APPLIED PSYCHOLOGY: AN INTERNAnONAL REVIEW, 1987,36 (2) 121-132 Absence from Work: Explanations and Attributions Nigel Nicholson and Roy Payne University of Shefield, Shefield, U.K. Quatre groupes de travailleurs (employes de bureau, employks de bureau, ouvriers et ouvrieres) ont ktk interrogks sur les chances qu'avaient douze kvenements particuliers de les contraindre i s'absenter de leur travail et si ces m2mes kvenements avaient effectivement ktk a l'origine d'absences spkcifiques rkcentes. Parmi ces Cvenements, on compte la maladie grave, les problemes familiaux mineurs et la difficult6 de se lever H l'heure. Ces derniers ont tti choisis pour reprknter un continuum d'bvihements q& different de par leur influence plus ou moins grande sur l'individu qui dtkide si il/elle devra s'absenter du travail. On a manifest6 un soutien que trk modM en faveur de I'hypothtse qu'il y aurait des diffkrences entre les quatre groupes profession- nels. Certaines divergences intkressantes sont apparues entre les estimations quant aux probabilitks qu'avaient certains kvknements de se produire et la frhuence B laquelle ces mEmes Cvhements ktaient signal& c o m e causes d'absence rkelles (par exemple maladie Gnigne). Ainsi les motifs attribub aux absences peuvent diffkrer des causes reelles. Les implications de ces rksultats pour les recherche futures ainsi que des suggestions destinks aux directeurs de sociktks confrontks au probleme de l'absentkisme sont exposies brievement. Four groups of workers (white-collar males, white-collar females, blue-collar males, and blue-collar females) were asked how likely it was that each of 12 different events would lead to their being absent from work, and then whether the same 12 events had actually been causes of recent specific problems. Examples of these events were serious illness, minor domestic problems and difficulties getting up on time. These have been chosen to represent a conti- nuum of events which vary in the degree to which they leave the individual free to decide if he/she will need to be absent from work if they occur. There was only moderate support for the hypothesis that there would be differences between the four occupational groups. Some interesting discrepancies occurred between the estimates of how likely some events were to occur, and the frequency with which the same events were reported as actual causes of absence (e.g. minor illness). Thus attributions for absences may differ from real causes. The implications for future research and suggestions for managers dealing with absenteeism are briefly outlined. Requests for reprints should be sent to Roy Payne, MRC/ESRC Social and Applied Psychology Unit, University of Sheffield, Sheffield S10 2TN, U.K. The authors would like to thank Gary Johns for his insightful comments on an earlier version of this paper. ~~~~ ~~~ 0 1987 International Association of Applied Psychology
Transcript
APPLIED PSYCHOLOGY: AN INTERNAnONAL REVIEW, 1987,36 (2) 121-132
Absence from Work: Explanations and Attributions
Nigel Nicholson and Roy Payne University of Shefield, Shefield, U.K.
Quatre groupes de travailleurs (employes de bureau, employks de bureau, ouvriers et ouvrieres) ont ktk interrogks sur les chances qu'avaient douze kvenements particuliers de les contraindre i s'absenter de leur travail et si ces m2mes kvenements avaient effectivement ktk a l'origine d'absences spkcifiques rkcentes. Parmi ces Cvenements, on compte la maladie grave, les problemes familiaux mineurs et la difficult6 de se lever H l'heure. C e s derniers ont tti choisis pour reprknter un continuum d'bvihements q& different de par leur influence plus ou moins grande sur l'individu qui dtkide si il/elle devra s'absenter du travail. On a manifest6 un soutien que trk m o d M en faveur de I'hypothtse qu'il y aurait des diffkrences entre les quatre groupes profession- nels. Certaines divergences intkressantes sont apparues entre les estimations quant aux probabilitks qu'avaient certains kvknements de se produire et la frhuence B laquelle ces mEmes Cvhements ktaient signal& c o m e causes d'absence rkelles (par exemple maladie Gnigne). Ainsi les motifs attribub aux absences peuvent diffkrer des causes reelles. Les implications de ces rksultats pour les recherche futures ainsi que des suggestions destinks aux directeurs de sociktks confrontks au probleme de l'absentkisme sont exposies brievement.
