a comparison of_final Running head: A COMPARISON OF ACADEMIC PERFORMANCE 1 Volume 13, Number 2 ISSN 1099-839X A Comparison of the Academic Performance of College Bound High School Students in Regional vs. Community High Schools in Connecticut Joseph P. Cullen Appalachian State University Citation Cullen, J.P. (2010). A Comparison of the Academic Performance of College Bound High School Students in Regional vs. Community High Schools in Connecticut. Current Issues in Education, 13(2). Retrieved from http://cie.asu.edu/ Abstract Consolidated regional high schools (RHSs) have replaced traditional community high schools (CHSs) in many nonmetropolitan communities. Consolidation purports to offer cost savings that, in theory, enable nonmetropolitan districts to provide a wider array of instructional opportunities to their students. Nonetheless, critics argue that the benefits of consolidation do not outweigh the costs. This inquiry adds to this discussion an examination of differences in performance on the 2006 - 2008 SAT I between RHS and CHS students in Connecticut. Results suggest that RHS students outperformed their CHS counterparts on 15 of 16 points of comparison. Four of these differences were statistically significant. Keywords: Rural Education, Educational Policy, School Reform, Secondary Education About the Author(s) Author: Joseph P. Cullen Affiliation: Appalachian State University Email: joe.cullen@familyinnovations.org Biographical information: Joseph P. Cullen received his Ph.D. in Special Education from the University of Connecticut in 1995. During his 26-year career in the fields of Education and Child Welfare, Dr. Cullen has served in a School Psychologist, Learning Disabilities Specialist, consultant, program director, and university faculty member. He currently serves in a dual capacity as the Director of Research and Evaluation at Appalachian Family Innovations, a university-affiliated research and training program, and as an Associate Professor in Special Education at Appalachian State University in Boone, North Carolina. Dr. Cullen’s research and professional interests include school reform, the prevention of child maltreatment, and strategies for working with teachers and families to support children with learning disabilities, ADHD, and emotional and behavioral challenges in school and community settings. A COMPARISON OF ACADEMIC PERFORMANCE 3 For more than a century, educational reformers have been discussing solutions to the challenges of rural schools. As Kannapel and DeYoung (1999) relate, rural schools have traditionally been described as lacking appropriate facilities, curricula, and personnel, leading many to regard them as inferior to schools in metropolitan communities. To improve rural school quality, advocates for reform in the first half of the 20th century relied heavily on the factory paradigm that emerged during the industrial revolution (Bard, Gardener, & Wieland, 2005). In particular, it was thought that rural schools would benefit from the concept of “economies of scale” (i.e., the principle that the production cost per unit is reduced when the size of the operation is increased). In this regard, large consolidated schools were thought to be more efficient and to have lower production costs than small community schools, leaving more resources for the improvement of facilities, curriculum, and the quality of teachers (Fanning, 1995). This trend of thought led to the conclusion that school consolidation -- the unification of two or more attendance areas into one large school (Peshkin, 1982) -- was the premier solution to the challenges of rural schools (Lasley, Leistritz, Lobao, & Meyer, 1995). By the middle of the 20th century, the pace of rural school consolidation was accelerated by demographic trends that included a decline in the population of rural areas due to a dying agricultural economy and falling birth rates. These factors resulted in diminished resources, especially in rural districts that received state funds on a per pupil basis, and made it difficult for them to upgrade school facilities or offer competitive salaries to teachers or administrators. Consequently, many rural districts could not provide the same educational services and specialty courses as their larger, better-funded, urban and suburban counterparts. Specifically, they were found to be less likely to offer advanced placement (AP) classes along with fewer courses in art, music, literature, foreign language, technology, and laboratory science (Schwartzbeck, 2003). To address these challenges, Conant (1959), in a highly influential report on secondary school reform, urged the elimination of small high schools as a means of offering a wider range of curricula and improving the cost-effectiveness of secondary education. In addition, elected officials implemented state policies in the 1970s, 80s, and 90s that encouraged school consolidation by requiring districts to meet mandated enrollment levels before they could receive state funds for new school buildings or capital improvements. Many districts were forced to consolidate in order to meet these mandates (DeYoung & Howley, 1990; Purdy, 1992). Advocates of consolidation at the secondary level also benefited from the political climate of the cold war era. After the launch of Sputnik in 1957, secondary education received a great deal of federal scrutiny, especially in the domains of math and science. The resulting effort to improve instruction in these areas necessitated significant upgrades to educational infrastructure including the modernization of laboratory facilities, the development of curricula, and the training of teachers and support staff. In order to afford these upgrades, many rural secondary schools were forced to consolidate into regional high schools wherein capital and operational costs could be shared among two or more communities (DeYoung, 1989; Ravitch, 1983). Further accelerating the consolidation movement was the A Nation at Risk report (USDE National Commission on Excellence in Education, 1983) and its scathing critique of the nation’s public school system. The effect of this report was to shift the emphasis in secondary educational policy away from broad-based curricula to a primary focus on preparation for college. It also resulted in a series of federal mandates aimed at raising professional standards for teachers and increasing facility and academic requirements. These mandates added significantly to the cost of A COMPARISON OF ACADEMIC PERFORMANCE 5 public schools, creating additional hardship for high schools in nonmetropolitan areas and forcing many to consolidate (Kannapel & DeYoung, 1999). By the end of the 20th century, rural school consolidation and associated efforts to increase the professionalization of the teaching field were so effectively implemented that large, centralized school districts controlled by credentialed professionals became the accepted standard in our society (Kannapel & DeYoung, 1999). It is, in fact, no longer accurate to refer to consolidation as an exclusively rural phenomenon in that many outlying suburban communities have also joined the consolidation movement (Plucker, Spradlin, Magaro, Chien, & Zapf, 2007). For this reason, this inquiry will use the term nonmetropolitan, as proposed by Hobbs (1994), to refer to the rural, small town, and outlying suburban districts that most often experience pressure to consolidate. This change in terminology is particularly well suited to the State of Connecticut, the locale for this inquiry. Connecticut, like many northeastern states, has experienced a demographic shift over the past 25 years. In 1986, approximately 67% of