Journal of Occupational Health Psychology
2016, Vol. 21, No. 1, 24 –36
© 2015 American Psychological Association
1076-8998/16/$12.00 http://dx.doi.org/10.1037/a0039138
Distinct Longitudinal Patterns of Absenteeism and Their Antecedents in
Full-Time Australian Employees
Christopher A. Magee, Peter Caputi, and Jeong Kyu Lee
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
University of Wollongong
This paper investigated distinct longitudinal trajectories of absenteeism over time, and underlying
demographic, work, and health antecedents. Data from the Household, Income, and Labor Dynamics in
Australia Survey were used; this is a panel study of a representative sample of Australian households.
This paper focused on 2,481 full-time employees across a 5-year period. Information on annual sick leave
and relevant sociodemographic, work, and health-related factors was collected through interviews and
self-completed surveys. Growth mixture modeling indicated 4 distinct longitudinal patterns of absenteeism over time. The moderate absenteeism trajectory (34.8%) of the sample had 4 –5 days of sick leave
per year and was used as the reference group. The low absenteeism trajectory (33.5%) had 1 – 2 days of
absenteeism per year, while the no absenteeism trajectory (23.6%) had very low rates of absenteeism (⬍1
day per year). Finally, a smaller trajectory accounting for 8.1% of the sample had high levels of
absenteeism (⬎11 days per year). Compared with the moderate absenteeism trajectory, the high
absenteeism trajectory was characterized by poor health; the no absenteeism and low absenteeism
trajectories had better health but may also reflect processes relating to presenteeism. These results
provide important insights into the nature of absenteeism in Australian employees, and suggest that
different patterns of absenteeism over time could reflect a range of demographic, work, and health related
factors.
Keywords: absenteeism, growth mixture modeling, job security, work hours, health
bank Private, 2005). Studies from North America and Europe also
indicate that the economic costs of absenteeism are considerable
(Confederation of British Industry, 2004; Cooper & Dewe, 2008;
Goetzel et al., 2004).
Although a vast body of literature has investigated antecedents
and consequences of absenteeism in numerous contexts (Johns,
2011), many aspects of absenteeism remain unclear. Few studies
have investigated whether there are distinct longitudinal patterns
of absenteeism in employees; this is an important consideration
because patterns of absenteeism over time could vary considerably
between individuals, reflecting the role of different antecedents.
The current paper aimed to investigate the presence of distinct
longitudinal trajectories of absenteeism, and their underlying antecedents to better understand the nature of absenteeism. The
remainder of this introduction outlines some key theories of absenteeism, and briefly reviews existing longitudinal research examining antecedents of absenteeism. The need to examine distinct
trajectories of absenteeism over time is then presented, followed
by the aims and main research questions of the present study.
Absenteeism, which refers to “the failure to report to work as
scheduled” (Johns, 2008, p. 160), is a major contributor to reduced
workplace productivity and has considerable financial costs
through health insurance claims, overtime wages, and legal claims
(Darr & Johns, 2008). Absenteeism can have longer term effects
on productivity by contributing to conditions such as depression
(Melchior et al., 2009) and leading to eventual withdrawal from
the workplace (Westman & Etzion, 2001). In Australia, average
rates of absenteeism range from 4 days per year (private sector
employees) to 8 days per year (public sector employees) and vary
by age, length of service, income, gender, organization size, and
industry type (Audit Office of New South Wales, 2010). It is
estimated that absenteeism costs the Australian economy at least
$7 billion a year in lost productivity and health care costs (Medi-
This article was published Online First May 4, 2015.
Christopher A. Magee, Centre for Health Initiatives, University of Wollongong; Peter Caputi, Centre for Health Initiatives and School of Psychology, University of Wollongong; Jeong Kyu Lee, Centre for Health
Initiatives, University of Wollongong.
This paper uses unit record data from the Household, Income, and Labor
Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated
and is funded by the Australian Government Department of Social Services
(DSS) and is managed by the Melbourne Institute of Applied Economic
and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the authors and should not be
attributed to either DSS or the Melbourne Institute.
Correspondence concerning this article should be addressed to Christopher A. Magee, Centre for Health Initiatives, University of Wollongong,
Wollongong NSW 2522, Australia. E-mail: [email protected]
Absenteeism Behavior
Absenteeism is a complex phenomenon, with many interrelated
factors having the potential to influence an employee’s decision
and ability to attend work. Historically, a distinction has been
drawn between involuntary absenteeism and voluntary absenteeism (Brooke, 1986; Johns, 2011; Nicholson, 1977; Steers & Rhodes, 1978). Involuntary absenteeism (also known as sickness absenteeism) refers to instances where employees do not attend work
due to poor physical or mental health (Brooke, 1986; Steers &
Rhodes, 1978). The term involuntary does not imply an employee
24
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ABSENTEEISM IN FULL-TIME AUSTRALIAN EMPLOYEES
lacks control over their decision to attend work; rather, it refers to
the influence of external factors in constraining an employee’s
ability to attend work (Beemsterboer, Stewart, Groothoff, & Nijhuis, 2009). In general, involuntary absenteeism behavior is characterized by duration rather than frequency of absenteeism spells
(Brooke, 1986). Voluntary absenteeism differs in that it reflects a
choice to withdraw or escape from a negative work environment
(Steers & Rhodes, 1978). In other words, employees may choose
to be absent from work as a way of escaping, avoiding, or compensating for adverse, demoralizing, or stressful work environments (Hardy, Woods, & Wall, 2003). Voluntary absenteeism is
reflected by duration rather than frequency of missed days of work
(Brooke, 1986).
Many conceptual models and frameworks have been proposed
to explain voluntary and involuntary absenteeism. It is beyond the
scope of this study to review all of these in detail; however some
key theoretical perspectives are briefly presented. Nicholson’s
(1977) attendance motivation model conceptualized absenteeism
behavior on a continuum from primarily unavoidable (involuntary)
to primarily avoidable (voluntary) absenteeism. Nicholson (1977)
argued that contextual factors (e.g., personal characteristics, job
characteristics, demographics) influence an employee’s level of
attachment to work, which in turn affects how motivated they are
to attend work. According to this model, attending work is the
norm behavior until proximal events interfere with an employee’s
ability to attend work or force them to make a decision about
whether to attend work. Nicholson (1977) suggested that employees with higher levels of attendance motivation are less affected by
these proximal events.
Steers and Rhodes (1978) proposed a widely cited conceptual
model of attendance at work that provides an insight into voluntary
and involuntary absenteeism. They proposed that demographic
characteristics have an indirect influence on attendance behavior
via two main variables: (a) an employee’s motivation to attend
work and (b) an employee’s ability to attend work. Motivation to
attend work reflects an employee’s affective response to their job
(e.g., job satisfaction) and a range of internal and external pressures (e.g., economic, social, or personal pressures). According to
this model, when an employee enjoys his or her work, they have a
strong motivation to attend work, and thus take fewer absenteeism
days (Steers & Rhodes, 1978). The model also proposes that
factors such as illness and accidents, family responsibilities, and
transportation problems may inhibit an individual’s ability to attend work regardless of motivation; these factors contribute to
involuntary absenteeism (Steers & Rhodes, 1978). This model was
extended by Brooke (1986) who outlined several exogenous (e.g.,
routinization, work involvement, role ambiguity, organizational
permissiveness) variables and five endogenous variables (job satisfaction, job involvement, organizational commitment, health,
and alcohol consumption), and hypothesized pathways by which
these variables were related with each other and with absenteeism.