Four groups of workers (white-collar males, white-collar females, blue-collar males, and blue-collar females) were asked how likely it was that each of 12 different events would lead to their being absent from work, and then whether the same 12 events had actually been causes of recent specific problems. Examples of these events were serious illness, minor domestic problems and difficulties getting up on time. These have been chosen to represent a conti- nuum of events which vary in the degree to which they leave the individual free to decide if he/she will need to be absent from work if they occur. There was only moderate support for the hypothesis that there would be differences between the four occupational groups. Some interesting discrepancies occurred between the estimates of how likely some events were to occur, and the frequency with which the same events were reported as actual causes of absence (e.g. minor illness). Thus attributions for absences may differ from real causes. The implications for future research and suggestions for managers dealing with absenteeism are briefly outlined.
Requests for reprints should be sent to Roy Payne, MRC/ESRC Social and Applied Psychology Unit, University of Sheffield, Sheffield S10 2TN, U.K. The authors would like to thank Gary Johns for his insightful comments on an earlier version of this paper.
~~~~ ~~~
0 1987 International Association of Applied Psychology
122 NICHOLSON AND PAYNE
I NTR 0 D U CTlO N
There is now a considerable literature on absence from work. Within this a comprehensive range of independent variables has been studied in the attempt to understand its causes. The sum total of this effort may be judged to have been disappointing and frustrating, inasmuch as few reliable predic- tors have emerged and the total amount of variance accounted for in absence behaviour remains low (Muchinsky, 1977; Johns, 1978; Landy, Vasey, & Smith, 1984).
Johns and Nicholson (1982) have argued that one reason for this has been the near-universal treatment of absence as an undifferentiated phenomenon and that one way forward is to decompose absence behaviour by focusing on different types of absentee, different work contexts, or different causes. The present paper is concerned with causes of absence. The approach to the study of attributed causes is guided by Nicholson’s (1 977) proposal that potentially absence-inducing events should be classified according to how much free- dom they leave for the individual to choose/decide whether or not event(s) will justify staying away from work. It is suggested that different events may be placed on a continuum called the A-B continuum (Chadwick-Jones, Brown, & Nicholson, 1973), at whose A-pole are events where individual choice cannot influence the probability of being absent (i.e. unavoidable absence), and at whose B-pole are events where the incidence of absence is entirely within the control of the person (i.e. avoidable absence). This formulation differs from conventional ideas about “voluntary” vs. “involun- tary” absence in two ways. First, it focuses on causes (events) rather than effects (absences), and second, it proposes that an event will have a different location on the A-B continuum according to the characteristics of the person and hisher work and personal circumstances. So, for example, it would be reasoned that a broken finger is an A-type event for a concert pianist but a B- type event for a singer. Nicholson suggested that A-B profiles could be constructed for occupationally or demographically distinctive groups or occupations, specifying for each group the location of potentially absence- inducing events on the A-B continuum and the frequency with which such events might be expected to occur for groups/occupations of that type. This opens the way to two-fresh approaches to the analysis of absence: (1) the comparison of group A-B event profiles to explain inter-group differences in the pattern and incidence of absence; and (2) improved prediction of absence at the individual level by identifying those cases where psychological variables have most potential influence, i.e. absences at the B-type end of the A-B continuum.
The purpose of this study -is to provide data on how people make attributions for their own absences and to assess the frequency with which those attributions are towards causes at the B-end of the A-B continuum.
ABSENCE FROM WORK 123
Four different occupational groups are used allowing us to compare their A- 3 profiles and so test the proposition that differences in the absence rates between occupations are partly due to differences in exposure to events which precipitate absence from work (i.e. A-type events).
METHOD
A sample of 280 employed persons was interviewed at their homes by trained interviewers from a professional survey organisation. The sampling range excluded people of less than age 25 and more than age 55 to reduce age effects known to be substantial in relation to absence (Nicholson, Brown, & Chadwick-Jones, 1977). The sample was stratified by sex and occupation class as follows: group 1: white-collar males, N=70; group 2: white-collar females, N = 70; group 3: blue-collar males, N = 73; group 4: blue-collar females, N = 67. Within these categories respondents were occupationally heterogeneous.
MEASURES
Respondents were asked about 12 classes of potential absence causes. This categorisation of events (see Table 1) was empirically derived from open- ended interview questions with 493 employees from various technologies in a previous study (Nicholson, 1975). First, event frequency: respondents were asked, “How often, if at all, have the following events happened to you during the last 2 years?’ The response scale was as follows: (1) not at all; (2) once or twice; (3) roughly once every 6 months; (4) roughly once every 3 months; (5) roughly once a week; (6) daily.
