 1998). Dropouts earn significantly less and contribute fewer tax dollars to the economy (Dianda, 2008). In a research study by the U.S. Bureau of Labor Statistics, and from analysis the of data, results indicated the unemployment rate for high school dropouts in August 2011 and four years after the recession began, was 143% higher compared to the 9.6% for high school graduates. The median income of a person age 18 through 67 who did not complete high school was roughly $25,000 in 2009. According to recent research if the graduation rate increased to 90% for just one cohort of students, our country would see a $7.2 billion increase in annual earnings and $1.1 billion increase in tax revenues (DePaoli, Bridgeland, & Balfanz, 2016). The high costs associated with dropping out make clear the need for programs to help students stay in school.

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In 2010, the Obama administration proposed a 50 million-dollar graduation initiative to promote strategies for increasing high school graduations rates. Despite dropout prevention efforts, many high school students still do not graduate (Mac Iver, A., 2009; Mac Iver, D., 2009). The Dropout Prevention Center/Network lists hundreds of dropout prevention programs in its online database of model programs. Only a few of these have been rigorously evaluated for effectiveness and even fewer were researched to be effective (Picciano, Seaman, Shea, & Swan, 2012). The concerns with high school graduation rates across the United States continuously become the topic of school reform. To address these problems, innovative and efficient dropout prevention programs for high school students were developed to provide alternative opportunities for reaching educational goals. According to a research report by Kennelly and Monrad (2007) for the National High School Center at the American Institute for Research, additional research on dropout prevention programs and effective strategies for increasing graduation rates is necessary. Background of the Problem Historical Overview In 1975 a significant proportion of U.S. workers, approximately 38%, did not complete a high school education (Swanson, 2009). Swanson (2009) further notes, with each successful level of completed schooling, income levels steadily rose for the workforce from 1975 to 2006. Educational attainment contributes to economic growth (Swanson, 2009). According to a report from the state of Virginia’s Department of Education (2005), each year approximately 5% of high school student’s dropout. Over the last decade 347,000 to 544,000 10th grade through 12th grade students left school without successfully completing a high school educational program.

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An Illinois Board of Education 2008 statistical analysis of school data, indicated 30,000 Illinois students dropped out of high school between the 2007-2008 school year. High school dropout rates are problems that directly affect the economic vitality of the United States. Students, who do not complete high school, will not have the sufficient background to find a livable wage job (Swanson, 2009). The Alliance for Excellent Education reviews each year the impact of high school dropout rates on lost wages and taxes. The total lost earning over a lifetime for U.S. high school dropouts was estimated at $325,622,960,000 (STEM Education, 2006). Educational research continues to make it clear that dropping out of high school is a serious issue for students, communities, states, and the nation (Dianda, 2008). Although the goal of education is to improve student skills, an unintentional outcome may be an increase in dropout rates (Martin, Tobin, & Sugai, 2002). Even with new policies and educational strategies in place, the dropout rate continues to rise. While investigating dropout rates and providing analysis of data, Chicago Public School District Official Hitt (2011) found that through a newly implemented Illinois state student tracking method, the graduation rate had actually plummeted to 76%. Dropout prevention is a National concern, and schools are in need of resources to implement new practices associated with improving academic progress, and graduation rate percentages. Prior research on evidence-based components of dropout prevention has shown schools can prevent students from dropping out by using data to identify which students are most at risk, and then provide these students with access to support and alternative programs for personalized relevant instruction (Pyle & Wexler, 2012). For schools across the United States meeting educational learning standards and statewide testing requirements, place an emphasis on improving student learning achievement and retention. The reauthorization of No

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Child Left Behind Act (NCLB) provides opportunities which extend support toward improving student attendance and dropout preventative measures. Current Overview Several U.S. high school districts have begun to take the initiative toward combating the dropout rate problem. Indiana for example, has enacted the Dropout Prevention Act of 2006 which requires high school districts to report the number of ninth graders without enough credits to go on to 10th grade (Kennelly & Monrad, 2011). Technology-based online educational curricula were originally offered as an option for adults wanting to further personal educational status while maintaining family and full time jobs, and have now emerged as an alternative face to face form of instruction (Kerr, 2011). According to Computer-based Instruction (CBI) a 2010 research study of technology-driven educational curricula indicates online and blended learning are becoming integral to several high school reform efforts, especially with regard to improving graduation rates, credit recovery, building connections for students toward future college careers, differentiating instruction, and supporting cost-efficiency for instruction (Kerr, 2011). Educational researchers identified development and application of computer-based instruction technologies as a way for managing differentiated learning strategies and approaches (Watson, S., 2011; Watson, W., 2011). Computer-based instructional curricula provides support for student achievement (Neill & Mathews, 2005). Educational reform researchers stress that using technology and Computer-based Instruction (CBI) is essential for shifting students toward a learner-centered paradigm of instruction (Watson, S., 2011; Watson, W., 2011). The International Association for K-12 Online Learning known as NACOL, assert that it is estimated 82% of U.S. school districts are now offering more online courses as alternative educational solutions and student achievement opportunity. Virtual schools exist in 32 states offering courses

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aligned with school curriculums. According to Kronholz (2011) students in a Florida virtual high school took approximately 220,000 online classes in the 2009-2010 school year. This research study seeks to determine the effectiveness of implementing online credit recovery educational curricula and as an alternative educational solution for increasing high school graduation rates. The study focused on the analysis of publicly available statistical graduation rate data. The statistical archived graduation rates data were for 350 state of Illinois urban and non-urban high school districts that have implemented online credit recovery curricula into their educational program. Relevant literature, publications, and studies were reviewed on the topics of United States and Illinois high school dropout crisis. Inclusion in the review of literature was high school dropout risk factors, economic concerns, concerns for school districts, community impacts, strategies for dropout prevention, calculating high school graduation rates, learning theories, and credit recovery programs. Credit recovery programs are an emerging technology-based dropout prevention strategy used through U.S. high school districts. Credit recovery programs are a dropout prevention strategy implemented in high school curricula toward increasing U.S. high school and Illinois high school graduation rates. Statement of the Problem A strong interest is now emerging in high school reform toward improving the quality of education for students and increasing graduation rates across United States school districts (Barton, 2011). The concerns about costs, quality, and effects on improving graduation rates continue to dominate educational leaders across the U.S and in Illinois as there is no single strategy or approach that will work for every state, district, or school, and only a few dropout prevention programs are effective for increasing graduation rates (Barton, 2011; Mac Iver, D., 2009; Mac Iver, M., 2009; Picciano et al., 2011). The state of Illinois report of high school

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graduation rates in 2009 were at 90.2%. In 2010, the Illinois high school graduation rate fell to 87.8% (Hitt, 2011). “Illinois’ dropout crisis not only threatens public safety, it also damages the Illinois economy” (Christenson, Lee, Schaefer, Peck, & Messner-Zidell, 2008, p. 5). Over the last decade steps have been taken to reform education, understand the dropout problems, and determine approaches to solving these problems (Balfanz, Bridgeland, Moore, & Fox, 2010). A key part of U.S. high school reform is the creation of schools or programs designed to reengage students who have fallen behind in credits and skills toward getting them back on track to earning a high school diploma (Balfanz, Almeida, & Steinberg, 2009). According to recent research on dropout prevention strategies, online credit recovery programs in which students can make up credit toward completion of high school, have emerged into the most popular type of online educational curricula offered at secondary levels in the United States. Many U.S. large urban high schools which historically have the lowest graduation rates of all schools in the country, are now embracing online credit recovery as a basis of their academic offering (Picciano, Seaman, Shea, & Swan, 2012). “Online credit recovery programs are proliferating across the country as well as Illinois” (Picciano et al., 2011, p. 23). A recent study was conducted by the American Institute for Research and the University of Chicago Consortium on School Research in which Heppen and others (2016) investigated whether or not early intervention of credit recovery helps 9th grade at-risk students get back on track for high school graduation. The research study consisted of credit recovery courses for 17 Chicago Public School (CPS) districts in the summer of 2011 and 2012 in which 1,224 first-time freshmen who were lacking credits in mathematics, and not on-track toward high school graduation. The analysis of data consisted of 613 students randomly selected to take an online recovery course and 611 assigned to a traditional face-to-face recovery class in an Algebra one

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course. Approximately 66% of the students in the online course compared to the 76% of students in the face-to-face course successfully recovered credits. However, even with a difference in pass rates for the either the online credit recovery or face-to-face credit recovery course, there were no significant differences in the likelihood of students being on-track toward graduation using online credit recovery programs (Heppen et al., 2016). The general problem, despite the rising presence of online credit recovery programs across U.S. school districts, is that there still exists scant evidence as to whether credit recovery programs increase high school graduation rates (Heppen et al., 2016; Le, 2015; Picciano, Seaman, Shea, & Swan, 2012). Educational researchers Picciano, Seaman, and Day (2011) completed a descriptive analysis of surveys from 210 Illinois high school principals in which at least 62% of the high schools had students enrolled in fully online recovery courses, and 23% had students enrolled in blended or hybrid course to examine their viewpoints, impacts on student achievement, and their concerns about the role online learning was playing in secondary academic programs. The specific problem is that although administrators in Illinois are providing more opportunities for students to enroll in online recovery courses there may not be a significant increase in graduation rates (Picciano et al., 2011). The focus of this non-experimental quantitative ex post facto causal comparative study was on Illinois high schools urban and non-urban districts that have implemented credit recovery programs. The results may contribute to the body of knowledge needed to address the goal for improving Illinois high school graduation rates. This study contributes to the existing knowledge since very little research has been conducted on the efficacy of credit recovery program impacts on graduation rates (Zapeda & Mayers, 2006).

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Purpose of the Study Over the last decade states, districts, and communities have been working to improve graduation rates by evaluating policies, coordinating resources, and investigating what is working nationwide. The state of Illinois is no exception in the existence of dropout problem or the intention to address it effectively (Wraight & Best, 2009). The desire to improve graduation rates for all students pushed educators to use credit recovery programs as the means for giving at-risk students a second chance (Picciano et al., 2011). The benefits, concerns, and costs related to these online learning credit recovery programs requires additional research since they have become the focus of graduation attainment (Picciano, Seaman, Shea, & Swan, 2012). Illinois public high school districts consist of 350 urban and non-urban schools in which half of its schools currently offer online credit recovery programs with students enrolled in one or more courses (Illinois State Board of Education, 2015). The purpose of this quantitative ex post facto research study was to examine the association between implementation of online credit recovery programs, and graduation rates for Illinois public high school districts that have and have not implemented credit recovery programs for increasing district graduation rates while controlling for attendance. The sample data analyzed consisted of pre-existing statistical graduation rate data for 350 Illinois urban and non-urban public high school districts, and for the years between 2007-2010, as compared to school years 2011-2014 after credit recovery programs were implemented. The sample was broken down to 175 public high schools that offer online credit recovery programs, with the mean difference compared to 175 like public high schools that do not. As randomization is not possible in an ex post facto study, the control procedure of comparing homogenous groups based on school attendance will support the equality of groups (Gall, Gall, & Borg, 2010)

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The Illinois high school 2007-2010 graduation rates were compared to the 2011-2014 graduation rates both among and between groups to determine if there was an association between implementing credit recovery and high school graduation rates over time. The sample data analyzed for the 350 Illinois public high school districts was acquired from archived publicly available data sources. The key variables in this study include the effect of credit recovery programs on graduation rates. The independent variables are the credit recovery programs and the dependent variables are the graduation rates. To control for extraneous interaction, which could affect graduation rates, attendance was held constant. The availability of data and clearly identified variables makes this a non-experimental, causal comparative approach and best for this study (Neuman, 2004). Research Significance According to research for the Center for Labor Market Studies (2009) the United States educational system continuously faces a persistent high school dropout crisis. This crisis has been a trend within the U.S. educational systems for many years. Obtaining a high school diploma is critical for achieving employability and avoiding poverty in a global economy. The educational consequences and economical costs of dropping out of high school are substantial to both dropouts and to the rest of society (Sum, Khatiwada, & McLaughlin, 2009). As with other states across the U.S, Illinois dropouts are more likely in highly populated urban districts. Midwest analysis of dropouts indicates that dropout problems do exist in rural and suburban communities, but are not as prevalent (Wraight & Best, 2009). Current advancements in technology have expanded the U.S. economy to one that is knowledge-based globally and digitally driven societally. Innovations in educational technology include online credit recovery programs to provide struggling students an alternative for obtaining missing credits and complete

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a high school education program (Johnston, 2012). The state of Illinois school districts continue to work toward improving high school graduation outcomes through reengagement strategies such as implementing online credit recovery programs (Wraight & Best, 2009). Although using online courses for credit recovery is becoming increasingly common, an analysis of its effectiveness and on graduation rates is limited (Hughes, Zhou, & Petcher, 2015). According to Picciano, Seaman, and Day (2011) assert research is necessary as online credit recovery is a fairly new phenomenon which have become a dominant form of course offering in many high schools across Illinois. Online credit recovery is becoming integral to the solution for retaining at risk students and increasing graduation rates in these schools. The results of Picciano et al., (2011) descriptive analysis relying on modified surveys in the 2010-2011 academic year, indicated a strong concern with credit recovery programs and their significance for improving graduation rates. This research study determined if there was a statistically significant difference in graduation rates after the implementation of online credit recovery programs, when controlling for attendance. Results of this study can contribute information toward helping Illinois high school district administrators make informed decisions regarding the use of online credit recovery programs, and their effectiveness at improving graduation rates for their districts. Nature of the Study As of 2010 the state of Illinois high school districts began to implement online credit recovery programs into their educational curricula to help at-risk students gain credits and graduate. This increase was fueled by pressure from state and federal accountability systems to increase high school graduation rates (Zehr, 2010). Out of 350 Illinois urban and non-urban public high school districts, half have implemented credit recovery programs for addressing at-

