Elementary School Education School Effectiveness in Mississippi
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Statement of the Problem
Researchers examining student performance consistently find that one of the most important influences on student achievement is socioeconomic status (SES) of students. The more affluent the student’s background, the better he or she will perform (Coleman, et. al., 1966; Jencks et. al., 1972). This research, often referred to as “status attainment research,” generally concludes that other school and teacher characteristics as well as school policies and spending decisions have minimal consequences for student achievement. Later studies continue to support these conclusions (Hanushek, 1989,1996).
Okpala (2002, p. 907), in one of many studies that examines resource usage in public schools concludes, “Some of the major factors that are theoretically under the control of a school… have little if anything to do with student performance.” These findings give little comfort to educators in economically disadvantaged schools who are facing heavy pressure to improve performance and close the gap between minority and white students.
Yet Verstegen and King (1998) claim that a growing body of research is using better databases and more sophisticated methodological strategies to provide evidence that school policies can make a positive difference in student outcomes. They also emphasize that resource patterns that optimize performance in one setting do not necessary work in others. Encouraged by this line of thinking, the researcher investigates factors that may explain the differences in performances in schools that share a common socioeconomic context.
Significance of the Study
This study will prove that among educators and administrators of economically disadvantage schools, decisions matter. These policy-makers can make conscious choices that affect the performance of their students. While the socioeconomic context is still a critical predictor of success, process variables are also important. It will also be proved that spending on bilingual programs can be an important resource for improving student achievement on standardized tests. This study will address an important niche for educators in economically disadvantaged schools that are facing greater pressure to improve performance on standardized tests and to reduce the gap between poorer students and those from more affluent backgrounds.
This study will include campuses with more than 50 students. Only those school campuses will be included in the study where 50% or more of the students are recognized to be economically disadvantaged. School campuses that do not have any regular expenditures, or have expenditures per pupil that are unrealistically low will be eliminated from the study.
This study will address the following questions:
Q.1. What factors contribute to the success of some and failure of other schools?
- 2. Is there any relationship between school size and achievement scores?
- 3. Is there any effect of class size on achievement scores?
- 4. Do instructional leadership and support play a particularly significant role in improving the performance of students in impoverished communities?
- 5. Do teacher characteristics, namely, the level of teacher salaries and experience affect achievement scores?
- 6. What are the effects of global resources, that is, per pupil expenditure (PPE), on the impact of performance?
- 7. Are there choices made by policymakers and administrators in economically disadvantaged schools that spark significant improvements in performance in these schools?
Definitions of terms
The allocation of resources by function refers to spending for instruction rather than non-instructional purposes.
Instruction refers to all activities dealing directly with the interaction between teachers and students, including instruction aided with computers.
Process variables include those variables that school systems more or less control.
Over the past 20 years, high school promotion and graduation requirements have increased significantly and recently many states and districts have further intensified their efforts to ensure that all students leave high school with the knowledge and skills needed for adult success by instituting standards based end-of-course and graduation exams (Committee for Economic Development, 2000).
Concerns that raising standards and instituting high-stakes tests will disadvantage students who have attended weak, unsuccessful, or under-resourced schools have typically been met with the counter-claim that poorly prepared students will be provided with the extra help and support they need to succeed (Achieve, 2001). To date, most of the support has centered on giving poorly prepared students more time. This has included providing second-chance test prep during summer school, offering students a fifth year of high school to become prepared, and attempting to enroll students in transition programs until they are ready to do high school level work.
Much less attention has been given to developing curricular and instructional means for poorly prepared high school students to accelerate their learning during the school year. A few such efforts are in their infancy. Several whole school reform models for high schools are developing catch-up courses, and several school districts have developed special prep courses for poorly prepared students that are given during the school day in addition to the standard grade courses (Balfanz, McPartland, & Shaw, 2002).
To date, however, in large part because of their infancy, the impact of these efforts has not been evaluated beyond small, formative studies typically involving a single school and one or two teachers. As a result, very little is known about the feasibility and rapidity with which the academic learning of students who enter high school multiple years behind grade level can be accelerated. This report takes a first step in this direction by reporting on the initial results and impacts of the Talent Development High Schools (TDHS) ninth grade instructional program in reading and mathematics. Its impact is examined across several cities and multiple high-poverty, non-selective high schools within each city.
