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Academic buoyancy: Towards an understanding of students' everyday academic resilience

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Abstract

Academic buoyancy is developed as a construct reflecting everyday academic resilience within a positive psychology context and is defined as students' ability to successfully deal with academic setbacks and challenges that are typical of the ordinary course of school life (e.g., poor grades, competing deadlines, exam pressure, difficult schoolwork). Data were collected from 598 students in Years 8 and 10 at five Australian high schools. Half-way through the school year and then again at the end of the year, students were asked to rate their academic buoyancy as well as a set of hypothesized predictors (self-efficacy, control, academic engagement, anxiety, teacher-student relationship) in the area of mathematics. Multilevel modeling found that the bulk of variance in academic buoyancy was explained at the student level. Confirmatory factor analysis and structural equation modeling showed that (a) Time 1 anxiety (negatively), self-efficacy, and academic engagement significantly predict Time 1 academic buoyancy; (b) Time 2 anxiety (negatively), self-efficacy, academic engagement, and teacher-student relationships explain variance in Time 2 academic buoyancy over and above that explained by academic buoyancy at Time 1; and (c) of the significant predictors, anxiety explains the bulk of variance in academic buoyancy.
Academic Buoyancy 1
Martin, A.J., & Marsh, H.W. (2008). Academic buoyancy: Towards an understanding
of students’ everyday academic resilience. Journal of School Psychology, 46,
53-83. DOI 10.1016/j.jsp.2007.01.002
This article may not exactly replicate the authoritative document published in the
journal. It is not the copy of record. The exact copy of record can be accessed via the
DOI 10.1016/j.jsp.2007.01.002
Academic Buoyancy 2
Running Head: ACADEMIC BUOYANCY
Academic Buoyancy:
Towards an Understanding of Students’ Everyday Academic Resilience
Andrew J. Martin
Faculty of Education and Social Work
University of Sydney
Herbert W. Marsh
Department of Educational Studies
Oxford University
October, 2006
Requests for further information about this investigation can be made to Associate Professor
Andrew J. Martin, Faculty of Education and Social Work, A35 – Education Building,
University of Sydney, NSW 2006, AUSTRALIA. E-Mail: a.martin@edfac.usyd.edu.au.
Academic Buoyancy 3
Academic Buoyancy:
Towards an Understanding of Students’ Everyday Academic Resilience
Academic buoyancy is developed as a construct reflecting everyday academic resilience
within a positive psychology context and is defined as students’ ability to successfully deal
with academic setbacks and challenges that are typical of the ordinary course of school life
(e.g., poor grades, competing deadlines, exam pressure, difficult schoolwork). Data were
collected from 598 students in Years 8 and 10 at five Australian high schools. Half-way
through the school year and then again at the end of the year, students were asked to rate
their academic buoyancy as well as a set of hypothesized predictors (self-efficacy, control,
academic engagement, anxiety, teacher-student relationship) in the area of mathematics.
Multilevel modeling found that the bulk of variance in academic buoyancy was explained at
the student level. Confirmatory factor analysis and structural equation modeling showed that
(a) Time 1 anxiety (negatively), self-efficacy, and academic engagement significantly predict
Time 1 academic buoyancy; (b) Time 2 anxiety (negatively), self-efficacy, academic
engagement, and teacher-student relationships explain variance in Time 2 academic
buoyancy over and above that explained by academic buoyancy at Time 1; and (c) of the
significant predictors, anxiety explains the bulk of variance in academic buoyancy.
KEYWORDS: Academic buoyancy; academic resilience; high school students; engagement;
motivation
Academic Buoyancy 4
Academic Buoyancy:
Towards an Understanding of Students’ Everyday Academic Resilience
We define academic buoyancy as students’ ability to successfully deal with academic
setbacks and challenges that are typical of the ordinary course of school life (e.g., poor
grades, competing deadlines, exam pressure, difficult schoolwork). As we argue below,
academic buoyancy is distinct from the traditional resilienceconstruct as well as constructs
reflecting ‘everyday hassles’ and ‘coping’. Notwithstanding this, it draws on these three
research domains to map onto the under-recognized notion of ‘everyday resilience’. The
present study aims to examine predictors of this everyday academic resilience, academic
buoyancy. In assessing this construct and in identifying salient predictors across time, we
hope to provide some guidance and shed some light on factors to target in counseling efforts
that seek to enhance students’ ability to deal with the inevitable ups and downs of everyday
life in the academic context.
