h. .. :. .. .39... 31.. :2} L I :3 1.9333311..- . fix-5.. 3., .23. Is : .133... . 33)): .1 .y‘xszv A? 3:31.. . 7:1. 3 .3231»: l. a :(3. wt??? 43).... 2. .. .t .a, \‘z . :33... a... -93.. at r.x§.:.?\ 333.!!- 5:\1f.115$) .1275 3:1.) . A 3.3.111... as .2. 1:33.: 1,: {1.3. ~ “-34.21; 93:2... :; ‘53:..5 {’13 .1 3:14?) B. r.s1\..|..>)l «55.1.7 \..\.‘|.(|\, THESIS Ill/IIll/IIl/l/II/l/I/l/ll/Il/Il/I/lllllllll/l/lll/ll/llll/I 7 3492 This is to certify that the dissertation entitled THE EFFECTS OF SPECIALIZATION 0N AT-RISK STUDENTS presented by RONALD SYLVESTER JONES A‘ has been accepted towards fulfillment of the requirements for P" . D - degree in EDUCATION Date September 14, 1994 MSU is an Affirmative Action/Equal Opportunity Institution 0—12771 LiBRARY Michigan State University PLACE IN RETURN BOX to remove thie checkout from your record. TO AVOID FINES return on or before date due. DATE DUE DATE DUE DATE DUE MSU Is An Affinnative Action/Equal Opportunity Institution Warns-p t THE EFFECTS OF SPECIALIZATION ON AT-RISK STUDENTS By Ronald Sylvester Jones A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Educational Administration 1994 ABSTRACT THE EFFECTS OF SPECIALIZATION 0N AT-RISK STUDENTS By Ronald Sylvester Jones Schools are bureaucracies and they exhibit bureaucratic characteristics. Thus, the focus of this study is upon the outcomes of bureaucratic characteristics, namely specialization. Specialization is the most prevalent bureaucratic characteristic employed in school. However, there are both proponents and opponents of the practice. Proponents of specialization or specialized interventions assert that schools employ this practice to treat idiosyncratic behavior (i.e. poor academic achievement, poor behavior, dropping out of school, and etc.) exhibited by poor and at-risk students. Critics, however, argue that specialized interventions may induce unintended effects or negative consequences such as those identified above. Thus, some opponents assert that specialized interventions harm rather than help students. To help illuminate this important issue, this study examined the effects of specialized interventions on at-risk students and the perceptions of those at-risk students to specialized interventions. This investigation was accomplished by comparing two groups of 9th and 10th grade, at-risk male students enrolled in Detroit Public Schools. One group of students were enrolled at Fredrick Douglas Academy and received more specialized interventions. A comparable group of students were from Mackenzie, Henry Ford, and Northwestern High Schools. They received fewer specialized interventions. Four research hypotheses were presented in this research. Those hypotheses stated that the students who received more specialized interventions would demonstrate significantly greater improvement in the areas of academic achievement (CAT Reading and Mathematics, and earned credit hours), behavior (suspensions and daily attendance), attitude toward school, and attitude toward expected accomplishments. While data on academic achievement and behavior were collected from student records, data on school attitude were collected by administering a 5 scale survey instrument (SAM scales A-E). Additionally, a 9 question survey instrument was administered to measure student attitude toward expected accomplishments. Statistical test indicated that the students who received more specialized interventions demonstrated significantly greater improvement on most of the variables than students who received fewer specialized interventions. Thus the researcher concluded that not only is bureaucratic specialization beneficial to these students but that programs like Fredrick Douglas Academy should be expanded to expose more students and at earlier grade levels. ACKNOWLEDGMENTS I wish to thank the chairperson of my guidance committee, Dr. Philip A. Cusick for his time, patience, encouragement, and guidance. Thanks also to committee members Dr. Frank Boster, Dr. Fred Ignatovich, and Dr. Perry Lanier for their statistical assistance, valuable suggestions, and help in organizing this study. I wish to acknowledge, with a deep sense of compassion and gratitude, my loving wife Janet Marie Jones who not only encouraged me to pursue advanced graduate studies at Michigan State University, but served as the motivation for me to complete the doctoral program. This study is dedicated in memory of my mother Mrs. Johnnie Mae Gorman. TABLE OF CONTENTS CHAPTER 1 1 Introduction ............ - -- - -- -- -- -- -- -- 1 Purpose--- - ----------- - - .................................................. 3 Conceptual Framework ........... - - - -3 Bureaucracy In Schools .......................................... - ............................ 8 Summary ............................................................... .............................. 10 Purpose and Research Questions ................................................................................. 11 Profile of an At-Risk Student ............ - ........... 15 Program Characteristics .................................................................................................. 18 Method and Sample ........................................................................................................ 23 Significance ....................................................................................................................... 26 Limitations ........................................................................................................................ 27 Definition of Terms ......................................................................................................... 28 References ...................................................................................................... 30 CHAPTERZ - - - -- 32 Review of Literature ....................................................................................................... 32 The Structure of Bureaucracy ....................................................................................... 34 Schools As Bureaucracy ................................................................................................. 38 Differentiation and Separation ..................................................................................... 43 Ability Group Sortin and Social Status ...................................................................... 47 Bureaucratic Contro ...................................................................................................... 52 Characteristics of At-Risk Students ............................................................................. 55 Specialized Intervention Programs ............................................................................... 62 Summary ........................................................................................................................... 67 References ........................................................................................................................ 70 CHAPTER 3 - - -- - - - 78 Methodology .................................................................................................................... 78 General Design ................................................................................................................ 78 Procedure and Data Collection ..................................................................................... 80 Data Analysis .................................................................................................................... 88 Instrumentation ............................................................................................................... 91 Pilot Study ......................................................................................................................... 96 Summary .......................................................................................................................... 98 CHAPTER 4-- - - - - - -- 100 Results and Analysis of Data ....................................................................................... 100 CHAPTER 5 -- - - - ------ -124 Summary ......................................................................................................................... 124 Conclusnons ..................................................................................................................... 129 Program Recommendations ........................................................................................ 133 References ...................................................................................................................... 136 ii Informed Consent Form .............................................................................................. 139 REFERENCES --140 iii LIST OF TABLES Table 1 Ninth and Tenth Grade Interventions for the Experimental and Control Group, School Year 1990-91 --------- - ..................... 82 2 Ninth and Tenth Grade Interventions for the Experimental and Control Group, School Year 1991-92 ........................................................ 83 3 Descriptive Statistics for all Dependent Variables as a Function of condition and grade ............................................................... 101 iv Figure \OQQQUIAOJN v—I-I H6 12 13 14 15 16 17 18 19 LIST OF FIGURES Mean CAT reading scores as a function of condition and grade. ...... 103 Mean CAT mathematics as a function of condition and grade. .......... 104 Mean Earned Credit Hours as a function of condition and grade. 105 Mean Attendance as a function of condition and grade. 106 Mean Suspensions as a function of condition and grade. .................... 107 Mean SAM Scale A as a function of condition and grade. ................... 108 Mean SAM Scale B as a function of condition and grade. ................... 110 Mean SAM Scale C as a function of condition and grade. ................... 111 Mean SAM Scale D as a function of condition and grade. ................... 112 Mean SAM Scale E as a function of condition and grade. ................... 113 Mean Expected Accomplishments, Statement 1 as a function of condition and grade. 115 Mean Expected Accomplishments, Statement 2 as a function of condition and grade. - -- 116 Mean Expected Accomplishments, Statement 3 as a function of condition and grade. ----- - 117 Mean Expected Accomplishments, Statement 4 as a function of condition and grade. - -- -- 118 Mean Expected Accomplishments, Statement 5 as a function of condition and grade. - 119 Mean Expected Accomplishments, Statement 6 as a function of condition and grade 120 Mean Expected Accomplishments, Statement 7 as a function of condition and grade. -- - 121 Mean Expected Accomplishments, Statement 8 as a function of condition and grade.- 122 Mean Expected Accomplishments, Statement 9 as a function of condition and grade. - ------ 123 Chapter 1 Introduction Schools are bureaucracies. They are large, complex, modern organizations and they contain all the characteristics of bureaucracies. The degree and form of bureaucratization may vary from one school to another (Perrow, 1979). The pure form of bureaucracy enumerated by Weber (1947) is structured or organized on seven rational-legal practices: ' Rules and Regulations (Standardization) ' Division of Labor (Specialization) ° Hierarchy of Authority Knowledge or Technical Skills Separation of Administrative Ownership ' Autonomy in Allocating Resources ' Written Records of Administrative Acts, Decisions and Rules Evidence of these practices is found in schools. Schools, however, are their own kind of bureaucracy because their product is students. To accommodate students, particularly those who exhibit low academic achievement and idiosyncratic behavior, schools increasingly rely on specialization, a practice that is embedded within the division of labor. 2 Specialization suggests that task are broken down or separated by skill and expertise and then assigned to persons who possess those skills and expertise. In schools, specialization involves the assignment of administrators, teachers and support staff to intervention programs to service student needs. Thus, staff members with skills in guidance, social work, special education, school psychology, mathematics, reading and etc. are assigned to provide specialized services for specific students. Specialization is the central focus of this study. It is also the bureaucratic practice that most strongly affects how schools manage and treat poor and at-risk students. However, there are both proponents and opponents of the practice. Proponents argue that specialization (specialized interventions) is a natural way for the organization to treat students with natural differences. But opponents point out that specialization tends to segment students along social and economic lines; that it is the poor, and at-risk students who are separated away and who receive the most specialized services. The debate then centers on whether students differ in the way they learn or in social and economic class. To help illuminate this important issue, this study will compare a group of at- risk students who receive more specialized interventions, with a group of at-risk students who receive fewer specialized interventions. One group of students selected for this study were 9th and 10th grade at-risk male students enrolled in Fredrick Douglas Academy (F.D.A.), a specialized intervention program in the Detroit Public Schools. The students at F.D.A. were selected because they exhibit troublesome behaviors and because F .D.A. provides students with more specialized interventions than comprehensive high schools. A comparable group of students who exhibit similar behaviors from Mackenzie, Henry Ford, and Northwestern High 3 Schools, also in Detroit, were selected. This latter group of students received fewer specialized interventions. When this study has been completed, the researcher will be able to add some scholarly thought to the debate. If the results support the argument posed by the proponents, this investigation as well as other research will provide plausible arguments for continued and expanded use of specialization. If the results are unfavorable, constructive recommendations must be made regarding the modification and/or abandonment of this practice. Purpose The purpose of this study was to examine the effects of Specialized interventions on at-risk students who have received the greatest share of these interventions. Also, since some at-risk students receive comparatively more specialized interventions than others, this study attempts to determine the perceptions of these at-risk students to the specialized interventions they received. Stated another way, this study asked whether or not specialized interventions achieve their intended or desired objectives? This study also asked whether or not these specialized interventions are perceived by the participants as helpful and leading to success? Conceptual Frgnework Schools are bureaucracies and bureaucracies exhibit certain behaviors. Bureaucracies according to Weber (1947) are characterized by power, authority, rules, and regulations, hierarchy, universalism, specialization and they segregate the 4 role from the person. The latter characteristic is particularly significant because idiosyncratic and possibly troublesome behaviors are segmented away from role behaviors. Further, bureaucracies are viewed as the most effective and efficient method of managing large organizations. Bureaucracies are not stagnant but dynamic. They utilize bureaucratic procedures to provide not only stability and rational environments, but to specify behaviors that will lead to predetermined outcomes. To ensure stability, rational environments, and achieve specific outcomes, bureaucracies establish rules, regulations, policies, and procedures. These bureaucratic characteristics save effort, prevent disorganization, and minimize uncertainty. Bureaucracies also provide the framework in which organizations adapt and adjust to changing environments. Change and adaptation are achieved through specializing to solve particular problems, co-opt needed resources, and moderate among competing interests inside and outside the organization. Specializing, co- opting, and moderating provide the dynamic by which organizations maintain a balance among competing forces. Bureaucracies are effective at solving problems, but they solve them bureaucratically. Therefore, to maintain or increase order, the organization develops and implements rules and regulations; to manage special needs, it provides specialized staff and resources; to increase compliance, the bureaucracy increases authority and sanctions. In essence, bureaucracies provide the mechanism by which organizations can function effectively and efficiently. 5 Schools have the characteristics of bureaucracies, and they are designed to operate with these characteristics. Schools serve large student enrollments of up to 1,000,000 in a single school district (New York) and several thousand students in a single school building. In all schools, students greatly out-number staff. Since schools are bureaucratic, they manage youthful populations by employing specialization, hierarchy of authority, knowledge or technical skills, and rules and regulations. When problems surface, schools respond bureaucratically. Schools offer specialized classes, assign specialized staff, allocate resources and particularize to solve problems. They enact rules and regulations to maintain order among students, expand the hierarchy with specialized staff, and place greater emphasis on separating the role from the person. Additionally, schools co-opt outside groups and interests, and they modify to survive and specialize to accommodate new groups (i.e. special education, bilingual education and dropouts). In short, schools are organized ‘ according to bureaucratic processes, and they solve problems bureaucratically. Given the premise that schools exhibit bureaucratic characteristics, the first piece of the argument is that bureaucracy is the most prevalent form of organization and it provides both stability and a mechanism for change. Similarly, since schools have adopted many of the characteristics of bureaucracy, they also exhibit a degree of order and stability. The second piece of the argument is that bureaucracy, according to some researchers, can induce unintended or negative effects and that those effects may diminish the bureaucracy’s primary goal, which is to educate and promote equality. 6 Katz (1975), Willis (1981), Calabrese (1988) and Bowles and Gintis (1976) argue that one of the negative effects of bureaucracy in schools is that it perpetuates a system that differentiates along social class lines rather than academic achievement lines. That is, while the ostensrble intent of the schools’ bureaucratic procedures is to provide specialized services for children of different needs and abilities, the actual result is to separate the poor from the wealthy and provide the poor with an inferior education. Stated another way, those authors argue that poor and at-risk students are segmented off into remedial and vocational programs and that such placements deny these students opportunities to advance socially and economically. Conversely, the affluent or wealthy students retain their advantage through placements in honors and college preparatory programs that ensure easier access to high-status jobs. The school bureaucracy may also induce negative effects on academic achievement. Katz (1975) and other researchers argue that poor and at-risk students are tested, labeled and treated as low achievers or academically disadvantaged. These students are grouped according to ability and as earlier argued, they are segmented off into remedial, special education and vocational programs. Additionally, poor and at-risk students receive specialized services designed to improve academic skills. However, many of these students never improve their academic skills sufficiently to meet school promotion standards and are retained at their current grade level. Another negative effect of bureaucracy is that it may induce poor and at-risk students to drop out of school. Poor academic performance is combined with what Willis (1981) asserts are contradictions practiced by schools. Essentially, Willis argues that the bureaucracy inculcates students with a belief that knowledge leads to 7 upward mobility. In actuality, the poor and academically disadvantaged student see little association between diplomas or qualifications and upward mobility and they view school bureaucracies as a means of controlling their behavior. Yet another negative effect of bureaucracy is that it may induce poor and at- risk students to display rebellious behavior. In essence, these students become disciplinary problems. According to Stinchcombe (1964), the low achieving and rebellious students are likely to have appropriated adult status long before they reach the age of maturity. The school, however, insists that all students are children and that they must be treated as children guided by rules and regulations. Schools then impose behavioral restraints via rules and regulations, but rules and regulations themselves exacerbate the conflict between the school and the students. Similarly, Gouldner (1954) noted that rules and regulations can encourage undesirable behavior by the subjects whose behavior the bureaucratic rules and regulations are designed to regulate. Such bureaucratic procedures, Gouldner suggests, may induce behaviors that are in direct conflict with the desired goals of the organization. So far the argument has been presented in two parts. First, it was argued that schools are bureaucratic, and they solve problems through bureaucratic means. Those means provide order, consistency, and stability for schools as well as appropriate services for students with identified needs. Second, school bureaucracies may induce unintended effects or negative consequences that counteract the bureaucracies primary goal; that is, to educate students and provide opportunities for social and economic advancement. Possible negative affects induced by the bureaucracy include: poor achievement; rebellious behavior; dropping out; sorting students by social class; and exposing poor students to an inferior education. Bureaucracy In Schools The third part of the argument is that the bureaucracy may not cure the problems of poor and at-risk students as evidenced by the amount of recidivism. Because of the repetitive nature of their problems (i.e. poor achievement, attendance, and behavior) poor and at-risk students may be assigned to one specialized program after another and sent from one specialist to another, to remediate those problems. Since these students not only pose the most problems for schools and are least tolerated by schools, they also receive the greatest exposure to bureaucratic specialization. As school staff experience difficulty with students who exhibit less than desirable academic performance and inappropriate behavior, they segment those students into specialized programs. Those students receive their first exposure to specialization through placement in developmental kindergarten, an extra-year intervention program for students who exhibit poor readiness skills in reading and mathematics. After completing developmental kindergarten, these students are placed in mainstream kindergarten classrooms. During first grade, some of these students begin to receive Chapter 1 services from reading and math specialists to improve academic performance. Those students also receive specialized services from psychologists, social workers, counselors, speech therapists and hearing consultants. At the second and third grade level, at-risk students may be placed into special education programs (i.e., 9 Learning Disabled, Educably Mentally Impaired and Emotionally Impaired), bilingual programs or all-male academies. In those programs, at-risk students receive attention from special education teachers, resource room staff and special education consultants. At the middle school level some at-risk students receive more or continued specialized services. After an Individualized Educational Planning Committee (I.E.P.C.) has evaluated the special education students, those students may be mainstreamed into regular education programs while still receiving specialized interventions from special education consultants and resource room teachers. Other at-risk students remain in special education or receive placements in Chapter 1 programs or they are placed into other programs such as those for students with attention deficit disorders (A.D.D.). In senior high school, poor and at-risk students not only receive Chapter 1, special education, and bilingual services, but they continue treatment through dropout prevention programs, substance abuse programs and programs for pregnant girls. Mentoring, attendance intervention programs, follow-up services from attendance agents and peer counselors are provided for problem students. Those services noted above represent the range of the attention afforded poor and at-risk students in grades K-12. Such programs are designed to address and improve the academic and behavioral problems displayed by these students. Since these students pose the most problems for the schools, they are also the students who receive the largest doses of bureaucracy. 10 The fourth piece of the argument is that despite Weber’s assertions relative to the efficiency and effectiveness of specialization, there is research that suggests that specialized programs may not be very effective and that the success rate of such programs may be alarmingly low. O’Sullivan (1990), Wilkerson (1989), Matranga and Mitchell (1988), Fernadez and Shu (1988) and Grannis (1988) suggest that programs for at-risk students fail to achieve desired goals. Poor and at-risk students, they argue, are placed in specialized programs because they exhibit poor attendance, low academic achievement and behavioral problems. Despite the design of these programs and the services provided, little improvement was found during treatment 01' post-treatment. Summary This study proceeds from the fact that schools are bureaucracies and bureaucracies employ power, authority, hierarchy, specialization, knowledge or technical skills, and rules and regulations in order to achieve their goals. Bureaucracy also provides a mechanism in which problems are solved through specialization. Since schools are bureaucracies, they too have adopted bureaucratic characteristics. They employ specialization, hierarchical structures and exercise power and control over students. Schools maintain order and stability through the use of rules and regulations. Those bureaucratic practices, however, may induce unintended effects or negative consequences on students, particularly those whom the schools label poor and at-risk. The unintended effects that are likely induced by these bureaucratic practices include: dropping out of school; poor academic 11 performance; inappropriate behavior; and placement in curricula that deny them the opportunity to improve socioeconomically. Yet another argument is that these bureaucratic methods are practiced to manage troublesome student behaviors by providing a multitude of specialized programs and services. Those services are designed to address the specific behaviors that cannot be addressed in a regular classroom environment. Students who exhibit such behaviors are, therefore, more likely to receive the larger doses of specialized programs and services. Finally, it is argued that poor and at-risk students are exposed to larger doses of specialized intervention programs that are designed to address troublesome behaviors and academic performance. But there is some evidence to suggest that specialized interventions may be least helpful for the students who receive the largest share of these services. Purpose and Research Questions The purpose of this study was to examine the effects of specialized interventions on at-risk students who have received the greatest share of these interventions. Also, since some at—risk students receive comparatively more specialized interventions than others, this study attempts to determine the perceptions of these at-risk students to the specialized interventions they received. Stated another way, this study asked whether or not specialized interventions achieve their intended or desired objectives? This study also asked whether or not these specialized interventions are perceived by the participants as helpful and 12 leading to success? The following research questions provide a framework for the investigation. 