The Need to Accelerate Learning in High-poverty High Schools
Analysis of existing achievement data in high-poverty high schools leads to two inescapable conclusions. First, students who attend high-poverty high schools typically perform significantly below national norms and dramatically short of the performance benchmarks increasingly employed to measure academic success. An analysis conducted by Education Week indicates, for example, that in the majority of large cities many students enter high school two or more years below grade level (Quality Counts ’98, 1998). The recent TIMSS R study shows that cities that educate primarily high-poverty students typically have performance levels equal to those in developing countries (Mullis et al., 2001).
When data is disaggregated to examine achievement at the school level, even larger gaps are revealed. In Philadelphia, for instance, more than 75% of high school students attend one of 22 non-selective neighborhood schools.
Approximately one quarter of these students are reading below the fifth grade level, another quarter is at the fifth or sixth grade level, a third quarter at the seventh or eighth grade level, and only slightly more than one in four students who attend a nonselective high school in Philadelphia read at grade level. In eight of the non-selective neighborhood schools, between two thirds and four fifths of the first-time ninth graders perform below the seventh grade level in both reading and mathematics (Neild & Balfanz, 2001).
One important conclusion that can be drawn from this data is that in many non-selective urban schools the majority, and in some cases nearly all, of the students need accelerated learning opportunities. What is required are not special programs for small numbers of students, but an organizational and instructional restructuring of the entire school, which will enable students to close achievement gaps and graduate prepared for college or post-secondary training (Legters,
Balfanz, Jordan, & McPartland, 2002; McPartland & Jordan, 2001).
The second conclusion is that the current level of academic performance in high-poverty high schools leads to multiple negative consequences for students and for society. It is too early to accurately gauge the impact of the high-stakes, standards based graduation tests that are increasingly becoming the norm in many states on the academic performance and dropout rate of students who enter high school with weak academic skills (Bishop & Mane, 2000; Hauser, 2001).
Several states at the forefront of this movement have recently slowed down their introduction, while in other states the first cohorts of students have not yet reached 12th grade. Nor is the impact of the minimum competency tests that were introduced primarily in the 1980s, and required in some states for graduation, unequivocal (Hauser, 2001). Existing data from Chicago (Roderick & Camburn, 1999) and Philadelphia, however, clearly show that poor academic preparation is a major factor in a downward path of course failure and retention that engulfs many high-poverty students during the ninth grade and culminates with them dropping out of school.
Neild and Balfanz (2001), for example, found that 43% of the first-time freshmen in Philadelphia who entered the ninth grade with math and reading skills below the seventh grade level were not promoted to the 10th grade, compared to 18% of the students who entered with skills above the 7th grade level. Logistic regression analysis further demonstrated that below-grade-level academic skills had a significant negative impact on promotion to 10th grade, controlling for attendance, eighth grade course failure, age, race, and gender.
Neild, Stoner-Eby, and Furstenberg (2001) in a longitudinal study, in turn, found that first-time freshmen not promoted to 10th grade had a dropout rate of nearly 60% compared to a rate of less than 12% for students who were promoted. The individual and social consequences of dropping out of high school are considerable. The economic returns to advanced education have been well documented (Committee for Economic Development, 2000). The social consequences of failing to complete high school are also well established (Hauser, 2001).
Balfanz and Legters (2001) estimate that there are about 250- 300 high schools in the nation’s 35 largest cities in which non-promotion is the norm. These schools are attended by about 60% of the African American and Latino students in public high schools in these cities. Thus, in an era when there is widespread consensus on the value of raising graduation requirements and standards, it is paramount that a means of accelerating student learning in high-poverty high schools be developed and evaluated.
What type of catching up and accelerated learning is needed in English and mathematics?
Grade level metrics and the percentage of students obtaining various proficiency levels provide a rough guide to the magnitude of catching up that needs to occur in high-poverty high schools.
They do not, however, provide a good guide to the skills, knowledge, and habits of mind that high school students with poor preparation need to acquire in an accelerated fashion to succeed in standards based courses, pass high-stakes tests, and become prepared to enter college or postsecondary training without remediation. On these questions, existing literature is sparse (Balfanz, McPartland, & Shaw, 2002).