Nicholson and Johns (1985) proposed that in addition to many
person-level factors captured in the models outlined above, two
social factors - absence cultures and the psychological contract—
also have the potential to influence absenteeism. Absence cultures
in a workplace can directly influence absenteeism for a given
group of employees through shared norms. Employees may also
observe the absence behaviors of other employees and the reactions to these behaviors (Nicholson & Johns, 1985). Furthermore,
25
absence cultures may facilitate or constrain the effects of person
level variables such as job satisfaction on absenteeism. Nicholson
and Johns (1985) specifically postulated that two interrelated factors influence absence culture: (a) beliefs about absence and (b)
assumptions about employment (the psychological contract).
Nicholson and Johns (1985) argued that absence culture and psychological contract are able to account for differences in absenteeism behavior within and between organizations. For example,
factors such as occupational status (e.g., white collar vs. blue collar
workers) may lead to differing beliefs regarding the legitimacy of
absences, which translates to different absenteeism behaviors.
These and several more recent conceptual frameworks and models (Halbesleben, Whitman, & Crawford, 2014; Johns, 2010;
Schaufeli, Bakker, & Van Rhenen, 2009) provide insight into a
range of factors that influence absenteeism behavior along the
involuntary-voluntary continuum. They suggest that a range of
demographic, work and job related, social, and health factors have
the potential to influence absenteeism behaviors.
Existing Longitudinal Studies
A large number of studies have examined absenteeism behavior
and underlying antecedents however, few studies have been theory
based and most have been inconsistent in terms of the type and
scope of antecedents examined. This section does not intend to
provide an exhaustive review of the longitudinal evidence base,
which is vast and encompasses a wide range of antecedents.
Instead, the aim is to provide an indication of the main antecedents
of absenteeism, consistent with established theoretical frameworks
such as Steers and Rhodes (1978). For clarity, the findings are
organized according to demographic characteristics, job satisfaction, psychosocial job characteristics, work characteristics, and
health.
Demographic Characteristics
Factors such as age, sex, and socioeconomic status (e.g., education) have been found to predict levels of absenteeism. For
example, age is inversely associated with absenteeism (Labriola,
Lund, & Burr, 2006; Martocchio, 1989; Ng & Feldman, 2008),
while in terms of sex females tend to have higher rates of absenteeism compared with males (Labriola et al., 2006; Mastekaasa &
Olsen, 1998). Consistent with Steers and Rhodes (1978), these
characteristics may have direct or indirect effects on absenteeism
(e.g., indirect effects via health and motivation) or moderate the
effects of the variables noted below on absenteeism.
Job Satisfaction
Job satisfaction is regarded as a key motivational factor underlying absenteeism behavior. In general, employees who have
higher levels of job satisfaction have lower rates of absenteeism
(Schaufeli et al., 2009; Ybema, Smulders, & Bongers, 2010). This
is consistent with the proposition of many existing frameworks
that employees who are more satisfied with aspects of their jobs
will be more motivated to attend work and thus have lower rates of
absenteeism (e.g., Brooke, 1986; Steers & Rhodes, 1978).
26
MAGEE, CAPUTI, AND LEE
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Psychosocial Job Characteristics
Many longitudinal studies utilizing theories such as the jobdemands control model (Karasek, 1979), job demands resources
model (Schaufeli et al., 2009), and the effort-reward imbalance
model (Siegrist, 1996) have demonstrated that aspects of the
psychosocial work environment are associated with absenteeism
behavior. For example, low job control (Nielsen, Rugulies, Christensen, Smith-Hansen, & Kristensen, 2006; Smulders & Nijhuis,
1999; Väänänen et al., 2003; Virtanen et al., 2007), high job strain
(Suominen et al., 2007), low social support (Melchior, Niedhammer, Berkman, & Goldberg, 2003), poor role clarity (Rugulies et
al., 2007), workplace bullying (McTernan, Dollard, & LaMontagne, 2013), reduced organizational commitment (Bakker, Demerouti, de Boer, & Schaufeli, 2003), poor organizational climate,
and poor leadership (Rugulies et al., 2007) have all been linked
with higher absenteeism. Findings for factors such as job demands
have been mixed (Smulders & Nijhuis, 1999; Virtanen et al.,
2007), with some studies finding that higher job demands predict
absenteeism in females but not males (Nielsen et al., 2006; Virtanen et al., 2007). Some studies have found that changes in
psychosocial characteristics over time (e.g., reductions in job control and increases in job demands) predict greater absenteeism
(Head et al., 2006; Vahtera, Kivimäki, Pentti, & Theorell, 2000).
According to the theoretical perspectives outlined in the previous
section, these psychosocial factors may lead to more absenteeism
via lower job satisfaction and motivation to attend work, or by
hindering the ability to attend work through elevated stress and
poor health (Brooke, 1986; Nicholson, 1977; Steers & Rhodes,
1978).
Studies have increasingly examined the role of job security in
absenteeism, and have revealed mixed results (Blekesaune, 2012).
Some studies report that lower levels of job security lead to more
absenteeism (Kivimäki et al., 1997); this can be interpreted in the
context of stressor perspectives, as lower job security could lead to
greater psychological distress and hence more sick leave (Blekesaune, 2012). In contrast, other studies have found that lower
levels of job security lead to less absenteeism (Blekesaune, 2012);
this finding can be explained by a disciplinary effect, as lower job
security may be associated with fear of losing one’s job which
leads to less absenteeism (Blekesaune, 2012). Blekesaune (2012)
suggested that the disciplinary effect is most likely to account for
short-term absenteeism, while the stressor effect accounts for
longer-term absenteeism.
Work Characteristics
Characteristics of an individual’s work such as their job type,
work schedule, and work hours have been linked with levels of
absenteeism. For example, white-collar workers have been found
to have lower absenteeism compared with blue-collar workers
(Pousette & Hanse, 2002), which is consistent with Nicholson and
Johns’ (1985) model. Studies have shown that working night shifts
is associated with increased absenteeism; for many employees
shift work is demanding and can be a source of stress, which could
lead to poorer health and thus higher absenteeism (Fekedulegn et
al., 2013). Findings for work hours are not clear, although some
studies indicate that longer work hours are associated with lower
absenteeism (Magee, Stefanic, Caputi, & Iverson, 2011). Other
factors such as no access to sick leave benefits or self-employed
status are also associated with lower rates of absenteeism (Benavides, Benach, Diez-Roux, & Roman, 2000).
