Abstract |
MATHEMATICS ACHIEVEMENT OF AFRICAN AMERICAN AND HISPANIC MIDDLE SCHOOL STUDENTS IN TITLE I AND NON-TITLE I SCHOOLS A Dissertation by JASMINE J. ERVINS Submitted to the Office of Graduate Studies of Texas A&M University-Commerce in partial fulfillment of the requirements for the degree of DOCTOR OF EDUCATION August 2016 MATHEMATICS ACHIEVEMENT OF AFRICAN AMERICAN AND HISPANIC MIDDLE SCHOOL STUDENTS IN TITLE I AND NON-TITLE I SCHOOLS A Dissertation by JASMINE J. ERVINS Approved by: Advisor: Melissa Arrambide Committee: Ava Munoz LaVelle Hendricks Head of Department: Chuck Holt Dean of the College: Timothy Letzring Dean of Graduate Studies: Arlene Horne iii Copyright © 2016 Jasmine Janay Ervins iv ABSTRACT MATHEMATICS ACHIEVEMENT OF AFRICAN AMERICAN AND HISPANIC MIDDLE SCHOOL STUDENTS IN TITLE I AND NON-TITLE I SCHOOLS Jasmine J. Ervins, Ed.D Texas A&M University-Commerce, 2016 Advisor: Melissa Arrambide, Ed.D The purpose of this quantitative study was to investigate whether school type, gender (male) percentage, Hispanic student percentage, and African American student percentage can be used to predict STAAR mathematics achievement. A complete list of campuses in the state of Texas was filtered by public schools that offer regular instruction to grades 7 and 8 within an independent school district. After the list was filtered, the remaining schools were identified as Title I and Non-Title I. Stratified random sampling was then performed on the filtered list, followed by random sampling within the two stratums to get the sample selection. Data for the study was obtained from the seventh and eighth grade Mathematics Campus Summary Report for each campus found on the Texas Education Agency’s website. A multiple linear regression analysis was conducted on 75 Title I schools and 75 Non-Title I schools for seventh grade and eighth grade separately to determine whether there is a correlation between school type, male student percentage, Hispanic student percentage, and African American student percentage and v average STAAR math scale score. The results of the study indicated for the seventh grade level, there was a statistically significant predictive relationship between the average STAAR math scale score and school type, gender percentage, Hispanic student percentage, and African American student percentage. There was also a negative correlation between average STAAR math scale scores and school type, Hispanic student percentage, and African American student percentage. The higher the Hispanic and/or African American percentage, the lower the average STAAR math scale score for the campus. The results of the study indicated for the eighth grade level, there was a statistically significant predictive relationship between the average STAAR math scale score and gender (male) percentage and African American student percentage. School type and Hispanic student percentages were not statistically significant predictors of average STAAR math scale scores. There was also a negative correlation between average STAAR math scale scores and school type, gender percentage, Hispanic student percentage, and African American student percentage. vi ACKNOWLEDGEMENTS This dissertation would not be possible without the grace and mercy of God. I am grateful for the strength, favor, wisdom, and unconditional love that He has provided me throughout this entire journey. A special thanks goes to my family and host of friends and colleagues who offered their prayers and support. Those silent prayers and times where we touched and agreed proved monumental along the way (you know who you are). My sincere gratitude goes to Phillip Randall for his unwavering support and for allowing God to use him to bless me as I matriculated through each obstacle. A huge thank you goes to my Pastor, Rev. Dr. Frederick D. Haynes, III, for being one of the inspirations for me to pursue a long-term career in education. I am grateful for my best friend, biggest supporter, and partner in life, Rakasha Hall. Your love, encouragement, support, and understanding helped carry me through four years of Doctoral ups and downs. It was your willingness to stay up late hours with me while I worked, to offer your shoulders when I collapsed in tears, and to keep pushing me when I wanted to give up that was a huge blessing, of which, I thank God. A heartfelt thanks goes to my advisor, Dr. Melissa Arrambide, for pushing me, staying with me, and standing by me constantly offering support and encouragement ensuring that I always remembered, “You can and will do this”. Thank you to my committee members, Dr. Ava Munoz and Dr. Lavelle Hendricks, for being committed to my dissertation journey. Each of you sacrificed valuable time to help prepare me every step of the way. Thank you to Dr. Mei Jiang for your statistical expertise and for being readily available each time I reached out to you. I am beyond grateful for EVERYONE who played an instrumental role throughout my four years of dedication to this degree. vii TABLE OF CONTENTS LIST OF TABLES ....................................................................................................................... x LIST OF FIGURES ..................................................................................................................... xi CHAPTER 1. INTRODUCTION ...................................................................................................... 1 Statement of the Problem ..................................................................................... 2 Purpose of the Study ............................................................................................ 4 Research Questions and Hypotheses ................................................................... 5 Significance of the Study ...................................................................................... 7 Method of Procedure ............................................................................................ 8 Selection of Sample ................................................................................. 9 Collection of Data .................................................................................... 9 Treatment of the Data ..............................................................................10 Definitions of Terms ............................................................................................11 Limitations ...........................................................................................................12 Delimitations ........................................................................................................12 Assumptions .........................................................................................................13 Organization of Dissertation Chapters .................................................................13 2. REVIEW OF THE LITERATURE ............................................................................14 No Child Left Behind ...........................................................................................15 Title I ...................................................................................................16 What is a High-stakes Test? .................................................................................18 State of Texas Assessment of Academic Readiness ............................................19 viii Accountability Systems .......................................................................................22 State Accountability ............................................................................22 STAAR Performance Standards .......................................22 State Measures ....................................................................................23 Academic Performance Reports ......................................24 Federal Accountability ........................................................................25 Achievement Gap .................................................................................................27 Race .....................................................................................................28 Gender .................................................................................................29 Mathematics Achievement Gap ...........................................................................30 Race as it Relates to Mathematics Achievement ................................32 Gender as it Relates to Mathematics Achievement ............................34 Theories Relating to Minority Achievement .......................................................35 Critical Race Theory ...........................................................................35 Social Constructivist Theory ...............................................................37 Socioeconomic Integration ..................................................................................38 Conclusion ...........................................................................................................40 CHAPTER 3. METHOD OF PROCEDURE .....................................................................................42 Design of the Study ..............................................................................................42 Instrumentation ....................................................................................................45 Sample Selection ..................................................................................................47 Data Gathering .....................................................................................................48 ix Treatment of Data ................................................................................................50 CHAPTER 4. PRESENTATION OF FINDINGS .................................................................................52 Data Collection ....................................................................................................56 Results ...................................................................................................................57 Descriptive Statistics .....................................................................57 Testing of Assumptions ................................................................59 Research Questions .......................................................................76 Summary .......................................................................................83 5. SUMMARY OF THE STUDY AND THE FINDINGS, CONCLUSIONS, IMPLICATIONS, AND RECOMMENDATIONS FOR FUTURE RESEARCH ........85 Summary ...............................................................................................................85 Findings .................................................................................................................86 Conclusions ...........................................................................................................88 Implications ...........................................................................................................90 Future Research ....................................................................................................92 REFERENCES ............................................................................................................................94 VITA .........................................................................................................................................104 x LIST OF TABLES TABLE 1. Descriptive and Demographic Characteristics of Seventh Grade ......................................58 2. Descriptive and Demographic Characteristics of Eighth Grade ........................................58 3. Shapiro-Wilk’s Test for Normality for Seventh Grade Data .............................................59 4. Shapiro-Wilk’s Test for Normality for Eight Grade Data .................................................64 5. Correlation Matrix of Study Variables for Seventh Grade ................................................74 6. Correlation Matrix of Study Variables for Eighth Grade ..................................................75 7. Collinearity Statistics of Study Variables for Seventh Grade ............................................75 8. Collinearity Statistics of Study Variables for Eighth Grade ..............................................76 9. Durbin-Watson Statistic for Seventh and Eighth Grade Multiple Linear Regression Models...............................................................................................................................76 10. Correlation Table for Seventh Grade .................................................................................78 11. Multiple Linear Regression Test Summary Table for Seventh Grade ...............................79 12. Correlation Table for Eighth Grade ...................................................................................81 13. Multiple Linear Regression Test Summary Table for Eighth Grade .................................83 xi LIST OF FIGURES FIGURE 1. Histogram of Average Math STAAR Score for Seventh Grade ........................................60 2. Q-Q Plot of Average Math STAAR Score for Seventh Grade ..........................................60 3. Histogram of Percentage of Male Students for Seventh Grade .........................................61 4. Q-Q Plot of Average Percentage of Male Students for Seventh Grade .............................61 5. Histogram of Percentage of Hispanic Students for Seventh Grade ...................................62 6. Q-Q Plot of Average Percentage of Hispanic Students for Seventh Grade .......................62 7. Histogram of Percentage of African American Students for Seventh Grade ....................63 8. Q-Q Plot of Average Percentage of African American Students for Seventh Grade ........63 9. Histogram of Average Math STAAR Score for Eighth Grade ..........................................64 10. Q-Q Plot of Average Math STAAR Score for Eighth Grade ............................................65 11. Histogram of Percentage of Male Students for Eighth Grade ...........................................65 12. Q-Q Plot of Average Percentage of Male Students for Eighth Grade ...............................66 13. Histogram of Percentage of Hispanic Students for Eighth Grade .....................................66 14. Q-Q Plot of Average Percentage of Hispanic Students for Eighth Grade .........................67 15. Histogram of Percentage of African American Students for Eighth Grade .......................67 16. Q-Q Plot of Average Percentage of African American Students for Eighth Grade ..........68 17. Boxplot of Average Math STAAR Score for Seventh Grade ...........................................69 18. Boxplot of Percentage of Male Students for Seventh Grade .............................................69 19. Boxplot of Percentage of Hispanic Students for Seventh Grade .......................................70 20. Boxplot of Percentage of African American Students for Seventh Grade .......................70 21. Boxplot of Average Math STAAR Score for Eighth Grade ..............................................71 xii FIGURE 22. Boxplot of Percentage of Male Students for Eighth Grade ...............................................71 23. Boxplot of Percentage of Hispanic Students for Eighth Grade .........................................72 24. Boxplot of Percentage of African American Students for Eighth Grade ...........................72 1 Chapter 1 INTRODUCTION For decades, minority students have been a constant focus of academic achievement throughout the United States. Moreover, high-stakes and standardized tests have revealed a widening achievement gap in mathematics