MSU LIBRARIES .—:—. RETURNING MATERIALS: PIace in book drop to remove this checkout from your record. FINES wil] be charged if book is returned after the date stamped beIow. EN \ IQ()H A STUDY OF THE RELATIONSHIP BETWEEN ELEMENTARY TEACHER ABSENTEEISM AND THE ACHIEVEMENT OF ELEMENTARY PUPILS IN READING AND MATHEMATICS By David B. Smith A DISSERTATION Submitted to Michigan State University in partiai fuifiiiment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Educationai Administration 1984 est ABSTRACT A STUDY OF THE RELATIONSHIP BETwEEN ELEMENTARY TEACHER ABSENTEEISM AND THE ACHIEVEMENT OF ELEMENTARY PUPILS IN READING AND MATHEMATICS By David B. Smith The financial loss resulting from employee absenteeism has been estimated at between SlS and $20 billion per year. In addition. there appears to be consensus that employee absenteeism also results in reduced productivity. However. there is little information regarding teacher absence and the educational effect of such absence. This study was designed to examine absenteeism rates for elementary teachers during two years and to determine if a relationship exists between absenteeism and student achievement. as measured by standardized norm-referenced reading and mathematics tests. ‘The study addressed the following specific concerns: (1) the relationship of elementary pupil (grades 1-6) achievement in mathematics to teacher absenteeism. as measured by total days absent and absence frequency and (2) the relationship of elementary pupil (grades 1-6) achievement in reading to teacher absenteeism. as measured by total days absent and absence frequency. The grades (1-6 The finding 1. areiations reacing or 2. rehtionshi reading and 3. “Iationshi Either reac 4. teacher abs in “than“ David B. Smith The study was constructed to examine the total elementary grades (1-6) as one group and each elementary grade as a separate group. The findings of the study were: 1. For all elementary grades (1-6) there did not appear to be a relationship between teacher absenteeism and pupil achievement in reading or mathematics for 1980/81. 2. For all elementary grades (1-6) there appeared to be a relationship between teacher absenteeism and pupil achievement in reading and mathematics for 1981/82. 3. For grades 1. 5. and 6 there did no]. appear to be a relationship between teacher absenteeism and pupil achievement in either reading or mathematics for 1980/81 or 1981/82. 4. For grade 2 there appeared to be a relationship between teacher absenteeism and pupil achievement in reading for 1981/82. and in mathematics for 1980/81 and 1981/82. 5. For grade 3 there appeared to be a relationship between teacher absenteeism and pupil achievement in reading for 1981/82. 6. For grade 4 there appeared to be a relationship between teacher absenteeism and pupil achievement in reading for 1981/82. and in mathematics for 1981/82. This dissertation is dedicated to my wife. Joan. and our Children. Anne and David. whose constant support made this project possible. fuiiy um a\ Ur. ( assi The ten: can this ACKNOWLEDGMENTS The help and encouragement of the following persons is grate- fully acknowledged: Dr. Herbert C. Rudman. whose encouragement and support were unwavering during the writing of this dissertation. The members of my committee. Dr. Lawrence W. Lezotte. Dr. C. Robert Muth. Dr. Mary Virginia Moore. and James F. Rainey. whose assistance was greatly appreciated. [Mu Grace Iverson and the members of the Evaluation Department. who provided technical assistance whenever needed. Marilyn Finney. who was always willing to assist. [ha I. Carl Candoli and Dr. Matthew Prophet. former Superin- tendents of the Lansing School District. and Dr. Robert J. Chamberlain. current Superintendent. who encouraged and supported the beginning of this degree. LIST 0i LIST ()1 Shapte I. Ii TABLE OF CONTENTS LIST OF TELES O O O I O O O O O O 0 LIST OF FIGURES . . . . . . . . . . Chapter I. mE meEM 0 O I O O O O 0 Background of the Problem Purpose of the Study . . . Importance of the Study Delimitations of the Study Definitions of Terms . . . Hypotheses . . . . . . . . General Hypothesis I General Hypothesis II General Hypothesis III General Hypothesis IV Organization of the Remainder O O O O O O O O O O s t D fi...0.00.00 (300.00.0000 3' of i t II. REVIEW OF RELATED LITERATURE . . . . . . . . . . Employee Absenteeismr-Business and Industry Employee Absenteeism: K-12 Smmmy .. ... .. III. METHODS AND PROCEDURES . Sample . . . . . . . . Pupil Sample . . . . Teacher Sample . . . Data-Collection Procedures Stanford Achievement Test Student Mobility Rate Parent Education . . . . . Aid to Families With Dependent Children Family Make-Up . . . . . . . . . . . . . Elementary School Teacher Absenteeism Design and Methodology of the Study . . . School Teachers . Page vi XV IV. a». T‘ZL Hypotheses . . . . . . . General Hypothesis I . General Hypothesis II General Hypothesis III General Hypothesis IV Summary Iv. FINDINGS O I O O O O O O O O O 0 Review of Data Analysis Results of Hypothesis Testing General Hypothesis I . . . General Hypothesis II . General Hypothesis III . General Hypothesis IV . Summary V. SUMMARY. DISCUSSION. CONCLUSION. FOR FURTHER RESEARCH . . . . . Summary Discussion . . . . . . . . . . Conclusion . . . . . . . . . . Suggestions for Further Research APPENDICES O O C O O O O O O O I I O O O O O O O O O SUGGESTIONS A. DEMOGRAPHIC CHARACTERISTICS OF PUPILS INCLUDED IN mE STUDY 0 O O O I 0 O O O O O O O O O O O B. INTERCORRELATIONS FOR SEVEN VARIABLES. BY GRADE C. EMPLOYEE TIME AND ABSENCE RECORD . . . . . . . . BIBLIMRWY O O O O O O O O O O O O O O O O O O O O O O 109 111 111 ll? 127 130 133 l3h Th5 158 160 10. ll. LIST OF TABLES Results of Olson's Elementary School Classroom Observations O O O O O O O O O O O O O O O O O O O 0 Rate of Teacher Absence for Illness and Personal Reasons for the Five Years. 1977/78 Through 1981/82 . Number of Elementary Pupils. by Grade. Taking Stanford Achievement Test in Reading. 1981/81 and 1981/82 . . Number of Elementary Pupils. by Grade. Taking Stanford Achievement Test in Mathematics. 1980/81 and 1981/82 Number of Teachers Included in Study During 1980/81 and 1981/82 0 O O O O O O O O 0 O O I O O O O O O O 0 Elementary Grade Level Summary Report for Stanford Achievement Test. 1980/81: Average Scaled Score. Standard Deviation. Average Percentile. and Number of Pupils . . . . . . . . . . . . . . . . . . . . . . Elementary Grade Level Summary Report for Stanford Achievement Test. 1981/82: Average Scaled Score. Standard Deviation. Average Percentile. and Number of Pupils . . . . . . . . . . . . . . . . . . . . . . Elementary School Building Summary Report for Stanford Achievement Test. 1980/8l and 1981/82: Building Reading Normal Curve Equivalent and Building Percentile . . . . . . . . . . . . . . . . . . . . . Elementary School Building Summary Report for Stanford Achievement Test. 1980/81 and 1981/82: Building Mathematics Normal Curve Equivalent and Building Percentile . . . . . . . . . . . . . . . . . . . . . Selected Elementary School Demographic Data of the Lansing School District for 1981/81 and 1981/82 . . . Days Absent and Frequency of Absence Per Elementary Teacher (Grades l-6). 1980/81 and 1981/82 . . . . . . vi Page 46 46 47 49 SO 51 52 53 58 130 20. 2]. .Su .Mu 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. Intercorrelations for Seven Variables Included in T1115 StUdy: Read1ng--1980/8] . 0 0 . . 0 Intercorrelations for Seven Variables Included in This Study: Reading--l981/82 . . . . . . Intercorrelations for Seven Variables Included Intercorrelations for Seven Variables Included in This Study: Mathematics--l981/82 . . . . . Multiple-Regression Analysis of Reading-1981 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Educat1 on) O O O O O O O I I O O O O O O O O O O O O 0 Summary of the Predicted Relationship Between Reading- 1981 and Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Parent Education-1981 Multiple-Regression Analysis of Grade 1 Reading-1981 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent, afld Parent Education) 0 0 0 0 0 0 0 0 0 0 0 0 Summary of the Predicted Relationship Between Grade 1 Reading-1981 and Frequency of Teacher Absence- l981. Total Teacher Days Absent-1981. and Parent Educati OFT-1981 O O O O I O O O O O O O O O O O O O Multiple-Regression Analysis of Grade 2 Reading-1981 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Student Mobility) . . . . . . . . Summary of the Predicted Relationship Between Grade 2 Reading-1981 and Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Student M0b111tY'1981..........o........ Multiple-Regression Analysis of Grade 3 Reading-198l Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent, alld Parent EducatTOn) 0 0 0 0 0 0 0 0 0 0 . vii Page 66 67 67 68 69 71 72 73 74 74 24. 25. 26. 27. 28. 29. 30. 31. Summary of the Predicted Relationship Between Grade 3 Reading-1981 and Frequency of Teacher Absence-1981. Tetal Teacher Days Absent-1981. and Parent Educa- tion-1981 . . . . . . . . . . . . . . . . . . . . . Multiple-Regression Analysis of Grade 4 Reading-1981 Correlated With Three Independent Variables (FreqUency of Teacher Absence. Total Teacher Days Absent. and Aid to Families With Dependent Children) . . . . . . . . . . . . . . . . . . . . . Summary of the Predicted Relationship Between Grade 4 Reading-1981 and Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Aid to Families With Dependent Children-1981 . . . . . . . . . . . Multiple-Regression Analysis of Grade 5 Reading-1981 Correlated With Two Independent Variables (Frequency of Teacher Absence and Total Teacher Days Absent) . . . . . . . . . . . . . . . . . . . Multiple-Regression Analysis of Grade 6 Reading-1981 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Education) . . . . . . . . . . . Summary of the Predicted Relationship Between Grade 6 Reading-1981 and Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Parent Education-1981 . . . . . . . . . . . . . . . . . . Multiple-Regression Analysis of Reading-1982 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families With Dependent Children) . . . . . . . . . . . . . . . . . . . . . Summary of the Predicted Relationship Between Reading-1981 and Frequency of Teacher Absence-1982. Tetal Teacher Days Absent-1982. and Aid to Families With Dependent Children-1982 . . . . . . . . . . . Multiple-Regression Analysis of Grade 1 Reading- l982 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up) . . . . . . . . . viii Page 75 76 77 78 79 BO 81 82 83 33. 34. 36. 37. 38. 39. Mi Mi '5. 32. 33. 34. 35. 36. 37. 38. 39. 40. Summary of the Predicted Relationship Between Grade 1 Reading-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Family Make-Up-l982 . . . . . . . . . . . . . . Multiple-Regression Analysis of Grade 2 Reading-1982 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up . . . . . . . . . . . . Summary of the Predicted Relationship Between Grade 2 Reading-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Aid to Families With Dependent Children-1982 . . . Multiple-Regression Analysis of Grade 3 Reading-1982 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Student Mobility) . . . . . . . . . . . Summary of the Predicted Relationship Between Grade 3 Reading-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Student Mobility-1982 . . . . . . . . . . . . . Multiple-Regression Analysis of Grade 4 Reading- 1982 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families With Dependent Children) . . . . . . . . . . . . . . . . . . . . . Summary of the Predicted Relationship Between Grade 4 Reading-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Aid to Families With Dependent Children-1982 . . . . . . . . . . . Multiple-Regression Analysis of Grade 5 Reading-1982 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families With Dependent Children) . . . . . . . . . . . . . . . . . . . . . Summary of the Predicted Relationship Between Grade 5 Reading-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Aid to Families With Dependent Children-1982 . . . . . . . . . . . Page 83 84 85 86 87 88 89 9O 91 ii. )4 42. Mi 43. Su 44. Mu Page Multiple-Regression Analysis of Grade 6 Reading-1982 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families With Dependent Children) . . . . . . . . . . . . . . . . . . . . . . 92 Multiple-Regression Analysis of Mathematics-1981 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up) . . . . . . . . . . 93 Summary of the Predicted Relationship Between Mathematics-1981 and Frequency of Teacher Absence- 1981. Total Teacher Days Absent-1981. and Family Make-Up-l981 . . . . . . . . . . . . . . . . . . . . 93 Multiple-Regression Analysis of Grade 1 Mathematics- 1981 Correlated With Two Independent Variables (Frequency of Teacher Absence and Total Teacher Days Absent) . . . . . . . . . . . . . . . . . . . . 94 Multiple-Regression Analysis of Grade 2 Mathematics- 1981 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families With Dependent Children) . . . . . . . . . . . . . . . . . . . . . . 95 Summary of the Predicted Relationship Between Grade 2 Mathematics-1981 and Frequency of Teacher Absence- 1981. Total Teacher Days Absent-1981. and Aid to Families With Dependent Children-1981 . . . . . . . . 96 Multiple-Regression Analysis of Grade 3 Mathematics- 1981 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up) . . . . . . . . . . 97 Multiple-Regression Analysis of Grade 4 Mathematics- 1981 Correlated With Two Independent Variables (Frequency of Teacher Absence and Total Teacher Days Absent) . . . . . . . . . . . . . . . . . . . . 97 Multiple-Regression Analysis of Grade 5 Mathematics- 1981 Correlated With Two Independent Variables (Frequency of Teacher Absence and Total Teacher Days Absent) . . . . . . . . . . . . . . . . . . . . 98 50. Si. 52. S3. 54. 55. 56. 57. 58. Mi ML 50. 51. 52. 53. S4. 55. 56. S7. 58. Multiple-Regression Analysis of Grade 6 Mathematics- 1981 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families With Dependent Children) . . . . . . . . . . . . . . . . . . . . . Multiple-Regression Analysis of Mathematics-1982 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families With Dependent Children) . . . . . . . . . . . . . . . . . . . . . Summary of the Predicted Relationship Between Mathematics-1982 and Frequency of Teacher Absence- 1982. Total Teacher Days Absent-1982. and Aid to Families With Dependent Children-1982 . . . . . . . Multiple-Regression Analysis of Grade 1 Mathematics- l982 Correlated with Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up) . . . . . . . . . . . . Summary of the Predicted Relationship Between Grade 1 Mathematics-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Family Make-Up-1982 . . . . . . . . . . . . . . . . Multiple-Regression Analysis of Grade 2 Mathematics- 1982 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up) . . . . . . . . . . . . Summary of the Predicted Relationship Between Grade 2 Mathematics-1982 and Frequency of Teacher Absence- 1982. Total Teacher Days Absent-1982. and Family Make-Up-l982 . . . . . . . . . . . . . . . . . . . Multiple-Regression Analysis of Grade 3 Mathematics- 1982 Correlated With Two Independent Variables (Frequency of Teacher Absence and Total Teacher Days Absent) . . . . . . . . . . . . . . . . . . . Multiple-Regression Analysis of Grade 4 Mathematics- 1982 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Education) . . . . . . . . . . . xi Page 99 100 101 102 103 104 105 105 106 59. 60. 61. 62. 63. 64. A1. A6. Summary of the Predicted Relationship Between Grade 4 Mathematics-1982 and Frequency of Teacher Absence- 1982. Total Teacher Days Absent-1982. and Parent Education-1982 . . . . . . . . . . . . . . . . . . Multiple-Regression Analysis of Grade 5 Mathematics- 1982 Correlated With Three Independent Variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families With Dependent Children) . . . . . . . . . . . . . . . . . . . . . Multiple-Regression Analysis of Mathematics-1982 Correlated With Two Independent Variables (Frequency of Teacher Absence and Total Teacher Days Absent) . . . . . . . . . . . . . . . . . . . Summary of Results of the Research Analysis for All Elementary Grades as a Total . . . . . . . . . . . Summary of Results of the Research Analysis. by Grade Lave] O O O O O O O O O O O O O O O O O O O O Absenteeism Control Methods. Ranked by Rated Effectiveness . . . . . . . . . . . . . . . . . . . Number of Elementary Pupils Taking Stanford Achieve- ment Tests in Reading in 1980/81. by Building Number and Grade Level . . . . . . . . . . . . . . Number of Elementary Pupils Taking Stanford Achieve- ment Tests in Mathematics in 1980/81. by Building Number and Grade Level . . . . . . . . . . . . . . Number of Elementary Pupils Taking Stanford Achieve- ment Tests in Reading in 1981/82. by Building Number and Grade Level . . . . . . . . . . . . . . Number of Elementary Pupils Taking Stanford Achieve- ment Tests in Mathematics in 1981/82. by Building Number and Grade Level . . . . . . . . . . . . . . Elementary Pupil Mobility Rates: Percentage of Pupils Entering or Leaving a School During the School Year Elementary-Student Parent Education Education Rates: Percentage of Parents Completing Twelfth Grade . . xii Page 107 108 108 115 116 122 135 136 137 138 139 1&0 A7. B7. B9. Aid to Families With Dependent Children Rates: Percentage of Families Within Each School Receiving Aid to Dependent Children . . . . . . . . . . . . . Elementary-Student Family Make-Up Report: Percentage of Families Reporting Two Parents Within the Home . Elementary Teacher Absenteeism Report. 1980/81 . . . Elementary Teacher Absenteeism Report. 1981/82 . . . Intercorrelations for Seven Variables Included in This Study: Grade 1 Reading and Mathematics-- 1980/81 0 O O O O O O O O O O O O O O O O O O O O O Intercorrelations for Seven Variables Included in This Study: Grade 1 Reading and Mathematics-- 1981,82 I O O I O O I O O O O O O O O O O O O O O O Intercorrelations for Seven Variables Included in This Study: Grade 2 Reading and Mathematics-- 1980, 81 O O O O O O O O O O O O O O O O O O O O O Intercorrelations for Seven Variables Included in This Study: Grade 2 Reading and Mathematics-- 1981/82 0 O O O O O O O O O O O O O O O O O O I O Intercorrelations for Seven Variables Included in This Study: Grade 3 Reading and Mathematics-- 1980,81 O O O O O O O O O O O O O O O O O O O I O Intercorrelations for Seven Variables Included in This Study: Grade 3 Reading and Mathematics-- 1981,82 O O O O O O O O O O O O O O O O O O O O O Intercorrelations for Seven Variables Included in This Study: Grade 4 Reading and Mathematics-- 1980,81 O O O O O O O O O O O O O O O O O O O O O Intercorrelations for Seven Variables Included in This Study: Grade 4 Reading and Mathematics-- 1981,82 O O O I O O O O O O O O O O O O O O O O O Intercorrelations for Seven Variables Included in This Study: Grade 5 Reading and Mathematics-- 1980,81 O O I O O O O O O O O O O ..... O O O xiii Page 1h] 1h2 1A3 144 1A6 1A7 1A8 149 150 151 152 153 15h 810. 1 Page 810. Intercorrelations for Seven Variables Included in This Study: Grade 5 Reading and Mathematics-- 1981,82 O O O O O O O O O O O O O O O O O O O O O O '55 811. Intercorrelations for Seven Variables Included in This Study: Grade 6 Reading and Mathematics-- 1980/81 0 O O O O O O O O O O 0 O I O O O O O O O O ‘56 B12. Intercorrelations for Seven Variables Included in This Study: Grade 6 Reading and Mathematics-- 1981,82 0 0 0 0 0 0 0 0 . 0 0 . 0 0 0 0 0 0 0 0 . 0 157 Figure LIST OF FIGURES Figure Page 1. Schooling Exposure and Achievement . . . . . . . . . . . 12 XV Proces W N the at i1Sen: 113931 miss: “)5 1 CHAPTER I THE PROBLEM .BQEKQLQHDQ_Q£_IDQ_E£QDlem Much has been written about the day-to-day absence of students and the effects of continued or excessive absenteeism on the learning process. Wiley summarized the effect of student absenteeism; he stated. "If schooling has an influence on a child. it does so on a day-to-day basis. when the child is present and subject to that influence. and cannot influence the child when the child is not there."1 If the child's attendance is important to the learning process. what role does the regular classroom teacher play in the entire learning process? Very little seems to have been written about the absence of teachers from their classrooms and the effect of such absence on the pupils they teach. Thus research on educational absenteeism is important in terms of pupil achievement. It is also necessary in terms of economic costs when school districts examine ways in which to reduce expenditures. Considerable research has been conducted on employee absenteeism rates in the private sector. During 1978. according to 1David E. Wiley. "Another Hour. Another Day: Quantity of Schooling. a Potent Path for Policy." in .in_Ame£1§an_§Qgiety. ed. William H. Sewell. Robert M. Hauser. and David L. Featherman (New York: Academic Press. 1975). Scott a percent work fc Rhodes MeUni days pe Rem vorkers 105 but Hilless SUD/e C 19821:, 7 pyOYee Unpaid A Of Natiol Scott and Markham. the national rate of employee absenteeism was 3 percent.2 This means that on any given day 3 percent of the scheduled work force did not show up for work. In an earlier report. Steers and Rhodes reported that an estimated 400 million work days are lost in the United States each year because of employee absenteeism--about 5.1 days per employee.3 As recently as November 1981. the W .Bexiew reported that. during a typical week. about five million workers are absent from their Jobs.‘4 These are employees who have a Job but are not at work for the entire week because of vacations. illnesses. and other reasons. The American Society for Personnel Administration reported that in 1978 the absenteeism rate for scheduled employees was 3 percent. but by 1980 this rate had fallen to 257 percent. and by 1981 it had been reduced even further--to 2.4 percent.5 In addition. the Society reported that in 1982 the absenteeism rate was still declining and was likely to be reported as 2.1 percent. This continued decline in employee absenteeism can be documented by a review of the average monthly absentee rates. as 2Dow Scott and Steve Markham. "Absenteeism Control Methods: A Survey of Practices and Results.".Eensnnnel_Admln151£ntnn 27 (June 1982): 73. 3Richard M. Steers and Susan R. Rhodes. "Major Forces on Employee Attendance: A Process Model." W 63.4 (1978): 391-407. 4Carol Boyd Leon. "Employed But Not at Work: A Review of Unpaid Absences." Monthly_LabQL_Bexiew (November 1981): 18-22. S"Job Absence and Turnover." W (The Bureau of National Affairs. Inc.). December 16. 1982. p. 2. reported the BNA r scheduled to be a 1 that the between Concern 1 Steers a; an empio benefit reported by the Bureau of National Affairs (BNA).6 In December 1982. the BNA reported the following average monthly absentee rates for scheduled work time: 198l--2.4 percent l980--2.6 percent l979--2.9 percent 1978-~2.9 percent Even though the rate of absenteeism is declining. it continues to be a large expenditure for American employers. Breaugh reported that the estimated annual cost of absenteeism to organizations is between 58.5 and $26.4 billion] Such cost becomes an even greater concern when one considers the expense of employee replacement. Steers and Rhodes estimated that. in 1977. the per-day absence cost of an employee was $66.8 This estimate included direct salary. fringe- benefit costs. and temporary employee-replacement costs. Within education. employee absenteeism appears to have increased recently. even though absenteeism in the private sector has shown a decline. Elliott and Manlove reported that teacher absences recently have increased dramatically in many American school systems. They went on to say that:"increased absences come with more generous sick leave policies bargained with teacher groups. When the regular 61bid.. p. 2. 7James A. Breaugh. "Predicting Absenteeism From Prior Absenteeism and Work Attitudes." Journal of Applied PsyngJng 66.5 (1981): 555-60. 8Steers and Rhodes. op. cit.. p. 391. teacher is absent. there are major costs. both instructional and financial."9 Writing about student and teacher absenteeism. Bamber indicated that there is no national monitoring of teacher absenteeism. which hinders the collection of data specifically related to teachers. But she stated that. as of May 1976. education employees were absent 3.6 percent of the time. which means that 86.000 classrooms per day were not being taught by the regular teacher:lo For the past three years. Michigan school districts have been forced. because of growing expenditure levels and reduced revenues with which to meet these demands. to review all expenditure or cost centers and to consider cost reductions and the implications of such reductions. These reductions have often resulted in cutting. or in some cases completely dropping. programs and services designed for students. Examination of the Lansing School District's 1981/82. 1982/83. and proposed 1983/84 budgets showed that more than 5400.000 was spent yearly for substitute teachers during 1981/82 and 1982/83. and nearly 3500.000 is projected for 1983/84.11 These figures do not take into account the cost of lost teaching time (that is. the salary paid to 9Peggy G. Elliott and Donald C. Manlove. "The Cost of Sky- rocketing Teacher Absenteeism.".Eh1_Qelta_Knnnnn 59 (December 1977): 269-70 0 1oChrissie Bamber. W. Fastback 126 (Bloomington. Indy: Phi Delta Kappa Educational Foundation. 1979), p. 150 1“Lansing School District Budget Projection 1983/84." compiled by the Finance Department. Lansing School District. June 1983. \ the This L006 each exper Dist: abser rates resul ectii 1t se StUde 317.15 F the absent teacher) but only the expense of replacing absent teachers. This expenditure level means that more than one-half of 1 percent 0006) of the budget is being devoted to paying substitute teachers each school year. Thus it can be seen that absenteeism is a large expenditure item for employers in general and for the Lansing School District in particular. In addition to simply calculating the costs associated with absenteeism. a number of researchers have considered absenteeism rates. or the percentage of time employees are not at work. and the resulting effect on the job being performed}2 Because teaching is an activity that relies almost exclusively on teacher-pupil interaction. it seems that the amount of absence of either the teacher or the student would have an effect on learning. This very point--diminished learning when the regular teacher is absent--was highlighted in a 1971 study of classroom quality conducted by Olson. He concluded that "substitute teachers in classrooms function in a role more akin to that of a 'babysitter' rather than that of a professionally trained educatorJflZ’ Olson drew this conclusion after analyzing classroom observations in 117 suburban school districts. During these observations. some of the areas examined were Class size. style of educational activity. number of adults in the classroom. and type of teacher. 12Steers and Rhodes. op. cit: Breaugh. op. cit. 13Martin N. Olson. "Identifying Quality in School Classrooms: Some Problems and Some Answers.".Cen1Lal_ldeas 21 (February 1971): 6. indic teaci resul subsi regul teach hand) Tobie SoUrc teach From these observations. Olson's ratings of various classrooms indicated that substitute teachers were the least effective type of teacher observed. below even student teachers and teacher aides. The results of these observations are summarized in Tablerl. Olson found substitute teachers' performance was abysmal in comparison to that of regular classroom teachers. Therefore. he said. either substitute teachers' performance must be improved. or less expensive methods of handling teacher absence should be initiated. Table 1.