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risk students and district graduation rates. Illinois public high school district pre-existing statistical graduation rates were the sample data analyzed through this non-experimental quantitative ex post facto research study. This study analyzed archived statistical public graduation rate data from 350 Illinois urban and non-urban public high school districts with credit recovery programs, and without credit recovery implemented into their high school curricula. Determining an association between graduation rates prior to and post implementation of credit recovery programs using a non-experimental quantitative ex post facto research design supports the effect of an intervention on existing data (Leedy & Ormrod, 2010). The ex post facto design is appropriate when the more powerful experimental method is not possible and desirable in social or educational context where the independent variable is not manipulated or currently exist (Cohen, Manion, & Morrison, 2011). This research study used a causal comparative approach which suggests causality more persuasively than correlational research and determines the degree of effect the independent variable has on the dependent variable (Belli, 2008). The causal comparative approach for this study was to examine pre-existing Illinois high school graduation rate data for schools with credit recovery and those without. Through a causal comparative research approach an examination of pre-existing Illinois public high school graduation rate data for school with and without credit recovery programs was analyzed to determine if there is a statically significant difference in graduation rates after credit recovery programs were implemented. This non-experimental ex post facto causal comparative design relied on pre-existing graduation rate data collected from state of Illinois school district public archival sources. Non-experimental research involves variables that are not manipulated by the

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researcher (Belli, 2008). A true experiment was not feasible in this study since implementation and evolution of the intervention takes time to matriculate, has already occurred, and the outcome measure is readily available through publicly accessible data. This research study does not manipulate the intervention, but rather determines if the intervention has made any statistically significant difference in Illinois high school graduation rates (Heffner, 2014). A quantitative research study collects some type of numerical data to answer a given research question. The non-experimental quantitative design is an accurate description of a situation or phenomenon to describe the size and direction of relationships among variables (Christensen, Johnson, & Turner, 2011). When conducting educational research, it is appropriate to use public domain databases and archived data sets, therefore no participants will need to be recruited and the data is already de-identified (Cohen, Manion, & Morrison, 2011; Christensen, Johnson, & Turner, 2011). The archived graduation rate data for Illinois urban and non-urban public high school districts were obtained through state of Illinois school public Report Card websites. The data for years 20072011 was obtained from the public website Illinois Interactive Report Card (IIRC), and data for years 2012-2014 was obtained from the public website Illinois State Board of Education (ISBE) Report Card. Data was obtained from two separate Illinois State Report Card websites due to the changes in 2011 for reporting school data beginning school year 2012. In 2011, the state of Illinois went to a new Report Card because the previous IIRC became cluttered and not useful to all audiences, however, the site is maintained for acquiring state of Illinois school data or additional educational information and resources (Smith, 2014). The new Illinois state Report Card as with the IIRC includes relevant information, however, it has been redesigned to be more readable and user-friendly. The new Report Card focuses on key areas of reporting student

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outcomes, student progress, and school environment. The new public Report Card website includes a one-page design for use by the broad community and a detailed report publicly available but, for use by district and school leaders. The last inclusion is comprehensive data from longitudinal information for use by educators and researchers (Smith, 2014). According to the Illinois State Board of Education Accountability Workbook, Section E (2010) the IIRC and ISBE Report Card websites provide reliable and valid information for reporting high school graduation rate data and school information regarding locale, demographics, attendance, population, race, and ethnicity percentages. The accountably report Section E. Reliably and Validity of The State Accountability System notes: Section E is designed to evaluate states’ validity and reliability evidence and approaches. Decisions regarding all schools and districts are based on the same valid and reliable information 95% participation, state assessments, academic indicators, and graduation at the high school level. The current assessment system has evidence of the validity and reliability. In addition, extensive simulations were performed to estimate the reliability and power of the proposed AYP system, as based on a 95% confidence interval approach. The State shall provide evidence that the State Report Card is available to the public and is accessible in languages of major populations in the state and districts, to the extent possible. The report cards were modified in 2002 so that the components met the requirements of NCLB. The report cards are distributed every fall, posted on the Illinois State Board of Education Web site, and linked to all school districts (p. 33-35). According to the National Forum on Education Statistics (2011) public data activity demands heightened vigilance in the areas of data quality, security, and privacy. Integrating a data governance program for Illinois State Board of Education creates accountability,

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collaboration, and standardization around the information. The data governance program allows ISBE to coordinate, properly identify data issues, and communicate clear data policies that govern access and data collection (Illinois State Board of Education, 2009; National Forum on Education Statistics, 2011). The state of Illinois school Report Card follows the federal education regulation 34 C.F.R. §200.19(b)(1)(i)(iv) adjusted four-year cohort graduation rate in which Title I federal regulations requires all state and local educational agencies by school years 2011 to report their annual report cards on the new definition called a four-year adjusted cohort graduation rate, and disaggregated by subgroups at the school and state (Illinois State Board of Education, 2016). According to the Illinois Part B State Performance Plan (SPP), Annual Performance Report (APR) for fiscal year 2014, Illinois school district public report card graduation rates are now based on the definition of four-year adjusted cohort graduation rate and first year 9th graders coded by their entrance year then assigned to that year cohort of graduates. If a student repeats a year they are assigned to the coded cohort year in which they entered as a 9th grader. Students who graduate with a GED, certificate, or other credential are not counted in the cohort graduate rate (Illinois State Board of Education, 2016). A multi-variant analysis of variance (MANOVA) was conducted to determine the difference in graduation rates between and among credit recovery programs, while holding attendance constant, using Statistical Package for Social Science (SPSS) software. First the difference among each group was determined, and then the mean difference between groups assessed. Because the attendance can influence the treatment effect, attendance was held constant during the analysis using a MANCOVA. Although there may be a difference in the credit recovery schools alone, to ensure validity schools with and without credit recovery were

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analyzed using repeated measures ANOVA to assure there was no difference between the groups prior to credit recovery implementation. In this way, if there was a difference in the credit recovery cohort and within a homogeneous cohort, then the findings can be generalized that credit recovery programs did in fact cause the difference in graduation rates when holding attendance constant. There has been a growing interest among educational research to identify successful strategies for student reengagement and increasing high school graduation rates. An increasing number of these strategies include the use online credit recovery programs (Belfanz, Bridgeland, Bruce, & Fox, 2012). The results of this quantitative ex post facto research study may contribute to essential information concerning the difference between Illinois high school graduation rates and implementing online credit recovery programs. Educational leaders may choose to use the results of this study to implement credit recovery programs in other school districts. Research Questions Illinois public high school district administrators have taken an initiative toward improving graduation rates. Several school district administrators have begun implementing online credit recovery as student reengagement strategies (Wraight & Best, 2009). Credit recovery is a promising mechanism for reengaging students, but there remains a lack in evidence about the degree to which credit recovery increases high school graduation rates (Heppen et al., 2016). The overall question concerns the difference between implementation of online credit recovery programs and graduation rates in Illinois public high schools. The following questions, null, and alternative hypothesis supported the purpose and significance of the study.

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RQ1 – What is the statistically significant difference between Illinois public high school graduation rates for schools with credit recovery programs and schools without credit recovery programs? RQ2 – What is the statistically significant difference between Illinois public high school graduation rates for schools with credit recovery programs and schools without credit recovery programs when controlling for attendance? Hypothesis H10 = There is no significant difference between Illinois Public high school graduation rates for schools with credit recovery programs and schools without credit recovery programs. H1a = There is a significant difference between Illinois public high school graduation rates for schools with credit recovery programs and schools without credit recovery programs. H20 = There is no significant difference between Illinois Public high school graduation rates for schools with credit recovery programs and schools without credit recovery programs when controlling for attendance. H2a = There is a significant difference between Illinois public high school graduation rates for schools with credit recovery programs and schools without credit recovery programs when controlling for attendance. Theoretical Framework A growing interest among researchers in education is the identification of reengagement programs for addressing the dropout crisis and increasing graduation rates across U.S. high schools. According to Ferdig (2010) published reports documenting dropout crisis have indicated case studies or brief descriptions on effective student retention and dropout prevention programs. The U.S. Department of Education documented research-based programs identifying their level

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of support for these claims. Some have seen success while others have not (Ferdig, 2010). An increasing number of learning theories include the use of technology, and online recovery programs for effective forms of student reengagement programs as they provide for flexibility and individualization (Wraight & Best, 2009). Previous and current research concerning online credit recovery programs indicate the implementation of credit recovery programs as an alternative pathway for students to acquire missing course credits within U.S. high school districts. Power, Roberts, and Patrick (2015) assert there is still limited research on their effectiveness for improving graduation rates. This study builds on a theoretical framework rooted in the constructivist theory of Jean Piaget (1896–1980) and Lev Vygotsky (1896–1934) in which learning needs to be authentic and meet real life experiences, and Knowles (1970) andragogy theory of adult learning in which a person moves from dependence toward increasing self-directedness and independence. According to Jean Piaget’s (1983) stages of development, adulthood begins around the age of twelve and humans are able to engage in abstract thought, and consider the moral implications of their actions. Knowles (1970) found within the principles of andragogy learners become ready to learn when their life situation creates a need to learn. Lawrence and Routten (2009) assert drawing from adult learning theories can support instruction that increases student engagement and perceived relevance. Computer-based learning is an area of application that supports the learning style and needs of potential dropouts. “Self-directed learning environments may be better suited for at riskstudents” (Lawrence & Routten, 2009, p. 20). Adult learning theory poses a theoretical framework which can serve as a foundation for alternative methods of instruction and delivery. However, Lawrence and Routten (2009) describe challenges to the application of adult learning

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theory are school educators cannot reasonably consider applying these theories without first determining whether they are appropriate for students who may not be developmentally adults. Learning styles differ among adolescents and this will decidedly produce academic achievement. Lawrence and Routten (2009) further note educators must be mindful of this and should not assume that borrowing from the adult learning theory can safeguard all students at risk of dropping out. Online learning environments provide much greater freedom of control to the learner. Hang (2002) describes constructivism and andragogy as similar learning theories as they stress ownership of the learning process by learners, experiential learning, and problem solving. The control of learning according to Haung (2002) is placed on the learner while narrowing the gap between the school world and real-life society. Constructivism learning theory according to Gilakjani, Leong, and Ismail (2013) holds that a learner actively constructs his/her own ways of thinking. “Technology is increasingly being touted as an optimal medium for the application of constructivist principles in learning” (Gilakjani et al., p. 57) Theories are online programs substantially aid in the dropout crisis. Ferdig (2010) claims research is ongoing as the issue of dropout, graduation rates, and credit recovery continues to receive attention in the online learning research community. The theories concerning credit recovery programs are that, these intervention strategies put students back on track to graduate and contribute to improved attendance. Chan (2010) claims under the constructivist learning theory, online learning programs allow students more control over the educational process and a hands-on style of learning. There is an increasing focus on experiential learning and participative learning opportunities (Chan, 2010). Chan (2010) research on the application of andragogy, indicated learners need more than a passive transfer of

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knowledge from one person. Learners need to be involved actively in the learning process to construct knowledge. Schell and Janicki (2013) indicate providing students more control of the learning process enables them to discover information for themselves. Self-discovery has been shown to increase the students perceived retention and their ability to incorporate learning into their daily lives. With the increase of online courses there is a debate in the way online learning compares too traditional face-to-face as it relates to the theories of learning. Gilakjani et al. (2013) assert the adult learning theory alone cannot alleviate the dropout crisis. The objectivist model of learning based on Skinner (1968) stimuli-response theory states learning is a change in behavioral disposition shaped by selective reinforcement such as teacher to student interventions (Schell & Janicki, 2013). Koohang, Riley, and Smith (2009) note the constructivist model entrenched in learning theories of Piaget (1972) and Vygotsky (1978), is defined as active construction of new knowledge based on a learners’ prior experience and student learning is active mental work, not passive reception of teaching such as learning through technological innovations. Virtual or online schools are known to provide personalized and individualized instruction. Educational research has demonstrated the importance of individualizing instruction to meet the remedial and advanced needs of students (Ferdig, 2010). There are theoretical and hypothetical reasons why online learning may positively impact student retention (Ferdig, 2010). From a constructivist learning theory, students are thought to learn more effectively when they are forced to discover knowledge for themselves rather than when they are instructed. Another aspect of constructivist learning is it lays the foundation for the concept of lifelong learning and prepares the student for real world experiences (Schell & Janicki, 2013). It has been argued that

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the constructivist model of learning is more appropriate where students already have an existing level of education and technological knowledge. Online learning requires the student to have experience with hypothesizing and predicting, mentally manipulating objects, posing questions, researching answers and investigating to be successful (Schell & Janicki, 2013). Research studies suggest that online learning can impact retention and dropout recovery. However, Ferdig (2010) claims theories in research also suggest that online credit recovery simply replicates existing face-to-face environments which replicates the negative behavioral, affective, and cognitive outcomes of at-risk students. Lehr, Johnson, Bremer, Casio, and Thompson (2004) indicate many theories have contributed to the development of dropout prevention and interventions toward promoting school completion. Lehr et al. (2004) further note the theory that has been most influential supports the notion that school engagement is integral to school completion. This theory in dropout prevention suggest students need to be active participants in school. Participation includes behavioral indicators such as attending school, being prepared, and being involved (Lehr et al., 2004). According to Merriam (2004) new theories of learning emerge from cognitive psychology known as situated cognition. This theory suggest learning is what is constructed by the interaction of people and resonates with what is known about adult learning theories. Critical perspectives involve the contextual perspectives. For example, Merriam (2004) states one might ask why a certificate of completion or GED is not as valued as a traditional high school diploma. Questions draw from philosophical and theoretical knowledge in critical theory. The newest addition to andragogy adult learning theories are the roles of emotions in learning. Where critical perspectives view the context by asking how race and gender shape learning, emotions play a role in the connections a student has within learning environments (Merriam, 2004).