There is a small, but growing, body of work that indicates that, concerning adolescent literacy, the greatest need is developing students’ reading comprehension and fluency (National Reading Panel, 2000). Nearly all adolescents can decode, but significant numbers of entering high school students have weak or limited reading comprehension skills (Campbell, Hombo, & Mazzeo, 2000).
In high-poverty high schools, the number of students who struggle to decode is higher than average but still typically represents a small minority of students. The overriding challenge in high-poverty high schools is that most, if not nearly all, students struggle to comprehend and fluently read high school level material (Greenleaf, Schoenbach, Cziko, & Mueller,
There is nothing approaching agreement in mathematics. Here there are several strongly held divergent views and only recently have there been attempts to use research to sort out differences and forge a consensus (Kilpatrick, Swafford, & Findell, 2001). One view holds that pre-collegiate mathematics is a sequential subject with a defined core of knowledge and procedures that need to be mastered in a largely prescribed order. Remediation efforts center on locating where a student lies on this continuum and providing instruction and practice around a set of defined procedures. In short, this view holds that arithmetic needs to be mastered before a student can learn algebra.
An alternative view holds that mathematics is a sense-making activity that employs a series of quantitative, algebraic, and geometric tools to solve problems. Kilpatrick, Swafford, & Findell (2001) find that this view proposes a different slant on accelerating learning that stresses both access to more advanced forms of mathematical thought and experience with mathematical problem solving. Recently, as witnessed by the revised National Council of Teachers of Mathematics (NCTM) standards (2000), there have been some attempts to argue that students need to both acquire and learn how to apply a core of mathematical knowledge.
What is clear is that the type of accelerated learning required by poorly prepared students in high-poverty high schools needs to involve more than narrow test preparation. It has to be substantial and sustained and enable students to rapidly develop declarative, procedural, and meta-cognitive knowledge (Kilpatrick et al., 2001). It also has to motivate students to learn and take advantage of the strengths they bring to the classroom. For example, adolescents with weak reading comprehension skills often have substantial spoken vocabularies and oral language skills.
Data collected as part of this study illustrate the uneven nature of the prior mathematical knowledge that poorly prepared students bring to the classroom. Entering ninth grade students in two high-poverty, non-selective high schools were given public release items from recent NAEP and TIMSS examinations. The results indicate that on any given item a substantial number of these students were successful, but overall, few students could solve more than a quarter of the items.
In short, some students had decent prior knowledge of geometry but not of data or operations, while others could solve operations problems but not the geometry questions. Thus, it is not surprising that traditional remedial courses that assume all students need to be taught from square one often result in high rates of student frustration. (Greenleaf et al., 2001).
Existing Research on Attempts to Accelerate Secondary Students’ Learning
Although many high schools offer some form of remediation in mathematics and reading, these efforts are typically not grounded in a well-developed research base or supported by solid evaluations of effectiveness. There are only a handful of catch-up programs for high school students that are supported by current research on the needs of adolescents and for which some evaluation data exists.
There are several recently developed extra-help or catch-up reading courses for high school students who can decode but who have weak fluency and struggle to comprehend advanced texts. These programs that focus on teaching students explicit reading comprehension strategies and giving them opportunities to apply these new skills have shown initial promising results.
In high implementing classrooms, students typically gain two years of reading level over one year of instruction. To date, however, these programs have been tested with only limited populations of students. Typically, these studies have examined the impact of the reading course at the teacher level with primarily one to two experimental teachers compared to a similar number of control teachers (Allen, 2001; Codding, 2001; Fischer, 1999; Greenleaf et al., 2001; Raiche & Showers, 2000; Showers, Joyce, Scalon, & Schnaubelt, 1998).
The research and evaluation base is even smaller in mathematics. White, Porter, Gamoran, and Smithson (1997) found generally positive effects for three high school transition courses they examined. Each of the courses—Math A in California, Stretch Regents in Rochester, New York, and the University of Chicago School Mathematics Project (UCSMP) Transition text as used in Buffalo, New York—attempted in somewhat different ways to provide under-prepared students with the knowledge, skills, and approaches they needed to succeed in college preparatory courses. To a significant degree, they succeeded. White and colleagues found that students who took these transition courses were “much more successful than those in the general math track in obtaining college preparatory math credits” (p. 77) and showed greater achievement gains.