Second, absence susceptibility: respondents were asked, “How likely are you to be absent from work when these events occur? Base your answers as closely as possible on what you did during the last 2 years. If the event has not occurred in that time, indicate what you would be most likely to do.” The response scale was as follows: (1) certain to be absent from work; (2) fairly likely to be absent from work; (3) 50-50 chance of being absent from work; (4) fairly likely to go to work; (5) certain to go to work.
Absence frequency, lost time, and absence causes were self-reported by a procedure designed to maximise reliability. Respondents were asked, “Can you please think of the last time you were off work-how long ago was that? How long were you off work? (Responses classified: 1 day, 2 days, 3-5 days, 6-10 days, more than 20 days.) Did any of the following play a part in your absence?” (Respondent is then again shown the list of 12 absence causes and allowed to nominate 1 or more causes for this absence incident.)
This procedure was then repeated for the next previous absence, and so on for a total of five absences or going back one calendar year, whichever
TAB
LE 1
Ev
ent F
requ
ency
in t
he P
revi
ous
Two
Year
s
Onc
e A
ppro
x.
App
rox.
Ap
prox
. A
ppro
x.
AN
Twic
e M
onth
s M
onth
s M
onth
W
eek
Daily
Iper
A
ppro
x.
Not
at
or
lper
6
Iper
3
I per
Min
or a
ilmen
t you
rsel
f (e.
g. c
old,
hea
dach
e, u
pset
18
.6 st
omac
h, h
ango
ver)
M
inor
dom
estic
pro
blem
s (e.
g. f
amily
mem
ber w
ith
min
or a
ilmen
ts)
Feel
ing
depr
esse
d Se
rious
ove
rload
of d
utie
s at w
ork
Find
ing
it di
ffic
ult t
o ge
t up
on ti
me
Pers
onal
bus
ines
s to
atte
nd to
in w
orki
ng h
ours
(e.g
.
Serio
us d
omes
tic p
robl
ems (
e.g.
illn
ess i
n yo
ur fa
mily
) Lo
cal e
vent
s of i
nter
est t
o yo
u du
ring
wor
king
hou
rs
(e.g
. so
ccer
mat
ches
) M
ajor
dis
agre
emen
t with
boss
Row
s with
wor
kmat
es
Acc
iden
t to
your
self
at w
ork
Serio
usly
ill y
ours
elf
buyi
ng a
hou
se)
42.3
19.0
10
.4
6.5
2.9
0.4
41.0
53
.8
63.4
74
.8
26.5
19
.4 10
.8
5.4
10.0
6.
8 2.
9 1.8
1.9
4.3
2.9
0.4
1.9
9.3
2.9
0.7
0.4
4.3
6.5
6.5
0.4
2.2
10.8
10.4
75.0
77
.0
14.6
19
.4
4.3 1.8
3.2
1.1
1.4
0.0
1.1
0.4
0.4
0.4
80.3
81
.8
86.0
87
.9
88.9
9.0 8.9
8.6
11.1
10
.7
2.9
2. I
2.2
1.1
0.4
2.9
3.6
1.8
0.0
0.0
2.5
I .4
I .4
0.0
0.0
1.4
0. I
0.0
0.0
0.0
1.1 I .4
0.0
0.0
0.0
ABSENCE FROM WORK 125
occurred sooner. This method enables one to calculate absence frequency (maximum = 5, minimum = 0), total days lost and causes for all periods of absence.
RESULTS
It should be noted that the research procedure allowed people to assign more than one cause to a single absence, and that the total number of attributed causes on which this table is based was 468. The total number of absences to which these causes was attributed was 344, i.e. most reported only a single cause per absence. The 344 absences were distributed among the sample as follows:
None 1 2 3 4 5 28.2% 39.3% 21.4% 6.4% 1.8% 2.9%
This distribution corresponds closely to the frequency distributions reported in other studies, even to the point of demonstrating some slight bimodalism in the absence-prone tail of the distribution (Nicholson, 1975; Chadwick-Jones, Nicholson, & Brown, 1982). This provides, albeit in- directly, some grounds for confidence in the validity of our self-report method.
Table 1 shows the percentage distributions of responses for the 12 potentially absence-inducing events, rank ordered by the number of respon- dents saying events had occurred one or more times in the previous 2 years.