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Andragogy has been challenged on whether its assumptions are true of adult learners. Lehr et al. (2004) assert some adult learners are not particularly self-directed and relay heavily on teachers for structure and guidance. Lehr et al. (2004) found current theories on reengaging students and developing successful dropout prevention strategies involve developing caring relationships with students, teachers, and administrators. Students need a shared sense of purpose with a common goal as defined in educational philosophy. Positive characteristics for an alternative learning program is where student progress is measured in terms of self-improvement rather than grades and recognition (Bland, Church, Neill, & Terry, 2008). Although constructivist and andrology adult learning theory according to Usher (2012) supports individualized alternative learning programs to reengage students, some students are more academically successful when they are provided individual counseling, behavior, or stress management opportunities in addition to their traditional learning environments. Knowles, Holton, and Swanson (2015) assert that andrology theory presents a challenge to static concepts of intelligence and standardized limitation of conventional education. There are a great majority of adult learners not interested in learning or motivated in the direction of continuing their education. Knowles et al. (2015) claim if learners possess these incentives they would take advantage of the numerous educational opportunities. This research study determines whether any difference exists between online credit recovery programs and Illinois high school graduation rates. However, to be successful in online learning environments such as credit recovery programs and toward graduation attainment, theories of constructivism and andragogy claim students also need to be self-directed, motivated, and actively engaged in their own learning.

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Scope, Limitation, and Delimitations The scope of this study was to examine the association between implementation of online credit recovery programs and graduation rates for Illinois urban and non-urban public high schools and when controlling for attendance. To determine if an association existed high school graduation rate data was examined for high schools that have implemented credit recovery programs and high schools that have not. Limitations included were the study results could not be generalized to other states, because data were from high school districts in the state of Illinois only. The study was limited to the collection of graduation and attendance data which had been previously collected, calculated, and archived. The archived graduation rate and attendance data were obtained from state of Illinois Interactive Report Card (IIRC) and Illinois Board of Education (ISBE) Report Card public websites. While beyond the scope of this research, there is no way to assure that the archived graduation rate and attendance data presented on the Illinois public websites was accurate. However, this limitation become a major assumption in the study. In addition, an ex post facto research examines events after the fact and cannot determine a cause and effect between the variables. Limitations in the study are matters and occurrences that may arise and are out of a researchers control (Simon & Goes, 2013). The study was delimited to 350 Illinois urban and non-urban public high school districts in which online credit recovery programs were implemented or not. Four consecutive years 2007-2010 of archived pre-existing high school graduation rate data and attendance percentages pre-implementation of credit recovery programs, and four consecutive years 2011-2014 post credit recovery programs was a final delimitation used in the study.

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Definition of Terms For this study, definition of the terms adapted from literature reviews has been included, and terminology specific to the research addressed. These definitions of terms are as follows: Andragogy: The contrasted concept by Malcom Knowles (1968) for the art and science of helping adults learn. Knowles asserted that adult learners were different from children and require a different learning model. The assumptions of andragogy were adults direct their learning, have accumulated life experiences, needs related to social roles, need immediate application of knowledge and are motived internally rather than by external factors (Lawrence & Routten, 2009). Constructivism: Teaching and learning strategies, tools, and practice theories in which Jean Piaget (1973) and Lev Vygotsky (1978) proposed that social interactions play a crucial role to learning, and learners construct new knowledge based on prior knowledge that are relevant and meaningful. Vygotsky placed more emphasis on the importance of social-cultural context in learning and how this impacts what is learned. Since Vygotsky emphasized social context of learning his theory was called social constructivism. Piaget’s theory placed an emphasis on cognitive and individual constructivism (Haung, 2002). Credit Recovery Program: An educational program which provides an opportunity for a student to retake a course in which the student was not academically successful in earning credit toward graduates. The federal government supports credit recovery programs through flexibly for the use of funds under the Title I of the Elementary and Secondary Education Act (ESEA) and the Individual with Disabilities Education Act (IDEA) (Powell, Roberts, & Patrick, 2015).

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Drop Out: In Illinois, dropout is any child enrolled in grade 1 to 12 who have been removed from the district enrollment for any reason other than his or her death, extended illness, or completed program of studies as in (105 ILCS 5/26-2a; Illinois General Assembly, n.d.-a.). Illinois law also mandates compulsory attendance law (105 ILCS 5/26-1; Illinois General Assembly, n.d.-b) that all student between 7 and 17 attend school. The minimum age at which a student may dropout was raised from 16 to 17 in 2005 (Wraight & Best, 2009). Dropout Prevention: Efforts to increase graduation rates and ensure access to educational opportunities for all students. State efforts to develop replicating new school models that reengage at-risk students. Developments in comprehensive and strategic approaches to reducing dropout rates and improving graduation rates (Almieda, Steinberg, Santos, & Le, 2009). Dropout Rates: The number of students who dropped out during the school term divided by the total expected to complete the school term in the specific school district (National Center for Educational Statistics, 2015). Economic Impact: Each year dropouts cost the United States more than $300 billion in lost wages and increased public-sector expenses. Low graduation rates serve as a barrier to state economic development. As dropouts search for work, they often turn to state services such as unemployment, welfare and health care assistance. Each individual who does not complete high school impacts the financial aspect of our economy (Princiotta & Reyna, 2009). Graduation Rate: The graduate rate is calculated by identifying a student who graduated with a regular high school diploma the reported year and with the group of students who started at the beginning of 9th grade. Illinois graduation rates are reported yearly to state and federal agencies. Each year the fall enrollment student count is reported as the starting 4-year cohort for that year (Illinois Report Card, 2015).

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NCLB: The No Child Left Behind Act of 2001 which was the reauthorization of the Elementary and Secondary Education ACT of 1965. The law was passed by Congress and signed into law by President George W. Bush on January 8, 2002 and significantly increased the federal role in holding schools responsible for the academic progress of all students (Klein, 2015). Online Educational Curricula: State and local virtual school lessons or academic content created by legislation or by state-level agencies which provide a variety of supplemental technology-based online learning opportunities for students across the country (Powell, Roberts, & Patrick, 2015). Summary The goal of this research is to assess whether a difference exists between online credit recovery programs and high school graduation rates through a non-experimental quantitative ex post facto causal comparative research approach. A quantitative research approach is an analysis of statistical data to substantiate or reject the research hypothesis (Christensen, Johnson, & Turner, 2011). The ex-post facto design was an effective research method for the analysis of the sample data, which consisted of graduation rates pre and post implementation credit recovery programs in Illinois public high schools. The results of this study may further assist in an understanding of the association between credit recovery programs and graduation rates. The review of literature with a theoretical framework for the research study and results from prior research is discussed in Chapter 2.

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Chapter 2 Review of Literature The condition of U.S. high school graduation rates and the growing number of dropouts is a central concern among both educators and policymakers. More importantly is how these conditions have impacted communities in the form of reduced economic vitality (Swanson, 2009). Heckman and LaFontaine (2008), conducted an analysis of school performance measures and noted the decline in high school graduation rates were an interest in its own right, as it is a measure of U.S. high school performance. To address the dropout crisis and improve graduation rates, high schools across the United States and especially in highly populated urban school districts, have begun to implement credit recovery programs. Mileaf, Paul, Rukobo, and Zyko, (2012) claim data which has been reported by school district administrators that have implemented recovery programs suggest credit recovery has a positive effect on earning credits toward graduation. Some school data indicate after implementation of credit recovery, graduation rates rose significantly. However, there is little research on the association of graduation rates and implementing credit recovery programs (Mileaf et al., 2012). Online credit recovery programs are self-directed learning environments rooted in the adult learning theory and the constructivist theory of transformational approach to learning, in which knowledge evolves through social negotiating and evaluation of the individual understandings. However, if students are not self-directed or motivated to pass a course in the classroom, they cannot be expected to be self-directed or motivated in an online course (Lawrence & Routten, 2009). To fully understand the relevance of this research study, the review of literature included a thematic review on dropouts, dropout risk factors, economic and high school district concerns, calculation of high school graduation rates, dropout prevention, credit

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recovery programs, and theories contributing to the associations between implementing online learning programs and high school graduation rates. Scant literature appeared in the review concerning the theories as they apply to credit recovery for increasing graduation rates, However, online learning has been noted as a form of the adult learning theory that can contribute to the participation outcomes of at-risk students in online credit recovery programs. Title Searches, Articles, Research Documentation, and Journals Title search terms used for this research study included a collection of historical and current research reviews of (a) dropouts, risk factors, and concerns, (b) calculating dropout and graduation rates, (c) dropout prevention strategies, (d) credit recovery programs, and (e) credit recovery impacts on graduation rates. The literature review included 200 sources from peerreviewed articles, books, dissertations, government statistics, and Internet sources. The use of online databases contributed to the research including the University of Phoenix Library, EBSCOhost, ProQuest, SAGE Journals, Google Scholar and Education Source. Sources reviewed included journals, articles, and research briefs, and publications. The literature review has been organized thematically, in which resources are organized in themes, theoretical concepts, and topics relevant to the research study. The study includes an analysis of the literature to identify weakness and strengths in investigation for addressing the research question (Randolph, 2009). Dropouts, Risk Factors, and Concerns Dropouts Research on dropouts according to Bridgeland, Dilulio, and Morison (2006) indicates our country has been trying to address the dropout crisis for some time, and the general public is almost entirely unaware of the severity of the problem due to inaccurate data. Ferdig (2010)

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notes the problem of dropouts is so prevalent it is called a crisis and tragic cycle. The Center on Reinventing Public Education (CRPE) completed research on existing pathways for supporting students at risk of dropping out in which researchers Marsh and Hill (2010) note American high schools lose about 7,000 dropouts per day and nearly 1.2 million per year. United States high school graduation rates have decreased for at least two decades, and with U.S. largest urban districts at the top of the dropout crisis. National graduation rates hover around 70% with the larger public urban districts well below that average. Chapman, Laird, and KewalRamani (2011) trend analysis using a t-statistics with a significance at p < .05 on dropouts indicated a pattern of decline in dropouts prior to 1980 with a brief upward trend form 1991 through 1995. Then another decline through 2009. Bland, Church, Neill, and Terry (2008) assert the cause of student dropout is an indication of the failure of public education today and it is important to examine why some students fail in traditional school settings. Jordan and Kostandini (2012) assert family characteristics are the main determinant of a high school completion rate. Previous research and theories on dropouts assert that students dropout more in large urban districts than surrounding suburban school districts (Swanson, 2009; Jordan & Kostandini, 2012). Researchers consistently look at how school size affects academic achievement and completion rates. Jordan and Kostandini (2012) assert assumptions are smaller schools have consistently higher completion rates. In a graduating class of 667 students for example, 6.4% fail to graduate compared to the 2,091 or 12.1% of students in larger schools who fail to graduate. Fitzgerald et al. (2013) completed a quantitative causal comparative research study to determine if there was a significance in dropouts between large, midsized, and small district schools. The additional variables were the completion rates between 2001-2008 among whites, blacks, and Hispanic students. The results indicated there were no significant differences among the three