among Hispanics and African Americans and their White peers. Hong and Youngs (2008) state that “one goal of the recent No Child Left Behind legislation is to reduce the gaps in achievement between White students and racial minority students and between middle-class students and low-income students” (p. 1). The NCES, or the National Center for Education Statistics, performed an analysis of mathematics data from 2003 to 2007 and discovered that African Americans’ mathematics scores for fourth graders increased by 2.7%, and for the Hispanic students, it had an increase of 2.5% for the entire five years (NCES, 2007). While there has been a slight improvement, the gap is still significantly high as the same NCES report indicated that the White students outperformed the Hispanics and African Americans by 12% (NCES, 2007). The type of school students attend and the socioeconomic status of students have also been linked to academic achievement. Brown-Jeffy (2008) noted in her study that, “schools with larger percentages of Black and Hispanic students also are more likely to be urban public institutions having a higher percentage of students that are from socioeconomically disadvantaged [low socioeconomic] backgrounds” (p. 390). According to Wyatt and Mattern (2011), if a student is classified as a low socioeconomic student (have an annual household income at or below $30,000), they receive lunch free or at a reduced rate. In Flores’s (2007) study, he found that “the majority of White eighth graders (64%) attend schools with less than one quarter of the students being eligible for free or reduced price lunch, but only 15% of 2 African Americans and 25% of Latino eighth graders do so” (p. 36). This signifies that many African Americans and Latinos are low socioeconomic students and perhaps attend a Title I school. This school categorization indicates “at least 40% of students are in the free or reduced lunch program” (Malburg, 2014, p. 1). The Title I label was designed as a component of the Elementary and Secondary Education Act of 1965 (ESEA). The label or categorization is “the foundation of the federal commitment to closing the achievement gap between low-income and other students” (National Association for the Education of Young Children, 2015). The United States Department of Education (2014) states that Title I was created to assist students of low socioeconomic status in achieving satisfactory performance on rigorous state academic performance standards. However, Brown-Jeffy (2008) says, “Black and Hispanic students often are disadvantaged because of the particular characteristics of the schools they attend” (p. 391). This could result in a higher number of students not achieving proficiency on state assessments. Many studies, such as those referenced above, discuss minority versus White achievement. The researcher, however, seeks to compare minority mathematics achievement among students attending a school identified as Title I to minority mathematics achievement among students attending a school identified as Non-Title I. Therefore, minority students were compared against each other, but in different settings. This comparison will help determine if setting impacts performance instead of socioeconomic status. Statement of the Problem There is a call on educators to prepare all students for state assessments and college level courses in reading, science, and mathematics. The U.S. Department of Education (2014) requires states to implement college and career ready standards and assessments that measure 3 student achievement and growth. The problem, however, is minority students consistently score below the proficient level as defined by each state in mathematics. “By the third grade, White students outperformed Black students by 14 points in math” (Rojas-LeBouef & Slate, 2011, p. 11). There has been noted improvement in academic performance for minority students but the same is true for Whites. Therefore, the gaps do not narrow much, if at all. Many studies previously conducted found that socioeconomic status is the reason for the poor academic achievement of minorities. For example, Brown-Jeffy (2008) conducted a study that analyzed mathematics achievement of minorities compared to White students and found that there is an achievement gap. Her analyses concluded that when at least 50% of the student population is Black or Hispanic, all student achievement is lower. In addition to race, she tested gender and social status; she found that “socioeconomic status has the strongest influence on student academic achievement indicating that an individual’s poverty status has a greater influence on their academic achievement than any other characteristic” (Brown-Jeffy, 2008, p. 402). In some cases, this may be true, but it is a possibility that mathematics achievement could impact low socioeconomic (SES) minority students regardless of the label their school has received. Previously mentioned studies provide evidence that the inverse relationship between the reading and mathematics achievement of minorities in comparison to their White peers throughout the United States exists, however, there has not been an investigation of the relationship between the mathematics achievement of low SES minorities in Title I schools in comparison to minorities in Non-Title I schools in Texas. In this study, there was a comparison between minorities in various schools in the state of Texas. With the achievement of minorities versus White, African Americans and Hispanics have consistently been found to perform 4 significantly lower than their peers. Thus, this population was selected as a point of reference because of the lack of studies available that only include Hispanics and African Americans. Purpose of the Study This quantitative study was developed to investigate the mathematics achievement of seventh and eighth grade Hispanic and African American students in Title I schools compared to those in Non-Title I schools. The independent variables identified in the study were school type, gender percentage, Hispanic students’ percentage, and African American students’ percentage. The dependent variable was the Mathematics STAAR achievement score. The study analyzed the mathematics achievement using the 2015 Math STAAR, or State of Texas Assessment of Academic Readiness. Moreover, this study closely examined whether school type, gender percentage, Hispanic percentage, and African American percentage can be used to predict STAAR mathematics achievement. Socioeconomic status and demographics can impact a student’s ability to focus and perform. Chukwuemeka (2013) says, “Children feel happy in a peaceful and friendly environment where as schools sited in noisy urban streets are associated with deficits in mental concentration leading to student’s poor performance” (p. 35). The quality of a student’s environment also affects student performance. According to Chukwuemeka (2013), “As culture has to do with beliefs, values, norms, and socializations, research evidence have shown that the environment whether urban or rural industrial also contributes to what a child learns and how it is being learned” (p. 35). This study aims to better understand how achievement in mathematics is impacted for low SES minority students when they receive an education in two different school settings. 5 Research Questions and Hypotheses 1. Is there a significant relationship between school type, seventh grade male percentage, seventh grade Hispanic student percentage, seventh grade African American student percentage, and the school's average Math STAAR score for seventh graders? a. Is there a significant relationship between school type and the school's average Math STAAR score for seventh graders? H1ao: There is no statistically significant correlation between school type and the school’s average Math STAAR score for seventh graders. b. Is there a significant relationship between male percentage and the school's average Math STAAR score for seventh graders? H1bo: There is no statistically significant correlation between male percentage and the school’s average Math STAAR score for seventh graders. c. Is there a significant relationship between Hispanic student percentage and the school's average Math STAAR score for seventh graders? H1co: There is no statistically significant correlation between Hispanic student percentage and the school’s average Math STAAR score for seventh graders. d. Is there a significant relationship between African American student percentage and the school's average Math STAAR score for seventh graders? H1do: There is no statistically significant correlation between African American student percentage and the school’s average Math STAAR score for seventh graders. 2. Are type of school, seventh grade gender percentage, seventh grade Hispanic student percentage, and seventh grade African American student percentage statistically significant predictors of the school's seventh grade average mathematics STAAR scores? 6 H2o: Type of school, seventh grade gender percentage, seventh grade Hispanic student percentage, and seventh grade African American student percentage are not significant predictors of the school’s average mathematics STAAR scores. 3. Is there a significant relationship between school type, eighth grade male percentage, eighth grade Hispanic student percentage, eighth grade African American student percentage, and the school's average Math STAAR score for eighth graders? a. Is there a significant relationship between school type and the school's average Math STAAR score for eighth graders? H3ao: There is no statistically significant correlation between school type and the school’s average Math STAAR score for eighth graders. b. Is there a significant relationship between male percentage and the school's average Math STAAR score for eighth graders? H3bo: There is no statistically significant correlation between male percentage and the school’s average Math STAAR score for eighth graders. c. Is there a significant relationship between Hispanic percentage and the school's average Math STAAR score for eighth graders? H3co: There is no statistically significant correlation between Hispanic student percentage and the school’s average Math STAAR score for eighth graders. d. Is there a significant relationship between African American student percentage and the school's average Math STAAR score for eighth graders? H3do: There is no statistically significant correlation between African American student percentage and the school’s average Math STAAR score for eighth graders. 7 4. Are type of school, eighth grade gender percentage, eighth grade Hispanic student percentage, and eighth grade African American student percentage statistically significant predictors of the school's eighth grade average mathematics STAAR scores? H4o: Type of school, eighth grade gender percentage, eighth grade Hispanic student percentage, and eighth grade African American student percentage are not significant predictors of the school’s average mathematics STAAR scores. Significance of the Study Minority students in low-income, Title I schools are required by the state and federal government to perform at the same level as students in every other school without consideration of factors such as the availability of additional tutoring, whether or not a meal is provided, or if the child is homeless, which are beyond their control. Minority students are largely found in Title I schools and research “suggests that schools with large minority populations provide a different quality school environment for their clientele mostly because of the insufficiency of schools with large minority populations” (Brown-Jeffy, 2008, p. 402). Murnane (2007) argues “Children living in poverty, disproportionately children of color, tend to be concentrated in schools with inadequate resources and poorly skilled teachers” (p. 162). In addition, it is found on a consistent basis that minority students are continuing to score below their White peers. According to Blazer (2011), “In fact, studies have shown that the negative effects of high-stakes testing appear to be greater for low-performing, low-income, and minority students than they are for more advantaged students” (p. 4). In a mathematics achievement gap study by Brown-Jeffy (2008), she found evidence that “Black and Hispanic students tend to have achievement scores that are lower than their peers throughout their educational experience” (p. 398). It showed that minorities start school behind 8 their White peers and do not gain enough each year to catch up in achievement. Thus, by high school graduation, the mathematics achievement gap “between Black and White students leaves Black students ten points behind, [and] the mathematics achievement gap between Hispanic and White students leaves Hispanic students eight points behind” (Brown-Jeffy, 2008, p. 398). While there is extensive research available on the minority and mathematics achievement gap independently, there is not any known research completed that examines the mathematics achievement of minorities in two different school settings in Texas. Study results sought to provide insight as to whether attending a low-income or more affluent school affects how well low SES minorities perform on STAAR mathematics state assessments. In addition, the findings could potentially initiate a discussion about socioeconomic integration in the state of Texas. This is a concept where low SES students are combined with middle class students in a middle class school; it is not effective in the reverse manner. Curtis (2013) adds, “Studies continue to show that disadvantaged students learn better and achieve higher academically when they are integrated with advantaged students” (p. 468). Method of Procedure This quantitative study used multiple linear regression to determine whether school type, gender percentage, Hispanic student percentage, and African American student percentage are statistically significant contributors to overall campus average Mathematics STAAR achievement scores. This study examined any correlation between school type and campus average STAAR mathematics achievement of seventh and eighth grade students, gender (male) percentage and campus average STAAR mathematics achievement of seventh and eighth grade students, Hispanic student percentage and campus average STAAR mathematics achievement of seventh and eighth grade students, and African American student percentage and campus 9 average STAAR mathematics achievement of seventh and eighth grade students. The researcher aimed to determine if campus average STAAR mathematics performance can be predicted by any of the above mentioned factors. Selection of Sample This study used archival campus average STAAR mathematics achievement data of seventh and eighth grade middle school students from various schools in the state of Texas for the year 2015. The data are available for public access from the Texas Education Agency (TEA). All schools selected serve seventh and eighth grade students. These schools were selected from an Excel list of campuses in Texas provided by TEA. The list was first filtered by public schools within an independent school district. The list was then filtered by schools who serve seventh and eighth grade students. Schools that offer regular instruction were filtered as well. TEA does not provide documentation or access to an accurate count of Title I and Non-Title I schools in a central location. Therefore, the selected schools were each manually identified with the appropriate label using the Title I and Non-Title I school list found on the Texas Education Agency’s website. After the filters were completed and the school types were added, stratified random sampling was conducted to ensure an adequate number of Title I and