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DÉPARTEMENT DES SCIENCES DE L'ÉDUCATION
DEPARTEMENT ERZIEHUNGSWISSENSCHAFTEN
DEPARTMENT OF PEDEGOGICAL SCIENCE
Prof. Dr. Margrit Stamm
Chair of Educational Sciences
Rue P.A. de Faucigny 2
CH-1700 Fribourg
[email protected]
perso.unifr.ch/margrit.stamm/
School Absenteeism: does the school make a difference?
Paper presented at the British Educational Research Association Annual Conference,
Institute of Education, University of London, 5-8 September 2007
Abstract
In this study we ask, who is actually to be held responsible for absenteeism, given that truancy
should not really exist on account of universally entrenched compulsory education and school
absence regulations. We focus on individual and institutional perspectives and answer three
questions: (a) What are the personal background characteristics associated with truancy behaviour? (b) Which patterns of school structure and social organisation are associated with
school absenteeism? (c) Are school structures and social and performance-related organisation independent of each other? The data record is taken from a representative sample of
3942 pupils aged between 12 and 17 in Sitzerland. The results support the assumption that
schools have significant organisational effects on the truancy behaviour of their pupils. The
demographic structure of the pupils of a school has practically no link to school absenteeism
when the pupils’ socio-economic backgrounds are considered. Schools with a performanceorientated profile are able to safeguard their pupils from truancy somewhat better than
schools whose profile offers very little in the way of demanding optional subjects. The size of
the school is of little significance in terms of truancy behaviour. Rather, other organisational
aspects are deemed relevant, for example, how teachers interact with pupils and the nature of
the regulatory system in which they are embedded.
1. School absenteeism between media and science
School absenteeism is a somewhat awkward subject; and it may well be that this is precisely
why the issue has recently gained notable impetus in the media. It is thought that half a million pupils regularly play truant from school in Germany, and that one hundred thousand skip
school on a daily basis. Despite the fact that these figures are purely conjecture - given that no
official truancy statistics exist - the numbers are, nevertheless, remarkable. Above all, when
one considers that practically all of the small number of studies focusing on the subject from a
criminological and criminal law point of view (WETZELS et al. 2000; WILMERS/GREVE 2002)
demonstrate that pupils who play truant commit crimes more frequently than those who do
not skip school, that they are often in a poorer psychological state, and that their truancy is a
reaction to difficult relationships within the family, at school, or with their peers, or related to
problems of self-esteem (WAGNER/DUNKAKE/WEISS 2005). In these parts, however, in contrast to the Anglo-American countries, not only is the subject practically devoid of systematic
research, it is also not discussed openly in educational practice and is mostly dealt with as a
trifling matter. As a result, a major lack of knowledge of the subject exists, both in terms of a
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knowledge base founded on theory and the differentiation between scientific questions and
the establishment of appropriate research methods.
If, despite the lack of representative data, it may be assumed that school absenteeism of one
form or another is relatively widespread throughout our schools, this raises a question that
goes beyond the extent of the school absenteeism itself; namely, who is actually to be held responsible for absenteeism, given that truancy should not really exist on account of universally
entrenched compulsory education and school absence regulations? There are two different
opinions in relation to this point. The traditional point of view considers school absenteeism
as a breach of obligation by the individual, whereas the alternative institutional view primarily
sees the school as being responsible for the problem. Recently, this institutional view has
gained ground in the Anglo-Saxon countries, where the effectiveness of the educational system not only addresses the quality of academic performance, but also discusses the level of
school absenteeism rates.
Starting from this point, this study examines the phenomenon of school absenteeism, while
considering which academic factors influence truancy behaviour. As such, the study sheds
light on a perspective barely addressed in absenteeism research carried out to date; namely,
the role of schools in absenteeism and the extent to which they differ in this respect. With the
spotlight on this question, the study initially presents the international status quo of research,
focusing on both the traditional explanatory model of individual responsibility as well as the
institutional perspective as a new paradigm. Analysis endeavouring to answer the question is
then set out in the empirical part of the study and is divided into two aspects. On the one
hand, the analysis examines the relationship between performance standards, personal background factors and the likely extent of school absenteeism, while, on the other hand, it also
examines the different relationship structures between the individual schools. Finally, the
findings are discussed in light of the question as to what schools can do to counter school absenteeism behaviour more effectively.
2. The status quo of research into school absenteeism
In Anglo-American research, the problem of school absenteeism is a keenly observed subject,
with the term taking root within professional discussion as a generic element of the overall
phenomenon being addressed here. Particularly in the UK, the subject has been discussed as
of late with the focus on attendance, whereby school attendance is understood to be an important factor to improving the quality of the school (DAVIES/LEE 2007; HALLAM, 2007).
School absenteeism is described as failure to attend lessons for a reason not sanctioned by
law, irrespective of whether the parents are aware of the non-attendance and have provided a
legitimate excuse for such. In this respect a distinction is made between truancy and school
refusal – also often referred to as school phobia throughout the English speaking regions
(SOMMER 1985; WHITNEY 1994; RICKING/NEUKÄTER 1997; REISSIG 2001). That school absenteeism is a major research subject in terms of the international perspective is evidenced,
not least, by the fact that enquiries via the ERIC databank unearthed some 4500 articles that
have been generated since 1990. Filtering out the – indeed numerous – studies of a purely educational-policy character, allows research activities to focus on six main areas:
(1) Personality traits and school absenteeism: these include works which concentrate on individual characteristics of pupils, such as gender, background, intelligence and academic
performance (BARRINGTON/HENDRICKS 1989; BARRO/KOLSTAD 1987; PINQUART/MASCHE
1999).