Health and Illness
Health-related factors have consistently been shown to predict
absenteeism behavior (Labriola et al., 2006; Schalk, 2011; ten
Brummelhuis, Ter Hoeven, De Jong, & Peper, 2013). Short-term
absences from work are often attributed to acute illnesses such as
the common cold and influenza (Schaufeli et al., 2009), whereas
long-term absences are attributed to chronic mental and physical
health conditions including pain (e.g., neck pain), long-term disability, hypertension, depression, and migraines (Kääriä, Laaksonen, Leino-Arjas, Saastamoinen, & Lahelma, 2012; Schaufeli et
al., 2009). Poor psychological health (e.g., depression, anxiety,
general psychological distress) and sleep disturbances (e.g., insomnia) also predict absenteeism (Knudsen, Harvey, Mykletun, &
Øverland, 2013; Roelen et al., 2014; Wada et al., 2013). Consistent
with Steers and Rhodes (1978), poor health may contribute to
involuntary absenteeism by constraining one’s ability to attend
work.
Trajectories of Absenteeism Behavior
A large body of longitudinal research therefore indicates that a
range of factors have the potential to influence absenteeism behavior. However, these studies provide only a partial insight into
the nature of absenteeism and its antecedents. This is because most
studies utilize analytic techniques that do not capture potential
interindividual and intraindividual differences in absenteeism over
time. These are important considerations as patterns of absenteeism behavior may vary considerably across time and between
employees. These variations could be reflected in distinct longitudinal trajectories of absenteeism, which may have distinct antecedents. For example, some absenteeism trajectories could reflect
the influence of motivational factors, some may reflect more
constraint factors (e.g., health), while others reflect a combination
of factors. Investigation of these intraindividual and interindividual
variations in absenteeism, along with their antecedents, is needed
to provide a more definitive insight into the nature of absenteeism.
To date, only a small number of studies have explored distinct
longitudinal trajectories of absenteeism (Dello Russo, Miraglia,
Borgogni, & Johns, 2013; Haukka et al., 2014; Haukka et al.,
2013). In a representative sample of 3,420 Finnish employees
followed for 7 years, Haukka and colleagues (2013) identified four
distinct trajectories of absenteeism spells (defined as sickness
absences periods spanning 10 or more consecutive working days).
The largest trajectory (“low” sick leave) included 59% of employees who had no occurrences of long sickness absence spells. A
small proportion of participants (9%) had a high occurrence of
sickness absences each year. The remaining two trajectories were
intermediary groups with ascending and descending patterns of
absenteeism occurrences. Factors such as increased age, poor
health (e.g., musculoskeletal, mental illness, obesity), physical
workload, and low job control predicted the higher sick leave
trajectories, suggesting that health and motivational factors could
underlie these trajectories. In a subsequent study of female kitchen
workers, Haukka et al. (2014) found three distinct absenteeism
trajectories over a 2-year period: no absenteeism; intermediate
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ABSENTEEISM IN FULL-TIME AUSTRALIAN EMPLOYEES
absenteeism, and high absenteeism. Bodily pain, smoking, and
obesity predicted the intermediate trajectory, while depression,
musculoskeletal disease, and bodily pain predicted the high absenteeism trajectory. These findings suggest that different health
factors have unique influences on patterns of absenteeism.
Dello Russo at al. (2013) adopted a different approach and
examined trajectories of absenteeism behavior in three groups of
employees on the basis of how long they had been at the organization (i.e., their duration of tenure). The three groups examined
were short-tenured (tenure ⬍3 years), medium-tenured (tenured
between 3 and 19 years), and high-tenured employees (tenure ⬎19
years). At baseline, short tenured employees showed low rates of
annual absenteeism (average 2 days a year) compared with the
other two groups. However, this group showed an increase in
absenteeism behavior over a 4-year period, such that levels of
absenteeism increased to 4.5 days per year, which was similar with
medium tenured employees. The results were interpreted within
the context of organizational norms. That is, employees new to an
organization initially showed lower rates of absenteeism, but a
gradual increase in absenteeism to be consistent with those with
longer job tenure. These results suggest that employees gradually
conform to the dominant norm of the organization in relation to
absenteeism behavior (Dello Russo, et al., 2013).
The Present Study
It has been well demonstrated that absenteeism is an important
indicator of workplace productivity. Furthermore, several conceptual frameworks and models have identified a number of antecedents of absenteeism behavior, which have subsequently been supported by longitudinal research. However, a key limitation of
existing studies is that intraindividual and interindividual variations in absenteeism over time have rarely been captured in previous studies. This is an important consideration because there
may be distinct longitudinal trajectories of absenteeism, which
have different underlying antecedents.
The aim of the present study was to investigate the presence and
nature of absenteeism in a sample of full-time Australian employees over a 5-year period. The present study involved using a
growth mixture modeling approach to identify and examine distinct longitudinal trajectories of absenteeism and their antecedents.
This involved examining the following primary research question:
Research Question 1: Are there distinct longitudinal trajectories of absenteeism in full-time employees?
We also aimed to examine antecedents that predicted and distinguished between these trajectories. Based on the widely cited
Steers and Rhodes (1978) model and existing empirical research,
we focused on antecedents reflecting demographic characteristics
(e.g., age, sex, education), job satisfaction, psychosocial work
characteristics (e.g., job demands, job control, job security), work
characteristics (e.g., work hours, work schedules, job type, sick
leave entitlements), and health. This involved addressing three
additional research questions.
Research Question 2: Do demographic characteristics (age,
sex, education) predict absenteeism trajectories?
27
Research Question 3: Are characteristics of an individual’s
job (e.g., work characteristics and psychosocial job characteristics) associated with absenteeism trajectories?
Research Question 4: Does health status distinguish between
absenteeism trajectories?
Method
Participants
The Household, Income, and Labor Dynamics in Australia
(HILDA) Survey is a representative longitudinal panel study of
Australian households (Wooden, Freidin, & Watson, 2002).
HILDA, which commenced in 2001, collects data from members
of Australian households every 12 months through a series of
interviews and self-completion surveys. In the first Wave of the
study, members from 11,693 Australian households were invited
to participate in the study. Of those households contacted, 7682
provided data from at least one household member leading to an
initial sample size of 19,910. Of these individuals, 4,787 were aged
younger than 15 years at the time of interview and were excluded,
leaving a sample size of 13,158.
This paper utilized data from five recent waves of data (Waves
7 to 11). In this paper, we included participants aged 18 years and
older who were employed full-time at each wave (n ⫽ 2,934);
participants with missing absenteeism data were excluded (n ⫽
453). This resulted in a final sample of 2,481 full-time employees.
Table 1 shows the characteristics of the final sample compared
with those with missing absenteeism data. This table indicates
some differences between the samples, particularly in terms of
health and sick leave benefits. The HILDA Survey received ethics
approval from the University of Melbourne Human Research
Ethics Committee. Ethical approval to use these data in the present
paper was obtained from our university’s Human Research Ethics
Committee.
Measures
Absenteeism. Absenteeism was measured in relation to two
interview questions that asked participants whether they had taken
any sick leave in the previous 12 months, and if so, the number of
sick leave days they had taken. The data were combined to create
a count indicator of the amount of annual sick leave (in days) taken
over the 5-year period.