Non-Title I schools were selected for the study. Once the schools were divided into the appropriate stratum, random sampling was conducted in each strata (group) to select the participants for the study. Collection of Data After the above referenced stratified random sampling of the school types and the random sampling of the school selections for the study were conducted, a list of schools to be used in the study was obtained. Archival data for these 150 schools included an even split of Title I (75) and Non-Title I (75) schools. The seventh grade STAAR Mathematics Campus Summary Report 10 and the eighth grade STAAR Mathematics Campus Summary Report were obtained for each of the 150 schools from TEA’s public database. The STAAR Mathematics Campus Summary Report includes a percentage breakdown as well as an exact number breakdown of the students who tested on the day the STAAR Mathematics exam was given. The report identifies the exact number and percentage of males, females, ethnicities, and subpopulations such as economically disadvantaged and special education. For the purpose intended in this study, the campus average STAAR mathematics scale scores for seventh and eighth grade were obtained, in addition to the gender percentage, Hispanic student percentage, and the African American student percentage. The campus average STAAR mathematics scale score served as the dependent variable in the study. This information was encoded into an Excel spreadsheet for easy transfer to SPSS. Treatment of Data The information obtained from the Excel spreadsheet was used to conduct the multiple linear regression analysis for the study. Brown (2009) states that multiple linear regression is used to represent the relationship between a dependent variable and several independent variables (p. 1). Prior to conducting this analysis, multiple linear regression requires that five assumptions are evaluated. These assumptions are: independence of observations, homoscedasticity of data, no multicollinearity, no significant outliers, and approximately normally distributed data. The data was obtained for seventh grade and eighth grade students at each school. Therefore, the multiple linear regression analysis was conducted for both grade levels. The independent variables in the study were the school type (Title I or Non-Title I), gender percentage, Hispanic student percentage, and African American student percentage. The dependent variable was the campus average STAAR mathematics scale score. The results of the 11 multiple linear regression analyses determined whether the independent variables of the study were predictors of the dependent variable (STAAR mathematics scale score). Definition of Terms No Child Left Behind Act of 2001 (NCLB). A federal law with “a number of measures designed to drive broad gains in student achievement and to hold states and schools more accountable for student progress” (Education Week, 2004, p. 1). Low-Socioeconomic Student. A low socioeconomic student is someone “whose family’s taxable income for the preceding year did not exceed 150 percent of the poverty level amount” (Office of Postsecondary Education, 2015, p. 1). State of Texas Assessment of Academic Readiness (STAAR). The State of Texas Assessment of Academic Readiness “is a new rigorous, standardized testing program for students in grades 3-12 that will focus on readiness for success in subsequent grades and courses, and ultimately, for college and career” (TEA, 2012, p. 1). Title I School. For a school to be “a Title I school, at least 40% of students must enroll in the free and reduced lunch program” (U.S. Department of Education, 2014, p. 1). Texas Education Agency (TEA). The Texas Education Agency is the administrative unit for primary and secondary education and provides leadership, guidance, and resources to help schools meet the educational needs of all students (TEA, 2011, p. 1). Academic Excellence Indicator System (AEIS). The AEIS pulls together a wide range of information on the performance of every district and campus in the state. These reports also provide extensive information on staff, finances, programs, and demographics for each school and district (TEA, 2011, p. 1). 12 Texas Academic Performance Report (TAPR). The TAPR replaces the AEIS and pulls together a wide range of information on the performance of students in each school and district in Texas every year. Performance is shown disaggregated by student groups, including ethnicity and low-income status. (TEA, 2013, p. 1). Socioeconomic Integration. Socioeconomic integration is combining students by socioeconomic status. Low socioeconomic students are placed in middle class schools, but “concentrations of poverty do not reach above the 50 percent level” (Kahlenberg, 2006, p. 25) Mathematics Achievement. For the intent of this study, mathematics achievement is defined as the score a student receives on the State of Texas Assessment of Academic Readiness (STAAR). Limitations The following limitations were present in this study: The math achievement identified in this study is based on the results/scores from the Mathematics STAAR test only. Private schools are not included in the study as they are not required to take the STAAR. Alternative schools are not included in the study as instruction is not given in the same manner of a regular instruction school. The ability to generalize to the entire population of seventh and eighth grade minority students attending each a Title I and a Non-Title I school beyond the state of Texas. Delimitations The researcher narrowed the study to include public, independent school districts in selected areas within the state of Texas. A nationwide study was not conducted as the testing measurements, requirements, and standards differ by state. The sample population was narrowed 13 to Title I and Non-Title I schools in the state of Texas. The sample, however, is similar in nature to the population of seventh and eighth grade minority students in various Title I schools in Texas and various Non-Title I schools in Texas and can thus be generalized. Assumptions The following assumptions were made with this study: The demographic and assessment data obtained from the TAPR are accurate and a true representation of information. The STAAR test is a valid and reliable testing instrument in Texas. The sample is representative of the Hispanic and African American population. Organization of Dissertation Chapters This research study is presented in five chapters. Chapter 1 includes the background of the study, statement of problem, purpose of the study, research questions, hypotheses, significance of the study, method of procedure, definition of terms, limitations, delimitations, and assumptions of the study. Chapter 2 delivers a review of the literature which includes the No Child Left Behind Act of 2001 as it relates to minority academic achievement, high-stakes testing as it relates to minority achievement, descriptive information about the STAAR, state and federal accountability systems, the overall achievement gap between minorities and Whites, the mathematics achievement gap between minorities and Whites, the theories that correlate with minority achievement, socioeconomic integration, and a conclusion of the chapter. Chapter 3 identifies and describes the methodology used for the quantitative research study including the participant selection, data collection apparatuses, and data analysis procedures. Chapter 4 presents the findings of the study. Chapter 5 discusses the research findings, conclusion, and recommendations. 14 Chapter 2 REVIEW OF THE LITERATURE This chapter will review the literature related to the math achievement of minority students in Title I and Non-Title I schools in the state of Texas. High-stakes testing was established as an accountability component of the No Child Left Behind Act of 2001 (NCLB); the terms of accountability, however, were in the hands of the state. According to Supovitz (2014), “The legislation required states to adopt test-based statewide accountability systems, testing annually in reading, math, and eventually science from grades 3 through 8 and one year of high school” (p. 4). Throughout the United States, schools have implemented different policies and techniques for accomplishing high-stakes testing goals as defined by each individual state. Supovitz (2014) also noted, “States were to define proficiency and adequate yearly progress to get all students to proficiency in 12 years” (p. 4). A concern, however, of stakeholders is that the federal government is allowing state governments, such as the Texas state government, to create the assessments students are required to take as well as develop the passing standards for those assessments. The problem with this tool of measurement and the current accountability standards is there is a consistent achievement gap between low-socioeconomic minorities and their more affluent White peers. According to Blazer (2011), high-stakes test have a greater impact on low-income, minority students than their more affluent peers. With each tested year, the high-stakes tests continue to show results that place low socioeconomic minorities at the lower end of the academic spectrum as well as a widening achievement gap between them and their White peers. Blazer (2011) believes this is because the [economically] disadvantaged students have been found to have lower than average scores and to be more likely to live in states with strong accountability systems (p. 4). 15 This review of literature is divided into nine sections. The first explains a brief history of NCLB as it relates to minority achievement and a brief overview of Title I schools. This is followed by the goal of high-stakes tests to benefit or improve minority achievement. The chapter will then describe the State of Texas Assessment of Academic Readiness, or STAAR, which is used to measure student performance in Texas. The fourth section breaks down the accountability systems. The racial and gender achievement gap across content areas will follow then move more specifically to the mathematics achievement gap for minority students. The next section discusses the theories that directly align with the problem of minority achievement. The eighth section discusses socioeconomic integration and follows with the conclusion in the final section. No Child Left Behind There was a need and desire of government officials to improve education in the United States during the era of President Lyndon B. Johnson. Therefore, Congress at the time authorized the Elementary and Secondary Education Act (ESEA) of 1965. In this law, “ESEA offered new grants to districts serving low-income students, federal grants for text and library books, it created special education centers, and created scholarships for low-income college students” (U.S. Department of Education, 2016, p. 1). The goal was to improve education for every child and allow for equal access to quality education. President Johnson was a former teacher and understood that children from low socioeconomic backgrounds require more academic resources (Social Welfare, 2015, p. 1). During his first United States Presidency term, George W. Bush, similar to President Johnson, aimed to address the student achievement gap in the United States. Therefore, he drafted the framework for education reform with the help of the ESEA of 1965 and Houston 16 Independent School District’s Superintendent at the time, Rod Paige. The U.S. Department of Education (2004a) noted that “President Bush emphasized his deep belief in our public schools, but an even greater concern that too many of our neediest children are being left behind” (p. 1). In order to increase academic achievement, Bush wanted every school aged child to receive a quality education. Hong & Youngs (2008) identified “One goal of the recent No Child Left Behind legislation is to reduce the gaps in achievement between White students and racial minority students and between middle-class students and low-income students” (p. 1). The law itself put the responsibility on each state to develop standards, assessments, and levels of proficiency for the schools within their state. Consequently, “student proficiency has little common meaning across states” (U.S. Department of Education, 2010, p. 30). Thus, to measure the progress of achieving this goal set forth by NCLB, high-stakes testing was reimagined and reintroduced in the public school systems. Title I Title I is “the largest federal program to assist school districts” and “provides funds to improve the education of children in high poverty schools” (Learning First Alliance, 2002, p. 4). The Title I section of the NLCB act was revised from the ESEA of 1965 to include “additional specificity and requirements, particularly in the areas of assessment and accountability” (Learning First Alliance, 2002, p. 4). The U.S. Department of Education (2015) confirms that “Title I is designed to help students served by the program to achieve proficiency on challenging state academic achievement standards” (p. 1). In order for a school to qualify for the classification of Title I, they must have at least 40 percent of the student population categorized as low-income. Within the classification of Title I, there are three levels: (a) highest poverty schools which is where “over 75% of the students are eligible for free or reduced-price lunches” 17 (U.S. Department of Education, 2014, p. 1), (b) high-poverty schools which is where “50-74% [of the students] are eligible for free or reduced-price lunches” (U.S. Department of Education, 2014, p. 1), and (c) poverty schools which is where “fewer than 50% of their students [are] eligible for free or reduced-price lunches” (U.S. Department of Education, 2014, p. 1). According to information obtained from the U.S. Department of Education (2004a), “In the school year 2009-10, more than 56,000 public schools across the country used Title I funds to provide additional academic support and learning opportunities to help low-achieving children master challenging curricula and meet state standards in core academic subjects” (p. 1). Title I schools are allowed to use the additional funding to create school-wide programs to improve academic achievement or targeted assistance programs which identify specific students who are struggling to be successful academically. This section of the ESEA of 1965 as well as the NLCB act of 2001 both aimed to provide resources to Title I schools with an effort to close the achievement gap between “high- and low-performing children, especially the achievement gaps between minority and nonminority students, and between disadvantaged children and their more advantaged peers” (US Department of Education, 2004, p. 1). While there is sufficient evidence based research that supports the theory that low socioeconomic minority students perform much lower than more advantaged, non-minority students on high-stakes tests, there is a need to study the relationship between the performance of minority students in a Title I school and the performance of the same group of students in a Non-Title I school to determine if the type of the school they attend impacts overall mathematics achievement. 