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ethnic groups for both small and midsized schools. However, in the larger schools compared to whites, black and Hispanics were at a significant disadvantage with respect to graduation rates Swanson (2009) analysis of 50 large city high schools in the United States indicated graduation rates for public urban districts are much higher than suburban districts as urban districts tend to have larger proportions of disadvantaged students and fewer resources than suburban districts. According to Heckman and LaFontaine (2010) found through research on dropout and graduation rates, found there is a large difference between urban and rural graduation rates with urban district graduation rates at about 60.9%, and rural districts slightly higher at 74%. Jordan and Kostandini (2012) conducted research using an estimate probability regression for urban and rural districts to determine if students are more likely to dropout in urban districts or rural districts. The analysis results indicated that dropout rates were very similar across both urban and rural districts. Sum, Khatiwadam, McLaughlin, and Palma (2011) argue the share of youths in the largest urban city of Chicago without a regular high school diploma was 15% higher than its rural areas at 9.7% and the state of Illinois at 11.5%. A 2009 and 2010 analysis of American Community Surveys indicated nearly 42,000 or 15% of 19 to 24 years old youths in the city of Chicago alone dropout. According to Balfanz, Almeidia, and Steinberg (2009) report from a collection of statistical data on U.S. high school graduation rates, Illinois, New York, Pennsylvania, and Tennessee had the lowest graduation rates within their highly populated urban areas of Chicago, New York City, Philadelphia, and Memphis. Schools urban districts routinely graduate less than 65% of their students (Balfanz, et al., 2009). Chappell (2011) conducted a research study on dropouts by gender and race in which a chi-square test was completed to analyze the research data. Results of the study indicated there

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was a statistically significance in the proportion of male dropouts to females. With a hypothesized value of 50% for each group, the analysis indicates male students represented 57.7% of the dropout sample compared to the 42.3% female dropout sample. Male youth are two times more likely to be dropouts than females according to Sum, Khatwada, McLaughlin, and Palma (2011) and at 19.2% versus the 10.2% of female dropouts. Among the major raceethnicity groups, dropouts vary with male non-Hispanics and white youth dropouts at 24%, blacks at 27%, and Hispanics at the highest rate of 30% (Sum et al., 2011). Lawrence and Routten (2009) indicate through research on dropout’s school administrators are now addressing the dropout issues through reform initiatives that explore the integration of adult learning theories into instruction. Lawrence and Routten (2009) argue the application of andragogy adult learning theory alone cannot alleviate the dropout crisis. However, this theory can support instructional methods that increases student engagement and perceived relevance. Risk Factors Ingrum (2006) indicates risk factors for high school dropouts are significantly correlated between learning disabilities and low socioeconomic status. Allensworth and Easton (2007) assert that drop out decisions and risk factors are affected by multiple contextual factors interacting in cumulative ways over a students’ life course. Allensworth and Easton (2007) conducted a longitudinal research study using quantitative data from all Chicago public schools built on earlier research from 2005 by the Consortium on Chicago School Research. The research study included 20,803 public school students in the 9th grade in the fall of 2000 and graduated or dropped out in 2005, and survey data for 24,894 ninth-graders in 2004-2005 concerning school characteristics such as attendance rates, failure rates, and grades. A strong correlation between course failure and GPA supported the fact that at least 75% of students who

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failed a course in 9th grade, and with a GPA below 2.0 are at-risk for dropping out compared to the 62% with a GPA of 1.5, 74% with a GPA 2.0 or 86% with GPA of 2.5 that would graduate. Attendance was also a contributing factor for students who drop out (Allensworth & Easton, 2007). Drop out risk factors according to Ferdig (2010) generally fall in one or more categories such as individual or instructional. Individual factors are a student’s life that impact decisions or abilities to stay in school. Instructional factors are related to family, school or communities that influence a student’s chance of dropping out. Researchers D. Mac Iver (2009) and M. Mac Iver (2009) assert a theoretical construct of student disengagement in school contributes to the primary factor for dropping out. An additional factor for dropping out is a process of the ABC’s of disengagement which manifests through increased absenteeism, behavior problems, and course failure. According to Lawrence and Routten (2009) a constructivist assumption in relation to dropouts is for adult learning to take place, the learning process and context must be relevant and meaningful to the student. “Almost half of high school dropouts leave because they find school uninteresting and irrelevant to their lives” (Lawrence & Routten, 2009, p. 14). According to Ferdig (2010) research studies confirm additional dropout risk factors are standardized test scores, transition from middle school to high school, and course failure lead to a disconnect for many high school students and as early as 9th grade. Researchers Doll, Eslami, and Walters (2013) analyzed data from seven nationally representative dropout research studies. An analysis of these studies was completed using a theoretical framework for the determining factors. The results of all seven research studies conclude that since 1955 there has been an increasing prevalence of factors in why students drop out. The bar for students has been lifted higher with the standards movement in education and culminating in the No Child left Behind

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Act Legislation of 2001 which mandates standardized testing (Doll, Eslami, & Walters, 2009). Picciano, Seaman, and Day (2011) research on dropout risk factors indicates students who cannot meet demands of higher academic requirements, tests, or coursework make up a significant portion of the high school student population that subsequently drop out. Researchers Nolan, Cole, Wroughton, Clayton-Code, and Riffe (2013) argue school mobility and truancy are risk factors and, ethnicity does not factor into the risks in either large, midsized or small district schools. Several research studies according to Jordan, Kostandini, and Myjerezi (2012) indicate a major determinant of dropping out are related too personal and family characteristics, industry structure likelihood of getting a job, school discipline, and the community. Past and current studies indicate dropouts arise from an accumulation of various risk factors that begin in early education and peaks in high school (Hammond, Linton, Smink, & Drew, 2007; American Psychological Association, 2012). Hauser and Koenig (2011) completed a research study using variables that minimized the chance of incorrectly classifying students at risk of dropping out or students who had a 75% chance or higher of not graduating on time for 6th graders. The variables included failing math, English, and an attendance of less than 80%. The individual indicators were designed to identify students with a 25% less chance of graduating. The research verified the need for early warning systems to capture student grades and attendance for supporting students at-risk (Hauser & Koenig, 2011). Bowers (2010) claims many school districts increase dropout risk factors by retaining students an additional year who are missing credits to advance a level. To test this theory, Bowers (2010) completed research to effectively study time dependent effects on students at-risk of dropping out. The effects of multiple variables on student probability of dropout were estimated employing analysis using logistic regression with person-period data set. Time-

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invariant dichotomous variables included were gender, ethnicity, and school district. Results confirmed repeating a grade level does not promote increased achievement and graduation. A key area of recent interest on dropout risk factors or student’s decision to drop out according to Ferdig (2010) is they fail to see the end goal of their work. Ferdig (2010) further asserts this can be related too recent neurological and psychological research that suggests adolescents lack abstract reasoning skills and are predisposed to risk behaviors. Springfield Illinois like many large urban districts across the United States has a highly mobile population. In 2010 23.2% of students entered or left school. One third of students who dropped out of school during 2008-2009 school year had changed schools in the past, demonstrating that high levels of mobility can be used as an indicator of dropout risk factors (Cash, Dawicki, & Sevick, 2011). Ferdig (2010) argues high dropout rates are correlated with students who have limited English proficiency. Cash et al. (2011) assert educational attainment has a major influence on household income and unemployed family members. Springfield for example, has 24.5% of city residents who failed to earn a high school diploma compared to the 11.6% statewide. In a research study on determinants of high school dropouts Cabus and DeWitte (2013) confirmed through results of data analysis that the probability of obtaining a diploma increases with study hours. Cabus and DeWitte (2013) further assert extrinsic motivation had a larger influence on the probability of dropping out than intrinsic motivations. Attendance increased the probability of earning a diploma. The theories on human capital accumulation, economic development, and technology change highly influences a student’s chance of dropping out. Constraints, motivation, job market aspirations, and current school policy measures also contributed to the determinants of a student’s decision to dropout (Cabus & DeWitte, 2013).

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The objective of research on dropout risk factors is to identify quality evidence-based programs already proven to address why students drop out of high school. Identification of risk factors is key to designing an effective intervention plan and program (Hammond, Linton, Smink, & Drew, 2007; Christenson, Reschly, & Wylie, 2012). “There is no single risk factor that can be used to accurately predict who is at risk of dropping out. Dropping out is often described as a process, not an event and the decisions to dropping out is determined through factors that build or compound over time” (Hammond et al., 2007, p.1). Latif and Hummayun (2015) found on the basis of a comparative analysis of different reasons for student dropouts, it was clear dropping out is not due to a single reason. However, limitations for their study were time and budget constraints which limited to consider causes of student dropouts in rural areas. Future research is recommended on conducting large scales to determine more specific reasons and risks for student dropouts to increase their graduation attainment. Economic Concerns States are facing the hardest fiscal environment of the past 25 years. According to Princiotta and Reyna (2009) even with funds from the American Recovery and Reinvestment Act, governors face diminishing state revenues and deficits totaling over $210 billion. High school dropouts economically impact these conditions. Princiotta and Rena (2009) argue dropouts cost public sectors approximately $209,000 over their lifetime. In the aggregate dropouts cost the United States more than $300 billion per year in financial assistance and decreased tax revenue. In the state of California for example, Belfield and Levin (2009) assert the negative social and economic losses from low educational attainment of its citizens are substantial. Deye (2011) notes our nation needs students today to fill the jobs of tomorrow which will require more skills and education than in the past. States and communities bear the brunt of

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students dropping out. Deye (2011) further asserts states are less attractive to business investments when its workforce is poorly educated and are challenged by increased public assistance for the unemployed. According to Princiotta and Reyna (2009) research of economic trends and educational attainment, years ago dropouts could easily find work but this is no longer the case as the demand on high skilled workers increase and low-skilled jobs being automated or moved overseas. The mean life time earnings of high school dropouts in the state of Illinois are typically at $13,400 versus the $21,700 for high school graduates (Sum, Khatiwadam McLaughlin, & Palma, 2011). Over an entire lifetime, the mean earnings for native born high school dropouts both male and female in Illinois will be approximately $595,000, which is well below the approximate $1,066,000 which is the mean earnings of high school graduates over a lifetime (Sum et al., 2011). A compendium report concerning high school completion rates for the United States between 1972 and 2009 according to Joseph (2014) indicates individuals who drop out of high school compared to those who complete a high school education on average costs the economy approximately $240,000 over their lifetime. Chapman, Laird, and KewalRamani (2011) assert dropouts impact lower tax contribution, higher reliance on Medicaid and Medicare, higher rates of criminal activity, and higher reliance on welfare. Burrus and Roberts (2012) ascertain dropouts represent billions of dollars annually in lost revenue for the U.S. economy. To keep students from dropping out, we need to effective intervention programs. Joseph (2014) through research on dropouts and the economic factors, confirmed the unemployment rate for dropouts is 9.1% higher than those with a diploma and college degree. Jordan, Kostandini, and Mykerezi (2012) argue most dropouts from large urban school districts already come from low-income families and become a statistic of society. In

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addition to becoming a statistic of low-income society or unemployed, students who do not complete a high school education are more likely to end up in prison, on public assistance, and even die at a younger age. Jordan et al. (2012) argue chances are most dropouts will be living in poverty and unemployed. Burrus and Roberts (2012) assert dropouts living in poverty and unemployed are highly dependent on government assistance programs. Sum et al. (2011) found through research the labor market problems of dropouts are severe in the city of Chicago, state of Illinois, and the United States. One in three households in Illinois is headed by a person lacking a high school diploma and has obtained government support such as food stamps in 2009-2012 compared to the 18% headed by high school graduates. Sum et al. (2011) further argue in the state of Illinois nearly 9% of high school dropouts obtained public assistance or Supplemental Security Income (SSI), during 2009-2010 compared to the 5% of high school graduates. Joseph (2014) notes approximately 1.2 million-students drop out of high school in the United States each year. Sum et al. (2011) argue dropouts in the U.S. commit 75% more of the countries crime. The incarceration rate of native-born male dropouts in the state of Illinois alone was at 15% compared to the rate of 2% for females. Black male dropouts in Illinois have the highest incarceration rates among race-ethnicity groups at 29%. The annual costs of housing dropouts in jail or prisons are substantial and contribute to the growing fiscal problems in states and local government (Sum et al., 2011). According to Cortiella (2013) current research on graduation rates results indicate the low rate of high school graduation with a regular diploma has a serious impact on our employment rates and earnings of students. The unemployment rate for those with less than a high school diploma continues to impact our economy. Stark and Noel

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(2015) ascertain reducing high school dropout rates should be everyone’s concern as dropouts hurt the nation’s competitive edge. Concerns for High School Districts In a 2008 research analysis of high school graduation rates for 50 of the largest cities in the United States and conducted by U.S. Department of Education, Swanson (2009) statistical analysis indicated that graduating from the U.S. largest cities is just over one-half of the students, or 53% in the principal school system completes high school with a diploma. Large U.S. urban school district graduation rates are well below the national graduation rate of about 71% and even shorter of the average for all school districts across the country at 61% (Swanson, 2009). With more importance being assigned to key education Barton (2009) notes indicators in an era of school accountability means graduation rate numbers across the U.S. school districts are being looked at much closer and especially by university based researchers. Swanson (2009) ascertains that research illustrates that the lowest graduation rates are within 50 of the U.S largest urban school systems, and contribute to the nation’s graduation rate crisis. Out of the 1.7 million public high school students educated within the 50 largest cities, approximately 279,000 students fail to graduate. Nineteen of the nation’s largest city school districts saw a decline in their high school completion rates over the last decade. The decrease in graduation rates have been part of a pattern since the 1970’s. In a bill introduced into United States Congress in March 2009, “the extent of U.S. high graduation rate problem was described as about 1,230,000 secondary school students which are approximately one-third of all secondary school students, fail to graduate each year” (Picciano & Seaman, 2010, p. 4). According to Swanson (2009) the No Child Left Behind Act (NCLB) federal law requires high schools held accountable in meaningful ways for increasing graduation rates, and

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performance on academic assessments. According to Wraight and Best (2009) the NCLB requires that state high school accountably be measured by graduation rates for the percentage of students graduating in the standard four-year cohort, and with a regular diploma. Several statelevel polices and initiative were implemented for the purpose of addressing dropouts and graduation rates across U.S. school districts. In the state of Illinois, several local programs specifically aimed at addressing the dropout problem were implemented. Hammond, Smink, Linton, and Drew (2007) argue that school budgets rely on tax revenues, and within inner-city urban districts there is a higher per-pupil expenditure because of the influx of federal funds targeted at high-poverty schools. A decrease in the number of student completion rate raises the threat of consolidation and loss of funding from state-based formula. If more students from these districts dropout than graduate, the school becomes a target of educational concern and in some cases dismantled. The dismantling of a school impacts students, families, teachers, administrators, and surrounding communities as many failing schools are closed or broken down into smaller charter schools (Swanson, 2009; Rumberger, 2011). To reduce the possibility of losing federal, state, and local funding for low performing school districts, Illinois implemented the SB 1796 Illinois Hope and Opportunity Pathway through Education program with the goal of re-engaging dropouts in programs to complete high school (Wraight & Best, 2009). Lawrence and Routten (2009) argue massive restructuring advocated by the National Center for Education and the economy must be approached carefully and viewed through the lens of adult learning theories. States need to consider reform initiatives that incorporate the andragogy model as self-directed learning to secondary education is part of an ideal restructuring formula.