Why Buoyancy? Why Not Resilience?
A critical aspect to our study is that buoyancy is proposed to be quite distinct from resilience.
To underscore this distinction, we propose the two differ in definitional terms, in terms of the
samples to which they relate, the operational differences, methodological distinctions, and
indeed the interventions that respond to them. In terms of definitional- and sample-related
differences, resilience has typically been characterized in terms of ‘acute’ and ‘chronic’
adversities that are seen as ‘major assaults’ on the developmental processes (e.g., see
Garmezy, 1981; Lindstroem, 2001; Luthar & Cicchetti, 2000; Masten, 2001; Werner, 2000).
The studies that deal with academic resilience tend to be focused on ethnic groups situated in
adverse conditions and situations (e.g., poverty – Overstreet & Braun, 1999; gang violence –
Catterall, 1998), chronic underachievers (e.g., Finn & Rock, 1997), and indeed, the
Academic Buoyancy 5
interaction of ethnicity and underachievement (e.g., Gonzalez & Padilla, 1997). Other
research touches on the issue of resilience in the academic setting for students with learning
disabilities (e.g., Margalit, 2004; Meltzer, 2004; Miller, 2002). Hence, traditional
constructions and operationalizations of resilience refer to a relatively small number of
individuals who experience quite extreme adversity.
Also in relation to definitional- and sample-related distinctions, we propose that the
traditional resilience concept does not address the many individuals who are faced with
setbacks, challenges, and pressures that are part of the ordinary course of life. This, we
contend, reflects an everyday resilience or a buoyancy that is relevant to the many who must
negotiate the ups and downs of everyday life as distinct from acute and chronic adversities
relevant to traditional constructions of resilience. Indeed, the positively-oriented buoyancy
concept aligns with recent developments in positive psychology that hypothesize about the
scope for positive dimensions of individuals’ lives to address aspects of their lives that are
not so adaptive. A positive focus along these lines has the capacity to not only reflect a
healthy end-state but also is a means to achieving psychological growth and improved well-
being over time (see Fredrickson, 2001). Positive psychologists refer to this as the broaden
and build theory of positive emotions (Fredrickson, 2001). The broaden and build theory
proposes that positive emotions and processes provide the potential to broaden individuals’
momentary thought-action repertoires and also increase individuals’ capacity to enhance their
personal resources. Hence, a focus on key principles underpinning academic buoyancy would
encompass building on strengths and emphasizing proactive rather than reactive approaches
to setback and challenge. It would also emphasize key catalysts to enhanced educational
outcomes that include healthy school environments, adaptive intrapersonal factors, positive
motivation and engagement, and constructive interests and attitudes – key factors under
investigation in the present study. Moreover, the concept of buoyancy may align more with a
Academic Buoyancy 6
positive psychology orientation that typically tries to better understand the many and the
‘healthy’ as opposed to resilience that is often confined to extreme cases at the problematic
end of the spectrum. Indeed, buoyancy may be the positive psychology version of resilience.
A further sample-related distinction is relevant to the fact that the concept of academic
buoyancy also resolves a challenge presented by Martin and Marsh (2006) who had
previously studied more everyday academic resilience across the full range of school
students. Their challenge was that traditional definitions of resilience were confined to the
relative few who experienced extreme adversity and yet the reality was that multitudes of
students face less extreme but nonetheless problematic setbacks and challenges as part of
everyday life at school. That study was an important one in that it was one of the first to
examine this issue across the full range of students. By proposing the concept of academic
buoyancy, this study bridges the gap between traditional treatments of academic resilience of
acute, chronic, intense, and sustained adversity experienced by the relative few (e.g.,
Garmezy, 1981; Lindstroem, 2001; Luthar & Cicchetti, 2000; Masten, 2001; Werner, 2000)
and Martin and Marsh’s extension of the concept to address all students.
In substantive, operational, and methodological terms, we propose that buoyancy and
resilience are demarcated on two primary dimensions: differences of degree and differences
of kind. In terms of differences of degree we argue that whereas academic resilience may be
relevant to chronic underachievement, academic buoyancy is relevant to the more typical
experience of isolated poor grades and ‘patches’ of poor performance; whereas academic
resilience may be relevant to overwhelming feelings of anxiety that are incapacitating,
academic buoyancy is relevant more to ‘typical’ stress levels and daily pressures; whereas
academic resilience may be relevant to debilitation in the face of chronic failure or anxiety,
academic buoyancy is relevant more to threats to confidence as a result of a poor grade.
