Running Head: TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

THE IMPACT OF CHRONIC TEACHER ABSENTEEISM ON STUDENT
ACHIEVEMENT

A Doctoral Capstone Project
Submitted to the School of Graduate Studies and Research
Department of Secondary Education and Administrative Leadership

In Partial Fulfillment of the
Requirements for the Degree of
Doctor of Education

Jason Reifsnyder
California University of Pennsylvania
July 2020

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

© Copyright by
Jason Reifsnyder
All Rights Reserved
July 2020

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TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

California University of Pennsylvania
School of Graduate Studies and Research
Department of Secondary Education and Administrative Leadership

We hereby approve the capstone of
Jason Reifsnyder
Candidate for the Degree of Doctor of Education

Dr. David Foley
Superintendent of Schools
South Butler County School District
Doctoral Capstone Faculty Committee Chair

Dr. Stacy Winslow
Assistant to the Superintendent
Derry Township School District
Doctoral Capstone External Committee Member

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Dedication
I dedicate this capstone project to my wife, Stephanie, who has been by my side and
supported me throughout this academic experience.
To my two wonderful boys, Broderick and Preston, with the hope that this journey
inspires them to be life-long learners.
To my parents, who have always believed in me and who have provided me with endless
amounts of love and support as a child and as an adult.

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Acknowledgements
I would like to thank my capstone committee members who guided me through this
process: Dr. David Foley and Dr. Stacy Winslow. Your encouragement, feedback, and assistance
was always appreciated. I would also like to thank, Dr. Silvia Braidic, Dr. Kevin Lordon, Dr.
Randal Lutz, Dr. John Smart, and Dr. Mary Wolf for their guidance and support throughout the
Education Administration and Leadership Doctorate program. Finally, I would like to thank the
Cal U Vulcan Learning Commons Writing Center for the countless hours they spent reviewing
my capstone project. A special thank you to Nathan Zisk from the writing center for his
expertise, insight, and support.

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Table of Contents
Dedication

iv

Acknowledgements

v

List of Tables

x

List of Figures

xiii

Abstract

xiv

CHAPTER I. Introduction

1

CHAPTER II. Literature Review

6

Introduction

6

Problems Associated with Absenteeism

7

Absenteeism in the United States workforce

7

Teacher absenteeism

8

Impact of substitute teachers

11

Impact on student achievement

12

Impact on student attendance

17

Reasons for Teacher Absenteeism

17

Size of the district

18

Socio-economic status of students

19

Class size

19

Collective bargaining agreements

19

District policies

20

Gender

20

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Age/experience

21

Grade level

22

Day of the week

22

Time of year

23

Job satisfaction

23

Leadership style

24

Workplace climate/culture

25

Solutions to Reduce Teacher Absenteeism

26

Board policies

26

Incentive plans

29

Conclusion
CHAPTER III. Methodology

34
37

Purpose

37

Setting and Participants

39

Research Plan and Data Collection

43

Research Question 1

44

Data collection

45

Data analysis

47

Research Question 2

48

Data collection

48

Data analysis

50

Research Question 3

50

Instruments

52

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Data collection

55

Data analysis

56

Research Question 4

56

Data collection

58

Data analysis

58

Research Question 5

58

Data collection

60

Data analysis

61

Secondary Research Questions

61

Validity

62

Summary

63

CHAPTER IV. Data Analysis and Results

65

Predicators of Teacher Absences

66

Correlations Between Teacher Demographics and Teacher Absences

83

Student Achievement Scores

86

Correlations Between Student Achievement Scores and Teacher Absences

113

Teacher Absence Data

115

Secondary Research Questions

118

Summary

122

CHAPTER V. Recommendations and Conclusions

126

Purpose of the Research

126

Correlations to Previous Studies

127

Recommendations for Future Research

143

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Recommendations for Derry Township School District

144

Limitations

150

Special Considerations

151

Summary

152

References

154

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List of Tables
Table 1. Frequencies and Percentages of Participant Demographics

42

Table 2. Description of Independent Variables

45

Table 3. Review of Demographic Variables

46

Table 4. Review of Demographic Variables

49

Table 5. Review of Student Achievement Variables

56

Table 6. Review of Student Achievement Variables

58

Table 7. Categories of Absences and Their Associated Descriptions

59

Table 8. Review of Leave Variables

61

Table 9. Mean Difference Absences by Teacher Age

68

Table 10. One-Way ANOVA of Teacher Age on the Number of Absences

69

Table 11. Mean Difference Absences by Gender

70

Table 12. One-Way ANOVA of Gender on the Number of Absences

70

Table 13. Mean Differences Absences by Race

72

Table 14. One-Way ANOVA of Race on the Number of Absences

72

Table 15. Mean Differences Absences by Experience

75

Table 16. One-Way ANOVA of Experience on the Number of Absences

76

Table 17. Mean Differences Absences by School Level

77

Table 18. One-Way ANOVA of School Level on the Number of Absences

78

Table 19. Mean Differences Absences by Degree

80

Table 20. One-Way ANOVA of Degree Attained on the Number of Absences

80

Table 21. Mean Differences Absences by Distance to Work

82

Table 22. One-Way ANOVA of Distance to Work on the Number of Absences

83

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Table 23. Correlations – All Demographic Variables Related Teacher Absences

85

Table 24. Mean Difference Grade 2 DIBELS Next Scores by Absence Classification

87

Table 25. One-Way ANOVA of Grade 2 DIBELS on Absence Classification

87

Table 26. Mean Difference Grade 3 DIBELS Next Scores by Absence Classification

89

Table 27. One-Way ANOVA of Grade 3 DIBELS on Absence Classification

89

Table 28. Mean Difference Grade 4 DIBELS Next Scores by Absence Classification

91

Table 29. One-Way ANOVA of Grade 4 DIBELS on Absence Classification

91

Table 30. Mean Difference Grade 5 DIBELS Next Scores by Absence Classification

93

Table 31. One-Way ANOVA of Grade 5 DIBELS on Absence Classification

93

Table 32. Mean Difference by Absence Classification on PVAAS Math Scores

95

Table 33. One-Way ANOVA of PVAAS Math Scores on Absence Classification

95

Table 34. Mean Difference by Absence Classification on PVAAS ELA Scores

97

Table 35. One-Way ANOVA of PVAAS ELA Scores on Absence Classification

97

Table 36. Mean Difference by Absence Classification on PVAAS ELA Scores

98

Table 37. One-Way ANOVA of PVAAS Science Scores on Absence Classification

99

Table 38. Mean Difference by Absence Classification on PVAAS Algebra I Scores

101

Table 39. One-Way ANOVA of PVAAS Algebra I Scores on Absence Classification

101

Table 40. Mean Difference by Absence Classification on PVAAS Literature Scores

103

Table 41. One-Way ANOVA of PVAAS Literature Scores on Absence Classification

103

Table 42. Mean Difference by Absence Classification on PVAAS Biology Scores

104

Table 43. One-Way ANOVA of PVAAS Biology Scores on Absence Classification

105

Table 44. Mean Difference by Absence Classification on Algebra I Final Exam

106

Table 45. One-Way ANOVA of Algebra I Final Exam on Absence Classification

106

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Table 46. Mean Difference by Absence Classification on English 9 Final Exam

107

Table 47. One-Way ANOVA of English 9 Final Exam on Absence Classification

107

Table 48. Mean Difference by Absence Classification on CP English 9 Final Exam

108

Table 49. One-Way ANOVA of CP English 9 Final Exam on Absence Classification

108

Table 50. Mean Difference by Absence Classification on Honors English 9 Final Exam

109

Table 51. One-Way ANOVA of Honors English 9 Final Exam on Absence Classification

109

Table 52. Mean Difference by Absence Classification on CP English 10 Final Exam

110

Table 53. One-Way ANOVA of CP English 10 Final Exam on Absence Classification

110

Table 54. Mean Difference by Absence Classification on CP Biology Final Exam

111

Table 55. One-Way ANOVA of CP Biology Final Exam on Absence Classification

111

Table 56. Mean Difference by Absence Classification on CP Chemistry Final Exam

112

Table 57. One-Way ANOVA of CP Chemistry Final Exam on Absence Classification

112

Table 58. Mean Difference by Absence Classification on Honors Chemistry Final Exam

113

Table 59. One-Way ANOVA of Honors Chemistry Final Exam on Absence Classification

113

Table 60. Correlations – All Demographic Variables Related to Teacher Absences

114

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List of Figures
Figure 1. 2016-19 Total Number of Absences Per Year by Leave Category

116

Figure 2. 2016-19 Total Number of Absences Per Year by Day of the Week

117

Figure 3. 2016-19 Mean Number of Absences Per Year by Teacher Age Group

129

Figure 4. 2016-19 Mean Number of Absences Per Year by Experience

132

Figure 5. 2016-19 Mean Number of Absences Per Year by School Level

134

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Abstract
The purpose of this quantitative study was to analyze the predictors of teacher absences
between the 2016-19 school years and the impact of teacher absences on student achievement
scores at Derry Township School District (DTSD), a school district located in Hershey,
Pennsylvania. The objectives of the study included: (a) an analysis of the predictors of teacher
absenteeism, (b) examining the costs associated with teacher absenteeism, (c) analyzing the
impact on student achievement, and (d) recommendations to reduce the frequency of teacher
absences and the associated costs. The desired outcome of this action research project was to
provide substantial recommendations to DTSD and other public school systems to meaningfully
address the problems associated with teacher absenteeism.
The results of the study indicated that more than 62% of teachers at DTSD were
considered to be chronically absent. The cost associated with securing substitutes between the
2016-19 school years exceeded $2.1 million. In addition, the substitute fill rate in the district
continued to decline. Although, the study determined that there were little to no significant
differences between the achievement scores for students instructed by chronically absent
teachers and those who were instructed by teachers who miss fewer than 10 days. The results of
the study suggested that significant relationships between the number of teacher absences and
student achievement scores did not exist. However, the demographic variables of age, gender,
and years of experience were all determined to be significant predicators of teachers absences.

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CHAPTER I
Introduction
Similar to many school districts across the nation, Derry Township School District
(DTSD) has experienced an increasingly difficult time securing certified substitute teachers to
fill day-to-day and long-term positions. As the Assistant to the Superintendent for Personnel and
Students Services, it is my responsibility to secure appropriately certified substitutes to fill the
district’s day-to-day and long-term positions caused by teacher absences. The district, which is
located in Hershey, Pennsylvania, consists of one campus that includes an Early Childhood
Center (Grades K and 1), a Primary and Intermediate Elementary School Building (Grades 2-5),
one Middle School (Grades 6-8), and one High School (Grades 9-12). The district serves
approximately 3,500 students and employs approximately 280 professional employees.
Historically, the district’s statewide assessment scores have been consistently well above state
and national averages, and annually, more than 90% of the graduating seniors pursue postsecondary education. As a result, expectations with regard to student achievement is high and is
directly correlated to the district’s motto, “every child, every day.” Subsequently, securing
appropriately certified substitute teachers who are capable of providing students with highquality instruction is of the utmost importance to all stakeholder groups in the district.
When I first started in this position seven years ago, it was relatively easy to find
substitutes who were appropriately certified in each content area. As a result, there were minimal
concerns with regard to a substitute’s ability to provide rigorous and meaningful instruction.
During my first few years serving as the Assistant to the Superintendent, when a teacher was
absent, the district would first reach out to substitutes who were certified in the same content
area as the teacher who requested leave. For example, if a biology teacher called off sick, the

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district would first contact all the substitutes who were certified in biology. In the event that the
district could not secure a substitute who was certified in the same content area as the teacher
who was absent, the district would then contact substitutes who held any Pennsylvania teaching
certificate to fill the void. On rare occasions, when the district could not find a substitute with a
valid Pennsylvania teaching certificate, a guest teacher who held an emergency permit would be
used to fill the vacancy. In Pennsylvania, guest teachers are substitutes who have a bachelor's
degree but have not obtained a Pennsylvania instructional teaching certificate. Unfortunately,
over the years, the district’s reliance on guest teachers to fill day-to-day teacher absences has
significantly increased. As a result, I began to wonder and worry that the quality of instruction
being provided by our substitute teachers was not at a level the district expects and/or desires.
The main concern was that the vast majority of the district’s substitute teachers were working
under an emergency permit, and those who were appropriately certified were no longer working
in the content area in which they were originally certified.
To gain a better understanding of the issues and concerns with regard to the increasing
number of guest teachers working as substitute teachers, I attended the Pennsylvania Association
of School Personnel Administrators (PASPA) 33rd Annual Conference. During a session that
focused on updates pertaining to Pennsylvania Teaching Certifications and Permits,
representatives from Pennsylvania’s Department of Education’s Bureau of School Leadership &
Teacher Quality (BSLTQ) suggested that the increase in the number of emergency teaching
certificates being issued by the Pennsylvania Department of Education (PDE) is due to a
declining number of new teachers entering the workforce. The presenters indicated that,
according to records obtained from Pennsylvania’s Teacher Information Management System
(TIMS), the number of instructional certificates and permits issued by PDE decreased from

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39,387 during the 2012-13 school year to 9,530 during the 2017-18 school year. The presenters
also noted that the number of emergency permits issued by PDE has significantly increased
during that same time period. Specifically, during the 2014-15 school year, PDE issued only
8,751 emergency permits, while 19,603 emergency permits were issued during the 2017-18
school year. The decrease of almost 30,000 new teachers entering the workforce coupled with
the increase in emergency permits directly mirrors the district’s experience with respect to the
growing number of guest teachers needed to fill day-to-day teacher absences. While the PASPA
session addressed my questions with respect to the reasons for the district’s increased reliance on
guest teachers, it failed to address my concerns with regard to what impact teacher attendance
and substitute teachers have on student achievement.
To complicate matters, teacher absenteeism rates at Derry Township School District have
increased during this time period. According to the district’s absentee records, the percentage of
teachers who were absent on any given day during the 2012-13 school year was roughly 7%.
This figure increased to approximately 11% during the 2017-18 school year. When analyzing the
number of teachers who were absent from work daily during this period, it was determined that
approximately 21 teachers missed work each day during the 2012-13 school year, while an
average of 29 teachers missed work daily during the 2017-18 school year. Consequently, the cost
associated with substitutes has continued to rise. During the 2012-13 school year, the district
spent roughly $487,500 on substitute teacher costs. Substitute teacher costs during the 2017-18
school year ballooned to more than $680,000.
In order to quantify my concerns with respect to teacher attendance and its impact on
student achievement as well as the budget, a quantitative research approach was used during my
project. I started by collecting teacher absentee data via the district’s absence management

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system. In addition, I downloaded teacher demographic data from the district employee
management system (eFinance) to determine if there were any attendance patterns with respect
to gender and experience. To increase the validity of the research, I collected and analyzed
teacher absentee data for the 2016-17, 2017-18, and 2018-19 school years. The quantitative data
that I collected included DIBELS Next Oral Fluency scores and high school final exam grades.
Furthermore, I used PVAAS data to analyze student growth based on teacher performance.
Similar to teacher absentee data, the student data that I collected included scores and results from
the 2016-17, 2017-18, and 2018-19 school years. Moreover, I collected financial data with
respect to teacher absences by accessing the district’s end-of-year financial reports.
The desired outcomes pertaining to my action research project include improved teacher
attendance rates, improved student performance, and decreased substitute costs. In addition to
the primary desired outcomes, the recommendations of my action research project could lead to a
number of ancillary benefits for the district. These secondary benefits may include, but are not
limited to, improved staff and student wellness, improved staff and student morale, increased
staff and student engagement, and decreased employee health care costs. Likewise, the goal of
my action research project is to provide meaningful and substantial recommendations to Derry
Township School District and other public school systems with respect to addressing student
performance through the lens of teacher absenteeism.
The primary research questions that I investigated included: (a) are age, gender, race,
experience, grade(s) taught, level of education, and distance from work predictors of teacher
absence; (b) what is the relationship between the frequency of teacher absences and factors such
as age, gender, race, experience, school level, degree, and distance from work; (c) are there
significant differences in student achievement scores between teachers who are chronically

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absent (defined as 10 or more absences per school year) and teachers who are not chronically
absent; (d) what is the relationship between student achievement scores and the frequency of
teacher absences; and (e) are there significant differences in teacher absenteeism rates by leave
category or days of the week?
In addition to answering the primary research question, secondary objectives with regard
to my action research plan included determining the following: (a) how many teachers at DTSD
are chronically absent, (b) what are the economic impacts associated with teacher absenteeism
from 2016-19, and (c) what organizational factors contribute to teacher absentee rates (board
policies and collective bargaining agreement, professional development) and to what extent?

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CHAPTER II
Literature Review
Introduction
The topic of teacher absenteeism and substitute teacher coverage was discussed at a
regional meeting of human resources supervisors in the fall of 2018. The meeting that took place
had intriguing dialogue that focused on the topic area of teacher absenteeism and student
achievement. During the initial searches on subject matter, it became apparent that a limited
number of researchers have actually explored this topic. The early search results produced a
limited number of studies and publications relevant to the topic of teacher absenteeism and
student achievement. To complicate matters, the majority of search results contained studies that
were conducted or published more than 20 years ago. It took an extensive and deep search to find
more recent and relevant research. These studies suggested that 36% of teachers in the United
States miss 20 or more days of school per year, and the financial impact of teacher absenteeism
costs school districts more than $5.6 billion annually. More importantly, the research suggested
teacher absenteeism seriously disrupts the consistency of the classroom environment (Folger,
2019; Griffith, 2012; Smith, 2001). However, the amount of research directly associated with
student achievement scores and teacher absenteeism was still minimal in comparison to other
topic areas. Fortunately, the research that was discovered provided the foundation and genesis
needed to generate additional research questions pertaining to the topic of teacher absenteeism.
The primary research question, along with the supplemental questions that were developed,
provided the framework for the literature. After reviewing and analyzing the relevant literature,
the topic of teacher absenteeism for this literature review was divided into three central themes:
the problems, the reasons for the cause of the problem, and the solutions to the problem.

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Problems Associated with Absenteeism
According to Pitcoff (1993), absenteeism is a costly problem that plagues all industries
and occupations in both public and private sectors of the United States. Until recently, teacher
absenteeism has received considerably less attention when compared to absences in other
occupations and industries (Ehrenberg, Ehrenberg, Rees, & Ehrenberg, 1991). In a state-wide
study of school personnel directors, 71% of the respondents indicated that adequately addressing
teacher absenteeism was the biggest challenge they faced (Norton, 1998). School personnel
directors also indicated on the same study that finding and securing substitute teachers was
another job challenge that they encountered daily. Although the study conducted by Norton
occurred roughly 20 years ago, the issues and concerns confronting school personnel directors
have remained constant.
Absenteeism in the United States workforce. Although it is generally acceptable for an
employee to miss a small number of days of work for justifiable reasons such as emergencies and
unexpected illness, the problem with absenteeism is that once it becomes a regular occurrence
for an employee or the employee is intentionally absent from work, the tendency to repeat the
same behavior increases (Porter & Steers, 1973). According to Gaziel (2004), employee
absences can be grouped into two categories: voluntary and involuntary absences. Voluntary
absences are when an employee intentionally misses work while involuntary absences are
beyond the control of an employee. Examples of involuntary absences include certified illness,
injury, bereavement, or emergency. Voluntary absences commonly include vacation, personal,
and uncertified sick leave. Gaziel further argued that voluntary absences generally occur in
patterns of short durations and high frequency. One of the difficulties in researching absenteeism
is determining how much freedom employees have to make their own decisions as to whether to

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be absent. For this reason, much of the research on absenteeism is focused on two important
variables: an employee’s motivation to attend and an employee’s ability to attend (Steers &
Rhodes, 1978).
From a fiscal perspective, the Society for Human Resource Management (SHRM) in
2013 estimated that the total direct cost of employee absences as a percentage of payroll was
8.1%. When calculated in terms of actual dollars, Losina, Yang, Deshpande, Katz, and Collins
(2017) determined that employee absenteeism in the United States costs employers roughly $250
billion per year in lost productivity. On an individual basis, unscheduled and unplanned worker
absences are estimated to cost employers approximately $2,650 annually for salaried workers
and about $3,600 a year for hourly employees (Forbes, 2013).
Research suggested that employee health has been determined to be one of the strongest
predictors of employee absences (Mullen & Rennane, 2017). According to the Bureau of Labor
and Statistics, the 2018 absence rate for full-time wage and salaried workers was 2%. This
percentage equates to the average American worker missing three to 3.7 days of work per year
due to illness and injury. Although the average number of days missed per worker per year is
relatively low, a small percentage of employees are absent at a considerably higher rate. It is
estimated that 6.5% of the workforce misses at least 10 days of work per year due to illness and
injury (Ahn & Yelowitz, 2016). As a result, the majority of research is focused on employees
with high rates of absences.
Teacher absenteeism. According to Clotfelter et al. (2009), understanding why teachers
are absent is important for four main reasons. These reasons include (a) the costs associated with
hiring a substitute teacher, (b) the effect on student achievement, (c) the correlation between
absence frequency and the poverty level of the school, and (d) the influence of school district

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policies. In a 2008 report released by the Center for American Progress, it was determined that
by the time typical students in the United States graduate, they would have been instructed by
substitute teachers for approximately two-thirds of a school year. Early research specific to
teacher absences indicated that absenteeism rates for educators in the United States was 5% per
year, the equivalent of nine days each school year (Erhenburg et al., 1991). However, a more
recent study conducted by the National Council on Teacher Quality (2014) suggested that the
actual number of days teachers miss work due to being sick and/or personal reasons has
increased to 12.7 days per school year. Regardless, the amount of annual discretionary leave
used by teachers is disproportionate when compared to the average American worker. In 2012,
the total costs associated with teacher absences in the United States were estimated to be in
excess of $4 billion per year (Miller, 2012). A more recent report estimates that teacher absences
cost school districts more than $5.6 billion per year (Folger, 2019; Kocakülâh, Bryan, & Lynch,
2019). When calculated on a per-teacher basis, absences cost school districts approximately
$1,800 annually for every teacher they employ (National Council on Teacher Quality, 2014).
While the total number of days teachers miss per school year is alarming, the percentage
of teachers who are identified as being chronically absent from work each year is even more
troubling. In a report issued by the Center for American Progress (2012), during the 2009-10
school year, 36% of public school teachers in the United States were absent from the classroom
for 10 or more days. As Miller (2012) noted, absentee rates vary greatly from state to state, with
the state of Utah reporting the fewest percentage of teachers who miss 10 or more days of school
per year. School districts in Rhode Island reported the greatest percentage of teachers being
chronically absent. The percentages ranged from a low of 20.9% to a high of 50.2%. The
absentee rate for teachers in Pennsylvania who were chronically absent during the 2009-10

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school year was 36.2% (Miller, 2012). According to Griffith (2012), a large part of the issue lies
within the number of sick and personal days that are annually afforded to teachers. The report
issued by Thomas Fordham Institute indicated the average teacher in the United States is
provided with a combined 12 sick and personal days per year while only one-third of the United
States workforce is provided with the equivalent amount of leave.
The rate at which teachers are absent from school varies greatly between public and
charter schools. As mentioned previously, 36% of public school teachers are chronically absent,
while only 10.3% of charter school teachers miss 10 or more days of school per year (Miller,
2012; Griffith, 2017). The research suggested the main factors that contribute to the
discrepancies in teacher attendance rates can be attributed to policy and collective bargaining
agreements.
Although the percentage of teachers in the United States who are chronically absent and
the frequency in which they miss work appears to be high, the numbers and percentages are far
more extreme in other parts of the world. For example, while the rates of teacher absenteeism in
the United States is roughly 5%, teachers in Papua New Guinea miss work 15% of the time and
teachers in Zambia annually experience absent rates of 18%. In a survey conducted by the World
Bank, teacher absenteeism rates in Peru, Indonesia, Uganda, and Kenya were 11%, 21%, 27%,
and 30%, respectively (Obiero, Mwebi, & Nyang’ara, 2017). The vast majority of research
indicated that teacher absentee rates are greatly influenced by student poverty levels (Obiero et
al., 2017; Rogers & Vegas, 2009). This theory is supported by the fact that the absentee rate for
teachers in developed countries is much lower than that of teachers in the developing world. For
example, teacher absentee rates in the United Kingdom and Australia are 3.2% and 3.1%,
respectively (Miller, 2008).

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Impact of substitute teachers. In a national study, it was determined that approximately
70% of public schools in the United States identified a shortage of substitute teachers as a
growing concern (Smith, 2001). To complicate matters, fears with respect to substitute teachers
go much deeper than just the financial implications. Additional concerns with respect to
substitute teachers extend to the quality of substitute training, the teaching skills of a substitute
teacher, and the overall perceptions and attitudes toward substitute teachers.
When school districts are fortunate enough to find enough substitutes to fill classrooms
vacancies, the costs associated with employing substitute teachers can often be financially
burdensome (Damle, 2009; Gonzalez, 2017). Data obtained from the National Education
Association (NEA) suggested that substitute pay rates vary greatly from state-to-state and
district-to-district. The most recent data on the NEA website indicated rates for substitute
teachers range from $35-135 per day.
The majority of substitutes receive minimal training before entering the classroom
(Damle, 2009). Due to the lack of preparation and training, substitute teachers quite frequently
are unable to provide instruction with the same continuity and rigor that the permanent classroom
teacher would likely have provided. Other factors, such as knowledge of the specific subject
matter and the ability to form relationships with students also contribute to the lack of continuity
in instruction (Woods & Montagno, 1997). Due to the contributing factors referenced above,
substitute teachers are often unable to provide instruction at the same level as the regular
classroom teacher (Miller et al., 2008). It should be noted that while substitute teachers in the
United States are often ill-prepared to enter the classroom and lack the necessary skills needed to
be successful, they are often better equipped and qualified than their counterparts in other
countries (Miller et al., 2008).

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An additional concern with respect to substitute teachers is general belief and perception
by classroom teachers that substitute teachers are inferior when compared to the permanent
classroom teacher. One of the major factors that contribute to this perception is the concept that
employees who have substandard qualifications often are paid lower rates when compared to
their highly qualified peers. Likewise, low pay is also associated with employees who lack
technical or specialized skills (Cardon, 2002). As a result, classroom teachers regularly assign
substitute teachers tasks and assignments that mirror that of an ill-informed babysitter. Quite
often, substitute lesson plans consist of showing movies and providing students with simple
worksheets to complete (Damle, 2009; Miller et al., 2008; Woods & Montagno, 1997). For these
reasons, the research implies that substitute teachers both directly and indirectly have a negative
influence on student achievement (Miller et al., 2008).
Impact on student achievement. There is a limited body of research with respect to the
impact of teacher absences on student performance. However, the literature that does exist
suggested that one of the first studies that attempted to correlate teacher absences to student
achievement scores occurred in 1986-87. Ehrenberg, Ehrenberg, Rees, and Ehrenberg (1991)
examined and analyzed teacher and student absenteeism at more than 700 school districts in the
state of New York to determine the impact teacher absences had on student achievement levels.
Unfortunately, the study analyzed only student pass rates on standardized tests. Therefore, the
researchers concluded that teacher absenteeism, for the most part, did not impact student pass
rates on standardized assessments. However, the researchers did note that additional research
should be conducted to see how teacher absenteeism impacts students who perform well above
the “minimal pass” level.