Beyond this single study, however, no other evaluations of high school catch-up courses in mathematics were found. Some school districts are trying different variations of providing some or all students with extra time and/or extra support to learn algebra and other college preparatory mathematics courses. But to date, the impact of these efforts has been reported primarily anecdotally (Olson, 2001). The one major exception is Equity 2000—a major effort launched by
The College Board to dramatically increase the number of minority students taking algebra and geometry. The program was field tested throughout the 1990s in a number of urban school districts.
Evaluations of Equity 2000 indicate that the elimination of lower or general track math courses, combined with sustained professional development for teachers and modest student supports primarily in the form of Saturday academies, enabled substantially more students to take and pass algebra and geometry (Everson & Dunham, 1996; Fields, 1997). The evaluations also indicate, however, that in several of the field test districts, only slightly more than half the students taking algebra and geometry passed and that the extra help provided through the Saturday academies was not a strong enough support for many students (Ham & Walker, 1999).
In 2001, the No Child Left Behind Act placed even stronger responsibility on states to raise student performance. As a result of these accountability standards, states must now administer standardized tests to “measure adequate yearly progress” of all students. They face costly federal mandates and must submit comprehensive accountability plans. The federal law also focuses on narrowing the achievement gap between races. It requires that states monitor the performance of racial and economic subgroups and undertake corrective action in failing schools (Wong, in Gray and Hanson, 2004, p. 376).
Researchers examining student performance consistently find that one of the most important influences on student achievement is socioeconomic status (SES) of students. The more affluent the student’s background, the better he or she will perform (Coleman, et. al., 1966; Jencks et. al., 1972). This research, often referred to as “status attainment research,” generally concludes that other school and teacher characteristics as well as school policies and spending decisions have minimal consequences for student achievement. Later studies continue to support these conclusions (Hanushek, 1989,1996). Okpala (2002, p. 907), in one of many studies that examines resource usage in public schools concludes, “Some of the major factors that are theoretically under the control of a school… have little if anything to do with student performance.”
These findings give little comfort to educators in economically disadvantaged schools who are facing heavy pressure to improve performance and close the gap between minority and white students. Yet Verstegen and King (1998) claim that a growing body of research is using better databases and more sophisticated methodological strategies to provide evidence that school policies can make a positive difference in student outcomes.
They also emphasize that resource patterns that optimize performance in one setting do not necessary work in others. Encouraged by this line of thinking, the researcher will investigate factors that may explain the differences in performances in schools that share a common socioeconomic context. That is, are there choices made by policymakers and administrators in economically disadvantaged schools that spark significant improvements in performance in these schools?
In this study, the researcher will assume the significance of SES or “input” factors in explaining achievement, and the researcher considers the impact of other “process” variables, that is, factors over which schools have some control.
Using the Mississippi Academic Excellence Indicator System (AEIS) data, the researcher will examine these variables to determine the elements that can impact success or failure of public school campuses. The measure of performance is the standardized test given in 2001 to students in Mississippi public schools, the MCT. The researcher will focus the study on Mississippi schools that are predominantly populated by students who come from economically disadvantaged backgrounds. From this pool of poor school campuses the researcher will select two groups of very “high-performing” and very “poor-performing” school campuses.
Impact of process variables
Although the statistical models will include measures for SES (percent of economically disadvantaged students and percent white students), the focus will be on process variables. The latter include those variables that school systems more or less control. The researcher categorizes these variables into three general areas: 1) school characteristics (school size, student/teacher ratio, and campus expenditures by function and program); 2) teacher characteristics (salary and experience levels); and 3) the global resource measure of per pupil expenditure (PPE).
One of the most important and controversial characteristics of schools is the size of school. Production function research on the effects of school size has been inconclusive, and both sides have their advocates. Supporters of small schools contend that students get more attention, school governance is simpler, and teachers and administrators are more accessible to parents.