(2) Social integration and the avoidance of school: the focus here includes studies relating to
conspicuous, refractory conduct, poor academic performance and the forms of truancy behaviour (THIMM 2000; WILMERS/GREVE 2002), which can also be associated with expulsion from school. Two Swiss evaluation studies on so-called time-out schools also refer to
such links (METTAUER/SZADEY 2005; HASCHER/KNAUS/HERSBERGER 2005).
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(3) Development of truancy behaviour: this includes analysis examining the academic career
of truants using longitudinal methods in order that risk factors can be generated. Such
studies include the work of FINE (1990); FINN (1989); GARNIER et al. (1997). Noteworthy
in this respect is the significant link between a later dropout from school (RUMBERGER/PALARDY 2005; WEHLAGE/RUTTER 1986), lower levels of qualification and greater
difficulty embarking on a career (ALEXANDER/ENTWISTLE/HORSEY 1997).
(4) School absenteeism and delinquency: empirical references on this main area exist, particularly in the USA (BROWN et al. 1990), but now also sporadically in Germany (WILMERS et al. 2002; WAGNER/DUNKAKE/WEISS 2005). Such studies demonstrate that extensive truancy behaviour acts as a risk marker for delinquency.
(5) Academic performance and truancy: this area includes studies that address absenteeism
from school within the broader context of academic performance (ECKSTROM ET AL. 1986;
COLEMAN 1990; CRONINGER/LEE 2001). The extent of truancy on the part of German pupils was likewise examined within the scope of the PISA study. Additional analyses include the link between truancy and class repetition (GRISSON/SHEPARD 1989; RODERICK
1994), as well as truancy, dropout rates and highly giftedness (RENZULLI/PARK 2001;
STAMM 2004).
(6) School quality and truancy: a number of studies focusing on this area are available in the
English-speaking regions. The groundbreaking work was the RUTTER study (RUTTER/MORTIMER/MORTIMER 1980), which pointed out major differences in absenteeism
rates between individual schools for the first time. Meanwhile, a large number of further
studies are now available from the USA, which examine the role of schools in relation to
absenteeism, attendance and instances of dropout from various perspectives
(LEE/BURKAM 1992, LEE/BURKAM 2003).
Despite their various approaches, the fact common to all six of these areas is that they recognise the multi-dimensionality of the phenomenon and afford it a multifaceted dynamism,
which can be a concomitant of performance problems and development of risk (cf. also the
overview in RICKING 2003). In response to the question of who can be held responsible for
truancy behaviour, the various studies differ greatly. In this respect, the first four main areas
are founded on an understanding of individual responsibility and consider truancy behaviour
as a form of social deviancy, explained by individual and characteristic personal traits. This
differs somewhat from the fifth main area, and explicitly from the sixth, which expand the
perspective to include the academic context and question the role played by the school in
connection with absenteeism. Collectively, these two different paradigms embody the status
quo of research into school absenteeism. The traditional and overriding view is that of the individual perspective. This approach explains truancy in terms of individual personality and
family traits that, along with socio-economic background factors, are the primary causes of
school absenteeism and, consequently, places sole responsibility upon the individual. As
such, truancy behaviour is viewed in terms of individual carelessness and a failure to fulfil
obligations. This contrasts with the second institutional perspective; whereby it is assumed
that various academic factors lying within the responsibility of the school can provoke, increase, but also minimise truancy behavioural patterns.
However, both in German language research and society, the individual perspective has been
far more extensively disseminated and enjoys far greater acceptance than the institutional
perspective, particularly amongst teaching staff and educational administrations. This is apparent in the overriding conviction that truancy is either merely an insignificant or troublesome disruption or, indeed, that it is an unfortunate, yet unavoidable and tolerable concomitant of everyday school, brought about by the changed conditions when growing up experienced in childhood and adolescence and by problematic family structures (HARTOCOLLIS
2001). However, in gaining understanding of this complex phenomenon, both approaches are
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important and helpful; particularly so against the background of the question posed by this
study. As such, the following text discusses the most important research conclusions, in
terms of both the individual and the institutional perspective.
2.1 Truancy as a breach of obligation by the individual
Initially of interest is the extent of school absenteeism, possible links between gender and age,
and the associated motives. According to the various available studies, which are all too often
restricted to regional catchment areas and is also the case for the survey carried out within the
scope of the PISA study (SCHÜMER et al. 2003), it may be assumed that school absenteeism is
relatively widespread amongst more than 20 percent of pupils and that between four and seven percent fall into the school refusal category (STURZBECHER/DIETRICH 1994; PINQUART/MASCHE 1999; SCHREIBER-KITTL/SCHRÖPFER 2002; RICKING 2003; WAGNER/DUNKAKE/WEISS 2005). The most common motives include an aversion to particular
subjects and the avoidance of tests, rejection of the school as a whole, conflict with teachers,
or being subjected to aggressive behaviour by other pupils. In terms of age, the findings are
somewhat inconsistent; nevertheless, various studies (BAKER et al. 2001; REID 2003a; 2003b;
WAGNER/DUNKAKE/WEISS 2005) clearly demonstrate that school absenteeism increases with
age. However, contrasting evidence is provided by the barely acknowledged studies ensuing
from research into the highly gifted, according to which pupils in the first and second grade
were identified as ‘school refusers’ more frequently than random chance would predict
(ROEDELL et al. 2000). If the various studies are correlated in relation to intelligence and
school absenteeism, the results are similarly contradictory. Indeed, some studies show a correlation between low intelligence and school absenteeism, whereas others contradict this
(RICKING 2003, p. 131). A few studies also point towards links between higher intelligence
and school refusal (GANTER-BÜHRER 1991), or indicate other forms of school absenteeism
(STAMM 2004). Also unclear is the link between school absenteeism and gender. Indeed,
while retaining a certain level of caution, research findings are lending weight to the theory
that where school absenteeism can be said to be determined by gender, for girls the question
is more one of school refusal, whereas for boys it is more one of truancy. Yet, here too, research into the highly gifted provides contrasting findings in that boys refused school more
frequently (STAPF 2003), or were identified as leaving school prematurely or becoming dropouts (SEELEY 1993; RENZULLI/PARK 2002).