18 What is a High-stakes Test? Across the United States, high-stakes tests are used for many purposes. A high-stakes test is “any test used to make important decisions about students, educators, schools, or districts, most commonly for the purpose of accountability” (edglossary.org, 2014, p. 1). The test may be used to determine whether students are promoted or graduate from high school. For teachers, this type of test may be used as a tool in the performance appraisal or to determine whether a bonus or raise will be issued. For schools, high-stakes test may be used to rate academic success or make executive changes in leadership and staff. In education, under NCLB, high-stakes tests are used as a means to measure whether there has been progress made to narrow the academic gap between low socioeconomic minorities and their more affluent White peers. Horn (2003) suggests, “Test scores give us important information, but they do not give us all the information necessary to make critical decisions” (p. 30). With respect to closing the achievement gap and minority achievement, “to date there is no consistent evidence that high-stakes testing works to increase achievement” (Berliner et al., 2005, p. 10). In a study conducted by Guisbond, Neill, and Schaeffer (2012), it was determined that “growth on NAEP [National Assessment of Educational Progress] was more rapid before NCLB became law and flattened after it took effect” (Guisbond et al., 2012, p. 3). Reading scores showed little to no movement and math scores showed a continued widening in the achievement gap. In the same study, “Fourth grade scores increased just 3 points to 221 between 2003 and 2011, remaining level since 2007” (Guisbond et al., 2012, p. 3). The same held true for the eighth grade findings in this study, only showing a movement of two points. On the other hand, “[Barak] Rosenshine found that average NAEP increases were greater in states with high-stakes testing policies than those in a control group of states without” (Berliner et al., 2005, p. 8). 19 Stakeholders feel the pressure from high-stakes testing because of the broad nature of its effects. It was predicted that “by 2014, less than 25 percent of Poor and Black students will achieve NAEP proficiency in reading, and less than 50 percent will achieve proficiency in math” (Lee, 2006, p. 11). Students of low socioeconomic status often attend schools that are classified as Title I and perhaps lack the resources needed to have a fair chance. Grant (2004) states, “The purpose of these tests is to measure not so much what the student has already learned, but how much the student is likely to learn in the future, or better, the student’s ability to learn” (p. 4) Thus, scholars suggest that minorities are set up to fail and if all students do not perform at proficient levels on high-stakes tests, the failure rate will continue to increase. Guisbond & Neill (2004) found, “The National Conference of State Legislatures estimated that, according to these standards, some 70 percent of schools nationwide will fail” (p. 13). Because there is inconsistent data available, there is a need to study the academic performance of minorities in two different settings using state assessments as the tool of measurement. State of Texas Assessment of Academic Readiness Each year, the state of Texas administers state assessments designed to measure whether students have mastered grade level and content specific state standards. In 2003, the Texas Assessment of Knowledge and Skills (TAKS) was the testing measure created to offer a more challenging assessment of student knowledge beyond basic skills. According to the Texas Education Agency (2006), “the TAKS test [was] designed to measure the extent to which a student has learned and is able to apply the defined knowledge and skills at each tested grade level” (p. 13). The grade level and content area curriculum standards created by the Texas Education Agency (TEA) were used to align each exam. In order for a high school senior to graduate, they must have mastered the required exit level exams first administered during their 20 eleventh grade year as outlined by the state of Texas. The grading standard for these exams was based on a three-tiered system. Students could receive a performance level of “Did Not the Meet Standard (unsatisfactory performance), Met the Standard (satisfactory performance), and Commended Performance (high academic achievement)” (TEA, 2012, p. 126-127). In 2013, the State of Texas Assessment of Academic Readiness (STAAR) replaced the TAKS. TEA (2015) states, “STAAR is an assessment program designed to measure the extent to which students have learned and are able to apply the knowledge and skills defined in the state-mandated curriculum standards, the Texas Essential Knowledge and Skills (TEKS)” (p. 1). According to UT Austin (2012), “The 74th Texas Legislature passed Senate Bill 1 calling for the State Board of Education to adopt Essential Knowledge and Skills as the required curriculum for Texas students” (p. 1). In addition, UT Austin (2012) notes that “TEKS are established by a committee of educators, curriculum and assessment specialists, business representatives, higher education faculty, and parents” (p. 1); they are the learning objectives and goals designed to ensure that teachers are clear on what should be taught throughout the school year. Curriculum standards covered on the assessments were reorganized to include readiness (most critical) and supporting standards (previously learned but currently reinforced). TEA (2015) adds that “Every STAAR question is directly aligned to the TEKS currently implemented for the grade/subject or course being assessed” (p. 1). For test takers, the test material and questions are expected to be more challenging than previous tests and use higher order thinking question stems. In Texas, according to TEA (2012), the following STAAR assessments are issued: 21 Grade 3 – Reading and Mathematics (English and Spanish) Grade 4 – Reading, Mathematics, and Writing (English and Spanish) Grade 5 – Reading, Mathematics, and Science (English and Spanish) Grade 6 – Reading and Mathematics (English Only) Grade 7 – Reading, Mathematics, and Writing (English Only) Grade 8 – Reading, Mathematics, Social Studies, and Science (English Only) EOC (High School) – English I (Reading and Writing), English II (Reading and Writing), English III (Reading and Writing), Algebra I, Geometry, Algebra II, Biology, Chemistry, Physics, World Geography, World History, and U.S. History When the STAAR was implemented, it was a mandate for high school students to exhibit mastery on the twelve exams listed above to graduate, however, “it is a reality that high-stakes standardized testing prevents some students who have otherwise completed all of their course work from graduating, just because they did not pass the exit exam” (Kritsonis & Walden, 2008, p. 5). In 2013, the Texas Legislature changed the STAAR End of Course graduation requirements. Now, students are required to exhibit satisfactory performance on the “Algebra I, English I Reading and Writing, English II Reading and Writing, Biology, and U.S. History end of course exams” to graduate from high school (TEA, 2012). In 2014, the Texas Education Agency implemented revised mathematics standards for grades K-8. With this change, STAAR Mathematics: 22 Will assess 35% of the 70% supporting standards and 65% of the 30% readiness standards (TEA, 2012, p. 5). Has more cognitively complex questions (TEA, 2012, p. 33). Has more questions that have multiple steps (TEA, 2012, p. 33). Has more questions that have application context (TEA, 2012, p. 33). Has more open-ended (griddable) questions (TEA, 2012, p. 33). Includes questions that are asked in a reverse manner (TEA, 2012, p. 34). Includes questions where the answer could be “Not here” or “None of the above” (TEA, 2012, p. 34). Includes questions that assess content and incorporate process skills (TEA, 2012, p. 34). Includes questions that require students to manipulate a ruler (TEA, 2012, p. 34). Will have process skills incorporated into at least 75% of the test questions (TEA, 2012, p. 40). The Spring 2015 administration of the grades 3-8 mathematics STAAR did not count against students, schools, or districts. The scores obtained will be used to determine a passing rate for subsequent years. Accountability Systems State Accountability STAAR Performance Standards. With the implementation of the new, more rigorous assessment, the performance measures in place from the TAKS (Commended, Met Standard, and Did Not Meet Standard) changed. According to TEA (2015), the new STAAR performance standards are: (a) Level I-Unsatisfactory Academic Performance, (b) Level II-Satisfactory 23 Academic Performance, and (c) Level III-Advanced Academic Performance. If a student reaches Level III, this signifies that they are extremely prepared for the next grade level and their ability to think critically is well above average (TEA, 2013, p. 1). Level II tells state and school officials “that students are prepared for the next grade level with room for improvement” (TEA, 2013, p. 1). Level I illustrates that the student did not meet the TEKS learning goals and objectives for the school year (TEA, 2013, p. 1). For the students already in high school prior to 2013, the graduation determination, grade promotion stipulations, and end of course requirements carried over from the TAKS test previously in place (TEA, 2012, p. 85-86). State Measures. In the state of Texas, an accountability system has been established in terms of measuring the performance of students on the STAAR test. Schools and teachers are challenged with preparing students for this exam, which in turn will give the Texas Education Agency the information needed to evaluate the campus. For 2013 and beyond, “a framework of four Performance Indexes will include a broad set of measures that provide a comprehensive evaluation of the entire campus or district” (TEA, 2013, p. 4). These four indexes are: “(1) Student Achievement, (2) Student Progress, (3) Closing Performance Gaps, and (4) Postsecondary Readiness” (TEA, 2013, p. 4). This system is designed to rate the campus and/or school district’s overall performance instead of accentuating a particular student group or content area. In each index, campuses and districts are to reach a target goal assigned by the state which indicates the campus is potentially on track for postsecondary readiness. The index scores or targets range from 0 to 100. In Index 1, the target goal in 2013 was 50, increasing to 55 in 2014. In Index 2, the target goal in 2013 was “based on 5th percentile of Index 2 outcomes based on the 2013 performance results by campus type: elementary, middle, or high school” 24 (TEA, 2014, p. 1). The 2014 target for Index 2 was based on 2013 performance results. In 2013, Index 3 had a numerical target of 55; but, in 2014, Index 3 target goal was “based on 5th percentile of Index 3 outcomes based on the 2014 performance results by campus type: elementary, middle, or high school” (TEA, 2014, p. 2). Index 4 was based on graduation plan and graduation score in 2013, changing in 2014 to “STAAR Final Level II, Graduation Score, Graduation Plan, and College-Ready Graduates” (TEA, 2014, p. 3). If campuses and districts fail to reach the target goal in one or more indexes, they “will receive a rating of Improvement Required” (TEA, 2014). A met standard rating is assigned if they meet the target goal in all four indexes (TEA, 2014). These are currently the only two ratings that can be received. According to the Texas Education Agency in 2014, 84.9% of the campuses in Texas received a Met Standard rating, while 8.7% received an Improvement Required rating (TEA, 2014, p. 1). Districts are assessed each year and in 2014, 90.1% of districts in Texas received a Met Standard rating, while 9.0% received Improvement Required (TEA, 2014, p. 1). Academic Performance Reports. The Texas Education Agency compiles an academic performance report each year based on the STAAR results for every campus and district in Texas for public viewing in the fall. Prior to 2012, this report was titled the Academic Excellence Indicator System Report, or AEIS. Today, this report is known as the Texas Academic Performance Report, or TAPR. The difference between the two reports is the financial section which is now “created by the Division of School Finance at TEA” (TEA, 2012). This section is accessed through an external link. On the TAPR, “performance is shown disaggregated by student groups, including ethnicity and low-income status” (TEA, 2014). In addition to 25 academic performance on state assessments, the TAPR includes information about the campus and district staff, such as average salary and years of experience. In Texas, a school’s report card is developed from information found in the TAPR. At any point in time, a person may find the current academic state of a campus based on the standards of TEA in these reports. Individuals who are interested in placing their child in a particular campus, teaching at a particular campus, or becoming an administrator on a particular campus can access the TAPR and school report card to retrieve academic information as well as demographic information about the campus. Federal Accountability Adequate Yearly Progress, or AYP, is a federal requirement determined by the states and is based on schools and local education agencies (LEA) served under Title I. The U.S. Department of Education (2009) states, “adequate yearly progress as defined by a state describes the amount of yearly improvement each Title I school and district is expected to make in order to enable low-achieving children to meet high performance levels expected of all children” (p. 1). While the state has accountability requirements for schools and districts, the state of Texas must also meet the federal requirement of reporting “performance measures with annual measurable objectives (AMOs)” (TEA, 2015, p. 90). According to the Center on Education Policy (2012), “To make AYP, a school or district must meet every AMO, not only for the overall student population but also for each of several student subgroups” (p. 4). These subgroups are students with disabilities, economically disadvantaged, English Language Learners (ELL), special education, low-income students, White, Asian American, Hispanics, Native American, and African American. On September 30, 2013, “the U.S. Department of Education approved the 26 Texas waiver request, [which waived the AYP calculations] and allowed the state’s existing systems of interventions to guide and support improvement of schools” (TEA, 2014, p. 77). Now, federal accountability requires that states develop a performance target percent that must be met for all students within the state. TEA (2015) identifies that “The federal accountability disaggregated safeguard measures include four components: performance rates, participation rates, graduation rates, and limits on the use of alternative assessments” (p. 90). This means that the state must report performance information in these categories to the U.S. Department of Education with an analysis of achievement for all students, as well as report the demographic specific results. In 2014, the state of Texas had a state performance target of 55% for all students in mathematics, writing, reading, science, and social studies as well as 55% for each subgroup in the same content areas (TEA, 2014, p. 78). According to TEA (2014), “The federal performance target for all students of 79% was assigned for mathematics and reading as well as for Hispanics, Whites, African Americans, Economically Disadvantaged, Special Education, and ELLs” (p. 78). The participation rates in mathematics and reading were 95% for all students and all subgroups. Four-year federal graduation rates were 80% for all students and 80% for all subgroups (TEA, 2014, p. 78), whereas 5-year federal graduation rates were 85% for all students and all subgroups. There is also a limit on the number of students allowed to take alternative STAAR assessments for each district to remain in federal compliance. TEA (2015) states that “Federal limitations require that the number of scores that meet the STAAR Alternate 2 performance standard not exceed one percent of the district’s total participation” (p. 92). STAAR Alternate 2 is a test designed