Academic Buoyancy 7
In terms of differences of kind we argue that whereas academic resilience might be
relevant to clinical types of affect such as anxiety and depression, academic buoyancy is
relevant more to low level stress and confidence; whereas academic resilience might be
relevant to truancy and total disaffection from school, academic buoyancy is relevant more to
dips in motivation and engagement; whereas academic resilience may be relevant to
comprehensive and consistent alienation or opposition to teachers, academic buoyancy is
more relevant to dealing with negative feedback on schoolwork.
Finally, in terms of intervention it is important to better understand buoyancy and how
it is distinct from resilience. If we recognize differences of degree between the two, then it is
probable that academic buoyancy is a necessary but not sufficient condition for academic
resilience. That is, resilient students are likely to also be buoyant. This implies something of
a hierarchy. Thus, in facilitating students’ resilience to more dramatic adverse academic and
life events it is important to help them deal with ongoing challenges and demands that
present themselves – that is, develop their buoyancy. Indeed, if developing resilience is in
part about helping individuals offset risk (Martin & Marsh, 2006) then buoyancy may be the
first part of this and interventions might do well to reflect this.
Taken together, we propose that buoyancy and resilience differ on a number of bases.
Specifically, there are some clear definitional distinctions and following from this buoyancy
and resilience do not equate to fully overlapping samples. There are also operational and
methodological distinctions that are important recognize in attempting to characterize the
diversity of demands and challenges that are part of the ordinary course of students’
academic lives. Furthermore, the joint operation of these distinctions gives rise to distinct
intervention implications. However as detailed below, whilst a good deal of research has
provided substantial understanding of resilience, no research has specifically recognized the
cognate construct buoyancy and the factors that underpin it.
Academic Buoyancy 8
Academic Buoyancy and Everyday Hassles and Coping
In contextualizing academic buoyancy we recognize and harness the two cognate areas of
everyday hassles’ and ‘coping’. Everyday hassles are those stresses and strains that
characterize everyday frustrations in life (see Bobo, Gilchrist, Elmer, Snow, & Schinke,
1986; French, Seidman, Allen, & Aber, 2000; Kanner, Coyne, Schaefer, & Lazarus, 1981;
Kohn, Lafreniere, & Gurevich, 1991; Pearlin & Leiberman, 1979; Seidman, Aber, Allen, &
French, 1996; Seidman, Lambert, Allen, & Aber, 2003; Seidman et al., 1995; Zeidner, 1992,
1994). Buoyancy is akin to hassles in that it draws on students’ everyday stresses and strains.
However, it is different from hassle-related research in that the hassle-related research
focuses almost exclusively on the stress of the situation and measurement around hassles
predominantly requires respondents to indicate the extent to which the hassles are a source of
frustration (see for example, the Daily Hassles Microsystem Scale: Seidman et al., 1995; the
Student Stress Inventory: Zeidner, 1990, 1992). That is, typically this research does not ask
how individuals deal with their hassles, it simply asks about the existence and extent of them.
Buoyancy on the other hand, is centered on an individual’s response to their everyday
challenges. Moreover, given the adaptive and positive focus of the buoyancy concept, it maps
more clearly and directly onto the emerging positive psychology literature than does the
hassle-related research.
Coping does relate more directly to individuals’ responses to stressful and
disruptive transactions with the environment. Specifically, it refers to an individual’s
cognitive and behavioral attempts to manage the demands of a stressful situation or
environment (Fry & Martin, 1994; Lazarus & Folkman, 1984; Speirs & Martin, 1999;
Zeidner, 1994; Zeidner & Hammer, 1990). Two of the classically defined coping
responses are problem-focused coping (i.e., referring to an individual’s efforts to
address the problem or stressor) and emotion-focused coping (i.e., referring to an
Academic Buoyancy 9
individual’s efforts to address the emotions of the stressful situation). Buoyancy is
more aligned with problem-focused coping in that it relates to individuals’ efforts to
deal with the problem or adversity.
It is proposed here that the hassle-related research and the coping research can
be integrated under the buoyancy concept. Specifically, it is proposed that academic
buoyancy as operationalized in the present study brings together key elements of the
hassle-related and coping research domains in that it: (a) explicitly addresses students’
problem-focused coping in response to (b) their everyday academic hassles, stressors,
and strains.