Traditional mainstays of the current debate generate conditional or risk factors which focus on
the behavioural patterns, abilities and moral concepts of pupils. The best-known predictors,
which are also the most effectively documented in terms of research, can be divided into three
categories: (a) social background (socio-economic background, family structure, gender, culture), (b) performance-related background (cognitive ability, school grades/test results, class
repetition), and (c) environment and personal factors (motivation to achieve, discipline problems, truancy). The relationship between school absenteeism and family circumstances has
been the subject of particularly intensive research. Indeed, recent German language studies
show that school absenteeism occurs within all social strata and family structures (PINQUART/MASCHe 1999; STAMM 2004); while Anglo-American research findings remain unanimous in their opinion that the breeding ground for absenteeism is primarily social depravation and a less educationally minded environment, or that it arises within families with a
background of migration (FOGELMAN et al. 1980; RUTTER/MORTIMER/MORTIMER 1980;
ROTHMAN 2001). In this respect, however, critical note should be taken of the fact that it is
precisely sociological correlatives of this nature that increase the risk of typifying school absenteeism with a particular social situation, that is, associating it with the ‘under classes’, a
‘less educationally minded’ environment, or a ‘migrational background’.
Astonishingly, relatively few studies are available on the significance of peers in connection
with school absenteeism (ATKINSON et al. 2000). Indisputable, however, is that young pupils
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are influenced by each other with respect to absenteeism patterns; and it is precisely because,
these days, they primarily go to school to meet with their friends, that the peripheral meeting
points available at school – break times, travelling to and from school, extra-curricular events
– have become attractive social gathering places where pupils at risk of truancy also have the
opportunity to join peer groups of an anti-school outlook (FARRINGTON 1980; GALLOWAY
1982). Such peer groups become all the more powerful, the more explicitly an anti-school culture dominates the class, the more isolated and unpopular the pupil, and the more dominant
the fear of extortion, threat or physical violence (CULLINGFORD/MORRISON 1997).
2.2 Truancy as a failure on the part of the school
According to Anglo-Saxon studies into school absenteeism (RUMBERGER 2001; DAVIS/LEE
2007), individual habits, attitudes and behavioural patterns are also determined by institutional settings. In the UK, this paradigm originates in studies carried out by CARROLL (1977)
and RUTTER (1980), which show that the absenteeism problem is less a question of the individual child, but rather lies to a far greater extent with the school. In the USA, the Report of
the National Research Council (1993) was the first to point out that research on dropout rates
was focusing too heavily on individual risk factors and the characteristics of family background, while devoting too little attention to the potentially equally problematic settings in
which pupils were being taught. Indeed, in Germany too, KORNMANN (1980) posed the question of whether truancy was a personal characteristic or rather a symptom of a need for improved teaching quality; without, however, succeeding in influencing research on school quality and the small number of studies on school absenteeism. Only recently, RICKING (2003) also adopted this point of view, ascertaining that “today’s standard of research has gone beyond
the point of considering school absenteeism as an intra-individual, personal characteristic determined by family genesis” (p.19), before calling for a synoptically integrative view to be
taken on the subject. This view of the role of the school has been applied by
WEHLAGE/RUTTER (1986), LEE/CRONINGER (2001), LEE/BURKAM (2003), and RUMBERGER/PALARDY (2005). The central question posed in these studies focuses on the role played
by the educational institution as regards behavioural patterns that lead to school avoidance
and dropout by pupils.
In this respect, LEE/BURKAM (2003) distinguish between a broad and a narrow school perspective. While the broad perspective focuses on explicit school structures and academic and
social organisation, the narrow perspective relates to all the implicit strategies of schools that
could be a trigger for absenteeism. In the following, these two perspectives are consequently
discussed as explicit and implicit school perspectives.
Explicit Strategies: On balance, the studies considered here clearly demonstrate that schools
do indeed influence absenteeism by way of their structure and organisation; however, they do
so in different ways. In terms of the factors that can be held responsible for these differences,
three aspects are most frequently referred to: the make up of pupils in the school, the teaching
environment and quality of the school (‘social capital’, cf. COLEMAN 1987), and the structural
nature of the school. The first factor, to a certain extent an input factor, encompasses socioeconomic background, family variables and specific behavioural characteristics. According to
RODERICK (1994), a high level of less capable pupils will also only have a marginal influence
on school absenteeism rates. In terms of the second factor, the social capital, again the findings are unequivocal: RUMBERGER/THOMAS (2000), for example, show that the quality of the
teacher as perceived by the pupils will influence the extent of truancy. In recent years, together with the environment of the school, this factor has also been increasingly recognised in
the German-speaking regions as a generator of academic well-being and school quality. Comprehensive data is available in this respect (cf. summary BOS et al. 1992; HASCHER 2004).
The study by PINQUART/MASCHE (1999) as well as the project by HOSENFELD/HELMKE
(2004) demonstrate the links between well-being, the environment of the school, and absen-
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teeism rates. The greater the pupils’ feeling of well-being and the more they value the relationship with teachers, the lower the dropout rates and levels of absenteeism. An environment
within the school that is able to curb absenteeism depends, first and foremost, on the way truancy is interpreted and dealt with by teachers and the school’s senior staff, that is, whether
they acknowledge the fact and nevertheless demonstrate social appreciation, take presence in
school seriously, and react immediately to absences from school. According to FEND (1986)
or RUMBERGER/PALARDY (2005), this also applies to large schools where support and control
systems present considerably greater problems than is the case with small schools.