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Through data collection and innovative methodologies for data analysis, researchers currently argue that the graduation rates have fallen for the past 45 years and children in the late 1940’s actually graduated at a higher rate than children today (Ferdig, 2010). According to Allensworth, Healey, Gwynne, and Crespin (2016) district leaders articulated the goals of recent graduation rate assessment is to determine whether policies and programs related to dropout prevention are being implemented with fidelity to produce better outcomes. There is a good reason to be optimistic about improving educational attainment. Rumberger (2011) asserts because of the NCLB act, high school is harder and success becomes more narrowly defined. Despite the increase in accountability, schools also have a strong incentive for increasing graduation rates due to revenue aspects. Calculation of High School Graduation Rates Calculations of dropout and graduation rates can be a confusing mishmash of data. Studies differ on what dropout and graduation means (Fields, 2008). The debate in educational research concerns how many students actually earn a diploma. “Dropout rates do not simply or directly translate to an accurate graduation rate. Multiple methods and definitions can result in conflicting information” (Lehr, Johnson, Bremer, Cosio, & Thompson, 2004, p.10). The disagreements on graduation rate calculation across the United States according to Barton (2009) indicate a need for exhaustive studies to be undertaken to determine the best way for calculating high school graduation rates implemented across all states. Barton (2009) notes the Averaged Freshman Graduation Rate (AFGR) developed by the National Center for Education Statistics (NCES) was enforced in 2012, and all U.S. high schools were required to calculate graduation rates using the same system. Barton (2009) further argues that the AFGR which also estimates by race and ethnicity was showing graduation rates across many states were on average 73.7% in

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1990-1991, and 73.7% in 1995-1996 then falling to 71% until 2003-2004 but were slightly up to 74.3%. The trend of increases and decreases in the AFGR graduation rate indicates a possible inconsistency in reporting high school graduation rates. Ferdig (2010) found multiple data collection and innovative research methodologies indicate graduation rates have fallen for the past 45 years and children in the late 1940’s actually graduated at a higher rate than children today. High school graduation rates reported were either high or rising, low and stable, or moderate and falling. They ranged across U.S. high schools from 67% to 90%. Ferdig (2010) argues new policies and data debates produced inconsistent graduation rates and trends. According to Bowers (2010) research on graduation and dropout rates focused on large-scale estimation of national dropout rates, and issues of indicators to dropping out. In 2003-2004 the estimated national graduation rate was at 74.3%. Bowers (2010) found through further studies the estimates and methods of national graduation rate calculations reported in 2005 was actually 70%. In Chicago Illinois Bowers (2010) found district graduation rates at 54% and a difference among ethnic groups with African American students at 39%, and Hispanic students at 51%, European at 71%, and 85% for Asian students. In a longitude study on dropout and graduation rate calculations, researchers Heckman and LaFontaine (2010) found the differences in data methodology across states produce large discrepancies in the estimates on graduation rate calculations that appear in recent literature. There is no one best way to measure high school dropout or completion rates. Different methods can be more or less useful and for different purposes. U.S. high schools use several methods to calculate dropout and completion rates called the status, event, cohort, and aggregate rate (Bowers, 2010; Heckman & LaFontaine, 2010).

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The Status, Event, Cohort, and Aggregate Graduation Rate Hauser and Koenig (2011) note the status dropout and completion rate is calculated using cross-sectional data on individuals in a target population. The status dropout and completion rates do not differentiate between those with a diploma and those with a General Education Diploma (GED) or another credential, nor when it was earned. The status rate is the population of students who fall into certain category at a given point in time. Hauser and Keonig (2011) further note in 2006 the United States dropout rates according to the status rate by the U.S. Department of Education, 93% of 16 to 24-year old were not enrolled in school and did not have a high school diploma. In the same month of that year, the status rate calculations were 87.8% of 16 to 24-year old status completers, meaning they were not in school but had a high school credential (Hauser & Koenig, 2011). Event rates according to Hauser and Koenig (2011) indicate the fraction of a population that experiences an event over a given time. The most frequently reported data in an event rate is the proportion of students who exit school during the academic year without completing high school. Event rates use longitudinal data. Research on national trends and differentials in event dropout rates estimated in the studies fell below the more extreme or higher estimates of status dropout rates. Hauser and Koenig (2011) refer to individual cohort rates are the longitudinal or retrospective data for students of the same age and in the same grade at certain points in time. Such as those entering in a given school year which includes all students to reduce the risk of selection bias. Aggregate cohort rates are designed to approximate true cohort rates. They count the number of students entering high school and leaving school by using a numerator or number of dropouts, and denominator or the number of students at risk of dropping out or completing. This

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calculation uses aggregated administrative data collected from all schools, however they can miscount dropouts and completers. Hauser and Koenig (2011) argued the aggregate rate does not meet the graduation rate definition spelled out by the National Governors Association (NGA) Compact or regulations of the No Child Left Behind (NCLB) Act and therefore not useful for accountability purposes. The Department of Education according to Fields (2008) wants to ensure all states begin using the same formula to calculate graduation rates and for determining the percent of dropout rates. High school dropout rates are calculated by number of students within a district who dropped out during the school term, then divided by the total expected to complete the school term (see Figure 1)1. The total expected to complete a school term may be more or less and based on the total adjustment for students who transfer in or out of the fall enrollment data (Fields, 2008).

80 Graduates in 2006 x 100 (100 9th Graders in 2002) + (20 transfers in) - (10 transfers out)

=

72% Grad Rate

Figure 1 High School Dropout Rate Calculation Formula. (Fields, 2008) Note: Figure created in Excel using he information in literature concerning calculations of graduation rates. ( Figure 2 High School Dropout Rate Calculation Formula. Figure created in Excel using he information in The United States graduation rate calculation according researchers Almeida, Steinberg, literature concerning calculations of graduation rates. (Fields (2008).

Santos, and Le (2010) uses a measurement called the Cumulative Promotion Index or CPI. Governors of all 50 states have recently committed to use of a research-based formula for counting and reporting graduates through a common cohort rate, to provide an accurate sense of how effective high schools are at retaining students (Almeida et al., 2010). To address the dropout rates and determine strategies for improving graduation rates Bridgeland, Dilulio, and Morison, (2006) assert schools need to develop state-wide early warning systems to identify at-

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risk of failing in school. According to research on additional concerns by schools and districts on the indicator to forecast graduates prior to determining overall district rates, Allensworth and Easton (2007) confirmed the indicators were freshman-year GPA, number of semester courses failure, and freshman absences. Stetser and Stilwell (2014) argue understanding how dropout and graduation rates are determined is the first step towards strategies for improvements. Dropout Prevention Strategies To respond to dropout problem and preventative measures researchers Dynarski, Clarke, Cobb, Finn, Rumgerger, and Smink, (2008) argue that states, districts, and schools need to first accurately understand its scope. Dropout prevention measures can include the use of longitudinal student databases with unique statewide identifiers which follow student progression from early childhood to high school. Hammond, Linton, Smink, and Drew (2007) argue education practitioners need to face the decision of which program or programs to implement toward addressing risk factors and graduation rates. Balfanz, Bridgeland, Bruce, and Fox (2012) further argue that success of dropout prevention efforts depends greatly on the types of programs used. Research into model policy elements that frame sound legislative strategies for dropout prevention, Almeida, Le, Santos, and Steinberg (2010) analyzed policies for a subset of 36 states that have passed new dropout legislation in 2002. Conclusions of results indicate that exemplary dropout prevention legislation are acceleration opportunities that target student who are at-risk and in which 15 U.S. school districts use one or more of these strategies. America’s Promise Alliance, a coalition of leading U.S. organizations implemented a research-based plan in March of 2010. The plan was called the Civic Marshall Plan toward increasing the nationwide high school graduation rate to 90 % by the year 2020. Balfanz, Bridgeland, Bruce, and Fox (2012) argue at a rate of 75.5% for the class of 2009 it would require

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school graduation rates to increase 1.3 % per year toward meeting the plan’s goal For a California Dropout Research Project, Duffy, Poland, Blum, and Sublet (2015) analyzed data from school districts in which improvement in graduation rates and other outcomes were reported by the school district administrators. Their analysis of school district data indicated that implementing systems to identify student’s at-risk, offering credit recovery opportunities, and developing early intervention systems were district-level strategies that influence the positive results for increasing graduation rates. According to a multiyear evaluation of graduation rates and research involving more than 21,000 students in New York small city school districts, Pierson, Fox, and Reid (2015) confirmed increased rigor and personalization for dropout prevention strategies demonstrated higher overall graduation rates. Neither Duffy et al (2015), or Pearson et al (2015) confirmed credit recovery programs were the primary reason for increased graduation rates. In Chicago, the graduation rates rose 47% in 1999 to 69% in 2013 using data to create dropout prevention strategies through individualized instruction for keeping 9th graders on track to graduate. The University of Chicago Urban Education Institute predicts this rate will exceed 80% over the next few years (Cardichon & Lovell, 2015). While dropout prevention intervention strategies have made progress, Pierson, Fox, and Reid (2015) argue it is clear many high schools still need comprehensive reforms implemented that reflect the latest research and best practices in the field of school reform. Powell et al. (2015) indicated positive results in this study were Dual Enrollment, Advance Placement, Online Learning, and Credit Recovery. Researched-based efforts which have shown some improvement for addressing the dropout risk factors are interventions for transitioning to 9th grade, academic and social supports, individualized instruction, and reengagement programs (Powell et al., 2015). In an evaluation study of online

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credit recovery programs for dropout prevention and increasing graduation rates in Springfield Illinois, researchers Cash, Dawicki, and Sevick (2011) relied on reports provided by district administrators which included student academic performance. Qualitative information was obtained through site visits and interviews with stakeholders determining it was impossible to obtain student-level data for determining the program impact due to district management systems. Cash et al (2011) found for the 300 students participating in night school credit recovery programs 109 failed to receive credit due to attendance and, the remaining 98 students received a grade of D- or high for a 90% success rate. In the fall of 2010-2011 according to Cash et al (2011), the first online credit recovery program as dropout prevention measure session included 84 students in which 22.6 passed coursework and 60% actually failed online coursework. A third session was added in which 46 students enrolled leading to 34.8% earning online course credits, and 37% of the enrolled students failed. The final analysis of this research indicated a high rate of failure was due to the rigor of coursework, not enough academic support, programs not being implemented strategically, and the strict attendance policy. Credit Recovery Programs Lawrence and Routten (2009) assert the application of adult learning theory can support preventative measures for at-risk students and increase student re-engagement. The flexibility of educational institutions adapting online course learning may help students organize their learning independently. Bland, Neill, Church, and Terry (2008) assert technology can create a setting identified as important delivery of independent instruction and learning outcomes. Technologydriven education allows for flexible schedules and accommodation of different learning styles. Technology-driven online credit recovery programs according to Johnston (2012) have become a

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central feature of dropout prevention and recovery based on reasons students give for dropping out which include not enough credits, failing courses, and average grade. However, Johnston (2012) further argues credit recovery is so varied and its implementation so malleable that there is little sense of its impact and effectiveness on improving graduation rates. Online credit recovery programs according to McCabe and Andrie (2012) are implemented into school curricula to help schools graduate more students. Credit recovery programs implemented into school curricula provide students a second change to recovery missing credits. McCabe and Andrie (2012) further assert dropout prevention strategy research studies confirm credit recovery as an effective tool for improving school district graduation rates “Online learning holds the promise of creating new, innovative personalized learning approaches. If a student has learned 40% of the material for a course, online credit recovery allows accelerated learning and flexibility” (Carr, 2014, p. 23). Mileaf, Paul, Rukobo, and Zyko (2012) ascertain research on credit recovery indicates, choosing effective credit recovery programs for a school or district requires the credit recovery program is aligned to the specific program goals of each school district. Educational researchers Almeida, Steinberg, Santos, and Le (2010) note that dropout prevention acceleration strategies include credit recovery programs that are flexible, high-quality options for students at-risk to obtain missing credits, and can be integrated into traditional school models or as an alternative setting. Brown (2011) found through a case study on implementing and the use of credit recovery programs in which a comprehensive sampling method was used for 13 research participants who worked directly with online credit recovery programs, credit recovery programs are effective for secondary curriculum toward assisting students in the area of dropout prevention, alternative education, and recovery of missing credits.