1. The bureaucratic model is a specialized model. One important exploratory question is: Have the students who received the tagger doses of specialized interventions progressed gs well academically as the at-risk students who received fewer ilterventions? To answer this question, the researcher used accumulated data as indicants of academic progress. The data will consist of math and reading scores on the California Achievement Test (C.A.T.) and earned credit hours. Research Hypothesis: At-risk students who have received more specialized interventions will Show significantly greater progress on the CAT. test and earned credit hours than a comparable group of at-risk students who have received fewer specialized interventions. 2. The bureaucratic model is also designed to improve student behavior. The second exploratory question is: Do the at-risk Merits who hgve been subjected to the larger doses of sgcializgd interventions display better behavior than at-risk students who have receivegljewer interventions? The data to be used in answering this question are accumulated data on attendance and discipline. 13 Research Hypothesis: At-risk students who have received more specialized interventions will Show significantly greater improvement in their average daily attendance and a reduction in the incidence of disciplinary action than at-risk students who have received fewer specialized intervention. One of the dangers of bureaucracy is that it can, according to sources cited earlier, induce negative attitudes toward school. Those attitudes are likely induced by continuous grade failure, exposure to differential and inferior educational programs and compliance with bureaucratic rules and regulations. The third research question is: Are the attitudes at at-risk students who receive more smcialized interventipps more positive toward school than a comparable gmup ot at-risk students who h_t_1ve received fewer smcializgd interventions. The indicant to answer this question will be a school attitude measure administered to both groups of at-risk students. Research Hypothesis: At-risk students who receive more specialized intervention processes will have a more positive attitude toward school than at-risk students who have received fewer specialized interventions. A further implication of the bureaucratic model is that it was designed to make less unequal the differences in social class. More specifically, the school programs are designed to give students of low socioeconomic background the attitude that 14 they can create a legitimate and respected place in society. The final exploratory question is: Given their backgmund and education do at-nlsk students who hdve received more spgcialized interventions have a more sitive attitude toward what t ct to have achieved or dccornplished within the neit five to ten rem, commred to at-risk students who hdve received fewer specialized interventions. The indicants to answer this question are included in a student survey instrument administered to both student groups, that measures expected accomplishments. This instrument was developed after the researcher asked a group of ninth and tenth grade high school students to write a short essay on the question: Given your background and education, please describe in one page what you realistically expect or believe that you will have achieved or accomplished within the next five (5) to ten (10) years? The responses indicated that students had identified specific goals relative to: high school completion, college, marriage and family, jobs, and home purchases. Research Hypothesis: At-risk students who have received increased specialized interventions will have a more positive attitude toward what they expect to accomplish within the next five to ten years when compared to students who have received fewer specialized interventions. The four areas of questioning presented above provided a frame work for conducting this investigation. These questions were also designed to explore the 15 students reactions to specialized interventions. While the data collected for this research was obtained through several survey instruments, the research questions were also designed to explain the effect of specialized programs and practices on at- risk students. If the line of questioning is appropriate, new insights will emerge that will maintain or redirect the method and focus of specialized programs servicing at- risk youth. Profile of an At-Risk Student The reader will recall that at-risk students are troubled students who present problems for the schools and subsequently have a history of bureaucratic interventions. To assist the reader in understanding the type and frequency of specialized interventions and activities imposed on at-risk students, a chronology of a student currently enrolled in the FDA. is illustrated in the following paragraphs. To maintain confidentiality, the student will be identified by the name "John". The first documented evidence of John’s exposure to specialized intervention programs and practices was found at the lst grade level. John received a psychological evaluation at the request of his mother because she noted that he sometimes behaved inappropriately and appeared to exhibit symptoms of dyslexia. The school district administered the Wechler’s Intelligence Scale for Children - Revised (WISC-R) to measure Students cognitive functioning. This two part assessment instrument measures verbal and performance skills, however, when both scores are combined, they generate a full scale I.Q. score. The overall 1.0. score achieved was below average. Additionally, the psychological evaluation recommended that John remain in the least restricted environment and that the school initiate special education teacher consultant services. 16 At the 3rd grade level all Detroit students are administered the California Achievement Test (C.A.T.) and during both 3rd and 4th grade the Assessment of Basic Curriculum Skills (ABCS) is given. On the CAT test, John scored at the 1.8 grade level in math and at the 1.7 grade level in reading, both scores were well below grade placement. On the ABCS, however, John scored at the 3rd grade level. Further, John was held back to repeat the 3rd grade as a result of his poor academic performance. By grade 4, John demonstrated continued evidence of low academic achievement. Scores on the ABCS reveal nonmastery in reading, writing and math. Additionally, grades on his transcript for all subjects were below average. During the 5th grade, John was again referred for a psychological evaluation. Two test batteries were administered to assess his abilities, namely the Wide Range Achievement Test (W.R.A.T.) and the Woodcock Language Proficiency Battery (W.L.P.B.) Scores on the W.R.A.T. test revealed proficiency at the 3rd grade level in both word recognition and math. On the W.L.P.B., John scored at the border line or below over age on all subtests. Because of John’s perpetual low academic achievement and poor performance on the test batteries, he was diagnosed as a slow learner. John participated in an Individualized Educational Planning Committee (I.E.P.C.) and was placed in a special education learning disabled program. By 7th grade, John was still performing below grade level. Stanford Diagnostic Test was at the 5.6 grade level in math and 6.2 grade level in reading. 17 Academically, John made some improvement in the special education program. Grades on his transcript revealed B’s and C’s. At grade 8, the same academic gains were noted, however, no testing data was available. Additionally, John was made a ward of the state and subsequently placed in a foster care home. Special education services continued at the 9th grade level. By the end of his freshman year, however, John had only completed 30 credit hours out of a required 50 credit hours required by the Detroit Public Schools to obtain sophomore status. By the 10th grade, there was some evidence that John was making academic improvement. The grade point average (g.p.a.) for the first card marking of the fall semester was a 3.6 while the overall g.p.a. was a 1.5. Not only was John continuing in special education, he had been mainstreamed in to a reading competency class. Reading competency is a regular education English class for students who demonstrate English deficiencies. Additionally, he was referred to the Fredrick Douglas Academy by the principal and counselor of his neighborhood high school. Because John met the criteria for enrollment in F.D.A., he immediately began receiving specialized services from a social worker and one of the at-risk counselors. The goal of this program was to provide a closely structured environment in order to assist John to complete the high school graduation requirements. In summary, John’s history of specialized or bureaucratic treatment is representative of those at-risk students who have received large doses of the specialized intervention process. The reader will recall that John was exposed to 18 specialized services at the first grade level and that those services are continuing at the sophomore level. Further, since specialized services are designed to improve student deficiencies, it is important to understand if the deficiencies have been remediated relative to students who have received fewer interventions. Progpam Characteristics Since this investigation focuses on specialization, it was necessary to identify a specialized intervention program that provides more specialized interventions to at-risk students and compare those interventions to those provided to at-risk students at three comprehensive high schools. Fredrick Douglas Academy was selected because it has at-risk students who not only exhibit troublesome behaviors, but receive more specialized interventions than at-risk students from the three comprehensive high schools. While the academic programs offered at F.D.A. are comparable (i.e. college prep, advanced placement, gifted and talented, special education, and etc.) major differences were found in the quality and quantity of supportive services. The most distinguishing feature of F.D.A. is the size of the student enrollment. During the 1991-92 school year, one-hundred and fifty students were enrolled in five classes each semester. Generally students must enroll in six classes per semester to meet the required 900 clock hours of instructions mandated by the state of Michigan. However, because of the type of students serviced, and the focus of the program, special permission was granted by the state to operate this facility. Additionally, small enrollment levels at F.D.A not only limit class size to twenty students but allow staff members to provide more individualized attention to each student. 19 It is argued that smaller class sizes enhance the staffs ability to provide and maintain a nurturing school climate. The nurturing that occurs not only allows staff to interact with students with greater frequency, but encourages students to ask questions without experiencing rejection or fear of participating in instructional activities in the classroom. More importantly, the school climate created a bonding process between the staff and students. It was assumed that bonding was beneficial to students because students began to interact with staff more frequently and sought the assistance of staff when problems surfaced. Students then began to develop positive relationships with the staff and recognized that the staff was interested in improving their self-concept, academic performance, attendance, and behavior. In comparison, the average enrollment in the three comprehensive high schools during the 1991-92 school year was two thousand four hundred students. Those students were enrolled in six classes each semester. Because of large student populations in these schools, and the districts collective bargaining agreement with teachers, the average class size was 35 students. Staff at each location complained that class sizes restricted their ability to provide individualized attention to regular education students as well as at-risk students. Further, larger class sizes tended to provide fewer opportunities to develop a positive student teacher rapport. Another feature of F .D.A. was the principal’s involvement in the recruitment and selection of staff members who were not only committed to educating troublesome and at-risk students but possessed unique skills in managing these students. Additionally, during the selection process, prospective staff members expressed a genuine interest in assisting the students to become successful. In 20 contrast, when the principals were assigned to the comprehensive high schools, little staff selection was possrble since the majority of their staffs were already in place when the principals arrived. Further, some staff members expressed their disinterest in working with at-risk students. The most noticeable feature of F.D.A. was the prevalence of more specialized interventions. Specialized interventions were provided by two academic counselors, one career counselor, a full-time social worker, three paraprofessionals, a school psychologist, and two reading and mathematics specialists. Since the student membership was small, each academic counselor was assigned 75 students. The counselors interacted with their counselees several times weekly through individual and/or group counseling sessions. When appropriate, counselors referred students to the school psychologist or social worker for one-hour sessions, to address personal and family related issues. The school psychologist and social worker would then provide weekly follow-up sessions for all referrals. Additionally, the school psychologist provided only one day of service per week to F.D.A. while the social worker serviced students five days per week. Individual career guidance was provided once each month, however, the career counselor scheduled speakers each week through the Detroit Urban Leagues’ Speakers Bureau to discuss various careers with F.D.A. students. The career counselor also arranged career related field trips and recruited local businesses to employ the students. Teachers and counselors at F.D.A. provided specialized interventions by monitoring the weekly academic progress and attendance of each student. As students experience academic difficulty, teachers and counselors refer them to the 21 paraprofessionals and the reading and mathematics specialists for 3, one-hour individualized tutiorial sessions before school, during class time, or after their schedules ended. Remedial reading and mathematics courses were also provided for students with deficiencies in those areas. Students attended remedial classes 5 days per week for 55 minutes daily. Further, as students exhibited attendance problems, an attendance administrator, the students academic counselor, and the vice principal provided daily interventions for the students and their parents. Comprehensive high schools on the other hand, provided fewer specialized interventions for students. For example, each high school had eight counselors, one of whom not only counseled at-risk students but college bound students as well. However, because of the per pupil-counselor ratio (300 students per counselor) counselors complained that they had little time to conduct individual and group counseling, or to develop a rapport with at-risk students. Two of the comprehensive high schools had a one semester intervention program to treat some at-risk students. The program (High School Intervention Center) provided students with group counseling, remedial reading and mathematics, and daily monitoring of attendance. After completing the intervention program, students are generally placed into remedial reading and mathematics classes with class sizes of 35 students. There were, however, no provisions to continue group counseling or to monitor daily attendance. Although career guidance, social work services, and reading and mathematics specialists were available to assist at-risk students, the frequency of service to many students were limited. Career guidance occurred usually at the ninth grade level when counselors met with students in a group setting to complete their four-year 22 plan of work form. A career program once a year also provide students with some information. Social work services were provided but this service was available only one day per week through counselor referrals. Additionally, remedial reading and mathematics classes were offered, however, because of the class size (35 students per class) little individualized instruction occurred. Finally, F .D.A. has promoted and encouraged increased parent involvement in the school. According to the FDA. staff, parents participate in the local school community organization (LSCO), volunteer as tutors, serve as hall monitors, and publish the schools newspaper. The comprehensive high schools have also encouraged parent participation; however, school administrators admit that parents were only active with the L.S.C.O. and served as chaperones at annual school dances. Administrators have attributed the parents lack of involvement to: low interest in their child’s academic achievement; work schedules that limit their ability to visit and volunteer for school activities and; the pursuit of their own interests. In summary, although F.D.A. and the comprehensive high schools offered similar academic programs, identifiable differences were found in the type and quantity of specialized interventions provided to both at-risk groups. The most striking differences at F.D.A. were small class sizes, more individualized attention from staff, more frequent and extensive interventions, and greater parent involvement. On the other hand, the comprehensive high schools have larger classes, less individualized attention, less frequent interventions and less parent involvement. These differences support the assertion that some at-risk students receive more specialized intervention processes than other at-risk students. This information provided further evidence to proceed with this study. 23 Method and Sample The researcher’s purpose in this study was to examine the effects of specialized interventions on at-risk students who have received the greatest share of these interventions. Also, since some at-risk students receive comparatively more specialized interventions than others, this study attempts to determine the perceptions of these students to specialized interventions. More specifically, interest was in determining whether these students exhibited improved academic performance, behavior, attitude toward school, and expected accomplishments compared to comparable at-risk students who received considerably less specialized interventions. To accomplish this the methodology selected was to identify an experimental and control group of at-risk male students enrolled in the Detroit Public Schools. Students in the experimental group were 9th and 10th grade at-risk male students enrolled at F.D.A., a specialized intervention program. These students exhibited poor achievement, poor attendance, and poor behavior. Students in the control group were also 9th and 10th grade at-risk male students who exhibited comparable characteristics. These students were from three comprehensive high schools (Mackenzie, Henry Ford and Northwestern). Additionally, 69 students were in the control group while 59 students comprised the experimental group. Since this study required the use of human subjects (students), the researcher requested permission from the University Committee on Research Involving Human Subjects (UCRIHS) at Michigan State University. When permission was granted, the researcher collected pre-treatment and treatment data on the experimental group by reviewing cumulative records. The data consisted of 24 CAT. scores, earned credit hours, attendance history, and discipline records for the 1990-91 and 1991-92 school years. Similar data was collected on the control group for statistical analysis. Further, the School Attitude Measure and the Student Survey on Expected Accomplishments were administered to both groups to gather data on their attitude toward school and what they expected to accomplish within the next 5 to 10 years. To provide some control against internal threats to validity such as history, time, and maturation, the control group was randomly selected using 9th and 10th grade at-risk male students, ages 14 to 16 years old. The experimental group, however, was selected after excluding special education students and students who were beyond the 10th grade. Since each subject in this investigation was under the age of 18, an informed consent form signed by the parent or legal guardian was obtained. The consent form explained the purpose of the study and that subjects were under no obligation to participant. The consent form also explained that the subjects could withdraw at any time without punitive consequences. There was no remuneration of any kind for participation. Further, measures were undertaken to maintain the anonymity of each subject. A pilot study with human subjects, was conducted to gather correlation and reliability data on the Student Survey on Expected Accomplishments. Since the survey was developed by the investigator, reliability data did not exist. The results of the data collection, however, provided an impetus for utilizing this instrument to gather data on the experimental and control group. Finally, it was the investigators 25 belief that this data would be sufficient to answer the final research question in this study. Significance The significance of this study was that the researcher focused on the effects of specialized interventions on at-risk students and the at-risk students perceptions of those intervention processes because specialized interventions are the standard bureaucratic behaviors practiced in schools. Since schools employ bureaucratic behaviors to manage at-risk students, it makes sense to examine the effects and perceptions of bureaucratic interventions on these students rather than evaluating the actual intervention strategies utilized in those programs. By focusing on the effects of specialized interventions on at-risk students new insights are gained on how at-risk students interact or react to those processes. For the purposes of future research, this study serves as a starting point and a guide to address how schools might more appropriately employ bureaucratic interventions to assist students at-risk. While this study does not proport to focus on all the questions relative to at- risk students and bureaucratic interventions the following contributions or potential outcomes may be found: 1. A broad understanding of the effect of specialized interventions on the academic achievement of at-risk students who have received fewer specialized interventions. 26 2. A broad understanding of the effect of specialized interventions on the behavior of at-risk students who have received fewer specialized interventions. 3. A broad understanding of the effects of specialized interventions on school attitudes of at-risk students who have received fewer specialized interventions. 4. A broad understanding of the effects of specialized interventions on the expected accomplishments of at-risk students who have received fewer specialized interventions. Limitations As with all quasi-experimental studies, there are concerns relative to generalizability. This study was limited to students who reside in a large urban school district. The ethnic make-up of this sample population was eighty-five (85) percent Black, five (5) percent White, eight (8) percent Hispanic, and two (2) percent Arab. While several ethnic groups are represented, the reader must exercise caution in applying generalizations about all student populations. The reader may, however, formulate some new insights on the interaction of the at-risk student with specialized intervention processes. Another limitation was the probability of grade inflation for the experimental group. Because this study does not test out this possibility, it was not known whether the grades received by the experimental group reflected actual grades achieved or grades given because the instructional staff believed that this group 27 simply could not improve their grades. Therefore, since grades cannot be controlled, they were excluded as a variable in this study. The grade level used in this study present yet another limitation. Since the Fredrick Douglas Academy enrolls students who are 9th and 10th graders, no other grade levels could be included in the sample population. Conclusions therefore can only be formulated on 9th and 10th grade students. A final limitation was the absence of statistical data on expected accomplishments of both at-risk student groups prior to the 1991-92 school year. As a result, statistical tests could not be performed to determine whether or not significant differences in expected accomplishments existed between groups during the 1990-91 school year. In order to provide some data to conduct a statistical test, the Student Survey On Expected Accomplishments was administered to a group of at-risk students enrolled in several of the districts comprehensive high schools during the 1992-93 school year. Those students had been identified by counselors and administrators in their schools, to enroll at F.D.A. in September of 1993. The responses of the students to the statements in the survey were used as the pretest measure for the experimental and control group. Additionally, it was assumed that their responses were representative and comparable to the attitudes of the subjects in this investigation during the 1990-91 school year. While conducting this investigation, the researcher was guided by the assumption that success in school is associated with the specialized programs and practices. In other words, the perspective of bureaucracy in an educational setting is critical to understanding how at-risk students respond or react to school 28 experiences. Therefore, a careful investigation of those effects will lead to a better understanding of the effects of specialized intervention processes on these students. Definition of Terms 1. More Specialized Interventions are defined in this study as more specialized treatment (i.e. counseling, social work services, specialized math and English classes and etc.) and greater frequency of treatment provided to at-risk students: (a. ) at their neighborhood high schools during the 1990-91 school year and (b.) at F.D.A. during the 1991-92 school year. 2. Fewer Specialized Interventions are defined in this study as less treatment and lower frequency of treatment provided to at-risk students during the 1990-91 and 1991-92 school year at their neighborhood comprehensive high schools. These student exhibit the characteristics listed above. 3. At-Risl_( Studen_t§ are defined as students who exhibited the following characteristics: a. One or more grade failures b. Poor attendance c. Poor or below average grade point average (G.P.A.) (1. Poor behavior or higher than average suspension rates e. Performance on the California Achievement Test (C.A.T.) one or more grade levels below current grade placement f. Achievement of less than 50 credit hours prior to enrollment in Fredrick Douglas Academy 4. 29 g. Poor attitude toward school A Semester is defined as 19 weeks of instruction in which students attend classes 5 days per week for one-hour per day. 30 REFERENCES Bowles, S. and Gintis, S. (1976). Schooling in capitalist America: Education and the contradictions of economic life. New York: Basic Books Calabrese, R., L. (1988, March). Schooling, alienation, and minority dropouts. The Education Digest. 