Demographic factors. Information on age, gender, and highest level of education (coded as high school, completed high
school, diploma/certificate, or university degree) was collected and
included in the analysis.
Psychosocial work characteristics. The HILDA Survey consisted of five items examining different aspects of job satisfaction,
including satisfaction with job security, hours of work, and work
life balance. These items were assessed on an 11-point scale, with
higher scores on all items indicative of higher levels of satisfaction.
Participants completed 12 items that examined aspects of their
perceived work environment (e.g., “I have to work fast in my job”;
“I have a lot of say in what I do at work”). Each item was assessed
on a 7-point Likert scale (strongly disagree to strongly agree).
MAGEE, CAPUTI, AND LEE
28
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Table 1
Characteristics of the Sample Compared With Those Excluded Due to Missing Data
Sex, n (%)
Female
Male
Age, mean (SD)
Bodily Pain subscale, mean (SD)
General Health subscale, mean (SD)
Satisfaction—job security, mean (SD)
Satisfaction—work hours, mean (SD)
Job control, mean (SD)
Work Schedule, n (%)
Nonstandard
Standard
Job type, n (%)
Professional
Manager
Trade/Technician
Laborer
Clerical/Administration/Sales
Sick leave benefits, n (%)
No benefits
Self-employed/Business owner
Access to benefits
Work hours, n (%)
35–39 hr
40–49 hr
50 hr or more
a
Final sample
(n ⫽ 2,481)
Excluded due to
missing data
(n ⫽ 453)
735 (84.7)
1746 (84.3)
39.75 (11.06)
79.80 (19.39)
73.56 (17.54)
8.28 (11.80)
7.13 (1.92)
26.34 (8.61)
137 (15.7)
316 (15.3)
39.47 (12.24)
74.48 (20.83)
69.02 (18.92)
8.05 (1.94)
6.98 (1.99)
27.63 (9.68)
408 (82.9)
2,073 (84.9)
369 (15.1)
84 (17.1)
428 (81.5)
667 (86.7)
432 (82.3)
629 (86.3)
325 (84.2)
97 (18.5)
102 (13.3)
93 (17.7)
100 (13.7)
61 (15.8)
139 (85.3)
334 (75.2)
2008 (86.3)
24 (14.7)
110 (24.8)
319 (13.7)
547 (84.3)
1,173 (86.4)
761 (82.1)
102 (15.7)
185 (13.6)
166 (17.9)
p Value for
differencea
.791
.621
.005
.005
.014
.135
.166
.272
.036
⬍.001
.020
p Values derived from chi-square tests (categorical variables) and ANOVAs (continuous variables).
Exploratory and confirmatory factor analysis conducted in this
paper indicated that there were two distinct factors. Factor 1
assessed issues surrounding flexibility and autonomy at work and
was consequently labeled job control (Cronbach’s alpha ⫽ .89).
Factor 2 assessed intensity of work and time pressures and was
labeled job demands (Cronbach’s alpha ⫽ .77). Higher scores are
indicative of high levels of job control and job demands respectively.
Work characteristics. The interviews and self-report questionnaires also collected information on the number of hours
worked each week (coded as 35–39 hr, 40 – 49 hr, and ⱖ50 hr a
week) and work schedule (coded as standard work hours [i.e.,
regular daytime shift] and nonstandard work hours [evening/night
shifts, rotating shifts, irregular schedules]). Information was also
collected on the type of job according to the Australian and New
Zealand Standard Classification of Occupations (Australian Bureau of Statistics, 2006). This classification system distinguishes
between eight major groupings of job types: (a) managers, (b)
professionals, (c) technicians and trades workers, (d) community
and personal service workers, (e) clerical and administrative workers, (f) sales workers, (g) machinery operators and drivers, and (h)
laborers. Due to some small cell sizes, we merged Categories d, e,
and f into a single category and g and h into a single category. This
resulted in five job type categories.
Health. Self-reported mental and physical health were assessed via the Short-Form Health Survey (SF-36), a 36-item scale
that assesses health across eight subscales (Ware, Kosinski, &
Gandek, 1993, 2000). The Physical Functioning subscale (10
items) examines how well individuals are able to perform normal
activities of daily living such as climbing stairs and kneeling. The
Role Physical subscale (four items) assesses the extent to which
physical health affects problems with work and other daily activities. The Bodily Pain subscale includes two items regarding the
extent to which individuals experience pain and the effect on
subsequent daily activities. Social Functioning (two items) asks
participants to indicate the extent to which their physical and
emotional problems affect their normal daily activities. Vitality
(four items) assesses individual’s levels of vigor and energy. Role
Emotional (three items) examines the effect of emotional problems
on work or daily activities. Finally, the mental health subscale
includes five items that assess levels of depression and anxiety. On
all subscales, higher scores are indicative of better health.
Statistical Analysis
Longitudinal trajectories of absenteeism were investigated using
growth mixture modeling (GMM), which is a statistical approach
that examines change in a given variable over time (Jung &
Wickrama, 2008). Conventional growth modeling assumes that a
single growth curve (or trajectory) is adequate for capturing
change in an entire population, and that covariates affect growth
factors in the same way for all individuals (Jung & Wickrama,
2008). However, there are often distinct subpopulations within a
sample that have different patterns of change over time (Jung &
Wickrama, 2008); these distinct patterns or trajectories are not
accounted for using conventional analytic approaches. This is
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ABSENTEEISM IN FULL-TIME AUSTRALIAN EMPLOYEES
particularly relevant in the context of absenteeism given some
recent findings suggesting identified distinct longitudinal patterns
of absenteeism (Dello Russo, et al., 2013; Haukka et al., 2014;
Haukka et al., 2013). In addition, it is feasible that potential
antecedents of absenteeism have a differential effect on these
trajectories, which can also be examined using GMM. Therefore,
GMM is an ideal approach for the present study because it can
identify distinct trajectories of absenteeism as well as covariates
that distinguish between the trajectories. Because absenteeism data
were counts, with a highly skewed distribution and a high proportion of zero values, zero-inflated Poisson GMMs were tested in
this paper (Muthén & Muthén, 1998 –2010).
Consistent with recent recommendations, the analyses involved
three main steps (Jung & Wickrama, 2008; Muthén, 2006; Muthén,
2004). The first step, performed using Mplus Version 6.11
(Muthén & Muthén, 1998 –2010), aimed to identify the number of
distinct trajectories of absenteeism. This step involved testing a
model with a single trajectory, following by a model with two
trajectories, then three trajectories and so on until the optimal
number of trajectories was identified. For this first step, covariates
were not included in the models. Several sources of information
were used to decide on the optimal number of trajectories. Three
information criteria—Akaike information criteria (AIC), Bayesian
information criteria (BIC), and sample-size–adjusted BIC—were
used to compare sequential models, with lower values for a model
with k trajectories (e.g., three trajectories) suggesting an improved
model fit compared with a model with k – 1 trajectories (e.g., two
trajectories). Relying on these criteria alone can overestimate the
number of trajectories, so bootstrap likelihood ratio tests (BLRT)
were also used (Nylund, Asparouhov, & Muthén, 2007). The
BLRT compares model fit between subsequent models (e.g., compares a three class model with a two class model); a significant
BLRT suggests that the model with one more trajectory provides
a significant improvement in model fit (Nylund et al., 2007). The
optimal number of trajectories is informed by the specification of
additional trajectories not leading to a significant BLRT. In deciding on the optimal number of latent classes, we additionally
considered classification accuracy (informed by entropy ⬎ .80),
trajectory size (to ensure trajectories were large enough to be
examined meaningfully), and the distinctiveness of trajectories (to
ensure identified trajectories were distinct from one another).