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A similar study to determine the negative effects of teacher attendance on student
achievement was conducted by Woods and Montagno (1997). This study examined reading
levels as determined by the Iowa Test of Basic Skills for two select school districts in Indiana
and Wyoming. In this particular study, the skills test was administered in the fall to third grade
students in the two selected schools and then again to the same students the following school
year. Woods and Montango concluded that the data supports the notion that teacher absenteeism
has a negative effect on student achievement. Similar to the previous study, the researchers
recommended further studies be conducted to continue exploring the impact teacher absenteeism
has on student achievement scores.
Although early research, with respect to teacher attendance and its impact on student
achievement, produced mixed results, a more recent study conducted by Clotfelder et al. (2009)
found a statistically significant correlation between teacher absences and student achievement
scores. The researchers in this study examined leave patterns for teachers in North Carolina from
the years 1994-2004 and the influence on student performance in both math and reading. The
results of the study indicated that students had reduced math scores compared to their peers when
instructed by a teacher who was absent from work for 10 or more days due to sickness. Likewise,
the achievement scores for students who were instructed by a reading teacher who missed 10 or
more days due to illness were lower than their peers. Although scores in both subject areas were
negatively impacted by the number of days a classroom teacher was absent, the researcher found
that teacher absences had a greater impact on math scores than reading scores.
A study conducted by Miller et al. (2008) analyzed the negative effects of teacher
absences on a single large urban school district in the northern part of the United States. The
study examined teacher leave patterns of 285 fourth grade teachers between the 2003-05 school

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14

years. The results of the study indicated that student test scores were lower in classes where the
teacher was absent from work 10 or more days during the course of the school year. Moreover,
the researchers suggested that for every 10 additional days a teacher is absent, the student
achievement scores decreased in math by 3.2% of a standard deviation. This study confirmed the
early findings of Clotfelder et al., which indicated student achievement scores are impacted by
teacher absenteeism rates at a substantially higher degree in mathematics than in other subject
areas.
Additional research by Brown and Arnell (2012) further supported the connection
between teacher absenteeism and student achievement. The study compared SAT 10 assessment
scores for elementary students who attended a Title I school in Montgomery, Alabama. The
researchers examined data for students and teachers in grades three through six between the
years 2006-09 to see if there was a correlation between student achievement and teacher
absenteeism. The authors concluded that student achievement scores decreased as teacher
absences increased. It was further determined that to minimize the detrimental impacts
associated with teacher absenteeism, school leaders should limit the number of days teachers
miss to no more than 10 days per year (Brown & Arnell, 2012).
According to the United States Department of Education, a teacher who misses 10 or
more days of work per year is classified as being chronically absent. Although the results are
somewhat mixed, the majority of research indicated that teachers who are chronically absent
negatively influence student achievement scores. While this body of research is significant, the
researchers in these previously mentioned studies provided no insight as to the correlation
between the actual number of days missed and/or a range of days missed by the classroom
teacher and student achievement scores (Brown & Arnell, 2012; Clotfelder et al., 2009;

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15

Erhenberg et al., 1991). Cantrell (2003), however, examined this very question. The study
analyzed teacher absentee rates in the Los Angeles Unified School District (LAUSD) during the
2001, 2002, and 2003 school years. The researchers divided teachers into five different groups
dependent on the percentage of days they missed per school year. For the purpose of comparison,
a teacher in the LAUSD who was absent 5-6% of the time was equivalent to a teacher missing 10
days of school per year. The study found that students who were instructed by teachers who were
absent less than 2% of the time outperformed their peers who were instructed by teachers in all
other comparison groups and in all subjects (math, reading, and language). The results were even
more dramatic when the researchers compared student achievement scores for teachers in the
group that missed work the least amount of time against the scores of teachers who missed work
the most.
Similarly, Colquitt (2009) set out to determine if student achievement scores were
influenced by the specific amount of leave a teacher used per year. In order to answer this
question, the researcher collected fifth grade student achievement scores on the statewide
mathematics assessment and compared the achievement data against attendance records for fifth
grade teachers who worked in a large suburban school district in Georgia. To determine the
impact on student achievement scores, the research divided teacher leave into four separate
categories that included: (a) teachers who missed four or less days of school per year, (b)
teachers who missed between five and 10 days, (c) teachers who were absent between 11 and 14
days, and (d) teachers who missed more than 14 days per year. While the purpose of the study
was to determine if student achievement scores were influenced by the specific amount of leave
a teacher missed per year, the researcher concluded that there was no statistical difference with

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16

respect to the amount of leave taken by a teacher and the academic achievement level of their
students.
Likewise, in a more recent study conducted by Niemeyer (2013), it was concluded that
there was no statistically significant difference between the number of days a teacher was absent
and reading proficiency levels for students in kindergarten through third grade. In this particular
study, the researcher examined the composite scores on the spring DIBELS Next literacy
assessment and compared them against the number of days a teacher was absent from class.
While Clotfelder et al. compared teachers who missed work 10 days or more for sick purposes,
Neimeyer compared teachers who were absent from the classroom for 10 days or more during
the school year for any reason. Niemeyer noted that more than 65% of the teaching staff who
participated in the study were absent from the classroom 10 or more days during the school year.
Since such a large percentage of teachers missed 10 or more days of work, the researcher divided
the total teacher absences into five levels ranging from zero to four days to 35 plus days. The
researcher then disaggregated the data to gain a better understanding as to how teacher
absenteeism impacted student achievement. However, as previously mentioned, Niemeyer found
no statistical differences between absence rates of teachers and student achievement scores.
Although there is conflicting evidence with regard to the correlation between teacher
absences and student performance, the majority of research indicated that teacher absences
negatively influence student achievement scores. In fact, one study found that every 10 times a
teacher misses work is the equivalent to a student being instructed by someone with two to three
years of less experience (Miller, 2008). Furthermore, a 2012 report released by Hanover
Research indicated that scholars from Harvard University also determined that mathematics
scores are significantly reduced each time a teacher misses 10 days of school. Finally, the

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17

literature implies that chronic teacher absenteeism impacts mathematics scores to a greater extent
when compared to other subject areas (Cantrell, 2003; Clotfelder et al., 2009).
Impact on student attendance. Bowers (2001) asserts that an increase in student
absenteeism should not correlate to an increase in teacher absenteeism and vice versa. However,
the small body of research that was conducted in this area implied that lower teacher absenteeism
led to lower student absenteeism (Bowers, 2001; Ehrenberg et al., 1991). Conversely, Bowers
(2001) contended that lower student absenteeism has been shown to have a positive influence on
teacher attendance rates. The research also suggested that student achievement increases as
student attendance rates increase (Ehrenberg et al., 1991). The research from this early study was
supported by a study conducted a few years later that examined pay incentives on teacher
absences in one New York district. This study concluded that it is reasonable to assume that
there is a positive correlation between teacher attendance rates and student absenteeism
(Jacobson, 1990). A more recent study that analyzed data from an anonymous, large urban
school district in the northern United States determined that when student attendance rates
increased, the teachers’ absentee rates decreased (Miller, 2008). It should be noted that each of
the studies that explored the connection between teacher and student absenteeism clearly
indicated the need for additional research in the topic area.
Reasons for Teacher Absenteeism
The research implied that it is extremely difficult to address the problem of teacher
absenteeism without first determining the degree and the extent in which the problem actually
exists (Rogers & Vegas, 2009). In terms of teacher absenteeism, if school districts are able to
determine the costs, frequency, and reasons for teacher absences, then they will be better
prepared to find solutions to the problem.

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A recent report indicated 71% of all teacher absences are a combination of sick and
personal leave. Sick leave alone accounted for more than 39% of the absences. The report also
noted that 20% of leave was for professional reasons. Professional leave in this instance referred
to teachers being out of the classroom for school or district business (National Council on
Teacher Quality, 2014). Because teachers can choose whether they want to be absent, leave taken
for personal or sick reasons are often referred to as voluntary or discretionary (Clotfelder et al.,
2009). Since a significant percentage of teacher leave is considered discretionary in nature,
several studies have examined various determinants of teacher absences. The determinants of
teacher leave generally consist of both individual and organizational characteristics. In order to
gain a better understanding as to the degree in which these characteristics impact teacher
behaviors, the following predictors of absenteeism are explored in this section of the literature
review: size of the district, socio-economic status of students, class size, collective bargaining
agreements, district policies, gender, age/experience, grade level, days of the week, time of year,
job satisfaction, leadership style, and workplace climate/culture.
Size of the district. The few studies that have examined the relationship between the size
of the school district and teacher absences indicated that there is a positive correlation between
teacher absences and student enrollment. As such, the research indicated that as student
enrollment increases, so does the rate of teacher absences (Miller, 2008; Miller et al., 2008). The
notion that school size is linked to teacher absenteeism is supported through a report released by
the Frontline Research and Learning Institute (2019), which indicated the average number of
absences per employee is far less for small school districts than for medium, large, or extra-large
school districts. The research did not yield any indications as to the reason for the correlation
between the size of the district and the number of teacher absences.

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19

Socio-economic status of students. According to Engle and Glen (2018), teachers were
absent more frequently in schools that had a larger percentage of free and reduced lunch. This
recent study supported the majority of existing research that suggested teachers had an increased
rate of absences in school buildings that have a higher percentage of students who are considered
to be economically disadvantaged (Clotfelter et al., 2009; Pitkoff, 1993). However, a report that
examined the data of 40 of the largest metropolitan school districts in the United States
concluded that the poverty level of the students does not significantly influence teacher
attendance (National Council on Teacher Quality, 2014).
Class size. Ost and Schiman (2017) conducted research that analyzed the correlation
between class size and teacher absentee rates. This study analyzed data on every public school
teacher and student in North Carolina between 1995 and 2007. However, the researchers focused
primarily on elementary teachers and students. The study concluded that larger class sizes in the
primary elementary grades are positively linked to lower teacher absenteeism rates. There is no
research that linked teacher absences to class size at the secondary level. However, additional
research into this variable was recommended.
Collective bargaining agreements. Griffith (2017) examined the differences in teacher
absenteeism rates between charter and public schools. The study was focused on examining the
differences between these two educational systems because charter schools are void of labor
agreements. While the study concluded that there was no clear evidence that collective
bargaining agreements impacted teacher attendance rates, the study did conclude that in states
where collective bargaining is illegal, the attendance gaps between charter school teachers and
public school teachers is significantly smaller than in states where school districts are required to
bargain. This study supported earlier research that suggested collective bargaining agreements

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20

directly influenced teacher absentee rates (Erhenberg et al., 1991). Although limited, the research
indicated that there is a correlation between collective bargaining agreements and teacher
attendance. Griffith, in part, attributed this correlation to the myriad of job protections that are
often contained in collective bargaining agreements.
District policies. Teachers in school districts that have policies that provide for a large
number of sick days and bereavement leave and have established sick leave banks generally have
higher absentee rates when compared to school districts that have policies that supply teachers
with a limited amount of leave. Moreover, teachers in school districts that have policies that
afford employees the opportunity to “cash-in” unused sick leave annually or upon retirement
generally have lower occurrences of teacher absences (Erhenberg et al., 1991; National Council
on Teacher Quality, 2014). Likewise, the research suggested policies that do not offer teachers
the ability to roll over personal or sick leave tend to indirectly encourage teachers to annually
exhaust their leave (Pitkoff, 2003). Rates of absences are generally lower in districts that have
policies that include bonuses for teachers with excellent attendance (Boyer, 1994; Ehrenberg et
al., 1991; Jacobson, 1990).
Gender. The report issued by the Center for American Progress in 2008 suggested that
female teachers are frequently more absent than their male counterparts. The basis for this
assertion is due to the fact that historically, women served as the primary caretakers for ill family
members. Likewise, women traditionally took more time off than men for the birth of a child.
This finding confirmed the conclusions of an earlier study that examined attendance data for
junior and senior high teachers in the Mid-Atlantic region of the United States. The study found
that males were absent less frequently than their female colleagues and that female teachers were
absent for a greater number of days per year when compared to male teachers (Scott &

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21

McClellan, 1990). The results of this study were even further supported by a more recent study
that examined teacher leave patterns and predictors of teacher absence. The study included
absence data for roughly 1,200 teachers in a single school division in Virginia who were
continuously employed for three consecutive school years (Pitts, 2014). However, in similar
studies conducted by Bermejo-Toro and Prieto-Ursúa (2014) and Capote Fermin (2018), the
researchers concluded that there was not a significant statistical difference between the average
number of sick days missed between male and female teachers.
Age/experience. Clotfelder et al. (2009) concluded that the experience level of teachers
impacted the number of days they are most likely to miss during a given year. Specifically, the
research suggested that second-year teachers are absent 2.8 more days than they were during
their first year of teaching. This leave trend continued during teachers’ third, fourth, and fifth
years of experience, with the number of days increasing annually until teachers reached their
fifth year of teaching. The study also indicated that this leave trend flattened out until the final
years of a teacher’s career, at which point the number of days a teacher is absent considerably
decreases. One of the most commonly noted reasons for the decrease in absence rates for
teachers nearing retirement is that the value of being able to cash out their unused leave days
becomes of greater importance to them (Miller, 2008). When comparing student achievement
results to the experience level of a teacher, the research suggested that students who are
instructed by teachers with three or less years of experience perform lower than students
instructed by teachers who have at least three or more years of experience. However, there was
no noticeable difference in student achievement scores between the time a teacher reaches three
years of experience and retirement (Cantrell, 2003).

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Grade level. There is a small body of research that suggested that the grade configuration
of school impacts teacher behavior and absenteeism rates (Clotfelter et al., 2009; Miller, 2008;
Miller et al., 2008). Specifically, a few studies suggested that the absenteeism rates of elementary
teachers are greater than middle school teachers, while the absenteeism rates of middle school
teachers are greater than high school teachers (Clotfelter et al., 2009; Miller et al., 2008). This
research is further supported by a study conducted by Miller (2008), which analyzed the absence
data of approximately 2,500 teachers during a four-year span. The result of the study concluded
that 37.8% of elementary teachers were chronically absent each year while the percentage of
middle school and high school teachers who were chronically absent was 36.7% and 33.3%,
respectively.
Day of the week. There is strong evidence to suggest that teachers are absent most often
on Fridays as compared to other days of the week (Miller et al., 2008; Pitts, 2010). A report
released by the Center for American Progress in 2008 noted that 5.9% of teachers were absent on
Fridays and 5.1% on Mondays while only 4.4% were absent during the middle of the workweek.
The high absentee rates on Fridays are a result of teachers wishing to extend their weekends, a
behavior that mirrors other occupations and industries (Miller, 2008; Miller et al., 2008).
Likewise, Pitts (2010) determined that teacher absentee rates increase the days prior to a holiday.
This data confirms an overarching belief that teachers commonly use discretionary leave to
extend their total number of consecutive days off work. Although many employees take
advantage of their abilities to extend their weekends, there is some conflicting research with
respect to the frequency that teachers are absent on Mondays. Some research suggested that
teachers are more commonly absent on Mondays while other research indicated that teachers are

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23

less commonly absent on Mondays when compared to other days of the week (Miller, 2008;
Miller et al., 2008; Pitts, 2010).
Time of year. Miller (2008) also found that teacher absentee rates increased steadily
during the fall and winter months before dropping in January. The steady rate of increase returns
during the remaining months of winter and early spring before peaking during the month of May.
The only other research that mentioned the correlation between teacher absences and time of
year was conducted by Unicomb, Alley, and Barack (1992). The authors suggested that teacher
absentee rates are higher during the months of November, January, and April. Additional
research with respect to teacher leave patterns based on the time of year should be conducted and
explored.
Job satisfaction. Job satisfaction is commonly defined as individuals’ general attitude
toward their jobs. As such, the guiding principle is that employees who are satisfied with their
jobs will miss work less often than employees who are dissatisfied. This belief was examined in
a study conducted by Ejere (2010), who analyzed survey data of more than 1,000 primary school
teachers in Nigeria. The results of the survey indicated that high levels of job satisfaction do not
necessarily result in lower rates of absenteeism. However, the author argued that teachers who
are extremely dissatisfied with their jobs are generally more likely to be absent from work. As a
result, Ejere concluded that a positive relationship exists between absenteeism and job
satisfaction. The study further concluded that some teachers are missing work solely because
they are dissatisfied with their jobs. Conversely, Diestel, Wegge, and Schmidt (2014) argued that
using job satisfaction to predict individual employee absenteeism rates is a flawed measure. The
authors contend that other variables may have a greater influence in determining if an employee
reports to work or not. For example, employees who are extremely satisfied with their jobs may

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24

be required to miss work due to an unexpected illness. Likewise, individuals who are dissatisfied
with their jobs may be forced to work due to potential negative consequences that may result
from being absent from work.
Leadership style. Imants and Van Zoelen (1995) found that teachers who work in
schools that are led by principals who exhibit a directive leadership style have lower absence
rates than teachers who are led by principals who prefer a supportive or restrictive style of
leadership. The study concluded that teachers have lower stress levels when led by principals
who play a central role in the decision-making process as it pertains to the rules and decisions
that govern the school. Owen (2010) determined that teachers generally believe strong principals
are leaders that are supportive in nature and provide the necessary physical resources and
emotional support needed for teachers to succeed in their classrooms.
While the majority of literature supported the notion that leadership style considerably
impacted employee attendance, Barge (2004) concluded that there was no significant positive
relationship between leadership style and teacher absenteeism. An additional study conducted in
2010 mirrored the results of the Barge study. Carter (2010) analyzed the managerial philosophies
of 90 principals throughout the state of Georgia. The data collected through the managerial
philosophy survey was then compared to the absence data for teachers who worked in each
principal’s respective building. The findings of the study concluded teacher absences increased
when principals had a more pessimistic view of the world that surrounded them. Conversely,
principals who had a more positive outlook generally experienced lower rates of teacher
absences. Although Carter found that there was a correlation between principal leadership style
and teacher absenteeism, the differences were still statistically insignificant due to the small
sample size and therefore cannot be generalized without additional research.

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25

Workplace climate/culture. Workplace climate and morale have been linked to
employee stress and consequently linked to absenteeism (Miller et al., 2008). As a result, the
general consensus is that as teacher morale improved, teacher absentee rates decreased. Owen
(2010) ascertained that teachers who were generally more positive with respect to their job duties
had lower rates of absenteeism. The same held true for teachers who had positive opinions and
attitudes with respect to their principal and colleagues. Specifically, the study showed that
teachers who were provided time during the day to complete non-instructional duties had lower
rates of absenteeism when compared to teachers who indicated that they were required to
complete these same tasks outside their contractual hours. Similarly, Capote Fermin (2018)
found that absentee rates decreased if teachers were afforded greater levels of autonomy in the
decision-making process with matters that related directly to their classroom environments.
However, the study concluded that the correlation between climate and absenteeism was
significant only when teachers missed work due to illness. Therefore, the researchers suggested
that climate impacts absenteeism rates only when teachers need to miss work due to unexpected
or unplanned discretionary reasons.
Regardless of the reason and contributing factor, the research clearly indicated teachers
most frequently miss work due to discretionary reasons. In a study that analyzed the leave
patterns of more than 5,000 teachers in a large urban school district in the northern part of the
United States, Miller (2008) found that short-term illness, which is defined as short periods of
leave that occur in blocks of one or two days, accounted for 41% of all sick leave. When
combined with medium and long-term illness, teacher absences for sick leave in this study
accounted for 59% of all absences. The reasons that teachers are absent from work vary
tremendously and can be attributed to a number of different determinants. However, the research

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26

suggested that individual and environmental characteristics may greatly influence the frequency
and duration of a teacher’s discretionary leave – this especially holds true for short-term personal
illness.
Solutions to Reduce Teacher Absenteeism
Rogers and Vegas (2009) suggested there are no simple answers with respect to
successfully addressing and improving teacher absenteeism rates. In fact, the solutions and
recommendations to combat teacher absenteeism, according to the case studies, have produced
mixed results. However, it is important to note that the research indicated that districts need to be
willing to take risks and develop plans that are specific to their individual situations in order to
maximize their chances for success (Rogers & Vegas, 2009). Plans should include incentives,
policies, and programs that reward the highest-performing staff members while providing the
opportunity for all teachers to participate (Jacobson, 1990).
Board policies. According to Ehrenberg et al. (1991), leave policies have a tremendous
impact on the number of days teachers are absent from school. The research conducted by
Ehrenberg et al. suggested that absenteeism rates were positively correlated to the amount of
leave that is afforded to each teacher. The study concluded that districts that have policies and
collective bargaining agreements that contain language that offers leave for bereavement
purposes but does not deduct bereavement leave from existing discretionary leave balances
experienced higher rates of absenteeism. On the other hand, the authors noted district policies
that allowed teachers to cash in unused sick leave noticed a decrease in the amount of leave that
was actually taken. In addition, the study deduced, without explicit evidence, that policies that
limit the number of days teachers can miss work to attend a conference or professional
development event also experienced lower teacher absence rates when compared to districts that

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27

do not limit the number of days a teacher can miss for professional development purposes. This
early body of research is supported by a more recent report released by the Thomas B. Fordham
Institute. The report authored by Griffith (2017) analyzed chronically absent teacher data from
the Office of Civil Rights Data Collections. The researcher concluded that decreasing the amount
of leave teachers are afforded is likely to reduce teacher absenteeism rates. However, the report
noted that there is only a slight relationship that exists between district policies and the
likelihood that a teacher will be frequently absent from the classroom.
Policies and collective bargaining agreements that provide teachers the ability to use
personal leave are also problematic in terms of curbing teacher absenteeism. According to
Pitkoff (2003), most personal leave policies do not provide teachers the ability to carry over or
cash in their unused personal leave at the end of the school year. Therefore, teachers generally
tend to use their personal leave rather than lose it. In order to remedy this situation, Pitkoff
suggested that in order to reduce the amount of personal leave teachers use per year, school
leaders should reclassify personal leave to emergency leave. The author argued that a change in
classification would allow teachers the ability to use emergency leave for only unexpected and
unavoidable situations, thus reducing the rate at which personal leave is used. Pitkoff also
concluded sick leave banks generally increased the rates of absenteeism and encouraged teachers
to use more sick leave than what is annually allotted to them. Pitkoff found that teachers in
districts that have sick leave bank provisions generally did not accumulate a large number of sick
leave in their individual leave banks because of their ability to access leave through the sick
leave bank. Therefore, since teachers had the ability to access a sick leave bank for catastrophic
injuries or illnesses, they had little to no incentive to accumulate sick leave. For this reason,

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28

Pitkoff encouraged districts to eliminate and remove language that provided for the use of sick
leave banks from policies and collective bargaining agreements.
The research also concluded that districts that had policies requiring teachers to report
their absences directly to their principal or supervisor experienced lower absence rates. Teachers
in districts that did not have such policies were generally required to only submit their absence
via an online absence management system or a district-wide call-in system (Miller et al., 2008).
The results of this study supported a previous study by Boudreau, Christian, and Theibadeau
(1993) that evaluated the effectiveness of reducing absentee rates by modifying employee calloff procedures. The study, which was conducted at a private, nonprofit residential program for
children with autism, found that absentee rates of unscheduled leave significantly decreased
when employees were required to call their immediate supervisor in addition to the person who
arranged substitute coverage. Specifically, the researchers found that unscheduled leave was
reduced by 56%, 66%, and 35% in the three group homes that participated in the study.
Although the vast majority of research indicated that limiting the amount of leave and
modifying reporting procedures generally lowered teacher absentee rates, there was a study
conducted by Boyer-Baker in 2008 that contradicted these widely held findings. The purpose of
the study was to determine if a new leave policy implemented in a large suburban school district
in Kansas City, Missouri, would improve teacher absenteeism rates. The former policy provided
teachers 10 sick days in addition to two personal days per year, while the new policy reduced the
amount of leave per year to 10 days. The new leave policy eliminated the previous absence
categories, thus allowing employees to choose how to use their discretionary leave. Some of the
tenets of the new policy required teachers to report their absences directly to their principal or
supervisor on Mondays and Fridays as well as submit their absence via the absence management

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29

system. In addition, the policy included an incentive that provided teachers a cash payment for
any paid leave days that were not used during the school year. Finally, teachers who had perfect
attendance during the first or second semester were eligible to receive an additional monetary
incentive. Boyer-Baker found that the new policy had a negative impact with respect to teacher
attendance rates. As a result, teacher absenteeism increased during the course of the study. The
researcher suggested the increase in absence rates was likely the result of teachers having the
flexibility and freedom to use leave as they so desired. The previous policy afforded teachers
with only two personal days per year, therefore limiting the amount of days teachers could miss
for absences not related to health issues. In addition to the overall increase in absences, leave on
Mondays and Fridays also increased during the course of the study. Boyer-Baker attributed the
increase to the new policy’s daily reporting requirements. As noted previously, teachers had to
report absences only to their immediate supervisor on Mondays and Fridays, while leave on the
other days of the week needed to be submitted via the district’s absence management system.
However, the research did note that a few of the districts that participated in the study required
their teachers to report absences directly to their principal. These districts experienced the
second-lowest amount of leave within the timeframe of the study and supported the notion that
reporting procedures directly influenced teacher absence rates.
Incentive plans. Rogers and Vegas (2009) suggested that while there is no simple answer
or recipe to reducing teacher absence rates, policy makers should be willing to experiment with
mechanisms to improve teacher attendance. However, the authors noted that there is still a
cumulative lack of evidence and research required to develop best practices with respect to
teacher incentive programs. Consequently, Rogers and Vegas argued that the best method for
addressing teacher absenteeism is solely dependent on the context and profile of each school

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30

district. The authors suggested that the most promising policies or incentives should include one
or more of the following components: (a) salaries and promotions contingent on performance
rather than dependent on solely qualifications and experience, (b) mechanisms for accountability,
and (c) an increase in intrinsic and non-monetary rewards for excellent attendance. Although
Rogers and Vegas imply that there is not enough evidence to develop best practices in the area of
improving teacher absenteeism, there were a few incentive plans that were referenced frequently
throughout the literature that had a direct impact on teacher absenteeism.
One of the earliest school district incentive programs implemented to improve teacher
attendance took place during the 1985-86 school year in the Dekalb County School System in
Georgia (Grant, 2000). The school system, which employed 7,700 full-time staff members
throughout its 100 schools, initiated an attendance incentive plan known as the Meritorious
Attendance Recognition Program. The program individually recognized employees who missed
four days of work or fewer during the school year. The program also recognized schools and
departments that had high attendance rankings when compared to their respective counterparts.
The goal of the program was to simply decrease absenteeism rates by one day for each staff
member. During the first year of implementation, employee absenteeism was reduced by an
average of 1.23 days per employee, which lowered substitute costs by $156,000 during the 198586 school year. The program offered employees a variety of incentives such as providing a
savings bond and a letter of commendation to employees who missed four days or fewer for the
year. The program also recognized employees who had perfect attendance by entering them into
a drawing in which they were eligible to win a personal computer. Another highlight was the
posting of all staff members who had perfect attendance. Lastly, at the end of the year, a trophy
was presented to the school that had the best overall attendance record, and schools that ranked

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

31

in the top 10 for attendance were recognized on a monthly basis. As a result of the incentives, the
number of employees who had perfect attendance increased from 338 to 931. Additionally, 90%
of the schools also experienced improved attendance, and teacher absenteeism was reduced by
14% (Grant, 2000).
The Sugar Hill School District, located in Western New York, which had a 187-day
school year, provided teachers with the opportunity to receive a share of the money from a
parimutuel pool for each additional day a teacher was present beyond 180 days (Jacobson, 1989).
It should be noted that the district did not create the incentive to curb absenteeism rates but rather
as a means to distribute the Excellence in Teacher (EIT) funds it received from the state of New
York. Regardless of the intent of the program, the objective of Jacobson’s study was to see if the
monetary incentive impacted teacher attendance rates. In the end, a total of 1,274 shares valued
at $57.16 were distributed to approximately 200 teachers. Jacobson found that while teacher sick
leave usage dropped significantly from 5.97 days to 3.84 days, the number of personal days
increased from 1.23 to 1.51. The researcher suggested that teachers likely used additional
personal days to take advantage of and maximize their reward with respect to the incentive. The
author noted that the number of teachers who missed fewer than seven days increased by 13%
when compared to the prior school year. Likewise, the percentage of teachers who had perfect
attendance increased 22%. Jacobson concluded that while some teachers may have substituted
their sick leave for personal leave, the data still suggested that monetary incentives have a
significant impact on teacher leave patterns regardless of how large the monetary incentive is in
relation to a teacher’s actual salary.
Another study conducted by Jacobson (1990) examined the impacts of work-units and
teacher absence in the North Forest School District located in the state of New York. In this

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

32

particular district, school administrators in cooperation with union leaders developed an
incentive program to address teacher absenteeism because the district’s absence rates were well
above the state average. Specifically, during the 1986-87 school year, the teacher absence rate in
the North Forest School District was 7.2%, or the equivalent of 13.4 absences per teacher per
year. In comparison, the state average for teachers in the state of New York during the same year
was 4.8%, or 8.9 days per year. The incentive plan that was created provided teachers with three
additional sick days per year if they were able to reduce the district’s overall teacher absentee
rate by 25%. In order to reach the goal, each teacher would need to use approximately three
fewer sick days than they had used the prior year. The results of the study concluded that
offering group rewards as a means to improve attendance is misguided. The researchers argued
that often, the individuals who have the most influence as to the program’s success or failure are
the same individuals who necessitated the need for the program in the first place. For example,
the study revealed that teachers in schools who already had a good attendance record believed
that they were unable to significantly impact the attendance behaviors of teachers who were
chronically absent at other schools in the district. Additionally, a principal at one of the schools
noted that there was a widely held belief that it was acceptable for teachers to annually use their
allotment of sick days. This belief was most evident in teachers who were nearing retirement.
Therefore, the researcher recommended that districts should create plans that are individualized
and that provide all teachers the opportunity to benefit from those plans. Moreover, in order to
maximize the success of the incentive plan, the program should be tiered so that the top
performers received the greatest benefit (Jacobson, 1990).
In addition to the above referenced case studies, there are a number of reports that offer
recommendations and suggestions to improving teacher attendance. In a report released by

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

33

Hanover Research in 2012, the authors suggested that school leaders should require teachers to
report their absences directly to their supervisors. This recommendation supported earlier
findings that indicated attendance rates improved when employees were required to report their
absences to a person instead of an automated system (Boudreau et al., 1993; Miller et al., 2008).
In addition to the reporting requirements, principals and supervisors should also receive training
so they can respond appropriately (Smith, 2001; National Council on Teacher Quality, 2012).
In the 2014 report released by the National Council on Teacher Quality, the researchers
suggested that in order to improve attendance, school districts should consider restricting leave
on specific dates. The report indicated that 27 of the 40 districts that were included in the report
implemented some form of leave restriction throughout the school year. Generally, leave was
restricted during state assessment testing windows, immediately before and after a scheduled or
holiday break and during times that professional development was scheduled (Hanover Research,
2012).
School districts were also encouraged to create clear guidelines and procedures to address
chronic absenteeism (Norton, 1988). Additionally, school leaders should be involved in all
aspects of the plan. This included being involved in the development, implementation, tracking,
and evaluation of employee attendance plans. The research also suggested school leaders that fail
to properly address teacher absenteeism should be held accountable by their superiors (Hanover
Research, 2012; Knoster, 2016; Norton, 1998).
In the Hanover Research report (2012), it suggested that principals and school leaders
should clearly articulate their expectations to teachers with respect to any attendance and
incentive plans that may exist in the district. In addition to setting attendance expectations, Smith
(2001) suggested school leaders should welcome back staff members regardless of the reason.