--Results of Olson's elementary school classroom observations. Observation Type of Teacher N Scores Regular 8.418 6.12 Specialist 1.164 5.82 Substitute 255 1.98 Student teacher 83 3.62 Teacher aide 7 3.21 Source: Martin N. Olson. "Identifying Quality in School Classrooms: Some Problems and Some Answers." Central Ideas 21 (February 1971): 6. The Lansing School District Personnel Department reviewed 'teacher absence over a five-year period. from 1977/78 through 1981/82. 'Teacher absences were defined as those absences permitted within the 1Veacher Master Agreement14 for either illness or personal leaves of 14"Lansing School District Master Agreement with Lansing s<=hool Employees Association." ratified for the years 1979 through 7 981. p. 44. absence. formula. The exam foiiouin Table 2. Year 1981/ 82 1980/81 1979/80 i978/ 79 i977/73 '_./ 'u u absencec ‘The absence rate was calculated by using the following formula.15 Number of Teacher Days Absence Rate = L9§I_Ihrnugh_lnh_Ansenses X 100 (Number of Employees) x (Number of Workdays) The examination of absence data for those five years showed the following results. (See Table 2.) Table 2.--Rate of teacher absence for illness and personal reasons for the five years. 1977/78 through 1981/82. Number of Days Absent Number of Number of Absence Year Illness Personal Employees Workdays Rate 1981/82 11.430.0 921.0 1.441 185 4.6% 1980/81 12.333.0 901.5 1.523 185 4.3% 1979/80 12.367.5 901.0 1.609 185 4.5% 1978/79 11.140.5 741.5 1.554 185 4.1% 1977/78 10.96S.0 707.5 1.579 185 4.0% ¥ Source: Taken from "Lansing School District Report of Sick and Per- sonal Leave Days Used 1981/82 Through 1977/78." compiled by the Employee Relations Department. Lansing School District. “Educational Research Service. W W (Arlington. Va.: Educational Research Service. 1980). Based on this preliminary analysis. it would appear that these absenteeism rates are much greater than the average of 244 percent being reported by the Bureau of National Affairs. or even the 3.6 percent reported for education employees as a whole.16 In contrast. absenteeism rates in the Lansing School District are not decreasing. as they have been in other sectors over the past several years. but rather are increasing. Not only is it important to consider the percentage of teach- ing time an instructor is absent. it is equally important to consider the frequency of absences. Breaugh emphasized the need to consider .absence_fnequen§y and total_dax§_nh§£nt as distinct measures of absen- teeism. as the two clearly are not related.T7 He defined frequency of absence as the total number of periods an employee was absent in a given year. regardless of the length of each absence.18 For example. an employee might be absent for an extended period of time such as two weeks. thereby missing ten days of work. or he/she might be absent ten separate times spread over ten weeks. In the latter example. the employee's frequency of absence would be much greater than in the former example. Breaugh defined total days absent as the total number of days an individual was absent in a given year."9 16"Job Absence and Turnover." op. cit.. p. 2. 17Breaugh. op. cit.. p. 556. 18Ib1d.. p. 557. 19Ib1d.. p. 558. no! fU‘ thr of pie stL tee da) abs stu The Test 0 outta Breaugh proposed that the frequency of absence is more reflec- tive of voluntary absenteeism than is the simple calculation of total number of days absent. and as such leads to the ability to predict future absenteeism.20 Being able to predict absenteeism. and assuming that a relationship exists between student achievement and frequency of absence. it would be possible to develop and implement specific plans to address the absenteeism problem as well as to strengthen student achievement. ‘Thus it is important to examine not only absen- teeism levels from the perspectives of absence frequency and total days absent. but also to attempt to determine the importance of the absent teacher in relation to student achievement. Many writers have focused their attention on the absent student. Harnischfeger and Wiley wrote. Evidence for other contextual factors seems to reveal potential contributory power to the explanation of test score decreases. One such factor is pupil absence rate which has steadily increased over the past decade. resulting in smaller average amounts of schooling forzpupils. but also burdening the teaching process considerably. The same authors also stated in the AdministnaIQLLS_NQIehQQk. It is obvious that if a child does not go to school at all. they will not directly benefit from schooling. It would also seem clear that if a child attends school less than the full year. but more than not at all. the benefits they derive from schooling should be in between. ‘That is. the quantigy of schooling should be a major determinant of school outcomes.2 201b1d0) p0 5590 21'Annegret Harnishchfeger and David E. Wiley. "Achievement TGSt Scores Drop--So What?" Wane: (March 1976): 5-12. 2zAnnegret Harnischfeger and David E. Wiley. "Schooling C'-l‘t:backs and Achievement Declines: Can We Afford Them?"Agm1m_s; ' NQIQDQQK (The University of Chicago) 24.1 (1975). fine ach fac sch que of. 10 In another article on the topic of student absenteeism. the American Association of School Administrators stated. Student absenteeism plays a critical role in decreasing actual learning time for the students involved. Each time a student misses class. the teacher has to repeat the assignment and review any material covered while the student was not in class. Absenteeism can also cause motivational problems that affect time on task. Students who have been gone for a number of days may feel left out of the classroom social system and develop an attitude of "why try to catch up." It's important for the teacher to take time tfiébring these students back into the mainstream of the classroom. With the collected evidence strongly suggesting that achievement is related to time devoted to learning and that such factors as student attendance. length of school day. and length of school year do make a difference are part of time on task. the questions then become: What part does the teacher play in the concept of exposure to schooling? Does it make a difference in learning if the regular teacher is in the classroom? Does it make a difference in learning if the teacher's frequency of absence (number of different absences) is high or low? Does it make a difference in elementary- school teaching if early-elementary teachers (grades 1. 2. 3) are absent a greater percentage of time than are later-elementary teachers (grades 4. 5. 6)? An examination of literature on absenteeism showed that although much study has been devoted to examining absenteeism in the l>rivate sector. very little research has been conducted on teacher absenteeism. The little research that has been carried out suggested \ 23American Association of School Administrators. IJHELJHI (Arlington. Va.: Merican Association of School Administrators. 1982). p. 31. that er 1977 51 l’teache probier inporta tive. d achieve: more an Pupn ee cOntinui by using achieve" SChool.n 11 that employee absenteeism in education is a definite problem. In a 1977 study. the Academy for Educational Development noted that "teacher absenteeism as a phenomenon has the potential to be a serious problem for the State of Illinois."24 Elliott and Manlove raised two important questions in this regard: "If substitutes are so ineffec- tive. do they constitute a cutback in schooling time and hence achievement? Are school districts bargaining away pupil progress with more and more 'sick days'?"25 In an article entitled "Accounti ng for Differences in Measured Pupil Performance." Lezotte and Passal acqua wrote: "Researchers are continuing to isolate and estimate the magnitude of 'school effects' by using various models. and by so doing demonstrating that poor achievement is not totally a function of the students who attend the school."26 This position was supported by the causal model related to schooling and achievement presented by Wiley and Harnischfeger. in which they included exposure to schooling as one of the characteris- tics that could explain student achievement.27 The model they pre- sented is shown in Figure 1. 24Academy for Educational Development. W31: :5 . .I :g -: ii I g: . ... 0 l0 0 : .... a . I .- .tjm (Illinois ffice of Education) (Indianapolis: The Academy for Educational Development. Public Policy Division. July 1977). 25Elliott and Manlove. op. cit.. p. 270. 26Lawrence W. Lezotte and Joseph Passalacqua. "Individual School Buildings--Accounting for Differences in Measured Pupil Per- formance.".l.1_r_b_an_liduc_aflg_n (October 1978): 283-91. 27David E. Wiley and Annegret Harnischfeger. "Explosion of a M.V‘l:h: Quantity of Schooling and Exposure to Instruction. Major Educa- tional Vehicles." When 3 (April 1974): 7-12. the on: 8an 12 A. Prior Pupil Characteristics 4% F. Achievement [\ 8. Attendance C. Length of School Day E. Exposure to Schooling 0. Length of School Year Maximal Quantity of Schooling Figure 1.--Schooling exposure and achievement. (From David E. Wiley and Annegret Harnischfeger. "Explosion of a Myth: Quantity of Schooling and Exposure to Instruction. Major Educational Vehicles.".Educational_BeseaLchen 3 (April 1974): 8.) The model appears to account for attendance only in terms of the student and does not allow for any variations in achievement based on teacher absence. The model would seem to be stating that the substitute teacher replacing the regular Classroom teacher will be requally effective in contributing to student achievement. W The purpose of this study was to examine the absenteeism rates for teachers in grades 1 through 6 in the Lansing School District and to determine if a relationship exists between absenteeism and student aCtl'iievement. as measured by standardized norm-referenced reading and Mathematics tests. The results of this study can be used to develop 13 programs related to teacher attendance. substitute-teacher use. and cost-savings measures that school districts might implement. Specifically. the writer's purpose was to investigate: l. The relationship of elementary pupil (grades 1-6) achievement in mathematics to teacher absenteeism. as measured by total days absent. 2. The relationship of elementary pupil (grades 1-6) achievement in mathematics to teacher absenteeism. as measured by absence frequency. 3. The relationship of elementary pupil (grades 1-6) achievement in reading to teacher absenteeism. as measured by total days absent. 4. The relationship of elementary pupil (grades 1-6) achievement in reading to teacher absenteeism. as measured by absence frequency. 5. The need for developing a specific teacher attendance program to address a situation in which excess absence may be affecting pupil achievement. mm It is important that the Lansing School District develop a plan to examine large expenditure areas for possible reductions. The «SXpenditure for teacher absence is a large. on-going expense that seems to lend itself to some program of cost containment. At the same 1t1|ne. it seems important to determine the effect of teacher absence of £31tudent achievement. If it can be demonstrated that high teacher albsence does have a negative effect on student achievement. this 14 finding might lead to the development of remedial programs aimed at reducing teacher absenteeism. It appears that as school districts seek ways to reduce expenditures. very little thought is given to the effect that such budget adjustments may have on the students they serve. If it is found that student achievement is related to teacher attendance. such information should be extremely useful in designing a teacher- attendance program. A school district could approach the teacher- absence problem with a dual purpose of not only reducing expenditures but also of correcting or adjusting so-called "school effects." thereby ameliorating the problem for the benefit of students. The research is also important in developing relationships and understanding between school district finance officers and school instructional personnel. for the ultimate benefit of the students they serve. W The study was delimited as follows: 1. The investigation was limited to studying the elementary schools. grades 1-6. in the Lansing School District. 2. It was limited to studying elementary school teacher absence for two school years: 1980/81 and 1981/82. 3. The study was limited to determining whether there were relationships between teachers' absence and pupil achievement in two subjects: mathematics and reading. 11 are used ‘L lg assigned t personnel : inciuded ir Eu; of this 5m £935 teacher ‘as Charged to ‘ tAether has i e \ 15 W The following terms are defined in the context in which they are used in this study. .Ieaghens: Those elementary school teachers who were actually assigned to a specific classroom during the years of study. Support personnel such as librarians or art and music teachers were not included in this definition. ‘Eypilsz The children enrolled in grades 1-6 during the period of this study. .Ieagnen_ab§enge: A day or a fraction of a day in which the teacher was absent from the assigned classroom. and the absence charged to either sick leave or personal leave. as defined by the teacher Master Agreement in effect during 1980/81 and 1981/82. Eregugngx_oi_ab§enge: ‘The total number of periods a teacher is absent in a given year. regardless of the length of each absence. flmgtbeses Four major hypotheses were tested in this study. ‘These hypotheses are stated in general form below and are restated in testable form in Chapter III. W The proposed relationship between average classroom reading achievement and teacher absenteeism (as defined by Frequency of Teacher Absence [PTA] and Total Teacher Days Absent [TTDAJ1 will contribute significantly (alpha 5, 0.05) to the overall relationship rbetween reading achievement and the selected demographic variables (of Student Mobility rate. Aid to Families with Dependent Children. F’amily Make-Up. and Parent Education for the 1980/81 school year. 16 WI The proposed relationship between average classroom reading achievement and teacher absenteeism (as defined by Frequency of Teacher Absence [FTA] and Total Teacher Days Absent [TTDA]) will contribute significantly (alpha < 0.05) to the overall relationship between reading achievement and the selected demographic variables of Student Mobility rate. Aid to Families with Dependent Children. Family Make-Up. and Parent Education for the 1981/82 school year. W The proposed relationship between average classroom mathematics achievement and teacher absenteeism (as defined by Frequency of Teacher Absence [FTA] and Total Teacher Days Absent [TTDA]) will contribute significantly (alpha < 0.05) to the overall relationship between mathematics achievement and the selected demographic variables of Student Mobility rate. Aid to Families with Dependent Children. Family Make-Up. and Parent Education for the 1980/81 school year. W The proposed relationship between average classroom mathematics achievement and teacher absenteeism (as defined by Frequency of Teacher Absence [FTA] and Total Teacher Days Absent [TTDA]) will contribute significantly (alpha < 0.05) to the overall relationship between mathematics achievement and the selected demographic variables of Student Mobility rate. Aid to Families with Dependent Children. Family Make-Up. and Parent Education for the 1981/82 school year. W In Chapter II. the review of related literature is presented. The review is primarily directed toward public-sector or teacher absenteeism; however. attention is also devoted to pertinent litera- ture on absenteeism in the private sector. Included in the literature review are items related to the use of substitute teachers. 17 The procedures and methodology of the study are discussed in Chapter III. which contains a detailed description of the data- gathering techniques used in the study. Included in the Chapter is a description of the statistical methods used to analyze the data. In Chapter IV. the analyses of the data gathered in the study are presented. Each hypothesis is outlined. and the findings obtained for each hypothesis are explained. The conclusions of the study and their implications are presented in Chapter V. Also included in this chapter are recommendations that may be useful for the development of related school-district programs or that suggest needed additional studies related to teacher absenteeism. CHAPTER II REVIEW OF RELATED LITERATURE The business community regularly examines employee absentee rates and relates those rates to cost of operations and the loss of productivity. By comparison. it appears that the educational community has spent little time analyzing absenteeism and its relationship to cost of operations or productivity (the teaching of students). In this chapter. the literature concerning employee absence and its relationship to cost and productivity is reviewed in two categories: (1) literature related to business and industry and (2) literature related to schools. W In May 1978. Taylor reported that wage and salary workers who normally work full time lost an average of 3.5 percent of their usual hours as a result of illnesses. injuries. and miscellaneous personal reasons.1 Nearly 7 of every 100 workers experienced at least one spell of absence during the reference week; illnesses and injuries accounted for most of the lost hours. Taylor went on to report that both the proportion of workers with an absence and the iDaniel E. Taylor. "Absent Workers and Lost Work Hours. May 1978." W (August 1979): 49. 18 19 proportion of time lost were about the same in May 1978 as they had been five years earlier.2 A year later. in May 1979. Taylor reported that American workers with full-time wage and salary jobs lost about 95 million hours a week as a result of illnesses. injuries. and miscellaneous personal reasons.3 Again he reported that the overall level of absence had shown no trend. The amount of time lost fluctuated narrowly between 3.3 and 3.5 percent from 1973 to 1979; the percentage of workers absent varied between 6.1 and 6.7 percent.4 At the close of the second quarter of 1983 (June). the Bureau of National Affairs (BNA) reported that the rate of unscheduled employee absence for that quarter had dropped to an unprecedented low in BNA's quarterly survey. averaging 1.8 percent of scheduled work time.5 By comparison. job absence rates for the second quarter of 1982 and for the first three months of 1983 averaged 2.1 percent of scheduled work time.6 Leon reported in November 1982 that most public attention was focused on the total count of the employed and the unemployed. yet a large segment of workers who were counted as employed were not 21bid. 3Daniel E. Taylor. "Absences From Work Among Full-Time Employees." W (March 1981): 68. 41bid. 5Bureau of National Affairs. "Job Absence and Turnover Second Quarter 1983." WWW. September 8. 1983. p. l. 61bid. 20 actually working.7 Leon reported further than during a typical week about five million workers are absent from their jobs for the entire week because of vacations. illnesses. and other reasons and therefore are removed from the economic stream for that period. For more than two million workers who receive no pay for the missed week of work. that absence may have unwelcome personal costs as well.8 The total number of week-long absentees (paid and unpaid) at a given time increased substantially between 1950 and 1980. rising from 2.0 to 5.1 million. Although employment grew during this period. absences increased even more. As a percentage of the employed. absen- tees increased from 4.2 to almost 6 percent.9 Most of this Change occurred in the 19505 and the late 19605. A slight rise in absen- teeism in the early 19705 has been largely offset by a decline toward the end of the decade. During the period from 1950 through 1980. the major reason for week-long absences was vacations. As shown in the following chart. vacations accounted for a large part of the absence data.'lo W 1950 _ 1980 With a job. but not at work 1.954.000 5.057.000 Percent 100.0 100.0 Vacation 54.2 59.6 Illness 28.2 24.7 Bad weather 2.9 1.5 Labor dispute 4.3 2.0 Other reasons (child care. 10.4 12.2 funerals. jury duty) 7Carol Boyd Leon. "Employed But Not at Work: A Review of Unpaid Absences.".MQnthl¥_LahQ£_Bex1ew (November 1981): 18. 8Ibid. 91bid. 1°Ib1d. 21 The American Society for Personnel Administration has been surveying absenteeism in the United States since 1974. The Society reported the following figures related to absence from work and unemploymen Year 1976 1977 1978 1979 1980 1981 1982 1976 1977 1978 1979 1980 1981 1982 Absenteeism Rates \D U") N 11'Robert Zager. "Employees Miss Less Work Time." Resource (American Association for Personnel Administration) (February 1983): 12. 22 Zager pointed out that employees are missing less time at work than at any time in recent years. Over the past four years. absenteeism has dropped as unemployment has risen. With about 99 million Americans currently employed. the difference between 2 percent calling in sick and 3 percent doing the same is close to one million employees.12 Not only do business and industry record absences in terms of the percentage of employees either working or not working. they are also interested in the relationship of such absences to productivity. In a report prepared for the American Society of Personnel Administrators. this point was highlighted as follows: "Absence may sometimes make the heart grow fonder--but never when you're running a company. Whether your employee has a genuine health problem. or one of attitude only. the end result is the same."13 Absenteeism problems mean not only lost time and money for the company. but also lost productivity. In a time of lagging productivity. absenteeism becomes an especially crucial problem. Kuzmits reported the cost of national absence is estimated to be between $15 and $20 billion a yean.‘4 He further stated that even if one chooses the "conservative" estimate of $15 billion a year. it still represents an awesome loss of productivity and a needless waste of human resources. As such. absenteeism relates to 13Prentice-Hall Editorial Staff. WWW. prepared for the American Society for Personnel Administration (Englewood Cliffs. N.J.: Prentice-Hall. Inc.. 1981). p. 2. ”Frank E. Kuzmits. "How Much Is Absenteeism Costing Your Organization." Ihe_Eensonnel_bdm1nistnaton (June 1979): 29-32. *1 23 loss of productivity because a business must consider the costs asso- ciated with production losses. machine downtime. quality problems. and inefficient use of materials. Kopelman. Schneller. and Silver also investigated absenteeism and loss of productivity and identified some of the costs associated with sick leave. They included: --Out-of-pocket expenses such as overtime. extra hours for part- time employees. and over-staffing --Fringe-benefits costs. which continue while the employee is absent --Maintenance of an absence-control system. whether it is effective or not --Increased supervisory time. as a need develops to revise work schedules and to check the output of substitutes --Lower morale as workers resent doing others' work. or higher turnover rate. more grievances. and/or increased tardiness --Reduced productivity because more unscheduledswork is done by people who are less experienced or fatigued. In another report dealing with employee absenteeism and productivity. Cruikshank estimated the cost of absences to be in the range of $15 to $20 billion a year just in wages paid for days when employees are absewnflé He went on to state that there are other substantial costs: the expense of training workers to fill in for absentees; disruption of production. which holds up deliveries; and. in many industries. perpetual overstaffing to minimize the effect of absenteeism. Cruikshank also found that. in the auto industry. absenteeism rates rise to 10 to 15 percent or even higher 15Richard E. Kopelman. George 0. Schneller IV. and John J. Silver. Jr.. "Parkinson's Law and Absenteeism: A Program to Rein in Sick Leave Costs.".EQLSanel_Adm101§I£nIQ£ (May 1981): 57. 16George E. Cruikshank. "No-Shows at Work: High Priced Headache." Natignls_fiu§1ne§5 (September 1976): 37. 24 at certain times of the year. 'This increased absenteeism causes havoc with production. upsets quality control. and builds resentment among the workers who do show up and who must be shifted into jobs they might not know or like.17 Cruikshank summarized his findings by stating that the cost of no-shows is recorded in tens of millions of lost worker hours every week. in idle machinery and unused plant . facilities. in materials spoilage. and in delayed shipments to customers.18 Concerning the relationship of absenteeism to productivity. Feinberg noted that those absences that are most devastating to productivity are the ones that occur on short notice or without any notice.19 Such absences do not allow the necessary planning to provide for a substitute or to readjust schedules. which planned absences permit. Allen and Higgins stated that we live in an absenteeism culture. Taking a day off and calling in sick is supported and encouraged by society. Many people's attitude is. "The time is coming to usJ' But these authors went on to ask. "What does this absenteeism culture cost?"20 Allen and Higgins estimated that the cost of absenteeism to American business exceeds $100 million a year. But this figure does not account for losses in productivity ‘7Ibrd.. p. 38. 13Ib1d.. p. 39. 19Mortimer R. Feinberg. "New Focus on Absenteeism." Bestaunant_8usiness. February 1. 1981. p. 82. 20Robert F. Allen and Michael Higgins. "The Absenteeism Culture: Becoming Attendance 0riented.".Ee£&an§l (January-February 1979): 30-34. 25 resulting from workers covering for one another. missed deadlines. missed orders and meetings. lost opportunities. and other substantial costs. 'The authors suggested that the only way to address the problem of absenteeism is to recognize that it is a cultural problem and that. as such. a cultural solution is required. It should be noted that employee absenteeism is not a new phenomenon. Johnson and Peterson indicated that. in a 1967 survey. business managers in 100 large and moderate-sized firms reported some kind of problem with absenteeism.21 For the private business sector it can easily be shown that. as Rothman stated. "staffing is a critical factor in any organiza- tion's ability to function effectively."21 She noted further that human error and illness block the attainment of organizational goals and are thus major concerns of management. Rothman proposed that when plans for staffing and production are developed. consideration should be given to the absenteeism rate experienced in the company as well as to the sick-leave-benefit plan used. The reliance on employees for productivity was discussed by Hayes. He reported that. according to the Council on Economic Affairs. lack of productivity is "one of the most significant 21Ronald D. Johnson and Tim 0. Peterson. "Absenteeism or Attendance: Which Is Industry's Problem?" W (November 1975): 568. 22Miriam Rothman. "Can Alternatives to Sick Pay Plans Reduce Absenteeism?" Eersonnel_lounnal (October 1981): 788. ir L4 31 1‘. 