7-10 Cusick, P., A. (1992). The educational system: It’s nature and logic. New York: McGraw Hill. Fernadez, R., R. and Shu, G. (1988). School dropouts: New approaches to an enduring problem. Education and Urban Society, 2(2), 363-86 Gouldner, A., W. (1954). Patterns of industrial bureaucracy. The Free Press. Grannis, J. A., Rich], C., Pallas A., Lerer, N. and Randolph, S. (1988). Evaluation of the New York city dropout prevention initiative: Final report on the high schools for year two, 1986-87. Institute for Urban and Minority Education. New York: Teacher College, Columbia University. Hollingshead, A., B. (1975). Elmtown’s youth and elmtown revisited. New York: John Wiley and Sons, Inc. Katz, M., B. (1975). Class bureaucracy and schools: The illusion of educational change in America. New York: Praeger Publishers. 31 Matronga, M. and Mitchell, D. E. (1988). Student dropout problem: Implications for policymakers. Washington, DC, Office of Educational Research and Improvement. O’Sullivan, R. G. (1990, April). Evaluating a model middle school dropout prevention program for at-risk students. Paper Presented at the Annual Meeting of the America Educational Research Association. Boston, MA. 16-20. Perrow, C. (1979). Complex organizations: A critical essay. Glenview, Illinois: Scott, Foresman and Company. Stinchcombe, A. (1964). Rebellion in a high school. Chicago: Quadrandle Press. Weber, M. (1947). The theory of social and economic organization. New York: Oxford University Press. 329-330. Reprinted in Robert K. Merton, Alisa P. Gray, Barbara Hockey and Hanan C. Selvin (eds) Reader in Bureaucracy, Glencoe, Ill.: The Free Press. 18-20. Wilkerson, D. (1989). New initiatives in dropout prevention: Project grad final report 1988-89. Austin Independent School District. Texas Office of Research and Evaluation. Willis, P. (1977). Learning to labor: How working class kids get working class jobs. New York: Columbia University Press. 1-226. Chapter Two Review of Literature One of the arguments described in Chapter 1 was that schools are bureaucracies and that they are organized and managed according to characteristics of the bureaucratic model. Some critics have asserted that those characteristics may, in fact, have a negative influence on student achievement, behavior, social and economic status. Critics also argue that students who behave appropriately; that is, within the rules and norms established by the schools are placed in a mainstream educational setting. However, when students exhibit idiosyncratic behaviors, they are separated, differentiated and placed in specialized intervention programs that are, according to some theorists, more structured, and controlled. Such programs, it is argued, are designed not only to accommodate at-risk students, but to reduce the disparity in achievement, behavior and social and economic status between mainstream and at- risk student. The purpose of this study was therefore to examine the effects of specialized interventions on at-risk students who have received a significant share of these specialized interventions. Also, since some at-risk students receive comparatively more specialized interventions than others, this study attempts to determine the perceptions of these students to specialized interventions. Given this purpose, Chapter 2 reviews literature in five areas: The structure of bureaucracy Schools as bureaucracies 32 33 Bureaucratic control ' Characteristics of at-risk students ' Specialized intervention programs To understand the effects of specialized interventions on at-risk students it is necessary to review the nature of bureaucracy and its utility in schools. The first section in this literature review, therefore, examines the bureaucratic nature of schools. Further, since certain bureaucratic characteristics are so prevalent in schools, they form the underlying structure of this investigation. In the second body of literature critics describe how bureaucratic methodologies employed in schools may inadvertently contrrbute to the poor and at-risk student’s problem. Some critics argue that because these students are sorted and segmented off from the mainstream, they are likely to receive fewer opportunities to advance academically, socially and economically. This argument, however, is examined in this investigation to determine whether or not the critics claims are valid. The third section of this chapter reviews the assertion that rules and regulations are employed to maintain classroom control and that control strategies may be overtly coercive or inconspicuously embedded in classroom lessons. Further, there is some criticism that such emphasis on control strategies may induce poor and at-risk students to disengage from or reject school. These assertions will also be examined in this investigation to determine what effect control strategies have on poor and at-risk students. 34 According to some scholars, bureaucracy may induce unintended effects or negative consequences that conflict with the desired goals of the organization. Those bureaucratic processes, they argue, may induce unintended effects or negative consequences on poor and at-risk students, namely, they reject school. The fourth section, however, adds to the literature review in the third section by suggesting that specific characteristics exhibited and experienced by these students may also influence their decision to disengage from or drop out of school. The fifth section of this chapter asserts that specialized programs were intended to close the achievement gap between at-risk and mainstream students. Those programs, according to some scholars may exacerbate the achievement gaps and encourage recidivism. In addition, the literature describes what should happen in specialized programs. This investigation, however, will consider whether or not specialized programs damage or help the students they were designed to assist. The Structure of Bureaucracy The theoretical framework that serves as the organizing structure of this study is Weber’s (1947) bureaucratic model. The bureaucratic model contains seven principles. However, embedded in the principle of division of labor is the concept of specialization. Since specialization is the most prominent characteristic utilized in schools, it will be the focus of this study. Three other principles, namely rules and regulations, hierarchy of authority, and knowledge or technical skills are examined because of their importance to specialization and utility in schools. A brief description and discussion of each characteristic is provided below: 35 1. A continuous organization of official functions bound by rules. (Standardization) 2. The rules which regulate the conduct of an office may be technical rules and norms. (Knowledge or technical skills) 3. The organization of offices follows the principle of hierarchy; that is, each lower office is under the control of a higher one. (Hierarchy) 4. A specific sphere of competence - division of labor. (Specialization) The first characteristic (Standardization) in the bureaucratic model emphasizes the importance of establishing a set of rules or procedures for the sake of continuity and equality. When formal organizations establish rules and regulations they avoid the cumbersome task of deriving new solutions for every problem or situation that arises. According to Weber (1947) rules save effort and they ensure that approved standards and equity are applied to all cases. Further, they obviate the laborious and complex task of handling each participant separately and individually. If the organizational rules or regulations are not established and maintained, Weber (1947) would argue that the organization is using both time and resources inefficiently. In order to ensure that participants comply with established rules and regulations, bureaucratic organizations employ coercive, remunerative and normative - social strategies (Etzioni, 1964). Compliance strategies are coercive 36 when the actions of the organization are oriented toward the application or threat of physical pain or restriction of movement. Remunerative strategies are evident when the behaviors or actions of the organization are based on the allocation of wages, commissions and services. Normative-social strategies, however, are oriented toward thecontrol over the allocation of symbolic rewards or punishment through deprivation of status esteem and prestige. Regardless of which strategy is practiced, the intent is to ensure achievement of organizational goals. The second characteristic of this model "knowledge or technical skills", suggests that the bureaucrat acquires and have the ability to demonstrate technical training to qualify as a member of an administrative team or organization. Bureaucrats are usually trained for their particular roles and have considerable knowledge and understanding of the organizations purpose and goals. Specialized training in essence was view by Weber (1947) as the essential element that underlies the bureaucrats authority. Technical skills or knowledge requiring specialized training is widely used in schools to manage students with special needs. Those skills are needed because of the diverse student populations that schools are mandated to service. This area, however, will be described in greater detail in a later section of this chapter. The third characteristic of the bureaucratic model is the hierarchical structure of organizations. Within this structure, each lower office is under the control and supervision of a higher office. Such supervision is said to induce and maintain compliance with organizational rules and goals. This characteristic also ensures that each lower office of the organization is vigorously and systematically monitored for consistency. —’ 37 The method in which schools employ the hierarchical structure is described in another section of this chapter. It should be noted however that this structure is not only found in administrative offices but in student curricula placements (i.e. advanced placement, college prep, remedial and etc.) The fourth bureaucratic characteristic that is relevant to this study is specialization. Weber describes specialization as a specific sphere of competence inclusive of the following elements: 1. A sphere of obligations to perform functions which have been marked off as a part of a systematic division of labor; 2. The provision of the incumbent with the necessary authority to carry out these functions; and 3. That the necessary means of compulsion are clearly defined and their use is subject to definite conditions. Because specialization is necessary to carry out the functions, activities and behaviors of the formal organization, the participant must possess the knowledge or technical skills to perform all tasks relevant to the job. Knowledge or technical skills are important because they are the root of the bureaucrats authority. Additionally, there are limitations or boundaries imposed on the participants degree of authority, to ensure that his/her duties do not infringe upon the responsibilities of others. 38 Schools, it will be argued, specialize by creating special programs for students of various abilities and students with unique needs. In order to provide those services, schools must employ staff with specialized skills. Those skills also promote stability in the school environment by focusing on specific idiosyncratic behavior. In summary, Weber (1947) asserts that the bureaucratic model provides the most effective framework in which modern democratic organizations can manipulate subjects, set goals, and endeavor to achieve goals. While rules and regulations save effort and provide consistency, the principles of hierarchy of authority, knowledge or technical skills are also important to specialization. For it is specialization, Weber argues, that ensures order and stability for goal achievement. Schoolw Bureaucracies To a considerable degree, what occurs in the educational arena reflects patterns of bureaucracy and specialization described previously. School boards and superintendents establish goals, policies, and procedures for the organization which includes staff and students. Those policies and procedures are designed to provide structure to the organization. Compliance strategies are employed in many facets of the school’s organization. Coercive strategies, for instance, are imposed on students through attendance policies, student codes of conduct, and dress codes. Such strategies are practiced to ensure stability in the school environment and to assist schools in achieving goals. 39 An example of a coercive strategy was observed by the writer during the spring of 1991 in one of Detroit’s nationally recognized high schools. In Detroit, California Achievement Tests (CAT) are administered annually to all 9th through 12th grade students to measure growth in academic achievement. CAT scores are also used to measure how effective schools are at teaching and educating students. High schools with high test scores are therefore labeled "Schools of Excellence." Those results also assist in attracting recruiters from major colleges and universities. During the spring of 1991 the principal approved a testing schedule mandating all 9th through 12th grade students to take the CAT test. Ninth through eleventh grade students reported and completed the test as required, however, the 12th grade students conspired to remain at home even though they were aware of the importance of the test results. The rationale provided by many of the 12th graders for their defiance was that the results would not impact their admissions to college. Their rationale had considerable validity since many of the students had already received college admissions letters. When the school staff reported the 12th graders failure to comply with this mandatory requirement, the principal scheduled a Special meeting for all 12th grade students and threatened to suspend every student who failed to report on a scheduled make-up day. Rather than face suspension for insubordination, the 12th graders begrudging appeared for the make-up test. On the make-up day, while the test instructions were being given, several staff members observed that some students had intentionally recorded incorrect answers on the answer sheet. In a few cases, students had sabotaged the test by placing responses to all questions on the answer sheet before the testing 40 coordinator had completed reading the test instructions to the group. Additionally, some students had verbalized their alienation over the coercive actions of the principal, prior to the make-up date. Students who had deliberately sabotaged the test were identified and suspended for misconduct. Their answer sheets were confiscated and destroyed to minimize the possibility of tainting the test data. In the end, the level of involvement demonstrated by the twelfth graders toward coercive strategies, generated at best, a low level of commitment. The students level of commitment was then reduced or altered to overt resistance. Remunerative strategies are practiced when schools establish control over the distribution of teachers pay, compensatory time and fringe benefits. Students, however, experience remunerative strategies through the distribution of grades, grade level promotions, college scholarships, coop job placements and monetary awards for fund raisers or contests. Normative - social strategies are observed in schools when instructional leaders share with their staffs, yearly goals and objective and the recognition of staff for special achievements. Those goals frequently include improving student achievement and improving student-teacher interaction. Students, on the other hand, are the recipients of normative-social strategies when teachers attempt to manage disruptive student behavior by appealing to the students sense of judgment or using classmates to control troublesome student behavior. Additionally, this strategy is noted when students receive recognition for achievement or hold leadership positions in school organizations (i.e. president of the senior class or student council). 41 This strategy is also employed when teachers establish goals and objectives for student success in various subjects. Each semester for example, teachers develop and distnbute course syllabi that detail the structure and content of a course. The syllabi also specify what students are expected to learn, the grading system, and the grading criteria. Students, therefore, who expect to achieve passing grades must comply with the criteria established by the teacher to self actualize their goals. Second, schools exemplify the bureaucratic hierarchical structure by appointing superintendents, principals, assistant principals and other administrative staff, each with specific functions to perform within certain guidelines established by the organization. This same administrative structure encourages and supports the formation of parent associations, booster clubs, student councils and other student organizations. Within these organizations are elected administrative offices such as presidents who establish the yearly goals, treasurers who manage the organizations funds and secretaries who record the minutes of each meeting. While schools organize and function within the guidelines and limitations of the hierarchical structure, they manifest clear evidence of specialization. All levels of K-12 education, for example, are organized by subjects or disciplines such as English, math and science. Within those disciplines, however, are categories into which students are to be divided (i.e. advanced placement, regular education and remedial courses). Additionally, attendance areas, busing routes and tiers and achievement levels represent other categories into which specialization treat student populations (The later category will receive further attention as the chapter progresses). 42 When problems arise in the organization or pressure is exerted by special interest groups, as a result of an educational issue, the bureaucracy reacts. The emphasis is on the utilization of resources within the bureaucracy to solve problems. According to the bureaucratic model problems are resolved through the various policies and procedures established by the bureaucracy. Earlier it was argued that rules, policies and procedures are established by the bureaucracy to provide stability for the organization (Weber, 1947). Those rules, policies and procedures, therefore, negate the need for the continuous invention of new methods for solving or resolving problems. However, when new issues surface or old issues recur because current policies and procedures become obsolete, new regulations and procedures are developed to redress those issues. What educational organizations do to manage new issues, then, is to provide more bureaucracy. Stated another way, educational reform becomes organizational reform or more accurately-~increased bureaucracy. More or increased bureaucracy in the schools emerged as a result of two philosophical mandates--universalism and egalitarianism. These mandate obligate schools to provide all students with opportunities for social, political and economic equality. In conjunction with this new responsibility, however, is the emergence of increased student enrollments, larger schools and differentiation of student abilities. In essence, schools inherited the obligation of educating all students regardless of academic, behavioral or physical deficits through specialized programs. As schools endeavor to service all students, educational reform, as noted above, engenders more bureaucracy. This process, according to some critics, is 43 achieved by creating different curricula for the accelerated, average and the academically disadvantaged student. The outcome of those practices becomes organizational differentiation and separation. Differentiation and Separation Differentiation and separation become increasingly evident as schools allocate resources to develop, implement and manage honors or alternative programs, bilingual programs for non-English speaking students and attendance programs for chronic absenteeism . While these programs focus on special student needs and abilities, a more profound concern has emerged over the last three decades — saving the at-risk student. Because of the propensity of the at-risk student to drop out, a plethora of educational reform programs in urban schools address the issue of how to motivate students to complete state graduation requirements. When the bureaucracy expands, it is done under the premise that more bureaucracy will solve students problems. Callahan (1962) asserts that while schools are providing and implementing a multitude of specialized programs and managing increased student enrollment, order must be maintained. It is through the formal organization of the bureaucratic structure that order can be maintained, thereby allowing programs to coexist. More importantly, Callahan (1962) noted that bureaucracy provides stability for the school. The bureaucracy in essence provides a mechanism in which schools address change. Therefore, when Special interest groups, parents, and the corporate community exert pressure for educational reform, the vehicle for initiating that 44 reform is already in place. Schools, as a result, manifest their ability to reform by co- opting programs initiated by outside groups (Cusick, 1992). In short, the programs sponsored by those groups become part of “the organization. Educational change has been implemented in the 20th century through bureaucratic specialization. Tropea’s (1987) research (Bureaucratic Order and Special Children: Urban Schools, 1950’s - 1960’s) found that schools dealt with at- risk or troublesome students through such strategies as expulsion/suspension, indiscriminate segregation, and lowering performance expectations. However, prior to the 1950’s schools had limited experience with troublesome students since many of these students made the decision to drop out of school to seek employment. More importantly, industrialization and compulsory attendance laws resulted in the enrollment of large numbers of students in public schools with heterogeneous abilities, especially those who were difficult to teach (at-risk students) in traditional classrooms. By the 1960’s other methods were used by school bureaucracies to separate and differentiate students who exhibited academic and behavior problems. Tropea (1987) for example, found that school bureaucrats in Washington, DC. designed and implemented the "Basic Track" program for troublesome students. Yet, during this same period another specialized program emerged in Washington, DC. and MIND (Meeting Individual Needs Daily). Although MIND was designed to treat students with behavioral and emotional problems, its objective was Similar to Basic Track, which was to remove troublesome students from the mainstream in order to maintain control in the classroom. 45 Educational communities also experienced federal intervention in the 1960’s. Many of those interventions are still evident in schools today. Federal mandates created specialized programs (Chapter 1) for students who exhibit low achievement in reading and mathematics. These students were to receive specialized and individualized instructions to improve their academic performance. Such programs often received more supplies, more specialist and had smaller class sizes. In addition, the bureaucratic and scientific management orientation of schools formalized the development of special education as a method of managing low ability or troublesome students. This new field legitimized the process of removing and containing the most recalcitrant students in special programs and classrooms in the interest of maintaining order in the rationalized school facility (Tropea, 1987; Lazerson, 1983; Sarason & Doris, 1979; and Page, 1991). The new procedure for removing troubled students was implemented through the teacher referral-diagnosis—prescription process. Tropea (1987) asserts that this method of removing students from regular education classrooms was further legitimized by the use of an office, called the "Office of Pupil Personnel Services". During the 1920’s and 1930’s this office was entitled the "Psycho- Educational Clinic". By the 1990’s, the educational communities in Baltimore, Milwaukee, Washington, DC. and Detroit, among others, had identified another burning issue- "decreasing achievement of black males" (Ebony, 1991). Again the bureaucracy moved with rapidity to create special programs to remedy the problem. Although the types of specialized programs being proposed and implemented in urban 46 settings (Black male academies and all male classrooms) are relatively new, they are lauded as the answer to educating Black males. Charles Whitaker, in his article, "Do Black Males Need Special Schools" (Ebony, 1991) cites the following observation of a Milwaukee middle school principal: What we’re basically saying is that we need to explore a different, more supportive system for African-American males to learn in because in the present system they’re being destroyed. (p. 18) Similarly, in Detroit the school board approved the design of an African- centered academy aimed at overcoming the problems of young black males (Nagler, 1991). A legal challenge was filed prior to the opening of the school, by the American Civil Liberties Union on behalf of parents who believed that similar accommodations should be provided for black female students. With some modifications of the schools enrollment criteria however, the school opened in September of 1991 as a coed educational facility. Although educators and various interest groups provide credible rationales for perpetuating the practice of specialization, the school bureaucracy provides the arena in which specialization and differentiation can occur. Yet with the proliferation of special and remedial programs designed not only to emancipate students from failure, but also to elevate their status socially and economically, some critics (Calabrese, 1988 & Cusick, 1992) argue that students make personal choices either to accept or reject school and its bureaucratic nature. 