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According to research on implementation of credit recovery programs for the National Center for Education Statistics Powell, Roberts, and Patrick (2015) determined that 88% of school districts around the country use credit recovery programs and courses to improve graduation rates. Powell et al. (2015) further argue in Illinois Chicago Public Schools (CPS) that have implemented the PASS credit recovery program in one of its underprivileged highly populated urban districts, there was no direct evidence concerning the impact on graduation rates after implementing. However, credit recovery programs did create an alternative for students failing to make up credits and especially teen parents. In a longitudinal research study Voshell (2013) completed an analysis of data for graduates requiring credits to graduate in one school district between the years 2009-2012, number of requests for enrolling in credit recovery courses, and cost associated to run the program. Data indicated according to Voshell (2013) that over a four-year period close to half the district students missing credits took credit recovery courses. In 2010 40% took one or two credit recovery courses and by 2011, 45% were taking more than two courses then by 2012, 43% were taking half their unearned credits through credit recovery programs. Voshell (2013) further asserts as the population of students missing credits grew, so did the demand for increasing credit recovery programs and improvements in number of students graduating. Hughes, Zhou, and Petscher (2015) used student transcripts and comprehensive assessment testing data to address research related to online learning and specific to credit recovery courses for the likelihood of a student earning a C or better. Results indicate that students taking online courses had a high probability of earning a C or better than face-to-face students taking the same courses. The likelihood of success for students enrolled in the online course was 7.3% higher in grades 9, 5.2% higher in grades 10 and 3.9% in grades 11. In grade 12

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however, the difference was negative or -0.1, meaning seniors did not fare as well in online courses as they did in face-to-face instruction. Mileaf, Paul, Rukobo and Zyko (2012) assert credit recovery programs since their implementation into secondary educational curricula, have been accepted as the solution for retaining at-risk students by providing them an opportunity to obtain credits and graduate with the rest of a cohort class. Hughes et al. (2015) argue although use of online credit recovery is becoming increasingly popular, an analysis of its effectiveness on graduation rates is limited. Credit Recovery Impacts on Graduation Rates Improving graduation rates is the most important aspect of implementing credit recovery programs (Heckman & LaFontaine, 2010). Allensworth. Healey, Gwynne, and Crespin (2016) assert high school graduation is the strongest predictor of most outcomes we care about as a society and school systems. Wraight and Best (2009) assert in Illinois, high school districts administrators, and communities are working to improve graduation rates by evaluating their own policies, coordinating existing resources, and investigating what is working in their region. Carr (2014) asserts school districts have begun to use online credit recovery programs to get students back on track and boost the achievement level of students and the district. A recent report from the National Center for Education Statistics (NCES) indicated that 55 % of U.S. school districts used online learning during 2009-2010 school years. More than 60 % of classes were taken for credit recovery. Carr (2014) found 80% of urban school administrators note credit recovery as an issue of importance and for many districts, graduation rates have increased since implementing. Archambault, et al. (2010) ascertain there has been a tremendous amount of activity over the past several years and aimed at getting more students to graduate. Several case studies were

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performed on U.S high schools in Florida, Wichita Kansas, Colorado, and Chicago in which each school district in the study implemented online credit recovery for improving graduation rates. From all the studies completed on these school districts, Powell, Roberts, and Patrick (2015) assert credit recovery programs were successful for supporting the academic needs of at-risk students and improving graduation rates. Johnston (2012) argues despite a slight increase in high school graduation rates in the United States after implementing credit recovery programs; it remains at a dismal 75% average and ranking in the nation 21st overall graduation rates. Volkerding (2012) conducted a research study to determine if there was a relationship between final course grades from NovaNET, an online credit recovery program, graduation rates, and previous failing grades through a simple correlation method. In the results of this study, over 70% of all students who participated in NovaNET were successful in this form of credit recovery. However, Volkerding (2012) indicated limitations in the study was using NovaNET scores from an entire school district, versus one school. An additional implication was the use of only one school did not account for the differences in other school policies for allowing students to participate in NovaNET. One or more schools limiting a student from NovaNET may alter the results and create bias, and specifically when another school may allow the same student to enroll in the class. Conversely, some students may have been omitted from the study results. Volkerding (2012) further asserts the fact that this study was completed over the period of one year yields less conclusive data than if collected over multiple years. In 2011, the National Center for Education Research (NCER) awarded a large research grant to the American Institutes for Research to study the impact of online credit recovery on Chicago students randomly assigned to these programs for acquiring missing credits. This was the first research grant awarded by the federal government to assess credit recovery. Although

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the study indicates recovery rates increased with expanded recovery options, most students still did not recover credits; graduation rate impacts were not identified (Allensworth, Michelman, Nomi, & Heppen, 2011; Le, 2015). Le (2015) further argues despite the rising presence and research on credit recovery programs, there exists scant evidence as to the effectiveness in increasing high school graduation rates. Strickland (2015) completed research on an alternative pathway diploma program in which students gained credits to graduate and earned their diploma outside of their traditional school program, and the impact on school district graduation rates. Independent t-tests were conducted to compare student end of course test scores for those who graduated through the participation of the alternative diploma program and those who graduated from traditional programs. Strickland (2015) found results indicated a significant difference in scores for the alternative diploma program students of (M=2.18, SD=.79) and traditional diploma students at (M=2.90, SD=.75); t (190) = -4.71, p=.00 which suggested that test performance influenced graduation diploma pathway or the alternative credit recovery program. However, in a further analysis, Stickland (2015) compared graduation rates for the entire school district prior to the alternative diploma program implemented indicated a (M = 89.12, SD = 3.14) and the graduation rates without the alternative diploma program, (M = 84.70, SD = 1.63); t (8) = 2.80, p = .023 suggested that the incorporation of an alternative diploma or credit recovery program, did not have an effect on the overall graduation rate of the school district. In the most recent research for Chicago Public School systems (CPS), and an analysis of dropout and graduation rates over time, Allensworth, Healey, Gwynne, and Crespin, (2016) found upon reviewing the failure rates and graduation rates over time for 9th graders, the decreases of course failure rates for 9th grade and the proportion of students earning above 3.0

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GPA in 9th grade has increased from 11% to 32% and the proportion of student with poor grades below a 1.0 GPA has decreased from 24% to 10%. Students who have fallen off track are provided with opportunities through credit recovery. Archambault et al. (2010) further argue there remain concerns many schools may still be miscoding student records either by accident or on purpose to encourage students to enroll in school with easier graduation requirements and credit recovery courses that replace traditional classrooms. According to Archambault et al. (2010) students who are able to create meaningful social connections which can provide emotional and academic level support, are more likely to be successful in educational and graduation attainment. Lawrence and Routten (2009) argue some adolescent learners may not be developmentally adults and according to adult learning theory by Malcolm S. Knowles (1980). Learning for some high school students can still be a challenge and especially in online learning which is self-directed. According to Cash et al. (2011) online credit recovery research in the summer of 2010 and data analysis of 204 students who participated in credit recovery programs, 171 or 83.8% earned credits while 33 or 16.2% did not. By the end of the first session for 20102011 school year in which 84 students participated, 22.6% completed credit coursework and 16.7% were still working toward earning credits. However, Cash et al. (2011) found 51% failed online credit recovery courses which accounted for 60% participants just in the first session. Online credit recovery programs do work for some students but not all according to this research study. A pilot study in Illinois of which 23 teacher-nominated students indicated positive results for a single high school within a self-paced summer program using online credit recovery and traditional curricula. Hughes, Zhou, and Petscher (2015) found limitations concerned the effects

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on graduation rates due to the sample size, the nomination of students by teachers, the limited contrast of online courses with similar face-to-face courses, and all students in the research study had passed the credit recovery course. Roberts, and Patrick (2015) argue there is still limited research on credit recovery program effectiveness for improving graduation rates. Allensworth, Healey, Gwynne, and Crespin (2016) assert graduation rates have improved for students of all racial, ethnic, and economic backgrounds and primarily in urban school districts after implementing credit recovery programs for addressing graduation rates. According to research concerning online virtual schools in which most students enroll through school districts to acquire missing credits and graduate, Miron and Gulosino (2016) found the graduation rates for virtual schools offering online courses and credit recovery have worsened by 3% over the past few years. Deye (2011) notes more research is recommended to increase an understanding of the inner workings of virtual online schools, and credit recovery programs in relation to academic rigor and their significance for improving school district graduation rates. However, Deye (2011) found through research on dropout prevention strategies, credit recovery programs are innovative and flexible programs for providing students an opportunity to earn and recovery credits and in turn improve the rates of graduation for school districts. In the most current study concerning online credit recovery programs for getting students back on track to graduate, a two school-level analysis and one student-level analysis was conducted on 17 Chicago Public Schools that offered both online and face to face credit recovery courses in the summer of 2011 and 2012. A total of 1,224 ninth graders participated in the study. Heppen et al. (2016) found despite the difference in credit recovery rates among students in the online programs who had an average of 2.51 credits earned, and face-to-face programs who had

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an average of 2.39 credits earned, the results were unable to identify any impacts on graduation rates. Hughes, Zhou and Petscher (2015) examined data on students enrolled in credit recovery programs from grades nine through 12. Students completing courses successfully decreased as the grade levels increased. The decrease in success were largest in grade 12. Students at this level were better served in face-to-face classrooms. In credit recovery programs, students were more likely to succeed in grades nine-11. Grade nine showed the largest success rate. The results did not determine a probability of increasing graduation rates and most in grade 12 did not complete the program. If students in grade nine-11 were actually succeeding to earn back credits and get back on track to graduate, but students in grade 12 were still lacking the credits to graduate, then the program may in itself be successful for helping student earn credits, but not necessarily graduate. Allensworth, Healey, Gwynne, and Crespin (2016) recent research for Chicago Illinois (CPS) school districts on failure rates and graduation rates over time, indicated CPS graduated about 6,000 more students in 2014 than 1999. Chicago Public High School graduation rates have increased by 22% over the last 16 years. Graduation rates increased for from 52.4% among students who tuned 19 in 1998 to 74.8% by 2014. Students are more on track to graduate as they now have alternative solutions such as additional school support and credit recovery programs. Huges, Zhou, and Petscher (2015) argue after considerable growth over the last several years and with more schools implementing online credit recovery programs; Illinois high school graduation rates have stagnated in the low 80 percentiles and more research is necessary to determine the actual connection between credit recovery programs and its impact on district graduation rates. Allensworth et al. (2016) ascertain graduation rates have improved for student of all racial,

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ethnic, and economic backgrounds and primarily in urban school districts after implementing credit recovery programs for addressing graduation rates. Powell, Roberts, and Patrick (2015) argue there is still limited research on credit recovery program effectiveness for improving graduation rates. Conclusion According to the review of literature the financial impact of dropping out is high for students, communities, and the nation. Researcher Deye (2011) found the number of jobs in the United States economy which require postsecondary education, increased from 28% to 59%. In a research poll using surveys of youths ages 10-18, results indicate 92% believe they will graduate leaving a 18% gap between number of students who believe they will, and those who actually do. Deye (2011) further assert U.S. school districts are setting ambitious goals for improving high school graduation rates. Fitzgerald et al., (2013) research results on credit recovery and graduation rates based on school size indicated there were no differences among small, midsized or large school districts. Heckman and LaFontaine (2008) and Tyler and Loftstrom (2009) found on the comparisons of race in relation to graduation rates, white-black differences are roughly the same as they were 30 years ago, about 15% difference favoring whites. Risk factors for drop outs were highly related to attendance, academic achievement, school size, and ethnicity. Nolan, Cole, Wroughton, Clayton-Code, and Riffe (2013) found risk factors for truancy and dropping out at a 95% confidence level from analysis of data, and a students’ socioeconomic status were a strong indicator they will become truant. Mobility was another risk factor to truancy and dropouts. Students who change schools are between 4% and 81% at risk of becoming truant and more likely as their age increases to dropout (Nolan et al., 2013). Nolan et al. (2013) further assert research on school size and ethnic groups, that whites

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have statistically significant higher graduation rates than African American or Hispanics across large, midsized, and small schools. However, Nolan et al. (2013) suggest further studies on groups such as Hispanic males and females are necessary. Several studies were performed on effective dropout prevention including credit recovery programs. At least one study on credit recovery program effectiveness was a randomized controlled trial or a quasi-experimental design. However, Dianda (2008) argues the methodology used by other research on credit recovery programs, was not without criticism and has been recently debated in the research community. Watson and Gemin (2008) found implementing credit recovery is a positive influence for supporting students and reengagement. “Online learning is proving to be an important and transformational tool in reaching at-risk students” (p. 7). Goals related to research studies on credit recovery and addressing at-risk students vary with each online program focusing on at least one or more, such as making up credits, preparing for state exams, getting dropouts back in school, and to meet budgetary concerns (Watson & Gemin, 2008). According to research data analysis from the School of Education, John Hopkins University on Illinois, Ohio, Florida, Nevada, California, Georgia, and New York, and for a total of 624,225 high school students combined taking some form of credit recovery programs, Cardichon and Lovell (2015) found these states still have an adjusted cohort graduation rate at or below 67%. Throughout the review of literature, there was a lack of researched information on whether or not credit recovery programs actually contribute to an increase in high school graduation rates (Cardichon & Lovell, 2015). Research studies discussed in the review of literature indicate the importance of developing early interventions for reducing dropout rates,

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identify risk factors such as attendance, and addressing accountability for school districts to ensure student success. Chapter Summary The goal of this research was to assess whether any difference exists between online credit recovery programs and high school graduation rates which may contribute to the existing literature concerning online credit recovery programs as a solution for increasing high school graduation rates. The review of literature indicates there are many different ways to gauge the impact of credit recovery program success and graduation rates for school districts across the United States and in Illinois school districts. Some school districts define success in terms of course completion, others measure as a decline in course drops, and still others base success assessment on grades, graduation rates, and reduction in student absences (Le, 2015). Chapter 2 contained discussions on dropouts, risk factors, credit recovery programs, and graduation rates concerning Illinois high school researched-based information on graduation rate improvements due to the implementation of credit recovery programs, which was the basis for this study. Chapter 3 provides a discussion of the research method, design, sample, population, data collection, analysis, and summary of the research process.