26 economic problems of recent years."23 Hayes stated that statistics from the National Center for Productivity and Quality of Working Life show that the past decade's productivity growth fell to an average annual rate of 1.6 percent. half the 3.2 percent rate during the 20-year period from 1947 to 1967. He reported that in the first quarter of 1978 there was a productivity gap (the difference between the amount and the cost of production) of TLS points. the worst in recent history. The author noted that one tends to forget that the definition of productivity is "output per worker" and that it is the worker who makes products out of inanimate resources. Hayes also stated that it is the employee who is the key link in the production process. The employee's performance determines whether the limited inanimate resources are optimally used. Hayes reported that even though human beings are the key to productivity. much of American industry regularly runs without its full complement of staff. resulting in a serious productivity gap.24 Moreover. he stated that although absenteeism directly affects produc- tivity. it is rarely considered a serious problem. Thus a vicious circle exists. for absenteeism flourishes precisely where it is ignored. Wm Staff absenteeism among educational personnel poses serious problems for effective school administration. Unlike many other ¥ 23James L. Hayes. "Absenteeism: The Death of Productivity." WW (December 1981): 25. 24Ibid. 27 occupations. teaching requires that classrooms be staffed at all times. either by the regular teacher or by a substitute teacher. to prevent disruption of the learning process and to maintain pupil supervision. From an instructional viewpoint. teacher absenteeism places a heavy strain on the continuity of student learning. and the value of substitute teachers is continually questioned. From a financial standpoint. teacher absenteeism is expensive because the salaries of both the regular and the substitute teacher must be paid when the regular teacher is absent.25 In addition to the interruption of the learning process and the financial aspects related to teacher absences. the expectation of attendance by both students and teachers must also be considered. Bamber stated that schools have a certain expectation for regular attendance of students and teachers. and when this does not occur. classroom performance suffers.26 Even occasional absences cause some learning disruption. but frequent absences of students or a teacher can severely hinder academic programs. When a student is absent. schooling is disrupted for that particular student. but more serious consequences may result when teachers are absent. Bamber went on to state that when substitute teachers are called in it is usually on short notice. with little time for preparation. Substitute teachers are then little more than babysitters in the 25Glenn Robinson. "Foreword." in Educational Research Servi cev Wm (Arl i ngtonv Va.: Educational Research Service. Inc.. 1980). p. vii. 26Chrissie Bamber‘. mmnleacbembsenteeism (Bloomington. Ind.: Phi Delta Kappa Educational Foundation. 1979). pp. 12-140 28 classroom. The author noted that absenteeism also has an economic effect on school districts: teacher absences cost schools money in hiring substitutes. Additional administrative expenses and record- keeping costs are also incurred in hiring substitute teachers. Bamber contrasted the apparent importance given to employee absenteeism by corporations with the relatively minor emphasis given this issue by school districts.27 She indicated that corporations keep close tabs on employee attendance; absent workers are a loss of money to the company. and any rise in absenteeism is countered quickly with measures to reduce it. Yet in schools. where taxpayers foot the bill. it may be several years before citizens become aware of excessive absences. Elliott and Manlove suggested that frequently absented class- rooms could be one of the many factors contributing to declining test scores and increasing vandalism.28 They went on to state that in recent years teacher absences have increased dramatically in many American school systems. and that when this occurs. costs. both finan- cial and instructional. are incurred. The increase in teacher absence and substitute use was also noted by Bundren. who concluded that such increases appeared to be universal.29 He stated that many districts are not conducting 271b1d.’ p. 16. 28Peggy G. Elliott and Donald C. Manlove. "The Cost Of Sky- rocketing Teacher Absenteeism.".Eh1_Delta_Kappan (December 1977): 209-210. 29Dorence L. Bundren. "The Influence of Situational and Demographic Factors on the Absentee Patterns of Teachers" ”HMO. dis- sertation. University of Southern California. 1974). 29 studies relating to absenteeism. and such diverse methods are used to organize and report absentee data that the subsequent utility of the tabulated data is seriously limited. This point was supported by the Educational Research Service. which reported that few local school systems or states have collected and published absence data for teachers and other educational personnel.30 The Service reported that the data that are available indicate school systems employed an average of'4.3 percent substitute teachers during a typical day in 1976/77. Drake reported that teacher absence is more a fact of life than it is a sudden emergency.“ Generous sick-leave policies and increased released time make it possible for a school system to be missing a number of regular faculty members each day. In fact. Drake stated. "published reports have shown that the statistically average student will have ten of their total classroom days each year supervised by a substitute teacher." He said that a substitute teacher is often thought of as the "spare time" of American education. the kind of resource used "to patch things up in an emergency. but then quickly put away as soon as the regular teacher returns."32 According to Drake. the saddest reality of all is that substitutes of 30Educational Research Service. We LLjflunmaLy_gj_BeseaL§n_(Arlington. Va.: Educational Research Service. 1980), p0 1410 31Jackson M. Drake. "Making Effective Use of the Substitute Teacher: An Administrative Opportunity." W (September 1981): 74. 321bid. 30 every type. including the most qualified and dedicated available. are seldom instructionally successful because of their stand-in role. Theoretically. a substitute teacher is a certified and qualified professional who replaces the regular classroom teacher for the purpose of continuing the instructional program. maintaining discipline. and generally promoting the educational welfare of the students. Yet there is little relationship between the intention and practice of substitute teaching. The practice rarely reflects the theoretical definition. and substitutes usually fall into one of the following categories: --The Baby Sitter--Discipline is the priority. All energy is spent on keeping students quiet. and "busy work" is used to maintain an atmosphere of guidance. --The Bare-Minimum Teacher--Ease of presentation is the priority. A minimal amount of energy is spent on instruction. ‘The substitute exercises little knowledge. skill. creativity. or authority. The materials and activities presented are chosen because they require a minimum amount of guidance. --The Improviser--Teaching their area of specialization is the priority. Teaching does take place; however. it has little or no relationship to the standard curriculum. The substitute in this category replaces the regular teacher's lesson plans with a personal curriculum. In a later study reported by McIntire and Hughes. the authors noted that the average student spends seven days out of every school year with a substitute teacher.34 That comes to 84 days (nearly half a school year) during 12 years of schooling. They also stated that the number of good substitute teachers is likely to decline just when the need for them increases. That is. if the teacher shortage that 33Ib1d0' p. 75. 34Ronald G. McIntire and Larry W. Hughes. "Houston Program Trains Effective Substitutes.".Eh1_Delta_Kappan (June 1982): 702. 31 many forecasters are predicting occurs. the most experienced and effective substitutes will obtain full-time teaching jobs. McIntire and Hughes also predicted that the number of days to be filled by substitutes is likely to increase and that the shortage of capable substitutes threatens to become acute. In the Detroit. Michigan. schools. teachers averaged 12.3 days off because of sickness in the first 167 days of classes in the 1979/80 school yean35 Not only was valuable instructional time lost. but an economic loss of $10.3 million was also realized because the district had to pay the absent teachers for the sick days and hire substitute teachers. if possible. In a 1974 study conducted by the State of New York Office of Education. it was found that in the 1971/72 school year the cost of hiring substitutes was $71.5 million for New York City.36 In addition to the financial cost of teacher absence. several other findings were noted: --Teacher absenteeism was greater in Title I schools than in non-Title I schools. --The $71.5 million represented almost 9 percent of the City's total expenditures for teacher salaries. --Sub5titute teachers were significantly less effective than regular teachers and specialists. and were even less effective than student teachers. Such a finding led to the remark that "a substitute teacher is no substitute for the teacher." --Ab5enteeism may create a harmful interruption in the conti- nuity of education. which may affect the child's learning. 35"Absentee Teachers Boost School Costs.".Lan&1ng_§Int§ Journal. November 1981. 36"Teacher Absenteeism in New York City and the Cost Effectiveness of Substitute Teachers." SIaI§_Q£_N§1_XQLKL_Qi£1§§ .QI_Egugatlgn_Eg££Q£m§ng§_B§¥1§n (Albany: New York Office of Educa- tion. January 1974). pp. 1-3. 32 --The absence of the regular teacher may also set a model fog7 student behavior. a major problem in the New York schools. In highlighting the lack of effectiveness of substitute teachers. the New York report stated that. in the last few years. many groups and individuals had critically appraised the performance of substitute teachers and noted that there had been little research to indicate their effectiveness. The report stated. "Conventional wisdom indicates that short-term substitute teachers seldom provide service to students at a level superior to a teacher aide or teacher assist- ant."38 The New York report went on to state that. in 1971. the Metropolitan School Study Council observed approximately 18.000 teachers and rated them for classroom effectiveness. by type. 'The Council's ratings are shown below: MEAN SCORE OF OBSERVATIONS BY TYPE OF TEACHER RANKED BY CLASSROOM EFFECTIVENESS MeaILSCDLQ ElementauSecondau Regular teacher 6.12 5.01 Specialist teacher 5.82 4.99 Student teacher 3.62 2.76 Substitute teacher 1.98 0.27 The Metropolitan School Study Council concluded that the substitute teacher's "being near zero leads to the conclusion that just nothing nnuch was going on)‘ The New York report summarized the ranking of ‘teacher effectiveness by stating that ¥ 37Ibid.. p. 18. 33Ib1d.. p. 17. 391bid. 33 What clearly stands out on the table is the abysmal performance of substitute teachers in contrast to that of the regular classroom teacher. ‘The low scores can only be interpreted as meaning that the substitute teachers in these classrooms function in the role more akin to that of a "baby sitter" rather than that of a professionally trained educator. Either substitute teacher performance must be improved or alternative. less expenslfie methods of handling teacher absence should be initiated. In an earlier report on teacher absenteeism in the New York City Schools. Zimet reported that absenteeism accounted for 1.500 uncovered classes daily--the equivalent of about 30 schools or one average school district.“ Zimet's report was based on a study released in 1967. which showed an average absence rate of 2.5 percent of the teachers. During the 1967/68 school year. the rate rose to 6.4 percent; the following year it rose to‘LS percent--an average of 4.500 teachers absent each school day. Zimet noted that as parents view the effects of decentralization of the New York City Schools. one problem is the periodic absences of teachers and the presence of substitutes who frequently do little more than mind children.42 To place some perspective on these rates of absenteeism. the Educational Research Service reported that literature concerning employee absence. in general. suggests a reasonable rate of absenteeism is from 3 to 6 percent of available work time. ‘The Service also reported that the average absence rate for all workers in the United States ranged from 2.9 to 3.5 percent in 1978.43 4°Ibrd.. p. 18. “Melvin Zimet. Willem (New York: Teachers College. Columbia University. 1973). p. 111. 421b1d. 43Educational Research Service. op. cit.. p. 110. 34 In a study of teacher absenteeism conducted by the Pennsyl- vania School Boards Association. the following findings were reported: --Pennsy1vania's school districts are spending approximately $27 million annually for substitute teachers to keep their schools operating during periods of short-term teacher absence and $88 million in total personnel costs associated with teacher absences. --The mean work absence rate increased steadily through the school year. with a year-end mean rate of 4.75%. --The "average" teacher in Pennsylvania was absent a total of 8.2 days during the 1977/78 school year. --E1ementary teachers have a slightly higher absence rate than secondary teachers. --Female professional staff members have a significantly higher absence rate than male professional staff members. --More absences occur on Friday than any other day of the week. --Sma11 districts (fewer than 200 professional employees) tend to have lower absence rates than do larger districts (200 employees or more). --The mean absence rate for teachers in Pennsylvania exceeds all major industry rates determined by the United States Bureau of Labor Statistics and is approximately one-third higher than the national average in the education industry. --0ver five million hours of regulas4instructional time are lost due to teacher absences annually. As a capstone to the findings of the Pennsylvania School Board Association. they discovered that teacher absence had increased by more than 106 percent in the past 16 years.45 As a response to the increase in teacher absenteeism. and in an attempt to develop and recommend alternatives to current staffing practices that would benefit the total educational program for students. the report concluded with the following recommendations for school districts to consider: 44Pennsylvania School Boards ASSOCIEtlone "Teacher Absenteeism: Professional Staff Absence Study" (Harrisburg: Pennsyl- vania School Boards Association. October 1978). p. v. 451b1d.. p. 37. 35 W --Loca1 school districts should develop a "reporting off" proce- dure which includes direct personal contact with the building principal. --Bui1ding principals should maintain personal contact with the absent teacher during the period of absence and should speak directly with the teacher upon return to work. --Systems of reporting off and reporting back to work should avoid the impersonal approaches found in many mechanical methods which may stress efficiency and ease of reporting procedures but lack the personal follow-through necessary for adequate control purposes. --School districts should maintain accurate. current records on teacher absences which are available for review at the building level for personal consultation purposes. --A monthly absentee report should be available in each district which identifies comparative information on absenteeism rates for the district schools and programs. --School management personnel. particularly school principals. should not delegate supenyisgny functions to building secre- taries or other support personnel in the development of report- ing procedures. --School boards should develop and enact policies dealing with absenteeism. including appropriate disciplinary actions for abuse of such policies. --The responsibility for coordinating district policies. building regulations. data gathering. and supervisory review of absen- teeism should be maintained at the central office level for effective control. . --A standard method for recording reasons for absence. employment of substitutes. and medical information used to verify absences should be developed and maintained. --Procedure5 should be developed which clearly identify the responsibility of absent employees to keep district officials informed of their return to work status in order that timely contact with substitute teachers can be maintained. WWW --Careful attention in the hiring process should be given to the prior history of new applicants related to prior absence records or other indications which denote a potential high absence risk. --Orientation programs for new and present faculty should review the policies. procedures. and forms associated with absence reporting systems on a regular basis. --In-service training programs should be developed which review the role of the regular teacher and employed substitutes when absences occur in order to maintain continuity in the instruc- tional process. 36 --Approved substitute teachers should receive appropriate orien- tation and written procedures which spell out the policy expectations of the district. the role of the substitute. and the necessary interaction with the regular teacher and building principal in order to provide a smooth transition during periods of substitute employment. --Teachers who have been determined to have a high incidence of absenteeism should receive special counseling to determine the reason(s) for the unusual absence rate. --School districts should review scheduled educational activities which tend to have an impact on teacher absences (e.g.. schedul- ing of faculty meetings. student assembly programs. group test- ing. in-service activities. etc.). --Consideration should be given to the yearly schedule of programs and activities to determine if the planned schedule contributes to the increased incidence of absence evident in most districts as the school term progresses. Winn: --School districts should carefully review the reasons for absence. particularly the use of sick leave as enumerated in Section 1154 of the School Code. to ensure that payments made under this authority are legal and permissible. --School districts should review the provisions of collective bargaining agreements which allow for teacher absences. Limi- tations and controls of "time off" provisions should be care- fully structured when such demands are made in the bargaining process. --Controls should be placed on the use of personal leave and other professional leave provisions which would limit the number of staff absences on a given day. or in a given month. for such reasons. --Consideration should be given to restrict use of personal and other professional leave provisions on Mondays and Fridays to discourage the "long weekend" on days which normally have the highest incidence of absence. --School districts should consider the effect of teacher absences in the development of building plans. --Consideration should be given to alternative plans for staff- ing absent positions to include the possible use of community volunteers. retired teachers. teaming with aides. honor stu- dents. and other educational resources which would permit greater flexibility in staffing and cost reductions. --The scheduling of staff for educational purposes which have direct contact with pupils should be given top priority over preparation periods. lunch assignments. or other non-educational pupil contact assignments when absences occur. 6 451b1d.. pp. 41-43. 37 Reporting that the real cost of teacher absenteeism is probably five to ten times greater than the amount typically computed. Lewis suggested that school districts introduce a record-keeping system to track employee absenteeism.47 Lewis reported that using such a system would not only be cost effective for the school district. but it would also improve the quality of education because it would increase the time classroom teachers spend with students. Lewis felt that school districts tend to overlook the true cost of absenteeism by considering only the daily substitute rate of pay as their cost. Actually. he contended. the cost is much greater when one considers such expenses as the absent teacher's salary; the salaries of administrators who must contact. instruct. and evaluate substitute teachers; and the money schools pay into various employee- benefit accounts. such as retirement. disability. and worker's- compensation funds. In summary. Lewis suggested that. by using a computer. schools can develop employee attendance profiles that show clearly when and how often employees are absent. With this information. employees will also be able to work toward improving their performance. which will directly affect the amount of instructional time they are providing to students. The National Association of Secondary School Principals reported that studies conducted in Las Vegas. Nevada; Merrick. New 47James Lewis. Jnu "Using a Computer to Monitor Teacher Absenteeism Can Save Schools Money and Increase the Time Teachers Spend in Class."Ihfi_AmfiL1fln_5£h9.9_1_B_QaLd_.19_umnl (December 1982): 30. 38 York: New York City; the northern suburbs of Chicago; Indiana; Illinois; and California all found an increase in teacher absenteeism during the course of the studies. Some of the important findings related to teacher absenteeism were as follows: --Demographic factors including age. gender. salary. continuous employment. and marital status do not have a significant impact on the amount of absenteeism. --Absenteeism has continued to increase since the passage of collective bargaining legislation. despite better pay. smaller classes. and more appropriate assignments. -The highest rate of absenteeism occurs the day before and day after the weekend. --High levels of absenteeism occur in school districts where there are low levels of faculty agreement toward the goals and policies of the community and school district. These high levels of absenteeism occur even in those school districts with high levels of material incentives and pleasant physical environ- ments. --Low levels of absenteeism among teachers occur in those dis- tricts with high levels of community support and policy agreement. regardless of low levels of material inducsgent and unpleasant physical conditions faced by the teachers. In addition. Pennsylvania school districts reported annual job-absence rates for 1977/78 ranging from a low of 1.51 percent to a high of 7.3 percent. with an overall group annual mean rate of 4.7 percent.‘19 They also reported that the "average" teacher was absent a total of 8.2 days for the period from September to the end of May. This rate of absenteeism had an economic effect on the various school districts taking part in the survey. The cost of professional staff 48"Absent Teachers: Another Handicap for Students.".Ih£ Ifluunujdnnen 5 (May 1979): 1. 49"Teacher Absenteeism: Professional Staff Absence Study," (Harrisburg: Pennsylvania School Boards Association. October 1978). p. 15. 39 absenteeism involves not only the salary paid to the absent teacher. but also remuneration to a replacement in the classroom. This dual payment almost doubles the cost of a dayWs work for the school district. while the amount of work accomplished is generally decreased.50 The concept of decreasing work effort in the public sector was highlighted further in a study conducted by Winkler.51 He reported that even less is known about public-sector absenteeism than about absences in the private sector and that. in all likelihood. public- sector absences are more expensive because they affect both the employer and the individuals receiving the public service. He noted that absent teachers are usually replaced by substitutes. who are likely to be less effective in the classroom than the regular teacher. Edwards evaluated several factors believed to be related to teacher absenteeism.52 He studied the teachers' own attitudes toward interpersonal. intrapersonal. and environmental stressors and how these stressors affect pupil control. administrator/teacher relations. teacher/parent relations. and teacher-to-teacher relations. According to Edwards. the findings of his study tended to indicate a need to: --Study the school-site situation from the administrator's position. --Lower class size. 5°Ib1d.. p. 35. 51Donald R. Winkler. "The Effects of Sick Leave Policy on 'Teacher Absenteeism.".IndusIL1al_nnfi_LahQL_Belntinn&_Bexlen 33 (January 1980): 232. 52Gregor 0. Edwards. "Teacher Absenteeism in Senior High SSChools: Economic. Educational. and Human Costs of Teacher Stress." 43 (July 1982): 29-A. 4O --Initiate fair and reliable discipline procedures. --Increase school security. --Work on the drug/weapon/violence problem. --Review administrative approaches. --Bring teacher training programs in line with needs experienced in teaching. These programs should include: multicultuggl training. stress-reduction methods. and time management. Edwards said that although the tangible economic effect of these stressors approximated $9 million in the schools sampled. possibly of even greater importance than the tangible costs are concerns related to low teacher or school morale. physical and mental disability. poor human relations. and poor social relations. Rawson conducted a study on the effectiveness of substitute teachers. He suggested that the following factors often hinder substitutes' effectiveness: --Low priority given to substitute teachers in the school system. --Lack of formal substitute teacher programs such as orientation or inservice. --Same rate of pay for differing levels of experience. --Lack of fringe benefits or collective negotiations. --Differing views of role expectations for substitute teachersS4 --Lack of feedback and evaluation of substitutes' performance. Goodman examined declining teacher morale and increasing teacher stress in inner-city situations. in which racial isolation presents specific stresses that are different from those found in integrated settings.55 He noted that poor teacher morale and the resulting teacher exit and absence in these schools also have enormous 53Ib1d. 54D. V. Rawson. "Increasing the Effectiveness of Substitute Teachers." .Bulletin (September 1981): 81. 55Victor B. Goodman. "Teacher Absenteeism. Stress in Selected Elementary Schools: An Assessment of Economic and Human Costs" (EdJL dissertation. University of California. Los Angeles. 1980). 41 legal. political. social. and economic significance. The findings of Goodman's study demonstrated that black. white. and Hispanic schools possess different stress patterns from each other. and that by knowing these patterns one can anticipate an elementary school's stress char- acteristics by virtue of its racial composition. In citing some of the differences between schools. the author noted that administrative stress characterizes schools with large black populations. whereas schools with large white populations are characterized by stress brought on by parents. Goodman listed several remedies proposed by teachers that would be expected to reduce stress and thus to affect the teacher- exit and teacher-absenteeism rates. Those suggestions included: --Schools with large populations of white students request increased workers compensation. and a desire for better com- munications with other teachers. --Physical security was the major concern in schools with large black populations. --Teachers at Hispanic elementary schools prefer more collegial team control and a better working relationship with the administration. Goodman concluded that. from a policy viewpoint. any attempt to offer a uniform. districtwide stress-management or moral e-enhancement program might be ineffective because of the unique stress patterns that characterize racially isolated schools. In research examining possible factors related to elementary- teacher absenteeism. Foster studied ten elementary schools in New York City. He reviewed several factors affecting teacher morale. including teacher perceptions of rapport with the principal. the individual's 55Ibid. 42 satisfaction with teaching. and teachers' perceptions of rapport among teachers. Foster concluded that --The schools with high teacher absenteeism and low teacher absen- teeism were related to percentages of low income and minority students in the total population. --Black and Hispanic students appeared to have a significant effect on teacher absenteeism. --There were no discernible effects on the average class means of the combined class reading and math achievement test scores in schools with high versus low teacher absenteeism in the schools studied. --Morale among teachers in schools with high versus low teacher absenteeism did not vary in terms of teacher perception of: .