Noguera (2002) states that in high schools where the majority of low-income students of color are achieving at high levels the one common characteristic is the small size of the schools. Lee and Burkam found that students are less likely to drop out of schools with fewer than 1,500 students (2003). However, others argue that large schools are able to offer students a wider range of educational offerings and services (“Still Stumped,” 2002).
Recent research indicates that the effects of school size may depend on the SES of students. Findings show consistently that the relationship between achievement and socioeconomic status was substantially weaker in smaller schools than larger schools, that is, students from impoverished communities are much more likely to benefit from smaller schools.
On the other hand, a positive relationship exists between larger schools and the output measures of affluent students (Lee and Smith, 1996; Howley and Bickel, 1999). Because this study will examine the performance of economically disadvantaged students, the researcher expects to find a negative relationship between school size and achievement scores. That is, the larger the school, the less likely students are to achieve on standardized tests.
The effect of class size on student achievement is another relationship that has been closely studied. In 2000, Congress allocated $1.3 billion for class size reduction as a provision of the Elementary and Secondary Education Act (ESEA) (Johnson, 2002). Most of the studies that examine the effect of class size on student performance have focused on primary schools. In the mid-1990s, findings from Tennessee’s Student
Achievement Ratio (STAR) study found that students from small classes had significantly higher scores on standardized tests in every subject tested (Mosteller, et.al., 1996; Finn and Achilles, 1999). However, Johnson (2000), citing a study at the Heritage Foundation examining National Assessment of Educational Progress (NAEP) reading data, asserted that the difference in reading assessment scores between students in small classes and students in large classes was insignificant. He criticized class size reduction programs citing California as example of how such programs exacerbate the problem of lack of qualified teachers to fill classrooms. His claim of the lack of association between class size and performance was consistent with Hanushek’s conclusions (1999). Studies of the effects of class size in secondary schools are much more rare and largely equivocal (Deutsch 2003; Grissmer 1999).
Many of those who advocate for smaller class sizes at the secondary level argue that small classes positively impact the school environment, thus, improving performance indirectly. In her review of the literature of class size and secondary schools, Deutsch (2003) highlights studies that conclude small classes stimulate student engagement, allow more innovative instructional strategies, increase teacher-student interactions, reduce the amount of time teachers devote to discipline, improve teacher morale, and minimize feelings of isolation and alienation in adolescence that can come from anonymity.
In addition to school and class size, a critical characteristic of the school is the allocation of resources by function and program. Indeed, researchers generally conclude that the specific allocation of funds is as important as the total amount or per pupil expenditure (PPE) (Hedges, Laine, and Greenwald, 1994; Harter 1999). Harter’s study is particularly interesting. She examined the effects of 17 different types of instructional expenditures in 2,800 elementary schools in Texas and found that the most significant correlates for achievement were money spent to reward highly rated teachers and for supplies and maintenance. These categories of expenditures were specifically effective in high-poverty schools.
In this study the researcher will examine the allocation of resources by function and by program. The researcher will also examine percent of funds spent on instructional leadership, i.e., managing, directing, supervising, and providing leadership for staff who provide instructional services. The researcher anticipates that instructional leadership and support play a particularly significant role in improving the performance of students in impoverished communities.
Instruction by program applies to the areas defined by the Mississippi AEIS data, i.e., percentage spent on regular instruction, bilingual education, compensatory programs, gifted and talented programs, and career and technology programs for secondary students. In general, one would expect that spending on regular instruction would be positively correlated with student performance. However, predictions about the effects of spending on regular instruction are difficult to make with respect to economically disadvantaged schools. This is because the resources these schools put into other programs such as bilingual or compensatory education may help to improve the performance of students on standardized tests.’
Another important process variable the researcher will investigate encompasses teacher characteristics, namely, the level of teacher salaries and experience. As with the other factors in this model, conclusions about the effects of both on student performance has been mixed, but recent studies seem to point to more positive correlations, particularly teacher experience (Hedges, Lane, and Greenwald 1994).