The aim of Step 2 was to identify covariates that were significantly associated with the trajectories identified in Step 1. This
involved conducting multinomial logistic regression modeling using
SPSS Version 19 to investigate covariates significantly associated
with trajectory membership. Covariates that were significantly associated with the trajectories were retained for inclusion in Step 3.
The third step of the analysis involved testing a full GMM,
whereby the significant covariates from Step 2 were added as
time-invariant covariates to the GMM (specifying the number of
distinct trajectories from Step 1). This step allowed the antecedents
to influence the growth factors of the trajectories, and provided
insight into how the trajectories varied by these antecedents
(Muthén, 2004). This final model thus indicated the nature of the
different trajectories and significant covariates that distinguished
between these trajectories. The differences between the trajectories
in regards to the antecedents are reported as odds ratios based on
logistic regressions conducted within the GMM.
29
Results
Table 2 shows the model fit results for models tested in Step 1.
These results indicate that specifying additional trajectories (e.g.,
two vs. one trajectory) led to improvements in model fit as indicated by significant BLRT results and lower relative values for the
AIC, BIC, and sample-size–adjusted BIC. Despite these improvements, the size of each additional trajectory identified became
smaller. In the four-class model, the smallest trajectory accounted
for approximately 8% of the sample, while in models with five or
more trajectories, the smallest trajectory accounted for ⬍3% of the
sample. These trajectories may be too small to examine meaningfully. As a consequence, the four-trajectory model was identified
as the optimal solution, which is consistent with the number
identified by Haukka et al. (2013, 2014).
In the second stage of the analyses, the multinomial logistic
regression models conducted with SPSS Version 19 indicated that
age, sex, education, job type, work hours, work schedules, sick
leave entitlements, job control, satisfaction with job security, satisfaction with hours worked, general health, and bodily pain were
significantly associated with class membership as specified by the
four-class model.
The full GMM was then tested in Mplus, with a four-trajectory
model specified and the significant covariates from Step 2 included as time-invariant covariates in the model. The four absenteeism trajectories resulting from the full model are shown in
Figure 1. The first trajectory (n ⫽ 879; 35.3%) included employees
who took 4 to 5 days of sick leave annually. The amount of
absenteeism increased over time at a decreasing rate as reflected
by the significant linear (B ⫽ .18, p ⬍ .001) and quadratic
functions (B ⫽ ⫺.03, p ⫽ .038). This trajectory was labeled
moderate absenteeism.
The second trajectory included 8.4% (n ⫽ 210) of the sample,
and had high levels of absenteeism compared with the other
trajectories. The linear (B ⫽ ⫺.01, p ⫽ .891) and quadratic
functions (B ⫽ .02, p ⫽ .573) were not significant for this
trajectory, indicating no significant changes in absenteeism over
time. This trajectory was labeled high absenteeism.
The third trajectory comprised 23.3% (n ⫽ 579) of the present
sample, and included employees who essentially took no sick leave
Table 2
Model Fit Results for Growth Mixture Models Specifying 1 to
8 Classes
Classes
AIC
BIC
Sample-size–
adjusted BIC
BLRT
p value
Entropy
1
2
3
4a
5
6
7
8
91,038.90
69,405.93
63,474.33
61,534.55
59,947.14
58,740.55
57,845.84
57,022.71
91,056.29
69,446.49
63,538.06
61,621.41
60,057.22
58,873.82
58,002.28
57,202.33
91,046.76
69,424.25
63,503.11
61,573.75
59,996.86
58,800.74
57,916.49
57,103.83
—
⬍.001
⬍.001
⬍.001
⬍.001
⬍.001
⬍.001
⬍.001
1.00
.94
.94
.91
.91
.92
.93
.92
Note. AIC ⫽ Akaike’s information criteria; BIC ⫽ Bayesian information
criteria; BLRT ⫽ bootstrap likelihood ratio test.
a
The four-class model was considered optimal, given that models with five
or more classes identified very small trajectories, despite improvements in
model fit.
MAGEE, CAPUTI, AND LEE
30
No sick leave
Low sick leave
Moderate sick leave
Number of sick leave days
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
High sick leave
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
Compared with the moderate absenteeism trajectory, the low
absenteeism trajectory had a lower proportion of females (OR ⫽
.50, p ⬍ .001), were older (OR ⫽ 1.01, p ⬍ .05), and had higher
general health (OR ⫽ 1.02, p ⬍ .001). These employees were also
more likely to be a manager (OR ⫽ 1.58, p ⬍ .05), self-employed/
business owner (OR ⫽ 3.72, p ⬍ .001), and work long hours
(OR ⫽ 1.73, p ⬍ .001) compared with moderate absenteeism
employees.
Compared with the moderate absenteeism trajectory, the high
absenteeism trajectory was characterized by older age (OR ⫽ 1.03,
p ⬍ .05), and poorer health as reflected by lower scores on the
Bodily Pain (OR ⫽ .99, p ⬍ .05), and General Health (OR ⫽ .99,
p ⬍ .05) subscales.
Discussion
2007
2008
2009
2010
2011
Years
Figure 1. The four distinct trajectories of annual absenteeism in the
sample of Australian full-time employees. Means and standard deviations
are shown for each trajectory.
annually across the 5-year period. There was a significant trend for
a decline in absenteeism over time as reflected by the linear
function (B ⫽ ⫺.78, p ⫽ .003); the quadratic function indicated
that this decline slowed with time (B ⫽ .19, p ⫽ .013). This
trajectory was labeled no absenteeism.
The final trajectory included 33.0% (n ⫽ 822) of the present
sample, and included employees who took 1 to 2 days of sick leave
over the 5-year period; levels of absenteeism remained stable over
time as reflected by nonsignificant linear (B ⫽ ⫺.05, p ⫽ .552)
and quadratic growth functions (B ⫽ .01, p ⫽ .709). This trajectory was labeled low absenteeism.
The characteristics of these trajectories are shown in Table 3
with the multivariate differences between the trajectories shown in
Table 4. In order to interpret the multivariate differences in these
characteristics between trajectories, it was necessary to select one
trajectory as the referent. We decided on the moderate absenteeism
trajectory as the referent for two main reasons. First, it was the
largest trajectory, which can be used as a justification for selecting
a suitable reference category. Second, the pattern of sick leave
corresponds most closely with average levels of sick leave in
Australia. Therefore, it may provide a useful indication of “typical” absenteeism patterns, and allow for meaningful comparisons
with the other trajectories.