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34

During this conversation, principals should take the opportunity to tell teachers that they were
missed during their absences. If teachers are deemed to be chronically absent, principals and
supervisors should plan to meet with those teachers for the purpose of reestablishing attendance
expectations and reiterating that the use of discretionary leave is a benefit and not an entitlement
(Smith, 2001).
Finally, the research suggested that some schools have had success by including
attendance as a measure in teacher evaluations (Hanover Research, 2012; National Council on
Teacher Quality, 2014). Many school districts are limited in their abilities to include absence
data as a component of teacher evaluations due to state policies or laws that restrict their
inclusion. For the most part, districts that have been successful in adding attendance as a
component to a teacher evaluation have incorporated the additional element into existing
measures that assess a teacher’s competency in the area of professionalism (National Council on
Teacher Quality, 2014).
Conclusion
The review of literature provides substantial evidence that the amount of annual
discretionary leave used by teachers is disproportionate when compared to the average American
worker, and the financial costs associated with teachers being absent from work exceeds $4
billion per year (Erhenburg et al., 1991; Miller, 2012). In addition to the financial implications,
the majority of the research has shown that teacher absenteeism negatively impacts the learning
outcomes of students both in terms of achievement and attendance (Brown & Arnell, 2012;
Bowers, 2001; Clotfelder et al., 2009; Erhenberg, et al., 1991; Jacobson, 1990; Miller et al.,
2008; Woods & Montagno, 1997). Moreover, the research suggested that achievement scores in
mathematics are influenced by teacher attendance to a greater extent than other subject areas

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35

(Clotfelder et al., 2009; Miller et al., 2008). To complicate matters, when teachers are absent
from the classroom, the likelihood of finding a qualified substitute teacher who can deliver the
same level of instruction when compared to the classroom teacher is very unlikely (Damle, 2009;
Miller et al., 2008; Woods & Montagno, 1997).
While the amount of discretionary leave provided to teachers varies greatly from state-tostate and district-to-district, the one constant that remains is that teachers are allowed to decide
for themselves whether to be absent from work (Clotfelder et al., 2009; Gaziel, 2004; Steers &
Rhodes, 1978). Since teachers have the ability to make their own decisions with regard to how
and when to use discretionary leave, determining the underlying reasons teachers are absent from
the classroom becomes of the utmost importance. As such, the research indicated that the
determinants of leave included individual and organizational characteristics that range from the
gender and age of the teacher to the leadership style of the principal. While there is no clear
evidence to suggest which determinant influences teacher absenteeism the most, the research
indicated that factors outside of illness reasons strongly influence a teacher’s decision whether to
report to work (Clotfelder et al., 2009; Miller et al., 2008).
As Rogers and Vegas (2009) noted, there are no standardized plans or blueprints to
successfully address the issue of teacher absenteeism. However, the literature suggested that
school districts should be willing to take risks when addressing the problem of absenteeism.
When developing plans, school leaders should be mindful of the determinants that specifically
contribute to teacher absenteeism rates in their local school district. Likewise, based on the
research, plans should be tailored in a way that not only provides all teachers the opportunity to
benefit from the plan but rewards the top-performing teachers the most. In order to lessen the
financial burden and improve the quality of learning for all students, a more standardized

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
approach that successfully addresses the issue of teacher absenteeism is worthy of further
exploration and study.

36

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

37

CHAPTER III
Methodology
Purpose
The research that has been published on the topic of teacher absenteeism has focused on
primarily the following themes: (a) predictors of teacher absenteeism, (b) the costs associated
with teacher absenteeism, (c) the impact on student achievement, (d) attendance incentive
programs, and (e) district attendance policies and procedures. As such, the purpose of this
quantitative study was to analyze how each of these themes impact the Derry Township School
District (DTSD), a school district located in Hershey, Pennsylvania. The objectives of the study
included: (a) an analysis of the predictors of teacher absenteeism, (b) examining the costs
associated with teacher absenteeism, (c) analyzing the impact on student achievement, and (d)
recommendations to reduce the frequency of teacher absences and the associated costs.
As Rogers and Vegas (2009) noted, it is extremely difficult to address the problem of
teacher absenteeism without first determining the degree and the extent to which the problem
actually exists. Consequently, it is important to have a thorough understanding of the predictors
and reasons why teachers are absent from work. In order to determine the degree and extent of
the teacher absenteeism problem at DTSD, the study examined the effects and correlations
among age, gender, race, experience, school level, degree, distance from work, and the frequency
of teacher absences. In addition, the study examined the effects and correlations between the
number of teacher absences by day of the week.
The review of literature indicated that recent reports estimate that teacher absences in the
United States cost school districts more than $5.6 billion per year (Folger, 2019; Kocakülâh,
Bryan, & Lynch, 2019). Moreover, The National Council on Teacher Quality (2014) noted, when

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

38

calculated on a per-teacher basis, absences cost school districts in the United States
approximately $1,800 annually for every teacher those school districts employ. To ascertain the
costs associated with teacher absenteeism, an examination of DTSD’s financial records was
conducted.
Previous studies that analyzed the effects of teacher absenteeism on student achievement
scores have produced mixed results (Brown & Arnell, 2012; Cantrell, 2003; Clotfelder et al.,
2009; Colquitt, 2009; Erhenberg et al., 1991; Niemeyer, 2013; Woods & Montagno, 1997).
However, the majority of studies indicated that teacher absences have a negative impact on
student achievement, particularly in the area of math (Cantrell, 2003; Clotfelder et al., 2009).
Therefore, one of the primary goals of this study was to contribute to the body of research that
examined the relationship between teacher absenteeism and student achievement.
According to Ehrenberg et al. (1991), leave policies have a tremendous impact on the
number of days teachers are absent from school. While there are no simple answers or recipes to
reducing teacher absence rates, policy makers should be willing to experiment with mechanisms
to improve teacher attendance (Rogers & Vegas, 2009). The goal of this study in terms of
policies, procedures, and incentive programs was to examine the district’s current policies,
procedures, incentive programs, and its collective bargaining agreement to determine the extent
that these factors contribute to teacher absenteeism. In order to address each of these themes, the
primary research questions that guided the study were:
1. Are age, distance from work, gender, experience, grade(s) taught, level of education, and
race predictors of teacher absence?

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

39

2. What is the relationship between the frequency of teacher absences and factors such as
age, distance from work, gender, experience, grade(s) taught, level of education, and
race?
3. Are there significant differences in student achievement scores between teachers who are
chronically absent (defined as 10 or more absences per school year) and those who are
not chronically absent?
4. What is the relationship between student achievement scores and the frequency of teacher
absences?
5. Are there significant differences in teacher absenteeism rates by leave category or days of
the week?
In addition to the primary questions that guided the study, the project also examined the
following questions in order to gain a better understanding of teacher absenteeism at DTSD. The
goal of addressing these additional questions was to assist the researcher in making
recommendations to address the problem of teacher absenteeism:
•

How many teachers at DTSD are chronically absent?

•

What are the economic impacts associated with teacher absenteeism from 2016-19?

•

What organizational factors contribute to teacher absentee rates (board policies and
collective bargaining agreement, professional development) and to what extent?

Setting and Participants
The setting for this study was the Derry Township School District. The community
enjoys a legacy that began with its namesake founder, famed confectioner and philanthropist,
Milton S. Hershey. DTSD encompasses approximately 27 square miles and is the site of the
well-known Hershey's Chocolate Company, Hershey Park amusement center, and various other

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

40

entertainment and resort establishments. Hershey is built on tourism with an average of 30,000
additional people entering the community on any given day. Although there are many long-term
residents, there are also individuals and families who are transient, migrant, or homeless.
The district consists of an Early Childhood Center that houses students in kindergartenGrade 1, a Primary Elementary School that serves students in Grades 2-3, an Intermediate
Elementary School that educates students in Grades 4-5, a Middle School that teaches students in
Grades 6-8, and a High School that instructs students in Grades 9-12. The district serves
approximately 3,500 students. At all assessed grade levels, statewide assessment scores are
consistently well above state and national averages. Annually, more than 90% of the graduating
seniors pursue post-secondary education. Hershey High School is consistently recognized as one
of the top public schools in America by various national publications (Niche, 2020; U.S. News
and World Report, 2020). A large percentage of the socioeconomic status of the student
population is in the middle to upper middle class with the overall range varying from wealthy to
very poor. Five-year comparisons indicate a rise in the number and percentage of students in
kindergarten through Grade 12 who qualify for free and reduced lunches. Specifically, the total
amount of students qualifying for free and reduced lunches has increased from 7% of the student
body to 21% of the student body between the 2012-13 and the 2019-20 school years.
DTSD offers a wide and significant range of special education services and supports.
These services and supports are accessed by approximately 350 students through a full range of
supplementary supports and services in a variety of locations throughout each building. Services
and supports are also accessed by and offered to students from consortium districts. Intensive
learning support and autism support classroom options have been added within the past 10 years.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

41

The district also provides gifted support to approximately 150 students from kindergarten
through 12th grade.
The student demographic population in the district is 70.3% percent white, 12.6 % Asian,
4.8% black, 8.3% Hispanic, 3.6% multi-racial, and 0.4% other. DTSD has seen a steady increase
in the number of English Learners (EL) receiving services. Languages of the EL students are
quite diverse, with 23 different languages being represented among the approximately 50 EL
students.
The teaching population for the study included all certificated professional employees,
which consisted of classroom teachers, school counselors, school psychologists, school nurses,
librarians, instructional coaches, and specialists. The average age of the professional employees
at DTSD during the three-year study was 41, and the average years of experience was 13.5. More
than 62% of the certificated staff at DTSD were deemed to be chronically absent during the
2016-19 school years. The percentage of teachers at DTSD who exceed the United States
Department of Education’s chronically absent threshold is significant considering that teachers
who are chronically absent negatively influence student achievement scores (Cantrell, 2003;
Clotfelder et al., 2009; Erhenberg et al., 1991). The number of chronically absent teachers at
DTSD is also extremely high when compared to the national average of 36% and the state
average of 36.2% (Miller, 2012; Griffith, 2017). Table 1 provides the descriptive statistics for the
323 professional staff members who were included in the study.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

42

Table 1
Frequencies and Percentages of Participant Demographics
Demographic

2016-17

2017-18

2018-19

n

%

n

%

n

%

13
40
51
41
53
27
18

4.53%
13.94%
17.77%
14.29%
18.47%
9.41%
6.27%

12
34
56
34
57
30
19

4.21%
11.93%
19.65%
11.93%
20.00%
10.53%
6.67%

23
31
55
33
50
38
17

7.85%
10.58%
18.77%
11.26%
17.06%
12.97%
5.80%

71
216

24.74%
75.26%

73
212

25.61%
74.39%

70
223

23.89%
76.11%

30
37
47
80
93

10.45%
12.89%
16.38%
27.87%
32.40%

30
37
47
76
95

10.53%
12.98%
16.49%
26.67%
33.33%

36
39
44
76
98

12.29%
13.31%
15.02%
25.94%
33.45%

Race
African American
Asian
Hispanic
Caucasian
Other

1
2
0
284
0

0.35%
0.70%
0.00%
98.95%
0.00%

3
1
0
281
0

1.05%
0.35%
0.00%
98.60%
0.00%

2
1
0
290
0

0.68%
0.34%
0.00%
98.98%
0.00%

Degree
Bachelor’s
Master’s
Master’s plus 10 credits
Master’s plus 20 credits
Master’s plus 30 credits
Master’s plus 45 credits

54
47
21
24
35
106

18.82%
16.38%
7.32%
8.36%
12.20%
36.93%

51
54
23
16
32
109

17.89%
18.95%
8.07%
5.61%
11.23%
38.25%

62
60
22
9
38
102

21.16%
20.48%
7.51%
3.07%
12.97%
34.81%

Age
21–25
26–30
31–35
36–40
46–50
51–55
56 or older
Gender
Male
Female
School level
Early Childhood Center
Primary Elementary
Intermediate Elementary
Middle School
High School

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
Demographic

43

2016-17

2017-18

2018-19

n

%

n

%

n

%

Years of experience
0–3 years
4–9 years
10–14 years
15–19 years
20–24 years
25–29 years
30 years or more

54
68
51
54
28
23
9

18.82%
23.69%
17.77%
18.82%
9.76%
8.01%
3.14%

50
60
59
54
25
26
11

17.54%
21.05%
20.70%
18.95%
8.77%
9.12%
3.86%

55
59
52
59
24
27
17

18.77%
20.14%
17.75%
20.14%
8.19%
9.22%
5.80%

Distance from school
0.0–3.9 miles
4.0–7.9 miles
8.0–11.9 miles
12.0–15.9 miles
16.0 miles or more

59
103
40
30
55

20.56%
35.89%
13.94%
10.45%
19.16%

57
104
39
29
56

20.00%
36.49%
13.68%
10.18%
19.65%

59
101
43
31
59

20.14%
34.47%
14.68%
10.58%
20.14%

Research Plan and Data Collection
This study used an Analysis of Variance (ANOVA) test to determine if differences in
teacher absentee rates were statistically significant based on teacher demographic data. In
addition, correlation tests were used to determine the relationship between teacher absence rates
and teacher demographic data. An ANOVA test was also used to determine if teacher absentee
rates were statistically significant based on the days of the week. Likewise, an ANOVA test was
used to determine if there was a statistically significant difference between achievement scores
for students who were instructed by chronically absent teachers and students who were not. A
separate research and data collection plan for this study was developed for each primary research
question. The research and data collection plan for the secondary research questions was
combined into one section.

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44

Research Question 1
This question examined how the effects of age, gender, race, experience, school level,
degree, and distance from work affected the predictability of a teacher being absent from work.
The hypothesis was formulated to examine differences between the various demographic factors
and their influences on teacher absences. Using an ANOVA test, the dependent variable (number
of teacher absences) was combined with a series of independent variables in order to determine if
the effect was significant. Table 2 describes the independent variables used to examine the
predictors of teacher absences.
Null hypotheses
H01: There are no statistically significant differences in teacher absenteeism rates by age.
H02: There are no statistically significant differences in teacher absenteeism rates by
gender.
H03: There are no statistically significant differences in teacher absenteeism rates by race.
H04: There are no statistically significant differences in teacher absenteeism rates by
experience.
H05: There are no statistically significant differences in teacher absenteeism rates by
school level.
H06: There are no statistically significant differences in teacher absenteeism rates by
degree.
H07: There are no statistically significant differences in teacher absenteeism rates by
distance from work.

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45

Table 2
Description of Independent Variables
Independent variable

Description

Age

Ages of the teachers

Gender

Gender of the teachers

Race

A teacher’s self-identification with one or more social groups

Years of experience

Number of years of teaching experience

Degree earned

Highest degree earned

School level

School level assignment

Distance from work

Number of miles between a teacher’s home address and school

Data collection. For this question, the demographic data such as age, gender, race, years
of experience, degree earned, school level, and mailing address were obtained and extracted from
the district’s payroll and human resources system (eFinance). The attendance data were
downloaded from the district’s absence management system (Frontline Education, Absence
Management). FileMaker Pro was then used to match and merge the demographic and
attendance data together into one document. GoogleMaps was used to obtain the distance
between home and work each professional staff member. In order to calculate the distance, each
home address was entered into GoogleMaps to determine the distance between a subject’s home
address and work location. After the data were entered and merged, all personally identifiable
information was removed from the data sets to protect the identity of the subjects. The
independent variables were then coded as described in the table 3.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

46

Table 3
Review of Demographic Variables
Variable

Type of Variable

Description

Code
1 = 21–25 years old
2 = 26–30 years old
3 = 31–35 years old
4 = 36–40 years old
5 = 41–45 years old
6 = 46–50 years old
7 = 51–55 years old
8 = 56 years or older

Age

Independent

Discrete variable

Gender

Independent

Dichotomous variable 1 = Male
2 = Female

Race

Independent

Discrete variable

1 = African American
2 = Asian
3 = Hispanic
4 = Caucasian
5 = Other

Years of experience

Independent

Discrete variable

1 = 0–3 years
2 = 4–9 years
3 = 10–14 years
4 = 15–19 years
5 = 20–24 years
6 = 25–29 years
7 = 30 years or more

School level

Independent

Discrete variable

1 = ECC
2 = Primary
3 = Intermediate
4 = Middle
5 = High

Degree earned

Independent

Discrete variable

1 = LTS
2 = Bachelor’s
3 = Master’s
4 = Master’s + 10
5 = Master’s + 20
6 = Master’s + 30
7 = Master’s + 45

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

Variable
Distance from work

Teacher absences

Type of Variable
Independent

Dependent

47

Description

Code

Discrete variable

1 = 0–3.9 miles
2 = 4–7.9 miles
3 = 8–11.9 miles
4 = 12–15.9 miles
5 = 16 miles or more

Continuous

Data analysis. One-way ANOVA tests were used to determine if there was a statistically
significant difference between each demographic variable and the frequency of teacher absences
over the three-year period. In addition, One-way ANOVA tests were performed separately for
each demographic variable per school year (2016-17, 2017-18, and 2018-19). The significance
level for each test was set at 0.05%. In addition, an effect size index, η2 (eta square), was
calculated to determine the overall extent of the relationship between each demographic variable
and the frequency of teacher absences over the three-year period. Effect sizes were interpreted as
follows: (a) small, .01 ≤ An η2; (b) medium, .06 < An η2; (c) large, .15 < An η2. The
Pennsylvania Department of Education stipulates that professional employees must work 140
days during the course of a school year to be credited with a year of services. Due to this
stipulation, the following number of teachers were removed from each school year, as they did
not work the required number of days to be credited with a year of service: 20 teachers for the
2016-17 school year, six teachers for the 2017-18 school year, and 19 teachers for the 2018-19
school year.

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48

Research Question 2
The second research question analyzed the relationship between age, gender, race,
experience, school level, degree, and distance from work on the predictability of a teacher being
absent from work. The hypothesis was formulated to examine the relationship between the
various demographic factors and their influence on teacher absences. Correlation tests were used
to determine the relationships between teacher absences and the various demographic variables.
The correlation tests were used to measure and describe the relationship between two variables.
The independent variable for age, years of experience, and distance from work was continuous,
while the independent variable for gender, race, degree earned, and school level was either
discrete or dichotomous. The dependent variable was continuous and included the number of
teacher absences.
Null hypotheses
H01: No correlation exists between the number of teacher absences and age.
H02: No correlation exists between the number of teacher absences and gender.
H03: No correlation exists between the number of teacher absences and race.
H04: No correlation exists between the number of teacher absences and experience.
H05: No correlation exists between the number of teacher absences and school level.
H06: No correlation exists between the number of teacher absences and degree.
H07: No correlation exists between the number of teacher absences and distance from
work.
Data collection. The data utilized to examine the second research question was obtained
using the same data collection procedures described in the first research question. Table 4

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

49

describes the variables used to examine the correlations between the number of teacher absences
and the various demographic factors.
Table 4
Review of Demographic Variables
Variable

Type of Variable

Description

Age

Independent

Continuous

Gender

Independent

Dichotomous variable 1 = Male
2 = Female

Race

Independent

Discrete variable

Years of experience

Independent

Continuous

Degree earned

Independent

Discrete variable

1 = Bachelor’s
2 = Master’s
3 = Master’s + 10
4 = Master’s + 20
5 = Master’s + 30
6 = Master’s + 45

School level

Independent

Discrete variable

1 = ECC
2 = Primary
3 = Intermediate
4 = Middle
5 = High

Distance from work

Independent

Continuous

Dependent

Continuous

Teacher absences

Code

1 = African American
2 = Asian
3 = Hispanic
4 = Caucasian
5 = Other

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50

Data analysis. The demographic and attendance data were loaded into IBM SPSS, and
correlation tests were conducted to test each null hypothesis. The purpose of the correlation test
was to assess the degree of the relationship between two variables. The degree of the relationship
is defined by the correlation coefficient, denoted r, and falls between the values of -1 and 1. If
the correlation coefficient equals +1, then there is a perfectly positive relationship between the
two variables, and if the correlation coefficient equals -1, then there is a perfectly negative
relationship between the two variables. If the correlation coefficient equals 0, then there is no
relationship between the two variables. The following guidelines were used to interpret the
correlation coefficient statistic in terms of the value of the relationship: very strong, (a) .90 ≤ | r |
≤ 1.0; (b) strong, .70 ≤ | r | ≤ .89; (c) moderate, .50 ≤ | r | ≤ .69; (d) weak, .30 ≤ | r | ≤ .49; and (e)
very weak, .00 ≤ | r | ≤ .29. Correlation tests were performed separately for each demographic
variable per school year (2016-17, 2017-18, and 2018-19). In addition, correlation tests were
performed for the aggregate totals for each demographic variable.
Research Question 3
This question examined the differences in student achievement scores between students
taught by teachers who were chronically absent and students taught by teachers who were not
chronically absent by using the following assessment data: (a) DIBELS Next Oral Reading
Fluency scores for students in Grades 2 through 5; (b) English language arts, mathematics, and
science achievement scores for students in Grades 3 through 8 as determined by the
Pennsylvania System of School Assessment (PSSA) but measured by Pennsylvania Value-Added
Assessment System (PVAAS) Teacher Value Added scores; (c) algebra I, biology, and literature
achievement scores for students in Grades 7 through 12 as determined by the Pennsylvania
Keystone Exams but measured by Pennsylvania Value-Added Assessment System (PVAAS)

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51

Teacher Value Added scores; and (d) final exam grades for students in Grades 9 through 12. The
hypothesis was formulated to examine differences between student achievement scores between
the two groups of teachers. Table 5 describes the variables used to examine the differences
between student achievement scores and teacher absence classification.
Null hypotheses
H01: There will be no statistically significant differences in the DIBELS Next Oral
Reading Fluency Scores for students in Grade 2 by teacher absence classification
(chronic or not chronic).
H02: There will be no statistically significant differences in the DIBELS Next Oral
Reading Fluency Scores for students in Grade 3 by teacher absence classification.
H03: There will be no statistically significant differences in the DIBELS Next Oral
Reading Fluency Scores for students in Grade 4 by teacher absence classification.
H04: There will be no statistically significant differences in the DIBELS Next Oral
Reading Fluency Scores for students in Grade 5 by teacher absence classification.
H05: There will be no statistically significant differences in PVAAS Teacher Value
Added Math Scores by teacher absence classification.
H06: There will be no statistically significant differences in PVAAS Teacher Value
Added English Language Arts Scores by teacher absence classification.
H07: There will be no statistically significant differences in PVAAS Teacher Value
Added Science Scores by teacher absence classification.
H08: There will be no statistically significant differences in PVAAS Teacher Value
Added Algebra I Scores by teacher absence classification.

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H09: There will be no statistically significant differences in PVAAS Teacher Value
Added Literature Scores by teacher absence classification.
H010: There will be no statistically significant differences in PVAAS Teacher Value
Added Biology Scores by teacher absence classification.
H011: There will be no statistically significant differences in final exam grades by teacher
absence classification.
Instruments. The instruments used to examine the third research question included (a)
the Pennsylvania System of School Assessment, (b) Keystone Exams, (c) DIBELS Next Oral
Reading Fluency Scores, and (d) Hershey High School final exam grades. A description of each
instrument is discussed and presented below.
The Pennsylvania System of School Assessment (PSSA) is a valid standards-based,
criterion-referenced assessment that has been used since 1992 to measure a student’s
understanding of academic standards in the English Language Arts (ELA), mathematics, and
science and technology. All students in Grades 3 through 8 are annually assessed in the areas of
English Language Arts and mathematics. In addition, students in Grades 4 and 8 are also
assessed in science and technology. All students receive a performance score based on their
proficiency as related to the academic standards in each content area. The four performance
levels for the PSSAs are advanced, proficient, basic, and below basic. The PSSA is annually
administered in the spring. Since 1992, there have been several versions of the PSSA. The
current version of the PSSA is scored by the Data Recognition Corporation (DRC). The validity
and reliability of the PSSA is documented in the Pennsylvania System of School Assessment
Technical Report that is annually published by the Pennsylvania Department of Education.

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53

The Keystone Exams are standards-based, criterion-referenced, end-of-course
assessments that have been used since 2013 by the Pennsylvania Department of Education to
measure a student’s understanding of the academic standards in algebra I, biology, and literature.
Students enrolled in algebra I, biology, and literature are required to take the Keystone Exam
prior to completion of the course. Keystone Exams are administered three times during the
school year (spring, summer, and winter). Since the Keystone Exam is a requirement for
graduation, students who do not attain proficiency on the first attempt are required to retake the
Keystone Exam. Similar to the PSSA, student performance levels for the Keystone Exam are
advanced, proficient, basic, and below basic. The current version of the Keystone Exam is scored
by the Data Recognition Corporation (DRC). The validity and reliability of the Keystone Exam
is documented in the Keystone Technical Report that is published annually by the Pennsylvania
Department of Education.
Pennsylvania Value-Added Assessment System (PVAAS) is based on a mixed-model,
multivariate longitudinal analysis of assessment data. PVAAS is based on the methodology of
the Education Value-Added System (EVAAS) and is used to measure the academic growth of
groups of students by analyzing existing PSSA and Keystone Exam assessment data. According
to the Pennsylvania Department of Education (2019), “PVAAS uses students’ scores rather than
their academic performance level across grades and subjects to generate a reliable estimate of the
true achievement level of a group of students. Then, these estimates of achievement are
compared to estimate growth for a group of students” (p. 8). Growth measures are broken into
five reporting categories: (a) red (growth measure is more than two standard errors below zero),
(b) yellow (growth measure is more than one but no more than two standard errors below zero),
(c) green (growth measure is less than one standard error above zero and no more than one

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54

standard error below zero), (d) light blue (growth measure is at least one but less than two
standards errors above zero), and (e) dark blue (growth measure is more than two standard errors
above zero). According to the Pennsylvania Department of Education (2019), the following
criteria must be met for a teacher to receive a Teacher Value-Added Score:
Teachers need to have at least 11 students’ scores for students enrolled with them
(in the PVAAS Roster Verification process) in a tested subject, grade, or course
during the school year in order to receive a Value Added report in that grade,
subject, or course. Additionally, teachers must have an “active n” count of 6
students (6 FTE/full time equivalent students) to receive a Value Added report;
the “active n” count is calculated by considering the instructional responsibility
claimed for each student. (p. 35)
The Pennsylvania Department of Education generally releases the Teacher Value-Added
PVAAS scores to school districts in the Fall of each school year. Teacher Value-Added PVAAS
scores are a component of the Pennsylvania Teacher Effectiveness System, which is used to
annually evaluate teachers in Pennsylvania.
According to Good, Kaminski, Dewey, Wallin, Powell-Smith, and Latimer (2011),
“DIBELS Oral Reading Fluency (DORF) is a measure of advanced phonics and word attack
skills, accurate and fluent reading of connected text, and reading comprehension” (p. 79). DORF
consists of two parts that include oral reading fluency and passage retail. The first measure, oral
reading fluency, is assessed by giving each student three separate on-grade level passages. The
passages should be unfamiliar to the students, and students are asked to read each passage for
one minute. Students are scored based on the number of words read correctly and the number of
errors for each passage. Median scores across the three passages are used to determine the

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55

student performance level. The passage retell component of DORF is used to assess a student’s
reading comprehension level. When prompted, students are asked to tell what they have read.
Students are assessed on the number of words in the retell that are related to the story. If a
student hesitates for five seconds or longer or responds for five seconds in a way that is not
relevant to the passage, the response is discontinued. The retell portion of DORF relies heavily
on the evaluators’ judgement and therefore compromises the reliability and validity of the data.
As a result, the retell score will not be used in the study. Student progress is monitored three
times a year (fall, winter, spring).
Final exams are end-of-course, criterion-referenced assessments that are administered to
high school students at Hershey High School. Final exams are used to measure a student’s
understanding of the materials presented in a specific course. Only courses that had common
final exams were used to determine the impact of teacher absenteeism on student performance.
For the purposes of this study, common final exam scores were used in only the data analysis if
there were multiple teachers who taught the same course and administered the same exam.
Data collection. The student achievement data for the DIBELS Next Oral Reading
Fluency Scores were obtained and extracted from the district’s student assessment data
warehouse management system (PerformancePlus). PVAAS Teacher Specific scores were
downloaded directly from the PVAAS website. PVAAS Teacher Specific scores are not publicly
accessible, and only authorized users can download teacher specific scores. The researcher for
this project was an authorized user for the district and was able to download directly from the
PVAAS website. Final exam grades were extracted from the district’s student information
system (eSchool Plus). Class rosters were also downloaded from the district’s student
information system. Teacher attendance data were downloaded from the district’s absence

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56

management system. The student achievement data and teacher attendance data were then
matched and merged into FileMaker Pro. After the data were merged, all personally identifiable
information was removed from the data sets to protect the identity of the subjects. The data sets
were loaded into IBM SPSS to perform the data analysis. The independent variable was then
coded and described in table 5.
Table 5
Review of Student Achievement Variables
Variable

Type of Variable

Description

DIBELS Next
Oral Reading Fluency

Dependent

Continuous

PVAAS
Teacher Value Added

Dependent

Continuous

Final exam grade

Dependent

Continuous

Teacher absence
classification

Independent

Code

Dichotomous variable 1 = Male
2 = Female

Data analysis. One-way ANOVA tests were used to determine if there was a statistically
significant difference between the student achievement scores and teacher absence classification
over the three-year period. In addition, One-way ANOVA tests were performed separately for
each student achievement variable per school year (2016-17, 2017-18, and 2018-19).
Research Question 4
The fourth research question analyzed the relationship between student achievement
scores and the frequency of teacher absences. The hypothesis was formulated to examine the
relationship between the various student achievement scores and their influence on teacher

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57

absences. Pearson correlation tests were used to determine the relationships between teacher
absences and the various student achievement scores. The dependent variable for DIBELS Next
Oral Reading Fluency Scores, PVAAS Teacher Value Added Scores, and final exam grades were
continuous, while the independent variable was continuous and included the number of teacher
absences.
Null hypotheses
H01: No correlation exists between the number of teacher absences and DIBELS Next
Oral Reading Fluency Scores for students in Grade 2.
H02: No correlation exists between the number of teacher absences and DIBELS Next
Oral Reading Fluency Scores for students in Grade 3.
H03: No correlation exists between the number of teacher absences and DIBELS Next
Oral Reading Fluency Scores for students in Grade 4.
H04: No correlation exists between the number of teacher absences and DIBELS Next
Oral Reading Fluency Scores for students in Grade 5.
H05: No correlation exists between the number of teacher absences and PVAAS Teacher
Value Added Math Scores.
H06: No correlation exists between the number of teacher absences and PVAAS Teacher
Value Added English Language Arts Scores.
H07: No correlation exists between the number of teacher absences and PVAAS Teacher
Value Added Science Scores.
H08: No correlation exists between the number of teacher absences and PVAAS Teacher
Value Added Algebra I Scores.