Connecticut school districts were described as small/rural in 1986 (Melnick, Shibles, and Gable, 1986). However, due to the phenomenon of urban sprawl, the population of many rural communities has swelled since the 1980’s. These shifts have changed the character of these communities from rural/agrarian to suburban. Consolidation and the effects of school size As the United States entered the 21st century, the justification for school consolidation encompassed research on the effects of school size. One such study was done by Horn (1986) who found that, relative to larger schools, teachers in small high schools were less qualified, lower paid, and had fewer opportunities for professional development. In addition, he found that course offerings in small high schools were more limited and guidance counselors and librarians were less likely to be available. Another study from this era (Monk, 1990) found that school size was one of the most powerful predictors of variation in curriculum offerings in secondary schools. Nonetheless, recent examinations of the issue of school size have reached conclusions that are at odds with earlier studies. For instance, Monk and Haller (1993) concluded that that the relationship between school size and curriculum was not linear. Rather, they found that it appears to be influenced by factors such as academic subject area, level of course difficulty, school setting, socioeconomic status, faculty unionization, and grade configuration. In addition, other authors have found evidence that small schools have distinct advantages over large schools. They include Cotton (1998) who wrote that the benefits of small schools appear to include higher numbers of students taking academic courses, more attention to student needs, a closer connection with one’s community, more positive staff attitudes, higher rates of participation in extracurricular activities, and better attendance. Furthermore, Lee and Burkham (2003) found that small schools have lower dropout rates than their larger counterparts while Lee and Loeb (2000) found that teachers in smaller schools took greater personal responsibility for student learning than teachers in larger schools. As a result, they tended to exhibit better relationships with their students and more confidence in their teaching. In the domain of school climate, Noguera (2004) cites evidence of student alienation as justification for reorganizing secondary schools into smaller, more personalized learning communities. Efforts to address this factor have resulted in the creation of smaller high schools in distressed inner city districts where student alienation is a significant problem (“Making Room for Literacy in Secondary Schools,” 2005; Rubenstein, Reisner, Coon, & Fabiano, 2005). Finally, recent research has provided evidence that smaller schools show higher overall A COMPARISON OF ACADEMIC PERFORMANCE 7 achievement, more inclusive decision-making processes, less tension between teachers and students, fewer resources devoted to discipline problems, higher rates of parent-teacher involvement, higher morale, and lower levels of frustration and alienation (Plucker, et al., 2007; Steward, 2009). The results of these studies have been used to advocate for changes in educational policy that would give a higher priority to creating and maintaining small community schools (DeYoung & Howley, 1990; Fanning, 1995; Howley, 1989; Kannapel & DeYoung, 1999). With regard to schools in Connecticut, the subject of this inquiry, Melnick et al. (1986), concluded that there were very few differences between small and large districts in terms of quality. Specifically, small and large schools were not found to vary with respect to per pupil expenditures, percent of students in need of remedial services, performance on state-mandated assessments, or student attendance and persistence rates. Differences favoring larger high schools were, however, found in the number of advanced courses offered and proportion of students who continue on to higher education. In addition, a difference favoring small schools was found in the number of school staff per 1,000 students. These results indicate that the effects of school size in Connecticut mirror national trends; that is, size appears irrelevant to school costs with the primary benefit of large schools being their potential for offering advanced curriculum and the primary benefit of small schools being their potential for offering more individualized attention to students. Consolidation and consumer satisfaction Like any government policy, the most important test of school consolidation may occur not in academia but in the court of public opinion. In this regard, the consolidation movement continues to thrive because some communities welcome consolidated schools and perceive them to be effective at achieving their intended benefits. Specifically, Self (2001) in an evaluation of the effects of school consolidation found that teachers, parents, and students held overwhelmingly positive attitudes toward the consolidation of schools in Ohio in the early 1990s. A follow up survey revealed that major stakeholders continued to perceive consolidation in a positive manner eight years later. In addition, studies supportive of consolidation report that, relative to community high schools, consolidated high schools exhibit: 1) financial advantages, 2) higher and more consistent standards of teacher preparation, 3) more variation in course offerings, and 4) higher faculty salaries (Cummins, Chance, & Steinhoff, 1997; Nelson, 1985; Schwartzbeck, 2003). Despite these positive perceptions, a growing number of stakeholders have expressed dissatisfaction with the practice of school consolidation. Their criticisms have been positioned within a broader discourse about the ends and means of education and the importance of schools as community centers, not just instructional settings. Specifically, the arguments of advocates for the preservation of small nonmetropolitan schools have focused on: 1) practical problems such as long bus rides and disincentives to participation in extra-curricular activities and 2) philosophical concerns such as social justice and maintaining a meaningful context for learning (Bard, et al., 2005; Fanning, 1995; Fitchen, 1991; Howley & Howley, 2001; Kannapel & DeYoung, 1999; Luloff & Swanson, 1990; Nachtigal, 1982, 1994; Peshkin, 1978). Regarding practical concerns, Howley and Howley (2001) write that students in consolidated schools are far more likely to have long bus rides than students in small community schools. These lengthy bus rides add significantly to transportation costs that rise more sharply, are less predictable, and offer fewer instructional benefits than the costs associated with educating children in their own communities. In addition, the logistics of providing bus A COMPARISON OF ACADEMIC PERFORMANCE 9 transportation across broad geographic areas make it difficult for many students to participate in extra-curricular activities. Research has found that consolidated schools show significantly lower rates of student participation in extra-curricular activities than do comparable community schools (Biere, 1995; Cotton, 1998; Fitchen, 1991; Howley & Howley, 1995; Luloff & Swanson, 1990; Nachtigal, 1982). This finding is particularly problematic in light of research indicating that many nonmetropolitan communities value extra-curricular and nonacademic activities as much as academic activities if not more so (DeYoung, 1995; Nachtigal, 1982; Peshkin, 1978; Stern, 1994). Other examiners have challenged consolidation on the basis of social justice and the importance of creating meaningful