It is worth noting that only 11-12% have had serious illness or accidents, but over 80% have had minor ailments. Around 25% report difficulties with getting up (1 1% daily!), 20% interest in local events, and 25% having to attend to personal business in working hours. All these are commonly cited as reasons for absence in the literature, particularly by management. Employees are more likely to attribute serious overload of work as a reason and over 35% of the present sample do report this as hzving occurred to them (1 1 YO saying it occurs daily). As Table 1 shows these events do occur quite frequently for large proportions of the working population. Further- more, the distributions of the different causes differ as one might expect. Serious illness and accidents occur once or twice or not at all whereas depression, minor domestic problems, sleep problems, and work overload can occur very regularly. These differences lend additional confidence to the research strategy.
Table 2 presents the frequency of absences broken down by attributed causes. The percentage of people reporting serious illness and accidents in Tables 1 and 2 are similar indicating that both these nearly always lead to absence from work. Minor ailments are far and away the most frequently cited cause of absence, and this matches the frequency of this event in Table
126 NICHOLSON AND PAYNE
TABLE 2 Absence Frequency by Cause
Minor ailment Serious illness Serious domestic problems Personal business matters Minor domestic problems Accident at work Feeling depressed Local events of interest Difficulties getting up on time Major disagreements with boss Rows with workmates Serious overload of work duties
1. For many of the other causes of absence, however, there is little correspondence between Tables 1 and 2. While difficulties getting up, disagreements with boss or workmates, and serious overload are mentioned as relatively frequently occurring events, they are hardly ever cited as causes of absence from work. But over 5% do admit that serious domestic problems, minor domestic problems, and personal business matters have been causes of absence.
These data suggest that many B-type events, where people have discretion about whether the event will be used as a justification for absence, are very rarely attributed as causes of absence.
Table 3 shows the mean scores for people’s ratings of how likely each of the 12 events is to be a cause of their absence. These basically confirm Table 2. Serious illness is almost 100% certain to lead to absence. Serious domestic problems and accidents at work score about half-way on the 5-point scale of certain to go to work-certain to be absent. According to these results, more often than not people claim they will go to work even if they do have a minor illness or personal business matters to attend to.
One interesting discrepancy between Tables 2 and 3 is the rating of 1.7 for minor ailments in Table 3. This indicates that people believe they are more likely than not to go to work if they have a minor ailment, but as Tables 1 and 2 show minor ailments occurTrequently and they are frequently given as a cause of being absent. Our respondents seem to be underestimating the rate at which they actually take absences for minor ailments.
ABSENCE FROM WORK 127
TABLE 3 Means and Standard Deviations for Self-rated Absence
Susceptibility
Absence Susceptibility'
Serious illness Serious domestic problems Accident at work Personal business matters Minor ailment Minor domestic problems Feeling depressed Local events of interest Major disagreement with boss Difficulties getting up on time Serious overload of work duties Rows with workmates
Scale 1-5: certain to go to work-certain to be absent from work.
Considering these data in terms of the A-B continuum it is evident from Table 3 that only one event, serious illness, is clearly considered to be an A- type event by all the sample (the low standard deviation indicates high agreement about this). Serious domestic problems and accidents at work are equivocally rated; both in terms of their means being close to the scale midpoint, and in terms of their high variance. Similar equivocality is also apparent for personal business matters, though the mean score classifies it as a middle range B-type event, along with minor ailments and domestic problems. The remaining events are unreservedly rated as B-type events by the sample, with their low standard deviations indicating a high measure of agreement. It is noticeable that this agreement increases for events as one descends the A-B continuum of susceptibility from the middle to the bottom B-end.
INTER-GROUP DIFFERENCES The data provided only moderate support for the hypothesis that there are differences between occupational groups. This was tested by two-way analysis of variance across the four sub-samples generated by sex (male/ female) vs. class (white collar/blue collar).
Looking first at the frequency of occurrence for potentially absence- inducing events the two-way ANOVAs produced six significant differences (P<O.O5). For four of the events the difference was due to a sex main effect.
128 NICHOLSON AND PAYNE
Males reported more “personal business matters” (F = 15.6, P < O.OOl), more “events of local interest” (F= 8.8, P < O.O05), more “disagreements with boss” (F= 13.3, P<O.OOI), and more “rows with workmates” (F= 8.0, P < 0.005). There was a significant main effect by class for event number 1 1, “serious overload of duties at work”, rated as more frequent by the white- collar groups (F= 12.9, P<O.OOI). The only event where both sex and class main effects were significant was “accident to self at work”. Blue-collar workers reported more events of this kind (F= 5.4, P< 0.02) and males more than females (F-4.7, PC0.03). There were no two-way interactions on any of the 12 events.