Predictors of Academic Buoyancy
If, as we argue, the concept of academic buoyancy has merit and is a construct relevant to
many students, it will be useful to identify salient predictors with a view to assisting
practitioners seeking to enhance students’ academic buoyancy. Indeed, identifying such
predictors is a central purpose of the present study. Given that buoyancy is a new concept, in
guiding the selection of predictors of academic buoyancy we find it helpful to draw on the
existing research into its cognate construct, academic resilience. The research conducted
investigating predictors of academic resilience has identified a broad array of factors that
contribute to students’ capacities to deal effectively with academic adversity and setback.
Research has generally focused on either distal factors (e.g., SES, single parent, ethnicity) or
proximal factors (e.g., psychological factors, school related factors). Because the proximal
factors are generally considered to be more manipulable and amenable to intervention
(Cappella & Weinstein, 2001), these are the focus of the present study. Broadly, these
proximal factors can be grouped into (a) psychological factors, (b) school and engagement
factors, and (c) family and peer factors.
Academic Buoyancy 10
Psychological factors include self-efficacy, control, sense of purpose, and motivation
(Finn & Rock, 1997; Masten & Coatsworth, 1998; Shumow, Vandell, & Posner, 1999;
Waxman, Huang, & Padron, 1997; Wayman, 2002). School and engagement factors include
class participation, educational aspirations, enjoyment of school, relationship with teachers,
teacher responsiveness, effective teacher feedback, attendance, value placed on school, extra-
curricular activity, and challenging curriculum (Alexander, Entwisle, & Dauber, 1993; Alva,
1991; Catterall, 1998; Finn & Rock, 1997; Floyd, 1996; Hymel, Comfort, Schonert-Reichl, &
McDougall, 1996; Masten & Coatsworth, 1998; McMillan & Reed, 1994; Waxman et al.,
1997). Family and peer factors include family support, positive bond with a pro-social adult,
informal network of friends, peer commitment to education, authoritative and caring
parenting, and connections to pro-social organizations (Alva, 1991; Catterall, 1998; Floyd,
1996; Gonzalez & Padilla, 1997; Masten & Coatsworth, 1998; McMillan & Reed, 1994;
Wayman, 2002; Voydanoff & Donnelly, 1999).
The question then arises as to which factors are the most salient in determining a
student’s capacity to deal effectively with academic setback, challenge, and adversity. Again,
research into academic resilience plays a guiding role. Borman and Rachuba (2001)
conducted an analysis of academic resilience that examined the relative salience of five
competing models. The first model, the individual characteristics model, examined the role
of factors such as self-esteem, control, and self-efficacy. The second, the effective schools
model, focused on developing students academically, enhancing belonging in school, strong
principal leadership, and clear school mission. The third, the school resources model,
examined the impact of school funding, resources, and class size. The fourth, the peer group
composition model, examined the extent to which students’ peers affected academic
resilience. The fifth, supportive school community model, explored the role of caring and
supportive teachers, a safe orderly school, positive expectations for students, and
Academic Buoyancy 11
opportunities for students to be actively involved in school. They found two models in
particular accounted for most variance in students’ ability to deal with academic setback and
adversity: the individual characteristics model and the supportive school community model.
In terms of individual characteristics, key factors were locus of control, academic
engagement, and self-efficacy. In terms of the supportive school community, amongst the
key factors were positive teacher-student relationships. These four factors (self-efficacy,
control, academic engagement, and teacher-student relationships), then, will be foci for the
present study with a view to examining their roles in the buoyancy process from a
longitudinal perspective.
Determining an Approach for the Present Study
Again, given that buoyancy is a new concept, in guiding the approach to the present study,
we find it helpful to draw on the existing research into academic resilience. According to
Masten (2001), there are predominantly two approaches to the study of resilience. The first is
the variable-focused approach that tests linkages among measures of degree of risk/adversity
and qualities that may protect the person from negative consequences and outcomes. The
second is the person-focused approach that compares people with different profiles to
ascertain what differentiates resilient individuals from non-resilient individuals. We consider
it important to conceptualize and assess academic buoyancy as a continuum on which all
individuals lie and this would imply the variable-focused approach is the one most pertinent
to the present study. Indeed, as Masten notes, the variable-focused approach has the
advantage of statistical power and is suited for searching for specific and differential links
between predictors and outcomes that can have implications for intervention. Accordingly,
the present study adopts a variable-focused approach to academic buoyancy. This also allows
us to test aspects of the validity of academic buoyancy in relation to correlates rather than to
leave this assumption untested. Also, because we assume that the construct varies as a
Academic Buoyancy 12
function of a combination of student level variables and environmental variables, it is critical
to consider how the variable changes over time with longitudinal data and what variables are
related to these changes.