~ teacher rapport with the principal; his or her satisfaction with teaching; and rapport among teachers. --The percentages of teachers filing grievances did not have a significant effect on the ratios of teacher absenteeism in the schools with high versus low teacher absenteeism.5 Beauchamp and Conran examined several factors relating both to students and teachers in an attempt to explain the relationship of selected factors and several areas of student achievement.58 In regard to teacher absenteeism and student achievement. findings indicated that teacher absence had a negative influence in 4 of 11 subtest areas measured and in total reading. total math. and total battery. That is. teacher absence had a negative influence on some portions of achievement but did not adversely affect achievement in most subtest areas. 57Seymour 0. Foster. "An Investigation of Selected Factors in Schools With High Versus Low Teacher Absenteeism in a New York Community School District" (Ed.D. dissertation. Fordham University. 1977). 58George A. Beauchamp and Patricia C. Conran. "Longitudinal Study in Curriculum Engineering-VI" (paper presented at the annual meeting of the American Educational Research Association. San Francisco. California. April 1976). (Mimeographed.) 43 In a 1980/81 study conducted in the 39 secondary schools in the Cleveland City School District. Zafirau found that teachers were absent somewhat less in those schools that had the highest student attendance.59 Summam The studies conducted in the areas of business and industry tended to agree that a large financial 1055 results from employee absenteeism. ‘This financial loss has been estimated at various levels but tends to fall within $15 to 20 billion per year. Going beyond the reports dealing with financial losses. there appears to be consensus that employee absenteeism also results in reduced productivity. ‘This position was highlighted by Kuzmits. who stated. "absenteeism relates to loss of productivity in that a business will have to consider the costs associated with production losses. machine downtime. quality problems and inefficient materials usage."60 Within the area of education. there appears to be less information regarding the absence of teachers and the educational effect of such absences. A number of studies. such as those conducted by the National Association of Secondary School Principals. the State of New York. and the Pennsylvania School Boards Association. have focused on the number of days teachers are absent. the financial effect of such absences. and the relative ineffectiveness of classroom 59James S. Zafirau. "A Study of Attendance Issues in a Desegregating School District." March 15. 1982. p. 4. (Mimeographed.) 60Kuzmits. op. cit.. p. 29. 44 substitutes. But very few studies have explored the loss in produc- tivity that might occur to pupils when the classroom teacher is absent. In one such study. Foster concluded that there were no noticeable effects on the average class means of combined reading and mathematics achievement test scores in New York high schools with high versus low teacher absenteeism.61 lFoster, op. cit. CHAPTER III METHODS AND PROCEDURES Sample The study sample comprised two groups chosen from the Lansing School District: (1) the entire student enrollments in grades 1 through 6 during the 1980/81 and 1981/82 school years and (2) all of the teachers assigned to those classes for the same two years. Wm]: All of the pupils in grades 1 through 6 were chosen as the sample because they provided a good representation of characteristics found in an urban school district. The reason for selecting all of the pupils was to obtain students with a range of socioeconomic levels and achievement abilities similar to what would be found if a sample had been chosen from the total population by some other means. It should be noted that even though all of the pupils in grades 1 through 6 were Chosen for this study. only those present and actually taking the standardized reading and mathematics tests were included in the analysis. ‘The number of pupils. by grade. for the two years of study is shown in Tables 3 and 4. Summary data related to the number of pupils from each elementary school building who took the Stanford Achievement Tests are shown in Tables A1. A2. A3. and A4. Appendix A. ‘15 46 Table 3.--Number of elementary pupils. by grade. taking Stanford Achievement Test in reading. 1980/81 and 1981/82. Grade 1980/81 1981/82 1 1.798 1.675 2 1.707 1.589 3 1.760 1.576 4 1.821 1.674 5 1.788 1.716 6 1.691 1.706 TOTAL 10.565 9.936 Source: Taken from the Overview of Stanford Achievement Test Analy- sis report of the Lansing School District for 1980/81 and 1981/82. Table 4.--Number of elementary pupils. by grade. taking Stanford Achievement Test in mathematics. 1980/81 and 1981/82. Grade 1980/81 1981/82 1 1.796 1.674 2 1.701 1.586 3 1.753 1.572 4 1.819 1.669 5 1.792 1.716 6 1.687 1.705 TOTAL 10.548 9.922 Source: Taken from the Overview of Stanford Achievement Test Analy- sis report of the Lansing School District for 1980/81 and 1981/82. 47 .IEnQDQILSfllefi All of the elementary school teachers who taught grades 1 through 6 during the 1980/81 and 1981/82 school years were included in the sample to provide a broad representation of teacher characteristics such as years of teaching experience. educational level. age. and sex. The sample was selected in this manner to be characteristic of teaching staffs in the larger population. The actual number of teachers included in the study during the two school years under investigation is shown in Table 5. Table S.--Number of teachers included in study during 1980/81 and 1981/82. Year Number of Teachers 1980/81 426 1981/82 399 Source: Taken from Personnel Department records of the Lansing School District for 1980/81 and 1981/82. MW Wiles: The Stanford Achievement Test (SAT). Form A.‘| is administered to every pupil in grades 1 through 6 each spring. The data used in 1Eric F. Gardner. Herbert C. Rudman. Bjorn Karlsen. and Jack c. Merwin. W (New York: The Psychological Corporation. 1974). 48 this research resulted from the spring 1981 and 1982 testings of pupils in the areas of reading and mathematics. The summaries of pupil achievement for the two years of the study were for the Total Reading and Tetal Mathematics batteries. reported as mean percentiles. ‘These data were reported in several different ways for use within the school district. The data were provided in a summarized form showing the results of the total school district. as well as the results of individual grades within each building. Table 6 shows the results of the testing conducted in spring 1981. Table 7 shows the results of the testing conducted in spring 1982. The normal curve equivalent and percentile rank on the reading and mathematics achievement tests for each elementary school building in each of the two years under study are shown in Table 8 and 9. W The student mobility rate is a comparison of the total pupil enrollment at the beginning of a school year with the total number of pupils who move into or out of an individual school during a given school yeah. By reviewing the records of those pupils who enter or withdraw from school. a percentage of change is calculated. The lower the percentage the fewer changes that have taken place. whereas the larger the percentage the greater the number of entries or withdrawals from the beginning of a school year. Examining the student mobility rate enables the researcher to determine the degree of student movement 49 Table 6.--Elementary grade level summary report for Stanford Achievement Test, 1980/81: Mean scaled score, standard deviation, average percentile, and number of pupils. Mean Standard Percentile Grade Scaled De iati 3 Rank of Mean N Score v on Scaled Scoreb Reading 1 117.7697 16.8881 60 1.798 2 135.0773 16.9148 60 1,707 3 146.8148 17.5564 60 1,760 4 154.6447 17.9516 52 1,821 5 162.8916 19.2981 52 1,873 6 172.5490 20.1699 56 1,765 Mathematics 1 124.7433 12.0292 72 1.796 2 134.4827 11.1334 62 1,701 3 146.1820 13.1950 60 1,753 4 155.8384 14.3863 54 1,819 5 166.3113 16.2868 50 1,876 6 176.2993 17.5422 56 1,761 Source: Taken from the ”Overview of Stanford Achievement Test Analysis” report of the Lansing School District, November 1981. a . In terms of scaled score pornts. bScaled scores were averaged first for each grade. Percentiles were then calculated from the mean scaled scores. 50 Table 7.--Elementary grade level summary report for Stanford Achievement Test, 1981/82: Mean scaled score, standard deviation, average percentile, and number of pupils. ' Mean St d d Percentile Grade Scaled 0 enter a Rank of Meaq) N Score evra '0" Scaled Score Reading 1 118.7600 16.8582 64 1,675 2 135.8263 15.9720 60 1,589 3 147.5057 17.6647 59 1.576 4 154.9474 17.4410 52 1,674 5 163.5256 19.0219 54 1,716 6 173.4007 19.6250 56 1.792 Mathematics 1 124.9630 11.1655 72 1,674 2 135.4836 10.9920 64 1,586 3 147.3009 13.0465 62 1.572 4 156.4955 13.9691 56 1,669 5 166.8969 16.4599 52 1,716 6 177.1971 17.3909 58 1,791 Source: Taken from the ”Overview of Stanford Achievement Test Analysis'I report of the Lansing School District, November 1982. a . In terms of scaled score points. bScaled scores were averaged first for each grade. Percentiles were then calculated from the mean scaled scores. Tabi Schc Nun! 4—__'-'—__.J-—.—g 51. Table 8.--Elementary school building summary report for Stanford Achievement Test, 1980/81 and 1981/82: Mean building reading normal curve equivalent and building per- centile rank. 1980/81 1981/82 332:2: Mean Building Building Mean Building Building Normal Curve Percentile Normal Curve Percentile Equivalent Ranka Equivalent Rank3 1 51.22 52 52.40 55 2 54.62 59 55.42 60 3 57.20 63 57.86 65 4 54.08 58 50.66 51 5 57.15 63 57.24 63 6 57.49 64 53.48 56 7 57.98 65 57.83 65 8 49.76 so 52.00 54 9 59.61 68 59.71 68 10 59.62 68 59.98 68 11 47.95 46 49.39 49 '2 54.86 59 54.31 58 13 46.18 43 45,09 41 14 53.71 57 56.36 62 15 47.49 45 47.83 46 16 49.81 50 51.64 53 17 51.31 53 51.49 53 18 51.72 53 54.45 58 l9 60.06 68 63.57 74 20 59.01 67 60.09 68 21 62.56 72 64.24 75 22 50.35 51 50.84 51 23 51.33 53 52.59 55 24 49.13 48 50.80 51 25 53 20 S6 54 62 59 26 51 51 53 53 50 57 27 47 53 45 50 64 51 28 49 23 49 48 02 46 29 52 ll 54 52 06 54 30 52 09 54 52 46 55 31 S9 75 '68 52 71 55 32 49 80 50 48 12 46 33 49 43 49 48 9o 48 34 48 38 47 51.96 54 35 48.04 46 building closed 36 55.72 61 57.14 63 37 54.33 58 57.46 64 38 46.68 44 45.29 41 39 56.75 62 61.20 70 40 49.10 48 51.24 52 41 55.91 61 55.66 61 Source: Taken from the "Overview of Stanford Achievement Test Analysis” report of the Lansing School District, 1981 and 1982. aTo obtain a building percentile rank that covers several grade levels, the following method was used: Scaled scores were averaged for each grade at each school. The closest corresponding percentile for each averaged scale score was found. Each percentile was then converted to the closest NCE score. NCE's fiareach grade were weighted for a building NCE. The corresponding building percentile was figured from the weighted NCE. WPWN— 52! Table 9.'-Elementary school building summary report for Stanford Achievement Test, l980/8l and l98l/82: Hean building mathematics normal curve equivalent and building per- centile rank. 1980/8| l981/82 School Mean Building Building Hean Building Building Number Normal Curve Percentile Normal Curve Percentile Equivalent Ranka Equivalent Ranka l 40.75 33 53.50 57 2 58.l8 65 54.23 58 3 64.03 75 62.39 72 4 5l.89 53 53.50 57 5 56.96 63 57.41 64 6 60.02 68 56.44 62 7 57.86 65 57.83 65 8 53.22 56 57.67 64 9 60.53 ' 69 60.4] 69 IO 63.40 74 63.33 74 ll 51.73 53 , Sl.4l 53 l2 55.39 60 57.26 64 l3 45.8l 42 48.4l 47 l4 56.96 63 56.9l 63 is . 57.68 64 57.08 , 63 l6 53.58 57 54.00 58 '7 56-70 63 59-‘9 67 18 56.79 63 58.06 65 19 61.38 71 65.38 77 20 58.01 65 6l.97 7| 2| 58.12 65 62.22 72 22 52-99 55 53.58 57 23 5l.39 53 54.97 59 24 49.27 49 52.57 55 25 53.52 57 55.07 59 26 52.48 55 53.27 56 27 46.26 43 52.3l 54 28 54.43 58 59.53 67 29 52.68 55 S4.l6 58 30 49.48 49 52.06 54 31 61.25 70 63.08 73 32 52.03 54 50.93 52 33 49.98 50 49.48 49 34 50.09 50 53.79 57 35 46.78 44 building closed 36 54.15 58 57.10 63 37 53.69 57 56.03 5' 38 53.59 57 49.00 48 39 60.07 68 62.49 72 40 52.20 54 53.64 56 4| 53.37 56 56.l7 6i Source: Taken from the "Overview of Stanford Achievement Test Analysis” report of the Lansing School District. l98l and l982. 3To obtain a building percentile rank that covers several grade levels, the following method was used: Scaled scores were averaged for each grade at each school. The closest correSponding percentile for each averaged scale score was found. Each percentile was then converted to the closest NCE score. NCE's for each grade were weighted for a building NCE. . The correSponding building percentile was figured from the weighted NCE. W’WN— e e o e 53 within the school district. It is believed that students' mobility affects their achievement. For the purpose of this study. student mobility rates were examined from the perspective of the total school district. as well as of individual schools within the district. The total school district mobility rates for 1980/Bl and 1981/82 are shown in Table 10. and the individual school mobility rates are reported in Table A5. Appendix A. Table lO.--Selected elementary school demographic data of the Lansing School District for l980/8l and 1981/82. Grades l-6 1980/Bl 1981/82 (5) (Z) Student Mobility 31.4 30.6 Parent Education 71.0 58.0 Aid to Families with Dependent Children 25.7 22.6 Family Make-Up 66.0 66.0 Source: Taken from the "Elementary Demographic Data Report" of the Lansing School District. l980/Bl and l981/82. W100 The elementary school Parent Education Report is used to gather data concerning the education of parents of currently enrolled elementary school pupils. This information is requested from each parent or guardian when the pupil is enrolled; not all parents divulge 54 this information. The parent education data are collected because it is believed that parents' education level may have a relationship to the educational achievement of their children. By reviewing parents! responses to the education report. it is possible to determine the percentage of parents who have completed l2 years of education or more. as compared to those who have not finished their high school education. For the purpose of this study. the elementary parent education rate was examined from the perspective of the total school district. The total school district parent education rate is shown in Table 10. and individual school rates are reported in Table A6. Appendix A. Minnie: 111W Aid to Families with Dependent Children (AFDC) is a reporting of the percentage of families who are receiving financial aid and have dependent children. The percentage of a school's population receiving AFDC is determined by comparing the actual number of children living within a school's attendance boundaries and receiving AFDC with the total number of children living in that attendance area. These data are collected because it is believed there may be a relationship between a family's economic level and the educational achievement of their children. For the purpose of this study. the AFDC rate was examined from the perspective of the total school district. as well as of individual schools within the district. The total school district AFDC rate is 55 shown in Table 10. and the individual school rates are reported in Table A7. Appendix A. W Family Make-Up is a report indicating the number of two-parent families as compared to single-parent families within the various elementary school attendance areas. ‘The two-parent family could include either the natural parents or step-parents. Family Make-Up information is requested of the parent or guardian when the pupil is enrolled. Such information is given voluntarily and is subject to change as family status changes. This information is collected because it is believed there may be a relationship between family make-up and children's educational achievement. For the purpose of this study. Family Make-Up data were examined from the perspective of the total school district. as well as of the individual schools within the district. The total school district Family Make-Up data are shown in Table l0; the individual school rates are reported in Table A8. Appendix A. W For the purpose of this research. data were collected regarding the absenteeism of elementary school teachers in grades l through 6 during the 1980/Bl and l981/82 school years. These data concerned teacher absenteeism for reasons of health and/or personal necessity. 56 Part of an individual teacher's contract with the Lansing School District is a provision in the Master Agreement that allows the teacher to be absent for reasons of health and/or personal business. The contract states in part: W A. Compensable leave of ten (10) days for the school year shall be credited to the compensable leave account of each teacher. This benefit will be pro-rated for teachers hired after the beginning of the school year. Each teacher shall be entitled to unlimited accumulation of the unused portion of each year's compensable leave which shall be available in future years. In addition. teachers shall have available two leave days per year under the provisions in Section 0. Any unused portion of the leave days shall become additional compensable leave. Compensable leave shall be granted in accordance with the schedule specified herein. subject to the following conditions: 1. Personal illness: Bonafide physical or mental incapacity of the teacher to report for and discharge duties to the extent of unused days credited. 2. Illness or serious injury in the immediate family: Absence necessitated because of the need of the personal attendance of the teacher. (Immediate family shall include the teacher's spouse. children. parents or foster parents. parents-in-law. brothers. sisters. and any other person for whose financial or physical care the teacher is principally responsible.) 3. Bereavement: Utilization of such leave shall be for the purpose of attending the funeral arrangements in the case of the death of a teacher's father. mother. father-in-law. mother-in-law. spouse. children. brother. sister. grandparents or grandchildren. This leave shall be for a maximum of five (5) days. 4. Funerals: One day leave may be granted for attending funerals for persons other than in the immediate family. One additional day may be requested ffr attending funerals held more than 200 miles from Lansing. 2Taken from the Master Agreement between the Lansing Schools Education Association and the Lansing School District for 1981. 1984. 57 If a teacher is absent for either health or personal reasons. he/she is required to complete an Employee Absence Form indicating the dates of absence and the reason for absence. (See Appendix C.) The teacher absences considered in this research were only those that were allowable under the agreement for reasons of health and/or personal business. The number of days absent was then totaled and analyzed by two separate measures. Total Teacher Days Absent and Frequency of Absence. for both the 1980/81 and 1981/82 school years. Total Teacher Days Absent is the total number of days a teacher was absent for reasons of health and/or personal business in a given school year. A summary of days absent for elementary school teachers is shown in Table ll. In addition to examining Total Teacher Days Absent. the investigator also reviewed Frequency of Teacher Absence to determine how many different times the teacher was absent. When a teacher returned from an absence and later was absent again. it increased the frequency of absence. 0r. stated another way. a teacher could be absent five consecutive days. or he/she could be absent one day a week for five weeks. In the former. the frequency-of-absence rate would be one; in the latter. the frequency-of-absence rate would be five. A summary of the Frequency of Elementary Teacher Absence for 1980/81 and 1981/82 is shown in Table 11. 58 Table ll.--Days absent and frequency of absence per elementary teacher (grades 1-6). 1980/81 and 1981/82. Number of Number of Average Days Year Teachers Days Absent Absent/Teacher Days Absent 1980/81 426 3.969.S 9.3 1981/82 399 3.290.0 8.2 Frequency of Absence 1980/81 426 2.394.0 5.6 1981/82 399 2.040.0 5.1 Source: Taken from teacher absenteeism records of the Lansing School District. 1980/81 and 1981/82. Breaugh described frequency of absence as "the total number of periods an employee was absent in a given year regardless of the length of each absence."3 He went on to indicate that it is important to consider the measure of frequency because it is less sensitive to one long period of absenteeism and reflects voluntary absenteeism more than does the total number of days an employee is absent. Following an examination of the teacher absenteeism data. the results were used as they pertained to total mean absence. grade-level mean absence. and building mean absence. ‘The absence data for individual buildings are reported in Tables A8 and A9. Appendix A. 3James A. Breaugh. "Predicting Absenteeism From Prior Absentee- ism and Work Attitudes." lQnfinil_Qf_Ainied_E§¥£hQngx 5 (1981): 557-580 59 W The experimental design used in this study was a multiple- regression analysis as described by Kerlinger.4 The key to this design is the ability to enter independent variables one by one on the basis of pre—established criteria. In the case of this study. it was possible to study the effects of teacher absenteeism on reading and mathematics achievement. Kerlinger stated. "educational researchers can study the combined and separate effects on school achievement. say of intelligence. aptitude. social class. race. home background. school atmosphere. teacher characteristics. and so on."5 The statistical design of this study consisted of a multiple- regression analysis. using a forward inclusion of two groups of independent variables. The first group of independent variables entered into the analysis comprised Frequency of Teacher Absence (FTA) and Total Teacher Days Absent (TTDA). After allowing these two independent variables to account for as much variability in the two dependent variables (reading and mathematics achievement) as they could. the second group of independent variables. Aid to Families with Dependent Children. Student Mobility Rate. Parent Education. and Family Make-Up. were given an opportunity to enter into the regression equation. 4Fred N. Kerlinger. W11. 2nd ed. (New York: Holt. Rinehart and Winston. Inc.. 1973). p. 150. 51bid. 60 The forward-inclusion approach. although favoring the first group of independent variables (Frequency of Teacher Absence and Tetal Teacher Days Absent) over the second group. would not result in distor- tion of the real-life situation because the relationship of reading and mathematics achievement to parental education and socioeconomic status has been largely confirmed by many researchers and was not under scru- tiny in this project. ‘The forward-inclusion strategy allows one to examine the relationship of teacher absence variables. however minimal. to students' SAT reading and mathematics scores. while at the same time giving an optimal-prediction equation with as few terms as possible. This study was designed to answer two major questions: matics? This question involved the study of elementary teachers' (grades 1-6) total time absent for reasons of health and/or personal business and the relationship of such absence to pupil achievement. This researcher posed the questions: (a) Does it make a difference how many days a teacher is absent? and (b) Does the total amount of teacher absence have a greater effect on pupil achievement than certain social factors such as student mobility rate. Family Make-Up. Aid to Families with Dependent Children. or parents! level of education? The study covered two years. 1980/81 and 1981/82. 61 Data were analyzed by using the Statistical Package for the Social Sciences (SPSS)6 to discover whether there was a relationship between the dependent and independent variables. In this section of the study. the dependent variable was pupils' achievement as demon- strated by their reading and mathematics scores on the Stanford Achievement Test for their grade level. The independent variables were total teacher days absent and selected demographic data related to pupils (student mobility rate. Aid to Families with Dependent Children. Family Make-Up. and parents' level of education). 2. WW: .. ;. .; H .. = .H.. . . . . . = ;”=. . .. . :. . ... .. animatbanatjgs? This question involved the study of elementary teachers' (grades 1-6) frequency of absence for reasons of health and/or personal business and the relationship of such absence to pupil achievement. This investigator posed the questions: (a) Does it make a difference how frequently or how many different periods of time a teacher is absent? (b) Does a high frequency of absence have a greater effect on pupil achievement than does a high total rate of absence? and (c) Does frequency of absence have a greater effect on pupil achievement than do certain socioeconomic factors. such as student mobility rate. single- parent status. Aid to Families with Dependent Children. or parents' level of education? 6Norman H. Nie et al.. Wail Sciences. 2nd ed. (New York: McGraw-Hill Book Co.. 1975). pp. 320-67. 62 The Statistical Package for the Social Sciences was used to determine whether there was a relationship between the dependent variable and the independent variables. In this portion of the study. the dependent variable was pupilsfl achievement as demonstrated by their reading and mathematics scores on the Stanford Achievement Test for their grade level. The independent variables were frequency of teacher absence and socioeconomic data related to pupils.(student mobility rate. Aid to Families with Dependent Children. Family Make-Up. and parents' level of education). Balm: Multiple-regression analyses were used to test the following hypotheses: W The proposed relationship between average classroom reading achievement and teacher absenteeism (as defined by Frequency of Teacher Absence [FTA] and Total Teacher Days Absent [TTDA]) will contribute significantly (alpha < 0.05) to the overall relationship between reading achievement and the selected demographic variables of Student Mobility rate. Aid to Families with Dependent Children. Family Make-Up. and Parent Education for the 1980/81 school year. Analyses of Hypothesis I were performed separately for grades 1 through 6 and for the total of all elementary classrooms. W The proposed relationship between average classroom reading achievement and teacher absenteeism (as defined by Frequency of Teacher Absence [FTA] and Total Teacher Days Absent [TTDA]) will contribute significantly (alpha < 0.05) to the overall relationship between reading achievement and the selected demo- graphic variables of Student Mobility rate. Aid to Families 63 with Dependent Children. Family Make-Up. and Parent Education for the 1981/82 school year. Analyses of Hypotheses II were performed separately for grades 1 through 6 and for the total of all elementary classrooms. W The proposed relationship between average classroom mathematics achievement and teacher absenteeism (as defined by Frequency of Teacher Absence [FTA] and Total Teacher Days Absent [TTDA]) will contribute significantly (alpha < 0.05) to the overall relationship between mathematics achievement and the selected demographic variables of Student Mobility rate. Aid to Families with Dependent Children. Family Make-Up. and Parent Education for the l980/81 school year. Analyses of Hypothesis III were performed separately for grades 1 through 6 and for the total of all elementary classrooms. W The proposed relationship between average classroom mathematics achievement and teacher absenteeism (as defined by Frequency of Teacher Absence [FTA] and Total Teacher Days Absent [TTDA]) will contribute significantly (alpha < 0.05) to the overall relationship between mathematics achievement.and the»selected demographic variables of Student Mobility rate. Aid to Families with Dependent Children. Family Make-Up. and Parent Education for the 1981/82 school year. Analyses of Hypothesis IV were performed separately for grades 1 through 6 and for the total of all elementary classrooms. Sumarx Elementary teacher absence data were gathered for two school years. 