None of these fly in the face of common sense or the literature on absence. It seems that for events rated as the main causes of absence, “sickness” of self or others, sex and social class have little effect on frequency. The wideIy reported higher incidence of depression among women is not found here. However, this is likely to be due to the fact that the subjects were asked only about “depressive feelings”, for research has shown that this sex difference in depression, found among the unemployed, disappears when only employed persons are compared (warr & Payne, 1982).
When the same ANOVA procedure is performed on actual causes of absences a slightly more complex picture emerges. There is a class main effect for “serious illness to self” with the blue-collar group reporting this as a more common cause of absence (F=4.7, PC 0.03). The only main effect by sex was for “serious domestic illness”, with women scoring higher than men for this cause of absence from work (F=4.4, P< 0.04). There were three two- way interactions. For “minor illnesses” white-collar males and blue-collar females reported the highest absence frequencies (F = 8.0, P<0.005). For “minor domestic problem” the interaction and the main effect for class is due to white-coIlar females having notably higher absence rates for this cause than the other groups (F=6.5, Pc0.01). For “accident at work” the interaction is due to the relatively high levels for blue-collar males and, to a lesser extent, white-collar females (F = 6.0, P< 0.01), though levels for all groups are so low as to suggest this finding is of marginal importance.
Overall, this set of differences is not too surprising. A possible exception is the higher level of minor illnesses among white-collar males. This is unexpec- ted given that other research has generally found white-collar sickness absence to be lower than for blue-collar workers (O.P.C.S., 1973). Our data suggest this difference may not hold for frequency of absence found to be high in some other white-collar samples (cf. Chadwick-Jones et al., 1982) and known to have different correlates to time lost. And it may also not apply to sickness resulting in short rather than long spells (i.e. minor ailments).
Table 4 looks at this directly by analysing total time lost and frequency across the four groups. Although there are no significant main effects or interactions for time lost, white-collar males do have the lowest level of time
ABSENCE FROM WORK 129
TABLE 4 Means and Two-way ANOVAs of Frequency and Time Lost for Each Group in the Sample
Mean Absence Frequency (Spells per Person)
Male Female
Class Sex class x sex
Male Female
Class Sex Class x sex
White-collar Blue-collar 1.34 1.03 1.10 1.47
F P 0.02 n.s. 0.50 ns. 6.30 0.01
Mean Absence Time Lost (Days per Person)
White-collar Blue-collar 9.83 14.56
1 1.24 I 1.45
F P 2.20 n.s. 0.24 I1.S.
1.78 n.s.
N=280
lost, as would be predicted by previous research. But for frequency there is a significant interaction in which white-collar males are revealed to have the highest levels followed by blue-collar females (F= 6.63, P < 0.02). These more frequent spells of short absences are, as we have seen, especially likely to be reported as caused by minor illnesses.
Other studies (Chadwick-Jones et al., 1982) show females usually have higher frequency and time lost. The difference in time lost between white- collar males and blue-collar males shown in Table 4 is consistent with published absence statistics and investigations looking at self-reported absence (O.P.C.S., 1973). The higher frequency figure for white-collar males is interesting because absence data from white-collar samples are rare in the literature. It also shows that the absence levels of white-collar workers may often be masked by the formal reporting of absence that applies to them in organisations.
130 NICHOLSON AND PAYNE
Finally, it is in absence susceptibility that inter-group differences may be most strongly expected in view of the differences in sickness absence levels by social class (O.P.C.S., 1973) and the employment conditions and general life circumstances associated with class (Townsend & Davidson, 1982). We find no overwhelming support for this. Only 3 of the 12 causes produce significant differences using a two-way ANOVA analysis. Of those three, two reflect the difference in the life roles of employed men compared to employed women. Females report they are more likely to be absent from work if “serious domestic problems” occur (F = 5.2, P < 0.03), with a similar result for “minor domestic problems” (F= 3.8, P < 0.05). There is also a significant interaction for the latter event (F = 5.6, P < 0.02) with blue-collar females the most likely, and white-collar females the least likely, to say they would stay away from work for “minor domestic problems”.