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Chapter 3 Methodology This chapter defines the relevant research design and methods used for the study. Conducting good research entails the use of many approaches for studying complex issues. The research includes the logistics and mechanics used for collection of data, sample size, and analysis to address the research question and hypothesis. The research study used a nonexperimental quantitative ex post facto design to determine the association between variables. Quantitative research is a form of objective study using data to derive conclusions and features objectivity, generalizability, and numbers (Vickers & Offredy, 2015). A quantitative ex post facto design is an after the fact research approach for examining the extent to which the independent variable may influence the dependent variable (Leedy & Ormrod, 2010). This research study involved the use of pre-existing statistical data, which was publicly available. There were no participants or individuals identified in the selected sample, nor were the variables manipulated (Christensen, Johnson, & Turner, 2011). Research Design A quantitative ex-post facto design was appropriate in this study for examining the association between high school graduation rates and implementation of online credit recovery curricula. Quantitative research explains phenomena through numeric data that are analyzed using mathematical based methods. The sample data obtained from the state of Illinois Interactive Report Card years 2007-2011 and Illinois Board of Education Report Card years 2012-2014 public websites and analyzed for this study, consisted of secondary statistical graduation rate and attendance data. The data collection used publicly available data obtained via the internet, and involved no interaction with the subject. In this non-experimental design, 350

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Illinois urban and non-urban public high school graduation rate and attendance data were collected to determine through analysis of data if there has been a delineative change because of online credit recovery program implementation. A qualitative design would not have been appropriate in this research study because to determine a causal effect between the variables, a statistical analysis was required (Leedy & Ormrod, 2010). Sample Method This quantitative ex post facto design examined 350 Illinois urban and non-urban public high schools archived statistical graduation rate data for periods of 2007-2010 preimplementation, and 2011-2014 post-implementation of credit recovery programs, acquired from state of Illinois Interactive Report Card and Illinois State Board of Education Report Card public websites. The research sample data consisted of the archived statistical high school graduation rate data to retrospectively determine the effect that online credit recovery programs have on graduation rates (Cohen, Manion, & Morison, 2011). The additional data sample included the 350 Illinois urban and non-urban high school districts attendance percentages to control for the extraneous effect they may have on graduation rates. A stratified sample was taken from the state of Illinois Report Card archived public data base, matching 175 high schools with and 175 high schools without credit recovery based size, location, and demographics to assure equivalent distribution in each group. Data obtained from the public websites, Illinois Interactive Report card for years 2007-2010 and Illinois Report Card website for years 2011-2014, was aggregated data which refers to numeric information collected from multiple sources or variables and compiled into data summaries. The data is typically used for purposes of public reporting or statistical analysis. School districts with 500 students for

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example, maintains a variety of student records and for each of the 500 students enrolled then aggregated for public reporting (Abbott, 2015). The 350 Illinois urban and non-urban high school district graduation rate data for this study was obtained from compiled public statistical reports that document district high school graduation rate and attendance data. The high school districts aggregated graduation rate and attendance data for each school year pre-implementation 2007-2010 and post-implementation 2010-2014, and for each of the 350 high school districts in state of Illinois, was obtained by first entering in the search criteria on the Illinois public Report Card websites by years, urban and non-urban location, and size small, midsized, or large for equal comparison of schools. The Illinois urban and non-urban high school districts which matched the target data of graduation rate and attendance data, was recorded in the excel spreadsheet for years’ pre-credit recovery programs 2007, 2008 2009, and 2010 and post credit recovery program years 2011, 2012, 2013, and 2014. The data was categorized by the 175 schools with and 175 schools without credit recovery programs. The Illinois high school districts were further stratified by location urban and non-urban with equal numbers of schools selected from each stratum. Stratified sampling was the best sampling method for this study to assure that schools with and without credit recovery were similar in size, location, and demographic distribution. Out of the 350 Illinois urban and non-urban public high school districts half had implemented credit recovery programs for addressing at-risk students and district graduation rates, while the other half have not. This study used pre-existing archived statistical graduation rate data from the Illinois Report Card pubic websites for the 350 Illinois urban and non-urban public high school districts with credit recovery programs, and the high school districts without credit recovery implemented into their curricula. The Illinois high school district graduation rate

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and attendance data collected was for the periods from 2007-2010 pre-implementation and 20112014 post-implementation. Determining a difference between graduation rates prior to and post implementation of credit recovery programs using a non-experimental quantitative ex post facto causal comparative research method supported a pre and post analysis of statistical data in the research to identify which variables are significant predictors of the variables being studied (Leedy & Ormrod, 2010). The quantitative ex post facto design is appropriate when the more powerful experimental method is not possible and desirable in social or educational context where the independent variable or variables lie outside the researchers control (Cohen, Manion, & Morrison, 2011; Johnson, 2000). A quantitative ex post facto research designs can make inferences about relations among the independent variable (credit recovery programs) and dependent variables (graduation rates) through a systematic empirical inquiry in which researchers do not have direct control of independent variables because their manifestations have already occurred or they cannot be manipulated (Cohen, Manion, & Marion, 2011). This non-experimental ex post facto causal comparative design aligned with quantitative research that is used to compare archival data between variables. The research variables in this quantitative ex post facto design were not manipulated as the treatment has already occurred (Belli, 2008). A quantitative design was appropriate for the study as the objective was to analyze public archived statistical graduation rate data to determine a difference between the independent variable specifically, the credit recovery programs in Illinois public high school districts pre-years 2007-2010 and post-years 2011-2014, and the dependent variable, district high school graduation rates. The research study involved the use of 350 Illinois high school district pre-existing data publicly available on state of Illinois Report

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Card websites, and no participants or high schools in the selected sample were directly identified (Christensen, Johnson, & Turner, 2011). High schools are now depending upon the use of online credit recovery and blended learning for many of their school programs and for improving student performance. However, administrators are still uncertain about the quality of online instruction and especially with the use of credit recovery programs as an effective solution for improving graduation rates (Heafner, Hartshorne, & Petty, 2015). With the increase in use of online credit recovery programs as an alternative solution for at-risk students and improving graduation rates in Illinois high school districts, this research may contribute to gaps in research for determining if credit recovery programs are effective for improving Illinois high school graduation rates. The following questions and hypothesis supported the research analysis and results. Research Questions RQ1 – What is the statistically significant difference between Illinois public high school graduation rates for schools with credit recovery programs and schools without credit recovery programs? RQ2 – What is the statistically significant difference between Illinois public high school graduation rates for schools with credit recovery programs and schools without credit recovery programs when controlling for attendance? Hypothesis H10 = There is no significant difference between Illinois Public high school graduation rates for schools with credit recovery programs and schools without credit recovery programs. H1a = There is a significant difference between Illinois public high school graduation rates for schools with credit recovery programs and schools without credit recovery programs.

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H20 = There is no significant difference between Illinois Public high school graduation rates for schools with credit recovery programs and schools without credit recovery programs when controlling for attendance. H2a = There is a significant difference between Illinois public high school graduation rates for schools with credit recovery programs and schools without credit recovery programs when controlling for attendance. Population Illinois public high schools began implementing online credit recovery programs after a 2010 research of 15 schools, which piloted the programs for their effectiveness (Allensworth, Michelman, Nomi, & Heppen, 2014). In Illinois, graduation rates are reported yearly to state and federal agencies and reported on the state of Illinois Report Card public websites. Each school year the fall enrollment student count is reported as the starting four-year cohort for that year. The graduate rate is calculated by identifying a student who graduated with a regular high school diploma in the reported year and with the group of students who started at the beginning of 9th grade (Illinois Report Card, 2015). School information and statistical data such as graduation rates and attendance are reported and publicly available on state of Illinois public Report Card websites, which can be accessed by the public, or parents wanting to review school demographic information and academic structures (Illinois State Board of Education, 2015). Illinois 350 urban and non-urban public high school graduation rate data for the school years between 2007 and 2014 that have implemented credit recovery programs and schools that have not, was the target population in this study. A power analysis was conducted based on the two groups, schools with credit recovery programs and schools without credit recovery, and 9 variables, graduation rates 2007-2010 and 2011-2014 and attendance, with a moderate effect size

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of .25; alpha of .05; power .95. Based on the power analysis, a total sample size of 350 schools, 175 with and 175 without credit recovery programs were required. Credit recovery implementation was the independent variable and graduation rate was the dependent variable used in this quantitative ex post facto research study. Data Collection Data was extracted from 350 Illinois urban and non-urban high school district aggregated statistical graduation and attendance rates published in the public domain. The 350 Illinois high school districts aggregated statistical graduation rate and attendance data for years 2007-2010 pre-implementation and years 2011-2014 post-implementation was categorized by school size by credit recovery implementation. The archived high school graduation rate and attendance data was first obtained from state of Illinois Interactive Report Card (IIRC) and Illinois Board of Education (ISBE) Report Card public websites. The high school aggregated graduation rate data collected was first categorized by schools with credit recovery (1=Yes) and without credit recovery (0=No). The high school graduation rate and attendance percentage data was further organized by years 2007-2014 into an MS Office Excel spreadsheet (See Appendix A). The data was then run through excel for comparison of duplications or missing values and identified through given conditions to ensure the accuracy of data entry. The data was then transferred into the Statistical Package for Social Science (SPSS) software to perform statistical analysis. The Illinois urban and non-urban public high schools that had missing graduation rate or attendance data were excluded. Instrument The instrument is a term that a researcher uses for measurement device such as survey, questionnaire, or test (Vogt, 2007). The collection of pre-existing statistical graduation rate and

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attendance data obtained from the state of Illinois Report Card public websites, was categorized by variables and organized into an excel spreadsheet which was the instruments for this research. Therefore, for this research study an instrument in the purest sense of the word was not used as data was not collected from individual subjects. Rather the researcher developed a data collection tool by which the data extracted from the State of Illinois public websites was entered into an excel spreadsheet (see Appendix A), and later uploaded into SPSS for data analysis purposes. Reliability and Validity This research study involves the use of preexisting statistical data in which the measurement of outcomes has occurred prior to the start of the study. Therefore, internal validity is not possible to assess. The study did not alter the collection instrument and relied on the accuracy of the graduation rate data as reported by Illinois high school administrators to the state of Illinois Board of Education and for reporting on the state of Illinois public Report Card websites. To ensure reliability only the archived aggregated statistical graduation rate and attendance data was collected from the Illinois State Interactive Report Card and Illinois Board of Education Report Card Public websites for the 350 Illinois public high schools and in which continuous variables are assumed. To ensure the reliability of reported data by Illinois high school districts, administrators are to follow the definition of a graduate as defined by state of Illinois accountability system. A graduate cohort is a group of students from the time they enter ninth grade until they reach grade 12, and graduate in the same cohort. Edit check are built into the process for reporting school district graduation rates to ensure schools submitting data that result in graduation rates outside of an acceptable range of 40% -100% are contacted for verification. In addition, school district