contexts for learning. Specifically, a robust body of research suggests that large consolidated schools primarily benefit affluent, college-bound students while small community schools have been found to achieve positive learning outcomes across a broader range of socioeconomic strata (Fowler & Walberg, 1991; Friedkin & Necochea, 1988; Howley, 1995; Lee & Smith, 1997). Another social justice issue concerns the fact that consolidation is often justified by its effectiveness at addressing national goals such as the preparation of workers to compete in the global economy (DeYoung, 1995; Howley, 1997; Howley & Howley, 1995; Post & Stambach, 1999; Theobald & Nachtigal, 1995). This justification weakens local control over public schools, resulting in policies that are less responsive to community values and priorities. For example, in farming communities, modern consolidated high schools that are designed first, and foremost, to prepare students for college often fail to provide learning experiences that are rooted in the community’s agricultural heritage (Fanning, 1995; Kannapel & DeYoung, 1999). Fanning (1995) indicates that this lack of connection between school and community results in “place-less” rather than “place-based” learning; a trend that undermines the meaningfulness of learning contexts and runs contrary to modern movements in curriculum reform such as constructivism (Haas & Lambert, 1995; Herzog & Pittman, 1995; Howley, 1997: Howley & Howley, 1995; Rosenfeld, 1983; Theobald and Nachtigal, 1995). To re-connect schools to the community, Fanning (1995) argues for a balance between “grounded knowing,” which helps children to interpret the events of their lives, and “technical knowing,” which enables them to understand the connection of these events with larger human experience (p. 4). Consolidation may be an effective model for promoting technical knowing. However, if it results in impersonal educational institutions devoted primarily to college preparation, then it becomes an ineffective model for promoting grounded knowing outcomes such as good citizenship, healthy relationships, or functional living skills. It also fails to value the importance of community schools as cultural and social centers that enrich the broader community (DeYoung & Lawrence, 1995; Herzog & Pittman, 1995; Nachtigal, 1982; Seal & Harmon, 1995; Stern, 1994). Current status of school consolidation Despite the arguments of its detractors, school consolidation is alive and well as an educational policy initiative. The state of Maine, for example, has recently proposed merging its 290 local school districts into 26 regional administrative districts. In addition, the 2007 state budget in Indiana appropriated funds for use by school districts wishing to study the feasibility of consolidation. Furthermore, the legislatures in Kansas, Nebraska and North Dakota have recently debated school consolidation initiatives. Over the past three years, these states, along with Idaho, South Dakota, and Arkansas, have either passed laws or established policies that encourage consolidation (Plucker et al., 2007; Schwartzbeck, 2003). A COMPARISON OF ACADEMIC PERFORMANCE 11 In light of these trends, there is a need for communities considering the question of consolidation to engage in rigorous cost-benefit analyses relative to community values, culture, and traditions. Do the benefits of an enhanced capacity to offer technical education outweigh the practical and social justice objections to consolidation or the loss of meaningful contexts for learning? Surprisingly, very few quantitative inquiries evaluate the effects of consolidation on the academic performance of students. To inform policy makers on this issue, this inquiry will explore this gap in the literature. It will do so by comparing the scores on college entrance examinations of students in consolidated (also known as regional) high schools with those of students in community high schools. For the purposes of this study, the term regional high school (RHS) will be used to describe a secondary school that was created to serve a consolidated student body from several nonmetropolitan districts. The term community high school (CHS) will be used to refer to a secondary school in a nonmetropolitan area whose student enrollment falls in the bottom quartile in the state census. In addition, CHSs resemble in size, demographics, and geographic location the constituent high schools of districts that have consolidated.   Methods This investigation utilized a causal-comparative design to address the following research question: On standardized college admissions tests, do students is regional high schools (RHSs) outperform students in community high schools (CHSs)? Given that the districts being studied cannot be randomly selected, a matching procedure was used to assure that the two groups were comparable on key demographic and geographic characteristics. To address the threat of location (Gall, Gall, & Borg, 2006), a standardized instrument (The College Board SAT I) with specific administrative guidelines was used to assure that data were collected under relatively uniform sets of circumstances. To address the threat of mortality, this inquiry focused on archival data collected from an authoritative source, the Connecticut Education Data and Research (CEDAR) database (2008). This approach assured a 100% response rate among districts in the sample. In addition, with regard to instrumentation, the use of an objectively administered and scored standardized achievement measure eliminated data collector bias as a significant rival hypothesis. Finally, because the data for this investigation were collected in a naturalistic setting, it is free of the threats to external validity mentioned by Campbell and Stanley (1963) with respect to experimental research designs. However, given that the sample for this study is geographically limited, results will not be generalized beyond school districts within the State of Connecticut. The dependent variable for this investigation was student performance on the College Board SAT I Reasoning Test (SAT I), the premier norm-referenced measure of academic preparedness for college. In that this study compared the performance of two discrete groups of participants, the independent variable was group membership; that is, comparisons were made between Group A: RHS Students and Group B: CHS Students. The sample for this investigation included 32 high schools – 16 regional and 16 community – with an enrollment in excess of 20,000 students. The two groups used for this investigation were matched using District Reference Group (DRG) as the control variable. The State of Connecticut created District Reference Groups (DRGs) – designated A (the highest) through I (the lowest) – as a means of classifying schools and school districts according to their needs and resources. Specifically, DRGs are groups of districts that have similar geographic, cultural, and socio-economic characteristics. The CSDE assigns districts to DRG groupings based upon data elements from the 2000 Census and 2004 Public School Information System A COMPARISON OF ACADEMIC PERFORMANCE 13 (PSIS) database. To measure socioeconomic status, the CSDE used three data elements from the 2000 Census: median family income; percentage of parents with a bachelor’s degree or higher; and percentage of parents holding jobs in executive, managerial or professional occupations. Three other indicators – the percentage of children living in families with a single parent, the percentage of children enrolled in public schools whose families have incomes that make them eligible to receive free or reduced-price