The GMM indicated that several covariates distinguished between the trajectories (see Table 4). Compared with the moderate
absenteeism trajectory, the no absenteeism trajectory had a lower
proportion of females (odds ratio [OR] ⫽ .47, p ⬍ .001), were
younger (OR ⫽ .96, p ⬍ .001), had better general health (OR ⫽
1.02, p ⬍ .001), and higher levels of job control (OR ⫽ 1.03, p ⬍
.05). In addition, individuals in the no absenteeism trajectory were
more likely to be managers (OR ⫽ 2.01, p ⬍ .001), self-employed/
business owner (OR ⫽ 39.65, p ⬍ .001), and work long hours
(OR ⫽ 2.01, p ⬍ .001) compared with moderate absenteeism
employees.
This study utilized a GMM approach to identify distinct trajectories of absenteeism and their antecedents in a sample of full-time
Australian employees. The results identified four distinct longitudinal trajectories of absenteeism, which were labeled no absenteeism (23.3%), low absenteeism (33.0%), moderate absenteeism
(35.3%), and high absenteeism (8.4%). A small proportion of
individuals displayed annual fluctuations in absenteeism that deviated from these trajectories, perhaps reflecting factors such as
injury, short-term illness, or organization changes that produce
short-term changes in absenteeism (Hansson, Vingård, Arnetz, &
Anderzén, 2008). However, in general, the mean levels of absenteeism within each trajectory remained fairly stable over time. The
present findings add to a small number of existing studies that have
identified distinct trajectories of absenteeism behavior (Dello
Russo, et al., 2013; Haukka et al., 2013, 2014).
A key strength of the present study is that potential antecedents
of these trajectories were examined. The antecedents explored in
this study were consistent with well-established theories of absenteeism, most notably Steers and Rhodes’ (1978) model which
proposes that absenteeism behavior reflects demographic characteristics, motivation to attend work, and ability to attend work. As
will be discussed in more detail, our results indicate that the
absenteeism trajectories differ on a range of these antecedents,
suggesting differences in motivation and ability to attend work. In
discussing these results, we utilize the moderate absenteeism trajectory as the reference trajectory. This is because this trajectory
was the largest and had rates of absenteeism (4 – 6 days per year)
that corresponded most closely with average absenteeism rates in
Australia. Therefore, it provides a meaningful point of comparison
for the other trajectories. The findings are discussed separately for
each absenteeism trajectory.
High Absenteeism Trajectory
The high absenteeism trajectory was the smallest of the four
trajectories and was characterized by 11–13 days of absenteeism
each year. There were several antecedents that distinguished the
high absenteeism trajectory from the moderate absenteeism trajectory. In particular, employees in the high absenteeism trajectory
had lower scores on the Bodily Pain and General Health subscales
of the SF-36, which indicates poorer health. The General Health
subscale assesses overall health, the likelihood of getting sick
relative to others, and whether the individual expects their health
ABSENTEEISM IN FULL-TIME AUSTRALIAN EMPLOYEES
31
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Table 3
Characteristics of the Four Sick Leave Trajectories Based on the Covariates Included in the Full Growth Mixture Model
Sick leave, mean (SD)
Year 1
Year 2
Year 3
Year 4
Year 5
Age mean (SD)
Sex, n (%)
Male
Female
Bodily Pain subscale, mean (SD)
General Health subscale, mean (SD)
Job security, mean (SD)
Satisfaction with work hours, mean (SD)
Job control, mean (SD)
Work schedule, n (%)
Standard
Nonstandard
Job type, n (%)
Professional
Manager
Trade/Technician
Laborer
Clerical, etc.
Sick leave benefits, n (%)
No benefits
Self-employed
Access to benefits
Work hours, n (%)
35–39 hr
40–49 hr
50 hr or more
No absenteeism
Low absenteeism
Moderate absenteeism
High absenteeism
.16 (.52)
.09 (.37)
.07 (.33)
.05 (.24)
.13 (.50)
42.94 (10.56)
1.92 (2.36)
1.95 (1.98)
1.85 (1.90)
1.69 (1.67)
1.91 (2.10)
39.29 (11.09)
4.25 (3.46)
5.01 (3.87)
5.34 (3.80)
5.63 (4.58)
5.31 (4.04)
37.64 (10.95)
11.08 (11.22)
11.09 (10.12)
11.06 (10.92)
12.30 (12.40)
13.00 (11.62)
41.58 (10.63)
487 (27.9)
93 (12.7)
80.39 (18.28)
75.44 (16.92)
8.13 (1.92)
6.80 (2.05)
30.37 (8.04)
626 (35.9)
195 (26.5)
82.56 (18.17)
75.92 (16.58)
8.31 (1.72)
7.15 (1.89)
25.92 (8.21)
526 (30.1)
347 (47.2)
79.09 (19.51)
72.01 (17.42)
8.30 (1.83)
7.37 (1.81)
24.76 (8.47)
107 (6.1)
100 (13.6)
70.17 (22.98)
65.60 (20.21)
8.49 (1.68)
6.92 (1.92)
23.37 (8.52)
455 (21.9)
125 (30.6)
707 (34.1)
114 (27.9)
732 (35.3)
141 (34.6)
179 (8.6)
28 (6.9)
165 (38.6)
115 (17.2)
122 (28.2)
101 (16.1)
77 (23.7)
151 (35.3)
220 (33.0)
143 (33.1)
196 (31.2)
111 (34.2)
92 (21.5)
259 (38.8)
144 (33.3)
264 (42.0)
114 (35.1)
20 (4.7)
73 (10.9)
23 (5.3)
68 (10.8)
23 (7.1)
71 (51.1)
263 (78.7)
246 (12.3)
42 (30.2)
55 (16.5)
724 (36.1)
24 (17.3)
12 (3.6)
837 (41.7)
2 (1.4)
4 (1.2)
201 (10.0)
61 (11.2)
215 (18.3)
304 (39.9)
152 (27.8)
401 (34.2)
268 (35.2)
262 (47.9)
458 (39.0)
153 (20.1)
72 (13.2)
99 (8.4)
36 (4.7)
to get worse over time (Ware et al., 1993, 2000). Although developed as a measure of physical health, research has consistently
demonstrated that the General Health subscale also reflects aspects
of mental health (Ware et al., 1993, 2000). The Bodily Pain
subscale of the SF-36 assesses the levels of pain experienced in the
past 4 weeks, and the extent to which pain interfered with work
and housework. In combination, these two results indicate that
employees in the high absenteeism trajectory experienced more
pain and poorer physical and mental health compared with the
moderate absenteeism trajectory. Consistent with Steers and Rhodes (1978), as well as many other theoretical perspectives (e.g.,
Brooke, 1986; Nicholson, 1977), it is feasible that poor health and
pain observed in this trajectory lead to higher rates of absenteeism
by constraining employee’s ability to attend work. These findings
suggest that high rates of absenteeism may primarily reflect health
constraint factors.