The Role of Anxiety
In a recent study of motivational determinants of students’ ability to deal with academic
setback, Martin and Marsh (2006) found that in addition to factors such as self-efficacy,
control, and engagement, anxiety played a pivotal part – in fact, accounting for the bulk of
variance in academic resilience. Anxiety is most likely to be experienced in situations of
threat. In the academic context, it is experienced under conditions of performance and
evaluative threat such as in the face of tests and exams that evoke fear of failure (Covington,
1992; Sarason & Sarason, 1990; Spielberger, 1985; Tobias, 1985; Zohar, 1998). There is a
large body of research demonstrating the negative effects of anxiety including performance
decrements, negative affect, negative cognition, and quite debilitating physical sensations
(Elliot & McGregor, 1999; Hancock, 2001; Newbegin & Owens, 1996; Sarason & Sarason,
1990; Skaalvik, 1997; Spielberger, 1985). If this is the case, it could be expected to be
negatively associated with students’ ability to deal with academic setback and challenge.
Indeed, the research into the related areas of academic hassles and academic coping finds
consistently significant associations with anxiety (Kohn et al., 1991; Lazarus, 1991; Lazarus
& Folkman, 1984; Shirom, 1986; Zeidner, 1992, 1994), attesting to the possible role of
anxiety in academic buoyancy.
On the other hand, there is some research showing that anxiety is not unambiguously
maladaptive from an achievement perspective or that the negative relationship between
anxiety and educational outcomes is not strong or is mediated by other factors (Ma, 1999).
For example, the negative effects of anxiety are not so marked in particular classroom
climates (Hancock, Nichols, Jones, Mayring, & Glaeser-Zikuda, 2000; McInerney,
Academic Buoyancy 13
McInerney, & Marsh, 1997) and the facilitating or debilitating effects of anxiety may depend
on the individual’s personality (Nyland, Ybarra, Sammut, Rienecker, & Kameda, 2000).
Indeed, it may be that academic anxiety may trigger a “fight” rather than “flight” response to
academic setback and challenge. If this is the case, it might be positively associated with
academic buoyancy. Accordingly, in addition to self-efficacy, control, teacher-student
relationships, and academic engagement, anxiety is to be included as a predictor of academic
buoyancy.
Academic Buoyancy in Mathematics
Whereas some researchers develop generalized measures of academic motivation and
engagement that are intended to broadly apply to all academic subjects, others are interested
in the development of students’ achievement-related motivations, beliefs, affects, and
behaviors that are domain specific (e.g., Eccles, Midgley, & Adler, 1984; Eccles, Wigfield,
Harold, & Blumenfeld, 1993; Marsh, 1990, 1993a). Therefore, a student may be highly
motivated or interested in an English subject but less motivated or perhaps display anxiety in
mathematic-based subjects (Bong, 1996; Marsh, Martin, & Debus, 2002; Pintrich, 2003;
Zimmerman, 2000).
In relation to motivational dimensions, Gottfried (1982) measured anxiety and
intrinsic motivation in four school subjects (reading, math, social studies, and science) and
concluded that the relationship between academic intrinsic motivation and anxiety varied
according to the school subject. Smith and Fouad (1999) also confirmed the existence of
different levels of self-efficacy, interests, outcome expectancies and goals for mathematics,
art, social science, and English subjects. Educational researchers have also demonstrated the
need to differentiate between math and verbal domains in a variety of other academic
constructs. For example, Marsh (1986) found domain specificity of attributions for academic
success and failure, but noted that it varied substantially depending on the particular
Academic Buoyancy 14
attribution. More specifically, attributions to effort and particularly external attributions (e.g.,
luck and task difficulty) showed greater generalizability across different academic domains
but attributions to ability as the basis for academic success and failure were very subject
specific. More recent research has also suggested that internal attributions, for example, are
very domain specific (Vispoel & Austin, 1995).