to “meet the diverse needs of students with significant cognitive disabilities enrolled in grades 3 through 8 and EOC subjects” (TEA, 2015, p. 20). According to the federal accountability standards, each of the targets must increase each year. 27 Based on the state and federal accountability systems, low socioeconomic, minority students have the same academic expectations set for them as the remaining subgroups. The dissemination of information shows a leveled playing field among minority students and non-minority students as well as among the disadvantaged and their advantaged peers. Yet, with the same standards and leveled playing field, low SES minorities continue to perform below their more affluent, White and Asian peers. Achievement Gap In the United States, achievement gap is defined “as the differences between the test scores of minority and/or low-income [low SES] students and the test scores of their White and Asian peers” (National Education Association, 2015, p. 1). The achievement gap is measured by using data from student performance on high-stakes and standardized tests such as the National Assessment of Educational Progress (NAEP). The student classification of low SES is “someone whose family’s taxable income for the preceding year did not exceed 150 percent of the poverty level amount” (Office of Postsecondary Education, 2015, p. 1). Low socioeconomic status (SES) is often associated with African Americans and Latinos, “because Blacks and Latinos are disproportionately represented in the low SES” (Brogan, 2009, p. 1). According to Burkam & Lee (2002), “for example, 34% of black children and 29% of Hispanic children are in the lowest quintile of SES compared with only 9% of White children” (p. 2). Baker & Johnston (2010) identified that “An inverse relationship exists between the percentage of students receiving subsidized lunches and the adjusted pass rates on these tests showing that students’ SES is related to their achievement” (p. 194). On state assessments, Baker and Johnston conducted a study including fifty-one equally funded Non-Title I and Title I schools in Florida. Upon concluding their study, they found that “of all students tested, sixty-six 28 percent attended Title I schools and of that group, only thirty-nine percent passed the Florida Comprehensive Assessment Test, or FCAT, whereas sixty-five percent of Non-Title I students passed” (Baker & Johnston, 2010, p. 197). Palardy (2008) found that “students from low SES schools entered high school 3.3 grade levels behind students from higher SES schools” (p. 37). At Texas State University, Opheim and Tajalli (2004) found that “for each percent increase in the number of economically disadvantaged students in a campus, the odds of the campus being a high-performing campus drops by 6.3% and 8.4% respectively for 4th and eighth grade campuses” (p. 51). Researchers are continuously conducting studies that identify socioeconomic status as a major influence on student academic achievement. Children from these households enter education lacking the academic skills because they are children of parents who are not educated or able to offer them academic assistance. According to the American Psychological Association (2015), “initial academic skills are correlated with the home environment, where low-literacy environments and chronic stress negatively affect a child’s pre-academic skills” (p. 1). Race The academic performance gap between racial subpopulations in education is a widely studied problem as well. Giroux & Schmidt (2004) found that “Current trends in the literature seem to indicate that student achievement is adversely affected by socioeconomic status and ethnic grouping” (p. 216). The reality is minorities have consistently scored below their White peers. Research shows “that a gap in achievement between White and minority students is already present before students enter kindergarten, and this gap often persists into adulthood” (Williams, 2011, p. 66). Throughout the country, researchers have studied the relationship between academic performance and race, continuously showing study after study that race is 29 connected to academic performance of students. Elizabeth Stearns found “at the end of the 2001 school year, 82 percent of Whites were performing at or above grade level, while 52 percent of African Americans, and 59 percent of Latino students were doing so” (Stearns, 2002, p. 2). There is still an achievement gap. Furthermore, “Armor has shown that for the past 15 years African American students have improved; however, the same study showed that the White students had also improved their grades so the gap still exists” (Shirvani, 2009, p. 53). Bartee & Hunter (2003) suggest that “some proposed reasons for achievement differences suggest that racial, environmental, and other institutional differences mitigate the ability of Blacks to acquire these skills at the same rate as Whites” (p. 155). The same has been said about Hispanics. Giroux & Schmidt (2004) note “the Commission’s findings conclude that Latino students are likely to attend under-achieving schools and that by the time they arrive in high school, Latino students are far behind others in academic achievement” (p. 216). American College Test (2012) argues that “Asian and White students start with the highest scores and grow at the fastest pace; African American and Hispanic students start with the lowest scores and grow at the slowest pace” (p. 2). Regardless of reason or content, minorities are continuously outperformed by their White peers on high-stakes and standardized tests. Researchers mostly looked at the achievement of Hispanic and African American students compared to White students without considering the school’s condition. It is important to look at the difference in achievement among minorities that are placed in a Non-Title I school as opposed to a Title I school. Gender There has also been discussion of gender and its impact on student achievement. John (2008) found that “female minority students have been outperforming their male counterparts, especially in the higher education setting, but there is evidence that the achievement gap stems 30 back as far as middle and elementary school” (p. 2). It has been argued by brain researchers such as Dr. Sandra Witelson and Dr. Apostolos Georgopoulos, that males and females have different brain types. However, Eliot (2010) found the “the truth is that neuroscientists have identified very few reliable differences between boys’ and girls’ brains” (p. 32). It is often noted that females are calmer and relaxed, thus leaning towards excellence in English, Reading and History. While males are more hands-on and hyper in movement, they would typically navigate to excellence in Math and Science. Eliot’s (2010) study argues that when students reach the 3rd grade, “20 percent more girls than boys score in the proficient range as readers, according to NAEP data – a gap that grows to 38 percent by eighth grade and a startling 47 percent by the end of high school” (p. 34). The reasons cited for this gap have to do with classroom teaching delivery such as direct instruction, inquiry based learning, or cooperative learning. According to Jeanfaivre (2009), the 21st century classroom “in which standardized tests are the measure of proficiency is well suited to girls’ learning habits, but not necessarily for boys, who are generally speaking, much more hands-on learners and full of energy” (p. 1). This idea suggests that the “sit and get” classrooms lose the male students. There is not much research that studies the achievement gap in terms of gender, but that which exists is geared toward all males and females regardless of racial group. For purposes of this study, it is a key element to look at the achievement of males and females within the minority population. This will give a better understanding as to how minorities fare in academics when compared to students with the same categories and classifications as themselves. Mathematics Achievement Gap Many individuals of school age, who have finished school, or chose to teach mathematics, would suggest that it is the hardest subject to teach and learn. Singham (2003) 31 agrees in that “mathematics teaching and learning has also been the toughest educational problem; the subject typically has the lowest pass rates in proficiency tests” (p. 588). Flores (2007) supports the idea as well in finding that “by eighth grade, 91% of African American and 87% of Latino students are not proficient in mathematics, as measured by the National Assessment of Educational Progress (NAEP)” (p. 30). When teaching the mathematics concepts, low socioeconomic students often make connections to the problems based on experience and their common sense. Therefore, the connections that they make are often not aligned with the concept being taught; “this reasoning puts them at a disadvantage when they missed the mathematical point of the problem” (Lubienski, 2007, p. 55). Low-socioeconomic students are “most in need of mathematics instruction that emphasizes questioning and problem solving” (Lubienski, 2007, p. 55). This is illustrated in the academic performance of students on assessments across the country. Hemphill (2011) found “at grade 4 in 2009, the 11-point achievement gap for eligible [free or reduced price lunch] students was smaller than the 16-point gap for not eligible students, and both gaps were smaller than the 21-point gap for all grade 4 students” (p. 14). These statistics can be slightly misleading as the number of Hispanic students representing eligibility for the school lunch program is larger than the White student population. In a study by Hemphill et al. (2011), they discovered “the achievement gap is larger when all students are considered because most Hispanic students (about 77 percent in 2009) come from low-income families-that is, are eligible for free or reduced price lunches” (p. 14). White students are the exact opposite, in that, “70 percent in 2009 come from families with higher incomes and are not eligible” (Hemphill et al., 2011, p. 14). These same statistics were similarly reflected in grade 8 of the same years. This signifies that “a large portion of the population 32 remains a generation behind their White peers in mathematics achievement” (Klum, 2007, p. 267). Race as it Relates to Mathematics Achievement As the years progress, minority students have consistently scored much lower than their White peers on mathematics assessments; for example, “in eighth grade math, the large gap between Whites and Blacks remained at 32 points from 2007 to 2009, closing by just one point in 2011” (Guisbond et al., 2012, p. 3). Johnson (2006) concluded, based on a report by the California Teacher Association, that “in 2003, of the fourth and eighth graders tested, African American and Latino students were found to perform on average, statistically, three years behind their White counterparts in math” (p. 2). This, however, is a nationwide gap. Cheema and Galluzo (2013) conducted a study measuring the mathematics achievement gap and found that there was a significant association with math achievement, socioeconomic status, and race. Cheema & Galluzzo (2013) also noted that “The White-black achievement gap was more strongly associated with math achievement as compared to the White-Hispanic achievement gap” (p. 104). More specifically, “the negative signs and magnitudes of these correlation coefficients suggest that the White-Hispanic achievement gap is smaller than the White-Black achievement gap, with White exceeding both Black and Hispanics, in terms of math achievement” (Cheema & Galluzzo, 2013, p. 104-105). Socioeconomic status could potentially put a stain on it all because “only 13% of students from poor families are at the proficient or advanced levels compared to 38% of students from non-poor families” (Flores, 2007, p. 30). In high school, the mathematics achievement gap continues to widen as students increase in grade level. Flores (2007) found “Only 49% of Latinos and 47% of African American students have taken pre-algebra or algebra in eighth grade compared to 68% of European 33 American students” (p. 35). This means that many minorities enter high school academically behind their White peers and lacking the foundational math skills to be successful on high-stakes or standardized assessments. The “2005 NAEP test found that a staggering 39% of U.S. high school seniors lack even a basic understanding of high school mathematics” (Mervis, 2007, p. 1485). Brown-Jeffy (2008) wanted to take a closer look at how the achievement gap is reflected in terms of race and found that “Black and Hispanic students tended to score lower on the mathematics test than White or Asian students in both the 10th grade and the 12th grade” (p. 393). She continued with the study and narrowed the data based on race. In this, she discovered that “Black students’ average 12th grade mathematics achievement score remained about 13 points lower than the White students’ average score” (Brown-Jeffy, 2008, p. 394). In the same study, “Hispanic students’ average 12th grade mathematics achievement score remained almost 11 points lower than the White students average score” (Brown-Jeffy, 2008, p. 394). Lastly, Lee (2004) examined the NAEP for the trends in the mathematics achievement gap and the different facets of inequity that exists within it. In this study, “the percentage of 12th grade Black students performing below the Basic proficiency level in mathematics was 3 times larger than that of their White peers” (Lee, 2004, p. 62). While these studies prove that there is a significant difference in academic achievement between minorities and Whites in mathematics, only one, Brown-Jeffy (2008), analyzes the type of school and environment that minorities are in when they are being taught and when they test. She suggests that “being in a school with a high concentration of Black and Hispanic students lessens all students’ chances of academic achievement” (Brown-Jeffy, 2008, p. 388). 34 Gender as it Relates to Mathematics Achievement There have not, however, been a significant number of studies conducted on the mathematics achievement gap among minority males and females. Brown-Jeffy (2008) extended her study of the mathematics achievement gap beyond race and found that “male students tend to have higher mathematics achievement scores than female students, and students from higher SES households have higher mathematics achievement scores” (Brown-Jeffy, 2008, p. 396). According to the National Center for Education Statistics (2011), “the Hispanic-White mathematics gap did not change significantly for either male or female students at either grade [4th or eighth] when comparing 2009 to 1990” (Hemphill et al., 2011, p. 12). On the contrary, using the same data set, Anderson et al (2009) found that “among [4th grade] females, the gap was narrower in 2007 as the average score gains of Black females were greater than those of their White peers” (p. 8). The males, however, did not have significant gains. With the lack of information available to analyze the mathematics performance among minority males and females, this study is significant as a contribution to the available literature. The research identified in this section provides evidence that an academic performance gap exists in mathematics between minorities and their White peers. However, a large portion of the information does not account for or take a deeper look at the type of school the students attend while learning and while testing. There is a need to study whether there is a relationship between low SES minority students’ mathematics performance when in a Title I school as opposed to a Non-Title I school. There are two theories that correlate with minority achievement on high-stakes and/or standardized testing that will help explain the consistent gap between them and their White peers. 