1980/81 and 1981/82. The absence data were recorded in two different forms: (1) the total number of days a teacher was absent each year and (2) the number of different times each year a teacher was 64 absent. 'The absence data were analyzed to determine if such absence had an effect on the SAT reading and/or mathematics scores of pupils in grades 1 through 6 those two years. The method of analysis was a multiple-regression approach. using a forward inclusion of two groups of independent variables. This design was used to determine the effect of teacher absenteeism on reading and mathematics before allowing other demographic variables to enter into the regression equation. CHAPTER IV FINDINGS The findings of the data analysis are contained in this chapter. The results of the hypotheses testing are presented. as are other related findings. The hypotheses were formulated to determine whether (1) e1 emen- tary teacher absenteeism in the Lansing School District had a signifi- cant effect on elementary pupils! reading achievement during the 1980/81 and/or 1981/82 school years and (2) whether elementary teacher absenteeism in the Lansing School District had a significant effect on el ementary pupils' mathematics achievement during the 1980/81 and/or l981/82 school years. Additional hypotheses were formulated to deter- mine if the number of times a teacher was absent had an effect on pupils' reading and/or mathematics achievement. .8911§I_QI_DBIn_Annl¥51§ Using regression analysis incorporating the forward-inclusion approach favored the first group of independent variables (Frequency of Teacher Absence [FTA] and Total Teacher Days Absent [TTDAJ) over the second group of independent variables (Aid to Families with Dependent Children [AFDC]. Parent Education [PED]. Student Mobility [SMOB]. and Family Make-Up [FAM]). It was determined that such an approach would 65 66 not distort the real-life situation because the relationship of reading and mathematics achievement.to parental education and economic status has been largely confirmed by many researchers and was not under scru- tiny in the present project. Pearson product-moment correlation analysis was used to illus- trate the relative strengths of dependency of the various independent variables in this study. Through this method it was shown that the dependent variable of mean Stanford Achievement Test (SAT) reading percentile appeared to be correlated with Aid to Families with Depend- ent Children. Student Mobility. Parent Education. Family Make-Up. and Total Teacher Days Absent but was not at all correlated with the inde- pendent variable—-Frequency of Teacher Absence. These findings are shown in Tables 12 and 13. Table 12.--Intercorrelations for seven variables included in this study: Reading--1980/81. Read AFDC SMOB PED FAM FTA Read AFDC -0.39* SMOB -0.32* 0.83* PED 0.39* -0.84* -O.71* FAM 0.33* -0.80* -0.66* 0.61* FTA 0.07 0.06 0.10 0.05 0.01 TTDA -0012“. 0003 0008 -0004 -0006 0040* *Significant at alpha < 0.05. 67 Table l3.--Intercorrelations for seven variables included in this study: Reading--1981/82. Read AFDC SMOB PED FAM FTA Read AFDC -0.42* SMOB -0.36* 0.73* PED 0.15* -0.26* -0.18* FM 0039* -0073* -0044* 0030* FTA -0.02 -0.07 -0.04 0.31 0.04 TTDA 0.17* -0.08 0.02 -0.08 0.13* 0.30* *Significant at alpha < 0.05. In the area of mathematics. the dependent variable of mean Stanford Achievement Test mathematics percentile was moderately corre- lated with Aid to Families with Dependent Children. Student Mobility. Parent Education. Family Make-Up. and Total Teacher Days Absent (1981/82 only) and was not at all correlated with the independent variables of Frequency of Teacher Absence and Total Teacher Days Absent (1980/81 only). These findings are shown in Tables 14 and 15. Table l4.-Intercorrelations for seven variables included in this study: Mathematics--1980/81. Math AFDC SMOB PED FAM FTA Math AFDC 0.26* SMOB -0.l4* 0.83* PED 0.24* -0.84* -0.71* FAM 0.27* -0.80* -0.66* 0.61* FTA 0.01 0.28 0.10 -0.04 —0.06 TTDA -0.10 0.18* 0.16* -0.l4* 0.40* -0 025* 68 Table 15.--Intercorrelations for seven variables included in this study: Mathematics--1981/82. Math AFDC SMOB PED FAM FTA Math AFDC -O.28* SMOB -0.13* 0.73* PED 0.18* -0.26* -0.18* FAM O.27* -O.73* -O.44* 0.30* FTA -0.04 -0.07 -0.04 0.03 0.04 TTDA 0.19* -0.08 0.02 -0.08 0.13* 0.30* *Significant at alpha < 0.05. W Multiple-regression analysis incorporating the forward inclusion of the variables Frequency of Teacher Absence and Total Teacher Days Absent was used to test the hypotheses formulated for this research. Each hypothesis was tested in two ways. In Part One. the data were analyzed for all elementary grades (1-6) as a whole. In Part Two. the data were analyzed for each of the elementary grades as a separate group. The findings of these analyses are presented below. .Genena1_flxnntbesbs_1 The proposed relationship between average classroom reading achievement and teacher absenteeism (as defined by Frequency of Teacher Absence [FTA] and Total Teacher Days Absent [TTDAll will contribute significantly (alpha < 0.05) to the overall relationship between reading achievement and the selected demographic variables of Student Mobility rate. Aid to Families with Dependent Children. Family Make-Up. and Parent Education for the 1980/81 school year. 69 Magr-Analysis of all elementary grades (1-6) as a total: 1980/81 school year. The test of Hypothesis I provided the following data relative to Reading-1981. (See Table 16.) Table l6.--Multiple-regression analysis of Reading-1981 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Education). Source df Mean Square F Signif. of F Regression 3 933.54169 13.10058 0.00* Residual 193 71.25957 *Significant at alpha < 0.05. Discussion: An overall statistically significant correlation existed between reading achievement and the three independent variables of Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Education; F (3.193) = 13.10058 at alpha < 0.05. The demographic variables of Student Mobility. Family Make-Up. and Aid to Families with Dependent Children were not included as inde- pendent contributors. However. they did influence the above relation- ship through their intercorrelations with the Parent Education vari— able. As the overall regression was found to be significant (Table 16). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent 70 components. Table 17 includes a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Reading-1981). Table l7.--Summary of the predicted relationship between Reading-1981 and Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Parent Education-1981. Variable Multiple R R2 F Signif. of F FTA-81 0.0700 0.0049 1.495 0.13 TTDA-81 0.1607 0.0258 1.579 0.11 FED-81 0.4113 0.1691 5.770 0.00* *Significant at alpha < 0.05. Table 17 shows that the statistically significant relationship between Reading-1981 and the three independent variables of Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Parent Education-1981 was caused primarily by its correlation with the Parent Education variable. Parent Education accounted for 17% of the variation in the Reading-1981 variable. as shown in the variable column. R2 = 0.1691. This value was significant for F = 5.770 at alpha = 0.00 m.—-Analysis of each elementary grade as a separate group. The test of Hypothesis I provided the following data. EnjuuLJ; The test of Hypothesis I provided the following data relative to Reading-1981 for Grade 1. (See Table 18.) 71 Table 18am-Multiple-regression analysis of Grade 1 Reading-1981 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Education). Source df Mean Square F Signif. of F Regression 3 314.92146 3.66638 0.02* Residual 31 85.89429 *Significant at alpha < 0.05. .Qisgussjgn: An overall statistically significant correlation existed between reading achievement and the three independent variables of Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Education; F (3.31) = 3.66638 at alpha < 0.05. The demographic variables of Student Mobility. Family Make-Up. and Aid to Families With Dependent Children were not included as inde- pendent contributors. However. they did influence the above relation- ship through their intercorrelations with the Parent Education vari- able. as shown in Appendix Table B1. As the overall regression was found to be significant (Table 18). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 19 includes the summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Reading-1981). 72 Table 19.--Summary of the predicted relationship between Grade 1 Reading 1981 and Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Parent Education-1981. Variable Multiple R R2 F Signif. of F FTA-81 0.07000 0.00490 1.251 0.22 TTDA-81 0.35193 0.12385 0.898 0.37 PED-Bl 0.51157 0.26189 2.408 0.02* *Significant at alpha < 0.05. Table 19 shows that the statistically significant relationship between Grade 1 Reading-1981 and the three independent variables of Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Parent Education-1981 was caused primarily by its correlation with the Parent Education variable. Parent Education accounted for 26.2% of the variation in the Reading-1981 variable. as shown in the variable column. R2 = 0.26189. This value was significant for F = 2.408 at alpha = 0.02. .Gnsgs_2: The test of Hypothesis I provided the following data relative to Reading-1981 for Grade 2 (Table 20). 'Djssussign: An overall statistically significant correlation existed between reading achievement and the three independent variables of Frequency of Teacher Absence. Total Teacher Days Absent. and Student Mobility; F (3.31) = 6.76429 at alpha < 0.05. 73 Table 20.--Multiple—regression analysis of Grade 2 Reading-1981 correlated with three independent variables (Frequency of Teacher Absence. Tetal Teacher Days Absent. and Student Mobility). Source df Mean Square F Signif. of F Regression 3 386.37637 6.76429 0.00* Residual 31 57.12002 *Significant at alpha < 0.05. The demographic variables of Parent Education. Family Make-Up. and Aid to Families with Dependent Children were not included as inde- pendent contributors. However. they did influence the above relation- ship through their high intercorrelations with the Student Mobility variable. as shown in Appendix Table BB. As the overall regression was found to be significant (Table 20). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 21 is a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Reading-1981). Table 21 shows that the significant relationship between Reading-1981 and the three independent variables of Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Student Mobility-1981 was caused primarily by its correlation with the Student Mobility variable. Student Mobility accounted for 39.6% of the varia- tion in the Reading-1981 variable. as shown in the variable column. T. 8x1 74 R2 = 0.39563. This value was significant for F = 4.294 at alpha = 0.00. Table 21.--Summary of the predicted relationship between Grade 2 Reading-1981 and Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Student Mobility-1981. Variable Multiple R R2 F Signif. of F FTA-81 0.07000 0.00490 1.457 0.15 TTDA—81 0.19019 0.03617 0.939 0.35 SMOB-81 0.62899 0.39563 4.294 0.00% *Significant at alpha < 0.05. .GLQQQJ3: 'The test of Hypothesis I provided the following data relative to Reading-1981 for Grade 3 (Table 22). Table 22.--Mu1tiple—regression analysis of Grade 3 Reading-1981 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Education). Source df Mean Square F Signif. of F Regression 3 182.68544 4.02269 0.01* Residual 31 45.41374 *Significant at alpha < 0.05. Djsgussign: An overall statistically significant correlation existed between reading achievement and the three independent variables of Ec ar pe sf V6 DE CC ti- 5‘8 75 of Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Education; F (3.31) = 4.01169 at alpha < 0.05. The demographic variables of Student Mobility. Family Make-Up. and Aid to Families with Dependent Children were not included as inde- pendent contributors. However. they did influence the above relation- ship through their moderate intercorrelations with the Parent Education variable. as shown in Appendix Table BS. As the overall regression was found to be significant (Table 22). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 23 is a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Reading-1981). Table 23.--Summary of the predicted relationship between Grade 3 Reading-1981 and Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Parent Education-1981. Variable Multiple R R2 F Signif. of F FTArBl 0.07000 0.00490 0.035 0.97 TTDA-81 0.36058 0.13002 1.398 0.17 FED-81 0.52935 0.28021 2.543 0.01* *Significant at alpha < 0.05. As shown in Table 23. the significant relationship between Reading-1981 and the three independent variables of Frequency of 76 Teacher Absence-1981. Total Teacher Days Absent~1981. and Parent Education-1981 was caused primarily by its correlation with the Parent Education variable. Parent Education accounted for 28% of the variation in the Reading-1981 variable. as shown in the variable column. R2 = 0.28021. This value was significant for F = 2.543 at alpha = 0.01. fihguuLfi; The test of Hypothesis I provided the following data relative to Reading-1981. Grade 4 (Table 24). Table 24.--Multiple—regression analysis of Grade 4 Reading-1981 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children). Source df Mean Square F Signif. of F Regression 3 150.72496 3.27956 0.03* Residual 31 45.95893 *Significant at alpha < 0.05. .Djsgussign: An overall statistically significant correlation existed between reading achievement and the three independent variables of Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children; F (3.31) = 3.27956 at alpha < 0.05. The demographic variables of Student Mobility. Family Make-Up. and Parent Education were not included as independent contributors. However. they did influence the above relationship through their high 77 intercorrelations with the variable Aid to Families with Dependent Children. as shown in Appendix Table B7. Because the overall regression was found to be significant (Table 24). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 25 shows a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Reading-1981). Table 25.--Summary of the predicted relationship between Grade 4 Reading-1981 and Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Aid to Families with Dependent Children-1981. Variable Multiple R R2 F Signif. of F FTA-81 0.07000 0.00490 1.379 0.17 TTDA-81 0.35575 0.12656 0.604 0.55 AFDC-81 0.49083 0.24092 2.161 0.03* *Significant at alpha < 0.05. Table 25 shows that the significant relationship between Reading-1981 and the three independent variables of Frequency of Teacher Absence-1981. Tetal Teacher Days Absent-1981. and Aid to Families with Dependent Children-1981 was caused primarily by its correlation with the Aid to Families with Dependent Children variable. Aid to Families with Dependent Children accounted for 24% of the 78 variation in the variable column. R2 = 0.24092. This value was significant for F = 2.161 at alpha = 0.03. finds}: The test of Hypothesis I provided the following data relative to Reading-1981. Grade 5 (Table 26). Table 26am-Multiple-regression analysis of Grade 5 Reading-1981 correlated with two independent variables (Frequency of Teacher Absence and Total Teacher Days Absent). Source df Mean Square F Signif. of F Regression 2 368.31977 3.04859 0.06 Residual 26 120.81652 .Qisgussjgn: An analysis of the results shown in Table 26 indicated that no statistically significant correlation existed between reading achievement and the two independent variables of Frequency of Teacher Absence and Total Teacher Days Absent at the alpha < 0.05 level. .Gnsns_§: The test of Hypothesis I provided the following data relative to Reading-1981. Grade 6 (Table 27). .Qisgnssion: An overall statistically significant correlation existed between reading achievement and the three independent variables of Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Education; F (3.24) = 3.14041 at alpha < 0.05. 79 Table 27.--Multiple-regression analysis of Grade 6 Reading-1981 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Education). Source df Mean Square F Signif. of F Regression 3 130.71852 3.14041 0.04* Residual 24 41.62466 *Significant at alpha < 0.05. The demographic variables of Student Mobility. Family Make-Up. and Aid to Families with Dependent Children were not included as independent contributors. However. they did influence the above relationship through their moderate intercorrelations with the Parent Education variable. as shown in Appendix Table 811. As the overall regression was found to be significant (Table 27). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 28 includes a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Reading-1981). As shown in Table 28. the statistically significant relationship between Reading-1981 and the three independent variables of Frequency of Teacher-Absence-l981. Total Teacher Days Absent-1981. and Parent Education-1981 was caused primarily by its correlation with the Parent Education variable. Parent Education accounted for 28% of the variation in the Reading-1981 variable. as shown in the variable 80 column. R2 = 0.28189. This value was significant for F = 3.027 at alpha = 0.00. Table 28.--Summary of the predicted relationship between Grade 6 Reading-1981 and Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Parent Education-1981. Variable Multiple R R2 F Signif. of F FTArBl 0.07000 0.00490 0.754 0.45 TTDA-81 0.08791 0.00773 0.093 0.92 FED-81 0.53094 0.28189 3.027 0.00% *Significant at alpha < 0.05. W The proposed relationship between average classroom reading achievement and teacher absenteeism (as defined by Frequency of Teacher Absence [FTA] and Total Teacher Days Absent [TTDA1) will contribute significantly (alpha < 0.05) to the overall relationship between reading achievement and the selected demographic variables of Student Mobility Rate. Aid to Families with Dependent Children. Family Make-Up. and Parent Education for the 1981/82 school year. £2uj;jnua--Analysis of all elementary grades (1-6) as a total: 1981/82 school year. The test of Hypothesis II provided the following data relative to Reading-1982. (See Table 29.) .Dlssussign: An overall statistically significant correlation existed between reading achievement and the three independent variables of Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children; F (3.193) = 16.41097 at alpha < 0.05. 81 Table 29.--Multiple—regression analysis of Reading-1982 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children). Source df Mean Square F Signif. of F Regression 3 1004.52860 16.41097 0.00* Residual 193 61.21079 *Significant at alpha < 0.05. The demographic variables of Student Mobility. Family Make-Up. and Parent Education were not included as independent contributors. However. they did influence the above relationship through their intercorrelations with the Aid to Families with Dependent Children variable. Because the overall regression was found to be significant (Table 29). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 30 shows a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Reading-1982). Table 30 shows that the significant relationship between Reading-1982 and the three independent variables of Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Aid to Families with Dependent Children-1982 was caused primarily by its correlation with the variable Aid to Families with Dependent Children. Aid to Families with Dependent Children accounted for 20% of the 82 variation in the Reading-1982 variable (R2 = 0.2032). whereas Total Teacher Days Absent accounted for 3.2% of the variation (R2 = 0.0323). These two values. respectively. were significant for F = 2.374 at alpha = 0.01 and for F = 6.433 at alpha = 0.00. Table 30.--Summary of the predicted relationship between Reading-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Aid to Families with Dependent Children- 1982. Variable Multiple R R2 F Signif. of F FTA-82 0.02000 0.0004 1.362 0.17 TTDA-82 0.07998 0.0323 2.374 0.01 AFDC-82 0.45083 0.2032 6.433 0.00* *Significant at alpha < 0.05. m.--Analysis of each elementary grade as a separate group. The test of Hypothesis 11 provided the following data. .Gnnds_1: ‘The test of Hypothesis II provided the following data relative to Reading-1982. Grade 1 (Table 31). .D1sgnss19n: An overall statistically significant correlation existed between reading achievement and the three independent variables of Frequency of Teacher Absence. Tetal Teacher Days Absent. and Family Make-Up; F (3.31) = 6.77701 at alpha < 0.05. The demographic variables of Student Mobility. Aid to Families with Dependent Children. and Parent Education were not included as 83 independent contributors. However. they did influence the above rela- tionship through their intercorrelations with the Family Make-Up vari- able. as shown in Appendix Table B2. Table 31.--Multiple-regression analysis of Grade 1 Reading-1982 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up). Source df Mean Square F Signif. of F Regression 3 488.31259 6.77701 0.00* Residual 31 72.05432 *Significant at alpha < 0.05. As the overall regression was found to be significant (Table 31). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 32 contains a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Reading-1982). Table 32.--Summary of the predicted relationship between Grade 1 Reading-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Family Make-Up-1982. Variable Multiple R R2 F Signif. of F FTAPBZ 0.02000 0.00040 1.500 0.14 FAM—82 0.62935 0.39608 4.356 0.00* *Significant at alpha < 0.05. 84 Table 32 shows that the significant relationship between Reading-1982 and the three independent variables of Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Family Make- Up-l982 was caused by its correlation with the Family Make-Up variable. Family Make-Up accounted for 39.6% of the variation in the Reading-1982 variable. as shown in the variable column. R2 = 1.39608. This value was significant for F = 4.356 at alpha = 0.00. W: The test of Hypothesis II provided the following data relative to Reading-1982. Grade 2 (Table 33). Table 33.--Multiple—regression analysis of Grade 2 Reading-1982 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up). Source df Mean Square F Signif. of F Regression 3 394.94803 5.03748 0.00* Residual 31 76.4.675 *Significant at alpha < 0.05. .Dlsgnss1on: An overall statistically significant correlation «existed between reading achievement and the three independent vari- ables of Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children; F (3.31) = 5.03748 at alpha < 0.05. 85 The demographic variables of Student Mobility. Family Make-Up. and Parent Education were not included as independent contributors. However. they did influence the above relationship through their intercorrelations with the Aid to Families with Dependent Children variable. as shown in Appendix Table B4. Because the overall regression was found to be significant (Table 33). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 34 includes a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Reading-1982). Table 34.--Summary of the predicted relationship between Grade 2 Reading-1982 and Frequency of Teacher Absence-1982. Tetal Teacher Days Absent-1982. and Aid to Families with Dependent Children-1982. Variable Multiple R R2 F Signif. of F FTA-82 0.02000 0.00040 0.078 0.93 TTDA-82 0 .36055 0 . 13000 1.958 0 .05* AFDC-82 0 .57248 0 .3 2773 3 .020 0 .00* *Significant at alpha < 0.05. As shown in Table 34. the significant relationship between Reading-1982 and the three independent variables of Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Aid to Fanrilies with Dependent Children-1982 was caused primarily by its 86 correlation with Aid to Families with Dependent Children. Aid to Families with Dependent Children accounted for 32.7% of the variation in the Reading-1982 variable (R2 = 0.32773). whereas Total Teacher Days Absent accounted for 13% of the variation (R2 = 0.13000). These two values. respectively. were significant for F = 3.020 at alpha = 0.00 and for F = 1.958 at alpha = 0.05. 51103.3: The test of Hypothesis II provided the following data relative to Reading-1982. Grade 3 (Table 35). Table 35.--Multiple—regression analysis of Grade 3 Reading-1982 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Student Mobility). Source df Mean Square F Signif. of F Regression 3 244.84603 6.10171 0.00* Residual 31 40.12742 *Significant at alpha < 0.05. .lecussign: An overall statistically significant correlation existed between reading achievement and the three independent variables of Frequency of Teacher Absence. Total Teacher Days Absent. and Student Mobility; F (3.31) = 6.10171 at alpha < 0.05. The demographic variables of Aid to Families with Dependent (Mlildren. Family Make-Up. and Parent Education were not included as independent contributors. However. they did influence the above 87 relationship through their intercorrelations with the Student Mobility variable. as shown in Appendix Table B6. As the overall regression was found to be significant (Table 35). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 36 contains a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Reading-1982). Table 36.--Summary of the predicted relationship between Grade 3 Reading-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Student Mobility-1982. Variable Multiple R R2 F Signif. of F FTA-82 0.02000 0.00040 0.138 0.89 TTDA-82 0.33952 0.11527 2.096 0.04% SMOB-82 0.60931 0.37126 3.553 0.00* *Significant at alpha < 0.05. Table 36 shows that the significant relationship between Reading-1982 and the three independent variables of Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Student Mobility-1982 was caused by its correlation with Student Mobility and Total Teacher Days Absent. Student Mobility accounted for 37% of the variation in the Reading-1982 variable (R2 = 0.37126). whereas Total Teacher Days Absent accounted for 11.5% of the variation (R2 = 88 0.11527). These two values. respectively. were significant for F = 2.096 at alpha = 0.04 and for F = 3.553 at alpha = 0.00. mm: The test of Hypothesis II provided the following data relative to Reading-1982. Grade 4 (Table 37). Table 37.