meals, and the proportion of children in the district whose families speak a language other than English at home – were used to assess need. In addition, enrollment in the district at the end of the 2003-2004 academic year was a minor factor in the analysis (CEDAR, 2008). The first step in sample selection was to establish an operational definition of a small high school. This process involved an analysis of two sources of information. The first was enrollment data on communities that participate in regional high schools. These data were examined to identify benchmarks for selecting a matched sample of schools that would be similar in size to the schools that would be operated by these communities if they did not participate in a regional high school. Based on State of Connecticut K-12 enrollment data (CEDAR, 2008), “head counts” in the elementary grades, which offer a valid prediction of the number of high school aged students in these districts, range from approximately 40 to approximately 800 students. To obtain more precise figures, a second source of information – the 2006 State of Connecticut School Census (CEDAR, 2008) – was analyzed. This document lists 123 comprehensive community high schools with a mean enrollment of 1147 students (standard deviation=589). Based on the mean and standard deviation, it was determined that schools with 750 or fewer students would constitute the bottom quartile in enrollment. Given the similarity between this figure and the upper limit of the elementary “head count” among communities with regional high schools, it was accepted as a defensible benchmark for selecting schools for the sample. When this benchmark was applied, an initial pool containing the 30 smallest comprehensive public high schools in Connecticut was identified. These schools ranged in size from 231 to 740 students with a mean of 509.13 and a standard deviation of 149.74. The second step in sample selection was to match regional high schools with community high schools based on the DRG system. To accomplish this in a defensible manner, it was necessary to narrow the range of DRGs in both groups. Therefore, given that the state’s 17 regional schools are spread across DRGs A through F, community high schools in lower DRGs (G though I) were eliminated from the CHS pool. By the same token, because there were no community high schools in DRG A, the lone DRG A regional school was eliminated from the RHS pool. In addition, since consolidation appeals primarily to schools in rural areas, small community high schools in suburbs on the fringes of urban centers were eliminated from the CHS pool. To achieve a similar distribution in the RHS and CHS groups on key demographic and geographic variables, schools were matched based on their DRG to the fullest extent possible. However, because of unequal distributions of schools across DRG groups, it was necessary, in some instances, to match schools in higher DRGs with schools in lower DRGs based on a careful examination of demographic data. This strategy was legitimized by the fact that the schools in DRGs C through E are all in middle-income communities that are very similar demographically. The two resulting groups had the following compositions: (1) The RHS Group was composed of 16 High Schools serving 12,231 students. The group includes one high school from DRG B, 14 from DRGs C through E, and one from DRG F. (2) The CHS Group was composed of 16 High Schools serving 7,893 students. The group includes one high school from DRG B, 14 from DRGs C through E, and one from DRG F. A COMPARISON OF ACADEMIC PERFORMANCE 15 Descriptive and inferential statistics were used to verify the comparability of the RHS and CHS groups on the demographic factors that the State of Connecticut uses to assign schools to DRGs; i.e., median family income; percentage of parents with a bachelor’s degree or higher; proportion of children’s parents who hold jobs in executive, managerial, and/or professional (i.e., “white collar”) occupations; percentage of children living with a single-parent; proportion of students who meet eligibility guidelines for free or reduced meals, and percentage of students whose families speak a language other than English at home. In addition, schools were compared on their minority enrollments, number of students per academic computer, and per pupil expenditures. Table 1.1 provides a complete profile of the RHS and CHS groups, including descriptive statistics and t-test results used to evaluate the significance of group differences. Table 1.1 Descriptions and Comparisons of Research Groups Source: Connecticut Education Data and Research (CEDAR, 2008) Schools Size D R G Median Income % Free/ Reduced Meals % Minority % College Degree % White Collar % Single Parent % Non- English Speaking Students Per Computer Per Pupil Spending Regional N=12,231 Region 1 562 E $56,591 12.6 3.7 29.4 44.5 30.1 1 1.6 $12,305 Region 4 594 C $82,620 7.3 6 43.9 50.8 16.3 1.7 1.9 $11,046 Region 5 1678 B $93,868 1.8 12.7 59.1 63.5 12 3.2 3.5 $10,397 Region 6 390 E $65,759 7.6 2.6 28.7 38.7 13.7 0.2 2.9 $10,395 Region 7 786 C $84,090 2.1 2 40.8 56.2 13 0.1 2.0 $11,392 Region 8 1026 C $81,862 3.6 3 45 51.2 15.1 0.1 2.4 $8,365 Region 10 775 C $84,246 3.1 4.9 42.5 55 5.3 2.1 2.2 $11,016 Region 11 200 F $64,732 14.8 5.8 21.5 32.6 20.3 0 1.4 $14,210 Region 12 392 C $83,514 2.6 6.1 39.9 42.7 10.3 0.8 2.1 $13,510 Region 13 1380 C $79,900 4.8 3.5 40.2 47.8 16.3 0.2 3.4 $10,040 Region 14 836 C $82,025 4.6 4.5 46.9 52.6 19.6 1.4 3.7 $9,205 Region 15 814 B $87,671 1.6 7 53 57.4 9.2 2.4 4.1 $9562 Region 16 679 E $77,260 8.2 4.9 27.6 35.8 8.8 1.9 3.1 $9,705 Region 17 459 C $88,307 5.6 3.4 48.3 57.9 9 0.4 3.7 $11,732 Region 18 459 C $78,025 3.8 5.7 55.5 51.9 18.2 1.8 2.9 $13,538 Region 19 1201 C $70,239 6.8 11.7 50 58.9 17.3 1.7 2.9 $10,291 Means 764.4 $78,794 5.58 5.47 42.02 49.84 14.66 1.19 2.74 $11,044 SD 408.8 $9,829 3.79 2.98 10.67 8.85 5.96 0.99 .82 $663 Community N=7,893 Bolton 285 C $81,293 6.2 6.4 47.4 54.1 16.2 0 3.6 $11,706 Canton 515 C $76,113 3.5 7.2 47.5 58.6 12.3 1.1 4.1 $10,888 Clinton 663 D $70,776 7.8 9.7 35.9 45.7 16.2 1.3 3.4 $11,854 Coventry 573 E $65,707 10.6 5 20.8 40.1 22.7 0.3 3.7 $8,234 E. Granby 241 D $77,852 1.3 11.2 29.8 49.5 20.6 3.6 3.1 $11,919 E. Haddam 387 E $68,393 5.4 4.7 29.7 41.1 11.1 1.2 3.3 $9,463 E. Hampton 567 D $70,400 7.8 5 32.6 44.1 22.1 1.4 4.9 $10,467 Ellington 738 C $81,196 4.4 7.2 36.1 48.5 13 1.7 3.0 $10,111 Granby 687 B $92,696 3.4 6.7 57.3 65.4 10.9 1.2 3.1 $10,201 Lebanon 581 E $66,652 8.1 4.3 26.8 43 13.3 0.7 3.6 $9,776 Litchfield 438 E $66,809 4.8 5.2 36.5 43.6 14 0 3.5 $11,420 Old Saybrook 462 D $73,409 7.7 11.1 43.3 44 19.9 5 4.3 $11,294 Plymouth 526 F $65,917 14 5.4 16.4 33.9 18.8 1.5 4.4 $9,013 Portland 362 E $68,802 9.9 8.4 38.4 41 25.3 0.6 3.0 $11,811 Somers 574 C $77,795 4.8 4.4 34.9 46.3 9.5 1.2 3.8 $9,747 Westbrook 294 E $75,568 9.6 6.5 31.9 35.4 19.4 1.7 2.5 $13,984 Means 493.3 $72,711 6.83 6.78 35.33 45.89 16.58 1.41 3.58 $10,527 SD 119 $7,354 3.24 2.26 10.25 8.10 4.80 1.28 .62 $1,148 t probability .026 .150 .337 .128 .058 .232 .414 .550 .003 .425 A COMPARISON OF ACADEMIC PERFORMANCE 17 The instrument used to measure the dependent variable in this investigation was the College Board SAT I Reasoning Test (SAT I), a norm-referenced measure of student readiness for college learning with three sections: Critical Reading, Writing, and Math. The SAT I meets the highest standards of technical adequacy in content, construct, and criterion-related validity as well as internal consistency and test re-test reliability. It was deemed an appropriate basis for the comparison of school districts in that it now measures, since