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that submit raw data on time are asked to verify their graduation rates when they are computed (ISBE Report Card Definitions and Sources Data, 2016). Since there are many types of sampling procedures, the basis is to avoid or reduce bias in the research analysis. The bias in research can be defined by a systematic tendency to produce a different outcome due to inaccuracy of the data collected (Pannucci & Wilkins, 2010). The collection of graduation rate and attendance data from 350 Illinois school districts will be of a significant size and from archived statistical data. To reduce bias, all schools were matched by urban and non-urban, small, midsize, and large, and included in the data set if graduation rate and attendance rates and were reported for each year 2007-2014. Any Illinois school districts matched but do not have all years reported graduation rate and attendance data were excluded. Descriptive statistics were used to describe the basic features of the data in this study and to provide summaries of the sample and measures used in the analysis to address the research hypothesis. To increase the reliability of the findings, an effect size of .95, rather than the traditional .80 was selected for this study (Lehman, O’Rourke, Hatcher, & Stepanski, 2013). Due to the longitudinal nature of the study design, data was analyzed between groups to assure that the difference within the intervention group was based on credit recovery and not by chance. The data will be maintained securely on a password protected USB drive until the research has been completed and stored for five years, at which time the data will be destroyed. Data Analysis Descriptive statistics were used to depict the demographic topography of the schools in the credit recovery programs and the non-credit recovery programs group. Descriptive statistics simplified the large amount of school data in to a sensible summary. The 350 Illinois high school

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graduation rate and attendance data was categorized by years 2007-2010 pre-implementation, and years 2011-2014 post-implementation of credit recovery programs, and urban or non-urban small, midsized, and large districts. The categories further summarized by 175 schools with credit recovery (Yes) and 175 schools without credit recovery programs (No). The use of descriptive statistics helped to provide a summary of the basic features of the data used in this study and formed the basis of the quantitative analysis of data. To ensure accuracy of data the Illinois school districts who did not report graduation rates or attendance data for all years 20072014, were eliminated from data analysis. A multivariate analysis of variance (MANOVA), was used in the analysis for determining a statically difference in graduation rates between and among high schools with or without credit recovery programs and using the Statistical Package for Social Science (SPSS) software. According to Tabachnick and Fidell (2012), the MANOVA extends the data analysis by testing multiple continuous dependent variables or for testing hypotheses regarding the effect of one or more independent variables on two or more dependent variables. The mean differences between groups was assessed and a multivariate analysis of covariance (MANCOVA) used for holding attendance constant during the analysis of data. The two sample data groups consisted of high schools with and without credit recovery programs. The number of measurements included four pre years 2007-2010 and four post years 2011-2014 credit recovery programs for a total of eight while holding attendance constant. The eight measurements tested both hypothesis in this study in which H10 states there is no statistically significant difference or H1a there is a statistically significant difference between Illinois high schools with and without credit recovery programs. The second hypotheses, H20 states there is no statistically significant difference or H2a there is a statistically significant

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difference between Illinois high schools with and without credit recovery programs and while holding attendance constant in the analysis. The MANOVA was used to assess whether mean difference among the high schools with credit recovery and schools without credit recovery have occurred by chance. The multivariate method can handle large numbers of covariates and confounders simultaneously (Pourhoseingholi, 2012). In this study, the MANOVA analysis is an overall comparison on whether or not group means differ for schools with credit recovery and schools without for the years 2007-2010 preimplementation and years 2011-2014 post-implementation credit recovery programs. If the results were larger than both the H10 and H20 null hypothesis, there is no statistically significant difference and while holding attendance constant, will be rejected and the alternative H1a and H2a will be retained. The assumptions of normality and homogeneity of covariance matrices was assessed. Normality assumed the scores were normally distributed and homogeneity of variance assumed that schools with and without credit recovery had equal variance. Chapter 4 supports the results of this study data analysis, and include any applicable tables, charts, or figures. Confidentiality The Illinois State Report Card websites provided a source of school statistical information in which data on the Illinois high school districts are reported and can be accessed by the general public. The Illinois State Board of Education Report Card websites for accessing school archived school statistical data and information was set up as a community resource and for parents to stay informed concerning their child’s school district (Illinois State Board of Education, 2015). No personal or individualized identification of students, teachers, administrators, and other individual identifiable information can be obtained or accessed from this site.

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The sample data for this study did not include any direct or identifiable individual data or school names were retained. The data was only reported in aggregate. The Illinois high school publicly available graduation rate and attendance data was coded only by nominal allocation for schools with and without credit recovery programs on the excel spreadsheet and no school name, information, or direct identification of any school was included on the spreadsheet. The sample data collected and categorized into the excel spreadsheet and the SPSS software analysis files were backed up on to a password protected USB flash drive. During the course of the study, when not in use, the flash drive was stored in a locked drawer and will be destroyed at the end of the study. Summary This study is a non-experimental quantitative ex post fact causal comparative analysis of archival Illinois public high school graduation rate data to determine if there is a difference between high school graduation rates and the implementation of online credit recovery programs. The possible relationships among variables that are not manipulated were explored in the research study. Chapter 3 included an overview of the research design, method, data sample, and analysis procedure to address the questions and hypothesis. Chapter 4 includes the research analysis results.

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Chapter 4 Analysis Results Chapter 3 discussed the research design, data collection procedures, instrumentation, and data analysis methodology. The quantitative ex post facto design was used to investigate whether significant differences existed among the research variables. The purpose of this research study was to determine if a statistically significant difference exists between Illinois high school graduation rates prior to years 2007-2010, and post years 2011-2014 implementation of online credit recovery programs. Online credit recovery programs implemented in to high school curricula provide an opportunity for students to earn credit for courses previously failed. Credit recovery programs help students earn credits to graduate and increase high school graduation rates (Franco & Patel, 2011). Chapter 4 is an overview of the current research study analysis, descriptive and summary statistics, research questions, and testing of the hypothesis results. Survey of Data Sample Size A total of 350 Illinois urban and non-urban high school district archived graduation rate and attendance percentage data were the target sample size. However, 38 high schools were excluded from the study and analysis because they did not provide graduation rate data either the pre-implementation years 2007-2010 and or post-implementation years 2011-2014 of credit recovery programs. The target sample size in this study was 312 Illinois urban and non-urban high school districts who had reported graduation rate and attendance data for all years, pre 2007-2010 and post 2011-2014 implementation of credit recovery programs. Examined in the study were the high school graduation rates (dependent variables) of the 156 Illinois high school

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districts with credit recovery programs, and 156 Illinois high school districts without credit recovery programs (independent variables). The reported and archived graduation rate and attendance data was obtained from the state of Illinois Board of Education public Report Card websites. The state of Illinois public Report Card websites contains secondary archived aggregated statistical graduation rate and attendance data reported by Illinois urban and non-urban school districts for all years 2007-2014. According to the state of Illinois Legislative Citation (3)(a) the public website Report Card shall include applicable indicators of attendance center, district, and statewide student performance. This will include the percent of students who exceed, meet, or do not meet standards established by the State Board of Education pursuant to Section 2-3.25a [105 ILCS 5/2-3.25a]. Reports will include school district student attendance rates, dropout rate, graduation rate, and student mobility. According to the U.S. Department of Education an accountably system can be said to have validity when evidence is strong enough for inferences that are aligned to the purposes and working in harmony to accomplish purposes what was intended (ISBE Report Card Definitions and Sources Data, 2016). Data Collection Data was obtained from a sample of 312 urban and non-urban high school districts in the state of Illinois, with and without credit recovery programs implemented into high school curricula. Measured in the study were the archived high school graduation rate and attendance percentage data for pre-years 2007-2010 and post-years 2011-2014 implementation of credit recovery programs. The archived high school graduation rate and attendance data were obtained through the state of Illinois Interactive Report Card and the Illinois Report Card websites. The target sample was reduced by 38 Illinois high schools matched in the collection of data but had

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missing graduation rate and attendance data reported. These 38 Illinois districts were eliminated from the analysis to reduce chance of bias and errors in the results. The state of Illinois public websites was broken into two sites in which the first website contained the archived graduation rate data for years 2007-2012 and the second (new) website contained archived data for all years after 2012. The state of Illinois changed the definition of the calculated graduation rates from 2010 and therefore, updated the Report Card website to include the new definition of graduation rate reporting to begin in 2012. The graduation rate and attendance data collected were a continuous measure, and the schools categorized as (1=Yes) credit recovery and (0=No) credit recovery were a categorical measure. The research questions and subsequent hypotheses, where based on previous literature regarding online credit recovery programs for addressing students at risk and increasing graduation rates and served as the basis for the data analysis. The Illinois high school district school archived graduation rate and attendance data for 312 urban and non-urban school districts was entered into a Microsoft Excel (2013) spreadsheet, and organized by years for uploading into SPSS (Version 24) software. Two groups categorized the high school graduation rate and attendance data, schools with credit recovery programs (1=Yes), and schools without credit recovery programs (0=No). The means and standard deviations were compute for each dependent variable. Data Analysis Data analysis procedures involved the use of SPSS software to address each research question and hypothesis. The Illinois high school districts, corresponding graduation rate, and attendance data were categorized by years 2007-2014. Credit recovery programs were categorized by whether the school had credit recovery (1=Yes) or whether it did not (0=No) and

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uploaded to SPSS software. The analysis of data categorized and uploaded into SPSS software included a multivariate analysis of variance (MANOVA) for addressing the first question and hypothesis, and to control for covariates the multivariate analysis of covariance (MANCOVA) was used to address the second question and hypothesis. Repeated measures analysis of variance (ANOVA) was first conducted to test if graduation rates among the 312 Illinois urban and nonurban school districts categorized as schools with credit recovery and without credit recovery, were statistically different pre-implementation credit recovery years 2007-2010. The overall results of this study were based on the graduation rates for 156 Illinois high school districts with credit recovery programs, and 156 Illinois high school districts without credit recovery programs for the years 2007-2010 pre-implementation and 2011-2014 post-implementation. Descriptive Statistics Descriptive statistics are typically used to describe or summarize the data. It is used as an exploratory method to examine the variables of interest, potentially before conducting inferential statistics on them. Descriptive statistics for the study were used to summarize the school graduation rate and attendance data by sample size, means, and standard deviation. The sample size column (N) depicts the number of schools with and without credit recovery data. The Mean column shows if there are any trends in the graduation and attendance data that may affect the results. The variation shows if each level is similar under the Std. Deviation column. Table 1 depicts the descriptive summary of numeric values and for the schools with and without credit recovery, graduation rates, and attendance percentages by pre-years and post-years.

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Table 1 Descriptive for Numeric Values: Number of Schools with/without Credit Recovery , Graduation Percentage, and Attendance percentage Data Variables

N

2007 Pre Attendance 2007 Pre Graduation Rates 2008 Pre Attendance 2008 Pre Graduation Rates 2009 Pre Attendance 2009 Pre Graduation Rates 2010 Pre Attendance 2010 Pre Graduation Rates 2011 Post Attendance 2011 Post Graduation Rates 2012 Post Attendance 2012 Post Graduation Rates 2013 Post Attendance 2013 Post Graduation Rates 2014 Post Attendance 2014 Post Graduation Rates Valid N (listwise)

312 312 312 312 312 312 312 312 312 312 312 312 312 312 312 312 312

Min

Max

71 38 55 28 60 34 64 31 65 36 58 33 66 18 68 44

96 100 98 100 100 100 100 100 96 100 96 100 97 100 97 100

Mean

89.54 85.26 89.02 85.49 89.31 85.68 90.27 85.68 90.33 82.71 89.94 82.60 90.88 83.85 91.99 86.20

Std. Deviation 2.491 14.119 4.353 14.918 3.448 15.275 3.239 15.411 3.127 13.540 4.376 13.216 4.038 13.304 4.143 10.091

The sum of categorical values was also calculated for schools with and without credit recovery programs. The frequencies and percentages were calculated for each nominal variable and depicted in Table 2. Table 2 Summary of Categorical Values: Schools with (Yes Credit Recovery) and School without (No Credit Recovery)

Valid

No Credit_Recovery Yes Credit_Recovery Total

Frequency

Percent

156 156 312

50% 50% 100%

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Valid Percent 50% 50% 100%

Cumulative Percent 50.0 100.0

The Repeated Measures Analysis of Variance A repeated measure analysis of variance (ANOVA) was conducted to assess if a significant difference exists among graduation rates pre-implementation years 2007-2010 and by schools with and without credit recovery. The repeated measures ANOVA examines differences in the dependent variables that can be matched. The repeated measures ANOVA is used to determine any statistically significant difference between the means of three or more levels of a within-subjects factor (Kirk, 2013). If more than two observations and significance is found in the ANOVA, pairwise comparisons are conducted to determine if the paired differences exist (Fields, 2009). Prior to the analysis, (ANOVA) the assumptions of multivariate normality, univariate normality, and sphericity were assessed. Assumptions First, the assumption of multivariate normality was assessed by plotting the quantiles of the model residuals against the quantiles of a Chi-square distribution, also called a Q-Q scatterplot for Mahalanobis distances. Mahalanobis distance is the distance between a data point and the overall mean to identify outliers (Rencher, 2002). It is more powerful for detecting outliers in the data. The scatterplot representing the graduation rates and over time are presented in Figure 2.

Figure 2: Q-Q scatter plot for Mahalanobis distances 74 Figure 3: Q-Q scatter plot for Mahalanobis distances

A Shapiro-Wilk test was completed to test the assumption of univariate normality. The test suggested that the variables for graduation rates pre-implementation of credit recovery years 2007, 2008, 2009, and 2010 did not come from a normally distributed population. The test for assumption of sphericity was violated as assessed by Mauchly's test of sphericity. The results showed variance of difference in scores between years 2007-2010 were significantly different, p