Although Hanushek (1989) originally found negligible effects between teacher salary and student achievement, this was not true for teacher experience. And, in a more recent review of research, he cites a positive correlation between teacher salary and student performance in 74 percent of the cases, and a positive correlation between teacher experience and student performance in 85 percent of the studies (Hanushek 1997, p. 144 as cited in Verstegen and King, 1998). In their review of production function research, Verstegen and King (1998) state that teacher experience was a significant predictor of student performance in 24 of 30 studies and teacher salary was significant in 17 or 19 studies.
Finally, the researcher will also examine the effects of global resources, that is, per pupil expenditure (PPE), on the impact of performance. In their review of production function research, Verstegen and King cite Hedges, Laine, and Greenwald’s assertion (1994) that “Global resource variables such as PPE, show positive, strong, and consistent relations with achievement” (1995, 57-58). However, other studies fail to yield significant results (Chubb and Moe 1990; Okpala 2002). Tajalli, in his examination of the wealth equalization or “Robin Hood” program in Texas, found that the transfer of nearly $3.4 billion of dollars to poor school districts did not have a significant impact on the improvement of performance in these districts (Tajalli, 2003).
It may be that expenditures in general have an indirect effect that is not apparent when using PPE as a direct measure. In his study of school spending Wenglinsky (1997) develops a “path” in which he concludes a school’s economic resources are associated with academic achievement. He posits that per-pupil expenditures on instruction and central office administration are positively related to class size, i.e., more spending on smaller classes. Smaller teacher/student ratios contribute to a cohesive school environment, which enhances achievement.
Data on finances, students, and school characteristics from over 437 Mississippi elementary school campuses will be collected from the Mississippi Education Agency and will be merged for the purposes of this study. The resulting master file will be screened to arrive at the final data set. The researcher will use several criteria for including a campus in this study.
First, the researcher will eliminate campuses that have less than 50 students. Second, the researcher will select only those school campuses where 50% or more of the students are recognized to be economically disadvantaged. The researcher will also eliminate those cases that will not seem to be appropriate for the study. For example, the researcher will delete school campuses that will not have any regular expenditure, or will have expenditures per pupil that will be unrealistically low. The resulting data file will provide the researcher with cases for the 4th grade data, cases for the 6th grade campuses and cases for the 8th grade schools.
Dependent variables of this study are three dichotomous variables each representing low-performing and high-performing school campuses. The researcher will use 2001 campus MCT passing rates of 4th, 6th, and 8th graders for discerning high and low performing schools. The researcher defines high-performing schools as those with 90 percent or more MCT passing rates, and low-performing schools as those that had 50 percent or lower MCT passing rates. School campuses with MCT passing rates between 50% and 90% will be excluded from this study.
Fourteen independent variables will be considered for this study. The regression models for 4th and 6th grade data will not include expenditure on “career and technology” since these campuses are not recipients of the revenue allocated for this expenditure. This variable will be used only for the 8th grade model. The 8th grade regression will not include “Compensatory Expenditure” because it is highly correlated with the regular expenditure in this model. At the end, each regression model will be consisted of 13 independent variables. A list of all independent variables includes:
- Campus size
- Percentage of students economically disadvantaged.
- Percentage of students White.
- Percentage of expenditure on regular program.
- Percentage of expenditure on bilingual program.
- Percentage of expenditure on compensatory program.
- Percentage of expenditure on gifted and talented program.
- Percentage of expenditure on career and technology program.
- Operating expenditure per pupil.
- Percentage of expenditure on instruction.
- Percentage of expenditure on instructional leadership.
- Teacher-student ratio.
- Average teacher base salary.
- Average teachers’ years of experience.
Three separate forward logistic regressions will be tested to determine which independent variables are predictors of school performance. These regressions will run on 4th, 6th and 8th grade high/low performing schools. The data will be screened for outlier cases and the existence of multi-co-linearity among the independent variables.
Achieve (2001). Standards: How high is enough? (Achieve Policy Brief #3). Washington, DC: Author.
Allen, J. (2001). Eliminating a “Yes, But” curriculum. Principal Leadership 2(2), 10-15.
Balfanz, R. & Legters, N. (2001, January). How many central city high schools have a severe dropout problem, where are they located, and who attends them? Initial estimates using the common core of data. Paper presented at Dropouts in America: How Severe is the Problem? What do we know about intervention and prevention? Conference of the Harvard Civil Rights Project and Achieve, Inc.: Cambridge, MA.