Another debate of note is that concerning structural characteristics, such as the size, location
and type of school, and their connection with school absenteeism. In this respect, some studies support a connection, while others reject such a link. Particularly well examined is the
connection between teacher-pupil relations, absenteeism and dropout rates, and the size of the
school. LEE/BURKAM (2003) succeeded in confirming such assumptions to a certain extent;
however, a correlation between positive teacher-pupil relations and low levels of absenteeism
could only be effectively demonstrated in small and medium-sized schools. In addition, various studies demonstrate that the formal and informal regulatory structure of a school is a central variable influencing absenteeism levels. WEHLAGE/RUTTER (1986) or BAKER et al.
(2001), for example, show that schools with disciplinary systems deemed ineffective and unjust by the pupils also have a high number of dropouts and absentees who are ignored and remain unpunished. These findings also apply with respect to the German-speaking regions. As
such, in his study on full-time education, HOLTAPPELS (1995) succeeded in demonstrating that
norm-breaking behaviour, discipline problems and tendencies towards dissociation increased
amongst pupils in problem-conducive, poorly regulated school environments; whereas,
schools with a promotion-orientated and structured regulatory environment and with clearly
structured curricula with lessons of a demanding quality, benefited from a better disciplinary
situation, with lower levels of truancy and a greater sense of identity. Similar findings are also
available from the USA by COLEMAN (1987; 1990), LEE/SMITH (1999) and CRONINGER/LEE
(2001).
Implicit Strategies: In recent years, various researchers such as RUMBERGER (1995), VALENZUELA (1999), BLAUG (2001), SCHULZE (2003) or GAUPP/HOFMANN-LUN (2005), have pointed to the fact that hidden factors also influence pupils’ sense of well-being and, thus, have a
contributory or preventive impact on dissociation from school – the subject of discourse here
being the implicit school processes. What do schools do specifically to prevent or, indeed,
provoke absenteeism? Current research refers to two courses. One course considers the commitment of pupils to be an indicator of alienation and absenteeism patterns. It disparages the
necessary social and learning-conducive conditions that would be deemed central for pupils’
commitment and academic success, thereby bolstering voluntary alienation. The other course
occurs through the implicit strategies of teachers, which push pupils towards involuntary alienation and, consequently, actually provoke increased absenteeism. These include practices
such as repression, sanctioning, showing pupils up or making them look foolish, fierce criticism and discouragement. Such action makes it impossible to create a basis of trust or the
necessary interaction, with the result that pupils are at risk of getting caught up in a vicious
circle of alienation towards school. If this is also accompanied by increasingly poor academic
performance, a further loss of prestige will occur, in turn causing teachers to stereotype pupils
and incite them towards behaviour that will encounter further negative reaction.
3. Consequences and research questions
The synopsis of the research demonstrates that school absenteeism can be addressed, theoretically and empirically, on two levels – the traditional view which places responsibility with the
individual, and the institutional view which primarily forms the subject of discussion in the
Anglo-American region. Initially, analysis of the literature deduces that there is no developed,
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consensual concept regarding school absenteeism; however, it can be said that a minimal consensus exists that recognises the multi-dimensional nature of the phenomenon and affords it a
multifaceted dynamism. On the basis of a number of conceptual and empirical studies, it is
now apparent that traditional explanations primarily associating truancy with an unfavourable
social background and inadequate performance or low intelligence have become just as questionable as the conviction that the problem is one of individual responsibility, upon which the
educational institution can only exert a minimal influence. Links between truancy and school
quality appear to exist insofar as schools play a part in the emergence and furtherance or, alternatively, the minimisation of behaviour associated with school absenteeism, and that high
intelligence or favourable individual variables may well also play a role. Thus, of far-reaching
consequence for the purposes of this discussion is the recognition that it is not purely a question of singling out certain pupils as being “at high risk of truancy” (LEE/BURKAM 2003, p.
354), but that this applies equally to schools which provide a platform for truancy on account
of their structure, organisation and/or social capital.
In this study, both the individual and the institutional perspectives are examined; whereby focus centres on examination of a possible relationship between the school organisation and
structure, the social capital and truancy. Initially, this is a matter of identifying the individual
factors, before considering the role of the school within this process and then, thirdly, examining the interaction of the structural, organisational and performance-related characteristics.
In essence, the focus lies on the following three questions:
1. What are the personal background characteristics associated with truancy behaviour?
It is anticipated that children of foreign origin and from a lower socio-economic background will be more likely to remain absent from school than Swiss children and those
from a higher socio-economic background. The same applies for children who have already repeated classes and are marked by poor academic performance.
2. Which patterns of school structure and social organisation are associated with school absenteeism?
It may be anticipated that schools with high social capital and well-organised structures
will have fewer absentees than schools with less social capital and poorer organisational
structures. In addition, the presumption is that school absenteeism will be less widespread
in smaller schools than in larger schools, and that institutions in which the teaching personal are themselves a model of punctuality and attendance will similarly have fewer truants than schools that are, to all intents and purposes, devoid of such models.
3. Are school structures and social and performance-related organisation independent of
each other?
It is anticipated that links will be established between these three aspects. The social organisation is operationalised in the form of school culture and is defined by the relationships between teachers and pupils. In this respect, it is assumed that differences will exist
between large and small schools, given that a positive teacher-pupil relationship is somewhat irrelevant in large schools as teachers teach a large number of classes and pupils
also have numerous other contacts.
4.