Support for the domain specificity of academic affect has been clearest in research
focusing on self-concept, which has, predominately echoed the need to explore this issue of
domain specificity of motivational constructs. In early research, Marsh, Byrne, and
Shavelson (1988) found that correlations between mathematics and English self-concepts
based on each of three different instruments were close to zero. Marsh and Craven (1997;
Marsh, 1990, 1993a) integrated a growing body of research showing that verbal and
mathematics self-concepts are nearly uncorrelated and that the effects of academic self-
concept on subsequent outcomes are also very specific to the subject domain.
Therefore, we suggest that a domain-specific approach to the study of academic
buoyancy is important. This raises the question as to which academic domain to focus on in
the present study. Previous research has found that students appear to experience a decline in
valuing of math after the junior high transition, whereas their valuing of English increases
(Eccles, Adler, Futterman, Goff, Kaczala, Meece & Midgley, 1983; Wigfield, Eccles,
MacIver, Reuman & Midgley, 1991). Other research finds relatively higher levels of anxiety
associated with mathematics (Bessant, 1995; Pajares & Urdan, 1996; Schneider & Nevid,
1993; Vance & Watson, 1994). Hence, mathematics seems to be an area in which a number
of students struggle and would also bring into consideration the issue of academic buoyancy.
Accordingly, the focus of the present study is on mathematics.
The Roles of Gender and Age
Academic Buoyancy 15
Discussion of anxiety and mathematics brings into consideration other potentially relevant
factors, gender and age being two. We envisage that there will be significant effects of
gender and age on at least some of the central factors. For example, Martin (2004) has found
that on the very scales assessed in this study, girls are significantly higher in engagement but
also significantly higher in domain general anxiety. In terms of domain specific mathematics
anxiety, a good deal of research has shown that females experience higher levels of anxiety
in mathematics-based subjects than males (e.g., see Bradley & Wygant, 1998; Flessati &
Jamieson, 1991; Martin & Marsh, 2005). On the other hand, there have been mixed gender
findings in previous academic resilience research with some research finding females to be
more academically resilient (e.g., Finn & Rock, 1997) but other research finding the opposite
(e.g., Martin & Marsh, 2006). Interesting to note is previous work finding females reporting
higher levels of academic hassles and emotion-focused coping (Zeidner, 1994) which is
supportive of Martin and Marsh’s data. In terms of age-related effects, Martin (2003, 2006, in
press) has found that senior and junior high school students reflect a more adaptive pattern of
engagement than middle high school students. He has also found that older school students
experience higher levels of anxiety than younger students. Moreover, Martin and Marsh
(2006) found older students to be significantly higher in mathematics anxiety and lower in
mathematics-based academic resilience. Hence, if we are hypothesizing that there exist
significant predictors in our central model, then researchers and practitioners will greatly
benefit from understanding how these predictors vary as a function of key characteristics of
their clientele – particularly gender and age.
The Hypothesized Model
Based on the review of previous literature, a number of conclusions can be drawn that guide
the development of a hypothesized model. Firstly, it is justifiable to focus on proximal
predictors, as these are more amenable to intervention (Cappella & Weinstein, 2001). Three
Academic Buoyancy 16
groups of proximal factors important to the buoyancy process include psychological factors,
school and engagement factors, and family and peer factors. Secondly, it has been found
(Borman & Rachuba, 2001) that the best models explaining responses to academic setback
and adversity comprise psychological factors (that include self-efficacy, control, and
academic engagement) and school community factors (that include teacher-student
relationships). In relation to the psychological factors, it is also suggested that anxiety might
play a meaningful role in predicting academic buoyancy. Thirdly, two approaches to
modeling buoyancy comprise the variable-focused approach that tests links between
variables and the person-focused approach that compares people and groups (Masten, 2001).
The variable-focused approach is best for exploring predictors and harnessing statistical
power.
In sum, these three conclusions point to a model in which self-efficacy, control,
anxiety, academic engagement, and teacher-student relationships predict academic buoyancy.
In identifying salient predictors, it is also important to look at the effect of these potential
predictors on academic buoyancy after controlling for prior levels of academic buoyancy.
Hence, a fourth conclusion rests in the importance of modeling this process over time so that
the variance in academic buoyancy accounted for by prior academic buoyancy can be
included in the one analytical framework. Taken together, these theoretical and analytical
decisions lead to the formulation of the hypothesized model presented in Figure 1.