Balfanz, R., McPartland, J., & Shaw, A. (2002, April). Re-conceptualizing extra help for high school students in a high standards era. Paper presented at Preparing America’s Future: High School Symposium, Washington, DC.
Bishop, J.H. & Mane, F. (2001). The impacts of minimum competency exam graduation requirements on college attendance and early labor market success of disadvantaged students. In G.
Campbell, J.R., Hombo, C.M., & Mazzeo, J. (2000). NAEP 1999 Trends in academic progress: Three decades of student performance (NCES2000-469). Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement, National Center for Education Statistics.
Chubb, J.E., & Moe, T.M. 1990. Politics, markets and America’s schools. Washington D.C: The Brookings Institution.
Codding, J. (2001). An up ramp for struggling readers. Principal Leadership, 2(2), 22-25. Committee for Economic Development (2000). Measuring what matters: Using assessment and accountability to improve student learning. New York: Author.
Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A.M., Weinfeld, F. D., & York, R. L. 1966. Equality of educational opportunity. Washington D.C: U.S. Department of Health, Education, and Welfare.
Deutsch, F.M. 2003. How small schools benefit high school students. NASSP Bulletin, 87, 35-44.
Everson, H.T., & Dunham, M.D. (1996). Signs of success: Equity 2000—Preliminary evidence of effectiveness. New York: The College Board. (ERIC Document Reproduction Service No. ED 455 109)
Fields, C.F. (1997). An equation for equity: Maryland’s Prince George’s County puts Equity 2000 to the test. Black Issues in Higher Education 13(26), 24-27, 28-29, 30.
Finn, J., & Achilles, C M. (1999). Tennessee’s class size study: Findings, implications, misconceptions. Educational Evaluation and Policy Analysis, 21, 97-109.
Fischer, C. (1999). An effective (and affordable) intervention model for at-risk high school readers. Journal of Adolescent & Adult Literacy, 43, 326-35.
Greenleaf, C.L., Schoenbach, R., Cziko, C., & Mueller, F.L. (2001). Apprenticing adolescent readers to academic literacy. Harvard Educational Review 71, 79-129.
Grissmer, D. 1999. Class size effects: Assessing the evidence, its policy, implications, and future research agenda. Educational Evaluation and Policy Analysis, 21, 231-248.
Ham, S., & Walker, E. (1999). Getting to the right algebra:The Equity 2000 initiative in Milwaukee public schools. Manpower Demonstration Research Corporation. Retrieved August 3, 2006 from http://www.mdrc.aa.psiweb.com/Reports99/Equity2000/Equity2000WrkgPpr.html
Hanushek, E. A. 1989. The Impact of differential expenditures on school performance. Educational Researcher, 18, 45-65.
Hanushek, E. A. 1996. School resources and student performance. In Gary T. Burtless (Ed.), Does Money Matter? The Effect of School Resources on Student Achievement and Adult Success (pp. 43-73). Washington D.C: Brookings Institution.
Hanushek, E. A. 1999. Some findings from an independent investigation of the Tennessee STAR experiment and from other investigations of class size effects. Educational Evaluation and Policy Analysis, 21, 143-64.
Harter, E. 1999. How educational expenditures were related to student achievement: insights from Texas elementary schools. Journal of Educational Finance, 24, 281-302.
Hauser, R.M. (2001). Should we end social promotion? Truth and consequences. In G. Ornfield & M.L. Kornhaber (Eds.), Raising standards or raising barriers? Inequality and high-stakes testing in public education. New York: Century Foundation.
Hedges, L. V., Laine, R. D., & Greenwald, R. (1994). Does money matter? A meta-analysis of studies ofthe effects of differential school inputs on student outcomes. Educational Researcher, 23, 5-14.
Howley, C & Bickel, R. (1999). The Matthew project: National report. Randolph, VT: Rural Challenge Policy Program (ERIC Document Reproduction Service No. ED433 174).
Jencks, C, M. Smith, Ackland, H. M. J., D. Cohen, Heyns, H. G. B., & Michelson, S. 1972. Inequality: A reassessment of the effect of family and schooling in America. New York: Basic Books.