Methods
4.1 Sample and Data
The data record is taken from the study «School absenteeism in Switzerland – the phenomenon and its consequences», which was commissioned by the Swiss National Science Foundation. The sample relates to 28 randomly selected schools in nine cantons of German-speaking
Switzerland, with a total of 3942 pupils aged between 11 and 18 who were attending the 6th,
7th, 8th or 9th grade of secondary level I. The average number of pupils per school was 140
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(range: 27 to 304). In terms of gender, boys were slightly over-represented in that the sample
included 1994 boys (50.6%), 1920 girls (48.7%), with 28 individuals failing to submit details
of gender (0.7%). Of the interviewees, 297 (7.5%) attended a small class, 914 (23.2%) a
school catering for the basic educational standards and 2577 (65.4%) a school catering to
higher educational standards. 887 pupils (22.5%) had repeated one or more classes and 81
(3.2%) had jumped one or more classes.
4.2
Survey instruments
Pupil data
The dependent variable of relevance here is the dichotomous measure of whether a pupil was
classified as a repeated truant at the time of the survey (cf. Table 1). In this respect, the focus
lies solely on those pupils that had played truant from school for a half day on more than five
occasions in the last six school months. So-called occasional truants were counted amongst
the “present”. The socio-demographical background characteristics were assessed using various measures: gender (girls = 1, boys = 0), ethnicity (dummy variables recording whether the
pupils originated from Central Europe, Eastern Europe or outside Europe, whereby a noncoded category was reserved for Swiss children), and the SES, a z-standardised value with M
= 0, SD = 1. The pupils’ performance-related background was recorded using the dummy variable, class repeated (coded with 1) or not (coded with 0). Also recorded was the grade obtained in mathematics in the last school report.
School data
The demographic composition of the schools was organised using various measures: average
school SES, share of foreign pupils, average grades in mathematics. School structure variables were recorded using dummy variables. The size of the school was recorded according to
four categories. Three of these categories (very small = 0-40 pupils; medium = 101-200 pupils; large = more than 201 pupils) were recorded as dummy variables (coded with 1), whereby each was compared with the category marked ‘small’ (coded = 0). The Academic orientation was recorded using the dummy variable ‘demanding optional subjects’ (coded with 1
where available, or with 0 where not available). Finally, the social organisation (social capital) was recorded in terms of positive teacher-pupil relationships, which were used here as an
indicator of a school’s social capital. This measure was calculated using two scales (cf. Table
1), in which the pupils expressed the extent that teachers supported them and how this was reflected in the atmosphere of the school. This calculated value was interpreted on the basis of
factor analysis and then aggregated at school level. In terms of the multi-variant analyses, all
variables were either applied as dummy variables (coded with 1 or 0), or as z-standardised
continuing variables (with M = 0; SD = 1).
Table 1: Scales applied with items and internal constancy, mean value and standard deviation
Pupil variables
Frequent truants
Female
Central Europe
Eastern Europe
Outside Europe
SES
Class repetition
Mathematics grade
Dummy-coded truancy variable; 1 = truant from school for more than
five half days in the last six months; 0 = constantly present or truant
from school for less than five half days in the last six months.
Dummy-coded gender variable, 1 = female; 0 = male.
Dummy-coded ethnic variable, 1 = Central Europe; 0 = other
Dummy-coded ethnic variable, 1 = Eastern Europe; 0 = other
Dummy-coded ethnic variable, 1 = outside Europe; 0 = other
Standardised level of socio-economic status, including parents’ education, their occupation and domestic residential situation (z-value
with M = 0, SD = 1).
Dummy-coded repetition variable, 1 = one or more classes repeated; 0
= no classes repeated.
Average grade in the last school report
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School variables
Average SES
Greater share of foreign pupils
Average mathematics grade
Demanding optional subjects
School size – very small
School size – medium
School size – large
Teacher-pupil relationship
Aggregated value of school-average SES
Dummy-coded demographic variable, 1 = 30% or more pupils from
other countries
Aggregated value of school-average in mathematics
Dummy-coded curriculum variable, 1 = demanding range of subjects
on offer; 0 = no demanding range of subjects on offer.
Dummy-coded school size variable, 1 = 0 – 40 pupils; 0 = other
Dummy-coded school size variable, 1 = 101 - 200 pupils; 0 = other
Dummy-coded school size variable, 1 = more than 201 pupils; 0 =
other
Aggregated value of social organisation, based on the scale ‘positive
teacher relationship’ (5 items,  = .803; example items: “I am unpopular with a number of teachers”, “I am quite afraid of some teachers”) and a positive school environment (4 items,  = .608; example
items: “The atmosphere at my school is generally very positive/only
somewhat positive/somewhat negative/very negative”).
Analytic approach and presentation of the findings
The three questions forming the focus of this element of the study are applicable to the individual and the institutional level. In order to deal appropriately with such a multi-level structure, the multi-level analysis strategy of HLM for dichotomous variables was applied (RAUDENBUSCH et al. 2004). HLM comprises two important steps. In the first step, within each
school, the relationship between performance levels and personal background factors and the
likely extent of school absenteeism is estimated; whereby the independent variables are regarded as fixed rather than random effects. The second step, looks at these relationship structures between the schools. As such, HLM portrays each level of the data structure as a formal
independent sub-model, with each sub-model representing the structural relationships on precisely one level.
Subsequently, the results are presented in a descriptive and analytical form. The descriptive
results are presented singularly in the form of group mean values vis-à-vis the educational
background of two groups of pupils: frequent truants and non-truants. These group differences were examined for statistical significance: continual variables by means of t-tests (mean
value and standard deviation were assessed), with category variables (all dummy variables)
examined using contingency tables. Data was also presented according to school size, with
the average values for very small, small, medium and large schools illustrated. The differences were assessed using univariate factor analyses. The group mean values for each school
size variable (very small, medium, large) were each compared separately with small size
schools.