47 Cusick (1992) illustrates the essence of this position: Along the way to failure, each dropout has been given help from reading and math specialists, counseling from the guidance office, progressive discipline from the vice principal, therapy from the psychologist and parent conferences with the principal. Special, vocational and alternative programs have all been tried. "We’ve done everything we can" say the schools and indeed, within the confines of a bureaucracy they have. (p. 226) In the final analysis, critics argue that after exposing students to program after program, the bureaucracy allows schools to divest themselves from any negative outcomes (Cusick, 1992; & Page, 1991). The person who ultimately shoulders the responsibility for failure or dropping out of school is the student. Further, despite all that the bureaucracy does, it is the arena in which reform efforts (i.e. specialization) can be administered. While stability has already been established through rules, regulations and procedures, the school bureaucracy is the setting where teachers demand that students learn and demonstrate achievement through performance. Consequently, if the student makes a conscious effort to reject all that the bureaucracy provides and stands for, the efforts exerted through special programs may not benefit the students they were designed to help. Ability Group Sorting and Social Status The literature thus far describes how the school bureaucracy employs specialization to differentiate and separate troublesome students, (i.e poor and at- risk) from the mainstream. But there is some research that suggest that specialization may induce unintended effects or negative consequences on the poor and at-risk student. Katz (1975) and Calebrese (1988) for example assert that 48 through the application of specialization, schools encourage, practice and support differentiation of ability, curriculum, and social status. These practices sort and separate high achievers from low achievers and the rich from the poor - a process Katz refers to as a class-bias environment. Katz (1975) and Calabrese (1988) also argue that sorting and differentiating not only separates and places the children from well-to-do or affluent families into the most attractive and innovative programs (i.e. college preparatory, advanced placement and honors curricula) but that sorting may also provide inferior and inequitable educational opportunities for the poor and at-risk student. Deprivation of similar opportunities may therefore induce unintended effects or negative consequences on poor and at-risk students. Katz (1975), Giroux (1981), Washburn (1989) and Calabrese (1988) found that poor and academically disadvantaged students are stratified and enrolled in remedial programs such as special education, bilingual, industrial and vocational education and general education programs. These programs they argue, were designed to provide schooling for the less academic and the poor student. But such programs were also designed to progress at a slower and less demanding pace because school bureaucrats have low expectations for these students. Thus, Katz (1975) and others concluded that sorting and differentiating provides the children of the well-to-do, not the children of the poor, with better educational opportunities and access to higher status jobs. Oakes (1985) Goodlad, (1984) and Tye’s (1985) research discovered that sorting and differentiated curricula are detrimental to the lower-track (at-risk) students achievement. They argue that not only are the differences between lower- 49 and upper-track curricula systematically and institutionally created, but that such hierarchical practices promote wider achievement gaps between upper-and lower- track students. Further, they found that the poor and racial minorities are disproportionately represented in lower-track classes and that the scholastic inequities created by these placements, transmits rather than reduces social inequities. Sorting and tracking are practiced however because it is assumed that many lower-track students will not value or cannot excel at scholastic pursuits. Giroux (1981) also noted in his research that public schools engage in a systematic and organized process in which some students have access to certain opportunities and others do not. Thus he concluded that schools are not neutral in their ideological underpinnings and that schools do not provide all students the same opportunity. Washburn (1989) found that the sorting process inherent in American public education occurs through both formal, institutionalized practices and informal, less obvious methods. Most schools, be asserted, offer special tracking for gifted or highly motivated students where such students are separated from the rest of the students and are placed in different schools or classrooms. Washburn summarizes his findings in the following excerpt: While the designation usually takes place as students enter high school, some school systems begin separating the performers from the non-performers during the elementary and middle school years. The formal sorting of students occurs within classrooms and administrative offices when teachers and administrators make decisions and judgments that influence the students future. For many students, how they are sorted will influence 50 whether they will ultimately graduate from high school and their ability to access opportunities beyond high school. (p. 21 ) Willis (1977) argues that school bureaucracies may contribute to the poor and at-risk students problem by virtue of its support of the extant system. The extant system, Willis (1977) noted, allows poor and at-risk to identify and formulate three contradictions regarding the goals of education and the goal of equality. Those contradictions include 1) an "opportunity-eosted" assessment of the rewards of conformity and obedience; 2) the assessment regarding the quality of available work and; 3) the ideological confusion of individual and group logic. These contradictions are described in the following paragraphs. First, Willis (1977) noted that the counter-school culture (poor and at-risk students) engages in an "opportunity - costed" assessment of the rewards of conforming and obedience expected by the school and exerted on working class students. Specifically, this involves a degree of skepticism about the value of qualifications relative to what one sacrifices to achieve those qualifications. Achieving those qualifications and the sacrifices to achieve them result in a delayed gratification and more importantly, qualifications are not viewed as a criteria for job selection. In essence, exerting energy to obtain qualification may not, in many cases, lead to upward mobility. Further, while the bureaucracy, embraces the philosophy of equal opportunity for all, it engages in practices that restrict social mobility for poor and at-risk students. Those practices, according to Willis (1977) have lead to distrust of the institution and formulate attitudes that predestine failure. Second, poor and at-risk students make assessments regarding the quality of available work. Employment for this group may involve apprenticeships or clerical 51 work (Willis, 1977). Those jobs do not require extensive training technical skill or opportunity for advancement. Further, Willis asserts that since those jobs require a different level of training, at-risk students distrust diplomas and certificates. Their distrust is founded on the belief that qualifications serve to maintain those who are currently at the top (Willis, 1977 and Katz 1975). Essentially, Willis (1977) found that the school bureaucracy engaged in a two tiered system that promotes more advantages for the affluent or dominant class. This class earlier noted by Katz (1975) receive the best jobs while perpetuating low or de-skilled jobs for the poor and at-risk students. For this lowest stratification the perception may be: that schools are covertly engaging in practices that deprive them of opportunities to advance to middle and upper-class status (Calebrese, 1981). Third, the counter-school culture makes a distinction between individual and group logics and the nature of their ideological confusion in modern education (Willis, 1977). For the individual working class person, upward mobility may be important, in fact some will make it. As a group or class however, mobility, acquiring knowledge and achieving success is relegated to a level of insignificance. What schools fail to teach according to Willis (1977) is that not all people can achieve upward mobility. In summary, the literature suggests that schools are bureaucracies because they exemplify principles of the bureaucratic model. Although specialization is the major concept practiced in schools, rules and regulations, hierarchy of authority and knowledge or technical skills are also utilized to provide stability and order in schools. There is some research, however, that asserts that these principles, particularly specialization, may induce unintended effects or negative consequences 52 on poor and at-risk students. Specialization, it was argued, allows schools to sort and differentiate students by ability and social status. The sorting process is also blamed for denying the poor and at-risk student equal access to educational opportunities. In short, Willis (1977) and others assert that specialization may be harmful rather than helpful to at-risk students. Bureaucratic Control In the previous section, the literature argues that schools embrace characteristics of the bureaucratic model to effect change and maintain stability. It is also argued that specialization encourages differentiation and ability sorting and that those processes may inadvertently induce unintended effects or negative consequences on poor and at-risk students. The following body of literature describes how the school bureaucracy’s emphasis on rule following and control may also have a deleterious effect on poor and at-risk students. Katz (1975) for example noted that schools perpetuate the practice of rule following and the teaching of obedience. In related research, Jones (1963), Johnson (1985), McNeil (1986) and Shanker (1993) found that an inordinate amount of classroom time is spent correcting student behavior and that teachers deliberately employ instructional techniques designed to establish and maintain control. In essence, they argue that instructional techniques have dual purposes. The primary and intended purpose is to teach compliance and the secondary and less emphasized purpose is to impart knowledge. Bowles and Gintis (1976), Willis (1977), Schwatz (1981), and DeRidder (1990) reported similar findings in their research of minority students in urban 53 schools. Their research discovered that urban schools with poor and at-risk students adopted coercive practices oriented toward control and rule following. These practices, they assert, may induce students to reject or disengage themselves from the purpose of schooling; that is, to become educated and productive members of society. Page (1991) noted however, that not all controlling strategies employed or emphasized in classrooms are coercive in nature. While many studies cited earlier reported the use of coercive strategies in poor urban institutions, Pages’ Maplehurst study was conducted in a predominantly white middle class community. Her observations on how schools gain control is describe in the following excerpt: Thus, anticipating "trouble" from students whom they regard as uninterested in academics and as "lacking the critical thinking skills to participate in a discussion," teachers structure lower-track lessons for control. (p 52) Teachers, according to Page (1991), achieved and maintained control by constantly exhibiting a high level of organization. Organization ensured little or no deviation from the classroom assignment and created an environment devoted to time-on-task. For teachers, controlling what occurred in the classroom became an art or science of regimentation. During a classroom visit Page (1991) made the following observation: The efficient regimented climate in Mr. Bauer’s Additional Needs American History classroom is characteristic of the other lower track classes in which I (Page) observed at Marshall. In both word and deed, veteran teachers set forth the principles of time and 54 training. They arrange the classroom situation as an invariant but commonsensical routine; they reconstruct a complex, "bookish" body of knowledge as reassuringly simple, step-by step, "practical" skills; and they dominate classroom talk without domineering. In short, they teach by remote control, without calling attention to them selves as teachers. (p. 147) Apple (1979; 1983) and Page (1991) discovered that control can assume an invisrble form; that is, when it is embedded technically in curriculum through the use of individualized worksheets, use of films, or note taking. By incorporating such instructional strategies, teachers minimize the need to belabor discipline in the classroom. Apple (1979; 1983) and Page (1991) suggest, however, that when teachers constantly engage students in a tightly structured climate, that control and trivia] lessons may justify the students disengagement in school. Willis’ (1977) Hammertown study suggests that poor and at-risk students perceive school to be a place where the bureaucracy exercises control and where education is relegated to a lower priority. Those students personalized and rejected bureaucratic control and authority and developed an allegiance to the counter- school culture (an informal group of non-conformist). Further, Willis (1977) argues that as the students rejected the coercive strategies employed by the school bureaucracy, they became members of the counter-school culture and adopted troublesome or non-compliant behaviors For schools, however the idea of teaching involves the exchange of knowledge from the teacher to the student. Knowledge however is exchanged for respect and guidance for control (Willis, 1977). It is presumed that knowledge gives the teacher moral authority and legitimacy to exert control over students. The conformist therefore views knowledge as a reward because it provides a delayed 55 gratification. For the conformist, knowledge and rule following provide the outcomes of qualifications and upward job mobility. lnversely Willis notes that the counter-school culture view school as a place where control is exerted and where the non-conformist must constantly reject authority. Opposition to the school is principally manifested in the struggle to win symbolic and physical space from the institution and its rules and to defeat its main perceived purpose: to make you work (Willis, 1977). In this section the literature has suggested that poor urban schools practice coercive strategies to establish and maintain control in classrooms. But, such strategies may induce unintended effects or negative consequences on poor and at- risk students, namely, disengagement from school. There is some research, however, that assert that controlling strategies need not be coercive to induce the same effects on students in middle-class communities. Additionally, it was suggested that when control strategies are emphasized, the imparting of knowledge may be relegated to a lower priority in lower-track classrooms Characteristics of At-Risk Studept In a previous section the literature suggested that the school bureaucracy may deprive poor and at-risk students of equal educational opportunities by differentiating and sorting them from the mainstream. Those bureaucratic processes it is argued may induce unintended effects or negative consequences on poor and at-risk students, namely, they reject school. This section, however, expands that argument by suggesting that specific characteristics exhibited and 56 experienced by poor and at-risk students may also influence their decision to disengage from or drop out of school. Ogden and Germinario (1988) assert that all children are at times students- at-risk and that there is a segment of every school population that consistently display a lack of intellectual, emotional and/or social skills necessary to take full advantage of the opportunities available. Frequently, these students become disenchanted, and actively or passively reject school. Their rejection of school subsequently places them in the population of students who are identified as students at-risk. In related research, Sartain (1989) defined at-risk students as children of school age, who, because of one or more factors in a syndrome of disadvantageous traits, behaviors, and circumstances are in danger of being unsuccessful in school and/or in danger of becoming entangled in personally debilitating social, emotional, physical, or economic difficulties currently or in the near future. Slavin and Madden (1989) describes at-risk students as students who exhibit certain characteristics indicative of school failure (i.e. poor achievement and social skill). Lehr and Harris (1988) provide a different perspective in their description of at-risk students. They describe an at-risk student as "one who is not performing up to his/her potential". This description asserts that at-risk students are low achievers. Further, the researchers determined that the terms at-risk and low achievement are relative and they illustrated Albert Einstein’s academic performance as an example of a low achiever. 57 There is a proliferation of literature on at-risk students that identify them as dropouts, particularly since some at-risk students fail to complete high school. This literature also describes a variety of variables associated with at-risk students. Mink and Kaplin (1970) and for example suggest that varying combinations of identifiable variables appear to be related to dropping out of school. The most prevalent variables are family background, which includes below-average socioeconomic and cultural status; employment of the father as an unskilled or semiskilled worker; and failure of parents to complete high school; few or no social relationships in school, little participation in extracurricular activities, early school failure, failing at least one subject, poor grades, grade retention of one or more years, overage for grade placement, low reading and mathematics ability, language difficulties, inadequate personality structure, high absenteeism in school, improper, inadequate, or changing school curricula, and low intelligence quotients. Liddle (1962) demonstrated that dropouts, as a group, have below average intellectual ability. This research also illustrated that dropouts achieve average or below average grades, are frequently absent from school, and are failing one or more courses. The NBA Research Division (1963), however, found conflicting evidence regarding the importance of intelligence quotients as a factor contributing to school dropouts. Some researchers found that intelligence is of little importance while others suggest that intelligence plays a major role in the at-risk students decision to drop out of school. 58 While the debate continues regarding LO. scores and their importance in the decision of at-risk students to drop out, there is research that strongly supports the notion of improving I.Q. scores through environmental change. One of the most significant of these studies was a longitudinal study conducted by Skodak (1949). Skodak placed 100 children who were less than six months old, into adoptive homes, all of which were in higher socioeconomic levels. All of the foster parents were working in managerial occupations. The intellectual growth of these children was measured through periodic testing over a 13 year period. The mean IQ score of the children at age 13 was 106 compared with a mean of 85.7 of 63 of the biological mothers, who were mostly from the lower socio-economic levels. The difference of 20 1.0. points was highly significant. An investigation conducted by Wehlage and Rutter (1986) discovered that at-risk students who become dropouts share a number of characteristics. Their investigation found that students from low socioeconomic background have the highest dropout rate; among ethnic groups, Hispanics have the highest dropout rate; followed by blacks, then whites. Other demographic factors which are associated with the dropout rate include: a single—parent family; a large family; or living in a city or in the urban or rural south. This investigation therefore suggests that low socioeconomic status (S.E.S.) combined with minority group status are strong predictors of dropping out. The High Schools and Beyond Study (1983) agreed with some of the finding of Wehlage and Rutter (1986). This study found that dropouts are disproportionately from low S.E.S. families and racial/minority groups. While 15 percent of students who were sophomores in 1980 did not complete high school two years latter, nearly 25 percent of black students dropped out. This study also 59 concluded that dropouts were also more likely to be older, to be males rather than females, and to be enrolled in public schools in the urban areas in the South or West. Research findings from four national studies utilized longitudinal data: Project TALENT, (Flanagan, 1961) Youth in Transition, (Backman, 1971) National Longitudinal Survey of Youth labor market Experience, (Sproat, 1985) and High School and Beyond, (Jones, 1983) confirm the assertion that family background characterized by low socioeconomic status, is strongly associated with dropping out. What these studies fail to clarify or identify are the elements in the family background of low SES families that produce youth who are at—risk of completing their schooling. Other findings in this research however, assert that poor school performance leads to low grades and course failure and that these two variables are associated with dropping out. Further, after controlling for family background, race is not a variable that predicts dropping out of school. Lehr and Harris (1988) argue that the following characteristics are pervasive in at-risk students. Although these characteristics are extensive, the researchers noted that all of the traits need not be present for a student to be labeled "at-risk". academic difficulties lack of structure inattentiveness distractibility short attention span low self-esteem health problems 60 excessive absenteeism dependence discipline problems narrow range of interest lack of social skills inability to face pressure fear of failure (feels threatened by learning) lack of motivation Although the characteristics cited above are frequently present among low achievers, none of these characteristics is mutually exclusive. Many studies cite early school failure as a common characteristic of dropouts (Wacker, 1981; Barr, 1987; Sartain, 1989; and Hampton, 1991). Allen (1956) however, asserts that not only are dropouts unsuccessful in school, but they are retained for one or more grades and they exhibit below average academic ability. Further, he noted that at-risk students tend to demonstrate low abilities in mathematics and reading. Slavin and Madden (1989) Swan (1981) and Howell & Frese (1982) found a variety of risk factors that describe and effect at-risk students. Many of those factors paralleled characteristics identified in research cited earlier. Those factors include low achievement, grade retention, behavior problems, poor attendance, low socioeconomic status, and enrollment at schools with large numbers of poor Students. However, of greater importance is that these researchers not only found a strong association between the factors identified above and the drop out rate but they also noted that these associations facilitated the process of predicting and 61 identifying students who exhibit a propensity to drop out and those who will likely complete their schooling. Further, they argue that these students can be identified with remarkable accuracy as early as the 3rd grade. This section presented literature describing characteristics of students at-risk of dropping out of school. But as some critics have argued, the school bureaucracy employs specialization to remediate those characteristics. For example, students who exhibit poor reading and mathematics skills receive specialized and individualized instructions from reading and mathematics specialist. Behavioral problems are referred to guidance counselors, school psychologist and social workers. Students with learning and emotional disorders may be placed in one of many areas of special education. Hearing and speech impaired students are serviced by hearing and speech consultants. Programs have also been developed for teenage mothers to allow them to continue their education. And, students who exhibit a multitude of characteristic described in this section (i.e. academic achievement, poor attendance, poor behavior and low self-esteem) are segmented off into specialized intervention programs to receive extensive treatment. In essence, the school bureaucracy makes an honest attempt to educate all students regardless of the troublesome characteristics they exhibit. These students experience among others, poor achievement, poor attendance, lack of social skills and low motivation for schooling. Those experiences, may induce negative consequences, more specifically, poor and at-risk students may reject school and drop out. 62 Specialized Intervention Programs The previous section reviewed literature that asserted that bureaucratic methodologies employed by schools may in fact contribute to or induce negative reactions from poor and at-risk students. More specifically, the literature suggests that rule-following, control-oriented classrooms and differentiated curricula may place poor and at-risk students at a disadvantage academically, socially and economically. The final body of literature examines the bureaucracies treatment of at—risk students in specialized intervention programs, how those programs may effect students achievement and how such interventions may be improved. Levin (1987) argues that progress toward improving the education of at-risk youths has been limited to how educators view and address the problem. Schools are cognizant of the fact that poor and at-risk youth begin school with learning gaps in areas that schools and mainstream economic and social institutions value. However, specialized interventions may not be adequate unless such interventions significantly reduce this gap by bringing poor and at-risk students up to the same level of academic performance as their peers. Some educators have assumed that at-risk students cannot maintain a normal instructional pace and that providing specialized interventions will in fact narrow the learning gap (Powell, Farrar, & Cohen 1985; Oakes, 1985, and Page 1991). These students are subsequently placed in less demanding instructional environments with no timetable for augmenting their level of achievement. Such instructional models they assert, may further exacerbate an already devastating condition. 63 According to Hampton (1991) and Page (1991) several alarming outcomes emerge when at-risk students are repeatedly exposed to inferior programs, remedial courses and lowered expectations. First, they argued that when at-risk students are stigmatized as slow learners and the teachers of these students are labeled educators of the less academic, the intervention model has a debilitating effect on the students it was ostensibly designed to assist. By assigning hierarchical labels to students and teachers, intervention models inadvertently contribute to weak social support for the activity, and low social status and negative self-images for students engaged in remediation. Stated another way, the combination of low social status and low expectations treats at-risk students and their teachers as educational discards, substandard to mainstream education. These circumstances they assert are unhealthy conditions under which to expect significant academic and social progress. Second, they noted that the differentiated and segmented treatment of at- risk students in remedial and other specialized intervention programs were not designed to bring these students up to grade level. Instead, such programs may maintain students at their current academic achievement level, and in some cases, the achievement levels regress. But some research (Grannis, 1988; Walker, 1988; & O’Sullivan, 1990) that supports these findings suggest that the lack of academic achievement may occur because no timetables are established to improve achievement and few incentives or provisions exist to ensure that at-risk students progress from remedial instructions to a mainstream environment. Moreover, Rist (1977) and Cakes (1985) argue that since we expect students in remedial programs to progress at a slower than "normal" pace, they achieve the self-fulfilling prophesy. Once these students are exposed to such programs, the achievement gap may grow significantly wider. 64 Hampton (1991) and Page (1991) also noted that, the existing intervention models emphasize slow pace basic skills and endless repetition of materials through drill-and-practice. Such practices only serve to reinforce the teachers low expectations and the students negative perception of school. The premise is that students must learn fundamentals before they can receive challenging instructions and materials. Thus, they concluded that at-risk students find little interest in both language and mathematics skills and that the lack of interest may diminish their motivation to achieve. Miller, Leinhardt, and Zigmond (1988) conducted research on at-risk students in an urban blue collar community. Their focus was to examine engagement with or disengagement from the social and academic aspects of school life. The researchers selected three (3) Learning Disabled students and three non- learning disabled students. Data was collected using interviews and observations. The researchers found that all of the students in the study exhibited low skill levels and that teacher expectations for the students were considerable low. More specifically, student discipline rules were bent; teacher selected academic materials that were not challenging; homework assignments were not graded for accuracy; students were allowed to use class notes during examinations; and testing covered only what was discussed in class rather than including a wider range of material covered in the textbooks. Consequently, when academic standards are lowered and schools establish low expectations for students, school is viewed as dull and encouraging student apathy. 