Johnson, K.A. (2000). Do small classes influence academic achievement? WTiat the National Assessment of educational progress shows. (CDA Report No. 00-07). Washington D.C: Heritage Foundation.
Johnson, K.A. (2002). The downside to small class policies. Educational Leadership, 59, 27-30.
Kilpatrick, J., Swafford, J., & Findell, B. (Eds.) (2001). Adding it up: Helping children learn mathematics. Washington, DC: National Academy Press.
Lee, V. E. & Burkam, D. T. (2003). Droppingout of high school: The role of school organization and structure. American Education Research Journal, 40, 353-93.
Legters, N.E., Balfanz, R., Jordan, W.J., & McPartland, J.M. (2002). Comprehensive reform for urban high schools: A Talent Development approach. New York: Teachers College.
McPartland, J. & Jordan, W. (2001, January). Essential components of high school dropout prevention reform. Paper presented at Dropouts in America: How Severe is the Problem? What do we know about intervention and prevention? Conference of the Harvard Civil Rights Project and Achieve, Inc.: Cambridge, MA.
Mosteller, F., Light, R. J., & Sachs, J. A. (1996). Sustained inquiry in education: Lessons from skill grouping and class size. Harvard Education Review, 66, 797-842.
Mullis, I.V.S., Martin, M.O., Gonzalez, E., O’Connor, K.M., Chrostowski, S.J., Gregory, K.D., Garden, R.A., & Smith, T.A. (2001). Mathematics benchmarking report: TIMMS 1999- eighth grade. Boston: International Association for the Evaluation of Educational Achievement.
National Council of Teachers of Mathematics (2000). Principals and standards for school mathematics. Reston, VA: Author.
Neild, R.C., & Balfanz, R. (2001). An extreme degree of difficulty: The educational demographics of the ninth grade in Philadelphia. Baltimore, MD: The Johns Hopkins University, Center for Social Organization of Schools.
Neild, R.C., Stoner-Eby, S., & Furstenberg, Jr., F. (2001, January). Connecting entrance and departure: The transition to ninth grade and high school dropout. Paper presented at Dropouts in America: How Severe is the Problem? What do we know about intervention and prevention? Conference of the Harvard Civil Rights Project and Achieve, Inc.: Cambridge, MA.
Noguera, P. .A. (2002). Beyond size: The challenge of high school reform. Educational Leadership, 59,60-3.
Okpala, C. 2002. Educational resources, student demographics and achievement scores. Journal of Education Finance, 27, 885-908.
Olson, L. (2001). A quiet crisis: Unprepared for high stakes. Education Week 20(31), 1,12,14, 16- 17.
Ornfield & M.L. Kornhaber (Eds.), Raising standards or raising barriers? Inequality and high-stakes testing in public education. New York: Century Foundation.
Quality Counts ’98 (1998). The urban challenge: Public education in the 50 states. Education Week, 17(17).
Raiche, N., & Showers, B. (2000). A second chance to learn to read. Leadership, 30(2), 18-20.
Roderick, M., & Camburn, E. (1999). Risk and recovery from course failure in the early years of high school. American Educational Research Journal, 36, 303-343.
Showers, B., Joyce, B., Scanlon, M., & Schnaubelt, C. (1998). A second chance to learn to read. Educational Leadership, 55(6), 27-30.
Still stumped over school size. (January 2002). Education digest, pp. 31-3.
Tajalli, Hassan. 2003. Paper delivered at the annual meeting of the Southwestern Political Science Association, San Antonio, Texas.
Verstegen, D. A. & King, R. 1998. The relationship between school spending and student achievement: A review and analysis of 35 years of production function research. Journal of Education Finance, 24, 243-62.
Wenglinsky, H. 1997. How money matters: The effect of school district spending on academic achievement. Sociology ofEducation, 70, 221-237.
White, P.A., Porter, A.C., Gamoran, A., & Smithson, J. (1997). Upgrading high school mathematics: A look at three transition courses. NASSP Bulletin 81(586), 72-83.
Wong, K. K. (2004). The Politics of education. In Virginia Gray and Russell L. Hanson (Eds.), Politics in the ameriean states (pp. 357-388). Washington D.C: CQ Press.