its 2006 revision, a set of skills that is closely aligned with college preparatory instruction. In addition, relative to state mastery tests, it offers an elevated ceiling capable of differentiating student performance at higher levels of achievement. Scores from 2006 through 2008 were used for this inquiry, as they are the only scores available since the revision. The Connecticut State Department of Education official database was used as the sole data source for this inquiry. This database compiles official statistics on Connecticut School districts based upon federal census information and superintendents’ yearly reports. With regard to the research question – On standardized achievement tests, do students in regional high schools outperform students in community high schools? – the SAT I scores for the RHS group were compared with those of the CHS group. To assure that group differences were not primarily a function of the size and diversity of the test-taking cohort, the two groups were also compared on the proportion of students who took the SAT I. Given that this inquiry used matched groups, correlated t-tests were used to evaluate group differences. In addition, two- tailed tests with an alpha level of .05 were used for all comparisons. All analyses were performed on the MS Excel spreadsheet with t-test results reported as probability values. Results In terms of demographics, Table 1.1 provides a profile of the two groups that were the focus of this inquiry. The reader will note that while the regional schools are consistently higher on most indicators of socioeconomic status, none of the differences between the groups crosses the threshold of statistical significance. However, one indicator, the percentage of parents who are college educated bordered on statistical significance (p=.058). Nonetheless, given that both groups exhibited socioeconomic characteristics that were solidly in the middle range, the decision to treat them as comparable is justified. Aside from demographic contrasts, there are two points of comparison between the two groups that are noteworthy. First of all, the RHS and CHS groups were found to be strikingly similar in terms of per pupil expenditures. This finding is intriguing given the economy of scale argument that is often used to justify consolidation. In fact, the data indicate that the regional high schools sampled spend approximately $600 more per year per student than their community high school counterparts. While this difference is not statistically significant, it is supportive of the conclusion that regional high schools do not appear to offer overall cost savings relative to community high schools. Secondly, there was a significant difference between the two groups in terms of students per academic computer. This difference suggests that the regional schools may offer significantly better access to technology than their community school counterparts. With regard to the research question, Table 2.1 provides a summary of the 2006, 2007 and 2008 SAT I scores for the two groups. A summary of the 2006-2008 SAT I mean scores can be found in Table 2.2. Both tables also provide the results of correlated t-tests on all 16 points of comparison between the RHS and CHS groups. As these results indicate, the groups were not found to differ in terms of the proportion of students taking the SAT I. However, in 2006, A COMPARISON OF ACADEMIC PERFORMANCE 19 students in regional high schools obtained higher scores than their community high school counterparts on three out of four points of comparison: Critical Reading (p =.002), Writing (p = .025), and Total Scale (p=.016). In addition, the difference between groups in Math was very close to the threshold of statistical significance (p=.057). In 2007, students in regional high schools earned slightly higher scores than their counterparts in community high schools on three of the four SAT I components (Critical Reading, Writing, and SAT I Total). In Math, the CHS group outperformed the RHS group by a slim margin. These differences, though, were not found to be statistically significant. Similarly, in 2008, students in regional high schools earned slightly higher scores than their counterparts in community high schools on all four SAT I components (Critical Reading, Math, Writing, and SAT I Total). However, these differences were, again, insignificant. Overall, when compared on the basis of 2006 through 2008 SAT I mean scores, students in regional schools outscored students in community high schools on all four components but the only statistically significant difference occurred in Critical Reading (p=.035). The approximate mean score differences in favor of the RHS group were as follows: 11 points in Critical Reading, five points in Math, ten points in Writing, and 26 points on the Total Scale. Table 2.1 2006 -2008 SAT I Scores for RHS & CHS Groups Tot=SAT Total; Cre=SAT Critical Reading; Mat = SAT Math; Wri=SAT Writing Source: Connecticut Education Data and Research (CEDAR, 2008) High Schools % of Students Tested 06-08 2006 SAT I 2007 SAT I 2008 SAT I Tot Cre Mat Wri Tot Cre Mat Wri Tot Cre Mat Wri Regional : Region 01 64.68 1579 539 519 524 1551 532 501 518 1528 513 505 510 Region 04 81.20 1563 521 525 524 1550 523 504 524 1576 530 519 526 Region 05 92.25 1677 552 566 559 1634 539 548 547 1655 545 551 559 Region 06 75.05 1563 520 517 522 1566 527 531 508 1601 531 531 539 Region 07 83.55 1628 544 548 538 1586 529 533 524 1612 526 551 535 Region 08 83.30 1608 536 542 528 1628 541 550 536 1627 540 552 536 Region 10 85.88 1601 532 527 540 1554 512 521 521 1556 515 520 521 Region 11 74.65 1579 534 515 530 1475 501 468 506 1509 521 477 511 Region 12 88.98 1576 527 527 522 1551 527 504 521 1536 513 506 516 Region 13 77.35 1579 540 530 517 1595 531 530 534 1569 520 522 526 Region 14 91.88 1552 520 518 515 1517 510 498 508 1560 524 508 528 Region 15 95.18 1568 522 528 525 1629 547 546 536 1633 541 551 541 Region 16 71.23 1489 500 499 491 1457 485 487 485 1463 485 490 488 Region 17 85.78 1598 530 531 529 1532 511 512 509 1578 522 528 527 Region 18 79.60 1660 542 567 553 1691 560 569 561 1687 557 565 565 Region 19 72.93 1668 563 558 550 1651 547 560 542 1657 550 561 547 Mean 81.5 1593 532.6 532.3 529.2 1572.9 526.4 522.6 523.8 1584.2 527.1 527.3 529.7 SD 8.5 47.9 14.8 19.2 16.5 63.4 19.1 28.1 18.70 59.8 17.4 26.2 19.1 Community: Bolton 77.13 1562 526 512 524 1626 544 538 543 1583 525 511 546 Canton 86.93 1624 531 546 543 1632 535 554 543 1625 537 545 543 Clinton 86.63 1538 515 512 512 1508 495 510 503 1572 521 523 528 Coventry 78.73 1518 510 507 499 1499 506 499 495 1527 516 507 504 E. Granby 71.83 1551 510 548 493 1620 533 561 526 1619 534 562 523 E. Haddam 79.03 1469 494 499 492 1523 497 501 525 1582 522 521 539 E. Hampton 82.10 1530 512 505 514 1527 503 514 511 1615 538 540 538 Ellington 86.90 1572 519 530 526 1549 512 529 508 1554 513 522 519 Granby 80.83 1646 554 549 544 1650 550 553 546 1617 539 542 536 Lebanon 86.78 1469 494 487 490 1490 499 494 497 1529 526 499 503 Litchfield 83.20 1564 509 523 528 1682 556 559 567 1607 528 539 539 Old Saybrook 90.35 1567 504 531 531 1543 510 516 517 1534 510 507 517 Plymouth 75.25 1516 499 512 505 1483 492 499 489 1416 470 482 464 Portland 89.53 1490 491 500 496 1573 522 541 510 1571 528 526 517 Somers 85.15 1568 520 533 519 1636 542 553 541 1551 512 524 515 Westbrook 77.30 1492 507 495 493 1552 515 522 516 1567 524 509 533 Mean 82.4 1542.3 512.2 518.1 513.1 1568.3 519.4 527.7 521.1 1566.8 521.4 522.4 522.8 SD 5.4 50.3 15.9 19.6 18.4 63.9 21.1 23.8 21.9 51.9 16.5 20.1 20.6 t probability .649 .016 .002 .057 .025 .840 .313 .619 .685 .324 .283 .538 .262 A COMPARISON OF