Low Absenteeism Trajectory
The low absenteeism trajectory accounted for one third of the
sample, with average rates of absenteeism in this trajectory ranging
from 2–3 days per year. Several characteristics distinguished employees in this trajectory from those in the moderate absenteeism
trajectory. First, employees in the low absenteeism trajectory were
older and more likely to be male. These findings are consistent
with existing studies demonstrating that older age and male gender
are associated with lower amounts of absenteeism (Darr & Johns,
2008; Feeney, North, Head, Canner, & Marmot, 1998; Martocchio,
1989; Mastekaasa & Olsen, 1998; Ng & Feldman, 2008; Steers &
Rhodes, 1978). The reasons for different rates of absenteeism in
males and females are not clear. Some existing studies have
suggested that a range of factors including differences in job type
and child care responsibilities could underlie these gender differences (Darr & Johns, 2008; Mastekaasa & Olsen, 1998), but this
requires further investigation using measures that are able to
examine these issues. In regards to age, existing research suggests
that older employees have lower rates of absenteeism because they
are better able to meet the requirements of their jobs (Martocchio,
1989) and/or have higher levels of job motivation (Ng & Feldman,
2008).
The results also indicated that employees in the low absenteeism
trajectory had better health compared with the moderate absenteeism trajectory as reflected by higher scores on the General Health
subscale of the SF-36. Consistent with Steers and Rhodes (1978),
better mental and physical health could mean that an individual is
more able to attend work, and have lower rates of absenteeism.
Individuals in the low absenteeism trajectory were also more
likely to be managers, work long hours, and be self-employed/a
business owner. Previous studies have indicated differences in
absenteeism rates by occupational status and job level (Darr &
Johns, 2008; Nicholson & Johns, 1985). For example, white collar
MAGEE, CAPUTI, AND LEE
32
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Table 4
Logistic Regression Results From the Full Growth Mixture Model
Sex
Female
Male
Age
Bodily Pain subscale
General Health subscale
Job Satisfaction—job security
Job Satisfaction—work hours
Job control
Work schedule
Nonstandard
Standard
Job type
Professional
Manager
Trade/Technician
Laborer
Clerical/Administration/Sales
Sick leave benefits
No benefits
Self-employed/Business owner
Access to benefits
Work hours
35–39 hr
40–49 hr
50 hr or more
No absenteeism
Low absenteeism
High absenteeism
0.47ⴱⴱ
Ref
0.96ⴱⴱ
1.01
1.02ⴱⴱ
1.02
0.94
1.03ⴱ
.50ⴱⴱ
Ref
1.01ⴱ
1.01
1.02ⴱⴱ
.99
.96
1.00
1.19
Ref
1.03ⴱ
0.99ⴱ
0.99ⴱ
1.11
0.91
.98
.82
Ref
0.68
Ref
.75
2.01ⴱⴱ
1.35
.97
Ref
.94
1.58ⴱ
1.04
.95
Ref
1.10
.85
.80
.83
Ref
2.39
39.65ⴱⴱ
Ref
1.39
3.72ⴱⴱ
Ref
1.36
2.56
Ref
Ref
1.13
2.01ⴱⴱ
Ref
.81
1.73ⴱⴱ
Ref
0.77
0.80
1.34
Ref
Note. Ref ⫽ referent category. The low absenteeism trajectory is the referent category. Results are presented
as odds ratios.
ⴱ
p ⬍ .05. ⴱⴱ p ⬍ .001.
workers such as managers and professionals have been shown to
have lower rates of absenteeism compared with blue collar workers (Darr & Johns, 2008; Nicholson & Johns, 1985). Although we
are unable to determine the reasons for these differences from the
present data, Nicholson and Johns’ (1985) work on absence culture
and psychological contract suggests that certain job types (including
managers) have lower rates of absenteeism because of guilt over
absences and a perception of absences as illegitimate (Nicholson &
Johns, 1985). Thus, some employees in managerial roles may avoid
taking time off work when needed because of these factors.
Employees in this trajectory were also more likely to be selfemployed or a business owner. Although all Australian employees,
with the exception of those employed casually, are eligible for paid
sick leave (Fair Work Ombudsman, 2010), individuals who are selfemployed may be reluctant to take time of work due to factors such
as illness because of potential for lost income (Benavides et al., 2000).
Individuals in this trajectory were also more likely to work longer
hours, which is consistent with some findings demonstrating that
longer work hours are linked with less absenteeism (Magee et al.,
2011).
The results discussed above suggest a range of different processes underlying the low levels of absenteeism in this trajectory.
On the one hand, it is feasible that because of their better general
health relative to the moderate absenteeism trajectory these employees do not need to take as many days off work each year. That
is, for these employees better general health means they are less
constrained to attend work. Thus, this could be a healthy group of
employees who do not need to take much time off work.
However, this low absenteeism trajectory may represent a group
of employees who are less inclined to take time off work when
they are unwell. This is consistent with the concept of presenteeism, which can be defined as employees attending work when they
are unwell and should be absent from work (Aronsson & Gustafsson, 2005; Dew, Keefe, & Small, 2005; Johns, 2010). Presenteeism is associated with productivity losses that are estimated to
outweigh losses associated with absenteeism (Cooper & Dewe,
2008; Hemp, 2004). A range of factors have the potential to
contribute to presenteeism, including organizational policies relating to sick leave and downsizing, ease of replacement, perceived
pressure for supervisors or coworkers to attend work while unwell,
and low levels of job security (Aronsson & Gustafsson, 2005;
Johns, 2011). The present study did not include a measure of
presenteeism, so it is not possible to determine whether some
employees in this trajectory had higher levels of presenteeism.
However, the findings for job type, long work hours, and selfemployed/business owner status are consistent with existing theories and findings relating to presenteeism. Therefore, it is feasible
that certain characteristics of jobs (e.g., job type, self-employed
status) increase the likelihood of low absenteeism and high presenteeism. These possibilities warrant investigation in future research.
No Absenteeism Trajectory
Employees in the no absenteeism trajectory reported very low
rates of absenteeism annually over the 5-year period, suggesting
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ABSENTEEISM IN FULL-TIME AUSTRALIAN EMPLOYEES
they essentially took no sick leave days each year. This is an
interesting finding because it suggests that although rates of absenteeism in Australia range from 4 – 8 days per year, a substantial
proportion of full-time employees (approximately 25% in this
sample) take very few (if any) sick days each year.
Similar to the low absenteeism trajectory, these individuals were
more likely to be male, and have higher levels of general health
relative to the moderate absenteeism trajectory. As noted above,
these factors may contribute to lower rates of absenteeism. However, these individuals were younger, which is an interesting
finding given that studies generally find that older (not younger)
age is linked with lower absenteeism (Martocchio, 1989; Ng &
Feldman, 2008). The reasons for this divergent finding are not
clear and require further investigation.