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H010: No correlation exists between the number of teacher absences and PVAAS Teacher
Value Added Biology Scores.
H011: No correlation exists between the number of teacher absences and final exam
grades.
Data collection. The data utilized to examine correlation between student achievement
and teacher absences was copied directly from student achievement tables used in the third
research question. The dependent variables were then coded and described in table 6.
Table 6
Review of Student Achievement Variables
Variable

Type of Variable

Description

DIBELS Next Oral Reading Fluency

Dependent

Continuous

PVAAS Teacher Value Added

Dependent

Continuous

Final exam grade

Dependent

Continuous

Teacher absences

Independent

Continuous

Data analysis. The student achievement scores and attendance data were loaded into
IBM SPSS, and Pearson correlation tests were conducted to test each null hypothesis. Pearson
correlation tests were performed separately for each student achievement variable per school
year (2016-17, 2017-18, and 2018-19). In addition, a Pearson correlation test was performed for
the aggregate totals for each student achievement variable.
Research Question 5
This last primary research question examined the effects of leave category and teacher
absences by day of the week on the predictability of a teacher being absent from work. The
hypothesis was formulated to examine differences between the various leave factors and their

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59

influence on teacher absences. The frequency and percentages of absences by leave category and
teacher absences by day of the week were then used to determine if the effect was significant.
Table 7 describes the categories of absences used to examine the predictors of teacher absences.
Null hypotheses
H01: There are no statistically significant differences in teacher absenteeism rates by
leave category.
H02: There are no statistically significant differences in teacher absenteeism rates by day
of the week.
Table 7
Categories of Absences and Their Associated Descriptions
Category

Description of Leave Categories

Emergency

Emergency leave is granted by the Superintendent for extenuating
circumstances that occur within 48 hours from the date of absence.
Approved emergency leave is deducted from an employee’s sick leave.
Examples of emergency leave include but are not limited to absences
related to car problems, emergency home repairs, flood, fire, and family
related issues.

Funeral

Funeral leave is taken without loss of pay as noted: (a) up to five days for
the spouse, parent, mother-in-law, father-in-law, son, or daughter of the
employee; (b) up to three days for the grandparents, grandchildren, or
siblings of the employee; (c) one day for the day of the funeral of the aunt,
uncle, niece, nephew, son-in-law, daughter-in-law, brother-in-law, sisterin-law, or first cousin of the employee. However, if the relative resided in
the employee’s household on the date of death, up to three days will be
provided; (d) for circumstances that do not meet the guidelines specified,
an employee may seek approval from the Superintendent to grant
additional funeral leave.

Jury duty

An employee who is required to appear under subpoena or jury summons
in a county common pleas or federal district court trial, other than as a
party, will be excused without loss of net pay.

Military

An employee who is called to active duty is entitled to use up to 15 days of
leave without loss of pay.

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Category

Description of Leave Categories

Personal

Employees may be granted three days of absence for personal reasons
without loss of pay provided a request is submitted at least 48 hours in
advance to the Building Principal. Personal days are not to be permitted
during in-service days or the first or the last five student days of the school
year. Any personal leave days not used can be added to the employee’s
accumulated sick leave total at the end of each school year, or the
employee may elect to be reimbursed at the then-current substitute rate per
day for each unused day.

Professional

Employees may use professional leave without loss of pay to attend a
professional meeting, workshop, or conference.

Sick

Leave taken without loss of pay for personal illness or to care for a spouse,
dependent, or parent who is sick. Sick leave may also be taken without
loss of pay to attend a personal medical appointment or to attend a medical
appointment for a spouse, dependent, or parent. Employees are granted 10
sick days per year, and unused sick leave can be accumulated.

Unpaid

Employees may take additional leave with the prior approval of the
Superintendent. This leave is granted without pay.
Data collection. For this question, the attendance data were downloaded from the district

absence management system. All personally identifiable information was removed from the data
sets to protect the identity of the subjects. The variables were then coded as described in table 8.

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Table 8
Review of Leave Variables
Variable

Description

Code

Category of leave

Discrete variable

1 = Emergency
2 = Funeral
3 = Jury Duty
4 = Military
5 = Personal
6 = Professional
7 = Sick
8 = Unpaid

Day of week

Discrete variable

1 = Monday
2 = Tuesday
3 = Wednesday
4 = Thursday
5 = Friday

Data analysis. Frequency distributions were used to determine if there was a significant
difference between each variable and the frequency of teacher absences over the three-year
period. In addition, frequency distributions were performed separately for every variable per
school year (2016-17, 2017-18, and 2018-19).
Secondary Research Questions
The secondary research questions examined the economic impacts associated with
teacher absenteeism, the number of chronically absent teachers, and the organizational factors
that contribute to teacher absentee rates. The data collection methods for the secondary research
questions included obtaining district financial records pertaining to substitute costs for the 201617, 2017-18, and 2018-19 school years from the business office. Teacher absentee data were
collected from the district’s absence management system. District policies addressing teacher

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62

leave were obtained from the district website, and the collective bargaining agreement for
professional staff was obtained from the personnel department.
The fiscal implications pertaining to teacher absences at Derry Township School District
include the substitute costs, teacher payouts for sick leave retirement, and unused personal days.
Substitutes at Derry Township School District earn between $100 to $150 per day based on their
specific assignment. However, the actual cost the district incurs per substitute ranges from
$130.90 to $197.10 per day. Teachers at Derry Township School District have the option to
annually cash out their unused personal days. Teachers who select the cash-out option are
provided $100 for each unused personal day. Similarly, upon retirement, teachers receive a
monetary sum for their unused sick days. The monetary sum is based on a formula that combines
years of service and the number of unused sick days.
The number of chronically absent teachers was calculated by determining the number of
teachers in the district who missed more than 10 days of work per year for any absence reason.
Teachers in Pennsylvania must work 140 or more days per school year to be credited with a year
of service. As a result, teachers who missed more than 50 days of school per year were excluded
from the chronically absent teacher counts. District attendance policies, procedures, and the
professional staff collective bargaining agreement were reviewed and examined to determine if
they influenced teacher attendance rates. Each policy, procedure, and collective bargaining
agreement was compared to the existing body of research and literature pertaining to teacher
absenteeism.
Validity
The primary purpose of this study was to examine and establish if a relationship or
statistically significant difference exists between teacher attendance rates and student

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63

achievement scores and teacher attendance rates and various teacher demographic variables. The
casual conditions in this study occurred prior to the research; thus, the intervention (teacher
absences) was not implemented by the researcher and occurred prior to the data collection.
Generally, when standardized measurements of student achievement are used, questions of
validity have been addressed by the test developers. To that end, the validity of the PSSA and
Keystone Exams are outlined in their respective technical reports. The DIBELS Next and
PVAAS scores have been validated by their respective organizations and are widely recognized
as quality instruments for use in assessing student achievement levels.
In order to increase the validity of the study, the researcher analyzed multiple types of
student achievement data that included DIBELS Next Oral Reading Fluency scores, PVAAS
Teacher Value Added scores, and final exam grades to determine if the trends and patterns were
consistent across the various student achievement data sets. To further increase the validity of the
data, the researcher analyzed the data sets for the duration of the three-year study and for each
individual school year (2016-17, 2017-18, and 2018-19) to determine if the trends and patterns
that emerged were consistent from year-to-year. This method was used to increase the validity of
the teacher demographic data.
Summary
This study examined the impact of teacher absences on student achievement scores and
analyzed the predictors of teacher absences. A quantitative research design that used both
primary and secondary data was conducted. The primary data collected included district policies,
procedures, and collective bargaining agreements. The secondary data collected included student
achievement, teacher demographic, and teacher leave data. The data were analyzed by using a
combination of one-way ANOVA and correlation tests. The tests were conducted to determine if

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
there was a significant statistical difference or relationship between the independent and
dependent variables. A detailed analysis of the data is presented in the next chapter of the
capstone project.

64

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65

CHAPTER IV
Data Analysis and Results
The primary purpose of the research project was to determine the impact of teacher
absenteeism on student achievement scores. The study also analyzed several probable
demographic predicators of teacher absenteeism at Derry Township School District (DTSD) and
the associated costs. The previous chapter outlined the research methods and statistical methods
used to examine the relationship among teacher absenteeism, student achievement, and various
demographic variables. The data collection and analysis provided in this chapter were guided by
five research questions:
1. Are age, gender, race, experience, grade(s) taught, level of education, and distance from
work predictors of teacher absence?
2. What is the relationship between the frequency of teacher absences and factors such as
age, gender, race, experience, school level, degree, and distance from work?
3. Are there significant differences in student achievement scores between teachers who are
chronically absent (defined as 10 or more absences per school year) and those who are
not chronically absent?
4. What is the relationship between student achievement scores and the frequency of teacher
absences?
5. Are there significant differences in teacher absenteeism rates by leave category, days of
the week, or absences connected to holiday?
To gain a comprehensive understanding of teacher absenteeism at DTSD, the study was also
guided by three secondary questions: (a) how many teachers at DTSD are chronically absent, (b)
what are the economic impacts associated with teacher absenteeism from 2016-19, and (c) what

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66

organizational factors contribute to teacher absentee rates (board policies and collective
bargaining agreement, professional development) and to what extent?
Predicators of Teacher Absences
Descriptive statistics and one-way Analysis of Variance (ANOVAs) were conducted to
determine if differences in teacher absentee rates were statistically significant based on age,
gender, experience, school level, degree earned, and distance from work. Correlation tests were
used to determine the relationship between teacher absence rates and teacher demographic data.
Descriptive statistics and one-way ANOVA tests were used to determine if there was a
statistically significant difference between the achievement scores for students who were
instructed by chronically absent teachers and those who were not. Chi-square goodness of fitness
tests were used to determine if teacher absentee rates were statistically significant based on the
day of the week and teacher leave categories.
To compare the effect of age on teacher absenteeism rates at DTSD over the three-year
period, one-way ANOVAs were conducted, and descriptive statistics were used determined the
means for each age variable. During the 2016-17 school year, an examination of the means
suggested that teachers in the 21-25 age group (M = 8.09, SD = 4.44) missed fewer days of work
when compared to teachers in the 26-30 age group (M = 12.48, SD = 6.13), 31-35 age group (M
= 14.65, SD = 6.5), 36-40 age group (M = 14.81, SD = 8.63), 41-45 age group (M = 14.14, SD =
7.32), 46-50 age group (M = 12.78, SD = 6.38), 51-55 age group (M = 13.12, SD = 6.78), or the
56 or older age group (M = 13.50, SD = 7.14). However, the analysis of variance showed that the
effect of age on the number of teacher absences was not statistically significant, F(7,259) = 1.58,
p = .143, h2 = 0.04.

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The descriptive statistics revealed that teachers who were between 21-25 years of age (M
= 10.29, SD = 8.00) during the 2017-18 school year were absent less often than their colleagues
in the 26-30 age group (M = 11.92, SD = 9.09), 31-35 age group (M = 14.25, SD = 7.84), 36-40
age group (M = 12.95, SD = 7.29), 41-45 age group (M = 14.49, SD = 7.24), 46-50 age group (M
= 13.31, SD = 7.61), 51-55 age group (M = 12.60, SD = 7.70), or the 56 or older age group (M =
13.24, SD = 5.45). The one-way ANOVA found that age does not have significant effect on the
number of days a teacher misses per year, F(7,271) = 0.72, p = .652, h2 = 0.02.
The data analysis for the 2018-19 school year revealed through a review of the
descriptive statistics that teachers ages 21-25 (M = 8.93, SD = 4.08) were absent less frequently
than teachers in 26-30 age group (M = 11.56, SD = 4.97), 31-35 age group (M = 13.46, SD =
7.15), 36-40 age group (M = 13.85, SD = 7.16), 41-45 age group (M = 14.97, SD = 8.31), 46-50
age group (M = 13.81, SD = 7.83), 51-55 age group (M = 12.18, SD = 6.47), or the 56 or older
age group (M = 11.68, SD = 5.83). The analysis of variance indicated that the effect of age on
the frequency of teacher absences was not significant, F(7,266) = 2.00, p = .055, h2 = 0.05.
An examination of the means over the three-year period of the study indicated that
teachers between the ages of 21-25 (M = 9.09, SD = 5.45) missed fewer days of school per year
than teachers in the 26-30 age group (M = 12.02, SD = 6.99), 31-35 age group (M = 14.11, SD =
7.19), 36-40 age group (M = 13.94, SD = 7.76), 41-45 age group (M = 14.53, SD = 7.60), 46-50
age group (M = 13.29, SD = 7.27), 51-55 age group (M = 12.58, SD = 6.90), or the 56 or older
age group (M = 12.82, SD = 6.09). The one-way ANOVA showed that the effect of age
significantly influenced the number of teacher absences, but the effect size was small, F(7,812) =
3.66, p = <.001, h2 = 0.03. Post hoc analyses were conducted using the Games-Howell post hoc
test. The Games-Howell post hoc test was used because the analysis of variance failed the

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68

Levine’s test for homogeneity of equal variances. The post hoc test indicated that there was a
significant difference between teachers in the 21-25 age range and all other age groups with the
exception of teachers in the 26-30 age group. The post hoc test also showed that no additional
significant differences among the groups existed. The results are presented in Table 9 and Table
10.
Table 9
Mean Difference Absences by Teacher Age
Year
2016-17

2017-18

2018-19

Age
21–25
26–30
31–35
36–40
41–45
46–50
51–55
56 or older
Total
21–25
26–30
31–35
36–40
41–45
46–50
51–55
56 or older
Total
21–25
26–30
31–35
36–40
41–45
46–50
51–55
56 or older
Total

M
8.09
12.48
14.65
14.81
14.14
12.78
13.12
13.50
13.46
10.29
11.92
14.25
12.95
14.49
13.31
12.60
13.24
13.25
8.93
11.56
13.46
13.85
14.97
13.81
12.18
11.68
13.01

N
11
33
44
40
44
52
26
17
267
12
33
52
33
43
57
30
19
279
20
27
47
31
45
50
37
17
274

SD
4.44
6.13
6.51
8.63
7.32
6.38
6.78
7.14
6.99
8.00
9.09
7.84
7.29
7.24
7.61
7.70
5.45
7.63
4.08
4.97
7.15
7.16
8.31
7.83
6.47
5.83
7.06

Range
15.50
30.50
30.00
35.50
35.50
25.50
25.50
26.50
38.00
26.00
47.00
44.00
28.00
26.00
42.50
28.50
20.50
48.00
13.00
18.00
36.00
27.50
41.00
37.50
21.50
19.00
41.00

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
Year
2016-19

Age
21–25
26–30
31–35
36–40
41–45
46–50
51–55
56 or older
Total

M
9.09
12.02
14.11
13.94
14.53
13.29
12.58
12.82
13.24

69
N
43
93
143
104
132
159
93
53
820

SD
5.45
6.99
7.19
7.76
7.60
7.27
6.90
6.09
7.23

Range
29.00
47.00
44.00
38.00
41.00
42.50
29.50
26.50
48.00

Table 10
One-Way ANOVA of Teacher Age on the Number of Absences
SS

MS

F

p

h2

7
259
266

531.08
12470.33
13001.31

75.86
48.15

1.58

.143

0.04

Between groups
Within groups
Total

7
271
278

296.71
15884.23
16181.94

42.39
58.61

0.72

.652

0.02

Between groups
Within groups
Total

7
266
273

682.67
12941.54
13624.21

97.52
48.65

2.00

.055

0.05

Between groups
Within groups
Total
Note: *Welch’s ANOVA

7
812
819

1310.00
41524.18
42834.18

187.14
51.14

3.66

<.001*

0.03

Year

Source

2016-17

Between groups
Within groups
Total

2017-18

2018-19

2016-19

df

To compare the effect of gender on teacher absenteeism rates, descriptive analysis and
one-way ANOVAs were conducted. The results showed that during the 2016-17 school year,
male teachers (M = 8.09, SD = 5.98) had a significantly lower absentee rate when compared to
female teachers (M = 12.48, SD = 7.19), but the effect size was small, F(1,265) = 9.98, p = .002,

h2 = 0.04. The descriptive statistics for the 2017-18 school year indicated that males (M = 11.86,
SD = 7.62) missed fewer days of work than females (M = 13.77, SD = 7.59). However, the

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

70

analysis of variance indicated that the effect of gender on the number of teacher absences was
not significant, F(1,277) = 3.49, p = .063, h2 = 0.01. Although the one-way ANOVA for the
2018-19 school year suggested that males (M = 12.39, SD = 7.19) missed work less frequently
than females (M = 13.23, SD = 7.02), there was not a significant effect for gender on the number
of absences, F(1,272) = 0.73, p = .392, h2 = <0.01. When the absentee data were combined for
the three years studied, the analysis of variance revealed that the effect of gender on the number
of teacher absences was significant, but the effect size was small, F(1,818) = 11.31, p = <.001,

h2 = 0.01. The descriptive statistics showed that males (M = 11.85, SD = 6.95) are absent less
often than females (M = 13.75, SD = 7.27). The results are presented in Table 11 and Table 12.
Table 11
Mean Difference Absences by Gender
Year

Gender

2016-17

Male
Female
Total
Male
Female
Total
Male
Female
Total
Male
Female
Total

2017-18
2018-19
2016-19

M
8.09
12.48
13.46
11.86
13.77
13.25
12.39
13.23
13.01
11.85
13.75
13.24

N
74
193
267
76
203
279
71
203
274
221
599
820

SD
5.98
7.19
6.99
7.62
7.59
7.63
7.19
7.02
7.06
6.95
7.27
7.23

Range

p
.002

h2

31.50
38.00
38.00
42.00
48.00
48.00
37.50
41.00
41.00
42.00
48.00
48.00

Table 12
One-Way ANOVA of Gender on the Number of Absences
Year
2016-17

Source
Between groups
Within groups
Total

df
1
265
266

SS
471.83
12529.58
13001.41

MS
471.83
47.28

F
9.98

0.04

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
Year
2017-18

2018-19

2016-19

Source
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total

df
1
277
278
7
272
273
7
818
819

SS
201.02
15979.92
16180.94
36.65
13587.56
13624.21
584.17
42250.00
42834.17

71

h2

MS
201.02
57.69

F
3.49

p
.063

0.01

36.65
49.95

0.73

.392

<0.01

584.17
51.65

11.31

<.001

0.01

A combination of descriptive statistics and one-way ANOVAs were conducted to analyze
the effect of race on the number to teacher absences. An examination of the means indicated that
during the 2016-17 school year, Caucasian teachers (M = 13.50, SD = 7.00) missed more days of
work than African American (M = 12.00, SD = 0.00) or Asian teachers (M = 4.50, SD = 0.00).
An analysis of variance showed that the effect of race on the number of teacher absences was not
significant, F(4,262) = 0.42, p = .794, h2 = <0.01. Although descriptive statistics for the 2017-18
school year showed that African American teachers (M = 20.5, SD = 12.32) missed work at a
higher rate than Asian (M = 7, SD = 0.00) or Caucasian teachers (M = 13.19, SD = 7.56)
teachers, there was not a significant effect for race on absentee rates, F(4,274) = 0.85, p = .496,

h2 = 0.01. The results for the 2018-19 school year suggested that no statistically significant
difference existed among the number of days of school missed by African American (M = 11.25,
SD = 1.77), Asian (M = 6.5, SD = 0.00) or Caucasian teachers (M = 13.05, SD = 7.09), F(4,269)
= 0.24, p = .914, h2 = <0.01. The absentee data for the three school years studied suggested that
African American teachers (M = 16.00, SD = 9.26) tended to be absent from the classroom more
often than Asian (M = 6.00, SD = 1.32) or Caucasian teachers (M = 13.25, SD = 7.22). Overall,

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

72

the effect of race on absentee rates was not significant, F(4,815) = 9.97, p = .423, h2 = <0.01.
The results are presented in Table 13 and Table 14.
Table 13
Mean Differences Absences by Race
Year

Race

2016-17

African American
Asian
Hispanic
Caucasian
Other
Total
African American
Asian
Hispanic
Caucasian
Other
Total
African American
Asian
Hispanic
Caucasian
Other
Total
African American
Asian
Hispanic
Caucasian
Other
Total

2017-18

2018-19

2016-19

M
12.00
4.50
13.50
13.46
20.50
7.00
13.19
13.25
11.25
6.50
13.05
13.25
16.00
6.00
13.25
13.24

N
1
1
0
265
0
267
3
1
0
275
0
279
2
1
0
271
0
279
6
3
0
811
0
820

SD
7.00
6.00
12.32
7.56
7.63
1.77
7.09
7.63
9.26
1.32
7.22
7.23

Range

p
.794

h2

38.00
38.00
24.50
48.00
48.00
2.50
41.00
41.00
24.50
2.5
48.00
48.00

Table 14
One-Way ANOVA of Race on the Number of Absences
Year
2016-17

Source
Between groups
Within groups
Total

df
4
262
266

SS
82.92
12918.50
13001.41

MS
20.73
49.31

F
0.42

<0.01

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
Year
2017-18

2018-19

2016-19

Source
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total

df
4
274
278
4
269
273
4
815
819

SS
197.65
15983.29
16180.94
49.00
13575.20
13624.20
202.99
42613.18
42834.17

73

h2

MS
49.41
58.33

F
0.85

p
.496

0.01

12.25
50.47

0.24

.914

<0.01

50.75
42.31

9.97

.423

<0.01

To analyze the effect of years of experience on teacher absenteeism rates, an examination
of the means and one-way ANOVAs were conducted. During the 2016-17 school year, the
descriptive statistics suggested that teachers who had 30 or more years of experience (M = 10.67,
SD = 5.42) missed fewer school days than teachers with 0-3 years of experience (M = 11.77, SD
= 6.13), 4-9 years of experience (M = 13.09, SD = 7.09), 10-14 years of experience (M = 14.72,
SD = 6.88), 15-19 years of experience (M = 13.31, SD = 7.17), 20-24 years of experience (M =
15.48, SD = 8.41), or teachers with 25-29 years of experience (M = 14.22, SD = 6.52), However,
an analysis of variance showed that the effect of experience on the number of teacher absences
was not statistically significant, F(6,260) = 1.41, p = .210, h2 = 0.03.
An examination of the means showed that teachers with 30 or more years of experience
(M = 10.82, SD = 5.99) during the 2017-18 school year were absent less often than their
colleagues with 0-3 years of experience (M = 11.15, SD = 7.68), 4-9 years of experience (M =
14.32, SD = 8.78), 10-14 years of experience (M = 13.16, SD = 6.45), 15-19 years of experience
(M = 13.35, SD = 7.11), 20-24 years of experience (M = 13.94, SD = 7.33), or 25-29 years of
experience (M = 15.35, SD = 8.77). A one-way ANOVA found that experience does not have

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

74

significant effect on the number of days a teacher is absent from work, F(6,272) = 1.38, p = .224,

h2 = 0.03.
The descriptive statistics for the 2018-19 school year revealed that teachers with 0-3
years of experience (M = 10.32, SD = 5.06) were absent from work less often than teachers with
4-9 years of experience (M = 13.67, SD = 6.09), 10-14 years of experience (M = 14.00, SD =
5.77), 15-19 years of experience (M = 13.29, SD = 7.96), 20-24 years of experience (M = 13.83,
SD = 9.56), 25-29 years of experience (M = 13.10, SD = 7.69), or 30 or more years of experience
(M = 13.84, SD = 6.30). The analysis of variance indicated that the effect of experience on the
number of teacher absences was not significant, F(6,267) = 1.51, p = .174, h2 = 0.03.
An examination of the means over the three-year period of the study suggested that
teachers with 0-3 years of experience (M = 11.08, SD = 6.37) are likely to miss fewer days of
school per year than teachers with 4-9 years of experience (M = 13.62, SD = 7.47), 10-14 years
of experience (M = 14.00, SD = 6.68), 15-19 years of experience (M = 13.31, SD = 7.39), 20-24
years of experience (M = 14.36, SD = 8.34), 25-29 years of experience (M = 14.18, SD = 7.67)
or teachers with 30 or more years of experience (M = 12.13, SD = 6.03). The results of the oneway ANOVA showed that over the course of the three-year study, the effect of experience
significantly influenced teacher absentee rates, F(6,813) = 3.21, p = .004, h2 = 0.02. Post hoc
analyses were conducted using the Tukey’s Honest Significant Difference test. The post hoc test
indicated that a statistically significant difference occurred among teachers with 0-3 years of
experience and teachers with 5-9 years of experience, 15-19 years of experience, 20-25 years of
experience, and 25-29 years of experience. The results are presented in Table 15 and Table 16.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

75

Table 15
Mean Differences Absences by Experience
Year
2016-17

2017-18

2018-19

2016-19

Years of Experience
0-3
4-9
10-14
15-19
20-24
25-29
30 or more
Total
0-3
4-9
10-14
15-19
20-24
25-29
30 or more
Total
0-3
4-9
10-14
15-19
20-24
25-29
30 or more
Total
0-3
4-9
10-14
15-19
20-24
25-29
30 or more
Total

M
11.77
13.09
14.72
13.31
15.48
14.22
10.67
13.46
11.15
14.32
13.16
13.35
13.94
15.35
10.82
13.25
10.32
13.67
14.00
13.29
13.83
13.10
13.84
13.01
11.08
13.62
14.00
13.31
14.36
14.18
12.13
13.24

N
46
60
51
52
26
23
9
267
50
56
58
53
25
26
11
279
47
46
46
56
23
26
16
260
144
168
158
163
75
76
36
820

SD
6.13
7.09
6.88
7.17
8.41
6.52
5.42
6.99
7.68
8.78
6.45
7.11
7.33
8.77
5.99
7.63
5.06
6.09
5.77
7.96
9.56
7.69
6.30
6.90
6.37
7.47
6.68
7.39
8.34
7.67
6.03
7.23

Range
33.00
31.50
35.50
31.00
35.50
20.50
14.50
38.00
33.00
45.50
28.50
29.50
26.00
39.00
21.50
48.00
20.00
27.50
22.50
37.00
38.00
37.00
18.50
41.00
33.50
47.00
36.00
38.00
41.00
40.50
21.50
48.00