ACADEMIC PERFORMANCE 21 Table 2.2 2006- 2008 SAT I Mean Scores for RHS & CHS Groups Tot=SAT Total; Cre=SAT Critical Reading; Mat = SAT Math; Wri=SAT Writing Source: Connecticut Education Data and Research (CEDAR, 2008) Mean 2006 – 2008 SAT I Tot Mean 2006-2008 SAT I Cre Mean 2006-2008 SAT I Mat Mean 2006-2008 SAT I Wri Regional High Schools: Region 01 1552.67 528.00 508.33 517.33 Region 04 1563.00 524.67 516.00 524.67 Region 05 1655.33 545.33 555.00 555.00 Region 06 1576.67 526.00 526.33 523.00 Region 07 1608.67 533.00 544.00 532.33 Region 08 1621.00 539.00 548.00 533.33 Region 10 1570.33 519.67 522.67 527.33 Region 11 1521.00 518.67 486.67 515.67 Region 12 1554.33 522.33 512.33 519.67 Region 13 1581.00 530.33 527.33 525.67 Region 14 1543.00 518.00 508.00 517.00 Region 15 1610.00 536.67 541.67 534.00 Region 16 1469.67 490.00 492.00 488.00 Region 17 1569.33 521.00 523.67 521.67 Region 18 1679.33 553.00 567.00 559.67 Region 19 1658.67 553.33 559.67 546.33 Mean 1583.38 528.69 527.42 527.54 SD 54.09 15.46 23.55 16.90 Community High Schools: Bolton 1590.33 531.67 520.33 537.67 Canton 1627.00 534.33 548.33 543.00 Clinton 1539.33 510.33 515.00 514.33 Coventry 1514.67 510.67 504.33 499.33 E. Granby 1596.67 525.67 557.00 514.00 E. Haddam 1524.67 504.33 507.00 518.67 E. Hampton 1557.33 517.67 519.67 521.00 Ellington 1558.33 514.67 527.00 517.67 Granby 1637.67 547.67 548.00 542.00 Lebanon 1496.00 506.33 493.33 496.67 Litchfield 1617.67 531.00 540.33 544.67 Old Saybrook 1548.00 508.00 518.00 521.67 Plymouth 1471.67 487.00 497.67 486.00 Portland 1544.67 513.67 522.33 507.67 Somers 1585.00 524.67 536.67 525.00 Westbrook 1537.00 515.33 508.67 514.00 Mean 1559.13 517.69 522.73 518.96 SD 47.10 14.46 18.91 17.00 t probability .183 .035 .549 .135 Discussion C urrent Issues in E ducation V ol. 13 N o. 2 22 The results of this inquiry suggest that, relative to community high schools (CHSs), regional high schools (RHSs) may provide a slight academic benefit to college preparatory students as measured by scores on the SAT I. However, this conclusion must be viewed as highly tentative in light of the fact that the most dramatic differences occurred in a single year, 2006, and were not consistent across the three-year period examined. Furthermore, those who would use these results to inform policy decisions should be mindful that the sample for this inquiry is small and drawn exclusively from nonmetropolitan regions of Connecticut. The results cannot, therefore, be generalized nationally or to urban or suburban districts where the relationship between school-level variables and student achievement appears to be much more complex. Another limitation of this inquiry is the fact that it is focused on the SAT I, a college entrance examination, and its findings are, therefore, limited to students who are bound for college. As a result, it offers few insights into the differences between students in regional and community high schools who are pursuing courses of study that prepare them for postsecondary objectives other than college. This is an especially important consideration in that it is these students that critics describe as being most in need of the more intimate environment of a small community school (Bard et al., 2005; Fitchen, 1991; Luloff & Swanson, 1990; Nachtigal, 1982; Plucker, et al., 2007; Purcell & Shakelford, 2005). When applied to the consolidation debate, advocates and opponents of consolidation will both find support for their positions in the results of this inquiry. Advocates will argue that their view is validated by the higher SAT I scores of students in regional high schools and data indicating that regional schools offer greater access to technology for a comparable per pupil cost. Opponents will point out that most of the differences between groups on the SAT I are A C om parison of A cadem ic P erform ance 23 insignificant and that the ones that reach statistical significance are not sufficiently robust to be considered an advantage for regional schools. In addition, they will point out that the gap between RHSs and CHSs appears to narrow over the three-year period examined in this study. Specifically, in reading, the performance of CHSs improved each year from a mean of 512.2 in 2006 to a mean of 519.4 in 2007 and 521.4 in 2008. Similarly, writing scores improved from 513.1 in 2006 to 521.1 in 2007 and 522.8 in 2008. These trends suggest that the “Regional Effect” may be fading. Furthermore, advocates of CHSs will argue that the data from this inquiry do not support the conclusion that regional schools are more cost effective that community schools. This conclusion is consistent with a previous study conducted on a similar sample in the same geographic region (Melnick et al., 1986). Finally, critics of consolidation will point out that the results of this inquiry do not speak to such core concerns as the effects of consolidation on transportation, student participation in extracurricular activities, educational equity, or the context for learning. Conclusions The results of this inquiry strongly indicate a need for further research. Specifically, given the size and geographic limitations of the sample, it would be necessary to replicate this study with larger samples, drawn from multiple locales, over a longer period of time before the stability and meaningfulness of these results can be determined. In addition, should meaningful differences be found, further inquiry would be needed to determine the reasons for these differences. A related issue would be to determine why these differences seem to occur more often in Reading and Writing than in Math. Inquiry in this area may also provide an explanation for the finding that, from 2006 through 2008, the gap between regional and community high schools on the SATI reading and writing subtests diminished steadily. Possible areas of inquiry C urrent Issues in E ducation V ol. 13 N o. 2 24 might include differences in the quality and preparation of teachers, the availability of instructional resources in regional versus community high schools, or the distribution of these resources across socioeconomic strata. Furthermore, since the demonstration of positive academic effects would not alone be sufficient to address the most daunting social, cultural, and philosophical challenges to consolidation, there is a need for policy makers to reflect on existing research and arrive at a consensus position on its benefits and limitations. While community leaders await the emergence of such a position, they would be wise to look beyond the fiscal benefits of consolidation and engage stakeholders in a deeper discussion of community beliefs regarding the goals and purposes of public education. The relevance of this study to this discussion is that an analysis of data from 32 nonmetropolitan communities in Connecticut provides additional support to the following three conclusions from the literature on school consolidation: (1) The cost benefits of modern school consolidation may be illusory given that regional schools and small community schools have similar per-pupil expenditures. Therefore, consolidating schools primarily as a cost saving measure may not be a defensible strategy. (2) Regional high schools appear to offer some limited advantages to college preparatory students as demonstrated by their higher performance on the literacy sections of the SAT I. These differences may be associated with the enhanced curriculum resources of larger schools, including the ability to offer advanced placement classes. However, these benefits may be diminishing as the performance gap between college- bound students in regional vs. community high schools