65 Levin and Hopfenberg (1991) research also supports Millers et al conclusions. They argue that at-risk students must learn at a faster rate than privileged students-not at a rate that drags them farther behind. Slowed learning rates only move at-risk students farther behind. Enriched and accelerated programs however provide the most promising hope for improving and increasing the rate of learning for at-risk students. Levin and Hopefenberg lend credibility to this assertion by citing a study in which at-risk students were assigned at random to remedial, average and honors classes in seventh-grade mathematics. At the end of the school year, the at-risk students assigned to the honors classes performed significantly better than those in the other two groups. Despite the recommendations of some researchers to expose at-risk students to mainstream and accelerated programs, many scholars have expressed concerns regarding the effect of these and other reform initiatives on at-risk students (McDill, Natriello, & Pallas 1985; Sedlak, Wheeler, Pullin, & Cusick, 1986). Some scholars believe that those initiatives have largely ignored the needs of at-risk students and that such abandonment will further exacerbate the differences between at-risk students and their counterpart/peers. Murphy (1989) contends however, that those concerns are ill founded because there is some evidence to suggest that current reform initiatives have had a positive effect on the academic performance of at-risk and minority students. For example, in 1988 the Business- I Education Partnership Committee and Guthrie and Kirst (1988) reported increases in achievement of at-risk and minority students at a faster rate than for white students. 66 Evidence of minority and at-risk student gains in mathematics were revealed in 1986 by the National Assessment of Educational Progress. More specifically, the NAEP data found that black and Hispanic students made constant gains in mathematics at every grade level and that those gains have been greater and more consistent than the gains of white students (Education Week, 1988). LeTendre (1991) argues that at-risk students enrolled in Chapter 1 programs should not be separated from the rest of the curriculum. Successful Chapter 1 programs should be aligned with regular education programs with comparable instructional strategies and materials. Further, LeTendre asserts that despite past program emphasis on basic skills, the 1988 reauthorized Chapter 1 program can benefit all students because it emphasizes higher order thinking skills, higher student expectations, and more challenging instructions. As educators engage in these program improvements, the achievement gap between at-risk students and higher achieving peers will narrow significantly. Tifft (1989) offers several recommendations to reform middle schools that would benefit students at-risk of being left behind. Among those recommendations are breaking up large schools into smaller communities for learning, emphasizing critical thinking rather than rote learning, and promoting "cooperative learning", in which groups of students with heterogeneous abilities work in teams. Good and Brophy (1991) also assert that tracking be replaced with cooperative learning where students of heterogeneous abilities can benefit. According to the researchers, properly structured cooperative learning not only improves achievement for all types of students but it also increases self-esteem, academic self-confidence, and students’ liking for their class and classmates. 67 The literature presented in this section suggest that specialized interventions (i.e. remedial programs) may not achieve their intended goal that is; to narrow the achievement gap between at-risk students and higher achieving students. According to researchers, some educators assume that at—risk students are incapable of learning at a normal instructional pace. These students are then placed in less demanding instructional environments that reinforce the low expectations of teachers. Researchers have suggested that poor and at-risk students may demonstrate greater academic achievement when exposed to more challenging instructions. Higher order thinking skills, cooperative learning and accelerated programs were recommended because there is some evidence that such strategies and interventions narrow the achievement gap between the higher achieving student and the poor and at-risk student. Summan Weber (1947), Callahan (1962), Tropea (1987) and others have asserted that schools are bureaucratic because they practice bureaucratic methods. More specifically, it was argued that specialization, rules and regulations, hierarchy of authority, and knowledge or technical skills are utilized in schools to ensure order and stability. however, specialization is the major concept utilized by the school bureaucracy to manage and treat students, particularly poor and at-risk students. Schools, it was argued, not only identify students who exhibit at-risk characteristics, but they provide specialized interventions to remediate those 68 characteristics. In 1965, the federal government created and funded Chapter 1 programs to improve reading and mathematics skills of poor and at-risk students. Vocational and industrial education, special education, bilingual education and general education programs were also developed to assist these students. Further, they argue that specialized interventions sort and differentiate poor and at-risk students from affluent and more academic students to maintain order and control in mainstream classrooms. In essence, bureaucrats have asserted that bureaucratic specialization is necessary because it helps poor and at-risk students. Critics of bureaucratic specialization (Katz, 1975; Willis, 1977; Calabrese, 1988; Page, 1991; Cusick, 1992) however, noted that while specialization is bureaucratically feasrble, it may induce unintended effects or negative consequences on poor and at—risk students, namely, they may reject school. Specialization, they argued, has legitimized the sorting and differentiation of students under the assumption that poor and at-risk students have little or no interesf in academic pursuits. Critics have also asserted that the bureaucracies emphasis on control may have a pejorative effect on academic achievement. Control may be achieved through coercive or instructional strategies, but those strategies place minimal focus on educating students. Such strategies, they noted, may induce poor and at-risk students to reject authority, develop non-compliant behaviors, and reject school. Additionally, Oakes (1985) Powell, Farrar, and Cohen (1985), Levin (1987) Walker (1988), Grannis (1988), O’Sullivan (1990) Hampton (1991) and Page (1991) have argued that specialized interventions, although well intentioned, may induce negative consequences on poor and at-risk students by minimizing academic 69 achievement gains and maintaining the students low perception of schooling. Many intervention programs not only segment students from the mainstream but they were not designed to narrow the learning gap between the poor and at-risk students and the more and less academic students. Specialized interventions are characterized as slow paced and repetitious. Such interventions, they argue may only reinforce the poor and at-risk students negative perceptions of schooling. In the final analysis some educators argue that specialization, particularly specialized intervention programs, help poor and at-risk students. Critics, on the other hand assert that while specialized interventions are well intentioned, overall they are harmful rather than helpful to students. Given these arguments, this research investigates the effects of specialized interventions on poor and at-risk students to determine which of these arguments is valid. 70 REFERENCES Allen, C .M. (1956). Combating the dropout problem: Handbook for teachers, counselors, and administrators in elementary and high schools. Science Research Apple, M. (1983). Curriculum reform and the logic of technical control. In M. Apple and L. Weis (Eds), Ideology and Practice in Schooling. Philadelphia: Temple University Press. 143-166. Apple, M. (1979). Ideology and curriculum. London: Routledge and Kegan Paul. Backman, J ., et al (1971). Youth in transition, Volume III: Dropping out - problem or sympton? Ann Arbor, Michigan: Institute for Social Research. Barr, R., B. (1987). An essay on school dropout for the San Diego Unified School District. San Diego, California: Planning, Research and Evaluation. Beck, L. and Muia, J .A. (1980, November). A portrait of a tragedy: Research findings on the dropout. High School Journal, 64 (2), 65-72. Bowles, S. and Gintis, S. (1976). Schooling in capitalist America: Education and the contradictions of economic life. New York: Basic Books. Business-Education Partnership Committee (1988). An evaluation of the education progress from South Carolina ’s educational improvement efforts: The fourth annual report on the South Carolina education improvement act. Columbia, SC. BEPC. 71 Callahan, R. (1962). Education and the cult of efliciency. Chicago: University of Chicago Press. Calabrese, R. L. (1988, March). Schooling, alienation, and minority dropouts. The M Education Digest. 7—10. Cusick, P. A. (1992). The educational system: It’s nature and logic. New York: McGraw Hill. DeRidder, L. M. (1991, February). How suspensions and expulsions contribute to dropping out. Education Digest. 44- 47. Education Week. (June 15, 1988). 29 Etzioni, A. (1964). Modern organizations. Englewood Cliffs, New Jersey: 50-54. Flanagan, J. C. (1961). Project talent: A progress report. Pittsburgh, Pa.: American Institute for Research. Giroux, H. A. (1981). Ideology, culture, and the process of schooling. London: The Falmar Press. Good, T. L. and Brophy, J. E. (1991). Teaching heterogeneous classes. New York: Harper -Collins. 382-438. 72 Goodlad, J. (1984). A place called school: Prospects for the future. New York: McGraw-Hill. Gouldner, A. W. (1954). Patterns of industrial bureaucracy. The Free Press. Grannis, J. A., Rich], C., Pallas A., Lerer, N. and Randolph, S. (1988). Evaluation of the New York City dropout prevention initiative: Final report on the middle schools for year two, 1986-87. Institute for Urban and Minority Education. New York: Teacher College, Columbia University. Gulick, L. and Urwick, L. (1937). Papers on the science of administration. Institute of Public Administration. New York: Columbia University. Guthrie, J. W., and Kirst, M. W. (1988, March). Conditions of education in California 1988. Policy Paper No. 88-3-2. Berkely, Calif.: Policy Analysis for California Education. Hampton, F. M. (1991). An evaluation of a dropout prevention program for middle school students in an urban setting. Unpublished doctoral dissertation, The University of North Carolina at Greensboro. Hobson V. H., 269F. Supp. 401 DC. (1967). Howell, F., and Frese, W. ( 1982). Early transition into adult roles: Some antecedents and outcomes. American Educational Research Journal. 19, 51-73. 73 Jones, C. et a1. (1983). High school and beyond 1980 sophomore cohort first follow-up 1982: Data file user manual. National Opinion Research Center, Contractor Report to the National Center for Education Statistics. Johnson, N. B. (1985). Westhaven: Classroom culture and society in a rural elementary school. Chapel Hill: The University of North Carolina Press. Katz, M. B. (1975). Class bureaucracy and schools: The illusion of educational change in America. New York: Praeger Publishers. Lehr, J. B., and Harris, H. W. (1988). At-risk, low-achieving students in the classroom. Washington, DC: National Education Association Professional Lrbrary. LeTendre, M. J. (1991). Improving chapter 1: Making the promise reality. Phi Delta Kappan, 72, 577-80. Levin, H. M. (1987, March). Accelerated schools for disadvantaged students. Educational Leadership. 44, 19—21. Levin, H. M. and Hopfenberg, W. S. (1991, January). Accelerated schools for at-risk students. Principal. 70, 11-13. Liddle, G. P. (1967). Education improvement for the disadvantaged in an elementary setting. Springfield, III: C. C. Thomas. McNeil, L. M. (1986). Contradictions of control: School structure and school knowledge. New York: Routledge and Kegan Paul. 74 McDill, E. L., Natriello, G. and Pallas, AM. (1985, Winter). Raising standards and retaining students: The impact of the reform recommendations on potential dropouts. Review of Educational Research. 55(4), 415-433 Miller, S., Leinhardt, G., and Zigmond, N. (1988). High school experiences of learning disabled students. American Educational Research Journal. Mink, O. G., and Kaplin, B. A. (1970). America’s problem youth: Education and guidance of the disadvantaged. Scranton, Pennsylvania: International Textbook Company. Murphy, J. (1989). Educational reform and equity: A reexamination of prevailing thought. A Paper Presented at the Annual Meeting of the American Educational Research Association. San Franciso. Nagler, H. F. (1991, April). Detroit approves all-male school. American Federation of School Administrators News. 17(18). Oakes, J. (1985). Keeping track: How schools structure inequality. New Haven, CT: Yale University Press. Ogden, MT. and Germinaries, V. (1988). The at-risk student. lancaster, Pennsylvania: Technomic Publishing Company, Inc. O’Sullivan, R. G. (1990, April). Evaluating a model middle school dropout prevention program for at-risk students. Paper Presented at the Annual Meeting of the American Educational Research Association. Boston, MA. 16-20. 75 Page, R. N. (1991). Lower-track classrooms: A cun'icular and cultural perspective. New York: Teachers College, Columbia University. Peterson, T. (1988, April). Building, passing, implementing, and assessing educational reform in South Carolina. Paper Presented at the Annual Meeting of the American Educational Research Association. New Orleans. Powell, A., Farrar, E., and Cohen, D. (1985). The shopping mall high school: Winners and losers in the education market place. Boston: Houghton Mifflin. Rist, R. (1977). On understanding the processes of schooling: The contributions of labeling theory. In J. Karabel and A. H. Halsey (Eds). Power and ideology in education. New York: Oxford University Press. 292-305. Sartain, H. W. (1989). Nonachieving students at-risk: School, family, and community intervention. Washington, DC: National Education Association Professional Library. Sedlak, M. W., Wheeler, C. W., Pullin, D. C., and Cusick, P. A. (1985). Selling students short: Classroom bargains and academic reform in the American high school. New York: Teachers College Press. Shanker, A. (1993, September). Is it fair to keep disrupters in class? The Detroit Teacher. 32(2). Skodak, M. and Skeels, H. M. (1949). Final follow-up study of one hundred adopted children. Pedagogical Seminar. 75, 85-125. 76 Slavin, R. E., and Madden, N. A. (1989, February). What works for students at-risk: A research synthesis. Educational Leadership. 4-6,(5)4. Sproat, K. (1985 ). The national longitudinal survey of labor market experience: An annotated bibliography of research. Lexington, Mass: Lexington Books. Swan, T. L. (1981). Dropouts in high school and after. American Educational Research Journal. 7, 343-367. Taylor, F. W. (1991). Scientific management. New York: Harper. Tifft, S. E. (1989, June 26). Help for at-risk kids: Carnegie proposal. Time. June 26 Tropea, J. L. (1987, Fall). Bureaucratic order and special children: Urban schools, 1950’s - 1960’s. History of Education Quarterly. 339-361. Tye, B. (1985). Multiple realities: A study of13 American high schools. Lanham, MD: University Press of America. Wacker, G. B. (1981, Fall). Vocational education’s role in serving dropouts and potential dropouts. Journal for Vocational Special Needs Education. 4(1), 19-22. Walker, R. (1988, April). School job-training programs boosted in new York, California. Education Week, April, Vol:7, Issue: 31, 9 Washburn, W. H. (1989). A reassessment of the dropout problem. Unpublished doctoral dissertation, Boston University. 77 Weber, M. (1947). The theory of social and economic organization. New York: Oxford University Press. 329-330. Reprinted in Robert K. Merton, Alisa P. Gray, Barbara Hockey and Hanan C. Selvin (eds) Reader in Bureaucracy, Glencoe, Ill.: The Free Press, 1952, 18-20. Wehlage, G. G., & Rutter, RA. (1986). Dropping out: How much do schools contribute to the problem? Teachers College Record. 87, 374-392. Willis, P. (1977). "Learning to labor, how working class kids get working class jobs. New York: Columbia University Press. 1-226. Willower, D. J. and Jones, R. G., (1963, November). When pupil control becomes an institutional theme. Phi Delta Kappan, XLV, 2 (10)7-9. Whitaker, C. (1991, March). Do black males need special schools? Ebony Magazine, Johnson Publications. XLVI (5) 17-22. Chapter 3 Methodology In the previous chapter the researcher reviewed criticisms from scholars who asserted that schools are organized and managed according to certain bureaucratic characteristics and that they expose poor and at-risk students to specialized intervention programs. Additionally, critics argued that exposure to specialized interventions may have a pejorative effect on the academic, social and economic status of poor and at-risk students. These criticisms thus prompted this researcher to examine the effects of specialized interventions and the students perceptions of those interventions. The students are those who have received the greatest share of specialized interventions. This chapter will include: the general design of this study; the identification of a sample population; the procedure for data collection, the statistical test used; the instrumentation; and the implementation of a pilot study to ascertain the reliability of a survey instrument developed by the researcher. General Design This study is an investigation applying a quasi-experimental design. To conduct this investigation, the researcher initiated a comparative analysis on two groups of at-risk male students. One group of at-risk students were 9th and 10th grade Students enrolled at Fredrick Douglas Academy (F.D.A.) during the 1991-92 school year. These students represent the experimental group. 78 79 Students enrolled in F.D.A. were referred by the principals at their neighborhood comprehensive high schools, after a review of their cumulative records, because they exhibited poor academic performance, poor attendance, and poor behavior during the 1990-91 school year. Other criteria used to select students were that they exhibited troublesome behaviors at the middle school level. For example, the mean g.p.a. was below average at the 7th grade level (1.75) and 8th grade level (1.90). Also, the mean absence rate attendance at the 7th grade was 12.5 and 15.0 at the 8th grade level. For the school principals, this data indicated that these students needed intensive specialized intervention. Principals also believed that these students had the potential to improve their high school performance and that they could benefit from the programs and services provided at Fredrick Douglas Academy. The second group of at-risk students were 9th and 10th grade students enrolled at Mackenzie, Henry Ford, and Northwestern High Schools, during the 1991-92 school year. These students represented the control group in this study. Cumulative records of the control group were reviewed by principals to determine which interventions strategies were appropriate for the 1991-92 school year. The cumulative records revealed that the mean g.p.a. was average at the 7th grade level (2.50) and at the 8th grade level (2.55). The mean absence rate at the 7th grade level was 8.5 and 8.0 at the 8th grade level. Since the cumulative records indicated that the subjects in the control group had demonstrated some potential in middle school, but had exhibited poor academic performance, poor attendance and poor behavior during the 1990-91 80 school year, principals allowed these students to remain at their neighborhood high schools. The rationale for allowing these students to remain at their neighborhood high schools was that the principals believed that the programs and services within their buildings could adequately address the needs of these students. Procedure and Data Collection In order to conduct this study the researcher obtained approval from the Detroit Public Schools and principals at each location. Once approval was granted the researcher began the selection process for an experimental and control group by reviewing the cumulative records of one hundred at-risk male students enrolled in Fredrick Douglas Academy. The subjects have birth dates between January 1977 and December 1978. This period was used because subjects are referred to F.D.A. at ages 14 and 15 and because they had completed one year of treatment as of June 1992. Thus, fifty-nine students were identified with birth dates within the specified time period after excluding students receiving special education services. Similarly, one hundred and fifty student records were reviewed from the three comprehensive high schools. These students were identified as at-risk students by their counselors and administrators because they had a record of poor academic achievement and troublesome behavior during the 1990-91 school year. Sixty-nine students were randomly selected for the control group after excluding students who received specialized services. These students also were comparable in age, gender and grade level. Since the subjects in this research exhibited comparable behaviors, it was necessary to collect data on the type and frequency of specialized interventions (i.e. 81 remedial mathematics and reading, social work and psychological referrals, and documentation of parent-teacher/counselor conferences) these students received during the 1990-91 and 1991-92 school years. Those specialized interventions are reported in tables 1 and 2. In remedial reading for example, a mean of 2.0 semesters of specialized interventions were provided to the 10th grade students in the experimental group during the 1990-91 school year. During the same period 9th grade students in the experimental group received a mean of 2.0 semesters of remedial reading. When comparing this data with the 9th and 10th grade students in the control group, the results indicate that 10th graders received only a mean of 1.0 semesters of remedial reading and the 9th graders, a mean of 0.91 semesters respectively (See Definition of Terms for definition of semester). Other differences are found in table 1 relative to remedial mathematics, social work, psychological, and parent-teacher/counselor conferences. Those differences indicate that the experimental group received more specialized interventions at their neighborhood high schools during the 1990-91 school year. The data reported in table 2 also indicates that the experimental group received more specialized interventions during the 1991-92 school year compared to the control group. This data provided further evidence to proceed with the study. 82 TABLE 1 Ninth and Tenth Grade Interventions For the Experimental and Control Group, School Year 1990-91. TEN TII GRADE EXPERIMENTAL GROUP - Fredrick Douglas Academy Parent-Teacher] Year Social Work Psychological (1) Rem. Reading (2) Rem. Math Counselor Conferences 1990-91 0.7 Referrals 0.6 Referrals 2.0 Semesters 2.0 Semesters 2.8 Conferences N=10 TENTH GRADE CONTROL GROUP - Comprehensive High Schools 1 2 Parent-Teacher] Your Social Work Psychologiall ( ) Rem. Reading ( ) Rem. Math Counselor Conferences 1990-91 0.27 Referrals 0.27 Referrals 1.0 Semesters 1.09 Semester 1.54 Conferences N = 11 NINTH GRADE EXPERIMENTAL GROUP - Fredrick Douglas Academy Parent-Teacher] Year Social Work Psychological (1) Rem. Reading (2) Rem. Math Counselor Conference 1990-91 0.12 Referrals 0.10 Referrals 2.0 Semesters 1.96 Semesters .076 Conferences N = 49 NINTH GRADE CONTROL GROUP - Comprehensive High Schools Parent-Teacher/ Year Social Work Psychological (1) Rem. Reading (2) Rem. Math Counselor Conferences 1990-91 0.05 Referrals 005 Referrals 0.91 Semesters 1.09 Semesters 0.52 Conferences N = 58 (l) Rem. Reading: Remedial Reading Classes (2) Rem. Math: Remedial math Classes 83 TABLE 2 Ninth and Tenth Grade Interventions For the Experimental and Control Group, School Year 1991-92. TENTH GRADE EXPERIMENTAL GROUP - Fredrick Douglas Academy l 2 Parent-Teacher/ Year Social Work Psychological ( )Reln. Reading ( )Rem. Math Counselor Conferences 1991-92 0.7 Referrals 0.3 Referrals 2.0 Semesters 2.0 Semesters 3.5 Conferences N=10 TENTH GRADE CONTROL GROUP - Comprehensive High Schools 1 (2 Parent Teacher/ Year Social Work Psychological ( )Reln. Reading )Reln. Math Counselor Conferences 1991-92 0.18 Referrals 0.09 Referrals 0.73 Semesters 0.90 Semester 0.90 Conferences N=ll NINTH GRADE EXPERIMENTAL GROUP - Fredrick Douglas Academy 1 2 Parent Teacher] Year Social Work Psychological ( )Reln. Reading ( )Rem. Math Counselor Conferences 1991-92 0.14 Referrals 0.10 Referrals 2.0 Semesters 1.96 Referrals 0.92 Conferences N=49 N INTI‘I GRADE CONTROL GROUP - Comprehensive High Schools . (1) (2 Parent Teacher/ Year Social Work Psychological Rem. Reading ) Rem. Math Counselor Conferences 1991-92 0.02 Referrals 0.02 Referrals 1.03 Semesters 1.12 Semesters 0.53 Conferences N=58 (1) Rem. Reading: Remedial Reading Classes (2) Rem. Math: Remedial Math Classes 84 Students referred to F.D.A. were less than 16 years old. To provide some control against internal threats to validity such as history, maturation, and time, all participants were enrolled in the 9th or 10th grade at the three comprehensive high schools or Fredrick Douglas Academy during the 1991-92 school year. Age and grade levels were consistent for both groups to make valid comparisons. After identifying both groups of students, the researcher compiled cumulative data on each group for the 1990-91 school year. The purpose of this data collection was to determine whether or not both groups had comparable California Achievement Test (C.A.T.) scores, earned credit hours, attendance records and suspension records. Cumulative data was also gathered on all four variables for the 1991-92 school year to determine whether or not there were significant differences between groups. Although grade point average (g.p.a.) was not included as a variable in this study, it was important to determine whether or not the academic evaluations recorded by the teachers of these students indicate that both groups level of achievement were comparable. The data indicates that the mean differences in g.p.a. between groups were not large. For example, the 9th graders in the experimental group earned a mean g.p.a. of .80 and the 9th graders in the control - group had a 1.00 g.p.a. For the 10th graders, however, the mean differences were even closer; that is, the 10th graders in the experimental group earned a mean g.p.a. of 1.10 and the 10th graders in the control group had a g.p.a. of 1.00. This data suggests that there were comparable mean differences in g.p.a. between groups for the 1990-91 school year. 85 Since the CAT. tests were administered by staff at each school in April 1991 and April 1992, scores were available in each students academic file. Earned credit hours and attendance data were also available from the subjects academic files. Disciplinary data, were obtained by reviewing student files in the Dean of Students Office, at each school. After collecting data on CAT reading and mathematics scores, earned credit hours, attendance, and suspensions, statistical analysis were completed on pretest data to determine whether or not both groups were comparable. The results indicated that there were no significant differences between groups at the 9th and 10th grade levels. In February 1993, the researcher visited Fredrick Douglas Academy and the three comprehensive high schools to administer the School Attitude Measure and the Student Survey of Expected Accomplishments. Prior to administering the survey instruments, the researcher obtained approval from the Detroit Public Schools, and principals at each location. After the approvals were granted, the researcher secured a listing of each subject’s address in order to mail copies of the Informed Consent Form to the parents/legal guardians. The consent form included an explanation of this investigation and requested permission to allow the subjects to participate in this study. Parents/legal guardians also received written instructions to return signed consent forms to the principal’s office for review. After the consent forms were returned to the school principals, both survey instruments were administered to the experimental and control groups to collect data on the students attitude toward school for the 1992-93 school year and their attitude toward what they expect to accomplish within the next 5 to 10 years. The 86 1992-93 data were used because there were no existing data on these instruments for the subjects for the 1991-92 school year. On the date the survey instruments were administered, only 52 subjects from the experimental group were present. Those subjects were then randomly assigned to one of two groups in order to create manageable group sizes. Group 1 had 29 subjects and group 2 had 23 subjects. Subjects in the control group were enrolled in one of three comprehensive high schools, therefore no random assignments were necessary to control for group size. Separate dates were identified for each school for the administration of both instruments. The researcher was careful not to schedule the administration of the survey instruments on Monday or Friday since schools tend to experience a higher rate of absenteeism on those days. However, only 18 subjects from Mackenzie, 17 subjects from Henry Ford, and 15 subjects from Northwestern High Schools were present when the survey instruments were administered. The researcher administered both instruments during a one hour and 10 minute morning session at each site. However, since F.D.A. had two groups, two separate morning sessions were conducted on the same day. The S.A.M. had one- hundred questions and required one hour to complete. The Student Survey on Expected Accomplishments had nine questions and required 10 minutes to complete. Prior to administering each survey the researcher instructed the subjects to place only their ID. numbers and grade level in the appropriate areas on the answer 87 sheets. Subjects were also instructed to read all statements carefully and to select responses that most closely or accurately represented their beliefs and attitudes. Each subject was presented with an information sheet describing the purpose of the study and the areas of questioning. The information sheet explained that the respondents were under no obligation to participate and that they could withdraw from the study at any time without punitive consequences. No promises of remuneration were offered to the participants. Further, every attempt was made to prevent the risk of exposing the identity of the respondents and to prevent a breach of confidentiality. During this investigation the researcher also discovered that no pretest data were available on both survey instruments for the experimental and control group for the 1990-91 school year. In order to obtain comparable data, principals at three additional comprehensive high schools in Detroit were asked to identify 9th and 10th grade at-risk male students who would be referred to F.D.A. for the 1994-95 school year. Those schools were also requested to identify at-risk male students who exhlbited academic, attendance and discipline problems and were, to be retained at their schools to receive specialized interventions. The comprehensive high schools collectively identified 61 students for F.D.A. and 75 students who would be retained at the schools for specialized intervention. However, to maintain consistency in sample size, the researcher randomly selected 42, 9th grade and 10, 10th grade students from the list of students being referred to F.D.A. for the fall 1993. Similarly, 40, 9th grade and 10, 10th grade students were randomly selected to complete a comparative analysis for the control group. 