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

76

Table 16
One-Way ANOVA of Experience on the Number of Absences
Year
2016-17

2017-18

2018-19

2016-19

Source
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups

df
6
260
266
6
272
278
6
267
273
6

SS
410.46
12590.96
13001.42
477.02
15703.92
16180.94
448.19
13176.01
13624.20
991.31

Within groups
Total

813
819

41842.86
42834.17

h2

MS
68.41
48.43

F
1.41

p
.210

0.03

79.50
57.73

1.38

.224

0.03

74.70
49.35

1.51

.174

0.03

165.22

3.21

.004

0.02

51.47

To compare the effect of school level on teacher absenteeism rates, descriptive analysis
and one-way ANOVAs were conducted. The results showed that teachers in the primary school
(M = 12.46, SD = 5.36) had a lower absentee rate during 2016-17 school year when compared to
teachers in the ECC (M = 14.67, SD = 7.87), intermediate school (M = 13.96, SD = 8.16),
middle school, (M = 12.84, SD = 6.05), or high school (M = 13.72, SD = 7.36), but there was not
a significant difference F(4,262) = 0.60, p = .665, h2 = <0.01.
An examination of the means for the 2017-18 school year indicated that teachers in the
intermediate school (M = 12.25, SD = 7.87) were absent from the classroom less often than
teachers in the ECC (M = 12.33, SD = 7.70), primary school (M = 16.22, SD = 8.08), middle
school (M = 12.96, SD = 7.84), or high school (M = 13.10, SD = 7.01). However, the one-way
ANOVA showed the effect of gender on the number of teacher absences was not significant,
F(4,274) = 1.72, p = .146, h2 = 0.02. Although the descriptive statistics for the 2018-19 school

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

77

year suggested that teachers in the intermediate school (M = 12.24, SD = 6.65) missed fewer
days of school than teachers in the ECC (M = 12.27, SD = 8.45), primary school (M = 13.15, SD
= 5.10), middle school (M = 13.31, SD = 6.75), or high school (M = 13.38, SD = 7.68), there was
not a significant effect for school level on the rates of absenteeism, F(4,269) = 0.31, p = .869, h2
= <0.01. When the absentee data were combined, the one-way ANOVA revealed that the effect
of school level on the frequency that a teacher is likely to miss work was not significant,
F(4,815) = 0.47, p = .757, h2 = <0.01. The descriptive statistics determined that teachers who
work in the intermediate school (M = 12.83, SD = 7.59) were absent less frequently than teachers
who work in the ECC (M = 12.99, SD = 8.02), primary school (M = 13.96, SD = 6.48), middle
school (M = 13.04, SD = 6.92), or high school (M = 13.40, SD = 7.33). The results are presented
in Table 17 and Table 18.
Table 17
Mean Differences Absences by School Level
Year
2016-17

2017-18

2018-19

School Level
ECC
Primary
Intermediate
Middle
High
Total
ECC
Primary
Intermediate
Middle
High
Total
ECC
Primary
Intermediate
Middle
High
Total

M
14.67
12.46
13.96
12.84
13.72
13.46
12.33
16.22
12.25
12.96
13.10
13.25
12.27
13.15
12.24
13.31
13.38
13.01

N
27
34
45
70
91
267
30
36
44
75
94
279
35
37
42
71
89
274

SD
7.87
5.36
8.16
6.05
7.36
6.99
7.70
8.08
7.87
7.84
7.01
7.63
8.45
5.10
6.65
6.75
7.68
7.06

Range
34.00
26.00
37.50
31.00
32.50
38.00
32.00
39.50
45.50
42.50
29.50
48.00
36.50
22.00
27.50
39.50
37.50
41.00

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
Year
2016-19

School Level
ECC
Primary
Intermediate
Middle
High
Total

M
12.99
13.96
12.83
13.04
13.40
13.24

78
N
92
107
131
216
274
820

SD
8.02
6.48
7.59
6.92
7.33
7.23

Range

h2

37.50
39.50
47.00
42.50
38.50
48.00

Table 18
One-Way ANOVA of School Level on the Number of Absences
Year
2016-17

Source
Between groups
Within groups
Total

df
4
262
266

SS
117.44
12883.97
13001.41

MS
29.36
49.18

F
0.60

p
.665

<0.01

2017-18

Between groups
Within groups
Total

4
274
278

395.63
15785.31
16180.94

98.91
57.61

1.72

.146

0.02

2018-19

Between groups
Within groups
Total

4
269
273

63.16
13561.05
13624.21

15.78
59.41

0.31

.869

<0.01

2016-19

Between groups
Within groups
Total

4
815
819

98.78
42735.39
42834.17

24.69
52.44

0.47

.757

<0.01

A combination of descriptive statistics and one-way ANOVAs were conducted to analyze
the effect of degree earned on the number of teacher absences. An examination of the means
indicated that during the 2016-17 school year, teachers with a bachelor’s (M = 10.71, SD = 5.73)
missed fewer days of work than teachers with a master’s (M = 13.37, SD = 6.58), master’s + 10
(M = 15.17, SD = 8.93), master’s + 20 (M = 12.23, SD = 7.04), master’s + 30 (M = 15.57, SD =
7.15), or master’s + 45 (M = 13.95, SD = 6.91). An analysis of variance showed the effect of
degree earned on absentee rates was significant, F(5,261) = 2.60, p = .026, h2 = 0.05. Post hoc
analyses were conducted using Tukey’s Honest Significant Difference test. The post hoc test

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

79

indicated that a statistically significant difference occurred between teachers with bachelor’s and
teachers with a master’s +30 and master’s +45.
The results for the 2017-18 school year suggested there were statistically significant
differences among the number of absences taken by teachers with a bachelor’s (M = 12.05, SD =
8.76), master’s (M = 12.63, SD = 7.26), master’s + 10 (M = 13.31, SD = 7.34), master’s + 20 (M
= 14.00, SD = 6.74), master’s + 30 (M = 14.40, SD = 8.26), or master’s + 45 (M = 13.62, SD =
7.33), F(5,273) = 0.56, p = .734, h2 = 0.02. An examination of the means for the 2018-19 school
year showed teachers with a master’s + 10 (M = 11.31, SD = 6.5) missed work less frequently
than teachers with a bachelor’s (M = 11.34, SD = 6.44), master’s (M = 13.89, SD = 7.96),
master’s + 20 (M = 11.67, SD = 5.88), master’s + 30 (M = 13.50, SD = 5.96), or master’s + 45
(M = 13.75, SD = 7.48). However, there was not a significant effect for degree earned on the
number of days a teacher is absent from work, F(5,268) = 1.30, p = .262, h2 = 0.02. The
combined absentee data for the three school years studied suggested that a teacher with a
bachelor’s degree (M = 11.39, SD = 7.09) is less likely to miss work than a teacher with a
master’s (M = 13.29, SD = 7.29), master’s + 10 (M = 13.82, SD = 7.97), master’s + 20 (M =
12.68, SD = 6.54), master’s + 30 (M = 14.47, SD = 7.10), or master’s + 45 (M = 13.77, SD =
7.22). However, the one-way ANOVA showed the effect of degree earned on teacher absentee
rates was not significant, F(5,814) = 3.11, p = .787, h2 = 0.02. The results are presented in Table
19 and Table 20.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

80

Table 19
Mean Differences Absences by Degree
Year
2016-17

Degree
Bachelor’s
Master’s
Master’s + 10
Master’s + 20
Master’s + 30
Master’s + 45
Total
Bachelor’s
Master’s
Master’s + 10
Master’s + 20
Master’s + 30
Master’s + 45
Total
Bachelor’s
Master’s
Master’s + 10
Master’s + 20
Master’s + 30
Master’s + 45
Total
Bachelor’s
Master’s
Master’s + 10
Master’s + 20
Master’s + 30
Master’s + 45
Total

2017-18

2018-19

2016-19

M
10.71
13.37
15.17
12.23
15.57
13.95
13.46
12.05
12.63
13.31
14.00
14.40
13.62
13.25
11.34
13.89
11.31
11.67
13.50
13.75
13.01
11.39
13.29
13.82
12.68
14.47
13.77
13.24

N
46
45
21
20
34
101
267
51
52
16
23
31
106
279
57
52
8
21
37
99
274
154
149
45
64
102
306
820

SD
5.73
6.58
8.93
7.04
7.15
6.91
6.99
8.76
7.26
7.34
6.74
8.26
7.33
7.63
6.44
7.96
6.50
5.88
5.96
7.48
7.06
7.09
7.29
7.97
6.54
7.10
7.22
7.23

Range
23.50
29.00
36.50
28.50
28.50
36.00
38.00
47.50
33.00
30.50
24.00
41.50
42.00
48.00
36.00
35.50
20.00
22.50
24.00
41.00
41.00
47.50
39.00
38.00
28.50
41.50
42.00
48.00

p
.026

h2

Table 20
One-Way ANOVA of Degree Attained on the Number of Absences
Year
2016-17

Source
Between groups
Within groups

df
5
261

SS
616.50
12384.91

Total

266

13001.41

MS
123.30
47.45

F
2.60

0.05

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
Year
2017-18

2018-19

2016-19

Source
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total

df
5
273
278
5
268
273
5
814
819

SS
162.82
16018.12
16180.94
323.64
13300.57
13624.21
804.48
42029.69
42834.17

81

h2

MS
32.56
58.67

F
0.56

p
.734

0.02

64.73
49.63

1.30

.262

0.02

160.89
51.63

3.11

.787

0.02

The final set of demographic variables analyzed the effect of distance to work on teacher
absenteeism rates. To compare the effect of distance to work on teacher absenteeism rates, an
examination of the means and one-way ANOVAs were conducted. During the 2016-17 school
year, the descriptive statistics indicated that teachers who lived 12.0-15.9 miles from work (M =
12.60, SD = 5.72) missed fewer days of school when compared to teachers who lived 0.0-3.9
miles from work (M = 13.26, SD = 6.89), 4.0-7.9 miles from work (M = 13.05, SD = 7.41), 8.011.9 miles from work (M = 14.86, SD = 7.78), or 16 or more miles from work (M = 13.88, SD =
6.31). However, the analysis of variance showed the effect of distance from work on the number
of days a teacher missed per year was not statistically significant, F(4,262) = 0.62, p = .651, h2 =
<0.01. An examination of the means for the 2017-18 school year showed teachers who lived 0.03.9 miles from work (M = 12.48, SD = 7.87) were absent less often than teachers who lived 4.07.9 miles from work (M = 13.16, SD = 6.98), 8.0-11.9 miles from work (M = 12.59, SD = 6.34),
12-15.9 miles from work (M = 13.18, SD = 9.46), or 16 or more miles from work (M = 14.69, SD
= 8.39). The one-way ANOVA found that distance from work does not have significant effect on
absentee rates, F(4,274) = 0.71, p = .588, h2 = 0.01.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

82

Although the descriptive statistics for the 2018-19 school year indicated teachers who
lived 0-3 miles from work (M = 11.68, SD = 6.64) missed fewer days per year than teachers who
lived 4.0-7.9 miles from work (M = 13.55, SD = 7.45), 8.0-11.9 miles from work (M = 13.69, SD
= 6.72), 12-15.9 miles from work (M = 12.98, SD = 7.85), or 16 or more miles from work (M =
12.89, SD = 6.62), the analysis of variance determined that the effect for school level on the
number of absences was not significant, F(4,269) = 0.72, p = .580, h2 = 0.01.
The absentee data for the aggregate data set suggested teachers who lived 0-3 miles from
work (M = 12.47, SD = 7.15) were more likely to be absent from the classroom than teachers
who lived 4.0-7.9 miles from work (M = 13.25, SD = 7.26), 8.0-11.9 miles from work (M =
13.70, SD = 6.96), 12-15.9 miles from work (M = 12.93, SD = 7.78), or 16 or more miles from
work (M = 13.83, SD = 7.18). However, the effect of distance to work on the number of days
missed per year was not significant, F(4,815) = 0.88, p = .473, h2 = <0.01. The results are
presented in Table 21 and Table 22.
Table 21
Mean Differences Absences by Distance to Work
Year
2016-17

2017-18

Distance to Work
0.0-3.9 miles
4.0-7.9 miles
8.0-11.9 miles
12.0-15.9 miles
16 miles or more
Total
0.0-3.9 miles
4.0-7.9 miles
8.0-11.9 miles
12.0-15.9 miles
16 miles or more
Total

M
13.26
13.05
14.86
12.60
13.88
13.46
12.48
13.16
12.59
13.18
14.69
13.25

N
53
98
38
26
52
267
56
101
39
28
55
279

SD
6.89
7.41
7.78
5.72
6.31
6.99
7.87
6.98
6.34
9.46
8.39
7.63

Range
31.50
32.00
35.00
20.50
36.50
38.00
42.50
32.50
23.50
47.50
41.50
48.00

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
Year
2018-19

2016-19

Distance to Work
0.0-3.9 miles
4.0-7.9 miles
8.0-11.9 miles
12.0-15.9 miles
16 miles or more
Total
0.0-3.9 miles
4.0-7.9 miles
8.0-11.9 miles
12.0-15.9 miles
16 miles or more
Total

M
11.68
13.55
13.69
12.98
12.89
13.01
12.47
13.25
13.70
12.93
13.83
13.24

83
N
54
98
40
29
53
274
163
297
117
83
160
820

SD
6.64
7.45
6.72
7.85
6.62
7.06
7.15
7.26
6.96
7.78
7.18
7.23

Range

h2

37.00
39.50
36.00
37.00
23.50
41.00
42.50
42.00
36.00
47.50
44.00
48.00

Table 22
One-Way ANOVA of Distance to Work on the Number of Absences
Year
2016-17

Source
Between groups
Within groups
Total

df
4
262
266

SS
121.17
12880.25
13001.42

MS
30.29
49.16

F
0.62

p
.651

<0.01

2017-18

Between groups
Within groups
Total

4
274
278

165.11
16015.83
16180.94

41.28
58.45

0.71

.588

0.01

2018-19

Between groups
Within groups
Total

4
269
273

143.98
13489.23
13624.21

35.99
50.11

0.72

.580

0.01

2016-19

Between groups
Within groups
Total

4
815
819

185.15
42649.03
42834.18

46.29
53.33

0.88

.473

<0.01

Correlations Between Teacher Demographics and Teacher Absences
Correlation tests were conducted to examine the relationship between the number of
teacher absences per year and age, gender, race, experience, school level, degree, and distance
from work. The following guidelines were used to describe the correlation coefficient values of
the relationship: (a) very strong, .90 ≤ | r | ≤ 1.0; (b) strong, .70 ≤ | r | ≤ .89; (c) moderate, .50 ≤ | r

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

84

| ≤ .69; (d) weak, .30 ≤ | r | ≤ .49; (e) very weak, .00 ≤ | r | ≤ .29. Pearson Correlation tests were
used to describe the relationship between teacher absentee rates and age, years of experience, and
distance from work. To examine the relationship between the number of teacher absences and
gender, a point-biserial correlation test was conducted. The point-biserial correlation test was
selected to analyze these two variables because the test is specifically used to measure the
strength and direction that exists between one dichotomous variable and one continuous variable,
whereas the Pearson Correlation test is used to measure the relationship between two or more
continuous variables. Point-biserial correlations were also used to measure the relationship
between the number of days a teacher missed per year and the demographic variables for race
and school level. To use the Point-biserial correlation to measure the strength and direction of the
association between teacher absentee rates and race and school level, the independent variables
were dummy coded into a series of dichotomous variables. The Kendall’s tau-b correlation
coefficient was used to measure the relationship between the ordinal variable, degree earned, and
the number of teacher absences per year. As displayed in Table 23, the data suggested that all the
relationships between absentee rates and age, gender, race, experience, school level, degree, and
distance from work were very weak. Although the results indicated that all the relationships were
very weak, there were four associations that were considered to be statistically significant. The
data suggested that there was a significant relationship between gender and the number of
teacher absences during the 2016-17 school year r(267) = 0.19, p =.002. When the data for the
three years were combined, the relationship between gender and absentee rates was also found to
be significant r(820) = 0.12, p =.001. The relationship between school level and the number of
days a teacher misses per year was shown to be significant for the primary school during the
2017-18 school year r(279) = - 0.12, p =.001. The correlation between degree earned and teacher

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

85

absences during the 2016-17 year showed a significant positive correlation r(267) = 0.10, p
=.027. The results suggest that as teachers earn more credits, they are also likely to miss more
days of work.
Table 23
Correlations – All Demographic Variables Related Teacher Absences
Variable

2016-17

2017-18

2018-19

2016-19

r

p

-0.03
0.19

.626
.002*

-0.05
0.11

.411
.063

-0.07
0.05

.236
.392

-0.05
0.12

.128
.001*

0.01

.834

-0.10

.098

0.02

.348

-0.03

.348

-0.08

.119

-0.05

.413

-0.06

.357

0.06

.082

Hispanic

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

Caucasian

-0.07

.291

0.06

.307

-0.05

.410

-0.01

.811

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

0.02

.773

-0.02

.776

0.06

.344

0.02

.531

-0.06

.347

0.04

.487

0.04

.507

0.01

.822

0.06

.369

-0.15

.012**

-0.01

.900

-0.04

.333

-0.03

.606

0.06

.345

0.05

.441

0.04

.333

0.05

.388

0.02

.702

-0.03

.681

0.03

.493

-0.03

.669

0.03

.672

-0.04

.555

-0.03

.403

Degree earned

0.10

.027**

0.09

.053

0.09

.051

0.01

.833

Distance from work

0.04

.509

0.09

.147

0.02

.710

0.05

.137

Age
Gender

r

p

r

p

r

p

Race
African American
Asian

Other
Years of experience
School level
ECC
Primary
Intermediate
Middle
High

Note: * Correlation is significant at the 0.01 level (2-tailed).
** Correlation is significant at the 0.05 level (2-tailed).

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

86

Student Achievement Scores
Descriptive statistics and one-way ANOVA tests were used to determine if there was a
statistically significant difference between achievement scores for students who were instructed
by chronically absent teachers and students who were not instructed by chronically absent
teachers. An examination of the means for the 2016-17 school year suggested that Grade 2
DIBELS Next Oral Reading Fluency scores were higher for students who were not instructed by
chronically absent teachers (M = 39.21, SD = 16.74) than students who were instructed by
chronically absent teachers (M = 30.56, SD = 22.18). The analysis of variance showed the effect
of chronically absent teachers on Grade 2 DIBELS Next scores was statistically significant, but
the effect size was small, F(1,174) = 6.00, p = .006, h2 = 0.03.
The descriptive statistics showed that Grade 2 DIBELS Next Oral Reading Fluency
scores for students who were taught by teachers who missed fewer than 10 days of school (M =
31.41, SD = 14.18) during the 2017-18 school year were lower than students who were instructed
by chronically absent teachers (M = 40.07, SD = 19.33). However, an analysis of variance found
that teacher absence classification does not have a significant effect on Grade 2 DIBELS Next
Oral Reading Fluency scores F(1,152) = 3.19, p = .076, h2 = 0.02.
The data for the 2018-19 school year revealed through an examination of the means that
students who were instructed by teachers who were regularly present (M = 36.84, SD = 22.23)
scored lower on the Grade 2 DIBELS Next Oral Reading Fluency assessment than students who
were instructed by teachers who missed 10 or more days of work (M = 38.76, SD = 20.13). The
one-way ANOVA indicated the effect of chronically absent teachers on the Grade 2 DIBELS
Next Oral Reading Fluency assessment was not significant, F(1,191) = 0.40, p = .530, h2 =
<0.01.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

87

An examination of the means over the three-year period of the study indicated students
who were instructed by teachers that were not classified as chronically absent (M = 36.98, SD =
19.90) had slightly higher Grade 2 DIBELS Next Oral Reading Fluency scores than their peers
who were educated by teachers who were classified as chronically absent (M = 36.41, SD =
20.97). An analysis of variance showed that the effect of teacher absence classification did not
significantly influence the Grade 2 DIBELS Next Oral Reading Fluency assessment scores
F(1,521) = 0.08, p = .773, h2 = <0.01. The results are presented in Table 24 and Table 25.
Table 24
Mean Difference Grade 2 DIBELS Next Scores by Absence Classification
Year

Absence Classification

2016-17

Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total

2017-18
2018-19
2016-19

M
39.21
30.56
32.92
31.41
40.07
39.11
36.84
38.76
37.89
36.98
36.41
36.58

N
48
128
176
17
137
154
88
105
193
153
370
523

SD
16.74
22.18
21.15
14.18
19.33
18.99
22.23
20.13
21.08
19.90
20.97
20.64

Range
82.00
107.00
112.00
51.00
89.00
89.00
109.00
130.00
130.00
109.00
133.00
133.00

Table 25
One-Way ANOVA of Grade 2 DIBELS on Absence Classification
Year
2016-17

2017-18

Source
Between groups
Within groups
Total
Between groups
Within groups
Total

df
1
174
175
1
152
153

SS
2609.47
75641.42
78250.89
1132.60
54044.53
55177.13

h2

MS
2609.47
434.72

F
6.00

p
.006*

0.03

1132.60

3.19

.076

0.02

355.56

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
Year
2018-19

Source
Between groups
Within groups
Total
2016-19 Between groups
Within groups
Total
Note: *Welch’s ANOVA

df
1
191
192
1
521
522

SS
176.67
85166.82
85343.49

MS
176.67

35.45
222408.32
222443.77

35.45
426.89

88

h2

F
0.40

p
.530

<0.01

0.08

.773

<0.01

445.90

To compare the effect of chronically absent teachers on Grade 3 DIBELS Next Oral
Reading Fluency scores, a combination of descriptive analyses and one-way ANOVAs were
conducted. The results showed students who were not instructed by chronically absent teachers
during the 2016-17 school year (M = 32.56, SD = 17.43) had significantly higher scores than
students who were taught by teachers who missed 10 or more days of school (M = 27.17, SD =
16.59), but the effect size was small, F(1,195) = 4.68, p = .032, h2 = 0.02. An examination of the
means for the 2017-18 school year indicated students who were instructed by teachers who were
absent from the classroom for fewer than 10 days of school (M = 32.18, SD = 18.38) scored
lower on the Grade 3 DIBELS Next Oral Reading Fluency assessment than students who were
instructed by chronically absent teachers (M = 33.53, SD = 17.94). However, the one-way
ANOVA indicated that the effect of teacher absence classification on Grade 3 DIBELS Next
Oral Reading Fluency scores was not significant, F(1,196) = 0.26, p = .614, h2 = <0.01.
Although the descriptive statistics for the 2018-19 Grade 3 DIBELS Next Oral Reading Fluency
scores showed that students who were instructed by teachers who were not chronically absent (M
= 27.80, SD = 14.95) scored lower than students in chronically absent teacher classrooms (M =
30.75, SD = 19.20), the effect was not significant, F(1,184) = 1.21, p = .273, h2 = <0.01. When
the absentee data were combined for the three school years studied, the one-way ANOVA
revealed that the effect of chronically absent teachers on Grade 3 DIBELS Next Oral Reading

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

89

Fluency scores was not significant, F(1,521) = 0.03, p = .868, h2 = <0.01. An examination of the
means revealed that student achievement scores of teachers who missed fewer than 10 days of
work (M = 30.99, SD = 17.09) were slightly higher than for chronically absent teachers (M =
30.74, SD = 18.14). The results are presented in Table 26 and Table 27.
Table 26
Mean Difference Grade 3 DIBELS Next Scores by Absence Classification
Year
2016-17
2017-18
2018-19
2016-19

Absence Classification
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total

M
32.56
27.17
29.71
32.18
33.53
33.04
27.80
30.75
29.64
30.99
30.74
30.84

N
88
99
187
72
126
198
70
116
186
230
341
571

SD
17.43
16.59
17.16
18.38
17.94
18.07
14.95
19.20
17.74
17.09
18.14
17.71

Range
86.00
115.00
115.00
92.00
84.00
92.00
70.00
121.00
121.00
93.00
126.00
126.00

h2

Table 27
One-Way ANOVA of Grade 3 DIBELS on Absence Classification
Year
2016-17

Source
Between groups
Within groups
Total
2017-18 Between groups
Within groups
Total
2018-19 Between groups
Within groups
Total
2016-19 Between groups
Within groups
Total
Note: *Welch’s ANOVA

df
1
195
186
1
196
197
1
184
185
1
521
522

SS
1351.03
53423.80
54774.82
83.65
64214.03
64297.68
379.92
57832.95
58212.87
8.74
178743.75
178752.49

MS
1351.03
288.78

F
4.68

p
.032

0.02

83.65
327.62

0.26

.614

<0.01

379.92
314.31

1.21

.273

<0.01

8.74
314.18

0.03

.868

<0.01

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

90

Descriptive statistics and one-way ANOVAs were conducted to analyze the effect of
teacher absence classification on Grade 4 DIBELS Next Oral Reading Fluency scores. An
examination of the means indicated that during the 2016-17 school year, student achievement
scores on the Grade 4 DIBELS Next Oral Reading Fluency assessment were lower if instructed
by teachers who missed fewer than 10 days of schools (M = 28.09, SD = 20.70) than if taught by
chronically absent teachers (M = 31.14, SD = 19.98). An analysis of variance showed the effect
of teacher absence classification on Grade 4 DIBELS Next Oral Reading Fluency scores was not
significant, F(1,202) = 0.90, p = .344, h2 = <0.01.
An examination of the means for the 2017-18 school year showed students who were
educated by teachers who were regularly in attendance (M = 28.86, SD = 15.27) scored lower on
the Grade 4 DIBELS Next Oral Reading Fluency assessment than students who were taught by
teachers who missed at least 10 days of school per year (M = 30.23, SD = 17.03). The one-way
ANOVA indicated that chronically absent teachers did not significantly impact Grade 4 DIBELS
Next Oral Reading Fluency scores, F(1,218) = 0.28, p = .619, h2 = <0.01.
The results for the 2018-19 school year suggested that there is no significant difference
between Grade 4 DIBELS Next Oral Reading Fluency scores for students instructed by teachers
who missed fewer than 10 days of work (M = 28.02, SD = 19.02), and scores for students
instructed by teachers who were chronically absent (M = 30.81, SD = 15.93), F(1,215) = 1.37, p
= .249, h2 = <0.01. The aggregate absentee data suggested that students instructed by teachers
who were not chronically absent (M = 28.38, SD = 18.18) scored lower on the Grade 4 DIBELS
Next Oral Reading Fluency assessments than students instructed by teachers who missed a
minimum of 10 days of work (M = 30.79, SD = 17.32). However, the effect of chronically absent

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

91

teachers on Grade 4 DIBELS Next Oral Reading Fluency scores was not significant, F(1,639) =
2.43, p = .120, h2 = <0.01. The results are presented in Table 28 and Table 29.
Table 28
Mean Difference Grade 4 DIBELS Next Scores by Absence Classification
Year

Absence Classification

2016-17

Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total

2017-18
2018-19
2016-19

M
28.09
31.14
28.93
28.86
30.23
29.10
28.02
30.81
29.28
28.38
30.79
29.11

N
148
56
204
181
39
220
119
98
217
448
193
641

SD
20.70
19.98
20.50
15.27
17.03
15.56
19.02
15.93
17.70
18.18
17.32
17.94

Range
95.00
114.00
131.00
92.00
84.00
92.00
91.00
87.00
91.00
106.00
120.00
131.00

Table 29
One-Way ANOVA of Grade 4 DIBELS on Absence Classification
Year
2016-17

Source
Between groups
Within groups
Total
2017-18 Between groups
Within groups
Total
2018-19 Between groups
Within groups
Total
2016-19 Between groups
Within groups
Total
Note: *Welch’s ANOVA

df
1
202
203
1
218
219
1
215
216
1
639
640

SS
379.18
84966.72
85345.90

MS
379.18

60.13
52974.47
53034.60

60.13

h2

F
0.90

p
.344

<0.01

0.28

.619

<0.01

1.37

.249

<0.01

2.43

.120

<0.01

288.78

244.00

418.13
67279.28
67697.41

418.13

780.77
205310.02
206090.79

780.77

312.93

321.30

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

92

To analyze the effects of chronically absent teachers on Grade 5 DIBELS Next Oral
Reading Fluency scores, an examination of the means and one-way ANOVAs were conducted.
During the 2016-17 school year, the descriptive statistics indicated students instructed by
teachers who were not classified as chronically absent (M = 21.59, SD = 19.45) scored lower on
the Grade 5 DIBELS Next Oral Reading Fluency assessment when compared to students
educated by chronically absent teachers (M = 24.58, SD = 14.13). However, the one-way
ANOVA showed the effect of teacher absence classification on the Grade 5 DIBELS Next Oral
Reading Fluency scores was not statistically significant, F(1,184) = 1.33, p = .250, h2 = <0.01.
The descriptive statistics showed students taught by teachers who missed fewer than 10 day of
work (M = 28.66, SD = 13.12) during the 2017-18 school year had lower Grade 5 DIBELS Next
Oral Reading Fluency scores than their peers who were instructed by teachers who missed 10 or
more days of work (M = 29.62, SD = 17.25). An analysis of variance found teacher absence
classification does not have a significant effect on Grade 5 DIBELS Next Oral Reading Fluency
scores, F(1,208) = 0.21, p = .651, h2 = <0.01.
Although the examination of the means for the 2018-19 school year showed students
instructed by teachers who were not chronically absent (M = 20.90, SD = 13.35) recorded lower
scores than students instructed by chronically absent teachers (M = 27.73, SD = 19.82), the effect
of chronically absent teachers on the Grade 5 DIBELS Next Oral Reading Fluency scores was
not significant, F(1,201) = 2.36, p = .126, h2 = 0.01.
The absentee data for the three school years studied suggested students taught by teachers
who missed fewer than 10 days of work (M = 24.63, SD = 16.74) were more likely to score
lower on the Grade 5 DIBELS Next Oral Reading Fluency assessment than students who were
instructed by teachers who were chronically absent (M = 27.63, SD = 18.05). The effect of