35 Theories Relating to Minority Achievement Critical Race Theory Critical race theory can also help explain the challenges minorities and low-socioeconomic students face in education each day. Hiraldo (2010) notes that in 1970, Derrick Bell and Alan Freeman joined forces to discuss racial reform in the United States and how it was transpiring at such a slow pace (p. 53). From this meeting, Critical Race Theory, or CRT, was created and used by many in the education field to explain the inequities that exist; as a result, “CRT analyzes the role of race and racism in perpetuating social disparities between dominant and marginalized racial groups” (Hiraldo, 2010, p. 54). The underlying idea of critical race theory is that “racism is a fundamental part of U.S. societal structure” (Hiraldo, 2010, p. 57). In order to fully understand Critical Race Theory, five tenets are addressed: (a) Counter-Storytelling, (b) The Permanence of Racism, (c) Whiteness as Property, (d) Interest Convergence, and (e) Critique of Liberalism (Hiraldo, 2010). The first tenet, counter-storytelling, is a tenet of critical race theory that “allows for the challenging of privileged discourses, the discourses of the majority, therefore, serving as a means for giving voice to the marginalized groups” (DeCuir & Dixson, 2004, p. 27). When telling the story of minorities and low socioeconomic students in education, more specifically, academic achievement on state-written standardized assessments, it allows for others to understand what life is like when you are a member of this group as opposed to how it is portrayed from the standpoint of the majority. The second tenet, the permanence of racism, is a “belief that racism is endemic to and permanent within American society given the history of racism in the U.S.” (Dixson, 2007, p. 11). Even though the United States has made small gains in attempting to eliminate racism, it is vital to accept the idea that racism, in some form, will always exist. Furthermore, “the notion of the 36 permanence of racism suggests that racist hierarchical structures govern all political, economic, and social domains” (DeCuir & Dixson, 2004, p. 27). This is present in education policies and laws as well as within the school when it comes to teaching practices, strategies, and disciplinary decisions. If there are racist hierarchical structures, these same structures make decisions centered on academic achievement that is consistently showing minorities and low-socioeconomic students at a disadvantage when compared to Whites. The third tenet, Whiteness as property, suggests that it is an asset to be White and only White people can benefit from it. Minorities and low socioeconomic students have limited options and are often restricted in terms of schools, academic environments, and an overall high quality education. If there is a limitation on these vital elements of education, this is placing minorities and low-socioeconomic students behind before they even start school, yet, they are required to master the same state assessments as Whites. Even though there are policies in place that create these restrictions, “school districts have served to reify this notion of Whiteness as property whereby the rights to possession, use and enjoyment, and disposition, have been enjoyed almost exclusively by Whites” (DeCuir & Dixson, 2004, p. 28). The fourth tenet, interest convergence, instructs that there must be a merge of interests before the racial divide ceases to exist in education. Dixson (2007) believes “the interest of African Americans and (people of color) in achieving racial equality will be accommodated only when that converges with the interests of Whites who are in policy-making positions” (p. 7). Therefore, policy makers must want minorities to be college ready and productive citizens of society just as they do for Whites. This means access to a high quality education should not be optional, based on socioeconomic status, or skin color. 37 Lastly, the critique of liberalism, “stems from the ideas of color-blindness, the neutrality of the law, and equal opportunity for all” (Hiraldo, 2010, p. 56). Furthermore, “the notion of colorblindness fails to take into consideration the persistence and permanence of racism and the construction of people of color as Other” (DeCuir & Dixson, 2004, p. 29). However, in just seeking equality, there is a huge void that is left untouched on the side of equity. DeCuir & Dixson (2004) argue that “race, and experiences based on race are not equal, thus, the experiences that people of color have with respect to race and racism create an unequal situation” (p. 29). The tenet suggests that incremental change will not benefit minority and low-socioeconomic students. The time and sense of urgency does not allow for the increment. Social Constructivist Theory Lee Vgotsky is a Russian born Psychologist who believed “that social interaction leads to continuous step-by-step changes in children’s thought and behavior that can vary greatly from culture to culture” (Gallagher, 1999). In other words, Vgotsky’s theory suggests that children develop based on the people and things they interact with the most. These interactions help form their views and shape their learning and actions. According to Oldfather et al., (1999), “a social constructivist perspective focuses on learning as sense making rather than on the acquisition of rote knowledge that exists somewhere outside the learner” (p. 9). This theory also suggests that learning should be based on teaching the whole child (academic and social). Oldfather et al., (1999) also notes “social constructivism stretches us to think beyond narrow, curricular goals and to reach toward broad purposes of learning such as students’ self-knowledge, development of identities, and belief that they can make a difference in the world” (p. 12). This transfers to the classroom when teachers “focus their efforts on helping their students find their passions, discover what they care about, create their own learning agendas, and most importantly connect who they are to what they do in school” (Oldfather et al., 1999, p. 16). As noted by Lam (2014) 38 “students from low socioeconomic backgrounds lack the prerequisites to track into class with good academic results” (p. 328). Whereas, high SES students come from households with two parents, both educated, and maintain an income well above the poverty line. With this, “parents from rich families are found to engage children in more meaningful conversations, read to their children more, and provide more teaching opportunities” (Lam, 2014, p. 328). Thus, they enter the academic environment more knowledgeable and academically more advanced than low SES students. This could contribute to the difference in academic performance among White and minority students given that low SES is associated with minority students. Socioeconomic Integration In an attempt to address the achievement gaps in the United States between minorities and their White peers, researchers believe that “socioeconomic integration must be the change that ends this cycle of inequality” (Curtis, 2013, p. 470). Prominent scholars suggest that combining students by socioeconomic status will improve test results and allow schools to meet the goals of high-stakes testing. This concept is called socioeconomic integration. It has been deeply examined by Richard Kahlenberg (2006), in which he believes, “socioeconomic integration may hold the key to reducing persistent achievement gaps” (p. 22). Gary Orfield, a professor at UCLA, “notes that educational research suggests that the basic damage inflicted by segregated education comes not from racial concentration but the concentration of children from poor families” (Kahlenberg, 2012, p. 3). Several districts (91) across the United States have already implemented socioeconomic integration and received noticeable gains in academic achievement on high-stakes tests. The key to the improvement is how the students are combined: low socioeconomic students must be placed in middle class schools and not vice versa. According to Kahlenberg (2006), “Middle class students do well in economically 39 integrated schools as long as concentrations of poverty do not reach above the 50 percent level” (p. 25). Wake County in North Carolina was one of the first areas to institute socioeconomic integration. In 2005, Wake County reported that “on the 2005 High School End-of-Course exams, 63.8 percent of Wake County’s low-income students passed, compared with 47.8 percent in Mecklenburg County, 47.9 percent in Guilford County, and 48.7 percent in Durham County” (Kahlenberg, 2006, p. 25). In 2006, a study using 22,000 schools was published that consisted of an analysis of data for approximately 18 million students. This study “found that minority students have greater gains in racially integrated schools, and that a substantial portion of the racial composition effect is really due to poverty and peer achievement” (Kahlenberg, 2012, p. 4). Socioeconomic integration has not been well received by all stakeholders. In an argument against socioeconomic integration, Kahlenberg noted a critique suggesting “that working one’s way up to buy a house in a good neighborhood with good schools for your children is the American way” (Kahlenberg, 2012, p. 3). This argument would not have low socioeconomic students placed in a middle class school, instead, this implies that they should stay in their low-income schools and work hard to get out of poverty. This would be sufficient, however, “equal educational opportunity for children, whether or not their parents can afford to live in a good neighborhood, is fundamental to the American Creed” (Kahlenberg, 2012, p. 3). Overall, Curtis (2013) identified “that through socioeconomic integration, low-income [SES], black, Hispanic, and White students all maintained an 88% high school graduation rate” (p. 473). With this, it can be concluded that these students were reaching proficiency on state exams given that states require students pass state exams to graduate. In the United States, many Title I schools have a population of students that come from low-income homes and are classified in education as economically disadvantaged. With 40 socioeconomic integration, a large portion of these students can be transferred to a Non-Title I school under the assumption that they will perform better because of their new academic setting. This could also reduce the number of schools that receive the negative connotation behind the label of Title I. Kahlenberg (2012) believes “the negative effects of concentrated poverty tend to kick in only where a clear majority of students are low-income” (p. 5). Economic and ethnic balance within schools could positively impact the performance of students according to socioeconomic integration. Kahlenberg (2012) also believes “the major problem with American schools is not teachers or their union, but poverty and economic segregation” (p. 14). Schools identified as Non-Title I and Title I are contrasted by the same two components: poverty and economic segregation. Conclusion Based on the literature presented, the achievement gap among Whites and Hispanic and African American students continues to widen which indicates that minorities are still being left behind. According to Orfield & Sunderman (2007), “the revolution begun by NCLB may be remembered as an experiment that resulted in massive data collection and better information systems-but very little educational gain and high political costs” (p. 139). There is still a gap in mathematics academic achievement as well among low SES minorities and non-low SES White students. In the United States, state assessments are designed to test the knowledge of students and narrow the achievement gap, but it can be argued that this is not happening. According to the National Dropout and Prevention Network, “of the nineteen states using exit exams, twelve supplied data showing that passing rates for minority students and students living in poverty are at least twenty percentage points lower than those of White students” (Klima, 2007, p. 18). Three years ago, the state of Texas began using the STAAR test as a measure of student 41 academic performance. Lathrop & Weiss (2014) argue that “In every test at every grade, groups of students who scored lower when STAAR rolled out three years ago are still behind-and in most cases, the gaps are growing” (p. 1). With socioeconomic integration being a new concept, there is a need to study the mathematics performance of minorities in a low-income, Title I school, as opposed to the same group of students in a more affluent school. This will help determine if socioeconomic integration is a step in the right direction towards closing the achievement gap. 42 Chapter 3 METHOD OF PROCEDURE Design of the Study Multiple linear regression analysis was used to examine whether the mathematics performance of seventh and eighth grade middle school students can be predicted based on school type, male percentage, African American student percentage, and Hispanic student percentage. Specifically, this study explored whether the type of school (i.e., Title I school vs. Non-Title I school), male percentage, Hispanic race percentage, and African American race percentage are statistically significant predictors of the mathematics achievement scores on the State of Texas Assessment of Academic Readiness (STAAR) for seventh and eighth middle school students. In this design, the independent variables were: type of school, gender (male) percentage, Hispanic student percentage, and African American student percentage, while the dependent variable was the school’s average mathematics STAAR scores for seventh and eighth grade middle school students. Since the researcher used multiple linear regression and the fact that school populations would most likely not be equivalent, percentages seemed to be appropriate. For the gender percentage, a reference must be selected, so it would either be male or female percentage. In the methods, the researcher selected male as the reference as using both when discussing percentage would be: male% + female%=100%. This would cause linearity issues. Research questions are delineated below: 1. Is there a significant relationship between school type, seventh grade male percentage, seventh grade Hispanic student percentage, seventh grade African American student percentage, and the school's average Math STAAR score for seventh graders? 43 a. Is there a significant relationship between school type and the school's average Math STAAR score for seventh graders? b. Is there a significant relationship between male percentage and the school's average Math STAAR score for seventh graders? c. Is there a significant relationship between Hispanic student percentage and the school's average Math STAAR score for seventh graders? d. Is there a significant relationship between African American student percentage and the school's average Math STAAR score for seventh graders? 2. Are type of school, seventh grade gender percentage, seventh grade Hispanic student percentage, and seventh grade African American student percentage statistically significant predictors of the school's seventh grade average mathematics STAAR scores? 3. Is there a significant relationship between school type, eighth grade male percentage, eighth grade Hispanic student percentage, eighth grade African American student percentage, and the school's average Math STAAR score for eighth graders? a. Is there a significant relationship between school type and the school's average Math STAAR score for eighth graders? b. Is there a significant relationship between male percentage and the school's average Math STAAR score for eighth graders? c. Is there a significant relationship between Hispanic percentage and the school's average Math STAAR score for eighth graders? d. Is there a significant relationship between African American student percentage and the school's average Math STAAR score for eighth graders? 