There is also a main effect by sex and significant interaction for “personal business matters”. Males are more likely to say they would be absent for this reason (F=6.2, P c 0 . 0 2 ) and the interaction is due to the especially high susceptibility of white-collar males and the low rating for white-collar females (F = 6.2, P< 0.02).
It is not predicted under the A-B theory that A-type events differ across groups for susceptibility (e.g. serious illness and minor illness) but under the A-B continuum hypothesis group differences are expected for the B-type events. Although it is true that the three significant differences reported here are all for B-type events, for most of the B-type events there are no significant differences. This is probably largely due to their very low level of occurrence.
SUMMARY AND DISCUSSION
These self-report data show that minor ailments are the most frequently occurring potential absence-inducing events, and by far the most frequent actual cause of absence. Other moderately frequent events-minor domestic problems, depression, work overload-are rarely reported as causes of absence. Two measures of people’s susceptibility to absence shed some light on this. The one clearly A-type or unavoidable reason, serious illness, and two intermediate-ranked reasons-serious domestic problems, accidents at work-are more often reported as causes of absence than they are as potentially absence-inducing events. This suggests that people are inclined to overestimate their susceptibility to absence due to serious domestic problems and, to a lesser degree, personal business matters. Perhaps, when it comes to actually facing these problems, there are more ways of circumventing the need for time off than people enGsage when considering them in the abstract. Susceptibility to serious illness is underestimated, to the degree that slightly more absences are attributed to this cause than there are reported events to
ABSENCE FROM WORK 131
cause them. This can be explained by the capacity of serious illness to spawn multiple spells of absence.
However, it is minor ailments that are of greatest interest here. They are the most frequently reported event and the most frequently reported cause of absence-over one in four incidents results in absence, i.e. towards the A- end of the continuum. However, this is inconsistent with the fact that people rate them towards the B-end of the susceptibility scale. It is arguable whether this constitutes an underestimate of actual susceptibility. If it is accepted as such, then one is drawn to the conclusion that either people choose to be absent for this reason more often than they anticipate they will, or that people’s attributions of the causes of their own previous absences are cognitively different from their estimates of susceptibility. On the latter point, it is plausible that people are more liable to use quasi-medical reasons to justify their absence when it comes to reporting on actual events than when rating their own hypothetical susceptibility. This implies that quite a number of the absences attributed to minor ailments could also have contained elements of other reasons. In our method people were disinclined to take the opportunity to give multiple reasons for their absences, but it is not unreasonable to expect mixed motives to underlie many of the absences associated with feeling marginally unwell. Attribution theory would support such an interpretation (Antaki, 1981): people might be expected to attribute the cause of negatively evaluated acts to factors outside their control (illness) in preference to factors potentially under their control (e.g. late rising). These “additional” absences attributed to minor illness might be absorbing and hence concealing those that might be otherwise attributable to the B-type causes which were conspicuously not cited as causes by our samples. In other words, since illness is a justifiable cause it may be that absences are remembered as minor illnesses even if that was not their original cause. Thus, negative attribution and poor memory may have distorted the actual causes of absence and the frequencies described in this paper.
In conclusion, the results support the suggestion that absence researchers need to look more deeply and closely at absences attributed to minor ailments. As has been argued elsewhere (Nicholson & Johns, 1985), the language of medical aetiology dominates the absence culture of employment, not just as a publicly made declaration for the benefit of doctors or employers, but also as a system of categories people use to think about the causes of their own actions. As such, the claim to have a minor illness constitutes a broad blanket attribution that obscures complex mixed motives.
A challenge remains for absence research as to how best to do this. These data suggest that merely asking people retrospectively invites bias. Asking people as they return to work equally invites bias by tapping into the rhetoric of absence explanation. Perhaps more immediate and detailed methods
132 NICHOLSON AND PAYNE
focusing on short and proximal time periods, could reduce both memory and attributional effects. For example, random telephone surveys of people actually off work might also produce different results from those reported here. Common experience and insight into one’s own absence behaviour surely suggests the B-type events listed here are more often real causes of absence than people have reported in these data. This is not to say the broad pattern of event occurrences is that inaccurate (Table l), nor the frequency of absences themselves, merely the attributed causes. It remains important to answer this question. If B-type events can be shown to be very rare actual causes of absence then managers can stop worrying about them. But the alternative hypothesis that they trigger or otherwise contribute covertly to socially acceptable overt causes, such as sickness, remains unexplored. This seems an important issue for future research to pursue.
Manuscript received December 1987
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