appears to be narrowing and A C om parison of A cadem ic P erform ance 25 affordable technology becomes available to add these enhancements to the curriculum of smaller schools. (3) Large consolidated schools may offer greater access to technology. However, this advantage may be of diminishing importance in light of the growing saturation of technology in American society. Given the advantages of small schools as described in the literature and the growing body of research on the benefits of small schools for a variety of historically under-performing groups, school leaders in Connecticut and across the country many need to re-examine the assumptions of school consolidation and consider the possibility that “small may be the new big.” In this regard, recent research indicates that “small is better” in poor inner-city communities where schools must counteract anomie, normlessness, and the breakdown of institutions that form the fabric of society (“Making Room for Literacy in Secondary Schools,” 2005; Rubenstein, Reisner, Coon, & Fabiano, 2005). Similarly, many have suggested that small may be better in remote rural areas where schools are the only institutions that can combat the effects of social isolation (Biere, 1995; Fanning, 1995; Fitchen, 1991; Luloff and Swanson, 1990; Nachtigal, 1982; Peshkin, 1978, 1982). Nonetheless, if we are to heed the admonition of Kannapel and DeYoung (1995) to avoid assuming that all schools suffer from generic problems that lend themselves to generic solutions, then we must also make room for the possibility that, in some communities, larger may continue to be better. In this regard, one practical recommendation would be to consider the question posed by Fanning (1995); “What should our young people have the chance to learn?” (p.5). If the answer to this question is, as Fanning suggests, a balance of “grounded” (i.e., personal and experiential) with “technical” (i.e., rational and empirical) knowing, then the results of this inquiry suggest C urrent Issues in E ducation V ol. 13 N o. 2 26 that community schools, which may be more effective at promoting grounded knowing, may wish to actively explore alternatives to consolidation for assisting their students in the development of technical knowing. By the same token, communities served by regional schools, which may offer advantages over community schools in promoting technical knowing, may wish to explore strategies for assisting their students in the development of grounded knowing. In the domain of technical knowing, communities that wish to preserve their community schools may find in this study the motivation to identify new ways to access talent and resources. One evidence-based option for accomplishing this objective is to develop partnerships with public and private institutions and foundations (Fanning, 1995; Nachtigal, 1994). In addition, to augment and/or avoid consolidation, Schwartzbeck (2003) recommends that districts explore: 1) Cooperative agreements with nearby districts that enable then to share staff, contractual services and/or buildings; 2) Four day work weeks (thus saving on utility and transportation costs); 3) Distance learning; and 4) Using Regional Educational Service Centers (RESCs) to achieve economies of scale in areas such as food and custodial services, special education, and payroll management. In the domain of grounded knowing, communities that are served by regional and/or consolidated schools may wish to explore the formation of smaller learning communities to promote a sense of cohesion and belongingness among students (Cotton, 1998; “Making Room for Literacy in Secondary Schools,” 2005; Noguera, 2004; and Rubenstein, Reisner, Coon, & Fabiano, 2005). This approach, based on the schools-within-schools model (Goodlad, 1984; Murphy, 1991), creates semi-autonomous small schools that are housed within larger schools to achieve learning environments characterized by greater intimacy, improved teacher engagement, and a more positive peer culture. A C om parison of A cadem ic P erform ance 27 Finally, the results of this inquiry provide insight into the thorny question of school size. It is noteworthy that although the regional high schools examined in this study were significantly larger than the community high schools sampled, they were not large schools relative to the state mean. Indeed, of the 16 schools studied, eight fell in the bottom quartile in terms of school size (N≤750) and only three were above the state mean (N≥1147). It is, therefore, difficult to conclude that the benefits (or lack thereof) of consolidation suggested by this inquiry have much to do with school size. This conclusion fits with prior research on this subject (Melnick et al., 1986) and with the lack of consensus in the literature on the benefits and disadvantages of small versus large schools. It also fits with the conclusion that the factors that mitigate the effects of school size are myriad and appear to include the demographic, socio-economic, cultural, and geographic characteristics of a given community. C urrent Issues in E ducation V ol. 13 N o. 2 28 References American Psychological Association. (2001). Publication Manual of the American Psychological Association (5th ed.). Washington, DC: Author Bard, J., Gardener, C., & Wieland, R. (2005). Rural school consolidation report: History, research summary, conclusions & recommendations. Report of the National Rural Education Association Consolidation Task Force. (ERIC Document Reproduction Service No. ED 497 049) Biere, A. 1995. Community change. In Lasley, et al. Beyond the amber waves of grain. Boulder, CO: Westview Press. Campbell, D., & Stanley, J. (1963). Experimental and quasi-experimental designs for research. Boston: Houghton Mifflin Company. Conant, J. (1959). The American high school today: A first report to interested citizens. New York: McGraw-Hill. Connecticut education data and research (CEDAR, 2008). Retrieved November 20, 2008, from http://www.sde.ct.us/public/cedar. Cotton, K. (1998). The academic and social effectiveness of small-scale schooling. Journal of Early Education and Family Review, 6, 1, 25-28. Cummins, C., Chance, E., & Steinhoff, C. (1997). A model for rural school consolidation: Making sense of the inevitable result of school reform. Tucson, AZ: Proceedings of the Annual NREA Convention. (ERIC Document Reproduction Service No. ED 413 148) DeYoung, A. (1989). Economics and American education: A historical and critical overview of the impact of economic theories on schooling in the United States. New York: Longman. DeYoung, A. (1995) The life and death of a rural American high school: Farewell Little Kanawha. New York: Garland. DeYoung, A., & Howley, C. (1990). The political economy of rural school consolidation. Peabody Journal of Education, 67, 63-89. DeYoung, A., & Lawrence, B. (1995). On hoosiers, yankees, and mountaineers. Phi Delta Kappan, 77, 105. A C om parison of A cadem ic P erform ance 29 Fanning, J. (1995). Rural school consolidation and student learning. Charleston, WV: U.S. Department of Education, Office of Educational Research and Improvement. (ERIC Document Reproduction Service No. ED 384 484) Fitchen, J. (1991). Endangered spaces, enduring places, change, identity and survival in rural America. Boulder, CO: Westview Press. Fowler, W. & Walberg, H. (1991). School size, characteristics, and outcomes. Educational Evaluation and Policy Analysis, 13, 189-202. Friedkin, N. & Necochea, J. (1988). School system size and performance: A contingency perspective. Educational Evaluation and Policy Analysis, 10, 237-249. Gall, M., Gall, J., & Borg, W. (2006). Educational research: An introduction (8th