44 4. Are type of school, eighth grade gender percentage, eighth grade Hispanic student percentage, and eighth grade African American student percentage statistically significant predictors of the school's eighth grade average mathematics STAAR scores? The study used multiple linear regression as the analytical technique and targeted the factors that could potentially contribute to the school’s math performance on the STAAR in the seventh and eighth grade levels. Multiple linear regression is a predictive analysis used to identify the relationship between a dependent variable and two or more independent variables (Brown, 2009, p. 1). It is known to be the most common linear regression analysis. This analysis helps to identify the effect the independent variables have on the dependent variable. The independent variables in the study were school type, male percentage, Hispanic student percentage, and African American student percentage, while the dependent variable was the average mathematics STAAR scores of the schools. The school type identifies as whether the chosen campus is a Title I school that is defined as having 40% or more students with free or reduced lunch or a Non-Title I school. As the gender percentages of male and female are additive to 100%, only the percentage of male students will be used in this study, where a higher male percentage indicates a lower female percentage, and vice versa. According to Brown-Jeffy (2008), Hispanic and African American students “often are disadvantaged because of the particular characteristics of the schools they attend” (p. 393), which could result in a higher number of these students not achieving proficiency on state assessments. Additionally, Hispanic and African American students have been found to consistently score below proficient level as defined by each state in mathematics (Rojas-LeBouef & Slate, 2011, p. 11). Therefore, in this study, both Hispanic and African American percentage based upon the total number of students 45 of the chosen campus were used. The current study aimed to use the 2015 performance data from the State of Texas Assessment of Academic Readiness (STAAR) to examine seventh grade and eighth grade, respectively, to represent middle school students. In summary, the design of this study employed the use of the Multiple Linear Regression model to assess correlational and predictive values of like student groups (race and gender) attending Title I and Non-Title I campuses regarding overall average STAAR Mathematics performance. Individual student data was not used. The campus average scale score for the STAAR Mathematics Assessment was used in completing the Multiple Linear Regression model. Instrumentation The STAAR is a standardized, high-stakes test used in Texas to measure how well the students have mastered the Texas Essentials of Knowledge and Skills standards and objectives developed and issued by the Texas Education Agency (TEA, 2012). The mathematics STAAR consists of 54 questions for seventh grade and 56 questions for eighth grade. According to TEA (2012), the questions on the mathematics STAAR have: “more cognitively complex questions, questions that have multiple steps, questions that have application context, and more open-ended griddable questions” (p. 33). The reliability and validity of all STAAR tests were reported in Chapter 4 of the Technical Digest released by the Texas Education Agency (2012). Any testing instruments created must be reliable and valid in order to be used as a basis for making decisions, assumptions, and interpretations based on the data obtained from them. Reliability addresses the “expectation that repeated administrations of the same test should generate consistent results” (TEA, 2012, p. 109). According to the Texas Education Agency (2012), the “reliability of the STAAR test score was estimated using statistical measures such as internal consistency, classical 46 standard error of measurement, conditional standard error of measurement, and classification accuracy” (TEA, 2012, p. 109). The internal consistency for the STAAR tests ranged from 0.81 to 0.93. The “classical standard error of measurement (SEM) represents the amount of variance in a score that results from factors other than what the assessment is intended to measure” (p. 110). The classical standard error of measurement for the STAAR test is between 2 and 4 raw score points. The conditional standard error of measurement “provides a reliability estimate at each score point on a test” (TEA, 2012, p. 110). The classification accuracy “provides an estimate of the accuracy of student classifications into performance categories based on current test results” (p. 110). The validity of the STAAR tests “refers to the extent to which a test measures what it is intended to measure” (TEA, 2012, p. 111). The Texas Education Agency performs validity testing each year “based on test content, response processes, internal structure, relationships to other variables, and consequences of testing” (TEA, 2012). The Texas Technical Advisory Committee (TTAC), which is “a panel of national testing experts created specifically for the Texas assessment program, provides ongoing input to TEA regarding STAAR validity evidence” (TEA-Technical Digest Chapter 4, 2014). According to TEA (2012), “To identify the appropriate assessment reporting categories for the STAAR assessments, teachers, curriculum specialists, test-development specialists, college educators, and TEA staff collaborated in advisory committees” (p. 1). These groups of educators meet on an annual basis for item development and review the STAAR items and make sure that each item is aligned appropriately to the TEKS it is intended to measure. Specifically, two distinct groups, item-review committees and content-validation committees, regularly convene to support the validity of the STAAR content. The item-review committees, made up of Texas K-12 educators, revise and edit items 47 as appropriate, while the content-validation committees, “made up of university faculty who are experts in the relevant subject matter, review items to ensure relevant content is being represented and measured fairly and appropriately by the test items” (TEA, 2012). For purposes of this study, average scale score from each school’s Mathematics STAAR Performance Summary Report was used. Sample Selection This study used archival data of the school’s average mathematics achievement scores on the State of Texas Assessment of Academic Readiness (STAAR) of the chosen schools in the state of Texas for the year 2015. Only public archival data were used. Permission did not need to be obtained as the data gathered from the Texas Education Agency are available for public access. The schools selected were all campuses in Texas that serve grades 7 and 8. The sample size was computed through power analysis using G*Power software (Buchner, 2013). In the current study, multiple linear regression with four independent variables (type of school, male percentage, Hispanic student percentage, and African American student percentage) was the analytical technique. A medium effect size (0.15), alpha error probability of 0.05, and medium power (0.8) were used. Results of the G*Power analysis showed that the minimum required sample size is 85. According to Tabachnick and Fidell (1996), a minimum sample size of 50 + 8m (where m is the number of IVs) can be used for testing the multiple correlation and 104 + m for testing individual predictors. Such rules of thumb are based upon a medium size relationship between the IVs and DV, α = 0.05, and β = 0.20, and suggest the minimum sample size of 150 for the current study. Given such, 150 schools were included in the current study. 48 As indicated on the Texas Education Website, there is a disproportionate number of Title I schools in Texas, therefore, stratified random sampling was used to make sure an adequate number of Title I and Non-Title I schools are included in the sample. Stratified random sampling is a sampling method that consists of a certain number of elements that can be divided into strata (groups). “This guarantees that a minimum sample size can be selected in each stratum or category” (Stehman, 1996, p. 401). Stratified sampling “is a potentially useful design for accuracy assessment” (Stehman, 1996, p. 401). For the current study, an Excel list of all campuses in Texas was obtained from the TEA website. The list was first be filtered by public schools within an independent school district who serve seventh and eighth graders. Then, schools that offer regular instruction only were included. Lastly, schools were coded for either Title I or Non-Title I schools. In order for a school to receive the Title I classification, they must have at least 40% of their students receiving free or reduced price lunch (Malburg, 2014, p. 1). After the schools were divided into their respective stratum (i.e., Title I and Non-Title I), random sampling was conducted in each stratum. Thus, 75 Title I schools and 75 Non-Title I schools were randomly selected from each strata. Data Gathering Every public school in Texas administers the STAAR to each student enrolled on the campus. The students take each test in the same testing window as identified by the Texas Education Agency each year. All schools operate during the same school days and hours with similar student breaks and holidays with one to two day variability (TEA, 2012, p. 1). Prior to gathering data, the researcher requested the review and approval of the Institutional Review Board (IRB). Upon approval, as outlined in the sample selection above, stratified random sampling was conducted to determine which schools to include in the sample. After identifying 49 the campuses, the seventh grade STAAR Mathematics Campus Summary Report and eighth grade STAAR Mathematics Campus Summary Report were obtained from the public database within TEA’s website. These reports include a breakdown by an administration summary which consists of information such as the number of male and female students tested, average test score, ethnicities, and subpopulations (e.g., economically disadvantage, special education, gifted/talented). For example, in a sample report of Byrd Middle School in Duncanville, Texas, there were 388 students who completed the seventh grade mathematics STAAR test in 2015. The average scale score among them was 1635. Of those 388 students, the report indicates that 107 (28%) received Level I: Unsatisfactory, 281 (72%) received Level II: Satisfactory, and 40 (10%) received Level III: Advanced on the Mathematics STAAR. There were 378 students who completed the eighth grade mathematics STAAR test in 2015. The average scale score among them was 1653. Of those 378 students, the report indicates that 115 (30%) received Level I: Unsatisfactory, 263 (70%) received Level II: Satisfactory, and 33 (9%) received Level III: Advanced on the Mathematics STAAR. The Campus Summary Report also indicates the number of females and males who took the exam and the average scale score. In the sample school above, the report indicates of the 388 seventh grade students who tested, there were 206 males with an average scale score of 1627. To find the percentage of males, the researcher would calculate 206 divided by 388 which would equal 53%. There were 182 females with an average scale score of 1644. Using the same calculations, this would equal 47%. For eighth grade, the report indicates of the 378 students who were tested, there were 208 males with an average scale score of 1648. To find the percentage of males, the researcher would calculate 208 divided by 378 which would equal 55%. 50 There were 170 females with an average scale score of 1658. Using the same calculations, this would equal 45%. Average school mathematics scores on the STAAR of seventh and eighth grade students, as well as the school type, gender percentage, Hispanic/Latino student percentage, and African American student percentage, were obtained from these reports. Such information was encoded into an Excel spreadsheet for easy viewing and easy transfer into SPSS. Treatment of Data Data was analyzed through multiple linear regression to determine whether school type, gender percentage, Hispanic student percentage, and African American student percentage are significant predictors of the average school mathematics STAAR scores of seventh and eighth grade middle school students, respectively. Prior to conducting the multiple linear regression analysis, the assumptions for conducting multiple linear regression were assessed: independence of observations, homoscedasticity of data, no multicollinearity, no significant outliers, and approximately normally distributed data. The Durbin-Watson statistic was computed to check for independence of observations. A correlation matrix was generated to check multicollinearity of variables, which is “when your model includes multiple factors that are correlated not just to your response variable, but also to each other” (Martz, 2013, p. 1). In the case where a significant correlation between two or more variables exists, one variable was chosen to represent the other variables with which it is highly correlated. To check for significant outliers, boxplots were plotted for the dependent variable. According to Hoffman (1981), “The box plot is a standardized way of displaying the distribution of data based on the five number summary: minimum, first quartile, median, third quartile, and maximum” (p. 1). Any school with average scores falling outside the allowable range of the math STAAR scores was removed from the 51 analysis. Normality was tested through running distribution, central tendency, and Shapiro-Wilk’s test to determine whether data were normally distributed. Additionally, descriptive data analysis was conducted to ensure accuracy of data file and to ensure there was no missing data. After checking the assumptions, two multiple linear regression tests were conducted, one for the seventh grade data and one for the eighth grade data, to find out whether type of school (i.e., Title I school vs. Non-Title I school), male percentage, Hispanic student percentage, and African American student percentage predict mathematics achievement scores on the STAAR for seventh and eighth grade middle school students. The confidence level for all statistical tests was 95%, which means that for the results of a statistical test to be significant, the p-value was 0.05 or lower. Correlation among school type, gender (male) student percentage, Hispano/Latino student percentage, and African American student percentage, and the average school mathematics STAAR scores were conducted for each grade level to determine whether each individual factor is statistically significantly correlated to the dependent variable. The purpose of this study was to determine whether school type, gender percentage, Hispanic student percentage, and African American student percentage can be used as predictors for the Mathematics STAAR scale scores for seventh and eighth graders. It aimed to find whether the environment in which students are placed impact their performance on state exams. Title I schools have at least 40% of their student population who come from low-income families with limited resources. It is important to determine whether factors such as these impact a child’s performance on exams required by the state, especially when promotion or retention is attached to whether a child passes or fails certain exams. The multiple linear regression analysis conducted in this study assists in determining whether there is a correlation between the independent variables and mathematics STAAR performance. 