--Multiple—regression analysis of Grade 4 Reading-1982 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children). Source df Mean Square F Signif. of F Regression 3 211.84521 3.90992 0.01* Residual 31 54.18153 *Significant at alpha < 0.05. Discussion: An overall statistically significant correlation existed between reading achievement and the three independent variables 15f Frequency of Teacher Absence. Tetal Teacher Days Absent. and Aid to Families with Dependent Children; F (3.31) = 3.90992 at alpha < 0.05. The demographic variables of Student Mobility. Family Make-Up. and Parent Education were not included as independent contributors. However. they did influence the above relationship through their intercorrelations with the Aid to Families with Dependent Children *variable. as shown in Appendix Table BB. Because the overall regression was found to be significant (Table 37). the contribution of each independent variable was tested by 89 partitioning the total explained sum of squares into its independent components. ‘Table 38 contains a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Reading-1982). Table 38.--Summary of the predicted relationship between Grade 4 Reading-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Aid to Families with Dependent Children-1982. Variable Multiple R R2 F Signif. of F FTArBZ 0.02000 0.00040 1.154 0.25 TTDA-82 0.35906 0.12892 2.018 0.05* AFDC-82 0.52394 0.27451 2.494 0.02* *Significant at alpha < 0.05. Table 38 shows that the significant relationship between Reading-1982 and the three independent variables of Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Aid to Families with Dependent Children-1982 was caused by its correlation with Aid to Families with Dependent Children and Total Teacher Days Absent. Aid to Families with Dependent Children accounted for 27.4% of the variation in the Reading-1982 variable (R2 = 0.2745). whereas Total Teacher Days Absent accounted for 12.8% of the variation (R2 = 0.12892). These two values. respectively. were significant for F = 2.494 at alpha = 0.02 and for F = 2.018 at alpha = 0.05. 90 .Gnsds_5; The test of Hypothesis II provided the following data relative to Reading-1982. Grade 5 (Table 39). Table 39.--Multip1e—regression analysis of Grade 5 Reading-1982 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children). Source df Mean Square F Signif. of F Regression 3 124.98931 3.10912 0.04* Residual 25 40.20090 *Significant at alpha < 0.05. ‘Disgussion: An overall statistically significant correlation existed between reading achievement and the three independent variables of Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children; F (3.25) = 3.10912 at alpha < 0.05. The demographic variables of Student Mobility. Family Make-Up. and Parent Education were not included as independent contributors. However. they did influence the above relationship through their intercorrelations with the Aid to Families with Dependent Children variable. as shown in Appendix Table 810. As the overall regression was found to be significant (Table 39). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 40 contains a summary of the independent variables 91 left in the regression equation and their respective individual correlations with the dependent variable (Reading-1982). Table 40.--Summary of the predicted relationship between Grade 5 Reading-1982 and Frequency of Teacher Absence-1982. Tetal Teacher Days Absent-1982. and Aid to Families with Dependent Children-1982. Variable Multiple R R2 F Signif. of F FTA-82 0.02000 0.00040 0.234 0.81 TTDA-82 0.10345 0.01070 0.003 0.99 AFDC-82 0.52127 0.27172 2.993 0.00* *Significant at alpha < 0.05. Table 40 shows that the significant relationship between Reading-1982 and the three independent variables of Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Aid to Fami- lies with Dependent Children-1982 was caused primarily by its correla- tion with Aid to Families with Dependent Children. Aid to Families *with Dependent Children accounted for 27% of the variation in the Reading-1982 variable. as shown in the variable column. R2 = 0.27172. This value was significant for F = 2.993 at alpha = 0.00. mm: The test of Hypothesis II provided the following data relative to Reading-1982. Grade 6 (Table 41). .Disgnssion: An analysis of the results shown in Table 27 indicated that no statistically significant correlation existed between reading achievement and the two independent variables of, Frequency of 92 Teacher Absence and Total Teacher Days Absent at the alpha < 0.05 level. Table 41.--Multip1e—regression analysis of Grade 6 Reading-1982 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children). Source df Mean Square F Signif. of F Regression 2 15.66617 0.42215 0.66 Residual 25 37.11029 WW1 The proposed relationship between average classroom mathematics achievement and teacher absenteeism (as defined by Frequency of Teacher Absence [FTA] and Total Teacher Days Absent [TTDA]) will contribute significantly (alpha < 0.05) to the overall relationship between mathematics achievement and the selected demographic vari- ables of Student Mobility Rate. Aid to Families with Dependent Children. Family Make-Up. and Parent Education for the 1980/81 school year. Mr-Analysis of all elementary grades (1-6) as a total: 1980/81 school year. (See Table 42.) .Qisgussiqn: An overall statistically significant correlation existed between mathematics achievement.and the three independent ‘variables of Frequency of Teacher Absence. Tbtal Teacher Days Absent. and Family Make-Up; F (3.193) = 5.27304 at alpha < 0.05. The demographic variables of Student Mobility. Parent Educa- tion. and Aid to Families with Dependent Children were not included as 93 independent contributors. However. they did influence the above rela- tionship through their intercorrelations with the Family Make-Up variable. as shown in Table 14. Table 42.--Multiple—regression analysis of Mathematics-1981 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up). Source df Mean Square F Signif. of F Regression 3 518.91268 5.26304 0.00* Residual 193 98.59559 *Significant at alpha < 0.05. Since the overall regression was found to be significant (Table 42). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. ‘Table 43 includes a summary of the independent variables left in the regression equation and their respective correlations with the dependent variable (Mathematics-1981). Table 43.--Summary of the predicted relationship between Mathematics- 1981 and Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Family Make-Up-l981. Variable Multiple R R2 F Signif. of F FTA-81 0.0700 0.0049 0.656 0.51 TTDA-81 0.1191 0.0142 0.760 0.44 FAM-81 0.2750 0.0756 3.581 0.00* *Significant at alpha < 0.05. 94 Table 43 shows that the significant relationship between Mathematics-1981 and the three independent variables of Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Family Make- Up-1981 was caused by its correlation with the Family Make-Up variable. Family Make-Up accounted for 7.6% of the variation in the Mathematics- 1981 variable. as shown in the variable column. R2 = 0.0756. This value was significant for F = 3.582 at alpha = 0.00. Ma-Analysis of each elementary grade as a separate group. The test of Hypothesis III provided the following data. fihxukLJJ The test of Hypothesis III provided the following data relative to Mathematics-1981. Grade 1 (Table 44). Table 44.--Multiple—regression analysis of Grade 1 Mathematics-1981 correlated with two independent variables (Frequency of Teacher Absence and Total Teacher Days Absent). Source df Mean Square F Signif. of F Regression 2 326.76849 3.10340 0.06 Residual 32 105.30602 .Disgussign: Analysis of the results shown in Table 44 indicated that no statistically significant correlation existed between loathematics achievement and the two independent variables of Frequency of Teacher Absence and Total Teacher Days Absent at the alpha < 0.05 level. 95 .GnadsLZ: The test of Hypothesis III provided the following data relative to Mathematics-1981. Grade 2 (Table 45). Table 45.--Multiple—regression analysis of Grade 2 Mathematics-1981 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children). Source df Mean Square F Signif. of F Regression 3 283.22698 4.77684 0.00* Residual 31 59.29116 *Significant at alpha < 0.05. .Qisgnssign: An overall statistically significant correlation existed between mathematics achievement and the three independent variables of Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children; F (3.31) = 4.77688 at alpha < 0.05. The demographic variables of Student Mobility. Parent Education. and Family Make-Up were not included as independent contributors. However. they did influence the above relationship through their high intercorrelations with the Aid to Families with Dependent Children variable. as shown in Appendix Table B3. As the overall regression was found to be significant (Table 45). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent 96 components. Table 46 includes a summary of the independent variables left in the regression equation and their respective correlations with the dependent variable (Mathematics-1981). Table 46.--Summary of the predicted relationship between Grade 2 Mathematics-1981 and Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Aid to Families with Dependent Children-1981. Variable Multiple R R2 F Signif. of F FTA-81 0.07000 0.00490 0.312 0.75 TTDA-81 0.34352 0.11800 2.137 0.04* AFDC-81 0.56226 0.31614 2.997 0.00* *Significant at alpha < 0.05. Table 46 shows that the significant relationship between Mathematics-1981 and the three independent variables of Frequency of Teacher Absence-1981. Total Teacher Days Absent-1981. and Aid to Families with Dependent Children-1981 was caused by its correlation with Aid to Families with Dependent Children and Total Teacher Days Absent. Aid to Families with Dependent Children accounted for 31.6% of the variation in the Mathematics-1981 variable (R2 = 0.31614). whereas Total Teacher Days Absent accounted for 11.8% of the variation (R2 = 0.11800). These two values. respectively. were significant for F = 2.997 at alpha = 0.00 and for F = 2.137 at alpha = 0.04. The test of Hypothesis III provided the following emu data relative to Mathematics-1981. Grade 3 (Table 47). 97 Table 47.--Multiple—regression analysis of Grade 3 Mathematics-1981 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up). Source df Mean Square F Signif. of F Regression 3 220.40073 2.64832 0.06 Residual 31 83.22274 Discussion: An analysis of the results shown in Table 47 indicated that no statistically significant correlation existed between mathematics achievement and the two independent variables of Frequency of Teacher Absence and Total Teacher Days Absent at the alpha < 0.05 level. fifljuELfifi The test of Hypothesis III provided the following data relative to Mathematics-1981. Grade 4 (Table 48). Table 48.--Multiple-regression analysis of Grade 4 Mathematics-1981 correlated with two independent variables (Frequency of Teacher Absence and Total Teacher Days Absent). Source df Mean Square F Signif. of F Regression 2 52.27680 0.60962 0.54 Residual 32 85.75320 98 .Qisgussign: Analysis of the results shown in Table 48 indicated that no statistically significant correlation existed between mathematics achievement and the two independent variables of Frequency of Teacher Absence and Total Teacher Days Absent at the alpha < 0.05 level. ‘Gngds_5: The test of Hypothesis III provided the following data relative to Mathematics-1981. Grade 5 (Table 49). Table 49.--Multiple-regression analysis of Grade 5 Mathematics-1981 correlated with two independent variables (Frequency of Teacher Absence and Total Teacher Days Absent). Source df Mean Square F Signif. of F Regression 2 399.61844 3.22554 0.06 Residual 26 123.89212 .Qissussign: Analysis of the results shown in Table 49 indicated that no statistically significant correlation existed between lnathematics achievement.and the two independent variables of Frequency of Teacher Absence and Total Teacher Days Absent at the alpha < 0.05 level. W: The test of Hypothesis III provided the following data relative to Mathematics-1981. Grade 6 (Table 50). 99 Table 50.--Mu1tiple—regression analysis of Grade 6 Mathematics-1981 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children). Source df Mean Square F Signif. of F Regression 3 80.41472 1.44269 0.25 Residual 24 55.73926 .Qisgnssign: An analysis of the results shown in Table 50 indicated that no statistically significant correlation existed between mathematics achievement and the two independent variables of Frequency of Teacher Absence and Total Teacher Days Absent at the alpha < 0.05 level. Wham! The proposed relationship between average classroom mathematics achievement and teacher absenteeism (as defined by Frequency of Teacher Absence [FTA] and Total Teacher Days Absent [TTDA]) will contribute significantly (alpha < 0.05) to the overall relationship between mathematics achievement and the selected demographic variables of Student Mobility Rate. Aid to Families with Dependent Children. Family Make-Up. and Parent Education for the 1981/82 school year. Ear_t_Qn.e.--Analysis of all elementary grades (1-6) as a total: 1981/82 school year. The test of Hypothesis IV provided the following data relative to Mathematics-1982. (See Table 51.) 100 Table Slam-Multiple-regression analysis of Mathematics-1982 correlated with three independent variables (Frequency of Teacher Absence. Tetal Teacher Days Absent. and Aid to Families with Dependent Children). Source df Mean Square F Signif. of F Regression 3 692.76946 8.77647 0.00* Residual 193 78.93484 *Significant at alpha < 0.05. .Qissussion: An overall statistically significant correlation existed between mathematics achievement and the three independent variables of Frequency of Teacher Absence. Tetal Teacher Days Absent. and Aid to Families with Dependent Children; F (3.193) = 8.77647 at alpha < 0.05. The demographic variables of Student Mobility. Family Make-Up. and Parent Education were not included as independent contributors. However. they did influence the above relationship even with their low intercorrelations with the Aid to Families with Dependent Children variable. as shown in Table 15. As the overall regression was found to be significant (Table 51). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 52 contains a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Mathematics-1982). 101 Table 52.--Summary of the predicted relationship between Mathematics- 1982 and Frequency of Teacher Absence-1982. Tetal Teacher Days Absent-1982. and Aid to Families with Dependent Children-1982. Variable Multiple R R2 F Signif. of F FTA-82 0.07000 0.0049 1.631 0.10 TTDA-82 0.21045 0.0442 2.800 0.00* AFDC-82 0.36460 0.1200 4.076 0.005 *Significant at alpha < 0.05. As shown in Table 52. the significant relationship between Mathematics-1982 and the three independent variables of Frequency of Teacher Absence—1982. Total Teacher Days Absent-1982. and Aid to Families with Dependent Children-1982 was caused by its correlation with Aid to Families with Dependent Children and Total Teacher Days Absent. Aid to Families with Dependent Children accounted for 12% of the variation in the Mathematics-1982 variable (R2 = 0.1200), whereas Tetal Teacher Days Absent accounted for'4.4% of the variation (R2 = 0.0442). These two values were significant for F = 2.800 at alpha = 0.00 and for F = 4.076 at alpha = 0.00. respectively. EaLt_Iw_Q.--Ana1ysi s of each elementary grade as a separate group. The test of Hypothesis IV provided the following data. Grads_1: 'The test of Hypothesis IV provided the following data relative to Mathematics-1982 for Grade 1 (Table 53). 102 Table 53.--Multip1e-regression analysis of Grade 1 Mathematics-1982 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up). Source df Mean Square F Signif. of F Regression 3 257.38716 2.91189 0.05* Residual 31 88.39188 *Significant at alpha < 0.05. ‘Disgussign: An overall statistically significant correlation existed between mathematics achievement and the three independent variables of Frequency of Teacher Absence. Tetal Teacher Days Absent. ‘and Family Make-Up; F (3.31) = 2.91189 at alpha < 0.05. The demographic variables of Student Mobility. Aid to Families with Dependent Children. and Parent Education were not included as independent contributors. However. they did influence the above rela- tionship through their intercorrelations with the Family MakePUp vari- able. as shown in Appendix Table 82. As the overall regression was found to be significant (Table 53). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 54 contains a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Mathematics-1982). 103 Table S4.--Summary of the predicted relationship between Grade 1 Mathematics-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Family Make-Up-1982. Variable Multiple R R2 F Signif. of F TTDA-82 0.11652 0.01358 0.766 0.44 FAM-82 0.46888 0.21984 2.863 0.00* *Significant at alpha < 0.05. Table 54 shows that the significant relationship between Mathematics-1982 and the three independent variables of Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Family Make- Up-l982 was caused primarily by its correlation with the Family Make-Up variable. Family Make-Up accounted for 22% of the variation in the Mathematics-1982 variable. as shown in the variable column. R2 = 0.21984. This value was significant for F = 2.863 at alpha = 0.00. 11:10.12: The test of Hypothesis IV provided the following data relative to Mathematics-1982. Grade 2 (Table 55). .Disgnssion: An overall statistically significant correlation existed between mathematics achievement and the three independent variables of Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up; F (3.31) = 5.67504 at alpha < 0.05. The demographic variables of Student Mobility. Aid to Families with Dependent Children. and Parent Education were not included as independent contributors. However. they did influence the above 104 relationship through their intercorrelations with the Family Make- Up variable. as shown in Appendix Table B4. Table 55.--Multiple—regression analysis of Grade 2 Mathematics-1982 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Family Make-Up). Source df Mean Square F Signif. of F Regression 3 405 . 14512 5 . 67504 0 . 00* Residual 31 71.39068 *Significant at alpha < 0.05. As the overall regression was found to be significant (Table 55). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 56 shows a summary of the independent variables left in the regression equation and their respective individual correlations with the dependent variable (Mathematics-1982). As shown in Table 56. the significant relationship between Mathematics-1982 and the three independent variables of Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Family Make- Up-7982 was caused by its correlation with Family Make-Up and Total Teacher Days Absent. The Family Make-Up variable accounted for 35% of the variation in the Mathematics-1982 variable (R2 = 0.3545). whereas Total Teacher Days Absent accounted for 24% of the variation (R2 = 105 0.24249). These two values were significant. respectively. for F = 2.403 at a1pha= 0.02 and for F = 2.319 at alpha = 0.02. Table 56.--Summary of the predicted relationship between Grade 2 Mathematics-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Family Make-Up-1982. Variable Multiple R R2 F Signif. of F FTA-82 0 . 07000 0 .00490 1.192 0 .24 TTDA-82 0.49244 0.24249 2.403 0.02* FAM-82 0.59540 0.35450 2.319 0.02* *Significant at alpha < 0.05. 911013,: The test of Hypothesis IV provided the following data relative to Mathematics-1982. Grade 3 (Table 57). Table 57.--Mu1tiple—regression analysis of Grade 3 Mathematics-1982 correlated with two independent variables (Frequency of Teacher Absence and Total Teacher Days Absent). Source df Mean Square F Signif. of F Regression 2 160.34046 1.77898 0 .18 Residual 32 90.13078 Discussion: An analysis of the results shown in Table 57 indicated that no statistically significant correlation existed between mathematics achievement and the two independent variables of Frequency of Teacher Absence and Total Teacher Days Absent at alpha < 0.05. 106 finxukLfig The test of Hypothesis IV provided the following data relative to Mathematics-1982. Grade 4 (Table 58). Table 58.--Mu1tiple—regression analysis of Grade 4 Mathematics-1982 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Education). Source df Mean Square F Signif. of F Regression 3 198.30395 3.64918 0.02* Residual 31 54.34213 *Significant at alpha < 0.05. .Qissussisn: An overall statistically significant correlation existed between mathematics achievement and the three independent variables of Frequency of Teacher Absence. Total Teacher Days Absent. and Parent Education; F (3.31) = 3.64918 at alpha < 0.05. The demographic variables of Student Mobility. Family Make-Up. and Aid to Families with Dependent Children were not included as inde- pendent contributors. However. they did influence the above relation- ship through their intercorrelations with the Parent Education vari- able. as shown in Appendix Table BB. As the overall regression was found to be significant (Table 58). the contribution of each independent variable was tested by partitioning the total explained sum of squares into its independent components. Table 59 contains a summary of the independent variables 107 left in the regression equation and their respective individual correlations with the dependent variable (Mathematics-1982). Table 59.--Summary of the predicted relationship between Grade 4 Mathematics-1982 and Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Parent Education-1982. Variable Multiple R R2 F Signif. of F FTA-82 0. 07000 0 .00490 1 .957 0 .05* TTDA-82 0.35502 0.12604 2.810 0.00* FED-82 0.51086 0.26098 2.379 0.02. ''Significant at alpha < 0.05. Table 59 shows that the significant relationship between Mathematics-1982 and the three independent variables of Frequency of Teacher Absence-1982. Total Teacher Days Absent-1982. and Parent Education-1982 was caused primarily by its correlation with the vari- ables Parent Education and Total Teacher Days Absent. Parent Education accounted for 26% of the variation in the Mathematics-1982 variable (R2 = 0.26098). Total Teacher Days Absent accounted for 12.6% of the varia- tion (R2 = 0.12604). and Frequency of Teacher Absence accounted for .5% of the variation (R2 = 0.0049). These values were significant for F = 2.379 at alpha = 0.02. for F = 2.810 at alpha = 0.00. and for F = 1.957 at alpha = 0.05. respectively. W: The test of Hypothesis IV provided the following data relative to Mathematics-1982. Grade 5 (Table 60). 108 Table 60.--Multiple—regression analysis of Grade 5 Mathematics-1982 correlated with three independent variables (Frequency of Teacher Absence. Total Teacher Days Absent. and Aid to Families with Dependent Children). Source df Mean Square F Signif. of F Regression 3 110.28302 2.55011 0.07 Residual 25 43.24645 .Disoussion: .An analysis of the results shown in Table 60 indicated that no statistically significant correlation existed between mathematics achievement and the two independent variables of Frequency of Teacher Absence and Total Teacher Days Absent at the alpha < 0.05 level. .Gnouo_o: The test of Hypothesis IV provided the following data relative to Mathematics-1982. Grade 6 (Table 61). Table 61.--Multiple—regression analysis of Mathematics-1982 correlated with two independent variables (Frequency of Teacher Absence and Total Teacher Days Absent). Source df Mean Square F Signif. of F Regression 2 53.74842 1.07171 » 0.35 Residual 25 50.15214 Discussion: An analysis of the results shown in Table 61 indicated that no statistically significant correlation existed between 109 mathematics achievement and the two independent variables of Frequency of Teacher Absence and Total Teacher Days Absent at alpha < 0.05. 50mm The analysis of data was performed using a multiple-regression approach. which included the forward inclusion of two independent variables: Frequency of Teacher Absence and Total Teacher Days Absent. The results indicated that for General Hypothesis I there was no relationship between teacher absenteeism (Frequency of Teacher Absence or Total Teacher Days Absent) and average classroom reading achievement for the 1980/81 school year. This finding was the same when considering each grade (1-6) separately. as well as when consid- ering the total of all elementary classrooms. The results indicated that for General Hypothesis II there was found to be a relationship between teacher absenteeism (Total Teacher Days Absent) and average classroom reading achievement for the 1981/82 school year in grades 2. 3. and 4. as well as for the total of all elementary classrooms. For General Hypothesis III the results indicated there was a relationship between teacher absenteeism (Total Teacher Days Absent) and average classroom mathematics achievement for the 1980/81 school year in grade 3. For General Hypothesis IV the results indicated there was a relationship between teacher absenteeism (Total Teacher Days Absent) and average classroom mathematics achievement for the 1981/82 school 110 year in grades 2 and 4. as well as for the total of all elementary classrooms. A relationship was also found to exist between teacher absenteeism (Frequency of Teacher Absence) and average classroom mathematics achievement for the 1981/82 school year in grade 4. These findings. their implications. and suggestions for using the results of this research are presented and discussed further in Chapter V. CHAPTER V SUMMARY AND CONCLUSIONS Summ This study was designed to determine whether elementary teachers' (grades 1-6) absenteeism had an effect on reading and/or mathematics achievement of pupils assigned to their classrooms. The investigator reviewed the teacher absence data and pupil achievement information for the school years 1980/81 and 1981/82. He sought to answer two specific questions: (1) What has been the effect of elemen- tary teachers' total absenteeism on elementary pupils' achievement in the areas of reading and mathematics? and (2) What has been the effect of elementary teachers' frequency of absenteeism on elementary pupils' achievement in the areas of reading and mathematics? This analysis was conducted by looking at grades 1 through 6 as a total group and by examining each grade level as a separate group. The research design consisted of a multiple-regression approach. incorporating the forward inclusion of two groups of independent vari- ables. The first group of independent variables consisted of Frequency of Teacher Absence and Total Teacher Days Absent. The second group included Aid to Families with Dependent Children. Student Mobility Rate. Parent Education. and Family Make-Up. The forward-inclusion approach allowed the first set of independent variables to be ill 112 considered before the second set. This technique did not result in distortion of the real-life situation because the relationship of reading and mathematics achievement to parental education and socio- economic status has been confirmed by many researchers and was not the primary subject or concern in this project. General Hypothesis I was formulated to determine whether there was a relationship between average classroom reading achievement in 1980/81 and teacher absenteeism. including both Frequency of Teacher Absence and Total Teacher Days Absent. The general hypothesis was examined from two perspectives: the total elementary pupils (grades 1-6) who took the Stanford Achievement Reading Test and each grade (1-6) separately. using the same test results. The results of the analysis performed using a multiple- regression analysis with the forward inclusion of the variables Fre- quency of Teacher Absence and Total Teacher Days Absent showed that General Hypothesis I was not_oonjinmou when viewed from the perspective of all elementary grades as a total. In addition. General Hypothesis I was not confirmed when the data were reviewed for each grade sep- arately. General Hypothesis II was formulated to determine whether there was a relationship between average classroom reading achievement in 1981/82 and teacher absenteeism. including both Frequency of Teacher Absence and Tetal Teacher Days Absent. The general hypothesis was examined from two perspectives: the total elementary pupils (grades il3 1-6) who took the Stanford Achievement Reading Test and each grade (1-6) separately. using the same test results. The data were analyzed using a multiple-regression approach with the forward inclusion of the variables Frequency of Teacher Absence and Total Teacher Days Absent. The results of the analysis showed that General Hypothesis II was oonfinmou from the perspective of all elementary grades as a total. The data showed that there was a statistically significant relationship between Tetal Teacher Days Absent and Reading Achievement for the 1981/82 school year. In addition. the data analysis osniisliy_ooniinmou General Hypothesis II when the analysis was conducted for each grade (1-6) separately. The data showed that there was a statistically significant relationship between Total Teacher Days Absent and reading achievement in grades 2. 