ed.). New York: Longman. Goodlad, J. (1984). A place called school. New York: McGraw-Hill. Haas, T. & Lambert, R. (1995). To establish the bonds of common purpose and mutual enjoyment. Phi Delta Kappan, 77, 136-142. Herzog, M., & Pittman, R. (1995). Home, family, and community. Phi Delta Kappan, 77, 113. Hobbs, D. (1994). Demographic trends in nonmetropolitan America. Journal of Research in Rural Education, 10,149-160. Horn, J. (1986). A study of rural/small schools and their graduates in a seven state area. Manhattan, KS: Kansas State University Center for Rural Education and Small Schools. (ERIC Document Reproduction Service No. ED 280 657) Howley, C. (1989). Synthesis of the effects of school and district size: What research says about achievement in small schools and school districts. Journal of Rural and Small Schools, 4, 1, 2-12. Howley, C. (1995). The Matthew principle: A West Virginia replication? Education Policy Analysis Archives, 3, 18. (ERIC Document Reproduction Service No. ED 389 503) C urrent Issues in E ducation V ol. 13 N o. 2 30 Howley, C. (1997). Studying the rural in education: Nation-building, globalization, and school improvement. Education Policy Analysis Archives, 5, 12. (ERIC Document Reproduction Service No. EJ 580 772) Howley, C., & Howley, A. (1995). The power of babble: Technology and rural education. Phi Delta Kappan, 77, 26-31. Howley, A. & Howley, C. (2001). Rural school busing. ERIC Digest. Eugene, OR: ERIC Clearinghouse on Rural Education and Small Schools. (ERIC Document Reproduction Service No. ED 459 969) Kannapel, P., & DeYoung, A. (1999). The rural school problem in 1999: A review and critique of the literature. Journal of Research in Rural Education, 15, 67-79. Lasley, P., Leistritz, L., Lobao, L., & Meyer, K (1995). Beyond the amber waves of grain. Boulder, CO: Westview Press. Lee, V. & Burkham, D. (2003). Dropping out of high school: The role of school organization and structure. American Educational Research Journal, 40, 353-393. Lee, V. & Loeb, S. (2000). School size in Chicago elementary schools: Effects on teachers’ attitudes and students’ achievement. American Educational Research Journal, 37, 3-31. Lee, V. & Smith, J. (1997). High school size: Which works best, and for whom? Educational Evaluation and Policy Analysis, 19, 205-227. Luloff, A. & Swanson, L. (Eds.) (1990). Education and community. Boulder: Westview Press. Making room for literacy in secondary schools (2005). R&D Alert, 7, 1. San Francisco: WestEd. Melnick, S., Shibles, M., & Gable, R. (1986). A comparative study of the relationships between school district size and selected indicators of educational quality: A report prepared for the Connecticut Association of School Administrators, Small/Rural Schools Committee. (ERIC Document Reproduction Service No. 305 215) A C om parison of A cadem ic P erform ance 31 Monk, D. (1990). Transcending the effects of school size on the high school curriculum. Los Vegas, NV: Annual Meeting of the American education finance association. (ERIC Document Reproduction Service No. ED 326 370) Monk, D. & Haller, E. (1993). Predictors of high school academic course offerings: The role of school size. American Educational Research Journal, 30, 3-21. Murphy, J. (1991). Restructuring schools: Capturing and assessing the phenomenon. New York: Teachers College Press. Nachtigal, P. (Ed.) (1982). Rural education: In search of a better way. Boulder, CO: Westview Press. Nachtigal, P. (1994). Political trends affecting nonmetropolitan America. New Orleans, LA: Paper presented at the Rural Education Symposium during the annual conference of the American Educational Research Association. (ERIC Document Reproduction Service No. ED 370 749) Nelson, E. (1985). School consolidation (Report No. 1985-00-00). Eugene, OR: ERIC Clearinghouse on Educational Management. (ERIC Document Reproduction Service No. ED 282 346) Noguera, P. (2004). Transforming high schools. Educational Leadership, 61, 8, 26-31. Peshkin, A. (1978). Growing up American. Chicago: University of Chicago Press. Peshkin, A. (1982). The imperfect union: School consolidation and community conflict. Chicago: The University of Chicago Press. Plucker, J. Spradlin, T. Magaro, M., Chien, R., & Zapf, J. (2007). Assessing the policy environment for school corporation collaboration, cooperation, and consolidation in Indiana. Bloomington, IN: University of Indiana Center for Evaluation & Educational Policy. (ERIC Document Reproduction Service No. ED 497 535) Post, D. & Stambach, A. (1999). District consolidation and rural school closure: E pluribus unum? Journal of Research in Rural Education, 15, 106-117. C urrent Issues in E ducation V ol. 13 N o. 2 32 Purcell, D. & Shakelford, R. (2005). An evaluation of the impact of rural school consolidation. What challenges may a new round of rural school consolidations have on the safety, educational performance and social environment of rural communities? West Lafayette, IN: NREA. (ERIC Document Reproduction Service No. ED 497 051) Purdy, D. (1997). An economical, thorough, and efficient school system: The West Virginia school building authority and economy of scale numbers. Journal of Research in Rural Education, 12, 170-182. Ravitch, D. (1983). The troubled crusade. New York: Basic Books. Rosenfeld, S. (1983). Something old, something new: The wedding of rural education and rural development. Phi Delta Kappan, 65, 270-273. Rubenstein, M., Reisner, E., Coon, M., Fabiano, L., (2005). New century high schools: Evaluation findings from the second year. New York: Policy Studies Associates, Inc. (ERIC Document Reproduction Service No. ED 491 882). Schwartzbeck, T. (2003). Declining counties, declining school enrollments. Arlington, VA: American Association of School Administrators. (ERIC Document Reproduction Service No. ED 478 349) Seal, S. & Harmon, H. (1995). Realities of rural school reform. Phi Delta Kappan, 77, 119-124. Self, T. (2001). Post-consolidation evaluation: The effects eight years later. Chicago, Il: Paper presented at the Annual Meeting of the Mid-Western Educational Research Association. (ERIC Document Reproduction Service No. ED 475 492) Stern, J. (1994). The condition of education in rural school. Charleston, WV: U.S. Department of Education, Office of Educational Research and Improvement (ERIC Document Reproduction Service No. ED371935) Steward, L. (2009). Achievement differences between large and small schools in Texas. The Rural Educator, 30, 2, 20-28. Theobald, P. & Nachtigal. P. (1995). Culture, community and the promise of rural education. Phi Delta Kappan, 77, 132-135. A C om parison of A cadem ic P erform ance 33 United States Department of Education National Commission on Excellence in Education. (1983). A nation at risk: The imperative for educational reform. Washington, DC: U.S. Government Printing Office. C urrent Issues in E ducation V ol. 13 N o. 2 34 Current Issues in Education http://cie.asu.edu Volume 13, Number 2 ISSN 1099-839X Authors hold the copyright to articles published in Current Issues in Education. Requests to reprint CIE articles in other journals should be addressed to the author. Reprints should credit CIE as the original publisher and include the URL of the CIE publication. Permission is hereby granted to copy any article, provided CIE is credited and copies are not sold. Editorial Team Executive Editors Jeffery Johnson Lori Ellingford Katy Hirsch Section Editors Krista Adams Hillary Andrelchik Miriam Emran Tracy Geiger Sarah Heaslip Melinda Hollis Amber Johnston Seong Hee Kim Lindsay Richerson Rory Schmitt Tapati Sen Jennifer Wojtulewicz Lucinda Watson Faculty Advisers Gustavo E. Fischman Jeanne M. Powers Debby Zambo