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

93

teacher absence classification on the aggregate Grade 5 DIBELS Next Oral Reading Fluency
scores was shown to be significant, F(1,597) = 4.13, p = .043, h2 = <0.01. The results are
presented in Table 30 and Table 31.
Table 30
Mean Difference Grade 5 DIBELS Next Scores by Absence Classification
Year

Absence Classification

2016-17

Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total

2017-18
2018-19
2016-19

M
21.59
24.58
22.83
28.66
29.62
29.15
20.90
27.73
27.02
24.63
27.63
26.47

N
109
77
186
102
108
210
21
182
203
232
367
599

SD
19.45
14.13
17.46
13.12
17.25
15.35
13.35
19.82
19.34
16.74
18.05
17.60

Range
161.00
66.00
161.00
62.00
117.00
117.00
44.00
128.00
128.00
161.00
128.00
200.00

Table 31
One-Way ANOVA of Grade 5 DIBELS on Absence Classification

h2

Year
2016-17

Source
Between groups
Within groups
Total

df
1
184
185

SS
405.37
56007.12
56412.49

MS
405.37
304.39

F
1.33

p
.250

<0.01

2017-18

Between groups
Within groups
Total

1
208
209

48.70
49224.43
49273.13

48.70
236.66

0.21

.651

<0.01

2018-19

Between groups
Within groups
Total

1
201
202

877.26
74687.62
75564.88

877.26
371.58

2.36

.126

0.01

2016-19

Between groups
Within groups
Total

1
597
598

1273.40
184017.72
185291.12

1273.40
308.28

4.13

.043

<0.01

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

94

Descriptive statistics and one-way ANOVA tests were used to establish if there were
statistically significant differences between PVAAS Teacher Value Added Math scores for
teachers who were chronically absent and scores for teachers who regularly attended work.
During the 2016-17 school year, an examination of the means suggested that PVAAS Teacher
Value Added Math scores were higher for teachers who were not chronically absent (M = 1.54,
SD = 1.57) when compared to those who were chronically absent (M = 1.01, SD = 1.36). An
analysis of variance showed the effect of chronically absent teachers on PVAAS Teacher Value
Added Math scores during the 2016-17 school year was not statistically significant, F(1,21) =
0.69, p = .415 h2 = 0.04.
An examination of the means showed PVAAS Teacher Value Added Math scores for
teachers who missed less than 10 days of work (M = 0.39, SD = 1.74) during the 2017-18 school
year were higher than chronically absent teachers (M = 0.12, SD = 2.33). The one-way ANOVA
that was performed for the 2017-18 school year found teacher absence classification does not
have significant effect on PVAAS Teacher Value Added Math scores F(1,22) = 0.11, p = .745 h2
= <0.01.
A review of the descriptive data for the 2018-19 school year suggested teachers who are
not chronically absent (M = 0.11, SD = 1.37) had lower PVAAS Teacher Value Added Math
scores than teachers who missed a minimum of 10 days of work (M = 0.53, SD = 1.64).
However, an analysis of variance indicated the effect of chronically absent teachers on PVAAS
Teacher Value Added Math scores was not significant, F(1,21) = 0.48, p = .511, h2 = 0.02.
The descriptive data for the three-year period of the study indicated that teachers who
were not classified as chronically absent (M = .072, SD = 1.66) had slightly higher PVAAS
Teacher Value Added Math scores than their colleagues who were chronically absent (M = 0.52,

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

95

SD = 1.83). A one-way ANOVA showed the effect of teacher absence classification did not
significantly influence PVAAS Teacher Value Added Math scores F(1,69) = 0.22, p = .643, h2 =
<0.01. The results are presented in Table 32 and Table 33.
Table 32
Mean Difference by Absence Classification on PVAAS Math Scores
Year

Absence Classification

2016-17

Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total

2017-18
2018-19
2016-19

M
1.54
1.01
1.33
0.39
0.12
0.27
0.11
0.53
0.31
0.72
0.52
0.63

N
14
9
23
13
11
24
12
11
23
39
31
70

SD
1.57
1.36
1.48
1.74
2.33
1.99
1.37
1.64
1.49
1.66
1.83
1.72

Range

MS
1.54
2.23

F
0.69

p
.415

h2
0.04

0.45
4.12

0.11

.745

<0.01

1.01
2.27

0.48

.511

0.02

0.65
3.01

0.22

.643

<0.01

5.85
4.53
5.85
6.11
8.27
8.34
4.52
5.61
5.61
8.05
8.27
8.34

Table 33
One-Way ANOVA of PVAAS Math Scores on Absence Classification
Year
2016-17

2017-18

2018-19

2016-19

Source
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total
Between groups
Within groups
Total

df
1
21
22
1
22
23
1
21
22
1
69
69

SS
1.54
46.84
43.38
.45
90.66
91.11
1.01
47.65
48.66
0.65
204.39
205.04

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

96

A combination of descriptive statistics and one-way ANOVAs were conducted to
determine the effect of teacher absence classification on PVAAS Teacher Value Added English
Language Arts (ELA) scores. The descriptive statistics indicated that during the 2016-17 school
year, teachers who missed 10 days or fewer (M = 0.64, SD = 2.10) had higher PVAAS Teacher
Value Added ELA scores than chronically absent teachers (M = -0.01, SD = 1.60). An analysis
of variance showed the effect of teacher absence classification on higher PVAAS Teacher Value
Added ELA scores was not significant, F(1,27) = 0.87 p = .359, h2 = 0.03.
An examination of the means for the 2017-18 school year showed teachers who were not
chronically absent (M = 0.68, SD = 1.33) scored slightly higher on the PVAAS Teacher Value
index for ELA than teachers who missed at least 10 days of school (M = 0.65, SD = 1.76). A
one-way ANOVA indicated chronically absent teachers did not have a significant effect on the
PVAAS Teacher Value Added ELA scores, F(1,29) = 0.00, p = .957, h2 = <0.01.
The results for the 2018-19 school year suggested that no statistically significant
difference existed between the PVAAS Teacher Value Added ELA scores for teachers who
missed fewer than 10 days of work (M = 0.95, SD = 1.41), and teachers who missed a minimum
of 10 days of school (M = 0.25, SD = 1.33), F(1,25) = 1.65, p = .211, h2 = 0.06. The data
analysis for the three school years studied suggested teachers who were not chronically absent
(M = 0.73, SD = 1.61) had higher PVAAS Teacher Value Added ELA scores than teachers who
were chronically absent (M = 0.28, SD = 1.54). However, the effect of teacher absence
classification on PVAAS Teacher Value Added ELA scores was not significant, F(1,85) = 1.18,
p = .187, h2 = 0.02. The results are presented in Table 34 and Table 35.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

97

Table 34
Mean Difference by Absence Classification on PVAAS ELA Scores
Year
2016-17
2017-18
2018-19
2016-19

Absence Classification
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total

M
0.64
-0.01
0.30
0.68
0.65
0.67
0.95
0.25
0.51
0.73
0.28
0.50

N
14
15
29
18
13
31
10
17
27
42
45
87

SD
2.10
1.60
1.85
1.33
1.76
1.50
1.41
1.33
1.38
1.61
1.54
1.58

Range
7.18
5.25
7.18
5.04
5.77
5.77
4.59
5.08
7.06
8.24
7.42
8.24

h2

Table 35
One-Way ANOVA of PVAAS ELA Scores on Absence Classification
Year
2016-17

Source
Between groups
Within groups
Total

df
1
27
28

SS
3.00
93.10
96.10

MS
3.00
3.45

F
0.87

p
.359

0.03

2017-18

Between groups
Within groups
Total

1
29
30

.01
67.08
67.09

0.01
2.31

0.00

.957

<0.01

2018-19

Between groups
Within groups
Total

1
25
26

3.04
46.10
49.14

3.04
1.84

1.65

.211

0.06

2016-19

Between groups
Within groups
Total

1
85
86

4.37
209.97
214.34

4.37
2.47

1.18

.187

0.02

To determine the effect of teacher absence classification on PVAAS Teacher Value
Added Science scores, descriptive analyses and one-way ANOVAs were conducted. The results
showed chronically absent teachers (M = 2.19, SD = 1.39) had higher scores on the PVAAS

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

98

Teacher Value Added index for Science than teachers who missed at least 10 days of school (M
= 2.20, SD = 1.38), F(1,7) = 0.00, p = .995, h2 = <0.01. An examination of the descriptive
statistics for the 2017-18 school year indicated teachers who were absent fewer than 10 days of
school per year (M = 0.73, SD = 1.48) had lower PVAAS Teacher Value Added Science scores
than chronically absent teachers (M = 0.76, SD = 2.62). However, the analysis of variance
indicated the effect of chronically absent teachers on PVAAS Teacher Value Added Science
scores was not significant, F(1,7) = 0.00, p = .985, h2 = <0.01. Although the examination of the
means for the 2018-19 school year showed teachers who were not chronically absent (M = 0.69,
SD = 1.08) had lower scores than chronically absent teachers (M = 1.06, SD = 1.35), there was
not a significant effect for teacher absence classification on PVAAS Teacher Value Added
Science scores, F(1,7) = 0.21, p = .662, h2 = 0.03. The combined data for the three school years
studied revealed chronically absent teachers did not have a significant effect on PVAAS Teacher
Value Added Science scores, F(1,25) = 0.79, p = .778, h2 = <.001. The descriptive statistics
revealed the scores on the PVAAS Teacher Value Added Science index for teachers who missed
fewer than 10 days of work (M = 1.21, SD = 1.46) were lower than chronically absent teachers
(M = 1.37, SD = 1.56). The results are presented in Table 36 and Table 37.
Table 36
Mean Difference by Absence Classification on PVAAS ELA Scores
Year
2016-17
2017-18

Absence Classification
Not chronic
Chronic
Total
Not chronic
Chronic
Total

M
2.19
2.20
2.19
0.73
0.76
0.74

N
6
3
9
7
2
9

SD
1.39
1.38
1.30
1.48
2.62
1.58

Range
3.91
2.47
3.91
3.75
3.70
3.81

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
Year
2018-19
2016-19

Absence Classification
Not chronic
Chronic
Total
Not chronic
Chronic
Total

M
0.69
1.06
0.86
1.21
1.37
1.26

99
N
5
4
9
18
9
27

SD
1.08
1.35
1.14
1.46
1.56
1.46

Range

h2

2.58
2.94
3.03
5.24
4.16
5.24

Table 37
One-Way ANOVA of PVAAS Science Scores on Absence Classification
Year
2016-17

Source
Between groups
Within groups
Total

df
1
7
8

SS
0.00
13.56
13.56

MS
0.00
1.94

F
0.00

p
.995

<0.01

2017-18

Between groups
Within groups
Total

1
7
8

0.00
20.04
20.04

0.00
2.86

0.00

.985

<0.01

2018-19

Between groups
Within groups
Total

1
7
8

0.30
10.16
10.46

0.30
1.45

0.21

.662

0.03

2016-19

Between groups
Within groups
Total

1
25
26

0.16
55.63
55.79

0.16
2.23

0.79

.788

<.0.01

To analyze the effects of teacher absence classification on PVAAS Teacher Value Added
Algebra I scores, an examination of the means and one-way ANOVAs were conducted. During
the 2016-17 school, the descriptive statistics indicated teachers who were not classified as
chronically absent (M = 2.06, SD = 2.00) scored lower on the PVAAS Teacher Value Added
Algebra I index than teachers who were classified as chronically absent (M = 2.99, SD = 0.00),
but an analysis of variance showed the effect of teacher absence classification on PVAAS
Teacher Value Added Algebra I scores was not statistically significant, F(1,9) = 0.20, p = .667,

h2 = 0.02.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

100

The examination of the means showed teachers who missed fewer than 10 days of school
(M = 2.15, SD = 1.45) during the 2017-18 school year had lower PVAAS Teacher Value Added
Algebra I scores than teachers who were absent from the classroom for 10 days or more (M =
2.41, SD = 3.61). An analysis of variance found chronically absent teachers do not have a
significant effect on PVAAS Teacher Value Added Algebra I scores, F(1,7) = 0.03, p = .913, h2
= <0.01. The results of the one-way ANOVA indicated that the homogeneity of variance
violated the assumption that all comparison groups have the same variance; therefore, the
Welch’s ANOVA was used to determine the significance level.
The descriptive statistics for the 2018-19 school year showed teachers who were not
chronically absent (M = 3.58, SD = 2.38) recorded lower scores on the PVAAS Teacher Value
Added Algebra I index than chronically absent teachers (M = 5.31, SD = 0.66). However, there
was not a significant effect for teacher absence classification on PVAAS Teacher Value Added
Algebra I scores, F(1,6) = 1.97, p = .210, h2 = 0.25.
The absentee data for the three school years studied suggested teachers who miss fewer
than 10 days of school (M = 2.13, SD = 1.71) were more likely to score lower on the PVAAS
Teacher Value Added Algebra I scores than teachers who missed 10 or more days of school (M =
2.36, SD = 2.28). An analysis of variance determined the effect of teacher absence classification
on PVAAS Teacher Value Added Algebra I scores was not significant, F(1,24) = 0.09, p = .771,

h2 = <0.01. The results are presented in Table 38 and Table 39.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

101

Table 38
Mean Difference by Absence Classification on PVAAS Algebra I Scores
Year

Absence Classification

2016-17

Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total

2017-18
2018-19
2016-19

M
2.06
2.99
2.14
2.15
2.41
2.23
3.58
5.31
4.44
2.13
2.36
2.21

N
10
1
11
6
3
9
4
4
8
17
9
26

SD
2.00
-1.91
1.45
3.61
2.14
2.38
0.66
1.86
1.71
2.28
1.88

Range
6.42
-6.42
4.39
6.55
6.55
5.52
1.38
5.52
6.35
6.55
6.55

h2

Table 39
One-Way ANOVA of PVAAS Algebra I Scores on Absence Classification
Year
2016-17

Source
Between groups
Within groups
Total

df
1
9
10

SS
0.79
35.87
36.66

MS
0.79
3.99

F
0.20

p
.667

0.02

2017-18

Between groups
Within groups
Total

1
7
8

0.14
36.58
36.72

0.14
5.23

0.03

.913*

<0.01

2018-19

Between groups
Within groups
Total

1
6
7

5.99
18.27
24.26

5.99
3.05

1.97

.210

0.25

Between groups
Within groups
Total
Note: *Welch’s ANOVA

1
24
25

0.32
88.33
88.65

0.32
3.69

0.09

.771

<.0.01

2016-19

Descriptive statistics and one-way ANOVAs were used to analyze the differences
between PVAAS Teacher Value Added Literature scores for teachers who were chronically

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

102

absent and teachers who were not classified as chronically absent. During the 2016-17 school
year, an examination of the means showed PVAAS Teacher Value Added Literature scores were
lower for teachers who were not chronically absent (M = 2.29, SD = 1.44) compared to teachers
who were chronically absent (M = 3.92, SD = 3.35). The analysis of variance indicated
chronically absent teachers did not have a statistically significant effect on PVAAS Teacher
Value Added Literature scores, F(1,5) = 0.41, p = .551 h2 = 0.08.
An examination of the means showed PVAAS Teacher Value Added Literature scores for
teachers who missed fewer than 10 days of school (M = 2.69, SD = 0.00) during the 2017-18
school year were higher than chronically absent teachers (M = 2.19, SD = 1.73). A one-way
ANOVA found teacher absence classification does not have a significant effect on PVAAS
Teacher Value Added Literature scores F(1,5) = 0.07, p = .799 h2 = 0.01.
The descriptive statistics for the 2018-19 school year revealed that teachers who are not
chronically absent (M = 2.94, SD = 2.25) had higher PVAAS Teacher Value Added Literature
scores than teachers who missed 10 or more days of school (M = 2.07, SD = 1.45). An analysis
of variance indicated chronically absent teachers do not have a significant effect on the PVAAS
Teacher Value Added Literature scores, F(1,6) = 0.44, p = .531, h2 = 0.07.
An examination of the means over the three-year period of the study indicated teachers
who were not classified as chronically absent (M = 2.63, SD = 1.37) had slightly lower PVAAS
Teacher Value Added Literature scores than their colleagues who were considered chronically
absent (M = 2.65, SD = 2.26). A one-way ANOVA showed the effect of teacher absence
classification did not significantly influence PVAAS Teacher Value Added Literature scores
F(1,20) = 0.00, p = .980, h2 = <0.01. The results are presented in Table 40 and Table 41.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

103

Table 40
Mean Difference by Absence Classification on PVAAS Literature Scores
Year
2016-17
2017-18
2018-19
2016-19

Absence Classification
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total

M
2.29
3.92
3.45
2.69
2.19
2.26
2.94
2.07
2.28
2.63
2.65
2.65

N
2
5
7
1
6
7
2
6
8
5
17
22

SD
1.44
3.35
2.91
-1.73
1.59
2.25
1.45
1.55
1.37
2.26
2.06

Range
2.03
8.08
8.08
-4.34
4.34
3.18
3.90
4.24
3.26
8.08
8.08

h2

Table 41
One-Way ANOVA of PVAAS Literature Scores on Absence Classification
Year
2016-17

Source
Between groups
Within groups
Total

df
1
5
6

SS
3.83
47.91
50.74

MS
3.83
9.38

F
0.41

p
.551

0.08

2017-18

Between groups
Within groups
Total

1
5
6

0.22
15.00
15.22

0.22
3.00

0.07

.799

0.01

2018-19

Between groups
Within groups
Total

1
6
7

1.15
15.61
16.76

1.15
2.60

0.44

.531

0.07

2016-19

Between groups
Within groups
Total

1
20
21

0.00
89.37
89.37

0.00
4.47

0.00

.980

<.0.01

A combination of descriptive statistics and one-way ANOVAs were conducted to analyze
the effect of teacher absence classification on PVAAS Teacher Value Added Biology scores. A
review of the descriptive statistics indicated that during the 2016-17 school year, teachers who

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

104

missed fewer than 10 days of school (M = 6.91, SD = 3.72) had higher PVAAS Teacher Value
Added Biology scores than chronically absent teachers (M = 0.05, SD = 1.87). However, an
analysis of variance showed the effect of teacher absence classification on PVAAS Teacher
Value Added Biology scores was not significant, F(1,2) = 5.43 p = .188, h2 = 0.73.
A one-way ANOVA for the 2017-18 school year could not be calculated because all the
high school biology teachers missed 10 or more days of school. The results for the 2018-19
school year suggested no statistically significant differences existed between PVAAS Teacher
Value Added Biology scores for teachers who missed fewer than 10 days of work (M = 2.10, SD
= 0.00) and teachers who missed at least 10 days of school (M = 4.60, SD = 5.15), F(1,3) = 0.19,
p = .693, h2 = 0.06. The absentee data for the three school years combined suggested teachers
who were not chronically absent (M = 5.31, SD = 3.82) had higher PVAAS Teacher Value
Added Biology scores than teachers who were chronically absent (M = 3.86, SD = 4.16).
However, the effect of teacher absence classification on the PVAAS Teacher Value Added
Biology scores was not significant, F(1,11) = 0.29, p = .603 h2 = 0.03. The results are presented
in Table 42 and Table 43.
Table 42
Mean Difference by Absence Classification on PVAAS Biology Scores
Year
2016-17
2017-18
2018-19

Absence Classification
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total

M
6.91
0.05
3.48
-5.03
5.03
2.10
4.60
4.10

N
2
2
4
0
4
4
1
4
5

SD
3.72
1.87
4.64
-3.45
3.45
-5.15
4.60

Range
5.26
2.65
10.82
-7.25
7.25
-9.54
9.54

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
Year
2016-19

Absence Classification
Not chronic
Chronic
Total

105

M
5.31
3.86
4.20

N
3
10
13

SD
3.82
4.16
3.98

Range

h2

7.44
10.82
10.82

Table 43
One-Way ANOVA of PVAAS Biology Scores on Absence Classification
Year
2016-17

Source
Between groups
Within groups
Total

df
1
2
3

SS
47.13
17.35
64.48

MS
47.13
8.63

F
5.43

p
.188

0.73

2017-18

Between groups
Within groups
Total

----

----

---

--

--

--

2018-19

Between groups
Within groups
Total

1
3
4

5.00
79.57
84.57

5.00
26.52

0.19

.693

0.06

2016-19

Between groups
Within groups
Total

1
11
12

4.82
184.86
189.68

4.82
16.81

0.29

.603

0.03

One-way ANOVAs were conducted to compare the effect of teacher absence
classification on high school final exam grades. For the purposes of this study, final exam grades
were calculated for only courses that had at least one teacher who met the chronically absent
teacher classification requirements and one teacher who did not meet the chronically absent
requirements. Descriptive statistics were used to make comparisons between the two teacher
absence classifications.
The examination of the means showed that Algebra I final exam grades for students who
were instructed by teachers who missed 10 or fewer days of school (M = 80.56, SD = 15.29)
during the 2018-19 school year were slightly higher than students who were instructed by
teachers who missed a minimum of 10 days of work (M = 79.28, SD = 13.78). An analysis of

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

106

variance found teacher absence classification does not have a significant effect on Algebra I final
exam grades F(1,126) = 0.22, p = .639, h2 = <0.01. The results are presented in Table 44 and
Table 45
Table 44
Mean Difference by Absence Classification on Algebra I Final Exam
Year

Absence Classification

2018-19

Not chronic
Chronic
Total

M
80.56
79.28
80.10

N
82
46
128

SD
15.29
13.78
14.72

Range

p
.639

h2

59.00
53.00
60.00

Table 45
One-Way ANOVA of Algebra I Final Exam on Absence Classification
Year
2018-19

Source
Between groups
Within groups
Total

df
1
126
127

SS
48.16
27475.52
27523.68

MS
48.16

F
0.22

<0.01

218.06

Descriptive statistics for the 2017-18 school year showed students who were not
instructed by a chronically absent teacher (M = 83.45, SD = 2.91) scored higher on the English 9
final exam than students who were instructed by a chronically absent teacher (M = 72.71, SD =
11.54). A one-way ANOVA indicated the effect of teacher absence classification on the English
9 final exam grades was not significant, F(1,26) = 9.05, p = .002, h2 = <0.01. The Levene’s test
indicated that the homogeneity of variance was violated. Therefore, the Welch’s ANOVA was
used to determine the significance level. The results are presented in Table 46 and Table 47.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

107

Table 46
Mean Difference by Absence Classification on English 9 Final Exam
Year
2017-18

Absence Classification
Not chronic
Chronic
Total

M
83.45
72.71
76.93

N
11
17
28

SD
2.91
11.54
10.52

Range
7.00
48.00
48.00

Table 47
One-Way ANOVA of English 9 Final Exam on Absence Classification
Year

Source

2017-18

Between groups
Within groups
Total
Note: *Welch’s ANOVA

df

SS

MS

F

1
26
27

771.6
2216.26
2987.86

771.6

9.05

h2

p
.002*

<0.01

85.24

An examination of the means for the 2017-18 school year showed students who were
instructed by teachers who were regularly in attendance (M = 84.56, SD = 4.88) scored higher on
the CP English 9 final exam than students who were taught by teachers who missed 10 or more
days of school (M = 74.03, SD = 12.79). An analysis of variance indicated there was a
significant effect for teacher absence classification on the CP English 9 final exam grades,
F(1,122) = 37.07, p = .000, h2 = <0.23. Likewise, the results for the 2018-19 school year
suggested a statistically significant difference existed between the CP English 9 final exam
grades for students instructed by teachers who missed fewer than 10 days of work (M = 84.02,
SD = 5.91) and teachers who missed at least 10 days of school (M = 74.00, SD = 10.72),
F(1,106) = 32.76, p = .000, h2 = 0.24. Moreover, an examination of the means for the two years
included in the study indicated students who were instructed by teachers who were not classified
as chronically absent (M = 84.33, SD = 5.32) had slightly higher CP English 9 final exam grades
than their peers who were educated by teachers who were considered chronically absent (M =

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

108

74.02, SD = 11.75). An analysis of variance showed the effect of teacher absence classification
had significant influence on the CP English 9 final exam grades F(1,230) = 71.12, p = .000, h2 =
0.24. The results of all three one-way ANOVAs indicated the homogeneity of variance violated
the assumption that all comparison groups have the same variance; therefore, the Welch’s
ANOVA was used to determine the significance level for CP English 9 final exam grades. The
results are presented in Table 48 and Table 49.
Table 48
Mean Difference by Absence Classification on CP English 9 Final Exam
Year

Absence Classification

2017-18

Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total

2018-19
2017-19

M
84.56
74.03
79.38
84.02
74.00
78.27
84.33
74.02
78.86

N
63
61
124
46
62
108
109
123
232

SD
4.88
12.79
10.94
5.91
10.72
10.25
5.32
11.75
10.62

Range
21.00
52.00
52.00
21.00
56.00
57.00
22.00
58.00
58.00

Table 49
One-Way ANOVA of CP English 9 Final Exam on Absence Classification
Year
2017-18

Source

Between groups
Within groups
Total
2018-19 Between groups
Within groups
Total
2017-19 Between groups
Within groups
Total
Note: *Welch’s ANOVA

df
1
122
123
1
106
107
1
230
231

SS

MS

3431.70
11293.49
14725.10

3431.70

2652.24
8580.98
11233.22

2652.24

6147.51
19882.08
26029.59

6147.51

F

p

h2

37.07

.000*

0.23

32.76

.000*

0.24

71.12

.000*

0.24

92.57

80.95

86.44

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

109

The descriptive statistics for the 2017-18 school year showed students instructed by
teachers who were not chronically absent (M = 72.81, SD = 14.46) scored lower on the CP
Honors English 9 final exam than students instructed by chronically absent teachers (M = 74.13,
SD = 12.99). The results of the one-way ANOVA showed there was not a significant effect for
teacher absence classification on the CP Honors English 9 final exam, F(1,99) = 0.19, p = .664,

h2 = <0.01. The results are presented in Table 50 and Table 51.
Table 50
Mean Difference by Absence Classification on Honors English 9 Final Exam
Year

Absence Classification

2017-18

Not chronic
Chronic
Total

M
72.81
74.13
73.79

N
26
75
101

SD
14.46
12.99
13.32

Range
56.00
62.00
62.00

Table 51
One-Way ANOVA of Honors English 9 Final Exam on Absence Classification
Year

Source

2017-18

Between groups
Within groups
Total

df
1
99
100

SS

MS

F

p

h2

33.93
17706.71
17740.64

33.93

0.19

.664

<0.01

178.86

The results for the 2017-18 school year suggested no statistically significant difference
existed between the CP English 10 final exam grades for students instructed by teachers who
missed fewer than 10 days of work (M = 72.81, SD = 14.46) and teachers who missed 10 or
more days of work (M = 74.13, SD = 12.99), F(1,99) = 0.19, p = .664, h2 = <0.01. The 2018-19
school year revealed that students who were not instructed by chronically absent teachers (M =
74.67, SD = 13.06) scored lower on the CP English 10 final exam when compared to students
who were educated by chronically absent teachers (M = 76.17, SD = 14.33). An analysis of

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

110

variance indicated the effect of chronically absent teachers on the English 10 final exam grades
was not significant, F(1,10) = 0.04, p = .854, h2 = <0.01. The combined final exam grades for
the two years studied suggested students instructed by teachers who missed fewer than 10 days
of school (M = 73.16, SD = 14.03) scored slightly lower on the CP English 10 final exam than
students instructed by teachers who missed at least 10 days of school (M = 74.28, SD = 13.00).
However, an analysis of variance showed the effect of teacher absence classification on the CP
English 10 final exam was not significant, F(1,111) = 0.17, p = .685, h2 = <0.01. The results are
presented in Table 52 and Table 53.
Table 52
Mean Difference by Absence Classification on CP English 10 Final Exam
Year
2017-18
2018-19
2017-19

Absence Classification
Not chronic
Chronic
Total
Not chronic
Chronic
Total
Not chronic
Chronic
Total

M
72.81
74.13
73.79
74.67
76.17
75.42
73.16
74.28
73.96

N
26
75
101
6
6
12
32
81
113

SD
14.46
12.99
13.32
13.06
14.33
13.10
14.03
13.00
13.25

Range
56.00
62.00
62.00
32.00
36.00
40.00
59.00
62.00
62.00

F
0.19

p
.664

h2
<0.01

0.04

.854

<0.01

Table 53
One-Way ANOVA of CP English 10 Final Exam on Absence Classification
Year
2017-18

2018-19

Source
Between groups
Within groups
Total

df
1
99
100

Between groups
Within groups
Total

1
10
11

SS
33.93
17706.71
17740.64
6.75
1880.17
1886.92

MS
33.93
178.86
6.75
188.02

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
Year
2017-19

Source
Between groups
Within groups
Total

df
1
111
112

SS
29.17
19626.69
19655.86

MS
29.17

111
F
0.17

p
.685

h2
<0.01

176.82

During the 2018-19 school year, the examination of the means indicated students who
had teachers who were not chronically absent (M = 81.81, SD = 10.81) scored higher on the CP
Biology final exam than students instructed by chronically absent teachers (M = 78.03, SD =
13.13). However, the one-way ANOVA revealed the effect of teacher absence classification on
the CP Biology final exam was not statistically significant, F(1,85) = 1.43, p = .234, h2 = 0.02.
The results are presented in Table 54 and Table 55.
Table 54
Mean Difference by Absence Classification on CP Biology Final Exam
Year
2018-19