3. and 4. General Hypothesis III was designed to determine whether there was.a relationship between average classroom mathematics achievement in 1980/81 and teacher absenteeism. including both Frequency of Teacher Absence and Total Teacher Days Absent. The general hypothesis was examined from two perspectives: total elementary pupils;(grades 1-6) who took the Stanford Achievement Mathematics Test and each grade (1-6) separately. using the same test results. The results were analyzed using a multiple-regression approach with the forward inclusion of the variables Frequency of Teacher Absence and Total Teacher Days Absent. The results showed that General Hypothesis III was noi_ooniinmou when viewed from the perspective of 114 all elementary grades as a total. 'The data oantia11y_oon£inmou General Hypothesis III when the analysis was conducted for each grade (1-6) separately. 'The data showed that there was a statistically significant relationship between Total Teacher Days Absent and mathematics achieve- ment in grade 2. General Hypothesis IV was formulated to determine whether there was.a relationship between average classroom mathematics achievement in 1981/82 and teacher absenteeism. including both Frequency of Teacher Absence and Total Teacher Days Absent. The general hypothesis was examined from two perspectives: total elementary pupils (grades 1-6) who took the Stanford Achievement Mathematics Test and each grade (1-6) separately. using the same test results. The results were analyzed using a multiple-regression approach with the forward inclusion of the variables Frequency of Teacher Absence and Total Teacher Days Absent. The results showed that General Hypothesis IV was ooniinmou when viewed from the perspective of all elementary grades as a total. The data showed that there was a statis- tically significant relationship between Total Teacher Days Absent and mathematics achievement for the 1981/82 school year. In addition. the data ouniiu11y_ooniinmou,General Hypothesis IV when the analysis was conducted for each grade (1-6) separately. There was a statistically significant relationship between Total Teacher Days Absent and mathe- matics achievement in grades 2 and 4. In addition. a statistically significant relationship existed between Frequency of Teacher Absence and mathematics achievement in grade 4. 115 The findings of this study related to all elementary grades as a total are illustrated in Table 62. Table 62.--Summary of results of the research analysis for all elementary grades as a total. Grades 1-6 Reading Mathematics 1980/81 Not Confirmed Not Confirmed 1981/82 Confirmed for Confirmed for Total Teacher Total Togcngn W1 9.415.895.9111 The findings of this study related to each individual grade level are illustrated in Table 63. Analysis of the data showed that teacher absenteeism did contribute to second graders' achievement in reading in both 1980/81 and 1981/82 and to their mathematics achievement in 1980/81; to third graders' reading achievement in 1981/82; and to fourth graders' reading and mathematics achievement in 1981/82. The data further showed that. when all grades were considered as a total. teacher absenteeism con- tributed to students' achievement in both reading and mathematics in 1981/82. It should be noted that in attempting to answer question one. "What has been the effect of elementary teachers' total absenteeism on elementary pupils' achievement in the area of reading and/or mathematics?." Total Teacher Days Absent was shown to contribute to studei during concel teachi the ar contri and on Table Grade ll6 student achievement in the middle grades (2. 3. and 4). particularly during the 1981/82 school year. Further. it should be pointed out that concerning question two. "What has been the effect of elementary teachers' frequency of absenteeism on elementary pupils' achievement in the areas of reading and mathematics?." Frequency of Teacher Absence contributed to pupil learning only in grade 4. for the year 1981/82. and only in mathematics. Table 63.--Summary of results of the research analysis. by grade level. Reading Mathematics Grade 1980/81 1981/82 1980/81 1981/82 1 Not Not Not Not Confirmed Confirmed Confirmed Confirmed 2 Not Confirmed for Confirmed for Confirmed for Confirmed Total Teacher Total Teacher Total Teacher Days Absent Days Absent Days Absent 3 Not Doniinmou_for Not Not Confirmed Total Teacher Confirmed Confirmed Days Absent 4 Not Confirmed for Not Confirmed for Confirmed Total Teacher Confirmed Total Teacher Days Absent Days Absent & Frequency of Teacher Absence 5 Not Not Not Not Confirmed Confirmed Confirmed Confirmed 6 Not Not Not Not Confirmed Confirmed Confirmed Confirmed 117 Discussion Harnischfeger and Wiley claimed that the cost in student learning when the regular teacher is absent is undoubtedly the most critical cost with which administrators must be concerned:l 'They suggested that as the relationship between teacher contact time and student progress becomes widely known. more difficult questions will be asked about the causes of lost instruction at school. One serious loss of teaching time occurs when regular teachers are absent and substi- tutes are in the classroom. Kopelman. Schneller. and Silver stated that "illnesses" are especially likely to occur in those organizations that have a paid-leave program. According to these authors. "a common behavioral outcome of such a program might be labeled Parkinson's Law of Sick Leave Abuse: the days lost due to sickness expand to equal the number of paid sick days allowedJ' Indeed. they stated. "there is evidence indicating that organizations with paid sick leave programs experience nearly twice the rate of absenteeism compared to organiza- tions without such programs."2 The attention to teacher absenteeism led Elliott and Manlove to observe that Among other things. schools may be bargaining away student progress. and neither party at the table has that right. Student irresponsibility is evidenced in such phenomena as increasing 1Annegret Harnischfeger and David E. Wiley. "Schooling Cutbacks and Achievement Declines: Can We Afford Them?" AdminisILAIQLEleuxfluufls 24 (1975). 2Richard E. Kopelman. George 0. Schneller. and John J. Silver. Jr.. "Parkinson's Law and Absenteeism: A Program to Rein in Sick Leave Costs." EensonneJJdmiantnatm: (May 1981): 57. 118 vandalism and falling test scores. We suggest the frequegtly absented teacher could be one of the many causal factors. Teacher absence was also highlighted by Bamber. who stated. "Public schools have certain expectations for regular attendance of students and teachers. When they are not met. classroom performance suffers." She continued: Even more dire consequences may result when teachers are absent. Most substitutes. called on short notice with no time for preparation. are little more than babysitters in the classroom. Even those who know their subject are at a disadvantage because they do not know the4students. and it may be harder for them to maintain discipline. Olson highlighted the lack of effectiveness of substitute teachers. In his study he found that "what clearly stands out was the abysmal performance of substitute teachers in contrast to that of the regular classroom teacher." He concluded that "either substitute teacher performance must be improved or alternatively less expensive methods of handling teacher absences should be initiated."5 In response to Olson's findings. the National Association of Secondary School Principals suggested that "if substitutes are as ineffective as the Olson Study reported. then there is concern that substitutes 3Peggy G. Elliott and Donald C. Manlove. "The Cost of Sky- rocketing Teacher Absenteeism.” Eni_Do1ta_Kaooan. (December 1977): 210. 4Chrissie Bamber. "Student and Teacher Absenteeisms." Phi Delta Kappa Educational Foundation. Fastback 126 (Bloomington. Ind.: Phi Delta Kappa. 1979). p. 12. 5M. N. Olson. "Identifying Qual ity in School Classroom: Some Problems and Some Answers." Special Report to the Metropolitan School Study Council. New York. New York. January 1971. ll9 actually constitute a cutback in real instructional time and conse- quently a cutback in student achievement."6 These statements. as well as the findings of other researchers. highlight the importance of teacher-student contact time and emphasize that the learning period cannot. in all likelihood. be duplicated through the use of substitutes. The findings of the present study have shown that teacher absenteeism can have a negative effect on student achievement in both reading and mathematics. It is of particu- lar interest that teacher absenteeism seemed to have the greatest effect on achievement in the second. third. and fourth grades. There appears to be sufficient evidence from this study to indicate that school districts should begin to take some steps either to reduce teacher absenteeism and thereby contribute to the improved achievement of elementary pupils. or to develop an alternative method of assigning substitute teachers in an attempt to reduce the negative effect of teacher absence. To address the continuing problem of teacher absenteeism. the National Association of Secondary School Principals proposed the following policies for teachers and substitutes: 1. Require all substitute teachers to have the credentials and skills of the regular contract teacher. 2. Provide inservice preparation time for persons to be employed as substitutes. 3. Provide some assistance to the Principal in monitoring those classrooms where a substitute is assigned. 6"Absent Teachers. Another Handicap for Students." Ins .Enooiiiionon (National Association of Secondary School Principals) 5 (May 1979): 5. an SUgge emplo Such; 544 Vi staff ing ti t0 Vhi SGFTOu is ign it is' °°mpan Opportl pr0p05£ Absente 1" Clas l20 4. Develop a system that recognizes good attendance and rewards it in some significant way. 5. Insist on more teacher responsibility for the continuity of instruction. 6. Principals could take a more personal interest in attendance: a. Praise good attendance. b. Show concern for teachers who have been absent. c. Keep building records on teacher attendance. d. Document excessive absence. show that absence is a serious matter. 7. Provide a metbod by which teachers can help monitor the attend- ance program. Yet another plan for dealing with teacher absenteeism was suggested by Lewis. who proposed developing a computer-produced employee absenteeism profile for each employee in the school district.‘3 Such a plan would allow employees to track their own attendance records and would enable administrators to identify absenteeism patterns among staff members. Lewis went on to propose that the first step in combat- ing the problem of employee absenteeism is to show everyone the extent to which absenteeism affects the schools. Hayes pointed out that absenteeism is rarely considered a serious problem. and therefore absenteeism flourishes precisely where it is ignored. Hayes stated. "People will come to work regularly only if it is to their advantage to do so. If the accepted behavior in a company includes frequent absences. most employees will take every opportunity to stay home and get paid for it)‘ Therefore. Hayes proposed. "it is up to the manager to create a positive. rewarding. 71b1dei pp. 7-9 8James Lewis. Jnu "Using a Computer to Monitor Teacher Absenteeism Can Save Schools Money and Increase the Time Teachers Spend in Class." WW (September 1982): 30-32. 'proc and 1 deve‘ Marki ”'Tfit-I‘l‘ UH With to de effec SUN/e" and M. and DI SupVey 1982); 121 'productive' environment where people feel impelled to appear regularly and perform as best they canJ' He formulated the following plan to develop an attendance-oriented staff: 1. Be committed to attendance. 2. Give recognition to those who report to work every day. 3. Don't be overstrict on time rules. 4. Pay personal attention to your employees. 5. Show people the importance of their work. 9 In another study on how to control absenteeism. Scott and Markham wrote. Although there is a large amount of research dealing with the reasons why employees are absent. there is surprisingly little written on the effectiveness of basic control policies and practices used to deal with this problem.. . . We have found that when managers talk with us about implementing a particular absenteeism control program. they often have not given any thought to their overall strategy or to hqb a new control method might affect other personnel practices. With this in mind. the authors conducted a survey of 987 organizations to determine what methods they used to control absenteeism and how effective those methods were. 'The findings of Scott and Markham's survey are shown in Table 64. After reviewing the methods used to control absenteeism. Scott and Markham provided the following guidelines for developing policies and practices: 9James L. Hayes. "Absenteeism. The Death of Productivity." WWW (December 1981): 25. 32. loDaw Scott and Steve Markham. "Absenteeism Control Methods: A Survey of Practices and Results." William 27 (June 1982): 73-76. .mmmucmwxzuomwkkmv UOuML >0 0.01:me amUO£um~E ~0LQCOU Emhvoutmmho_aso :0 come Em_ooucomnm mo comma—0:. .m— Nm.: wm.: N_~ m~.m mo_:cozum x503 o_n_xo_u .N. NN.: N:.: Nmm mN.m oucomnm cm coumm eozm_>cuuc_ oo>o_aEm ..— oe_u co_umum> . . . m.cmo> uxuc cu venom Lo .uumc Loom— NN : NM : we. aw m m an ammucoucoa m an :_ cozmmu on 0» :xcmn oucumnm: c.ma m c__:n cu muo>o_aso zo__< .o. No.: ww.m wmo .m.m mLOm_>Lua:m >3 ooc_muc_me mccouoc oucmccouum >__mo .m N:.: NN.: New mm.m >o__oa oucmccouum couu_c3->_cmu_u < .m NM.: wm.n Nam mm.m oucomnm we ou_u0c o>_m cu c_l__mo oo>o_aEm .m N_.: No.: Nw: om.m ucoEucmaoo .occOmcoa >n eoc_mu:_me museums uucmccouum >__mo .m N_.: wm.: Mum wm.m comumELOec_ oucm necouum >__mc mo m_m>_mcm >_cocoe ammo. u< .m wm.: ww.e Nam mm.m mo_o__oa oucmccouum mc_m:nm moo>o_aeo mo oc__o_ummc ccm co_umu_m_ucuo_ .e Nm.: Nw.: N_m m:.m Em_ooucomnm o>_mmouxo to» oc__a_um_c o>_mmocmoLa .m wm.: N:.: New ~:.m Em_00ucomnm o>_mmooxo co comma co_uoc_ELoh .N wN.: Nm.: Nmn n:.m >o__0q oucmccouum oo__aam >_ucoum_mcou < .— mcom: mcomacOz om: mmoco>~uoommm "boom "boom c. coumx venue: _ocuc0u oucomn< oucomn< acoucoa ommcu>< .nnocu>_uuocco coco. >a euxcou .noocuoe .ocucou secoeacomc<...ec Q~Qm~ .UWJCeuCOUll .QW ”~0th 123 45.5 No.5 Nan me.m .ueue_uua\..ua___ to» omsuxo m.c0u00c couu_c3 oc_:oo¢ .eu Nm.: Nm.: w.~ so.m muc_; 30: co» EoLmoLa comumuco_co oo>o_an .mEEOm m cw oucmccouum co acocoaeou < .m~ NN.: wm.: MN. mo.m Em_ooucomnm ouscoc cu coucoeo_ae_ co_umu0c LO\ucoEomcm_co\ucoEcu_Eco new .NN A.ouu .mcuuuo_m30c co Nm.m Mo.: NmN o_.m mccmon c_uo__:n unsecuc_ ..o._v oocmecouum coom m.oo>o_aeu mo co_umcmouoc u__n:a ..N N:.: N~.e Nun m_.m new use :0 >uommm mo mucueo>oLaE_ .o~ wm.: Nm.: Nu: :_.m oucmccouum coco ucoaoomnsm >n ccouoc m.ou>o_aeo Eo_n0ca m coo—u mc_q_3 .m— N~.: N:.: Mm. m_.m .mm_mcaom oucmELOmcoa m.c0mm>coa:m co Em_ooucomnm u_c: xcoz e0 co_m:_uc. .m— wm.: N:.: wmm m_.m _oLucou oucmccuuum c_ mc_c_ocu >c0m_>coa:m .m— N~.: wm.: wmm w_.m :o_m_uuc co_uoo_om m mc_me oLOwun mucouuc oucmccouum ammo .mu_:cooc coocuw .m— N:.: w~.: Nu: m_.m EmLmOLQ mc_c_mcu >uowmm xcoz _oEcou .m— Ne.m 45.5 am e_.m seose.uu ecnsn cam panacea oucmccouum cOOmxuuomcoa .e. meow: mcomscoz om: mmoco>_uuommm "poem "puma c_ coumm venue: _oLu:0u oocomn< oucomn< acoucom ommco>< .coacmucowlu.qw w~cnh 124 .lemm “ANmm. been. nu Leumcum.c.Ec< .occomcua :.u.:mo¢ new moo.uumcm mo >o>cam < "meccuoz .oLucoQ Em.ouucumnoum cam uuoum 3mm "ouL30m Nw.. N:.: N. om.N Em.ooucomnm cu one oo>o.aeo .o mmc.ccmo umo. mc.umu.oc. omaoam cu canoe; .am am.m Nm.: N. om.~ >mc meccaiucoc.uum co. macu>co.n ucmcu .mm N:.: wm.: wm: ~0.N oo>o.aEu ucomnm to» c. .... cu mcooa mc.u.:ooc >n oommcaooco ocammoca Loom .mm ”m.a am.a N. ...N .ecasu. soccmcv Eoum>m Loxoa co >Louuo. oucmccouu< ..m Nm.m Na.a NM. .m.~ saueen oEo;\uo.c\:u.mo; c. mEmcmOLa co.umuaom .om N..: N:.: Nm. om.~ oucmccouum Hoomcoo Lo. A>cmoocoev mscon oo>o.aEm .mN Mm.: wm.: wmm mm.~ uumcucou co.c: one c. ovum.u0moc coon mm; >0..oa .oLucou Em.ooucomnm och .ww NN.: N:.: Nmu mm.~ ..ouo ..o;ou.m .mmacc. EmLmOLQ omnnm oucmumnam .NN wo.m Mm.: N. oo.m “coaucmauc m.oo>o.aso co. memo >mv mo co.umcoao .oN oo>o.an Lonuo\u>.uoouoc Nm.: wm.: w.~ oo.m \omc:c\c0u00c >3 oucoc.moc oo>o.asu um a: xoozu cu A..mu mecca Lo. co.umu.m.> uoam .mN meow: mcomacoz om: mmoco>.uuomwm ”comm "mama c. ooumx cecuoz .oLucOU oucomn< oucomn< unease. ummco>< .euse_ueoo--.se u_ao. 125 1. Identify and re—examine the current methods being used to con- trol absenteeism within the organization. According to the survey certain methods of controlling absenteeism do not have much influence on controlling absenteeism. 2. If you have a policy of terminating employees for excessive absenteeism. examine those policies carefully for loopholes and inconsistencies. 3. Consider the value of using positive inducements to reduce absenteeism. Positive inducements were usually associated with the lower absenteeism. 4. Develop a centralized system for the collection of absenteeism data. It is important to remember that. although simply col- lecting absenteeism data will somewhat reduce absenteeism. a larger effect will occur if this data is analyzed periodi- cally. 5. Develop a comprehensive program for the control of absenteeism rather than relying on one or two methods to solve the prob- lem. In another study related to absenteeism. Allen and Higgins stated that "every organization is a culture. It has its own cultural norms that constitute the expected. supported. and accepted ways of behaving. These norms are mostly unwritten and tell people the way things really are."12 They went on to state that Absenteeism has its own subtle but complex norms. and the norms in the following areas influence people either to work or stay home. thus helping to create the absenteeism culture. 1. Leadership commitment. Managerial commitment to attendance goals and its views toward absenteeism do have an important impact on attendance. 2. Leadership modeling. What leaders say about absenteeism is often less important than the way their behavior is viewed by other organization members. 3. Recognition and compensation systems. Employees frequently remark that there is no advantage in reporting for work every day. because no one seems to care. A supervisor reduced HIbid. 12Robert F. Allen and Michael Higgins. "The Absenteeisms Culture: Becoming Attendance Oriented." Eonsonnol 56 (January-February 1979): 31. 10 6. 9. 10. ll. 126 absenteeism by 40 percent when letters were written for person- nel files at the end of each six month period of perfect attendance. with copies sent to conscientious employees. Organization policies and procedures. Regulations can some- times cause more problems than they prevent. Sometimes they actually get in the way of good attendance practices. Being five minutes late. even for a good reason. is often looked on less favorably than taking a sick day. Supervisory interpretation and implementation of policies. The personal link between employees and supervisors can be used in shaping a program. One supervisor tried a positive approach to absenteeism by starting a telephone follow-up to absent employ- ees. expressing concern for the cause of their absence and offering help to them and their families. Recruitment and selection. Employers ask for information about attendance in reference requests too infrequently. Employee orientation and training. Attendance norms are estab- lished the first day on the job. In a supermarket with low absenteeism. the importance of good attendance and exposure to high-attendance employees were stressed during orientations for new cashiers. Performance appraisal. Performance appraisal procedures can boost good attendance practices. If attendance rates make a difference in raises and appraisals of performance. and if employees are aware that this information is part of ongoing performance appraisals. attendance patterns are affected. Health factors. The connection between health and absenteeism is often overlooked or narrowly defined. with little attention being paid to alcoholism. drug abuse. and other stress-related factors. Job satisfaction. Boredom on the job is frequently ignored; little attention is paid to making the job more interesting or explaining its importance within the organization framework. The relationship of attendance to specific events. Vacations. holidays. meetings. training sessiggs. and other events influence the rate of absenteeism. For an organization to improve the existing absenteeism culture. Allen and Higgins suggested a systematic effort must be based on: 1. Involvement of employees at all levels. From the chief execu- tive officer to the new employee. involvement is crucial. Each has some kind of direct impact on an organization and contrib- utes to the modeling. rewarding. or supporting of attendance norms. Involvement means more than assigning tasks. Since everyone is affected by change. everyone must participate 131bid. varie ics i betg. rel 3 But . ibl e nut tEQQ 1001 Shot 127 in shaping change from goal setting at the start to final implementation. 2. Results orientation. Baseline and periodic measurement of attendance. productivity related to attendance. and attendance norms produce data that can be clearly communicated to the entire organization. 3. Sound data. Accurate program analysis and record keeping assures getting the sound data a program should be based on. This information enables managers to make higher. quality decisions in setting attendance goals. 4. A positive focus. Punishment intensifies resistance. Giving managers the tools to recognize and reward employees who maintain good attendance records stimulates cultural change. 5. A systematic approach. Change must be concerned with the factors that influence attendance norms so that managers can improve their skills and build a more effective organization. 6. Follow-through. Since the change process is an ongoing participatory commitment. management has the opportunity t2 periodically review. renew. and sustain attendance goals. Conclusion The findings of this research indicate again that demographic variables show a strong relationship to pupil achievement in mathemat- ics and reading. Although in some cases there was a relationship between teacher absenteeism and pupil learning. the magnitude of that relationship was not nearly as strong as the demographic variables. But it is the opinion of this researcher that as schools may not be able to change demographic factors. they would be negligent if they did not deal with an in-school factor that affects achievement. such as teacher attendance. Thus it seems that if a school district can make changes that would have such a positive effect on pupils! achievement. every effort should be made to ensure that appropriate programs are implemented to ‘_ 14Ibid.. p. 32. . ' - ;4 f 128 accomplish this end. Of course. many plans could be devised for a school district developing an overall strategy for dealing with absenteeism. but this investigator proposes that the following steps be taken in establishing an initial program: 1. Organize an attendance committee composed of individuals from a cross-section of all employee bargaining units. 2. Review all absence data currently being collected. Develop programs through which data can be analyzed in many different ways (employee absence. bargaining unit absence. and building absence). 3. Review the absenteeism control methods listed in Table 67 of this dissertation and develop. with the committee. a program for addressing the school district's absenteeism problem. Recommend this program to the superintendent of schools for approval. 4. Develop evaluation criteria that can be used to measure the effectiveness of the designed program. 5. Review current substitute-teacher—use practices and con- sider such alternatives as: 8. Increasing the use of building substitutes. b. Increasing the amount of inservice training provided to substitute teachers. concentrating on essential skills required for teaching. c. Reviewing assignment practices in an attempt to place substitute teachers in classrooms best suited to their skills. d. Establishing a Substitute Teacher Advisory Committee to address concerns of both substitute and classroom teachers. 129 e. Developing an evaluation process by which to review the program's effectiveness. 