88 Both survey instruments were administered to these students in May 1993. Arrangements were completed at each school for the administration of the survey instruments at different times during the day so that the F.D.A. referrals would have no contact with the participants in the control group. Further, the same procedures for testing were followed for each group relative to securing consent, scheduling of participants and administering the survey instruments. Data Analysis Some data analysis were completed during the selection of the experimental and control group for the 1990-91 school year, to determine whether or not both groups were comparable. The reader will recall that the data collected included C.A.T. scores, earned credit hours, attendance and discipline. Similar data were collected for the 1991-92 school year for a comparative analysis to determine what differences, if any, were found within groups and between groups. The statistical test selected to compare group data was the t-test. If the t- tests are significant at the p<.05 level for the 1991-92 school year, it can be concluded that research hypotheses for research questions 1 through 4 are consistent with the data. However, when the 1990-91 school year or pretest data was significant, the Analysis of Covariance (AN COVA) was employed. Research question number 1 asks whether or not the students who received the larger doses of specialized interventions progressed as well academically as the at-risk students who received fewer interventions. This question was answered by comparing reading and math scores on the California Achievement Test for each 89 group and earned credit hours. C.A.T. scores were listed on student records as grade mean equivalent. A math score of 10.7 is therefore interpreted as 10th grade, 7th month. For statistical analysis however, those scores were converted to mean scale scores (i.e. 785). Earned credit hours were based on total credit hours each subject earned at the end of the 1990-91 and 1991-92 school years. Subjects who received passing grades (i.e. grades A, B, C, or D) at the end of each semester received 5 credit hours for each class. Since each student was enrolled in 6 classes per semester, the total credit hours a student could earn each school year was 60 credit hours. Research question number 2 asks whether or not the at-risk students who have been subjected to the larger doses of specialized interventions display better behavior than at-risk students who have received fewer interventions. This question was answered by comparing the number of absences from class and incidence of suspension from school for each subject per school year. During the data collection process, only absences were recorded for analysis. The researcher therefore excluded from this study any reference to the incidence of tardiness to class. Additionally, suspension from school were recorded by the number of incidence per subject, per group. Research question number 3 asks whether or not the attitudes of at-risk students who received more specialized interventions are more positive toward school than a comparable group of at-risk students who have received fewer specialized interventions. To answer this question, the School Attitude Measure was administered to the subjects to make comparisons between groups. The respondents selected one of four responses to answer the questions. The answers 90 are coded with a numerical value ranging from 1-4 (i.e. always agree = 4, to never agree = 1). A weighted raw score was used to determine whether or not school attitude differences existed between groups. Research question number 4 asks whether or not the at-risk students who have received more specialized interventions have a more positive attitude toward what they expect to have achieved or accomplished within the next 5 to 10 years, compared to at-risk students who have received fewer specialized interventions. This question was answered by comparing the results of the participants responses on the Student Survey On Expected Accomplishments. Again, each question had four responses coded with a numerical value ranging from 1-4 (i.e. most likely =4, to most unlikely = 1). The t-test was employed at the p<.05 level of significance to determine whether or not differences existed between groups relative to expected student accomplishments for the 1992-93 school year. The same statistical test was applied to determine if significant differences existed between groups for the 1990- 91 school year. Following the completion of the surveys, the answer sheets were collected and inspected to determine if any data was missing. The inspection revealed that there was no missing data and that the subjects had responded to all statements. Additionally, to control against human error in scoring the S.A.M. instrument all answer sheets were computer scored. However, since the Student Survey On Expected Accomplishments had only nine statements, the scores were placed on a computer by the researcher. 91 INSTRUMENTATION The California Achievement Test (CI'B/McGraw-Hill, 1986, Form B, Level 19) was administered to participants to collect partial data for research hypothesis number 1. Research hypothesis number 1 states that at-risk students who have received larger doses of specialized interventions will show significantly greater progress on the CAT tests and earned credit hours than a comparable group of students who have received fewer specialized interventions. The CAT test is a nationally recognized assessment instrument comprised of two subtest, reading and mathematics. The reliability coefficient on each subtest is .98. Additionally, the answers for each subtest were multiple choice. Data collection for research hypothesis number 3 was achieved by administering the School Attitude Measure (Scott, Foresman and Company, 1980, Level 9-12). Research hypothesis number 3 states that at-risk students who received larger doses of specialized interventions will have a more positive attitude toward school than a comparable group of at-risk students who have received less specialized interventions. The School Attitude Measure (SAM) is a nationally recognized standardized instrument designed to examine several dimensions of student attitude expression. Each dimension or scale consisted of 20 statements. The attitude scales examined the following areas: 92 Scale A: Motivation for Schooling This scale was designed to measure the effect of the students’ reactions to past school experience and its impact on their motivation in school. The way students have come to feel about their total school experience can influence how hard they want to work in school, how highly they value school, and how much they want to pursue further schooling (Teacher’s manual, Scott, Foresman and Company, 1980). Scale B: Academic Self-Concept-Performance Based The statements in this scale are associated with the students’ confidence in their academic abilities and their feelings about their school performance. The authors of this scale believe that the students’ feelings about their academic abilities can impact his/her success or lack of success in school. Scale C: Academic Self-Concept-Reference Based This scale was designed to measure how students think other people (i.e. teachers, family and friends) feel about their school performance and ability to succeed academically. 93 Scale D: Student’s Sense of Control Over Performance This scale was developed to measure students’ feelings about being able to exercise control over situations that effect them at school and to take responsibility for the outcome of relevant school events (i.e. grades, promotions, etc.) Scale E: Student’s Instructional Mastery This scale differs from the other scales in the School Attitude Measure because the other scales measure student feelings. The Instructional Mastery Scale focuses on a self report of the state of the students actual school skills. Since there are certain skills that all students need in order to organize school life and to succeed in school, those skills include the student’s evaluation of his/her: ability to use school time effectively and efficiently persistence in instructional tasks ability to focus attention or concentrate on instructional tasks 94 ability to seek and use feedback (criticism, advice, and help from others) ability to evaluate his/her own work The S.A.M. was developed for elementary ( grades 4 to 6) middle (grades 7 to 8), and high school students (grades 9-12) with a reliability coefficient of .95. To maintain the validity and reliability across all three levels, certain items from each scale were included at each level. In some cases those items were rewritten to conform with language usage requirements (Scott, F oresman and Company, 1980). Data collection for research question number 4 was achieved with the Student Survey On Expected Accomplishments. This survey instrument was designed to measure what at-risk students realistically expected to achieve or accomplish within the next 5 to 10 years. This instrument was developed after the researcher asked 40 students to write a one page response to the following question: "Given your education and background, what do you realistically expect to achieve or accomplish in the next 5 to 10 years?" The answers provided by the respondents were used as a guide in developing this survey instrument. Since the Student Survey On Expected Accomplishments was developed by the researcher a pilot study was conducted. The details of the pilot study are described in the following section. Additionally, a posttest reliability measure was conducted on the experimental and control group. The posttest reliability coefficient was .90. 95 The questions contained in this instrument are as follows: (ML) (51-) (SU) (MU) 1. I am likely to graduate from high school ( ) ( ) ( ) ( ) 2. I am likely to graduate from college ( ) ( ) ( ) ( ) 3. I am likely to receive a second (advanced) college degree. ( ) ( ) ( ) ( ) 4. I am likely to be able to get a job after I finish my schooling. ( ) ( ) ( ) ( ) 5. I am likely to get married after I finish my schooling. ( ) ( ) ( ) ( ) 6. I am likely to start a family after finishing my schooling. ( ) ( ) ( ) ( ) 7. I am likely to purchase a nice home. ( ) ( ) ( ) ( ) 8. I am likely to own my own business. ( ) ( ) ( ) ( ) 9. I am likely to be better off financially than my parents. ( ) ( ) ( ) ( ) The response (ML) and (SL) indicates that the respondent believes he/she is MOSTLY LIKELY OR SOMEWHAT LIKELY to achieve a goal. The responses (SU) and (MU) indicates that the respondent is SOMEWHAT UNLIKELY or MOST UNLIKELY to achieve a goal. Further, the subjects were instructed to provide only one response to each statement. 96 Pilot Study A pilot study was planned and conducted by the researcher in March of 1993 to collect data on a survey instrument designed for the purpose of measuring students expected accomplishments. Since this instrument was developed by the researcher to answer research question number 4, the primary focus of the pilot study was to validate the reliability of the instrument. In order to conduct this pilot study, the researcher randomly selected 40 students (male and female) from a comprehensive high school in the city of Detroit. These students were enrolled in honors courses, college preparatory and remedial programs. The grade levels of the participants ranged from the 9th through 12th grade. The grade level and ability levels were needed to provide as wide a range as possrble for the sample. The selection of participants/volunteers were made in cooperation with the school principal, a counselor, and three English teachers. The selections were made by reviewing the enrollment lists of the teachers. From those lists, 21 male and 19 female students were selected to complete the Student Survey on Expected Accomplishments. Prior to conducting the survey, the researcher verbally instructed the participants on how to select responses to the statements on the survey and that only one response per statement was required. Further, the participants were informed that the survey would take fewer than 10 minutes to complete. 97 The participants were advised that their participation was voluntary and that they were not obligated to complete the survey. In addition, the participants were informed that they could withdraw from the study at any time without consequences. After administering the survey, the researcher applied the Pearson r Correlation to provide a preliminary view of the relationship among the statements in the survey. Although several statements show weak relationships the correlation matrix results were sufficiently strong enough to gather data on both at—risk student groups for use in this study. As reported earlier, the correlation coefficient was .70. This investigator planned and conducted a pilot study using the Student Survey on Expected Accomplishments in April of 1993. The focus of the pilot study was to gather data on a sample of college prep and at-risk students to determine the reliability of the instrument. Despite the range of student abilities in this pilot study, the most consistent finding was that all of the participants had adopted the goal of completing high school. Most of the participants had also responded positively to securing a job after completing their schooling, getting married, starting a family and purchasing a nice home. In addition, the participants responded positively to the statement which asked if they believed that they would be better off than their parents. Based on the findings from the pilot study and the efforts of the participants to provide accurate responses, the decision was made to proceed with this instrument as a valid measure of student attitudes toward what they expect to accomplish within the next 5 to 10 years. 98 Swarm The method of investigation was to conduct a comparative analysis of two groups of 9th and 10th grade at-risk male students. Both groups were enrolled in Detroit Public High Schools. The experimental group were enrolled at F.D.A. and the control group were from Mackenzie, Henry Ford and Northwestern High Schools. Students in the control group were randomly selected, however, the random selection process was not employed for the experimental group. Additionally, special education students were excluded from both groups since they were not the focus of this investigation. Data were collected for the 1990-91 and 1991-92 school years to determine whether or not the results were consistent with the research hypotheses. The research hypotheses state that students who receive more specialized interventions will demonstrate significantly greater improvement on academic achievement, behavior, school attitude, and expected accomplishments. Data on academic achievement consisted of CAT Reading and Mathematics score and earned credit hours. Data on student behavior included student daily attendance and suspension rates. However, since no pretest and posttest data were available on school attitude and expected accomplishments the SAM scale A-E and the Student Survey On Expected Accomplishments were administered to the experimental and control group in February 1993 to collect posttest data. Pretest data were collected by administering both survey instruments to a group of at-risk male students from 3 other comprehensive high schools who were scheduled to enroll at F.D.A. for the fall 1994 semester. Additionally, both survey instruments were administered to a group of at-risk male students at the same schools, for 99 pretest data, who would be retained to receive specialized interventions at those schools. Since the Student Survey On Expected Accomplishments was designed by the researcher, a pilot study was conducted to test the reliability of the instrument. The results indicated that the correlations were sufficiently strong enough to use this instrument to collect data for this study. Chapter 4 Results and Analysis of Data The purpose of this study was to examine the effect of specialized interventions and perceptions of those intervention processes on at-risk students who have received the greatest share of these specialized interventions. More specifically, this study sought to determine whether or not specialized interventions achieve their intended objectives. This study also sought to determine whether or not specialized interventions are perceived by the participants as helpful and leading to a successful conclusion. The data presented in this chapter compare the results of cumulative data as well as pretest and posttest achievement tests and survey data for subjects in the experimental and control group. The descriptive statistics provided in table 2, therefore, includes the pretest (1990-91) and posttest (1991-92) mean, standard deviation, number of subjects per group, and where applicable, the grade mean equivalent for all variables in this investigation. Given these data, t-tests were performed for between group comparisons. Additionally, the reader should recall that both groups were comprised of a small membership of 10th graders but included a significant membership of 9th graders. 100 ’1 101 TABLE 3 Descriptive Statistics for all Dependent Variables as a Function of condition and grade. £1!§!1!§!IAL.§BQ!E £9!IBQL.§BQ!£ ____2I!_§BAQE ____191!_§!AEE ____2I!_§£AQE ____AQI!_§BAQE VARIABLE 2311551 POSTTEST PRETEST rosrrtsr PRETEST rosrrEsr PRETEST POSTTEST MEAN 718.3 750.7 739.4 757.2 708.4 713.0 744.6 721.3 C.A.r. (AME) (5.7) (8.0) (6.9) (8.5) (5.2) (5.4) (7.4) (5.8) (nEAnruC) 5.0. 40.3 25.7 19.1 19.5 47.85 40.31 13.2 49.95 N 49 49 10 10 59 59 10 10 MEAN 730.0 751.2 754.7 771.8 726.7 714.4 754.1 730.1 C.A.r. (cME) (5.6) (7.0) (7.2) (8.4) (5.5) (4.9) (7.2) (5.6) (MAruEuATICS) 5.0. 30.06 28.54 13.06 15.69 41.64 40.73 17.95 49.72 N 49 49 10 10 59 59 10 10 MEAN 16.8 38.4 17.5 37.5 16.3 27.1 15.7 23.2 EARNED CREDIt 5.0. 11.35 8.36 9.20 12.07 11.32 11.14 6.01 8.97 nouns N 49 49 10 10 59 59 10 10 MEAN 28.2 13.7 23.4 10.0 28.0 24.5 26.7 24.3 ABSENCES 5.0. 14.58 7.24 13.38 6.11 12.70 10.38 10.76 6.92 N 49 49 10 10 59 59 10 10 MEAN 1.2 0.2 1.9 0.5 1.1 0.5 2.1 1.9 SUSPENSIONS 5.0. 0.96 0.40 0.73 0.52 1.05 0.67 0.73 0.73 N 49 49 10 10 59 59 10 10 S.A.M. MEAN 58.2 66.9 60.2 69.7 55.1 59.3 57.5 62.1 SCALE A 5.0. 9.07 9.15 7.62 8.01 10.46 10.81 7.23 7.47 N 42 42 10 10 40 40 10 10 S.A.M. MEAN 53.1 61.1 54.4 62.8 47.9 52.4 47.3 51.9 SCALE 8 5.0. 8.78 8.93 9.19 9.12 6.09 6.17 5.98 6.17 N 42 42 10 10 40 40 10 10 S.A.M. MEAN 49.7 58.38 50.7 58.1 45.5 50.2 49.8 54.4 SCALE c 5.0. 6.97 6.93 8.21 8.31 7.34 7.04 7.61 7.80 N 42 42 10 10 40 40 10 10 MEAN 59.2 67.95 60 68.2 59.5 63.9 60.7 64.2 SCALE 0 5.0. 9.47 9.86 11.20 11.65 8.46 8.44 8.79 6.81 N 42 42 10 10 40 40 10 10 S.A.M. MEAN 54.6 63.0 57 65.2 49.1 53.8 51.6 56.2 SCALE E 5.0. 8.83 8.54 8.62 8.43 9.02 9.21 7.21 7.25 N 42 42 10 10 40 40 10 10 102 TABLE 3 (CONTINUED) W W 91'" GRADE 10TH CRADE 9Tll GRADE M VARIABLE PRETEST PDSTTEST PRETEST PDSTTEST PRETEST PDSTTEST PRETEST PDSTTEST EXPECTED AC- MEAN 3.4 3.9 2.5 3.6 3.45 3.5 2.4 3.0 CDMPLISRMENTS 5.0. 0.73 0.29 1.08 0.51 0.81 0.67 0.69 0.47 (QUESTION 1) N 42 42 10 10 40 40 10 10 EXPECTED AC- MEAN 3.0 3.2 2.8 3.7 2.8 2.8 2.5 2.7 COMPLISIIMENTS 5.0. 0.92 0.89 0.78 0.48 0.81 0.94 0.84 0.48 (QUESTION 2) N 42 42 10 10 40 40 10 10 EXPECTED AC- MEAN 2.47 3.0 2.9 3.7 2.4 2.4 2.7 2.8 CDMPLISRMENTS 5.0. 1.17 0.96 0.56 0.48 0.98 1.17 0.67 0.42 (oursrroN 3) N 42 42 10 10 40 40 10 10 EXPECTED AC- MEAN 3.45 3.8 2.8 3.6 3.27 3.5 2.7 2.8 CDMPLISIIMERTS 5.0. 0.77 0.48 0.63 0.51 0.84 0.87 0.67 0.42 (QUESTION (4) N 42 42 10 10 40 40 10 10 EXPECTED AC- MEAN 2.59 2.6 2.8 3.3 2.37 2.2 2.6 2.5 COMPLISRMENTS 5.0. 1.10 1.20 0.63 0.67 1.31 1.06 0.51 0.52 (QUESTION 5) N 42 42 10 10 40 40 10 10 EXPECTED AC- MEAN 2.54 2.6 2.1 3.3 2.67 2.0 1.8 2.1 COMPLISIIMENTS 5.0. 1.19 1.22 0.73 0.48 1.18 1.14 0.78 0.73 (oussrroN 6) N 42 42 10 10 40 40 10 10 EXPECTED AC- MEAN 3.26 3.7 2.4 3.5 3.12 3.3 2.1 2.5 COMPLISIIIIENTS 5.0. 0.82 0.55 0.69 0.52 0.96 0.83 0.73 0.52 (QUESTION 7) N 42 42 10 10 40 40 10 10 EXPECTED AC- MEAN 2.1 3.1 2.7 3.6 2.3 2.3 2.2 2.8 CDMPLrSllMEuTS 5.0. 0.91 0.83 0.48 0.51 1.06 1.12 0.63 0.78 (QUESTION 8) N 42 42 10 10 40 40 10 10 EXPECTED AC- MEAN 3.1 3.5 2.4 3.7 2.97 2.9 2.3 2.9 COMPLISIIMENTS 5.0. 0.87 0.74 0.51 0.48 0.97 0.82 0.48 0.56 (QUESTION 9) N 42 42 10 10 40 40 10 10 ’1 103 Research hypothesis number 1 states that at-risk students who have received larger doses of specialized interventions will show significantly greater progress on the CAT. tests (reading and mathematics) and earned credit hours than a group of comparable students who have received fewer specialized interventions. As can be seen in Table 3, and figure 1 the pretest results indicate that the mean differences between groups were not large and the statistical tests revealed that there were no significant pretest differences at the 9th grade (t(106)=1.13, p>.05) and the 10th grade level (t(18)=—0.70, p>.05). Posttest results, however, indicate that the mean differences were large and that the statistical tests were significant for the experimental group at the 9th grade (t(106)=5.66, p<.01) and the 10th grade level (t(18)=2.11, p<.04). These differences indicate that the students who received more specialized interventions performed better on CAT reading than the students who received fewer specialized interventions. CA T Reading 9TH GRADE IOTH GRADE SCORES Exper inental Control Exper inentel Contra I - Pretest 91 - Posttest 92 Figure I . Mean CAT reading scores as a function of condition and grade. 104 The pretest results of the CAT. mathematics test (Table 3 and Figure 2) indicate that the mean differences between groups were not large and the statistical tests revealed that there were no significant pretest differences at the 9th grade (t(106)=-0.45, p>.05) and the 10th grade level (t(18)=0.08, p>.05). Posttest results, however, indicate that the mean differences were large and that the statistical tests were significant for the experimental group at the 9th grade (t(106)=5.33, p<.01) and the 10th grade level (t(18)=2.52, p<.02). These differences indicate that students who received more specialized interventions performed better on CAT mathematics than students who received fewer specialized interventions. CA T Mathematics 9TH GRADE I 0TH GRADE SCORES - Pretest 91 III] Posttest 92 Figure 2. Mean CAT mathematics scores as a function of condition and grade. The pretest results of the variable "earned credit hours" (Table 3 and Figure 3) indicates that the mean differences between groups were not large and the statistical tests revealed that there were no significant pretest differences between groups at 105 the 9th grade (t(106)=-0.21, p>.05) and the 10th grade level (t(18)=0.50, p>.05). Posttest results, however, indicate that the mean differences were large and that the statistical tests were significant for the experimental group at the 9th grade (t(106)=5.88, p<.01) and the 10th grade level (t(18)=2.99, p<.01). These differences indicate that the students who received more specialized interventions earned more credit hours than students who received fewer specialized interventions. Statistical analysis on the CAT reading and mathematics tests and earned credit hours therefore indicated that the data are consistent with research hypothesis number 1. EARNED CREDIT HOURS 9TH GRADE I 0TH GRADE ‘0 38.4 37.5 CREDIT K1116 Exper inentsl Control Expcr inentnl Control - Pretest. 