The analytical results were recorded in two HLM steps, with truancy behaviour forming the
dependent variable in both cases. In the first step («within-school-model»), the relationships
between truancy and pupil-related background variables are explored in order to answer question 1 above, and the results presented in a log odds metric. On account of the difficulties involving interpretation, the log of odds were translated into a log of odds ratio (ratio between
p, the probability of truancy and 1 – p, the probability of non-truancy). Through the simplified
interpretation, the odds ratio enables the estimation of an increase or decrease in the probability of absenteeism. A change in the odds ratio of 1.68 demonstrates a 68% increase in the
probability of truancy behaviour, whereas a change of 0.55 represents a 55% decrease. The
second step («between-school-model») indicates the relationship between the various levels
of school organisation and the number of truants as estimated by the school, that is, the rate of
absenteeism per school.
Significance and standard errors: the significance of HLM factors is heavily influenced by the
«within-school» sample sizes - a question of power. The average in the within-model, more
than 20 pupils, is more than sufficient for HLM estimates. Nevertheless, borderline signifi-
10
cance values are also included (p<.10). In addition, standard errors are detailed in the multivariate tables.
5. Results
5.1 Descriptive results
Table 2 demonstrates that, of our sample, 4.5% of pupils questioned can be categorised as
frequent truants. Falling into this category are those pupils who admitted having played truant
from school for one or more half days or whole days on more than five occasions in the last
six school months. In relation to this figure, it should be noted that the absenteeism rate
amongst the individual schools varies somewhat, ranging from a lower rate of 0.9% to an upper rate of 10.6%. In calculating the descriptive data, all the data provided by those interviewed was used, irrespective of whether demographic information or other data was complete. The resulting rate of truancy of 4.5% is slightly below the average for other Germanlanguage studies, which lies at around 7% (PINQUART/MASCHE 1999; EHMANN/RADEMACKER
2003; WAGNER/DUNKAKE/WEISS 2005). However, it may be presumed that the rate ascertained in this study would also have been higher, had the extent of frequent truancy over the
last twelve months (as opposed to merely the last six months) been examined.
Table 2: Pupil characteristics and school absenteeism
Sample size
Frequently truant
167
4.5%
Present
3 775
95.5%
Variables
% Female
% Central Europe
% Eastern Europe
% Outside Europe
43.2%
46.3%
1.2
6.2**
21.5**
3.4
13.7
11.5
-.52
.04**
Mean SES (S)
(1.02)
(0.97)
% Class repeated
3.6*
1.3*
% Class jumped
0.3
2.1**
4.12
4.65***
Average mathematics grade (S)
(0.97)
(0.82)
Significant values are represented by the greater of the two figures: *p<.05; ** p<.01; *** p<.001
S = standard deviation
The descriptive comparison of social background demonstrates that gender has no major impact on frequent truancy. By contrast, ethnicity plays a significant role. In comparison to
Swiss pupils, the number of young students from Central European countries (Spain, Italy,
France, Germany) who play truant, is lower than the number of young students from Eastern
Europe. The socio-economic background is also closely related to school absenteeism,
whereby the SES of frequent truants is 0.56 lower than that of pupils who do not play truant.
Also closely linked to frequent truancy is the academic performance; with the mathematics
grades of truants 0.53 lower than those of non-truants, which represents a very significant difference. The result in relation to class repetition was to be expected. In line with the findings
of previous research, the result of this study also shows that the class repetition factor is a risk
indicator of school absenteeism. However, also of note is that a significant number of nontruants (2.1%, N = 78) fall into the category of those who have jumped a class or started
school early, a fact that also applies to 0.3% of frequent truants (N = 18). Although these differences are significant, at the same time it must be recognised that frequent truants may also
have notable school careers. Not least, this result demonstrates that various achievement
curves exist within the group of frequent truants, which relate to both poor performance and
over expectation as well as strong performance and lack of mental challenge (STAMM 2004).
11
Overall, these descriptive findings point towards a relatively close connection between truancy, social background and school performance.
Further descriptive information on the 28 schools sampled in this study is provided in Table 2.
Despite the fact that the size of the participating schools is not equally balanced, the number
per category appears to be sufficient. As a first consideration, the Table shows that school absenteeism is associated with the size of the school, although the relationship is not linear. This
means that medium-sized schools (6.6%) have a larger proportion of frequent truants than
very small (2.1%), small (5.2%) and large schools (4.8%). Moreover, certain school characteristics are linked to the size of the school. The group of small schools can be characterised
by the highest SES and the lowest proportion of pupils of minority status. Small schools also
have the highest average grades in mathematics. However, the most surprising findings in Table 2 are the teacher-pupil relationships. In this respect, very small schools have averages
well-above those of other schools. These differences may be explained by the fact that the
small schools examined in this study tended to be located in rural areas, and the level of urbaninity may well play a role. This factor was not, however, examined further in this study.
Table 2: School characteristics and school absenteeism
Sample size
Variables
% Frequent truants
Mean SES
% Central Europe
% Eastern Europe
% Outside Europe
High proportion of minority status
Average mathematics grade
Average teacher-pupil relationship
*p<.05; ** p<.01; *** p<.001
Very small
(N=≥ 50)
4
13.7
Small
(N=41-100)
6
20.6
Medium
(N=101-200)
13
44.8
Large
(N=201-400)
6
20.6
2.1
0.22
3.1
4.5***
1.3*
25.4
4.10
0.52**
5.2
0.60***
2.1
6.5
8.2
5.0
4.34**
-.04
6.6**
0.25
4.3
7.2
4.2
27.2
4.12
-.03
4.8
-0.10**
5.2
9.3
12.6***
41.5***
4.02**
-.18
Overall, from Tables 1 and 2 it is clear that links exist between frequent truancy, the size of
the school, ethnicity and economic status. However, as these bivariate connections could be
misleading or indeed incorrect, multivariate and multi-level analyses are considered in the
section below.