Absence Classification
Not chronic
Chronic
Total

M
81.81
78.03
78.94

N
21
66
87

SD
10.81
13.13
12.66

Range
42.00
52.00
52.00

p
.234

h2

Table 55
One-Way ANOVA of CP Biology Final Exam on Absence Classification
Year
2018-19

Source
Between groups
Within groups
Total

df
1
85
86

SS
227.54
13547.17
13774.71

MS
227.54

F
1.43

0.02

159.38

An examination of the means showed students instructed by teachers who missed fewer
than 10 days of school (M = 84.60, SD = 7.68) during the 2017-18 school year scored higher on
the CP Chemistry final exam than their peers who were instructed by teachers who missed a
minimum of 10 days of school (M = 73.80, SD = 13.05). An analysis of variance found teacher

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

112

absence classification did have a significant effect on the CP Chemistry final exam scores, but
the effect size was small, F(1,78) = 20.33, p = .002, h2 = 0.21. The Levene’s test indicated the
homogeneity of variance was violated. Therefore, the Welch’s ANOVA was used to determine
the significance level. The results are presented in Table 56 and Table 57.
Table 56
Mean Difference by Absence Classification on CP Chemistry Final Exam
Year

Absence Classification

2017-18

Not chronic
Chronic
Total

M
84.60
73.80
81.90

N
60
20
80

SD
7.68
13.05
10.35

Range

p
.002*

h2

32.00
51.00
51.00

Table 57
One-Way ANOVA of CP Chemistry Final Exam on Absence Classification
Year
2017-18

Source
Between groups

Within groups
Total
Note: *Welch’s ANOVA

df
1
78
79

SS
1749.60
6713.60
8463.20

MS
1749.60

F
20.33

0.21

86.07

The descriptive statistics for the 2018-19 school year showed students who were
instructed by teachers who were not chronically absent (M = 82.95, SD = 9.22) scored higher on
the Honors Chemistry final exam than students who were enrolled in chronically absent teacher
classrooms (M = 80.55, SD = 9.40). An analysis of variance indicated chronically absent
teachers did not have a significant effect on the Honors Chemistry final exam scores, F(1,175) =
2.90, p = .090, h2 = 0.02. The results are presented in Table 58 and Table 59.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

113

Table 58
Mean Difference by Absence Classification on Honors Chemistry Final Exam
Year
2018-19

Absence Classification
Not chronic
Chronic
Total

M
82.95
80.55
81.60

N
78
99
177

SD
9.22
9.40
9.37

Range
40.00
44.00
45.00

p
.090

h2

Table 59
One-Way ANOVA of Honors Chemistry Final Exam on Absence Classification
Year
2018-19

Source
Between groups
Within groups
Total

df
1
175
176

SS
251.98
15206.34
15458.32

MS
251.98

F
2.90

0.02

86.89

Correlations Between Student Achievement Scores and Teacher Absences
Pearson Correlation tests were conducted to measure the strength of the linear association
between the number of teacher absences per year and student achievement scores. A value of r =
1 indicated a perfect positive correlation, while a value of r = -1 signified a perfect negative
correlation, and a value of r = 0 meant that no relationship existed between the two variables. A
review of the data suggested that very weak relationships existed between the majority of
independent and dependent variables. However, there were a number of associations that were
shown to be either moderately or highly correlated. The correlation tests revealed there was a
significant relationship between PVAAS Teacher Value Added Biology scores and the number
of teacher absences during the 2017-18 school year r(4) = -0.95, p = .045. The results indicated
that the relationship between the two variables had a very strong negative correlation.
Additionally, the data indicated a moderately negative correlation existed between PVAAS
Teacher Value Added Biology scores and the number of teacher absences during the 2016-17
school year, but that relationship was not significant r(4) = -0.62, p = .378.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

114

The relationship between the final exam grades for English 9 and the number of teacher
absences during the 2017-18 school year was shown to be significant with a moderate negative
correlation r(28) = -0.51, p = .006. The results showed a significantly weak relationship between:
(a) the CP English 9 final exam grades and teacher absences for the 2017-18 school year, r(124)
= -0.48, p = .000; (b) the 2018-19 school year, r(108) = -0.49, p = .000); and (c) the aggregate
grades for CP English final exams, r(232) = -0.47, p = .00. The correlation between the CP
Chemistry final exam grades and teacher absences for the 2017-18 school year indicated a
significantly weak correlation r(80) = -0.46, p = .000.
The data suggested there was a significant relationship between Grade 2 DIBELS Next
Oral Reading Fluency scores and the number of teacher absences during the 2016-17 school year
r(175) = -0.22, p =.004, and between the number of teacher absences and the combined Grade 4
DIBELS Next Oral Reading Fluency scores r(640) = 0.08, p =.000. The correlation between the
Grade 2 DIBELS Next Oral Reading Fluency scores was categorized as a very weak negative
relationship, while the Grade 4 DIBELS Next Oral Reading Fluency scores indicated a very
weak positive relationship. The results are presented in Table 60.
Table 60
Correlations – All Demographic Variables Related to Teacher Absences
Variable

2016-17

2017-18

2018-19
r

p

2016-19

r

p

r

p

r

p

DIBELS
Grade 2

-0.22

.004*

0.03

.718

-0.05

.071

-0.04

.346

Grade 3

-0.14

.054

0.03

.732

0.07

.338

0.03

.519

Grade 4

0.08

.214

0.07

.327

0.08

.239

0.08

.045**

Grade 5

0.07

.334

0.07

.318

0.11

.134

0.05

.273

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

Variable

2016-17

2017-18

2018-19

p

r

2016-19

r

p

PVAAS
Math

-0.35

.101

-0.16

.453

-0.11

.606

-0.19

.113

ELA

-0.17

.367

0.03

.877

-0.15

.466

-0.10

.358

Science

-0.02

.959

-0.11

.788

0.02

.961

0.01

.946

Algebra I

0.12

.728

0.10

.796

0.17

.696

-0.01

.974

Literature

0.34

.450

-0.21

.650

-0.30

.468

0.13

.581

-0.62

.378

-0.95

.045*

0.26

.669

-0.29

.332

Algebra I

--

--

--

--

-0.04

.639

--

--

English 9

--

--

--

--

-0.51

.006*

--

--

CP English 9

--

--

-0.48

.000*

-0.49

.000*

-0.47

.000*

Honors English 9

--

--

0.01

.232

--

--

--

--

CP English 10

--

--

0.04

.664

0.06

.854

0.03

.738

CP Biology

--

--

--

--

-0.12

.288

--

--

CP Chemistry

--

--

-0.46

.000*

--

--

--

--

Honors Chemistry

--

--

--

--

-0.13

.090

--

--

Biology

r

115

p

r

p

Final Exam Grades

Note: * Correlation is significant at the 0.01 level (2-tailed).
Teacher Absence Data
A chi-square goodness of fit test was used to determine if teacher absenteeism rates by
leave category differed from randomness. During the 2016-17 school year, absenteeism rates
among sick (N = 1889), professional (N = 1290.5), personal (N = 618.5), emergency (N = 32),
unpaid (N = 318), funeral (N = 87.5), jury duty (N = 13), and military leave (N = 15) were
determined to be statistically significant, c2 (7, N = 4032.5) = 6012.37, p = .000. Likewise, the
absentee rates by leave category for the 2017-18 school year were also found statistically
different among sick (N = 1764), professional (N = 1204), personal (N = 654), emergency (N =
39.5), unpaid (N = 250.5), funeral (N = 91), jury duty (N = 18), and military leave (N = 11), c2

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

116

(7, N = 4376) = 5148.75, p = .000. Moreover, the absentee rates by leave category for the 201819 school year were statistically different among sick (N = 2031), professional (N = 1153),
personal (N = 656.5), emergency (N = 26.5), unpaid (N = 407), funeral (N = 99.5), jury duty (N =
29), and military leave (N = 0), c2 (7, N = 3527.5) = 4713.41, p = .000. Over the three-year
period of the study, the absentee rates by leave category indicated that there was a statistically
significant difference among sick (N = 5658), professional (N = 3647.5), personal (1929),
emergency (N = 98), unpaid N = (975.5), funeral (N = 27), jury duty (N = 60), and military leave
(26), c2 (7, N = 12643) = 19010.47, p = .000. The results of the aggregated data are presented in
Figure 1 below.

Figure 1. 2016-19 Total Number of Absences Per Year by Leave Category
A chi-square goodness of fit test was used to determine if teacher absenteeism rates by
day of the week differed from randomness. During the 2016-17 school year, absenteeism rates
among Monday (N = 698.5), Tuesday (N = 817), Wednesday (N = 816.5), Thursday (N = 878.5),

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

117

and Friday (N = 1024) were found to be significantly different, c2 (4, N = 4234.5) = 66.18, p =
.000. The number of absences by day of the week for the 2017-18 school year were also shown
to be statistically different: Monday (N = 682.5), Tuesday (N = 751.5), Wednesday (N = 749),
Thursday (N = 870.5), and Friday (N = 979), c2 (4, N = 4032.5) = 68.72, p = .000. Likewise, the
absentee rates by day of the week for the 2018-19 school year were statistically different:
Monday (N = 740.5), Tuesday (N = 849), Wednesday (N = 783.5), Thursday (N = 952), and
Friday (N = 1051), c2 (4, N = 4376) = 72.90, p = .000. Throughout the three-year period of the
study, the absentee rates by day of the week indicated there were statistically significant
differences among Monday (N = 2121.5), Tuesday (N = 2417.5), Wednesday (N = 2349),
Thursday (N = 2701), and Friday (N = 3054), c2 (4, N = 12643) = 203.88, p = .000. The results
of the aggregated data are presented in Figure 2 below.

Figure 2. 2016-19 Total Number of Absences Per Year by Day of the Week

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

118

Secondary Research Questions
A review of the district’s absentee records revealed that 180 teachers were deemed
chronically absent during the 2016-17 school year. The data for the 2017-18 school year
indicated that 167 teachers were classified as chronically absent. The district’s absentee records
also showed that 178 teachers missed more than 10 days during the 2018-19 school year. The
percentage of chronically absent teachers during each year of the study was 63% for the 2016-17
school year, 59% during the 2017-18 school year, and 61% for the 2018-19 school year.
A review of the district’s financial records indicated the substitute costs for the 2016-17
school year totaled $741,643.03. The cost to secure substitutes for the 2017-18 and 2018-19
school years decreased to $684,952.96 and $676,820.27, respectively. The combined cost for
substitutes during the three-year span of the study totaled $2,103,416.26. The district’s
agreement with Educational Staffing Solutions (ESS), which serves as the district’s substitute
staffing agency, indicated that the rate for day-to-day substitutes was $100.00 per day, while
building level and long-term substitutes were paid at a rate of $150.00 per day. The agreement
noted that the mark-up rate for the 2016-17 and 2017-18 school years was 30.9% and 31.4% for
the 2018-19 school year. As a result, the actual cost incurred by the district for a full-day
substitute during the 2016-17 and 2017-18 school years was $130.90 for day-to-day substitutes
and $196.35 for both building level and long-term substitutes. The day-to-day substitute costs for
the 2018-19 school year increased to $131.40, and the cost for long-term and building level
substitutes increased to $197.10 per day.
The collective bargaining agreement for the Hershey Education Association (HEA)
indicates the following leave provisions are provided to professional employees: sick, personal,
professional, doctoral study, emergency, funeral, jury duty, military, child rearing, and unpaid

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

119

leave. According to the agreement, sick leave may be taken without loss of pay for personal
illness or to care for a spouse, dependent, or parent who is sick. Sick leave may also be taken
without loss of pay to attend a personal medical appointment or to attend a medical appointment
for a spouse, dependent, or parent. Professional employees are granted 10 sick days per year, and
any unused sick leave can be accumulated from year-to-year.
Professional employees are granted three days of professional leave per year and can
accumulate up to five days per year. Employees may request professional leaves of absence for
personal reasons without loss of pay, provided the request is submitted at least 48 hours in
advance to the building principal. Personal days are not permitted during in-service days or the
first or last five student days of the school year. Personal leave days not used during the year can
be: (a) carried over to the following year to a maximum of five days, (b) added to the employee’s
accumulated sick leave total, or (c) reimbursed at the current substitute rate per day for each
unused day. The substitute rate during the course of the study was valued at $100.00 per day.
The agreement notes that professional employees must contact their principal at least 24 hours in
advance if they wish to cancel their personal leave request, or otherwise, the employee will be
required to pay the difference between his or her per diem rate and any costs associated with
securing the substitute. There was no language in the collective bargaining agreement or board
policy that limited the number or percentage of teachers who could be absent per day due to
personal leave reasons.
Doctoral study leave is available to employees who are doctoral candidate students.
Employees can request up to five days of leave per school year without loss of personal leave
days or pay provided the leave is approved by the Superintendent. Teachers are entitled to jury
duty leave if required to appear under subpoena or jury summons in a county common pleas or

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT

120

federal district court trial. Teachers required to appear under a subpoena or jury summons are
excused from work without loss of net pay. Teachers who are called into active duty are entitled
to use a maximum of 15 days of leave without loss of pay.
Funeral leave may be taken by professional employees without loss of pay as follows: (a)
up to five days for the spouse, parent, mother-in-law, father-in-law, son, or daughter of the
employee; (b) up to three days for the grandparents, grandchildren, or siblings of the employee;
(c) one day for the day of the funeral of the aunt, uncle, niece, nephew, son-in-law, daughter-inlaw, brother-in-law, sister-in-law, or first cousin of the employee. However, if the relative
resided in the employee’s household on the date of death, up to three days will be provided; or
(d) for circumstances that do not meet the guidelines specified, an employee may seek approval
from the Superintendent to grant additional funeral leave.
Employees have the option of using emergency leave provided the leave request is
granted by the Superintendent. Emergency leave is approved for only extenuating circumstances
that occur within 48 hours from the date of absence. Approved emergency leave is deducted
from an employee’s sick leave bank. Examples of emergency leave include but are not limited to
absences related to car problems, emergency home repairs, flood, fire, and family related issues.
Teachers may use professional leave without loss of pay to attend professional meetings,
workshops, or conferences. Similar to personal leave, there are no limits as to the number of
teachers who can be approved for professional leave on any given day throughout the school
year. Employees have the ability to request unpaid leave. However, unpaid leave must be
approved by the Superintendent, and the leave is granted without pay. The agreement indicates
that all leave options can be used in either half-day or full-day increments.

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Professional employees who retire with unused sick days are provided a monetary sum
based on a formula that combines years of service and the number of unused sick days. The
collective bargaining agreement indicates that a maximum of 175 days can be cashed-in at the
time of retirement. Teachers who retire with at least 30 years of service with the district have the
ability to earn up to $17,500.00 by cashing-in their unused sick days.
The agreement also notes that professional employees who have earned three years of
credited service with the district are eligible for child rearing leave for a total period of up to one
calendar year. However, the agreement notes that child rearing leave is inclusive of any FMLA
leave taken for the birth, adoption, or foster care of a child. Since the District employs more than
50 workers, professional employees are eligible for the leave entitlements associated with the
Family and Medical Leave Act (FMLA). The FMLA entitlements provide 12 weeks of job
protected leave for the following reasons: (a) an employee’s serious health condition; (b) the
birth of a child or placement of child for adoption or foster care; (c) to care for a spouse, child or
parent who has a serious health condition; (d) because of qualifying exigency arising from the
fact that the employee’s spouse, child, or parent is on covered active duty or call to covered
active duty status with the Armed Forces; or (e) because the employee is the spouse, child,
parent, or next of kin of a covered servicemember with a serious health condition or injury.
Derry Township School District board policy also provides employees the ability to
request uncompensated leave for a maximum period of two years. This option is available to
only employees who are unable to work because of personal illness or disability and who have
exhausted all other leave options. Additionally, professional employees have the ability to apply
for compensated professional or restoration of health leave. Both leave types provide employees
with at least one-half of the employee’s regular salary. The maximum amount of time an

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employee can request off due to compensated professional leave or restoration of health leave is
one year.
Summary
The data analysis methods used to determine if age, gender, race, experience, grade(s)
taught, level of education, and distance from work are statistically significant predictors of work
involved the computation of descriptive statistics and an examination of one-way ANOVAs. A
combination of descriptive statistics and one-way ANOVAs were also used to determine if there
was a significant difference between achievement scores for students instructed by chronically
absent teachers and students who were not taught by chronically absent teachers. Correlation
tests were performed to establish if there were any significant relationships between any of the
independent and dependent variables. A chi-square goodness of fitness test was used to
determine if there were significant differences between the numbers of absences by day of the
week and by leave category. The data for each school year was analyzed individually and also
compared against each other to determine if any trends or patterns existed between the school
years. The aggregate data sets for the three school years were analyzed and compared against the
individual data sets for each school year. In addition, the data analysis involved a review of the
district’s collective bargaining agreement, board policy, and financial records to determine the
organizational factors that contribute to teacher absenteeism and the cost of teacher absences.
The results of the one-way ANOVAs indicated that there was a significant difference
between the number of absences and teacher age. The analysis of variance showed that teachers
in the 21-25 age range missed significantly fewer days of work when compared to the other age
groups, with the exception of teachers in the 26-30 age range. Gender was also found to be a
significant predictor of teacher absence, with females being absent from the classroom at a

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higher rate than males. A post hoc test for years of experience determined that teachers with 0-3
years of experience miss significantly fewer days of work than teachers with more years of
experience, with the exception of teachers in the 10-14 years of experience category. The oneway ANOVA showed that during the 2016-17 school year, there was a significant difference
among teachers with a bachelor’s and teachers with either a master’s + 30 or master’s + 45. The
results indicated that teachers with a bachelor’s missed fewer days of school than the other two
groups. One-way ANOVAs indicated that there were no additional statistically significant
differences between the various demographic variables and the number of teacher absences.
The correlation tests showed that there were four significant relationships between the
number of teacher absences and the various demographic variables. The significant correlations
included a weak correlation between gender and the number of teacher absences during the
2016-17 school years and for the three combined years of the study. The results revealed that
during the 2016-17 school year, a very weak correlation existed between the number of teacher
absences and degree earned. The final significant correlation indicated that teachers in the
primary school missed more days of work than teachers in the other buildings.
The one-way ANOVAs used to determine if significant differences existed between
teacher absence classification and student achievement scores found there were eight student
achievement variables that were deemed to be statistically significant. The analysis of variance
showed that students instructed by teachers who missed 10 or fewer days of school had
significantly higher scores on the following student achievement assessments: (a) the 2016-17
Grade 2 DIBELS Oral Reading Fluency assessment, (b) the Grade 3 DIBELS Oral Reading
Fluency assessment, (c) the 2016-19 Grade 5 DIBELS Oral Reading Fluency assessment, (d) the
2017-18 English 9 final exam, (e) the 2017-2018 CP English 9 final exam, (f) the 2016-17 CP

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English 9 final exam, (g) the 2018-19 CP English 9 final exam, and (h) the 2017-18 CP
Chemistry final exam.
The one-way ANOVAs for each assessment found to be significantly different indicated
that students perform better if instructed by a teacher who is not chronically absent. However,
the one-way ANOVA for the Grade 5 DIBELS assessment revealed students instructed by
chronically absent teachers scored higher than their peers who were not instructed by chronically
absent teachers.
Pearson correlation tests indicated that there were two significant relationships between
the number of teacher absences and the DIBELS Next scores. The correlation tests also revealed
five significant relationships between the number of teacher absences and final exam grades. The
Grade 2 DIBELS Next scores for the 2016-17 school year indicated that there was a negative
relationship between the two variables, which means student achievement scores decreased as
the number of teacher absences increased. A negative relationship existed between the 2017-18
and 2018-19 CP English 9 final exam grades and the number of teacher absences. Additionally,
the combined grades for the CP English 9 final exam showed a negative relationship as did the
relationship between the 2017-18 CP Chemistry final exam and the number of teacher absences.
Conversely, the results for the Grade 4 DIBELS Next Oral Reading Fluency assessment
indicated a positive relationship, meaning that student achievement scores increased as the
number of teacher absences increased.
The chi-square goodness of fit test indicated that a significant difference existed between
the number of teacher absences and both day of the week and leave category. Frequency
distributions showed that teachers were absent on Fridays at a greater rate than any other day of

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the week. The results showed that teachers used sick leave to a greater extent than any of the
other leave types.
In summary, the results indicated that only a few statistically significant differences
existed between the number of teacher absences and age, gender, race, experience, grade(s)
taught, level of education, and distance from work. Likewise, there were a minimal number of
significant correlations between the demographic variables and the number of teacher absences.
The data analysis also revealed that the majority of student achievement results were not
significantly different based on teacher leave classification and that few correlations existed
between the student achievement variables and teacher leave classification.

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CHAPTER V
Conclusions and Recommendations
The following section discusses and analyzes the results of the study within the
conceptual framework and the current literature. The section begins by restating the purpose of
the project and the research questions that guided the study. Correlations between the primary
research questions and previous studies are then discussed, followed by recommendations for
future research. Additionally, the section provides recommendations that are specific to the
Derry Township School District (DTSD), which are intended to reduce substitute teacher costs
and teacher absenteeism rates. Limitations and special considerations that resulted from the 2020
Novel Coronavirus pandemic are addressed, and the section concludes by summarizing the
overall findings of the research project.
Purpose of the Research
The purpose of this action research project includes three primary objectives: (a) improve
teacher attendance rates at DTSD by reviewing previous studies, (b) analyze the impact of
teacher absenteeism on student achievement at DTSD, and (c) decrease the costs associated with
teacher absenteeism at DTSD. The ancillary goals of the study include improving staff and
student wellness, improving staff and student morale, increasing staff and student engagement,
and decreasing employee health care costs. Furthermore, the desired outcome of this action
research project is to provide substantial recommendations to Derry Township School District
and other public school systems to meaningfully address the problems associated with teacher
absenteeism.
The primary research questions that guided the study included: (a) are age, distance from
work, experience, gender, grade(s) taught, level of education, and race predictors of teacher

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absence; (b) what is the relationship between the frequency of teacher absences and factors such
as age, distance from work, experience, gender, grade(s) taught, level of education, and race; (c)
are there significant differences in student achievement scores between teachers who are
chronically absent (defined as 10 or more absences per school year) and teachers who are not
chronically absent; (d) is there a relationship between student achievement scores and the
frequency of teacher absences; and (e) are there significant differences in teacher absenteeism
rates by leave category or days of the week?
The study also analyzed three secondary questions in order to gain a better appreciation
for the impacts and costs associated with teacher absenteeism at DTSD. The three secondary
questions that were investigated included the following: (a) how many teachers at DTSD are
chronically absent per year, (b) what are the economic impacts associated with teacher
absenteeism from 2016-19, and (c) what organizational factors contribute to teacher absentee
rates (board policies and collective bargaining agreement, professional development) and to what
extent?
Correlations to Previous Studies
By analyzing the predictors of teacher absenteeism, the study aimed to determine if
previous research that suggested age, distance from work, experience, gender, grade(s) taught,
level of education, and race had a statistically significant influence on the number of days a
teacher at Derry Township School District is likely to miss per year. Prior studies have also
implied that leave type and day of the week are significant predictors of teacher absenteeism.
Therefore, a goal of this project was to compare the absentee data at DTSD to the results of prior
research studies to determine if patterns and trends exist between the two sets of data. More
importantly, the study examined the relationship between chronic teacher absenteeism and

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student achievement scores to determine if the amount of time a teacher is absent from the
classroom impacts student achievement scores at DTSD, and if so, how does the data connect to
other research studies?
Porter and Steers (1973) argued that age is positively related to absenteeism, meaning
that as employees get older, the number of workdays missed increases. Conversely, Martocchio
(1989) found an inverse relationship between age and the absenteeism rates of employees. The
aggregate data for this study determined that age is a significant indicator of teacher absenteeism.
The results indicated that during the three years studied, teachers who were between the ages 2125 missed significantly fewer days of school than the other age groups examined, with the
exception of teachers in the 26-30 age group. However, the results of the individual school years
suggested there was no significant relationship between the two variables. Contrary to the
previous studies, the results of this project revealed that the relationship between age and
absenteeism was neither positively nor negatively correlated but rather more representative of a
bell-shape curve, as illustrated in Figure 3. The illustration suggests that the number of absences
per year drastically increases during the first 10 years of a teacher’s career, peaking and then
flattening out during the middle portion of a teacher’s career, while finally decreasing at a slower
rate near the end of a teacher’s career. The findings do not allow for the acceptance or rejection
of the null hypothesis for Research Question 1 but do allow for the acceptance of the null
hypothesis for Research Question 2.
Question 1 – H01: There are no statistically significant differences in teacher absenteeism
rates by age.
Question 2 – H01: No correlation exists between the number of teacher absences and age.

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Figure 3. 2016-19 Mean Number of Absences Per Year by Teacher Age Group
Previous studies have produced mixed results in terms of the influence gender has on
teacher absenteeism rates. The majority of research suggests that female teachers miss more days
of work when compared to their male colleagues (Miller, 2008; Pitts, 2014; Scott & McClellan,
1990). However, there are a few studies that indicate there is not a statistically significant
difference between the absenteeism rates of men and women (Bermejo-Toro & Prieto-Ursúa,
2014; Capote Fermin, 2018). The aggregate data for this project supports the majority of research
that suggests gender is a significant predicator of absenteeism. Although the data sets for the
2017-18 and 2018-19 school years indicate that there was not a significant difference between
the two variables, the data for the 2016-17 school year found that gender had a significant effect
on the number of teacher absences at Derry Township School District, with women being absent
more often than men. The differences between teacher absentee rates and gender were also found
to be significant when analyzing the absentee data over the three-year period of the study. The

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mean difference in the number of absences per year between the two genders ranged from a high
of 4.39 days during the 2016-17 school year to a low of 0.94 days during the 2018-19 school
year. The combined data set indicated that female teachers miss 1.90 more days of school per
year than male teachers. Although the correlation between the number of days missed between
men and women was determined to be very weak, the relationship was still deemed to be
significant for both the aggregate data set and the 2016-17 school year. There was no significant
correlation between the two variables for the 2017-18 and 2018-19 school years. The findings do
not allow for the acceptance or the rejection of the null hypotheses for Research Question 1 or
Research Question 2.
Question 1 – H03: There are no statistically significant differences in teacher absenteeism
rates by gender.
Question 2 – H03: No correlation exists between the number of teacher absences and
gender.
Losina, Yang, Deshpande, Katz, and Collins (2017) found that Caucasian workers missed
work at a statistically significant higher rate than non-white employees regardless of the absence
reason. The aggregate data of this research project indicated that African American teachers
missed more days of work than Asian or Caucasian teachers. However, the one-way ANOVA
showed that there was not a significant difference among the number of days missed by African
American, Asian, and Caucasian teachers. Likewise, the correlation tests indicated no significant
linear relationships existed among the races and absentee rates. Therefore, the results of this
study neither support nor contradict the research conducted by Losina et al. The findings allow
for the acceptance of the null hypotheses for Research Questions 1 and 2.

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Question 1 – H03: There are no statistically significant differences in teacher absenteeism
rates by race.
Question 2 – H03: No correlation exists between the number of teacher absences and
race.
Although the one-way ANOVAs for the individual school years suggested there was no
statistical differences between the independent and dependent variables for years of experience,
the results of the aggregate indicated that experience was a significant predictor of teacher
absenteeism in the Derry Township School District. The results of the post hoc test for the three
years studied showed that teachers with 0-3 years of experience missed significantly fewer days
of work, with the exception of teachers who were approaching retirement. An analysis of the
means found that during 2016-17 and 2017-18 school years, teachers with 30 or more years of
experience were less likely to miss work than teachers with fewer years of experience. The data
sets, when combined, also indicated that teachers with 30 or more years of experience missed
fewer days of work. The aggregate data for this project supports the research conducted by
Clotfelder et al. (2009) that showed teacher leave increases annually until a teacher acquires five
years of experience. The study conducted by Clotfelder et al., determined that after a teacher
accumulates five years of experience, the number of leave days used per year flattens out until
the final portion of a teacher’s career, at which point the number of absences per year decreases.
The leave pattern described by Clotfelder et al. mirrored the leave trend found in this study, as
illustrated in Figure 4. The findings did not allow for the acceptance or rejection of the null
hypothesis for Research Question 1 but did allow for acceptance of the null hypothesis for
Research Question 2.

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Question 1 – H04: There are no statistically significant differences in teacher absenteeism
rates by experience.
Question 2 – H04: No correlation exists between the number of teacher absences and
experience.