91 - Posttest 92 Figure 3. Mean Earned Credit Hours as a function of condition and grade. Research hypothesis number 2 states that at—risk students who have received larger doses of specialized interventions will show significantly greater improvement in their average daily attendance and a reduction in the incidence of disciplinary actions than at-risk students who have received fewer specialized interventions. As 106 can be seen in Table 3, and Figure 4, the pretest results on average daily attendance indicate that the mean differences between groups were not large and the statistical tests revealed that there were no significant pretest differences between groups at the 9th grade (t(106)=0.05, p>.05) and the 10th grade level (t(18)=-0.60, p>.05). Posttest results, however, indicate that the mean differences were large and that the statistical tests were significant for the experimental group at the 9th grade (t(106)=-6.18, p<.01) and the 10th grade level (t(18)=-4.89, p<.01). These differences indicate that students who received more specialized interventions achieved better average daily attendance than students who received fewer specialized interventions. Statistical analysis on average daily attendance therefore indicated that the data are consistent with research hypothesis number 2. A TTENDAN CE 9TH GRADE I 0TH GRADE RBSDKZB - Pretest ‘31 III] Posttest 92 Figure 4. Mean Attendance as a function of condition and grade. The pretest results of the variable "suspensions" (Table 3 and Figure 5) indicate that the mean differences between groups were not large and the statistical tests revealed that there were no significant pretest differences between groups at 107 the 9th grade (t(106)=0.55, p>.05) and the 10th grade level (t(18)=0.60, p>.05). Posttest results, however, indicate that the mean differences were large and that the statistical test were significant for the experimental group at the 9th grade (t(106)=- 2.75, p<.01) and the 10th grade level (t(18)=-4.88, p<.01). These differences indicate that the students who received more specialized interventions were suspended less frequently than students who received fewer specialized interventions. Statistical analysis on student suspensions, therefore, indicated that the data are consistent with research hypothesis number 2. CODE WOLA TI ONS 9TH GRADE I 0TH GRADE Lo SUSPENS IIIB - , . Ix ' .2 Control Exper Inento I Control - Pretest. ‘31 [III Posttest 92 Exper incnto I Figure 5. Mean Suspensions as a function of condition and grade. Research hypothesis number 3 states that at-risk students who received larger doses of specialized intervention processes will have a more positive attitude toward school than at-risk students who have received fewer specialized intervention processes. As can be seen in Table 3, and Figure 6, the pretest results on SAM Scale A, (Motivation for Schooling) indicate that the mean differences 108 between groups were not large and the statistical tests revealed that there were no significant pretest differences in mean scores between groups at the 9th grade (t(80)=1.43, p>.05) and the 10th grade level (t(18)=-0.81, p>.05). Posttest results, however, indicate that the mean differences were large and that the statistical tests were significant for the experimental group at the 9th grade (t(80)=3.44, p<.01) and the 10th grade level (t(18)=2.19, p<.04). These differences indicate that students who received more specialized interventions scored higher on the SAM Scale A than students who received fewer specialized interventions. Statistical analysis on SAM Scale A, therefore, indicated that the data area consistent with research hypothesis number 3. S TUDEN TA TTITUDE MEASURE 9TH GRADE I 0TH GRADE 3011.129 - Pretest 91 [ml Posttest 92 Figure 6. Mean SAM Scale A as a function of condition and grade. Student Attitude Measure, Scale B measures Academic Self-Concept (Performance Based). The pretest results at the 9th grade level for SAM Scale B (Table 3 and Figure 7) indicated that the mean differences between groups were 109 large and that the statistical test was significant for the experimental group (t(80)=3.09, p<.02). However, at the 10th grade level, the pretest results indicated that the mean differences were not large and the statistical test revealed that there were no significant differences between groups (t(18)=2.04, p>.05). Posttest results however, indicated that the mean differences were large and that the statistical tests were significant for the experimental group at the 9th grade (t(80)=5.11, p<.02) and the 10th grade level (t(18)=3.12, p<.05). Because there were significant pretest differences for the SAM Scale B data for 9th graders, an analysis of covariance was performed in which the pretest score served as a covariate, and the effect of the treatment on the posttest scores was assessed. The results of this analysis indicates that the treatment still had a statistically significant impact on the posttest scores when controlling for the effect of the pretest (F(1,79)=76.75, p<.01). These differences therefore indicate that the 9th and 10th grade students who received more specialized interventions scored higher on the SAM Scale B than the 9th and 10th grade students who received fewer specialized interventions. Statistical analysis on SAM Scale B, therefore, indicated that the data are consistent with research hypothesis number 3. 110 S TUDEN TATT I TUDE MEASURE 9TH GRADE I 0TH GRADE SCALEB - Pretest 91 ml Posttest 92 Figure 7. Mean SAM Scale B as a function of condition and grade. Student Attitude Measure, Scale C measures Academic Self-Concept (Reference Based). The pretest results at the 9th grade level for SAM Scale C (Table 3 and Figure 8) indicated that the mean differences between groups were large and that the statistical test was significant for the experimental group (t(80)=2.63, p<.01). However, at the 10th grade level, the pretest results indicated that the mean differences were not large and the statistical test revealed that there were no significant differences between groups (t(18)=0.25, p>.05). Posttest results at the 9th grade level indicated that the mean differences between groups were large and that the statistical tests were significant for the experimental group (t(80)=5.26, p<.01). However, the posttest results at the 10th grade level indicated that the mean differences between groups were not large and that the statistical test was not significant for the experimental group (t(18)=1.02,p > .05). Because there were significant pretest differences for the SAM Scale C data for 9th graders and analysis of covariance was performed in which the pretest score 111 served as a covariate, and the effect of the treatment on the posttest score was assessed. The results of this analysis indicates that the treatment still had a statistically significant impact on the pretest scores when controlling for the effect of the treatment (F(1,79)=139.42, p<.01). These differences therefore indicate that the 9th grade students who received more specialized interventions scored higher on the SAM Scale C than the 9th grade students who received fewer specialized interventions. The 10th grade students however who received more specialized intervention scored no better than the 10th graders who received fewer specialized interventions. Statistical analysis on SAM Scale C, therefore, indicate that the data are consistent with research hypothesis number 3 for 9th grade students only. S TUDEN TATTI TUDE MEASURE 9TH GRADE I 0TH GRADE SCALE C , :. .. tull, _ Exper Inentn I Control Expcr inentol Control - Pretest 91 IE] Posttest 92 Figure 8. Mean SAM Scale C as a function of condition and grade. Student Attitude Measure, Scale D measures Student’s Sense of Control Over Performance. The pretest results of SAM Scale D (Table 3 and Figure 9) indicated that the mean differences between groups were not large and the statistical tests revealed that there were no significant pretest differences between 112 groups at the 9th grade (t(80)=-0.14, p>.05) and the 10th grade level (t(18)=-0.15, p>.05). The posttest results also revealed that the mean differences between groups were not large and that the statistical tests were not significant between groups at the 9th grade (t(80)=1.96, p>.05) and the 10th grade level (t(18)=0.93, p>.05). These statistical test therefore indicate that the students who received more specialized interventions scored no better on SAM Scale D than the students who received fewer specialized interventions. Statistical analysis on SAM Scale D, therefore, indicated that the data are consistent with research hypothesis number 3. S TUDEN TATTI TUDE MEASURE 9TH GRADE I 0TH GRADE SCALZD - Pretest 91 III] Posttest 92 Figure 9. Mean SAM Scale D as a function ofcondition and grade. Student Attitude Measure, Scale E measures Student Instructional Mastery. The pretest results at the 9th grade level for SAM Scale C (Table 3 and Figure 10) indicated that the mean differences between groups were large and that the statistical tests were significant for the experimental group (t(80)=2.77, p<.01). However, at the 10th grade level, the pretest results indicated that the mean differences were not large and the statistical test revealed that there were no significant differences between groups (t(18)=1.51, p>.05). Posttest results, 113 however, indicated that the mean differences were large and the statistical tests were significant for the experimental group at the 9th grade (t(80)=4.67, p<.01) and the 10th grade level (t(18)=2.5, p<.01). Because there were significant pretest differences for the SAM Scale E data for 9th graders, an analysis of covariance was performed in which the pretest score served as a covariate, and the effect of the treatment on the posttest score was assessed. The results of this analysis indicated that the treatment still had a statistically significant impact on the posttest scores when controlling for the effect of the pretest (F(1,78)=206.20, p<.01). These differences therefore indicate that the students who received more specialized interventions scored higher on the SAM Scale E compared to the students who received fewer specialized interventions.The statistical tests therefore indicated that the data are consistent with research hypothesis number 3. S TUDEN TA TTITUDE MEASURE 9TH GRADE I 0TH GRADE SCALE E . i . , _ i : , n Experimental Control Exper luental Control - Pretest 91 ml Posttest 92 Figure I 0. Mean SAM Scale E as a function of condition and grade. 114 Research hypothesis number 4 states that at-risk students who received larger doses of specialized intervention processes will have a more positive attitude toward what they expect to accomplish within the next 5 to 10 years when compared to students who have received fewer specialized interventions. As can be seen, in Table 3 and Figure 11, the pretest results for statement number 1 (I am likely to graduate from high school) indicate that the mean differences between groups were not large and the statistical tests revealed that there were no significant pretest differences between groups at the 9th grade (t(80)=—0.12, p>.05) and the 10th grade level (t(18)=0.24, p>.05). However, the posttest results indicate that the mean differences were large and the statistical tests were significant for the experimental group at the 9th grade (t(80)=3.09, p<.01) and the 10th grade level (t(18)=2.71, p<.01). These differences indicate that the students who received more specialized interventions scored higher on statement number 1 of the student survey compared to students who received fewer specialized interventions. Statistical analysis on statement number 1, therefore, indicated that the data are consistent with research hypothesis number 4. 115 EXPECTED A C COMPLIS HMEN TS 9TH GRADE 10TH GRADE srnrmr 1 Figure 11. Mean Expected Accomplishment, Statement 1 as a function of condition and grade. Statement number 2 on the student survey states "I am likely to graduate from college." The pretest results of statement number 2 (Table 3 and Figure 12) indicate that the mean differences between groups were not large and the statistical tests revealed that there were no significant pretest differences between groups at the 9th grade (t(80)=1.03, p>.05) and the 10th grade level (t(18)=0.81, p>.05). However, the posttest results indicate that the mean differences were large and the statistical tests were significant for the experimental group at the 9th grade (t(80)=2.14, p<.03) and the 10th grade level (t(18)=4.62, p<.02). These differences indicate that the students who received more specialized interventions scored higher on statement number 2 of the student survey compared to students who received fewer specialized interventions. Statistical analysis on statement number 2, therefore, indicated that the data are consistent with research hypothesis number 4. 116 EXPECTED A C COMPLISHMEN T S 97?! GRADE 10TH GRADE S‘I'fi'l'm'l' Z Experimental Control Exper lncntal Control - Pretest 51 El] Posttest 92 Figure 12. Mean Expected Accomplishments, Statement 2 as a function of condition and grade. Statement number 3 on the student survey states "I am likely to receive a second (college) degree." The pretest results of statement number 3 (Table 3 and Figure 13) indicate that the mean differences between groups were not large and the statistical tests revealed that there were no significant pretest differences between groups at the 9th grade (t(80)=0.31, p>.05) and the 10th grade level (t(18)=0.71, p>.05). However, the posttest results indicate that the mean differences were large and the statistical tests were significant for the experimental group at the 9th grade (t(80)= 2.53, p<.01) and the 10th grade level (t(18)=4.43, p<.01). These differences indicate that the students who received more specialized interventions scored higher on statement number 3 of the student survey compared to students who received fewer specialized interventions. Statistical analysis on statement number 3, therefore, indicated that the data are consistent with research hypothesis number 4. 117 EXPECTED ACCOMPLISHMENTS 9TH GRADE 10TH GRADE 819m! 3 ! . Experlocntal Control Exper mental Control Figure 13. Mean Expected Accomplishments, Statement 3 as a function of condition and grade. Statement number 4 on the student survey states "I am likely to be able to get a job afler I finish my schooling." The pretest results of statement number 4 (Table 3 and Figure 14) indicate that the mean differences between groups were not large and the statistical tests revealed that there were no significant pretest differences between groups at the 9th grade (t(80)=0.99, p>.05) and the 10th grade level (t(18)=0.34, p>.05). Also the posttest results indicate that the mean differences between groups were not large and the statistical tests were not significant at the 9th grade level (t(80)=1.97, p>.05). However, at the 10th grade level the results indicate that the mean differences between groups were large and that the statistical tests were significant (t(18)=3.79, p<.01). These differences therefore indicate that the 9th grade students who received more specialized interventions scored no better on statement number 4 than the 9th grade students who received fewer specialized interventions. The 10th grade students however, who received more specialized interventions scored higher on statement number 4 than the students who received fewer specialized interventions. Statistical analysis on statement number 4, 118 therefore, indicated that the data are consistent with research hypothesis number 4 at the 10th grade only. EXPECTED ACCOMPLISHMENTS 9TH GRADE 10TH GRADE srnrmr 1 Control Experlwnul Contra - Pretest 91 ml Posttest 92 Figure 14. Mean Expected Accomplishments, Statement 4 as a function of condition and grade. Statement number 5 on the student survey states "I am likely to get married after I finish my schooling." The pretest results of statement number 5 (Table 3 and Figure 15) indicate that the mean differences between groups were not large and the statistical tests revealed that there were no significant pretest differences between groups at the 9th grade (t(80)=0.82, p>.05) and the 10th grade level (t(18)=0.77, p>.05). Also, the posttest results at the 9th grade level indicate that the mean differences between groups were not large and the statistical tests were not significant (t(80)=1.46, p>.05). However, at the 10th grade level the results indicate that the mean differences between groups were large and that the statistical tests were significant for the experimental group (t(18)=2.95, p<.01). These differences therefore, indicate that the 9th grade students who received more specialized interventions score no better on statement number 4 than the 9th grade 119 students who received fewer specialized interventions. The 10th grade students, however, who received more specialized interventions scored higher on statement number 5, than the students who received fewer specialized interventions. Statistical analysis on statement number 5, therefore, indicated that the data are consistent with research hypothesis number 4 at the 10th grade level. EXPECTED ACCOMPLISHMENTS 9TH GRADE I 0TH GRADE smmnm 5 - Pretest 91 ml Posttest 92 Figure 15. Mean Expected Accomplishments, Statement 5 as a function of condition and grade. Statement number 6 on the student survey states "I am likely to start a family after finishing my schooling." The pretest results of statement number 6 (Table 3 and Figure 16) indicated that the mean differences between groups were not large and the statistical tests revealed that there were no significant differences between groups at the 9th grade (t(80) = -0.48, p>.05) and the 10th grade level (t(18)=0.87, p>.05). However, the posttest results indicate that the mean differences were large and that the statistical tests were significant for the experimental group at the 9th grade (t(80)=2.16, p<.03) and the 10th grade level (t(18)=4.30, p<.01). These differences indicate that the students who received more specialized interventions 120 scored higher on statement number 6 of the student survey compared to students who received fewer specialized interventions. Statistical analysis on statement number 6, therefore indicate that the data are consistent with research hypothesis number 4. EXPECTED ACCOMPLISHMENTS 9TH GRADE 10TH GRADE SMWT 6 Figure 16. Mean Expected Accomplishments, Statement 6 as a function of condition and grade. Statement number 7 on the student survey states "I am likely to purchase a nice home." The pretest results of statement number 7 (Table 3 and Figure 17) indicated that the mean differences between groups were not large and the statistical tests revealed that there were no significant differences between groups at the 9th grade (t(80)=0.69, p>.05) and the 10th grade level (t(18)=0.93, p>.05). However, the posttest results indicate that the mean differences were large and that the statistical tests were significant for the experiment group at the 9th grade (t(80)=2.34, p<.02) and the 10th grade level (t(18)=4.24, p<.01). These differences indicate that the students who received more specialized interventions scored higher on statement number 7 on the student survey compared to students 121 who received fewer specialized interventions. Statistical analysis on statement number 7, therefore, indicated that the data are consistent with research hypothesis number 4. EXPECTED A C C OMPLIS HMEN TS 9TH GRADE 10TH GRADE srnrmmr 7 - Premt 91 En Posttest 92 Figure 1 7. Mean Expected Accomplishments, Statement 7 as a function of condition and grade. Statement number 8 on the student survey states "I am likely to own my own business." The pretest results of statement number 8 (Table 3 and Figure 18) indicate that the mean differences between groups were not large and the statistical tests revealed that there were no significant differences at the 9th grade (t(80)=- 0.49, p>.05) and the 10th grade level (t(18)=1.98, p>.05). However, the posttest results indicate that the mean differences were large and the statistical tests were significant for the experimental group at the 9th grade (t(80)=3.74, p<.01) and the 10th grade level (t(18)=2.68, p<.01). These differences indicate that the students who received more specialized interventions scored higher on statement number 8 on the student survey compared to students who received fewer specialized 122 interventions. Statistical analysis on statement number 8, therefore, indicated that the data are consistent with research hypothesis number 4. EXPECTED ACCOMPLISHMENTS 9TH GRADE 10TH GRADE srnrmr 8 - Pretest 91 ml Posttest 92 Figure 18. Mean Expected Accomplishments, Statement 8 as a function of condition and grade. Statement number 9 on the student survey states "I am likely to be better 017‘ financially than my parents." The pretest results of statement number 9 (Table 3 and Figure 19) indicated that the mean differences between groups were not large and the statistical tests revealed that there were no significant differences at the 9th grade (t(80)=0.82, p>.05) and the 10th grade level (t(18)=0.44, p>.05). However, the posttest results indicate that the mean differences were large and the statistical tests were significant for the experimental group at the 9th grade (t(80)=3.45, p<.01) and the 10th grade level (t(18)=3.39, p<.01). These differences indicate that the students who received more specialized interventions scored higher on statement number 9 on the student survey compared to students who received fewer specialized interventions. Statistical analysis on statement number 9, therefore, indicated that the data are consistent with research hypothesis number 4. 123 EXPECTED ACCOMPLISHMENTS 9TH GRADE 10TH GRADE smrmmr 9 Exper luental Control Expcr lnental Contra - Pretest 91 En Posttest 92 Figure I 9. Mean Expected Accomplishments, Statement 9 as afunction of condition and grade. In summary, the results of the student survey indicated that the data are consistent with research hypothesis number 4 at the 9th and 10th grade level except for statements 4 and 5. On statements 4 and 5, the posttest statistical analysis at the 9th grade level were not significant. Chapter 5 SUMMARY Schools are bureaucracies because they practice bureaucratic methodologies (i.e. specialization, rules and regulations, hierarchy of authority, and knowledge or technical skills). Bureaucratic methodologies are practiced because they provide not only stability and rational environments, but they specify behaviors as a means to achieve certain outcomes. Schools also practice bureaucratic methodologies because, as Weber (1947) asserts, bureaucracy is the most effective and efficient mechanism for managing large scale affairs such as public education. Schools must service all students regardless of academic ability, social background and economic status. Schools service students, particularly poor and at- risk students, through bureaucratic specialization. Specialization has some proponents and critics. According to the proponents (Tropea, 1987; Whitaker, 1991; and Nagler, 1991) poor and at-risk students exhibit idiosyncratic behaviors (i.e. poor academic achievement, behavior, and attitude toward school) that can be managed and treated through specializing, or more specifically, through specialized interventions. Specialized interventions, it was argued, sorts students by academic ability, provides a differentiated curricula, and provide remediation for the problematic behaviors exhibited by poor and at-risk students. Specialized interventions also assist schools to maintain order and control by emphasizing rules and regulations; employing sorting techniques which isolate students from one another; and 124 125 providing differentiated curricula, which serves to isolate troublesome students away from the mainstream. But more importantly, since specialized interventions provide remediation, proponents assert that specialized interventions help poor and at-risk students. Critics of bureaucratic specialization (Katz; 1975; Willis, 1977; Oakes, 1985; Calabrese, 1988; and Page 1991), however, argued that although specialization is bureaucratically feasible, it may induce unintended effects or negative consequences on poor and at-risk students. They assert that sorting and differentiating is an artificial process; that it is done primarily according to social class-not ability. Additionally, critics indicate that sorting and differentiating not only separates poor and at-risk students from affluent students, but such processes perpetuate an unequal social system. Critics have also argued that the bureaucratic emphasis on control may have a negative effect on academic achievement. Control may be achieved through the enforcement of rules, regulations, policies and procedures, but they may also be enforced inconspicuously through instructional strategies that place minimal emphasis on educating students, while heightening those practices that serve to control students. Control strategies are emphasized because bureaucratic procedures are characterized primarily by specialization. In essence, it was reasoned that the students who received the greater share of specialized services will be those who have received the most bureaucracy. Additionally, critics asserted that specialized interventions, although well intentioned, were not designed to narrow the learning and achievement gaps of poor and at-risk students and the affluent and more academic students. Critics 126 noted (Rist, 1977; Levin, 1987; Miller, Leinhardt and Zigmond, 1988; and Hampton, 1991) that specialized interventions were designed to be slower paced because it was assumed that poor and at-risk students were not interested in academic pursuits. Specialized interventions such as remedial reading and mathematics, special education and bilingual education served as a reservoir for placing poor and at-risk students. These placements served not only to control poor and at-risk students but they also denied them the same educational opportunities (accelerated programs and advanced placement courses) afforded to the mainstream. Thus, critics concluded that poor and at-risk students may develop a negative perception toward schooling. To shed some light on this argument, that is, whether or not more specialized interventions are helpful or harmful, this study compared a group of at-risk students who received more specialized interventions, with a group of at-risk students who received fewer specialized interventions. One group of students were 9th and 10th grade at-risk male students from Fredrick Douglas Academy, a specialized intervention program in the Detroit Public Schools. These students were selected because they exhibited troublesome behaviors (i.e. poor academic achievements, poor attendance, poor behavior, and poor attitude toward school) and received more specialized interventions. A comparable group (i.e. age, gender, grade level, achievement and attitude toward school) of students from Mackenzie, Henry Ford, and Northwestern High Schools were selected because they too exhibited troublesome behaviors. These students; however, received fewer specialized interventions. The purpose of this study was to examine the effects of specialized interventions on at-risk students who have received the greatest share of these 127 interventions. Also, since some at-risk students received comparatively more specialized interventions than others, this study attempted to determine the perceived reactions of these students to specialized interventions. Prior to conducting this investigation, the researcher hypothesized that the experimental group (F.D.A subjects) would demonstrate greater progress or improvement on the CAT. reading and mathematics test, earned credit hours, attendance, suspension rates, attitude toward school and attitude toward expected accomplishments, compared to the control group (comprehensive high school subjects) during the 1991-92 school year. However, to conduct this investigation pretest (1990-91) and posttest (1991-92) data were collected on all variables for statistical tests. When statistical tests were performed for 10th grade subjects, on all variables for the 1990-91 (pretest) school year, the results indicated that there were no significant differences between the experimental and control group. However, the results of the statistical test performed on all variables for 10th grade subjects during the 1991-92 (posttest) school year indicated that the data were consistent with research hypotheses 1-4 except on SAM Scale C. Stated another way, the 10th grade subjects who received more specialized interventions exhibited significantly greater improvement on most of the variables during the 1991-92 school year compared to the 10th grade subjects who received fewer specialized interventions. The results of SAM Scale C may indicate a Type I error. A Type I error suggests that there is the probability that null (research) hypothesis number 3 was rejected on the SAM Scale C variable when in fact it should have been accepted. The research hypothesis states that—risk students who received more specialized 128 interventions will have a more positive attitude toward school than at-risk students who received fewer specialized interventions. Another possrble explanation for the Type I error may be that the pretest data on the Student Attitude Measure and the Student Survey on Expected Accomplishments were collected on different subjects. The reader will recall that there was no existing pretest data on the SAM and the Student Survey On Expected Accomplishments for the experiment and control group. In order to examine the pretest and posttest effects of specialized interventions, the researcher administered both survey instruments to a group of at-risk male students who were being referred to F.D.A. for the 1994-95 school year. The survey instruments were also administered to a group of at—risk male students who would remain at their neighborhood high schools to receive specialized interventions for the 1994-95 school year. Thus, it is possrble that the pretest data may not have been a true or accurate representation of the experimental and control groups attitudes toward school and expected accomplishments. Pretest results for the 9th grade subjects varied on some variables. For example, CAT reading and mathematics, SAM Scales A and D, and all statements on the Student Survey on Expected Accomplishments indicated no significant differences between the experimental and control group for the 1990-91 school year. However, significant pretest differences were found at the 9th grade level for the experimental group on SAM Scale B, C and E. These differences might be explained by the effect of more specialized interventions received during the 1990- 91 school year (See table 1 and 2) and other factors not examined in this study. 