5.2 Analytical results
The initial issue is to provide an answer to question 1 above, whereby the aim is to elucidate
the relationship between social and academic background and the probability of truancy. The
results in this respect are presented in Table 3. As detailed above, all independent variables
are assessed in terms of fixed factors, and each variable is z-standardised (MW=0; S=1). For
this reason, the adjusted log odds of school absenteeism of -3.25 are translated into an adjusted absenteeism rate of 4%. In terms of Tables 3 and 4, this means that schools with positive log odds coefficients have a greater probability of truancy, whereas negative log odds coefficients indicate a lower probability.
Table 3: The ‘within-school’ HLM model of school absenteeism
Adjusted log odds of school absenteeism
Intercepta
Fixed factors
Female
Change in log
odds
SE
Change in
odds
-3.25
0.16
0.04
0.14
0.22
1.16
12
Central Europe
Eastern Europe
Outside Europe
SES
Class repetition
Mathematics grade
-1.36*
-.38**
0.23
-.54***
-1.21**
0.36**
Variance
1.165
0.61
0.15
0.22
0.10
0.28
0.12
df
29
0.26*
0.67*
1.24
0.52**
1.31*
0.62**
χ2
318.6
S
Intercept
1.076
*p<.05; ** p<.01; *** p<.001
a
Intercept as a value, which assumes the dependent variable in the respective level 2 unit, if all predictors are 0.
Table 3 shows that the results differ to some extent as compared with the descriptive findings.
The multivariate analysis does, however, confirm the descriptive results in that gender has no
significant impact on truancy, while the differences between ethnicity persist. The multivariate analyses indicate a 74% decrease in probability that, in comparison to Swiss children,
children from Central European countries will be frequent truants (change in odds .26, p<.05).
However, the results for young students from Eastern Europe and outside Europe contrast
with the hypothesis expressed in the explanation of question 1 above. In the context of multivariate and multi-level analysis, according to assessment of the SES and performance, the
number of absentees in Eastern European pupils is lower (change in odds .37, p<.05 33% decrease), while pupils from outside Europe do not regularly play truant from school any more
frequently than Swiss students. The socio-economic background of pupils remains closely associated with school absenteeism and the increase in a standard deviation results in a 46%
drop in the odds (p<.01). Once again, the question of class repetition is also of interest in that,
similar to the descriptive results, those having to repeat classes are the pupils who are more
frequently absent from school (31% increase). A moderate, individual factor associated with
school absenteeism is that of mathematical performance, where an increase from a standard
deviation (0.10) represents a decrease of 38% in relation to the probability of truancy (p<.01).
Although the descriptive data exposed major differences between frequent truants and nontruants, on examination of the SES, no relationship exists between mathematical performance
and truancy. The adjusted intercept value of each school varies significantly between the
schools, including following examination of the social and performance-related background.
Results of the ‘Between-school-model’ are presented in Table 4 and incorporate adjustments
for the entire group of school characteristics, as detailed in Table 3. The analysis also includes
variables that define schools with respect to their demographic structure, size, academic orientation (demanding range of optional subjects) and their social organisation (teacher-pupil
relationships). Also of note, is that the fixed factors of Table 3 have barely altered.
Table 4: The ‘Between-school-model’: how schools influence school absenteeism
Independent variable
Adjusted log odds regarding school absenteeism
Intercepta
Demographic characteristics of the school
Average SES
High proportion of minority status
Average performance in mathematics
Academic organisation of the school
Demanding optional subjects
Size of school
Very smalla
Mediuma
Largea
Social organisation of the school
Average teacher-pupil relationships
Change in log
odds
SE
Change in
odds
-3.65
0.36
0.04
0.65
-.33
-.23
0.42
.30
0.15
1.89
0.78
0.66*
-.67**
0.23
0.46**
.67
1.25**
0.69*
0.42
0.28
0.37
1.98
2.65**
2.01*
-1.55***
0.59
0.19**
13
Interaction
Teacher-pupil relationships x medium
Teacher-pupil relationships x large
Fixed factors
Female
Central Europe
Eastern Europe
Outside Europe
SES
Class repetition
Mathematics grade
S
Intercept
1.032
*p<.05; ** p<.01; *** p<.001
a
in comparison to small-sized schools (N=41-100)
2.44**
2.67**
0.87
0.99
7.23**
8.23*
0.15
-1.22*
-.32
0.50
-.65*
-1.13**
-.34*
Variance
1.056
0.30
0.60
0.18
0.33
0.15
0.33
0.11
df
29
1.22
0.30*
0.70
1.55
0.66**
0.32*
0.69***
χ2
246.3**
In terms of demographic structure, the schools show that an improvement in mathematical
performance by a standard deviation is generally accompanied by a fall in the likelihood of
absenteeism of 32% (p. 05). The other demographic attributes of average school SES and a
high level of minorities do not show any statistical relationship with the adjusted truancy
rates. The academic organisation of the school demonstrates that schools with a demanding
range of optional subjects have lower truancy rates. The primary effects of the school structures are linear. In comparison to small schools, medium-sized and large schools have higher
truancy rates than schools with average teacher-pupil relationships. Above all, this applies for
medium-sized schools in that they show an increase of 250%. Very small schools have higher
rates of absenteeism than smaller schools (almost a 100% increase).
Significant findings are also apparent with respect to question 2 above, in that pupils who
consider themselves to have good teacher-pupil relationships play truant from school less than
those who define the relationship structure somewhat more negatively (an increase by a standard deviation in teacher-pupil relationships results in a decrease in probability of absenteeism by 81% (p<.001)). That this effect does not, however, apply in all cases can be observed
from the interaction effects, according to which the absenteeism impact of the average teacher-pupil relationship differs according to the size of the school. In smaller and medium-sized
schools, an improvement in the teacher-pupil relationship by a standard deviation results in an
81% fall in the probability of absenteeism (p<.01). As the size of the school increases, the impact of the positive teacher-pupil relationship diminishes. For medium-sized schools: -1.55
plus 2.44 = .89, for large schools -1.55 plus 2.67 = 1.11. Neither effects are significant.