Figure 4. 2016-19 Mean Number of Absences Per Year by Experience
The data analysis for the research project found that the number of absences was not
significantly influenced by the grade configuration of the school. While the results were not
statistically significant, the descriptive statistics indicated the absentee rates of second and third
grade teachers during the course of the study were higher than the absentee rates of teachers in
the other school buildings. The data further suggested that high school teachers had the secondhighest rate of teacher absenteeism, followed by middle school teachers. The district’s
intermediate elementary school was found to have the lowest number of teacher absences per
year, closely followed by teachers at Early Childhood Center (ECC). As depicted in Figure 5, the

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results of this study do not support prior research that suggests absentee rates are highly
correlated with elementary teachers missing more days of work than middle school teachers,
while middle school teachers are absent at a higher rate than high school teachers (Clotfelter et
al., 2009; Miller, 2008; Miller et al., 2008). Although previous research indicated a linear
relationship existed between absentee rates and grade configurations, this study found that no
significant relationship existed between the school buildings and the number of days missed per
year, with the exception of the 2017-18 school year, where correlations between the primary
school and the number of teacher absences per year was found to be very weak but significant.
The findings allow for the acceptance of the null hypothesis for Research Question 1, but
because of the very weak yet significant correlation between the primary school and the absentee
rates during the 2017-18 school year, the hypothesis for Research Question 2 cannot be accepted
or rejected.
Question 1 – H05: There are no statistically significant differences in teacher absenteeism
rates by school level.
Question 2 – H05: No correlation exists between the number of teacher absences and
school level.

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Figure 5. 2016-19 Mean Number of Absences Per Year by School Level
Research suggests the education level of an employee is a significant predicator of
absenteeism. The literature indicates employees with higher degrees tend to have lower rates of
absenteeism than employees with lower levels of education (Wee, Yeap, Chan, Wong, Jamil,
Natha, & Siau, 2019). The results of this study found that the level of education had a
statistically significant effect on the rate of teacher absenteeism during the 2016-17 school year,
but the results were not significant during the 2017-18 or 2018-19 school years. Likewise, the
aggregate data showed that there was not a significant effect between the degree earned and the
number of times a teacher was absent per year. However, an analysis of the means indicated
teachers with a bachelor’s were likely to miss fewer days of work than teachers who had attained
higher levels of education. Similarly, the results of the study indicated that during the 2016-17
school year, there was a very weak but significant linear relationship between degree earned and
the number of teacher absences, but the relationship between the two variables was found not to
be significant for any of the other data sets that were analyzed. The results of this project do not

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support the previous findings of Wee et al., which suggests level of education significantly
influences employee absentee rates. The results of the study do not allow for the acceptance or
the rejection of the null hypotheses for Research Questions 1 and 2.
Question 1 – H06: There are no statistically significant differences in teacher absenteeism
rates by degree.
Question 2 – H06: No correlation exists between the number of teacher absences and
degree.
An examination of the means indicated that over the three-year period of the study,
teachers who lived closer to school (0-3 miles) missed fewer days of work than teachers who
lived further away. The descriptive statistics also suggested that teachers who had the longest
commute (16 or more miles) were absent at a higher rate than teachers who lived closer to work.
However, the one-way ANOVAs conducted for each school year and the results of the aggregate
data found that distance to work did not have a statistically significant effect on the number of
days a teacher missed per year. The data sets for both the aggregate years and the individual
school year showed that there was no significant correlation between the two variables. The
results of the study do not support the body of research, which implies that commuting distance
is a significant predicator of teacher absenteeism (Miller, 2008; Steers & Rhodes, 1978). The
findings allow for the acceptance of the null hypotheses for both Research Question 1 and 2.
Question 1 – H07: There are no statistically significant differences in teacher absenteeism
rates by distance from work.
Question 2 – H07: No correlation exists between the number of teacher absences and
distance from work.

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The study found that 13 out of the 16 one-way ANOVAs that were conducted to examine
the effect between teacher absence classification and DIBELS Next Oral Reading Fluency scores
indicated no statistically significant differences existed between the two variables. The results of
this project support the findings of a recent study that concluded there was no statistically
significant difference between the number of days missed and the reading proficiency levels for
students in kindergarten through third grade on DIBELS Next composite scores (Niemeyer,
2013). The three analyses of variances that showed a significant difference existed between the
two variables included: (a) the 2016-17 Grade 2 DIBELS Next Oral Reading Fluency
assessment, (b) 2016-17 Grade 3 DIBELS Next Oral Reading Fluency assessment, and (c) the
aggregate scores for the Grade 5 DIBELS Next Oral Reading Fluency assessment. The 2016-17
Grade 2 and Grade 3 DIBELS Oral Reading Fluency scores indicated that students who were
instructed by teachers who were not chronically absent scored higher on the assessment than
students who were educated by teachers who were chronically absent. Conversely, the aggregate
data for the Grade 5 DIBELS Next Oral Reading Fluency scores indicated that students achieve
higher scores if instructed by a teacher who was chronically absent. Although the results were
mixed, the majority of the data sets indicated teacher absence classification did not significantly
affect student achievement scores on the DIBELS Next Oral Reading Fluency assessment.
Similarly, only two of the 16 correlations indicated a statistically significant relationship
between the two variables. The two correlations found to be significant included the 2016-17
Grade 2 DIBELS Next Oral Reading Fluency scores and the combined data sets for the Grade 4
DIBELS Next Oral Reading Fluency assessment. The 2016-17 Grade 2 DIBELS Next Oral
Reading Fluency correlation indicated a very weak negative relationship, while the aggregate
data for the Grade 4 DIBELS Next Oral Reading Fluency showed a very weak positive

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relationship. The findings allow for the acceptance of the null hypothesis H03, which is included
in Research Question 3, and for the acceptance of the null hypotheses H02 and H04, which are
included in Research Question 4.
Question 3 – H03: There are no statistically significant differences in the DIBELS Next
Oral Reading Fluency scores for students in Grade 4 by teacher absence
classification.
Question 4 – H02: No correlation exists between the number of teacher absences and
DIBELS Next Oral Reading Fluency scores for students in Grade 3.
Question 4 – H04: No correlation exists between the number of teacher absences and
DIBELS Next Oral Reading Fluency scores for students in Grade 4.
The findings do not allow for the acceptance or rejection of null hypotheses H01, H02, or
H04, which are included in Research Question 3, or null hypotheses H01 and H03, which are
included in Research Question 4.
Question 3 – H01: There are no statistically significant differences in the DIBELS Next
Oral Reading Fluency scores for students in Grade 2 by teacher absence
classification.
Question 3 – H02: There are no statistically significant differences in the DIBELS Next
Oral Reading Fluency scores for students in Grade 3 by teacher absence
classification.
Question 3 – H04: There are no statistically significant differences in the DIBELS Next
Oral Reading Fluency scores for students in Grade 5 by teacher absence
classification.

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Question 4 – H01: No correlation exists between the number of teacher absences and
DIBELS Next Oral Reading Fluency scores for students in Grade 2.
Question 4 – H04: No correlation exists between the number of teacher absences and
DIBELS Next Oral Reading Fluency scores for students in Grade 5.
Previous research studies that analyzed the effect of teacher classification and student
achievement scores have produced mixed results. A study that examined the effect of absentee
rates of teachers on student achievement scores found that students who were instructed by
teachers who were absent less than 2% of the school year outperformed their peers who were
instructed by teachers in all other absence classifications (Cantrel, 2003). Conversely, Colquit
(2009) divided teacher leave into four separate classifications to determine if student
achievement scores are significantly influenced by the amount of leave a teacher uses per year.
The study conducted by Colquit found no statistically significant differences among the four
teacher leave categories and student achievement scores. The results of the study indicated no
significant differences existed between PVAAS Teacher Value Added Math, English Language
Arts (ELA), Science, Algebra I, Literature or Biology scores, and teacher absence classification.
The study also found no significant differences existed between the final exam grades for
Algebra I, Honors English 9, CP English 10, CP Biology, or Honors Chemistry, and teacher
absence classification. However, one-way ANOVAs conducted for English 9, CP English 9, and
CP Chemistry indicated there was a significant difference between final exam grades and teacher
absence classification. The analysis for variance for the three courses showed that students
instructed by a teacher who missed fewer than 10 days of school per year scored higher on the
final exam than students who were taught by teachers who were chronically absent. The result of

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this project, with the exception of final exam grades for English 9, CP English 9, and CP
Chemistry, support the research conducted by Colquit.
The findings allow for the acceptance of null hypotheses H05, H06, H07, H08, H09, and
H010, which are included in Research Question 3.
Question 3 – H05: There are no statistically significant differences in PVAAS Teacher
Value Added Scores Math sores by teacher absence classification.
Question 3 – H06: There are no statistically significant differences in PVAAS Teacher
Value Added Scores English Language Arts scores by teacher absence
classification.
Question 3 – H07: There are no statistically significant differences in PVAAS Teacher
Value Added Scores Science scores by teacher absence classification.
Question 3 – H08: There are no statistically significant differences in PVAAS Teacher
Value Added Scores Algebra I scores by teacher absence classification.
Question 3 – H09: There are no statistically significant differences in PVAAS Teacher
Value Added Scores Literature scores by teacher absence classification.
Question 3 – H010: There are no statistically significant differences in PVAAS Teacher
Value Added Scores Biology scores by teacher absence classification.
The findings do not allow for the acceptance or rejection of the null hypothesis H011,
which is included in Research Question 3.
Question 3 – H011: There are no statistically significant differences in final exam grades
by teacher absence classification.
Although early studies generated conflicting results, the majority of recent research
indicated that teacher absenteeism rates and student achievement scores are significantly

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correlated (Brown & Arnell, 2012; Clotfelder et al., 2009; Ehrenberg et al., 1991; Miller et al.,
2008; Woods & Montagno, 1997). A notable study conducted by Clotfelder et al. (2009) found a
significant relationship existed between teacher absences and student achievement scores.
Likewise, Miller et al. (2008) suggested for every 10 days a teacher is absent from the classroom,
achievement scores in math decrease by 3.2% of a standard deviation. The results of these two
studies were further supported by the research of Brown and Arnell (2012), who concluded that
student achievement scores decrease as teacher absences increase. However, the results of this
project indicated that a significant linear relationship existed in only six of the 52 correlations
that were performed. The six correlations that showed a significant relationship included: (a) the
2016-17 Grade 2 DIBELS Next Oral Reading Fluency scores, (b) the combined data sets for the
Grade 4 DIBELS Next Oral Reading Fluency scores, (c) the 2017-18 PVAAS Teacher Value
Added Biology scores, (d) the 2017-18 CP English 9 Final Exam grades, (e) the 2018-19 CP
English 9 Final Exam grades, and (f) the aggregate CP English 9 Final Exam grades. Five of the
six correlations that were statistically significant indicated a negative relationship between the
number of teacher absences and student achievement scores. The Grade 4 DIBELS Next Oral
Reading Fluency Assessment was the only correlation that demonstrated a positive relationship
between the two variables. Overall, the correlation data suggests that student achievement scores
and teacher absentee rates are not significantly related. The findings allow for the acceptance of
null hypotheses H02, H04, H05, H06, H07, H08, and H09, which are included in Research Question
4.
Question 4 – H02: No correlation exists between the number of teacher absences and
DIBELS Next Oral Reading Fluency scores for students in Grade 3.

TEACHER ABSENTEEISM AND STUDENT ACHIEVEMENT
Question 4 – H04: No correlation exists between the number of teacher absences and
DIBELS Next Oral Reading Fluency scores for students in Grade 5.
Question 4 – H05: No correlation exists between the number of teacher absences and
PVAAS Teacher Value Added Math scores.
Question 4 – H06: No correlation exists between the number of teacher absences and
PVAAS Teacher Value Added English Language Arts scores.
Question 4 – H07: No correlation exists between the number of teacher absences and
PVAAS Teacher Value Added Science scores.
Question 4 – H08: No correlation exists between the number of teacher absences and
PVAAS Teacher Value Added Algebra I scores.
Question 4 – H09: No correlation exists between the number of teacher absences and
PVAAS Teacher Value Added Literature scores.
The findings do not allow for the acceptance or rejection of null hypotheses H01, H03,
H010, and H011, which are included in Research Question 4.
Question 4 – H01: No correlation exists between the number of teacher absences and
DIBELS Next Oral Reading Fluency scores for students in Grade 2.
Question 4 – H03: No correlation exists between the number of teacher absences and
DIBELS Next Oral Reading Fluency scores for students in Grade 4.
Question 4 – H010: No correlation exists between the number of teacher absences and
PVAAS Teacher Value Added Biology scores.
Question 4 – H011: No correlation exists between the number of teacher absences and
final exam grades.

141

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142

The results of this study showed a statistically significant difference between teacher
absentee rates and day of the week. The data analysis revealed teachers in the Derry Township
School District were absent the most on Friday and missed the least amount of work on Monday.
Specifically, 24.7% of teacher absences for the three years studied occurred on Friday, while
only 16.6% of the absences occurred on Monday. The findings of this study support prior
research that strongly suggested that teachers are absent from classrooms at higher rates on
Fridays when compared to other days of the week (Miller et al., 2008; Pitts, 2010). Although the
research strongly supports the assumption that teachers are absent at a higher rate on Friday,
there is mixed evidence as to the day of the week teachers are least likely to be absent. Research
conducted by Miller (2008) implies that teachers are commonly absent on Monday, while the
study conducted by Pitts (2010) suggests teachers miss fewer days of work on Monday than any
other day of the week. The result of this project supports the research that suggested teachers are
least likely to be absent on Monday. A chi-square goodness of fitness test determined there is a
significant difference between the amount of leave used by leave category. The aggregate results
of the study indicated that sick leave accounted for 43.8% of all teacher absences, followed by
professional leave (32.9%) and then personal leave (16.9%). The remaining leave categories
combined accounted for 6.4% of leave. The findings allow for the rejection of null hypotheses
H01 and H02, which are included in Research Question 5.
Question 5 – H01: There are no statistically significant differences in teacher absenteeism
rates by leave category.
Question 5 – H02: There are no statistically significant differences in teacher absenteeism
rates by day of the week.

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Recommendations for Future Research
The aggregate data set for age indicated that younger teachers are absent less often than
other teachers, but the reason for the significant difference was not explored or examined in the
context of the research project. Therefore, since age was found to be a strong predictor of teacher
absenteeism, additional research should be conducted to determine the reasons behind the
discrepancies.
While the study revealed that gender had a significant effect on the number of days a
teacher missed annually at Derry Township School District, the research question failed to
address the reasons for the differences in absentee rates between male and female teachers.
Previous research has suggested that the disparity is likely due to a combination of factors that
include the fact that women have traditionally served as the primary caretaker for sick family
members and that historically, mothers have generally taken more time off than fathers after the
birth of a child (Miller, 2018). Although the results of the research project may support the
findings of previous studies, additional research should be conducted to establish if the
customary roles associated with motherhood account for the differences in absentee rates at
DTSD.
Miller (2008) suggested that one of the most common reasons for the decrease in the
number of workdays missed by teachers nearing retirement is a result of the teacher’s ability to
cash out unused sick leave. Since the Derry Township School District does have language in the
collective bargaining agreement that provides a retirement sick leave benefit to teachers, the
results of the study suggest that the cash-out option may contribute to the decrease in absence
rates for teachers approaching retirement. However, further research is recommended before any

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definitive conclusions can be made as to the reasons why the absenteeism rate of teachers
nearing retirement begins to decline.
Although the conclusions reached in this project do not support previous research studies
that suggest employees with higher degrees miss less work than employees with lower levels of
education, further exploration of this predicator of absenteeism should be investigated. This
recommendation is due in part because previous studies have implied that highly educated
employees are hesitant to call off sick because they believe their talent and skill sets cannot be
replaced at work. In the field of education, the general assumption is that all educators have a
comparable set of skills, regardless of their level of education. As a result, this assumption likely
explains the difference between the results of this project and previous studies.
Cantrell (2003) suggested that significant differences in student achievement scores was
even more dramatic when comparing teachers who missed the most amount of days against
teachers who were absent from the classroom the least amount of time. While this study
compared the differences between teachers who missed 10 or more days of school and those who
missed fewer than 10 days of work, the project did not examine differences between teachers on
opposite ends of the absentee spectrum. A more in-depth analysis of the extremes is worth
further consideration to determine if student achievement results are impacted at DTSD by
teachers who have absentee rates at opposite sides of the attendance spectrum.
Recommendations for Derry Township School District
The overall results of the study indicated that student achievement scores were not
significantly impacted by teacher attendance. However, as noted in the literature review, recent
studies estimate teacher absences cost school districts in excess of $5.6 billion annually (Folger,
2019; Kocakülâh, Bryan, & Lynch, 2019). The National Council on Teacher Quality (2014)

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determined the cost associated with teacher absences equates to roughly $1,800 per-teacher, peryear. For the three years studied, Derry Township School District spent approximately $700,000
per year on substitute costs, or the equivalent of about $2,400 a year per teacher. The financial
data clearly indicates the district’s expenditures for substitute costs on a per-teacher basis far
outpace the national average, creating a need for corrective actions to reduce the district’s
financial burden associated with teacher absences. The results of this research project and the
review of literature serve as the driving force behind the following set of recommendations that
are designed to decrease absenteeism rates, improve substitute fill rates, and ultimately reduce
the costs associated with teacher leave in the Derry Township School District.
The first recommendation the Derry Township School District should consider is the
implementation of a policy that requires teachers to report absences directly to their building
principal or immediate supervisor. The review of literature found that school districts that
required their teachers to submit absences only via an online management system or district-wide
call-in system generally had higher rates of absenteeism (Miller et al., 2008). Moreover, a study
conducted by Boudreau et al. (1993) suggested that unplanned leave could be reduced by at least
35% if employees were required to call their immediate supervisor. The current absence
notification procedure at DTSD requires teachers to submit absences only via the district’s online
absence management system. If Derry Township School District were able to reduce the use of
emergency and sick leave by 35% by requiring teachers to report their absences directly to their
principal or immediate supervisor as suggested by Boudreau et al. (1993), the district could save
at least $50,000 per year on substitute teacher costs. To reduce the use of discretionary sick and
emergency leave and to realize the potential savings, the district should consider revising the

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current absence reporting protocol to include a provision that requires teachers to report sick and
emergency leave absences directly to their building principal or immediate supervisor.
An examination of the district’s leave requests indicated teachers in the Derry Township
School District were absent from the classroom a total of 3647.5 days during the course of the
study for reasons associated with professional leave. The number of days teachers missed for
professional leave during the three years studied accounted for roughly 29% of teacher absences,
while sick leave accounted for only approximately 45% of teacher leave. The result of this
project conflicts with a study conducted by Miller (2008) that examined the leave patterns of
more than 5,000 teachers in a large urban district in the northern part of the United States. The
author concluded that sick leave accounted for 59% of all teacher absences. Furthermore, a
report issued by the National Council on Teacher Quality (2014) found only 20% of teacher
absences are due to professional leave reasons. The district’s high rate of professional leave is
likely the contributing factor for the discrepancies in the percentage of sick and personal leave
usage among the studies.
The results of previous studies indicate the district’s rate of professional leave appears to
be excessive. Therefore, the district is encouraged to limit the number of times a teacher can be
approved for professional leave to five days per year. The rationale for this recommendation is
based on the fact that the district’s absentee data showed 36% of the teaching staff were absent
for five or more days for professional leave reasons during the 2016-17 school year, followed by
29%, surpassing the proposed threshold during the 2017-18 school year, and during the 2018-19
school year, 29% of teachers were found to have been absent from work for professional leave
on at least five occasions. The financial costs to secure substitutes for professional leave was
approximately $121,000, $115,000, and $105,000, respectively, during the course of the study.

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The district could have saved an estimated $25,000 a year if the recommended limits would have
been in place. These figures are based on the then-contracted substitute rate for a day-to-day
substitute and included only professional leave requests that required a substitute teacher. To
realize these potential savings, the district is encouraged to set maximum limits on the amount of
professional leave a teacher can use per year.
A review of the Hershey Education Association collective bargaining agreement and
Derry Township School District’s leave policies determined that there are no maximum limits on
the number of teachers who can miss work due to planned absences in the district or school
building. The researcher recommends the district cap the number of teachers who miss work due
to a combination of personal and professional leave by building. The recommended limits are as
follows: high school (5), middle school (5), intermediate school (3), primary school (3), and ECC
(3). An analysis of the district’s absentee records indicate that Derry Township School District
could have saved a minimum of $76,000 if the recommended limits would have been enforced
during the time of the study.
In a study that analyzed 10 years of teacher leave data for the entire state of North
Carolina, Clotfelder et al. (2009) suggested that charging teachers a $50 fee for every sick leave
absence taken beyond 12 days would reduce the mean number of sick leave absences taken to
5.8 and would cost the average teacher $300 per year. Although this recommendation does not
go to the extreme of requiring teachers to pay for the use of sick leave, the recommendation does
include charging educators for the cost of the substitute teacher for every unpaid leave absence
taken that does not qualify for FMLA. An examination of the district absence data indicated that
teachers used 43.5 days of unpaid leave in the 2016-17 school year, 69.5 days in the 2017-18
school year, and 86.5 days during the 2018-19 school year. The total cost savings for the Derry

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Township School District during the span of the study would have been approximately $26,000
if teachers would have been charged a fee for the use of unpaid leave. The leave trend shows the
number of unpaid leave days increased each year of the study. If the trend continues, the savings
could exceed $11,000 each year. A secondary goal of this recommendation is to decrease the
number of unpaid leave days teachers take per year. Although it would be difficult to predict if
the cost associated with acquiring a substitute would reduce the number of days teachers use
unpaid leave in the Derry Township School District, the study conducted by Clotfelder et al.
suggested the district could expect the use of unpaid leave to decline.
The next recommendation is not intended to result in a cost savings for the district but
rather to improve the overall substitute fill rate. The result of this study concluded teachers in the
Derry Township School District use personal leave at a much higher rate on Fridays and
Mondays when compared to the other days of the week. During the three-year span of the study,
teachers used personal leave 42.1% on Friday, 18.8% on Monday, 12.3% on Tuesday, 10.9% on
Wednesday, and 15.9% on Thursday. The results of this study support previous research that
implied teachers tend to use personal leave connected to previously established days off to
extend their weekends and holidays (Miller, 2008; Miller et al., 2008; Pitts, 2010). An analysis
of the district’s substitute fill rate indicated that during the 2016-17 school year, the combined
fill rate for Mondays and Fridays in May was 97.5% and 98.4% for all other school days. The
differences between fill rates on Mondays and Fridays in May and the rest of the days in the
school year widened to 94% and 97.4%, respectively, during the 2017-18 school year. The
disparity in fill rates was even more dramatic during the 2018-19 school year, with only 84.2%
of teacher positions being filled on Mondays and Fridays in May compared to 95.1% of the
substitute requests being filled on the other days of the year. The goal of this recommendation is

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to restrict the use of personal leave on days that have historically been known to be difficult to
secure substitute teachers.
Pitkoff (2003) concluded sick leave banks generally lead to increased teacher absentee
rates. Furthermore, the researcher argued that sick leave banks actually encourage teachers to use
sick leave. The study found teachers in districts that have sick leave bank provisions generally
did not accumulate as many sick leave days in their individual leave banks when compared to
teachers in districts that did not have district sponsored sick leave banks. The assumption was
that since teachers have the sick leave bank available to them for catastrophic injuries or
illnesses, there was little incentive to accumulate sick leave. A review of the district’s absences
records revealed that at the end of the study, 32% of teachers had fewer than 30 days of sick
leave. This data implies that Pitkoff’s analysis of sick leave banks has some merit in terms of
teachers failing to accumulate adequate amounts of sick leave in the event of a catastrophic
illness or injury. For these reasons, the district should consider removing the sick leave bank
provisions from the Hershey Education Association collective bargaining agreement during the
next round of negotiations.
A review of the district’s student attendance policy indicated that students who miss more
than 10 days of school are required to furnish a doctor’s note for any additional days of school
missed during the course of the year. However, comparable language could not be found when
examining the district’s employee leave policy. Although the overall results of the study suggest
that teacher absenteeism does not impact student achievement scores, Brown and Arnell (2012)
suggest that school leaders should limit the number of days teachers miss a year to no more than
10. Furthermore, the Pennsylvania Public School Code of 1949 provides districts the ability to
require a teacher to submit a note from a physician or health care provider if the teacher was

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unable to perform his or her duties and was compensated for the time missed. This
recommendation is based on the belief that if teachers are required to furnish a doctor’s note, the
number of sick leave absences will likely decrease. Although there is no research indicating that
a doctor’s note will reduce teacher absentee rates, research does suggest that reporting absences
directly to a supervisor reduces employee absenteeism (Boudreau et al., 1993; Miller et al.,
2008). Therefore, requiring teachers to take an extra step when reporting sick leave absences in
excess of 10 days should likely result in lower absenteeism rates.
Limitations
This study examined several demographic variables that could be used to predict teacher
absences. However, the study did not analyze teacher or student background variables such as
income, mental, or physical health. Therefore, the results of this study as it relates to teacher
absenteeism and student achievement do not account for the complex relationships among the
vast array of demographic and social variables and their connections between students and
teachers. This intertwined web of connections may in part explain the lack of a significant
relationship and significant difference between student achievement scores and teacher
absenteeism.
Similarly, the study did not consider the impact of the substitute on student achievement
scores. Damle (2009) suggested the majority of substitute teachers receive minimal training
before entering classrooms. Conversely, many substitutes who work in the Derry Township
School District have teaching certificates and are adequately prepared to provide quality
instruction to students. However, without knowing the educational background of the substitute,
classroom teachers often prepare lesson plans that lack the quality and rigor that students

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typically receive on a day-to-day basis (Damle, 2009; Miller et al., 2008; Woods & Montagno,
1997).
Student achievement scores for all three assessments captured only student performance
at single points in time and did not account for the overall academic knowledge or growth of a
student during the course of the year. The scores that were analyzed in the study failed to account
for any mitigating factors that may have had a significant effect on a student’s performance on
the day of the assessment. For example, a student who was physically or mentally ill on the day
of the assessment most likely would have scored lower on the assessment than if that student had
been in good health.
Another limitation of the study involved the self-reporting of teacher absences via the
district’s online absence management system. Although not common, teachers occasionally
select the wrong leave category or at times indicate a substitute is not required when a substitute
is in fact needed. Furthermore, there have been a few times that teachers have completely
forgotten to enter leave.
Special Considerations
On March 13, 2020, Pennsylvania Governor Tom Wolf ordered the closing of all public
and private schools in the Commonwealth due to the Novel Coronavirus pandemic (COVID-19).
The order was originally issued to be in effect until May 1, 2020, but was ultimately extended to
June 30, 2020. The result of this order forced school districts to quickly transition from an inperson instructional delivery model to an emergency remote-distance learning model. The
transition required many teachers in the Derry Township School District to work additional
hours beyond the eight hours negotiated in the collective bargaining agreement. The additional
hours, the transition to emergency distance learning, and the fear of the unknown caused many

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teachers to experience increased amounts of stress and anxiety throughout the pandemic, as
noted by the District’s Director of Safe and Support Schools. As a result, the committee for this
capstone project believed it was in the best interest of the staff to eliminate a portion of the study
that included a teacher survey. The survey was designed to explore the reasons behind teacher
leave that could not effectively be captured by merely examining the demographic variables.
Additionally, the survey included questions asking what ideas and suggestions teachers had with
respect to improving teacher attendance. Although the survey was not included in the study, the
primary research questions that guided the project were not affected.
Summary
Overall, this research project determined that there are little to no significant differences
between the achievement scores for students instructed by chronically absent teachers and
students instructed by teachers who miss fewer than 10 days of work per year. Likewise, the
results of the study suggest significant relationships between the number of teacher absences and
student achievement scores do not exist. The demographic variables of age, gender, and years of
experience were all determined to be significant predicators of teacher absences at Derry
Township School District. However, with the exception of gender, there were no significant
linear relationships between absentee rates and the demographic variables of age, race, years of
experience, degree earned, school level, and distance to work.
The leave data confirmed prior research that suggested there is a significant difference
between the number of days teachers use by day of the week and leave category (Miller, 2008;
Miller et al., 2008; Pitts, 2010). The results indicated that teachers are absent from classrooms
the most on Fridays and the least on Mondays. In terms of leave category, sick leave is the most
frequent cause of teacher absences, followed by professional leave and personal leave.

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The results of the study indicated that more than 62% of teachers in the Derry Township
School District are considered to be chronically absent. The cost associated with securing
substitutes between the 2016-19 school years exceeded $2.1 million. In addition, the substitute
fill rate in the district continues to decline. In order to effectively address these concerns, the
district is strongly urged to consider implementing one or more of the following
recommendations:
1. Require teachers to report absences directly to the building principal or designee.
2. Limit the use of professional leave.
3. Establish limits for personal and professional leave use by school building.
4. Require teachers to pay the substitute teachers costs for unpaid leave.
5. Prevent the use of personal leave on Mondays and Fridays during the month of May.
6. Eliminate the Hershey Education Association (HEA) Sick Leave Bank.
7. Require teachers to submit a note from a physician for excessive sick leave absences.
In conclusion, while some absences are unavoidable due to colds, flu, or other health
related reasons, a teacher’s presence in the classroom is crucial to the academic and emotional
success of students (Miller, 2008). Therefore, school administrators should closely examine the
absentee data in their school buildings to understand the trends and patterns associated with
teacher leave. By doing so, school administrators can effectively combat teacher absenteeism and
reduce the more than $5.6 billion districts annually spend on substitute teacher costs (Folger,
2019; Kocakülâh, Bryan, & Lynch, 2019).

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