129 When statistical tests were performed on all variables for the 1991-92 (posttest) school year, for 9th grade subjects, most of the results were consistent with research hypothesis 1-4. Stated another way, the 9th grade subjects who received more specialized interventions demonstrated greater improvement on all variables except SAM Scale D (Student’s Instructional Mastery), Expected Accomplishments statement 4 (I am likely to be able to get a job after I finish my schooling) and statement 5 (I am likely to get married after I finish my schooling) compared to the 9th grade subjects who received fewer specialized interventions. Since there were no significant differences between groups on SAM Scale D, and Excepted Accomplishments statements 4 and 5, these results may indicate that a Type I error occurred. As indicated above, a Type I error suggests that there is the probability that the null (research) hypothesis was rejected on these variables when in fact they should have been accepted. Thus, the results, indicate that the 9th grade subjects who received more specialized interventions performed no better on those three variables than the 9th grade subjects who received fewer specialized interventions. Conclusions This study was guided by two bureaucratic arguments relative to specialization. One argument suggested that specialization helps to remediate the troublesome behaviors exhibited by poor and at-risk students (i.e. poor achievement, poor behavior, poor attitude toward school and poor attitude toward expected accomplishments). Specialization also assist schools to maintain order and control in schools through the use of rules and regulations. 130 The opposing argument; however, asserts that specialization may induce unintended effects or negative consequences on poor and at-risk students. The negative effects induced by specialization include, rejecting authority and school, dropping out of school, recidivism, poor achievement, poor behavior and the development of poor attitudes toward school. Although critics assert that specialization is harmful to students, this study provides evidence that further specialization does not harm students. The analysis of data indicated that the 10th grade subjects who received more specialized interventions during the 1991-92 school year demonstrated significantly greater improvement on academic achievement, behavior, attendance, attitude toward school, and attitude toward expected accomplishments compared to the 10th grade subjects who received fewer specialized interventions except on SAM Scale C. Thus the researcher concluded that specialization had a positive effect on the 10th grade experimental subjects in this study. Similar results were found when reviewing the analysis of data for the 9th grade experimental subjects, except on SAM Scale D, and statements 4 and 5 of the Student Survey On Expected Accomplishments. The analysis of data indicated that the 9th grade subjects who received more specialized interventions demonstrated significantly greater improvement on most of the variables compared to the 9th grade subjects who received fewer specialized interventions. Therefore, the researcher concluded that the specialized interventions received by the 9th grade subjects in the experimental group had a positive effect on student achievement, behavior, attitude toward school and expected accomplishments for the 1991-92 school year. 131 In the final analysis, this study indicates that the 9th and 10th grade experimental subjects who received more specialized interventions demonstrated significantly greater improvement on most of the variables studied. These results also indicate that more specialization, not less specialization, is beneficial to poor and at-risk students. The advantages of specializing thus outweighs the disadvantages, at least for the subjects in this study. Given these findings, this study lends support to the proponents argument that bureaucratic specialization is helpful to poor and at-risk students. What this investigation suggests is that the opponents of specialization or anti-bureaucratic researchers (e.g. Willis, 1977; Apple, 1983; McNeil, 1986; Page, 1991; and Cusick, 1973) may not have been wrong in their criticisms about specialization, they simply focused their research on field studies. Field studies limit themselves to small samples and they provide functional explanations; that is, they draw relations or associations between student achievement and the bureaucratic structure of the organization. These associations; however, are usually negative explanations about the organizations. This study adds a new perspective to the argument surrounding specialization, because the researcher used a quasi-experimental design to investigate at-risk male students who reside in a large urban community. Further, not only was this investigation conducted in a large urban setting, but the sample size (128 students) and the number of schools (Fredrick Douglass Academy, Mackenzie, Henry Ford and Northwestern High Schools) included in this research were sufficiently larger than those reported in the field studies. 132 Anti-bureaucratic researchers also limited their research by examining bureaucratic specialization as a single entity. Such research did not explore the qualitative differences among specialized intervention programs like Fredrick Douglass Academy. For example, F.D.A. provided a lot of pre-counseling to students and their families prior to enrolling students at F.D.A. Pre-counseling thus allows the students and their families an opportunity to gain some insights into the school’s goals, objectives and expectations for both students and parents. Another qualitative difference is that the F.D.A. staff was personally selected by the principal, through and interview process. Staff members were interviewed and requested to work at F.D.A. because they had a desire to work with at-risk students and they possessed some skills and expertise in working with this population. Further, the staff not only had the expertise to educate these students, but they established high expectations for student academic achievement. Thus, students were exposed to honors and advanced placement programs, and a multitude of career opportunities. While this study has demonstrated that specialized interventions had a positive effect on student achievement, behavior, and attitude toward school, other factor deserve credit because they too have enhanced the effectiveness of this intervention program. These factors include greater parent involvement in the school, a more committed staff, and high student expectations by the F.D.A. staff. Together, qualitative differences and human factors have encouraged the poor and at-risk students to view schooling more positively. In summary, this study suggests that if poor and at-risk students are treated as educational discards; that is, by placing them in specialized intervention programs 133 that inadequately address the concerns of the opponents, such interventions will inevitably harm students. These students will then respond by rejecting authority, dropping out of school, demonstrating poor academic achievement, and developing poor attitudes regarding school. However, if poor and at-risk students are placed in a supportive environment; that is, an environment that address their academic and social needs, specialized intervention programs will enhance student achievement, behavior, attitude toward school, and attitude toward expected accomplishments. Program Recommendations In the previous section, the researcher concluded that not only was bureaucratic specialization helpful to the subjects in this investigation, but that the advantages of specializing with these students outweighs the disadvantages. Since these results support bureaucratic specialization, five recommendations are provided for consideration and implementation. First since the specialized intervention program at F.D.A. has manifested many successes, this intervention model should be expanded (i.e. physical space) in order to accommodate a greater population of at-risk male students. In addition, this model should be duplicated in other buildings so that students who reside in neighborhoods that are not in close proximity to F.D.A. will not have to travel long distances to the school campus. As this intervention model is expanded and duplicated, however, small class sizes of 15 to 20 students should be maintained. Small classes will permit a higher level of student-teacher interaction and promote more personalized instructions. 134 Second, and important component of the F.D.A. intervention model is parent involvement. Parent involvement should not only be encouraged but mandated for all parents of F.D.A. students. Perhaps if parents are encouraged or required to participate in their child’s school, they will develop an interest in their child’s education and the school. Research indicates that students excel academically when parents develop an interest in their child’s schooling, participate in school activities, and visit their child’s school frequently. Sartain (1989) for example found that 70 percent of children of interested parents were rated as hard workers, while only 33 percent of disinterested parents’ children received the same rating. Additionally, Dornbusch and Ritter (1988) found that parental attendance at school events were associated with higher academic achievement. Third, the researcher recommends that the Detroit Public Schools identify at-risk male students at the K-5 grade level and implement comparable F.D.A. intervention models to ensure their future academic success. The reader will recall that the literature review detailed specific characteristics of at-risk students and noted that those characteristics were observable in students as early as the 3rd grade. Early identification and intervention through an F.D.A. model may assist in reducing the achievement gap between at-risk and mainstream students. Early intervention may also have a positive impact on student behavior, attitude toward school and disengagement from school by identifying and treating the characteristics that negatively influence students. Fourth, this research investigated the effects of specialized interventions and the perceptions of those intervention processes on at-risk male students in an urban setting. However, this research did not address the teachers and parents perceptions of this intervention process and its effect on academic achievement, 135 behavior, attitude toward school, and attitude toward expected accomplishments. Further research should address those perceptions to determine whether or not there is a positive effect on the variables cited in this study. Fifth, although there was some evidence of accelerated programs at F.D.A. more attention should be given to accelerated programs and cooperative learning. There is some research that suggests that cooperative learning (mixed ability groups) and exposure to accelerated programs have made a positive impact on at- risk students motivation and achievement. Cuban (1989), LeTendre (1991), Murphy (1989) and others noted that when at-risk students are placed in accelerated programs and heterogeneous ability groups they: learn at a faster rate; make significant achievement gains; increase their academic self confidence, improve their attitude toward school and; increase their prosocial interactions. In the final analysis, at-risk student benefit when accelerated programs and cooperative learning is provided. These students, according to some research, must accelerate their learning pace in order to close the achievement gap that exist between themselves and mainstream students. 136 REFERENCES Apple, M. (1983). Curriculum reform and the logic of technical control. In M. Apple and L. Weis (Eds.) Ideology and Practice in Schooling. Philadelphia: Temple University Press. 143-166 Calabrese, R. L. (1988, March). Schooling, alienation, and minority dropouts. The Clearing House. Washington, DC: the Helen Dwight Reid Educational Foundation. 325-28 Cuban, L. (1989, June). The "at-risk" label and the problem of urban school reform. Phi Delta Kappan. 70, 780-801. Cusick, RA. (1973). Inside high school: The student’s world. New York: Holt, Rinehart and Winston. Dornbusch, S. M. and Ritter, P. L. (1988, May). Parents of high school student’s: A neglected resource. Education Horizons. (Ref. Education Digest, L111 (9) 16- 19). Katz, M. B. (1975). Class bureaucracy and schools: the illusion of educational change in America. New York: Praeger Publishers. Levin, H. M. and Hopfenberg, W. S. (1991, January). Accelerated schools for at-risk students. Principal. 70,11-13. 137 LeTendre, M. J. (1991). Improving chapter 1: making the promise reality. Phi Delta Kappan, 72. 577-80. McNeil, L. M. (1986). Contradictions of control: School structures and school knowledge. New York: Routledge and Kegan Paul. Miller, S., Leinhardt, G., and Zigmond, N. (1988). High school experiences of learning disabled students. American Educational Research Journal Murphy, J. (1989). Educational reform and equity: a reexamination of prevailing thought. a paper presented at the annual meeting of the American educational research association. San Francisco. Nagler, H. F. (1991, April). Detroit approves all-male school. American Federation of School Administrators News. Vol. 17(18) Oakes, J. (1985). Keeping track: How schools structure inequality. New Haven, CT: Yale University Press. Page, R. N. (1991). Lower-track classrooms: A Curricular and cultural perspective. New York: Teachers College, Columbia University. Rist, R. (1977). On understanding the processes of schooling: The contributions of labeling theory. In J. Karabel and A. H. Halsey (Eds.) Power and ideology in education. New York: Oxford University Press. 292-305. Sartain, H. W. (1989). Nonachieving students at-risk: School, family, and community intervention. Washington, DC: National Education Association Professional Library. 138 Tropea, J. L. (1987, Fall). Bureaucratic order and special children: Urban schools, 1950’s - 1960’s. History of Education Quarterly. 339-361. Weber, M. (1947). The theory of social and economic organization. New York: Oxford University Press. 329-330. Reprinted in Robert K. Merton, Alisa P. Gray, Barbara Hockey and Hanan C. Selvin (eds.) Reader in Bureaucracy, Glencoe, Ill: The Free Press, 1952, 18-20. Whitaker, C. (1991, March). Do black males need special schools. Ebony Magazine, Johnson Publication. XLVI(5) 17-22. Willis, P. (1977). Learning to labor, how working class kids get working class jobs. New York: Columbia University Press. 1-226. INFORMED CONSENT FORM The Research, Evaluation and Testing Department of the Detroit Public Schools has granted permission for this research study. The researcher is a doctoral candidate at Michigan State University, who is conducting a research study on 9th and 10th grade at-risk students. The purpose of this study is to examine the effect of specialized services and the perceptions of those services on at-risk students enrolled at the Fredrick Douglas Academy, Mackenzie, Northwestern and Henry Ford High School. Since these students have exhibited poor academic performance, poor attendance and poor behavior, they will be compared to a comparable group of at-risk students enrolled at three (3) comprehensive high schools in Detroit. Data collection for both student groups will consist of CAT. scores, earned credit hours, attendance and discipline records for the 1990-91 and 1991-92 school years. Additionally, two (2) survey instruments will be administered to the participants. One of the surveys will measure student attitudes about school. The other survey will measure what at-risk students realistically expect to accomplish within the next 5-10 years. The two survey instruments that will be used in this study will take approximately one hour and ten minutes to complete. Students are requested to respond to each statement on both surveys by selecting one of four responses provided. However, the participant is under no obligation to respond to statements in which he may have an objection. Further, since participation is strictly voluntary, students may discontinue or withdraw from this study at any time without penalty. Please be advised that no responses will be associated with individual students. Anonymity will be maintained by requesting that respondents place only their grade level and school name at the top of the answer sheets. The results of this study will be made available to the Department of Research, Evaluation and Testing. Students and parents however who wish to receive copies of the results of this research may contact Dr. Doris Hodge at 494-2022. If you approve of your child’s participation in this study please sign this document and return it to the principals office. Parent Signature (Date) Student Name 139 LIST OF REFERENCES REFERENCES Allen, C. M. (1956). Combating the dropout problem: Handbook for teachers, counselors, and administrators in elementary and high schools. Seience Research. Apple, M. (1983). Curriculum reform and the logic of technical control. In_M. Apple and L. Weis (Eds.), Ideology and Practice in Schooling. Philadelphia: Temple University Press. 143-166 Apple, M. (1979). Ideology and curriculum. London: Routledge and Kegan Paul. Backman, J ., et al (1971). Youth in transition, Volume III: Dropping out - problem or symptom? Ann Arbor, Michigan: Institute for Social Research Barr, R., B. ( 1987). An essay on school dropout for the San Diego Unified School District. Sand Diego, California: Planning, Research and Evaluation Beck, L. and Muia, J. A. (1980, November . A portrait of a tragedy: Research findings on the dropout. High School J ourna , 64(2), 65-72. Bowles, S. and Gintis, S. (1976). Schooling in capitalist America: Education and the contradictions of economic life. New York: Basic Books Business-Education Partnership Committee (1988). An evaluation of the education progress from South Carolina ’s educational improvement efl'orts: The fourth annual report on the South Carolina education improvement act. Columbia, SC. BEPC. Callahan, R. (1962). Education and the cult of efliciency. Chicago: University of Chicago Press Calabrese, R. L. (1988, March). Schooling, alienation, and minority dropouts. The Clearing House. Washington, DC: the Helen Dwight Reid Educational Foundation. 325-28 Cuban, L. (1989). The "at-risk" label and the problem of urban school reform. Phi Delta Kappan. June 1989, 70, 780—801. Cusick, RA. (1992). The educational system: It’s nature and logic. New York: McGraw Hill. DeRidder, L. M. (1991, February). How suspensions and expulsions contribute to dropping out. Education Digest. 44-47. Dornbusch, S. M. and Ritter, P. L. (1988, May). Parents of high school students: A naglected resource. Education Horizons. (Ref. Education Digest, L111, 9, 16- 1 . Education Week. (June 15, 1988). 29 140 141 Etzioni, A. (1964). Modern organizations. Englewood Cliffs, New Jersey: Prentice- Hall, Inc. 50- 54. Fernadez, R., R. and Shu, G. (1988). School dropouts New approachestoan enduring problem. Education and Urban Society. 2(2), 363- 86 Flanagan, J. C. (1961). Project talent: A progress report. Pittsburgh, Pa.: American Institute for Research. Giroux, H. A. (1981). Ideology, culture, and the process of schooling. London: The Falmar Press. Good, T. L. and Brophy, J. E. (1991). Teaching heterogeneous classes. New York: Harper-Collins. 382-438. Goodlad, J. (1984). A place called school: Prospects for the fiature. New York: McGraw- Hill. Gouldner, A. W. (1954). Patterns of industrial bureaucracy. The Free Press. Grannis, J. A., Rich], C., Pallas A., Lerer, N. and Randolph, S. (1988). Evaluation of the New York City dropout prevention initiative: Final report on the middle schools for year two, 1986-87. Institute for Urban and Minority Education. New York: Teacher College, Columbia University. Grannis, J. A. and Others (1989). Evaluation of the New York City dropout prevention initiative: Final report on the high schools for year two. Institute for Urban and Minority Education. New York: Columbia University. Gulick, L. and Urwick, L. (1937). Papers on the science of administration Institute of Public Administration. New York: Columbia University. Guthrie, J. W. and Kirst, M. W. (March 1988). Conditions of education in California 1988. Policy Paper No. 88- 3- 2. Berkeley, Calif. Policy Analysis for California Education. Hampton, F. M. (1991). An evaluation of a dropout prevention program for middle school students in an urban setting. Unpublished doctoral dissertation, The University of North Carolina at Greensboro. Hobson V. H., 269F. Supp. 401 DC. (1967). Hollingshead, A., B. (1975). Elmtown’ s youth and elmtown revisited. New York: John Wiley and Sons, Inc. Howell, F., and Frese, W. (1982). Early transition into adult roles: Some antecedents and outcomes. American Educational Research J ournal. 19, 51-73. Johnson, N. B. 1985?. Westhaven: Classroom culture and society in a rural elementary school. C ape Hill: The University of North Carolina Press. 142 Jones, C. et al. (1983). High School and beyond 1980 sophomore cohort first follow-up 1982: Data file user manual. National Opinion Research Center, Contractor Report to the National Center for Education Statistics. Katz, M. B. (1975). Class bureaucracy and schools: The illusion of educational change in America. New York: Praeger Publishers. Lehr, J. B., and Harris, H. W. (1988). At-risk, low-achieving students in theclassroom. Washington, DC: National Education Association Profess1onal Library. LeTendre, M. J. 31991). Improving chapter 1: Making the promise reality. Phi Delta Kappan, 7 , 577-80. Levin, H. M. ( 1987, March). Accelerated schools for disadvantaged students. Educational Leadership. 44, 19-21. Levin, H. M. and Hopfenberg, W. S. (1991, January). Accelerated schools for at-risk students. Principal. 70,11-13. Liddle, G. P. (1967). Education improvement for the disadvantaged in an elementary setting. Springfield, 1]]: C. C. Thomas. Matronga, M. and Mitchell, D. E. (1988). Student dropout problem: Implications for policymakers. Washington, D. C., Office of Educational Research and Improvement. McNeil, L. M. (1986). Contradictions of control: School structure and school knowledge. New York: Routledge and Kegan Paul. McDill, E. L., Natriello, G. & Pallas, A. M. (1985, Winter). Raising standards and retaining students: The impact of the reform recommendations on potential dropouts. Review of Educational Research. 55(4), 415-433 Miller, S., Leinhardt, G., and Zigmond, N. (1988). High school experiences of learning disabled students. American Educational Research Journal Mink, O. G., and Kaplin, B. A. (1970). America’s problem youth: Education and guidance of the disadvantaged. Scranton, Pennsylvania: International Textbook Company. Murphy, J. (1989). Educational reform and equity: A reexamination of prevailing thought. A Paper Presented at the Annual Meeting of the American Educational Research Association. San Francisco. I Nagler, H. F. (1991, April). Detroit approves all-male school. American Federation of School Administrators News. Vol. 17(18) Oakes, J. (1985). Keeping track: How schools structure inequality. New Haven, CT: Yale University Press. Ogden, M. T. and Germinaries, V. (1988). The at-risk student. Lancaster, Pennsylvania: Technomic Publishing Company, Inc. 143 O’Sullivan, R. G. (1990, April). Evaluating a model middle school dropout prevention program for at-risk students. Pa er Presented at the Annual Meeting of the American Educational Researc Association. Boston, MA. 16-20. Page, R. N. (1991). Lower-track classrooms: A Curricular and cultural perspective. New York: Teachers College, Columbia Universny. Perrow, C. (1979). Complex organizations: A critical essay. Glenview, Illinois: Scott, Foresman and Company. Peterson, T. (1988, April). Building, passing, implementing, and assessing educational reform in South Carolina. Paper Presented at the Annual Meeting of the American Educational Research Association. New Orleans. Powell, A., Farrar, E., and Cohen, D. (1985). The shopping mall high school: Winners and losers in the education marketplace. Boston: Houghton Mifflin. Rist, R. (1977). On understanding the processes of schooling: The contributions of labeling theory. In J. Karabel and A. H. Halsey (Eds.) Power and ideology in education. New York: Oxford University Press. 292-305. Sartain, H. W. (1989). Nonachieving students at-risk: School, family, and community intervention. Washington, D. C.: National Education Association Professional Library. Sedlak, M. W., Wheeler, C. W., Pullin, D. C., and Cusick RA. (1985). Selling students short: Classroom bargains and academic reform in the American high school. New York: Teachers College Press. Shanker, A. (1993, September). Is itfair to keep disrupters in class? The Detroit Teacher. September 32(2). Skodak, M. and Keels, H. M. (1949). Final follow-up study of one hundred adopted children. Pedagogical Seminar. 75, 85-125. Slavin, RE, and Madden, N. A. (1989, February). What works for students at-risk: A research synthesis. Educational Leadership. 4-6, (5)4 Sproat, K. (1985). The national longitudinal survey of labor market experience: An annotated bibliography of research. Lexington, Mass: Lexington Books. Stinchcombe, A. (1964). Rebellion in a high school. Chicago: Quadrandle Press. Swan, T. L. 1981). Dropouts in high school and afler. American Educational Researc Journal. 7, 343-367. Taylor, F. W. (1991). Scientific management. New York: Harper. Tifft, S. E. (1989, June 26). Help for at-risk kids: Carnegie proposal. Time. June 26 Tropea, J. L. (1987, Fall). Bureaucratic order and special children: Urban schools, ' 1950’s - 1960’s. History of Education Quarterly. 339-361. 144 Tye, B. (1985). Multiple realities: A study of 13 American high schools. Lanham, MD: University Press of America. Wacker, G., B. (1981, Fall). Vocational education’s role in serving dropouts and potential dropouts. Journal for Vocational Special Needs Education. 4(1), 19- 22. Walker, R. (1988, April). School job-training programs boosted in New York and California. Education Week. 7(31)9. Washburn, W. H. (1989). A reassessment of the dropout problem. Unpublished doctoral dissertation, Boston University. Weber, M. (1947). The theory of social and economic organization. New York: Oxford University Press. 329-330. Reprinted in Robert K. Merton, Alisa P. Gray, Barbara Hockey and Hanan C. Selvin (eds.) Reader in Bureaucracy, Glencoe, Ill: The Free Press, 1952, 18-20. Wehlage, G. G., & Rutter, R. A. (1986). Dropping out: How much do schools contribute to the problem? Teachers College Record. 87, 374-392 Wilkerson, D. (1989). New initiatives in dropout prevention: Project grad final report 1988-89. Austin Independent School District. Texas Office of Research and Evaluation. Willis, P. (1977). Learning to labor: How working class kids get working class jobs. New York: Columbia University Press. 1-226. Willower, D. J. and Jones, R. G., (1963, November). When pupil control becomes an institutional theme. Phi Delta Kappan, XLV, 2(10)7—9. Whitaker, C. (1991, March). Do black males need special schools. Ebony Magazine, Johnson Publication. XLVI(5) 17-22. jl LIBRARIES l "ill/Ll. 0L l l l l 31L93 l V I N U E T n T 5" N An 6 I H C I H