14
very small schools (=40)
5.0
4.0
3.0
2.0
1.0
large schools
(>201)
small schools (41100)
0.0
-1 SD
+1 SD
medium-sized schools (101-200)
Figure 1: Adjusted percentage values in relation to regular truancy for schools with different
teacher-pupil relationships
In Figure 1, the effects of teacher-pupil relationships are broken down into adjusted truancy
rates. Weak teacher-pupil relationships are represented with a standard deviation of –1 and
strong teacher-pupil relationships with a standard deviation of + 1; each represented separately according to the four sizes of school - very small, small, medium and large. The results
of Table 4 and Figure 1 give rise to the conclusion that the level of truancy is reduced by high
social capital within the school; although this does not apply to the same extent for all
schools. The form of social capital reduces truancy levels to greater effect in small schools or,
in other words, positive teacher-pupil relationships do not generally reduce the level of truancy in medium-sized and larger schools. This could be due to two factors: firstly, low truancy rates may well be the result of a well-established system of control, or it may be the case
that the impact of good teacher-pupil relationships is neutralised by other organisational and
contextual disadvantages - for example, anti-school peer groups.
The model presented in this study accounts for only 12% of the variance of truancy rates between schools. From these modest results it is apparent that many other school-related factors
influencing truancy patterns remain unknown and that other school factors could be of significance.
6. Discussion
The results of this study support the assumption that the individual perspective presented at
the start of this paper requires qualification in favour of an increased institutional perspective.
Schools have significant organisational effects on the truancy behaviour of their pupils; and
this also applies following examination of their risk factors. Nonetheless, such a change in
perspective may well not be greeted with rapid acceptance, particularly by teachers, in that the
individual perspective allows them to place responsibility for behaviour with the pupils and
their families. According to recent research and the data presented in this study, this opinion
amounts to a misunderstanding, or at least, a highly one-sided understanding of the issue.
15
In this respect, the question naturally arises as to what schools can do in order to get a grip on
the problem of school absenteeism. Based on the results presented here, the limitations on a
school’s ability to act are initially apparent. Thus, schools cannot change the demographic
structure of their pupils; however, such factors have practically no link to school absenteeism
when the pupils’ socio-economic backgrounds are considered. Following examination of the
school characteristics, it is also clear that a high proportion of pupils from modest socio-economic backgrounds and/or with minority status will have very little influence on truancy patterns and that the same applies for high proportions of poor performers. These may well all be
non-findings, nonetheless they considerably expand our understanding of the subject. In what
ways, therefore, can schools and education departments play their part?
A significant finding of this study is that pupils are less likely to play truant from school when
their relationship with teachers is perceived to be a good one. This is also true following examination of the SES. Although schools cannot generally influence who their “clients” are,
the teachers and school administrations are in a position to determine the nature of interaction
with their pupils. The effect of positive teacher-pupil relationships operates on both the individual and the institutional level. On an individual level, this means that the pupil’s relationship to their various teachers can influence any decision to play truant. In particular, Table 2
clearly shows that there is a notable variability in the quality of teacher-pupil relationships
and that school-aggregated standard deviations were not significant in relation to school-aggregated mean values in respect of school sizes (within-school standard deviations between
0.9 and 1.0). Accordingly, the variability within the schools is practically the same as that between schools without consideration of school size. Within school variation, schools defined
by positive teacher-pupil relationships presented a similar picture as those defined by negative
teacher-pupil relationships. With respect to demanding optional subjects, it is apparent that
schools with a performance-orientated profile are able to safeguard their pupils from truancy
somewhat better than schools whose profile offers very little in the way of demanding optional subjects. This applies irrespective of consideration of the pupils’ academic performance and
their performance background.
A number of points are also apparent as regards the question of school size – a variable that
likewise cannot be influenced by teachers per se. Research carried out in recent years has
once again increasingly advocated the idea of the smaller school (DUBS 2005); whereby it has
been established that the school should be of a minimum size in order for it to be able to offer
an appropriate curriculum (STEVENSON 2006). Such concepts are based on a direct link between school size and performance. However, this study gives rise to somewhat contrasting
results in that the size of the school has practically no direct link with the academic performance of pupils. The same applies to school absenteeism: in all probability, the size of the
school is of little significance in terms of truancy behaviour. Rather, other organisational aspects are deemed relevant, for example, how teachers interact with pupils and the nature of
the regulatory system in which they are embedded. If this aspect is considered, direct residual
effects in terms of the size of the school arise. Thus, from the aspect of relationship quality,
the size of the school takes on a certain significance in relation to truancy. Regarded from this
angle, the size of the school is a factor not to be ignored. The extent to which smaller school
sizes are actually preferable and whether they can be distinguished from large schools by other social benefits (such as other forms of participation, increased commitment by individuals
vis-à-vis general objectives of the school, less binding relationship with teachers) remains
open. This aspect also represents a limitation of the study, given that the span of variables applied here barely allows for a more in-depth reflection of relationship patterns. Moreover, the
sample does not include any really large schools, which, in turn, restricts the opportunities for
identifying the social distribution of the findings.
So what is the essential finding of this study? In the first instance, somewhat trivial, yet nonetheless empirically underlined, is the significance of inter-personal relationships in relation to
16
truancy behaviour and, secondly, the significance of the school in its various facets. Naturally,
not all variables can be adjusted to the same extent; the size of the school or context variables,
for example. However, altering personal relationships does lie within the responsibility and
power of the school. As such, the response to the question posed in the title of this study,
‘Does the school make a difference’, is a resounding ‘yes’. The risk group does not solely refer to pupils classed as frequent truants, but also includes ‘risk schools’ that – particularly by
means of their social capital – also promote truancy behaviour.
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