52 Chapter 4 PRESENTATION OF FINDINGS Introduction The purpose of the study was to investigate whether the mathematics achievement of seventh and eighth grade middle school students can be predicted based on school type, gender percentage, African American student percentage, and Hispanic student percentage. Specifically, this study explored whether the type of school (i.e. Title I school vs. Non-Title I school), male percentage (as a reference of the gender type), African American student percentage, and Hispanic student percentage are statistically significant predictors of the mathematics scores on the State of Texas Assessment Readiness (STAAR) for seventh and eighth grade middle school students, respectively. The researcher analyzed the mathematics achievement using the 2015 Math STAAR, or State of Texas Assessment of Academic Readiness. In addition, the researcher examined whether school type, gender percentage, Hispanic student percentage, and African American student percentage can be used to predict overall mathematics achievement of schools as measured by STAAR mathematics achievement of seventh and eighth grade levels. This chapter presents the results of the correlation tests and multiple linear regressions to test the hypotheses and address the respective research questions as discussed in the previous chapter. Archival data of average school mathematics achievement scores on the STAAR from the year 2015, which were obtained from the Texas Education Agency (TEA) website, were used, using average school mathematics achievement scores on the STAAR of Title I and Non-Title I schools in the state of Texas from the year 2015. The research questions guided the study, and the respective hypotheses that were tested, are as follows: 53 1. Is there a significant relationship between school type, seventh grade male percentage, seventh grade Hispanic student percentage, seventh grade African American student percentage, and the school's average Math STAAR score for seventh graders? a. Is there a significant relationship between school type and the school's average Math STAAR score for seventh graders? H1ao: There is no statistically significant correlation between school type and the school’s average Math STAAR score for seventh graders. H1a1: There is a statistically significant correlation between school type and the school’s average Math STAAR score for seventh graders. b. Is there a significant relationship between male percentage and the school's average Math STAAR score for seventh graders? H1bo: There is no statistically significant correlation between male percentage and the school’s average Math STAAR score for seventh graders. H1b1: There is a statistically significant correlation between male percentage and the school’s average Math STAAR score for seventh graders. c. Is there a significant relationship between Hispanic student percentage and the school's average Math STAAR score for seventh graders? H1co: There is no statistically significant correlation between Hispanic student percentage and the school’s average Math STAAR score for seventh graders. H1c1: There is a statistically significant correlation between Hispanic student percentage and the school’s average Math STAAR score for seventh graders. d. Is there a significant relationship between African American student percentage and the school's average Math STAAR score for seventh graders? 54 H1do: There is no statistically significant correlation between African American student percentage and the school’s average Math STAAR score for seventh graders. H1d1: There is a statistically significant correlation between African American student percentage and the school’s average Math STAAR score for seventh graders. 2. Are type of school, seventh grade gender percentage, seventh grade Hispanic student percentage, and seventh grade African American student percentage statistically significant predictors of the school's seventh grade average mathematics STAAR scores? H2o: Type of school, seventh grade gender percentage, seventh grade Hispanic student percentage, and seventh grade African American student percentage are not significant predictors of the school’s average mathematics STAAR scores. H2a1: Type of school, seventh grade gender percentage, seventh grade Hispanic student percentage, and seventh grade African American student percentage are statistically significant predictors of the school’s average mathematics STAAR scores. 3. Is there a significant relationship between school type, eighth grade male percentage, eighth grade Hispanic student percentage, eighth grade African American student percentage, and the school's average Math STAAR score for eighth graders? a. Is there a significant relationship between school type and the school's average Math STAAR score for eighth graders? H3ao: There is no statistically significant correlation between school type and the school’s average Math STAAR score for eighth graders. H3a1: There is a statistically significant correlation between school type and the school’s average Math STAAR score for eighth graders. 55 b. Is there a significant relationship between male percentage and the school's average Math STAAR score for eighth graders? H3bo: There is no statistically significant correlation between male percentage and the school’s average Math STAAR score for eighth graders. H3b1: There is a statistically significant correlation between male percentage and the school’s average Math STAAR score for eighth graders. c. Is there a significant relationship between Hispanic percentage and the school's average Math STAAR score for eighth graders? H3co: There is no statistically significant correlation between Hispanic student percentage and the school’s average Math STAAR score for eighth graders. H3c1: There is a statistically significant correlation between Hispanic student percentage and the school’s average Math STAAR score for eighth graders. d. Is there a significant relationship between African American student percentage and the school's average Math STAAR score for eighth graders? H3do: There is no statistically significant correlation between African American student percentage and the school’s average Math STAAR score for eighth graders. H3d1: There is a statistically significant correlation between African American student percentage and the school’s average Math STAAR score for eighth graders. 4. Are type of school, eighth grade gender percentage, eighth grade Hispanic student percentage, and eighth grade African American student percentage statistically significant predictors of the school's eighth grade average mathematics STAAR scores? 56 H4o: Type of school, eighth grade gender percentage, eighth grade Hispanic student percentage, and eighth grade African American student percentage are not significant predictors of the school’s average mathematics STAAR scores. H41: Type of school, eighth grade gender percentage, eighth grade Hispanic student percentage, and eighth grade African American student percentage are statistically significant predictors of the school’s average mathematics STAAR scores. Data Collection The data used for the study were collected from the TEA website. As the data are accessible to the public, there was no need to obtain permission. The data used were archival data of average school mathematics achievement scores on the STAAR of Title I and Non-Title I schools in the state of Texas from the year 2015. As described in Chapter 3, stratified random sampling was conducted to obtain the 150 schools, with an even split of Title I (75) and Non-Title I (75) schools. The data collected includes the average STAAR mathematics scale scores for seventh and eighth grade, as well as gender percentage, Hispanic student percentage, and the African American student percentage of each school. The 150 schools were selected through stratified random sampling of all identified schools in the state of Texas, with Title I or Non-Title I being the strata, and each stratum containing 75 schools. After which, a random number was assigned to each school using Excel’s random number generator function. The random numbers were then ranked from lowest to highest, and the top 75 schools in each school type (Title I and Non-Title I) were used as the sample for the study. 57 Results This section presents the results of the statistical analyses conducted. Correlational tests and multiple linear regression analyses were conducted using the archival data to test the hypotheses and address the research questions of the study. In accordance with the formulated hypotheses and research questions, the tests were conducted for each grade level, seventh and eighth grade, respectively. Descriptive statistics Descriptive statistics of the study variables were presented for the respective grade levels (seventh and eighth) in Tables 1 and 2, respectively. While the descriptive statistics present percentages for both male and female students, the analyses will only use one gender percentage (male), as the selected gender (male) will serve as the reference, since for each school, the sum of male and female percentage would equate to 100%. The descriptive statistics of the study variables for the seventh grade level are presented in Table 1. The average Math STAAR score for the seventh grade level ranged from 1502 to 1786, with an average of 1640.39 (SD = 49.92). As observed in Table 1, there were schools with no Hispanic students or African American students in the seventh grade level. For the gender percentages, male percentage of seventh grade students ranged from 41% to 68% with an average of 51.58% (SD = 4.23%), while that of female percentage of seventh grade students ranged from 32% to 86% with an average of 48.72% (SD = 5.22%). The percentage of seventh grade Hispanic students ranged from 0 to 100%, with an average of 40.76% (SD = 26.61%). The percentage of seventh grade African American students ranged from 0 to 79%, with an average of 11.85% (SD = 11.97%). 58 Table 1 Descriptive and demographic characteristics of seventh grade N Minimum Maximum Mean Std. Deviation Percentage of male students 150 .41 .68 .5158 .04234 Percentage of female students 150 .33 .86 .4872 .05218 Percentage of Hispanic students 150 0.00 1.00 .4076 .26607 Percentage of African American students 150 0.00 .79 .1185 .11971 The descriptive statistics of the study variables for the eighth grade level are presented in Table 2. The average Math STAAR score for the eighth grade level ranged from 1529 to 1864, with an average of 1663.26 (SD = 54.04). As observed in Table 2, there were schools with no Hispanic students or African American students in the eighth grade level. For the gender percentages, male percentage of eighth grade students ranged from 31% to 75% with an average of 51.58% (SD = 4.51%), while that of female percentage of eighth grade students ranged from 41% to 69% with an average of 48.42% (SD = 4.51%). The percentage of eighth grade Hispanic students ranged from 0 to 99%, with an average of 41.32% (SD = 26.33%). The percentage of eighth grade African American students ranged from 0 to 66%, with an average of 11.83% (SD = 11.49%). Table 2 Descriptive and demographic characteristics of eighth grade N Minimum Maximum Mean Std. Deviation Percentage of male students 150 .31 .75 .5158 .04509 Percentage of female students 150 .41 .69 .4842 .04509 Percentage of Hispanic students 150 0.00 .99 .4132 .26334 Percentage of African American students 150 0.00 .66 .1183 .11493 59 Testing of assumptions This section presents the testing of assumptions for the correlation and multiple linear regression tests. The first assumption tested was the approximate normality of the data. Normality of data were tested through Shapiro-Wilk’s test for normality, plotting histograms, and Q-Q plots. For the seventh grade level, the Shapiro-Wilk’s test showed that only the average math STAAR score was normally distributed (p = 0.421), while the other study variables of percentage of male students, percentage of Hispanic students, and percentage of African American students, were not normally distributed (p < 0.001) (see Table 3). However, for percentage of male students, the histogram (see Figure 3) and Q-Q plot (see Figure 4) showed that the data were approximately normally distributed. For the percentages of Hispanic (see Figure 6) and African American students (see Figure 8), the Q-Q plots showed that the data were approximately normally distributed. Table 3 Shapiro-Wilk’s test for normality for seventh grade data Shapiro-Wilk Statistic df Sig. Average Math STAAR score .991 150 .421 Percentage of male students .951 150 .000 Percentage of Hispanic students .917 150 .000 Percentage of African American students .838 150 .000 60 Figure 1. Histogram of average math STAAR score for seventh grade Figure 2. Q-Q plot of average math STAAR score for seventh grade 61 Figure 3. Histogram of percentage of male students for seventh grade Figure 4. Q-Q plot of average percentage of male students for seventh grade 62 Figure 5. Histogram of percentage of Hispanic students for seventh grade Figure 6. Q-Q plot of average percentage of Hispanic students for seventh grade 63 Figure 7. Histogram of percentage of African American students for seventh grade Figure 8. Q-Q plot of average percentage of African American students for seventh grade For the eighth grade level, the Shapiro-Wilk’s test showed that only the average math STAAR score was normally distributed (p = 0.365), while the other study variables of percentage of male students, percentage of Hispanic students, and percentage of African American students, 64 were not normally distributed (p < 0.001) (see Table 4). However, for percentage of male students, the histogram (see Figure 11) and Q-Q plot (Figure 12) showed that the data were approximately normally distributed. For the percentages of Hispanic (see Figure 14) and African American students (see Figure 16), the Q-Q plots showed that the data were approximately normally distributed. Table 4 Shapiro-Wilk’s test for normality for eighth grade data Shapiro-Wilk Statistic df Sig. Average Math STAAR score .990 150 .365 Percentage of male students .900 150 .000 Percentage of Hispanic students .905 150 .000 Percentage of African American students .865 150 .000 Figure 9. Histogram of average math STAAR score for eighth grade 65 Figure 10. Q-Q plot of average math STAAR score for eighth grade Figure 11. Histogram of percentage of male students for eighth grade 66 Figure 12. Q-Q plot of average percentage of male students for eighth grade Figure 13. Histogram of percentage of Hispanic students for eighth grade 67 Figure 14. Q-Q plot of average percentage of Hispanic students for eighth grade Figure 15. Histogram of percentage of African American students for eighth grade 68 Figure 16. Q-Q plot of average percentage of African American students for eighth grade Boxplots for the data were then examined to see whether there were any significant outliers that were out of the allowable range for the study variables. For the average math STAAR scores of both seventh and eighth grade level, while there were outliers as shown in Tables 17 and 21, there were no significant outliers in the data. For percentage of male students of both seventh and eighth grade level, while there were significant outliers as shown in Tables 18 and 22, these significant outliers still fall within the allowable range of 0 to 100%, and as such, will be included in the analyses. There were no outliers for the percentage of Hispanic students for both seventh and eighth grade levels as shown in |