Employees in the no absenteeism trajectory had higher levels of
job control, suggesting more autonomy and flexibility; previous
research suggests that more job control could benefit health and
well-being by allowing individuals to better balance their work and
nonwork commitments, and recover from work stress (AlaMursula et al., 2006). It is therefore feasible that higher levels of
job control could contribute to lower rates of absenteeism over
time (Nielsen et al., 2006; Smulders & Nijhuis, 1999; Väänänen et
al., 2003; Virtanen et al., 2007).
However, other findings suggest that this trajectory could be
characterized, at least to some extent, by presenteeism in a similar
manner to the low absenteeism trajectory. For example, employees
in this trajectory had higher odds of being self-employed or a
business owner. As noted above, they may be less inclined to take
time off work due to financial concerns or loss of business (Benavides et al., 2000). Employees in this trajectory were also more
likely to be mangers. Consistent with the low absenteeism trajectory and Nicholson and Johns’ (1985) theory, some managers may
avoid taking time off work when unwell due to perceptions of sick
leave as not being legitimate.
These findings in combination with those for low absenteeism
trajectory, suggest caution when interpreting levels of absenteeism. It is possible that some employees are healthier or have better
control over the working arrangements and thus do not need to
take as much time off work. This could translate into low rates of
absenteeism over time. However, low absenteeism is not necessarily an indicator of healthy and productive employees. This is
because some employees may feel pressure to avoid taking time
off work when they are unwell because of workplace cultural
factors (e.g., perceptions of absence as less legitimate) or financial
reasons (e.g., loss of income for self-employees). This may indicate higher rates of presenteeism which also has the potential to
contribute to lost productivity, perhaps at levels higher than absenteeism (Hemp, 2004).
Strengths and Limitations
The present study provides an important extension on existing
absenteeism literature. First, the sophisticated modeling approach
to examine longitudinal data across five time points provided
insights into intraindividual and interindividual differences in patterns of absenteeism over time. This supports a small number of
studies that have recently identified distinct longitudinal trajectories of absenteeism (Dello Russo, et al., 2013; Haukka et al., 2014;
Haukka et al., 2013). The sample size was also relatively large,
33
providing sufficient statistical power to identify and distinguish
between the four absenteeism trajectories. In addition, we were
able to examine whether demographic, work and job characteristics, and health status differentiated between these groups. This is
important because well-established theories of absenteeism, such
as Steers and Rhodes’ (1978) model, suggest that these factors are
important predictors of absenteeism behaviors. Thus, our results
provide insight into some of the factors that may predict certain
patterns of absenteeism over time.
There are some limitations of the present study that warrant
discussion. First, absenteeism data were derived from self-reported
retrospective recall of absenteeism over a 12-month period. Selfreport measures are widely used but can lead to underestimates of
absenteeism; this may explain why the rates of absenteeism reported in this paper were lower than studies using records-based
data (Johns & Miraglia, 2015). However, Johns and Miraglia
(2015) indicated that absenteeism data from self-report measures
have reasonable rank order convergence with data obtained
through more objective measures (e.g., organizational records).
Therefore, although the identified trajectories may not provide a
precise indication of absenteeism amount, they are likely to accurately capture patterns of absenteeism over time.
Another consideration is that research has demonstrated that the
frequency and duration of absenteeism spells is important in distinguishing between voluntary and involuntary forms of absenteeism (Bakker, Demerouti, de Boer, & Schaufeli, 2003). For example, more frequent spells of absenteeism could reflect motivational
factors (such as low job satisfaction), whereas longer spells are due
to health impairment (Bakker et al., 2003). Future research capturing frequency and duration, as well as the underlying reasons
for absences will be important in further delineating the nature of
longitudinal trajectories.
The present study also focused on absenteeism only. In order to
make clearer conclusions about the implications of each trajectory
for employee productivity, future research will need to explore
other components of productivity such as presenteeism. This is
particularly important for the low absenteeism trajectories, where
lower rates of absenteeism could potentially be offset by higher
rates of presenteeism.
There are some other issues that require consideration. A small
proportion of participants (15.4% of the initial, eligible sample)
were excluded due to missing absenteeism data. The characteristics of these participants differed significantly from those who
were included in the final sample. Most notably, excluded participants had poorer self-reported health, lower job security, and were
more likely to be self-employed compared with included participants. As discussed above, these factors have important implications for patterns of sick leave over time and thus it is feasible that
excluded individuals could represent a unique subgroup of employees who have differing patterns of sick leave over time. Thus
a potential limitation of this paper is that the exclusion of individuals due to missing data could have implications for the nature and
size of the identified trajectories.
The present paper also focused on full-time employees, and it is
possible that employees who work part-time hours have very
different patterns of absenteeism reflecting differing antecedents.
Part-time employees are an important population, with existing
research demonstrating differences between part-time and fulltime employees in relation to a number of domains such as job
MAGEE, CAPUTI, AND LEE
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34
involvement; these associations vary depending on specific work
arrangements (e.g., permanent/temporary, voluntary/involuntary;
Thorsteinson, 2003). These and other factors may lead to differing
patterns of absenteeism reflecting differing antecedents compared
with full-time employees. We propose that the unique characteristics and considerable heterogeneity of part-time employees warrant separate investigations to fully understand trajectories of
absenteeism and the associated antecedents. Therefore, it is suggested that in addition to clarifying the nature of distinct absenteeism trajectories in full-time employees, future studies are also
conducted to explore absenteeism trajectories and their antecedents in part-time employees.
Furthermore, some of the potential antecedents assessed in this
study were assessed using short-scales or single items scales (e.g.,
job satisfaction). This is a potential limitation because these measures may not provide a comprehensive insight into these factors.
Despite this, single-item scales generally provide appropriate and
valid measures of these constructs and correspond well with multiple item scales (Wanous, Reichers, & Hudy, 1997). Factors not
examined in this paper such as insomnia, work-life balance, and
organization factors such as specific policies and cultural factors
regarding sick leave and leadership could also be important predictors of these trajectories and require investigation in future
research.
Conclusions
This paper provides a novel insight into different patterns of
absenteeism over time in a large, heterogonous sample of Australian employees. The results demonstrate that several sociodemographic, health, and work-related factors underlie these trajectories, which is consistent with existing theories of absenteeism. The
pattern of results for the high absenteeism trajectory suggests that
poor health may constrain an employee’s ability to attend work.
The findings for low absenteeism and no absenteeism suggest a
range of potential underlying factors, which may reflect a greater
ability and motivation to attend work or higher levels of presenteeism. These findings require further investigation, with measures
of presenteeism needed to better understand these trajectories.
Nevertheless, these results have important implications for employers and organizations, as they could inform strategies to minimize the effects of absenteeism on lost workplace productivity.
These may include providing employees with more flexible work
conditions (e.g., options to work from home, or have variable start
and finish times), greater autonomy, and improved coping skills.
Although there is a need to limit avoidable absenteeism (e.g.,
through prevention strategies), it is also important that organizations foster workplace cultures that encourage employees to take
sick leave when they are unwell to prevent presenteeism which
could ultimately lead to greater productivity losses.
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Received April 27, 2014
Revision received February 3, 2015
Accepted February 16, 2015 䡲
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