6. Continue to review teacher absenteeism and pupil achievement through the analysis methods used in this study. By implementing such a program. the Lansing School District might be able to reduce at least one barrier to elementary pupils' achieving their greatest potentiaL. Such a program would be in keeping with the philosophy of Lezotte and Passalacqua. who encouraged researchers to ”isolate and estimate the magnitude of 'school effects' by various models. and by so doing [demonstrate] that poor achievement is not totally a function of the students who attend school.".'5 Even though this research was designed to examine teacher absenteeism and pupil achievement within the Lansing School District. it is the opinion of this researcher that the findings may indeed have implications for other school districts. It is proposed that school districts should plan to review their teacher absenteeism records just as carefully as they examine those of pupil absences. In developing a program or study that could expand on this research. it is important to realize the lack of any organized data related to teacher absenteeism. In an informal survey of several Michigan school districts. it appeared that very little information related to teacher absenteeism is being gathered and analyzed. And. k 15Lawrence W. Lezotte and Joseph Passalacqua. "Individual School Buildings--Accounting for Differences in Measured Pupil Perform- ance." Limouoation (October 1978): 283-91. ir us at 1:. ti Ii 130 indeed. if such data are being gathered. they are most likely to be used only for the purpose of projecting the economic effect of teacher absences on the school district. This researcher would suggest that the topic of teacher absence is of such importance. both from the economic and educational effect on a school district. that a plan should be developed that expands the overall knowledge of the topic to include several Michigan school districts. It is suggested that possibly a statewide educational organization develop a study that would not only examine several districts. but could also serve as developing a self-examination guide that could be used by other districts. This guide would provide for the standardization of the information collected. thus allowing for analysis from one district to another. By implementing such a study program. Michigan schools could be provided with an extremely valuable guide for examining one problem common to all school districts that could be affecting the learning of its pupils. W This research was limited to teachers and pupils at the elemen- tary school level. grades 1-6. Future investigators might include teachers and students at both the middle school and senior high school levels. In addition. this research was designed to compare certain idemographics with the school-related variable of teacher absenteeism. Ill future studies. consideration should be given to further exploring 131 the relationship of teacher absenteeism to other school-related factors. such as relaxed learning standards. less substantive content. automatic promotions. and other such variables. This research was limited to an analysis of the total elemen- tary pupils in grades 1 through 6 and to each separate grade level. Future researchers might attempt to analyze teacher absenteeism in individual elementary school buildings. Further. this research was limited to»considering elementary teacher absence for reasons of personal illness and personal reasons only and. as such. did not consider additional days absent for reasons of conferences or inservice education. These additional absences may well add an additional burden to the pupils in the elementary grades. In addition. research might be considered that would evaluate the relationship between pupil absence and teacher absence. Is it possible that the absence of one leads to or contributes to the absence of the other? A reapplication and analysis of the present research in other school districts could determine whether the findings of this study are applicable to other districts. Assuming the suggested absenteeism program is implemented. a follow-up study of the progranfls effectiveness in eliminating teacher absenteeism as a factor inhibiting pupil achievement would be approp- riate. No one investigator could hope to answer all of the questions related to the effect of teacher absenteeism on elementary pupils' achi info dire inpo sonic 10001 on si 132 achievement. This researcher believes that interesting and useful information was obtained from the study. which. if acted upon. could directly influence the achievement of elementary pupils. In addition. an attempt has been made to identify other questions that seem to be important in understanding elementary pupils or students in middle or senior high school. The answers to these questions could have an important influence on planning for improved staff attendance and hence on student achievement at all grade levels. APPENDICES 133 APPENDIX A DEMOGRAPHIC CHARACTERISTICS OF PUPILS INCLUDED IN THE STUDY I34 "'31 135 Table Al.--Number of elementary pupils taking Stanford Achievement Tests in reading in 1980/81, by building number and grade level. Grade Grade Grade Grade Grade Grade Bldg. ' 2 3 4 5 6 Total 1 93 75 71 63 -- -- 302 2 62 44 67 50 67 56 346 3 52 50 56 65 62 55 340 4 32 35 31 40 34 36 208 5 57 51 51 52 -- -- 211 6 49 4O 50 49 57 49 284 7 -- -- -- -- 139 115 254 8 49 41 47 49 -- -- 186 9 59 54 48 6O -- -- 221 10 46 53 64 63 66 54 346 ll 44 45 41 41 3O 35 236 12 -- -- -- 50 63 74 187 13 53 38 53 48 53 54 304 l4 -- -- -- -- 112 121 233 15 50 67 52 -- -- -- 169 16 20 29 29 29 36 38 181 17 42 52 39 46 -- -- 179 18 60 55 51 38 -- -- 204 19 77 62 81 68 -- -- 288 20 33 34 19 27 20 23 156 21 36 43 42 42 -- -- I63 22 62 62 47 66 68 57 362 23 32 45 36 41 32 41 227 24 30 48 42 40 32 35 227 25 -- -- -- -- 200 148 348 26 84 69 88 70 78 79 468 27 31 43 36 44 40 39 233 28 42 35 32 36 -- -- 145 29 55 34 39 52 50 50 280 30 53 58 56 57 60 61 345 31 94 66 99 92 -- -- 351 32 51 35 47 55 46 41 275 33 -- -- -- -- 154 152 306 34 51 41 52 49 39 51 283 35 building closed 36 31 42 37 48 42 36 236 37 42 48 47 82 65 69 353 38 36 4O 22 28 34 30 190 39 39 43 43 55 47 41 263 40 85 72 79 61 -- -- 297 41 66 58 66 75 57 52 374 Source: Taken from the ”Overview of Stanford Achievement Test Analysis” report of the Lansing School District, 1980/81. 136 Table A2.--Number of elementary pupils taking Stanford Achievement Tests in mathematics in 1980/81, by building number and grade level. Grade Grade Grade Grade Grade Grade Bldg. 1 2 3 4 5 6 Total I 93 74 70 63 -- -- 300 2 62 44 67 50 67 56 346 3 52 50 56 65 62 55 340 4 32 35 31 40 34 35 207 5 57 51 51 51 -- -- 210 6 49 40 SO 39 57 49 284 7 -- -- -- -- 139 114 253 8 49 41 46 49 -- -- 185 9 58 54 48 6O -- -- 220 10 46 53 64 63 66 54 346 ll 44 45 41 41 30 35 236 12 -- -- -- 51 63 74 188 13 53 38 53 48 58 53 303 14 -- -- -- -- 113 121 234 15 51 67 52 -- -- -- 170 16 20 29 29 29 36 38 181 17 42 52 39 45 -- -- 178 18 60 55 50 38 -- -- 203 19 77 62 82 68 -- -- 289 20 33 34 18 27 20 23 155 21 36 43 42 42 -- -- 163 22 62 62 47 66 68 57 362 23 32 42 36 41 32 41 224 24 30 48 41 40 33 35 227 25 -- -- -- -- 200 147 347 26 83 69 89 70 78 81 470 27 31 42 36 44 40 39 232 28 42 35 32 36 -- -- 145 29 55 34 33 52 50 49 273 30 53 58 56 57 60 61 345 31 94 66 99 92 -- -- 351 32 51 35 46 55 46 41 274 33 '- -- -- -- 153 152 305 34 50 42 52 49 39 51 233 35 building closed 36 30 41 36 48 43 36 234 37 42 48 47 81 65 67 350 38 36 40 22 28 36 30 192 39 40 43 42 55 47 41 268 40 85 72 79 61 -- -- 297 41 66 57 66 75 57 52 373 Source: Taken from the ”Overview of Stanford Achievement Test Analysis“ report of the Lansing School District, 1980/81. 137 Table A3.--Number of elementary pupils taking Stanford Achievement Tests in reading in 1981/82, by building number and grade level. Grade Grade Grade Grade Grade Grade Bldg. 1 2 3 4 5 6 Total 1 69 79 59 62 -- -- 269 2 70 54 45 58 55 64 346 3 4O 47 4O 50 60 54 291 4 51 33 37 36 44 35 236 5 47 48 45 48 -- -- 188 6 40 43 35 53 40 51 262 7 -- -- -- -- 108 133 241 8 54 44 40 45 -- -- 183 9 57 56 49 48 -- -- 210 10 57 43 51 64 54 61 330 11 -- -- -- 52 47 53 152 12 38 50 38 50 45 57 278 13 -- -- -- -- 117 110 227 14 58 45 53 -- -- -- 156 15 48 40 44 41 41 35 249 16 22 18 30 25 19 30 144 17 51 35 43 40 -- -- 169 18 47 49 46 53 r- -- 195 19 66 70 57 78 -- -- 271 20 27 24 30 16 29 24 150 21 32 28 41 39 -- -- 140 22 57 56 56 42 60 71 342 23 42 26 39 25 39 27 198 24 37 27 39 37 29 32 201 25 -- -- -- -- 158 196 354 26 72 80 71 84 76 74 457 27 34 29 39 36 39 37 214 28 37 25 31 30 -- -- 123 29 35 38 36 39 54 44 296 30 50 45 57 58 59 60 329 31 77 82 61 90 -- -- 310 32 35 45 30 43 42 33 233 33 -- -- -- -- 134 147 281 34 43 53 40 41 48 38 263 35 building closed 36 28 25 38 36 50 42 219 37 48 43 41 44 73 56 305 38 40 43 52 27 31 35 228 39 35 33 39 40 50 43 245 40 76 75 68 82 54 -- 355 41 55 53 56 62 61 59 346 Source: Taken from the "Overview of Stanford Achievement Test Analysis” report of the Lansing School District, 1981/82. 138 Table A4.--Number of elementary pupils taking Stanford Achievement Tests in mathematics in 1981/82, by building number and grade level. Bldg Grade Grade Grade Grade Grade Grade 1 2 3 ‘1 5 6 Total 1 69 78 59 63 -- -- 269 2 70 54 45 58 55 64 346 3 4o 47 4o 50 61 54 292 4 51 34 37 36 44 35 237 5 47 48 45 49 -- -- 189 6 4o 42 35 53 4o 50 260 7 -- -- -- -- 108 132 240 8 54 44 4o 44 -- -- 182 9 57 56 49 48 -- -- 210 10 57 43 51 64 54 61 330 11 48 4o 44 41 41 35 249 12 -- -- -- 52 47 52 151 I3 38 50 38 50 45 S7 278 14 -- -- -- -- 125 113 238 15 58 45 55 -- -- -- 158 16 22 18 3o 23 19 29 141 17 51 35 43 4o -- -- 169 l8 46 49 46 53 -- -- 194 19 66 70 57 78 -- -- 271 20 27 24 3o 16 29 24 150 21 32 29 41 39 -- -- 141 22 57 56 55 42 6o 71 341 23 42 26 39 25 39 27 198 24 37 26 39 37 29 32 200 25 -- -- -- -- 158 196 354 26 72 80 71 83 76 74 456 27 34 28 37 36 39 37 211 28 37 25 31 3o -- -- 123 29 3S 38 36 39 53 45 246 30 50 45 55 57 56 59 322 31 77 82 61 89 -- -- 309 32 35 45 30 43 42 38 233 33 -- -- -- -- 131 147 278 34 43 53 4o 41 46 38 261 35 building closed 36 29 25 38 36 49 42 219 37 48 43 41 44 73 56 305 38 39 43 51 26 31 35 225 39 35 38 39 40 50 43 245 40 76 74 68 82 54 -- 354 41 55 53 56 62 62 59 347 Source: Taken from the ”Overview of Stanford Achievement Test Analysis” report of the Lansing School District, 1981/82. 139 Table A5.--Elementary pupil mobility rates: Percentage of pupils enter- ing or leaving a school during the school year. School Percentage of Mobility "umber 1980/81 1981/82 1 53.3 48.2 2 17.5 18.7 3 15.1 16.3 4 54.0 50.2 5 26.6 31.4 6 15.4 13.2 7 22.3 15.3 8 36.3 29.1 9 27.9 35.5 10 30.4 34.7 11 35.0 26.3 12 36.1 41.4 13 31.5 33.0 14 37.9 34.5 15 46.7 42.8 16 47.1 38.0 17 36.9 48.0 18 43.4 35.7 19 22.5 17.3 20 18.8 27.9 21 21.5 21.0 22 21.8 19.0 23 29.6 26.1 24 48.8 50.7 25 27.8 26.3 26 32.5 32.9 27 39.6 28.0 28 59.2 74.9 29 29.0 27.6 30 25.9 21.7 31 32.0 39.4 32 30.1 30.2 33 28.1 28.6 34 26.5 32.8 35 building closed 36 19.1 22.9 37 32.3 21.9 38 71.4 57.4 39 27.3 18.2 40 28.8 31.3 4] 20.0 25.2 _‘ isource: Taken from the ”Elementary Demographic Data Report” of the Lansing School District, 1980/81 and 1981/82. Ta S0Ur 140 Table A6.--Elementary-student parent education rates: Percentage of parents completing twelfth grade. School Percentage Number 1980/81 1981/82 1 60.0 55.0 2 84.0 54.0 3 89.0 86.0 4 55.0 61.0 5 77.0 56.0 6 84.0 61.0 7 84.0 61.0 8 74.0 59.0 9 78.0 50.0 10 85.0 55.0 11 63.0 46.0 12 62.0 66.0 13 56.0 62.0 14 56.0 53.0 15 65.0 70.0 16 35.0 47.0 17 59.0 61.0 18 82.0 62.0 19 87.0 64.0 20 75.0 58.0 21 84.0 70.0 22 83.0 53.0 23 66.0 51.0 24 57.0 61.0 25 67.0 56.0 26 83.0 56.0 27 74.0 60.0 28 42.0 53.0 29 55.0 56.0 30 65.0 54.0 31 73.0 58.0 32 78.0 61.0 33 66.0 62.0 34 59.0 47.0 35 building closed 36 83.0 55.0 37 87.0 56.0 38 48.0 56.0 39 75.0 60.0 40 68.0 61.0 41 87.0 74.0 5Source: Taken from the ”Elementary Demographic Data Report” of the Lansing School District, 1980/81 and 1981/82. 141 Table A7.--Aid to Families with Dependent Children rates: Percentage of families within each school receiving Aid to Dependent Children (in percent). School Percent Receiving AFDC "umber 1980/81 1981/82 1 39.3% 38.92 2 9.4 8.4 3 9.8 7.1 4 52.2 47.1 5 29.0 18.2 6 14.9 12.9 7 11.2 13.2 8 27.4 18.6 9 21.0 18.1 10 15.6 9.8 1' 33.0 25.7 12 29.4 22.9 13 33.5 31.2 14 34.0 36.9 15 25.0 22.5 16 43.8 37.1 17 37.4 36.7 18 28.5 26.5 19 18.2 9.3 20 17.3 11.0 21 11.4 24.6 22 20.8 16.4 23 31.6 35.6 24 48.5 49.5 25 28.4 29.3 26 25.4 19.1 27 32.0 27.6 28 49.6 42.5 29 23.6 15.8 30 33.8 26.8 31 20.5 19.7 32 21.5 17.5 33 30.2 39.0 34 24.1 20.0 35 building closed 36 22.5 23.4 37 11.1 8.4 38 45.8 45.5 39 23.2 17.4 40 27.3 23.5 41 10.7 6.2 \ SSource: Taken from the Elementary Demographic Data Report of the Lansing School District, 1980/81 and 1981/82. ' yum: 4:14-71! .FL” . a 142 Table A8.--Elementary-student family make-up report: Percentage of families reporting two parents within the home. Elementary Family Make-Up School (Percentage) "umber 1980/81 1981/82 1 61.0 64.0 2 71.0 75.0 3 79.0 78.0 4 51.0 53.0 5 67.0 67.0 6 79.0 71.0 7 78.0 73.0 8 71.0 71.0 9 69.0 65.0 10 75.0 76.0 11 61.0 55.0 12 57.0 60.0 13 63.0 64.0 14 65.0 64.0 15 65.0 61.0 16 66.0 66.0 17 63.0 57.0 18 64.0 60.0 19 69.0 72.0 20 66.0 79.0 21 63.0 75.0 22 56.0 55.0 23 64.0 41.0 24 48.0 53.0 25 66.0 63.0 26 69.0 69.0 27 65.0 63.0 28 53.0 56.0 29 65.0 67.0 30 57.0 65.0 31 73.0 69.0 32 67.0 64.0 33 60.0 63.0 34 73.0 75.0 35 building closed 36 65.0 64.0 37 75.0 71.0 38 49.0 55.0 39 66.0 64.0 40 63.0 65.0 41 73.0 72.0 \ 1:§<>urc:e: Taken from the “Elementary Demographic Data Report” of the Lansing School District, 1980/81 and 1981/82. 143 Table A9.--Elementary teacher absenteeism report, 1980/81. School Number of Fre uenc Mean Number of Mean Number Number Teachers q y Frequency Days Used of Days Used 1 12 95 7.9 117.5 9.8 2 14 93 6.6 133.5 9.5 3 10 41 4.1 40.0 4.0 4 9 53 5.9 110.5 12.3 5 9 43 4.8 52.0 5.8 6 10 38 3.8 57.5 5.8 7 10 77 7.7 97.0 9.7 8 7 32 4.6 37.0 5.3 9 20 58 5.8 99.0 9.9 10 13 111 8.5 149.0 11.5 11 9 66 7.3 230.0 25.6 12 8 62 7.8 80.0 10.0 13 12 87 7.3 110.5 9.2 14 9 32 3.6 43.0 4.8 15 7 54 7.7 84.0 12.0 16 7 30 4.3 58.5 8.4 17 7 42 6.0 58.0 8.3 18 8 27 9.6 81.5 10.2 19 12 77 6.4 101.5 8.5 20 7 33 4.7 83.0 11.9 21 7 34 4.9 61.0 8.7 22 15 76 5.1 153.0 10.2 23 10 58 5.8 82.0 8.2 24 9 41 4.6 125.0 13.9 25 14 67 4.8 93.5 6.7 26 18 102 5.7 197.0 10.9 27 9 55 6.1 79.5 8.8 28 6 26 4.3 44.5 7.4 29 11 71 6.5 142.0 12.9 30 13 54 4.2 95.0 7.3 31 14 64 4.6 117.5 8.4 32 12 68 5.7 102.0 8.5 33 11 96 8.7 123.0 11.2 34 12 58 4.8 72.0 6.0 35 6 25 4.2 64.0 10.7 36 9 33 3.7 47.5 5.3 37 14 86 6.1 114.5 8.2 38 9 55 6.1 94.5 10.5 39 10 70 7.0 110.0 11.0 40 12 45 3.8 78.0 6.5 41 15 59 3.9 151.0 10.1 TOTAL 426 2.394 5.6 AV 3,969.5 9.3 Siource: Taken from Personnel Department records of the Lansing School District for 1980/81. Table A10.--Elementary teacher absenteeism report, 1981/82. 144 School Number of Fre uenc Mean Number of Mean Number Number Teachers q y Frequency Days Used of Days Used 1 12 66 5.5 75.0 6.3 2 14 71 5.1 111.5 8.0 3 10 43 4.3 48.5 4.9 4 10 38 3.8 46.0 4.6 5 8 42 5.3 48.0 6.0 6 9 38 4.2 54.5 6.1 7 9 59 6.6 87.0 9.7 8 6 26 4.3 66.5 11.1 9 9 31 3.4 107.0 11.9 10 13 80 6.2 143.0 11.0 11 10 48 4.8 79.0 7.9 12 7 41 5.9 112.5 16.1 13 12 82 6.8 115.0 9.6 14 9 23 2.6 26.0 2.9 15 7 41 5.9 67.5 9.6 16 6 28 4.7 80.5 13.4 17 7 35 5.0 58.0 8.3 18 8 44 5.5 134.0 16.8 19 11 52 4.7 65.0 5.9 20 6 36 6.0 115.0 19.2 21 6 42 7.0 52.0 8.7 22 14 72 5.1 91.0 6.5 23 9 55 6.1 71.5 7.9 24 9 35 3.9 47.0 5.2 25 14 62 4.4 91.5 6.5 26 18 111 6.2 184.5 10.3 27 9 38 4.2 54.0 6.0 28 7 28 4.0 35.5 5.1 29 10 57 5.7 80.0 8.0 30 11 49 4.5 69.5 6.3 31 13 44 3.4 74.0 5.7 32 11 80 7.3 105.0 9.5 33 11 85 7.7 129.0 11.7 34 11 55 5.0 130.5 11.9 35 building closed 36 9 22 2.4 32.0 3.6 37 12 55 4.6 84.0 7.0 38 10 51 5.1 83.0 8.3 39 10 61 6.1 94.0 9.4 40 14 57 4.1 73.5 5.3 41 14 57 4.1 68.0 4.6 TOTAL 399 2,040 5.1 AV 3,290.0 8.2 Source: Taken from Personnel Department records of the Lansing School District for 1981/82. t‘ ~ that!“ W. I. ’4. i APPENDIX B INTERCORRELATIONS FOR SEVEN VARIABLES, BY GRADE 14S Table Bl.--Intercorrelations for seven variables included in Grade 1 Reading and Mathematics-- this study: 146 1980/81. Reading Read AFDC SMOB PED FAM FTA Read AFDC 'D.42* SMOB -0.38* 0.83* FTA '0.27 0.08 0.07 -0.03 -0.20 Mathematics Math AFDC SMOB PED FAM FTA Math AFDC '0.25 SMOB -0.16 0.83* PED 0.24 -0.84* -0.72* FAM 0.24 -0.79* '0.66* 0.51* FTA -0032* 0008 0.07 -0003 -0020 TTDA -0.34* 0.28* 0.15 -0.22 -D.39* 0.33* *Significant at alpha < 0.05. 157““; fi‘nmmmxm 147 Table BZ.--Intercorrelations for seven variables included in this study: Grade 1 Reading and Mathematics-- 1981/82. Reading Read AFDC SMOB PED FAM FTA Read AFDC -0.51* SMOB -0.46* 0.75* PED 0.11 -0.26 -0.19 TTDA 0.14 0.07 0.01 -0.22 0.04 0.29* Mathematics Math AFDC SMOB PED FAM FTA AFDC Math AFDC -0.38* SMOB -0.33* 0.75* PED 0.07 '0.26 -0.19 FAM 0.43* -0.74* -0.46* 0.30* FTA -0.04 -0.20 -0.22 0.01 0.21 TTDA 0.10 0.07 0.01 -0.22 0.04 0.29* *Significant at alpha < 0.05. Tab Reac AFDC Hath AFDC SMOB PED FAM FTA Table 83.--Intercorrelations for seven variables included in Grade 2 Reading and Mathematics-- this study: 148 1980/81. Reading Read AFDC SMOB PED FAM FTA Read AFDC -O.58* SMOB -0.59* 0.83* PED 0.54* -0.84* -0.72* FAM 0.53* -0.79* -0.66* 0.61* FTA -0.10 0.28* 0.44* -0.34* -0.22 TTDA -0.18 0.04 0.15 -0.09 -0.16 0.23 Mathematics Math AFDC SMOB PED FAM FTA Math AFDC -0.46* SMOB -0.43* 0.83* PED 0.44* -0.84* -0.72* FAM 0.48* -0.79* -0.66* 0.61* FTA -0.16 0.28* 0.44* -O.34* -0.22 TTDA -0.33* 0.04 0.15 -0.09 -0.16 0.23 *Significant at alpha < 0.05. Tabli Read AFDC SMOB PED FAM FTA TTDA Math AFDC SMOB PED FAM FTA TTDA 149 Table B4.--Intercorre1ations for seven variables included in this study: Grade 2 Reading and Mathematics-- 1981/82. Reading Read AFDC SMOB PED FAM FTA Read AFDC '0.50* SMOB -0.43* 0.75* FAM 0.52* -0.74* -0.46* 0.30* "DA 0036* -0014 -0000 0.01 0.25 -0001 Mathematics Math AFDC SMOB PED FAM FTA Math AFDC -0.40* SMOB -0.15 0.75* PED 0.13 -0.26 -0.19 FAM 0.45* -0.74* -0.46* 0.30* FTA -0.21 0.11 0.01 0.01 -0.10 TTDA 0.45* -0.14 0.00 0.05 0.25 -0.01 *Significant at alpha < 0.05. 150 Table BS.--Intercorrelations for seven variables included in this study: Grade 3 Reading and Mathematics-- 1980/81. Reading Read AFDC SMOB PED FAM FTA Read AFDC -0.42* SMOB -0.30* 0.83* PED 0.46* -0.84* -0.72* FAM 0.40* -D.79* -0.66* 0.61* FTA -0022 0020 0033* -0017 -0015 TTDA -0.36* 0.23 0.27* -0.23 -0.31* 0.58* Mathematics Math AFDC SMOB PED FAM FTA Math AFDC -0.30* SMOB -O.15 0.83* PED 0.23 -0.84* -O.72* FTA 0.02 0.20 0.33* -0.17 -O.15 TTDA -0.23 0.23 0.27* -0.23 -0.31* 0.58* *Significant at alpha < 0.05. 151 Table BG.--Intercorrelations for seven variables included in this study: Grade 3 Reading and Mathematics- 1981/82. Reading Read AFDC SMOB PED FAM FTA Read AFDC -D.4l* SMOB -0.49* 0.75* PED O.31* -O.26 -0.19 FAM 0.32* -0.74* -0.46* 0.30* FTA -0.17 0.05 -0.05 -0.03 -0.17 Mathematics Math AFDC SMOB PED FAM FTA Math AFDC -0.20 SMOB -0.19 0.75* PED 0.23 -0.26 -O.19 FAM 0.11 -0.74* -0.46* 0.30* FTA -0.25 0.05 -0.05 —0.03 -D.l7 TTDA -D.29* -0.04 -0.04 -O.19 0.00 0.51* *Significant at alpha < 0.05. 152 Table B7.--Intercorrelations for seven variables included in this study: Grade 4 Reading and Mathematics-- 1980/81. Reading Read AFDC SMOB PED FAM FTA Read AFDC -0.44* SMOB -0.24 0.85* PED 0.41* -0.84* -0.72* FAM 0.35* -0.79* -0.67* 0.62* TTDA -0.14 0.32* 0.23 -0.21 -0.27 0.36* Mathematics Math AFDC SMOB PED FAM FTA lMath AFDC -0.20 SMOB 0.04 0.85* FTA 0.04 -O.14 -0.15 0.21 0.16 'TTDA -O.l6 0.32* 0.23 -0.21 -O.27 0.36* *Significant at alpha < 0.05. Tab Natl AF D1 31101 PED FAM Tml Table BB.--Intercorrelations for seven variables included in this study: 153 Grade 4 Reading and Mathematics-- 1981/82. Reading Read AFDC SMOB PED FAM FTA Read AFDC -0.41* SMOB -0.28* 0.75* PED 0.16 -0.26 -0.21 PM 0023 -0075* -0046* 0031* FTA 0.08 0.07 0.14 0.17 0.05 TTDA 0.28* -0.01 0.18 -0.07 -0.01 0.78* Mathematics Math AFDC SMOB PED FAM FTA Math AFDC -0.18 SMOB 0.14 0.75* PED 0.26 -0.26 -0.21 FTA 0.12 0.07 0.14 0.17 0.05 'TTDA 0.30* -0.01 0.18 -0.07 -0.01 0.78* *Significant at alpha < 0.05. Tat 154 Table BQ.--Intercorrelations for seven variables included in this study: Grade 5 Reading and Mathematics-- 1980/81. Reading Read AFDC SMOB PED FAM FTA Read AFDC -0.l4 SMOB -0.16 0.83* PED 0.16 -0.84* -0.70* FAM 0.16 -0.83* -0.67* 0.61* FTA 0.40* -0.29 -0.19 0.18 0.17 TTDA 0.13 0.07 0.02 0.00 -0.15 0.65* Mathematics Math AFDC SMOB PED FAM FTA Math AFDC -0.12 SMOB -0.05 0.83* FTA. 0.42* -0.29 -0.19 0.18 0.17 ‘TTDA 0.17 0.07 0.02 0.00 -0.15 0.65* *Significant at alpha < 0.05. 155 Table BlO.--Intercorrelations for seven variables included in this study: Grade 5 Reading and Mathematics-- 1981/82. Reading Read AFDC SMOB PED FAM FTA Read AFDC “0.52* FTA 0.06 -0.20 0.03 '0.01 0.13 Mathematics Math AFDC SMOB PED FAM FTA Math AFDC -D.46* PED 0.23 '0.27 -0.13 FTA 0.23 -0.20 0.03 -0.01 0.13 TTDA 0.15 -0.22 0.08 -0.02 0.25 0.38* *Significant at alpha < 0.05. 156 Table Bll.--Intercorrelations for seven variables included in this study: Grade 6 Reading and Mathematics-- 1980/81. Reading Read AFDC SMOB PED FAM FTA Read AFDC -0.48* SMOB -0.42* 0.83* PED 0.51* -0.84* -0.71* FAM 0.37* -0.83* -0.68* 0.61* FTA 0.08 0.04 0.12 -0.12 -0.15 TTDA 0.02 0.20 0.20 -0.09 -0.33* 0.56* Mathematics Math AFDC SMOB PED FAM FTA Math AFDC -0.37* PED 0.34* -0.84* -0.71* FAM 0.31* -0.83* -0.68* 0.61* FTA -0.03 0.04 0.12 -0.12 -0.15 TTDA 0.00 0.20 0.20 -0.09 -0.33* 0.56* *Significant at alpha < 0.05. 157 Table BlZ.--Intercorrelations for seven variables included in this study: Grade 6 Reading and Mathematics-- 1981/82. Reading Read AFDC SMOB PED FAM FTA Read AFDC '0.24 SMOB '0.20 0.72* PED 0.21 '0.27 -0.14 Mathematics Math AFDC SMOB PED FAM FTA Math AFDC '0.27 SMOB '0.23 0.72* FM 0026 -0073* -0042* 0028 FTA -0.09 '0.21 '0.04 0.03 0.09 *Significant at alpha < 0.05. APPENDIX C EMPLOYEE TIME AND ABSENCE RECORD 158 159 .E .38:ng . :86 .38 153:... .5350 .38 =22: . :23 u29555.5 a<—°— 3 3.5!: 2 .8 .3... l> I l8. .5 I1. I33 .15.! u. 3.6 s). 5. 31.1.3 2 .2303? ES. 9.8.. 5808 040 n. 18 1 _:o On: .000 1 :Om> >8 6: :3... .26...- X 53.2 Sou .> 8 92 250: a? . :3 __< .2 on: - WwUZwmm< 0.0 OZ 2:0 3: - ..Ewo deZOmmwm ,.) 2.0 quO U<> may“. :03 2.325an 50 92(2 0:302, was: $2: :22: Ni: ~55 :28. OMOUum uUmem< OZ< 52.... mm>0._.=2u BIBLIOGRAPHY 160 BIBLIOGRAPHY "Absent Teachers: Another Handicap for Students." .Ihe.ELactitignen (National Association of Secondary School Principals) 5 (May l979): l-S. "Absentee Teachers Boost School Costs." .Lansing.1M19h19anl.§tate .lnunnal. November l98l. Academy for Educational Development. Regan: on league]: Anselm in mmmflnmmmmamnnimm (Illinois Office of Education). Indianapolis: The Academy for Educational Development. Public Policy Division. July l977. tfifi'fi‘fl Allen. Robert F.. and Higgins. Michael. "The Absenteeism Culture: Becoming Attendance Oriented." ‘Eensgnnel 56 (January-February 1979): 30-34. American Association of School Administrators. Ilme.gn.IaskL.u51ng Instnuctmnal lime More mm. Arlington. Va.: American Association of School Administrators. 1982. Bamber. Chrissie. Student and league: Absenteeism. Fastback 126. Bloomington. Ind.: Phi Delta Kappa Educational Foundation. 1979. Beauchamp. George A.. and Conran. Patricia C. "Longitudinal Study in Curriculum Engineering-VI." Paper presented at the annual meeting of the American Educational Research Association. San Francisco. California. April l976. (Mimeographed.) Breaugh. James A. "Predicting Absenteeism From Prior Absenteeism and Work Attitudes." .lgunnal of Applied Esyghglggy 66 (1981): 555-60. Bundren. Dorence L. "The Influence of Situational and Demographic Factors on the Absentee Patterns of Teachers." Ph.D. disserta- tion. University of Southern California. 1974. Bureau of National Affairs. "Job Absence and Turnover Second Quarter l983." .Bulletin.to.Management. September 8. 1983. p. l. Cruikshank. George E. "No-Shows at Work: High Priced Headache." Natinnls.Bu51ness (September 1976): 37. I61 I62 Drake. Jackson M. "Making Effective Use of the Substitute Teacher: An Administrative Opportunity." ‘NASSE.Bulletin (September 1981): 74. Educational Research Service. ,Emplgyee.Absenteeism:.A.Summany.gf .Beseangh. Arlington. Va.: Educational Research Service. l980. Edwards. Gregor 0. ”Teacher Absenteeism in Senior High Schools: Economic. Educational. and Human Costs of Teacher Stress." Dissentatinn Abstracts lntannatinnal 43 (July 1982): 29-A- Elliott. Peggy G.. and Manlove. Donald C. "The Cost of Skyrocketing Teacher Absenteeism." .Eh1.D§lIa Kannan 59 (December 1977): 209-10. Feinberg. Mortimer R. "New Focus on Absenteeism." .Bestannant .Business. February 1. 1981. p. 82. Foster. Seymour D. "An Investigation of Selected Factors in Schools With High Versus Low Teacher Absenteeism in a New York Community School District." Ed.D. dissertation. Fordham Uni- versity. 1977. Gardner. Eric F.: Rudman. Herbert C.: Karlsen. Bjorn: and Merwin. Jack C. Stanton! Achievement lest iEQcm A1. New York: The Psychological Corporation. 1974. Goodman. Victor B. "Teacher Absenteeism. Stress in Selected Elemen- tary Schools: An Assessment of Economic and Human Costs." Ed.D. dissertation. University of California. Los Angeles. 1980. Harnischfeger. Annegret. and Wiley. David E. "Achievement Test Scores Drop--So What?" ‘Edugatlgna1.3eseanghen (March 1976): 5-12. . "Schooling Cutbacks and Achievement Declines: Can We Afford Them?" .Administnatgzls.Ngtebggk (The University of Chicago) 24 (1975). Hayes. James L. "Absenteeism. The Death of Productivity." ‘Qnedit.and financial Management (December 1981): 25. "Job Absence and Turnover." .Bnllfitin.IQ.Mana9§m§nt (The Bureau of National Affairs. Inc.). December l6. 1982. p. 2. Johnson. Ronald D.. and Peterson. Tim 0. "Absenteeism or Attendance: Which Is Industry's Problem?" .Eensonnel‘lnnnnal (November 1975): 568. Kerlinger. Fred N. inundation: ni BenaanLal Basaancb. 2nd ed. New York: Holt. Rinehart and Winston. Inc.. 1973. 163 Kopelman. Richard E.: Schneller. George 0. IV: and Silver. John J.. Jr. "Parkinson's Law and Absenteeism: A Program to Rein in Sick Leave Costs." Eemnnfl Administnatc: (May 1981): 57. Kuzmits. Frank E. "How Much Is Absenteeism Costing Your Organization." Inc Eenscnncl Administrator: (June 1979): 29-32. "Lansing School District Budget Projection l983/84." Compiled by the Finance Department. Lansing School District. June 1983. "Lansing School District Master Agreement with Lansing School Employees Association." Ratified for the years l979 through l981. "Lansing School District Report of Sick and Personal Leave Days Used 198l/82 Through l977/78." Compiled by the Employee Relations Department. Lansing School District. Leon. Carol Boyd. "Employed But Not at Work: A Review of Unpaid Absences." .Mgnthly Labor Review (November l981): 18-22. Lewis. James. Jr. "Using a Computer to Monitor Teacher Absenteeism Can Save Schools Money and Increase the Time Teachers Spend in Class." Inc American Scnncl Board lcurnal (December 1982): 30-32. Lezotte. Lawrence W.. and Passalacqua. Joseph. "Individual School Buildings--Accounting for Differences in Measured Pupil Per- formance." .uLnan.Enucatign (October 1978): 283-9l. Master Agreement Between the Lansing Schools Education Association and the Lansing School District for 1981 and 1984. Article XV. p. 34. McIntire. Ronald G.. and Hughes. Larry W. "Houston Program Trains Effective Substitutes." .Ehi Delta Kappan (June l982): 702. Nie. Norman H.. et al. Statisticalfiackageionthaficcialficicncca. 2nd ed. New York: McGraw-Hill Book Co.. 1975. Olson. M. N. "Identifying Quality in School Classrooms: Some Problems and Some Answers." .Qentnal Ideas 2l (February 197l): 6. Pennsylvania School Boards Association. "Teacher Absenteeism: Pro- fessional Staff Absence Study." Harrisburg: Pennsylvania School Boards Association. October 1978. 16h Prentice-Hall Editorial Staff. .Absenteeism and Lateness. Prepared for the American Society for Personnel Administration. Englewood Cliffs. N.J.: Prentice-Hall. Inc.. l981. Rawson. D. V. "Increasing the Effectiveness of Substitute Teachers." .Natienal.Asseciatien.ei.Secendanx.§cnee1.Enincinals.8ulletin (September 198l): Bl. Robinson. Glenn. "Foreword." In Educational Research Service. Absenteeism A Summarx 9.15 Beseanch. Arlington. Va.: Educational Research Service. Inc.. 1980. Rothman. Miriank "Can Alternatives to Sick Pay Plans Reduce Absenteeism?" ‘Eensgnnel.lgunna1 (October 198l): 788. Scott. Dow. and Markham. Steve. "Absenteeism Control Methods: A Survey of Practices and Results." .Eensgnne1.Adm1nistnatgn 27 (June 1982): 73-76. Steers. Richard M.. and Rhodes. Susan R. "Major Forces on Employee Attendance: A Process Model." .Jentna1.QI.Annlied.Esxchnlegx 63 (1978): 391-407. Taylor. Daniel E. "Absences From Work Among Full—Time Employees." Menthx Label: Beyieu (March 1981): 68. . "Absent Workers and Lost Work Hours. May 1978." ‘Mgntnly .LabgL.Bexiew (August 1979): 49. "Teacher Absenteeism in New York City and the Cost Effectiveness of Substitute Teachers." State of New lock. Diche cf. Education ,Eenignmance.flexiew. Albany: New York Office of Education. January 1974. "Teacher Absenteeism: Professional Staff Absence Study." Harrisburg: Pennsylvania School Boards Association. October l978. Wiley. David E. "Another Hour. Another Day: Quantity of Schooling. a Potent Path for Policy." In Schooling.and.Acniexement.in ‘Amenican.§gciety. Edited by William H. Sewell. Robert M. Hauser. and David L. Featherman. New York: Academic Press. 1975. . and Harnischfeger. Annegret. "Explosion of a Myth: Quantity of Schooling and Exposure to Instruction. Major Educational Vehicles." .Educatienal.Beseancnen 3 (April 1974): 7-12. Winkler. Donald R. "The Effects of Sick Leave Policy on Teacher Absenteeism." Industrial and Labs: 39.111.19.05 Raids! 33 (January 1980): 232. I65 Zafirau. James S. "A Study of Attendance Issues in a Desegregating School District." March l5. 1982. (Mimeographed.) Zager. Robert. "Employees Miss Less Work Time." ‘Besounce (American Association for Personnel Administration) (February 1983): 12. Zimet. Melvin. Decentralization and School Eflectixeness. New York: Teachers College. Columbia University. l973. HICHI RN TATE c s H llll (I E3 1 312 30 @9500 UN V. Ill Illlll nnnxss WWW I405