We suggest that a strength of the study is the theoretically-derived model formulation.
Following from this, we pursue confirmatory structural equation modeling to test the fit of
this hypothesized model. Hence, we adopt the position recommended by Jöreskog and
Sörbom (1993) that holds that model testing should be based on a priori conceptual rationale
rather than being generated by the data themselves. We recognize that given the number of
factors included in this study, numerous alternative models are possible. However, based on
Academic Buoyancy 17
theorizing about predictors of academic buoyancy and the need to control for prior levels of
academic buoyancy, we suggest that the proposed model is a defensible one. We accept that
ultimately it is a judgment call as to the “best” model, but contend that if there is sound fit of
the data to the model that has emanated from theory, this model can be considered
defensible.
Aims of the Present Study
Having proposed that academic buoyancy is a concept that is distinct from resilience and
thereby in need of examination in its own right, the present study aims to examine an
hypothesized model of academic buoyancy. In this model: (a) Time 1 self-efficacy,
engagement, anxiety, uncertain control, and teacher-student relationship predict Time 1
academic buoyancy and also their Time 2 counterparts; (b) Time 1 academic buoyancy
predicts Time 2 self-efficacy, engagement, anxiety, uncertain control, teacher-student
relationship, and academic buoyancy; and (c) Time 2 self-efficacy, engagement, anxiety,
uncertain control, and teacher-student relationship predict Time 2 academic buoyancy over
and above that explained by Time 1 academic buoyancy. This model is presented in Figure 1.
Also presented in Figure 1 is gender, as this has been linked to academic resilience in
previous research. For completeness age is also included in the model. We include direct
paths between both gender and age and all Time 1 variables (not just academic buoyancy) for
two reasons. First, as argued above, we envisage that there will in fact be significant effects
of gender and age on at least some of these predictors (but the specific nature of effects on a
number of dimensions is unclear). Second, if we are hypothesizing that there exist significant
predictors of academic buoyancy, then researchers and practitioners will greatly benefit from
understanding how these predictors vary as a function of key characteristics – particularly
gender and age.
Academic Buoyancy 18
Method
Sample and Procedure
Respondents were 598 students from five Australian government high schools in Years 8
(58%) and 10 (42%). All schools were located in urban areas of Canberra and Sydney.
Schools were comprehensive institutions of mixed ability. In the Australian setting, both
systems subscribe to comparable curriculum and examinations. Schools primarily drew on
middle class areas. In total, 41% of students were females and 59% males. The mean age of
students was 14.3 years (SD = 1.1). Teachers administered the instrument to students during
class. The rating scale was first explained and a sample item presented. Students were then
asked to complete the instrument on their own and to return it once completed to the teacher
at the end of class. Students completed the instrument twice, with the two administrations
separated by approximately three months. Feedback from teachers indicated students
completed the instrument diligently. Because they were completing the mathematics-oriented
instrument in their mathematics class, they saw the relevance of the items to their context.
Because the instrument was not excessively lengthy, students completed the instrument again
at the end of the year with apparent good will. Although no formal validity-check items were
included in the instrument, the psychometric properties (see below), the inter-factor
correlations (see below), and data from previous research (e.g., Martin, 2001, 2003; Martin &
Marsh, 2005, 2006) jointly indicate that the items and factors ‘behave’ in the ways intended.
The research was approved by the university’s Human Ethics Review Committee and the
relevant government education authority. Participation in the study was voluntary, students
were informed they could withdraw from the process at any stage with no penalty to them,
and schools managed the consent process in accordance with their respective procedures.
This led to attrition in only one school which was subsequently dropped from the analyses
(yielding five schools for analyses instead of the invited six).
Academic Buoyancy 19
Materials
The instrument assessed numerous dimensions of students’ academic buoyancy, self-
efficacy, control, anxiety, academic engagement, and teacher-student relationships. To each
item, students rated themselves on a 1 to 7 scale (1=Strongly disagree; 2=Disagree;
3=Disagree somewhat; 4=Neither agree nor disagree; 5=Agree somewhat; 6=Agree; 6=Agree
strongly). Mathematics was the target area for ratings. Students rated themselves in their
mathematics class and were specifically instructed to think about mathematics when making
their ratings. Although the instrument was worded in such a way that items referred to
‘schoolwork’, instructions at the top of the page and by the administering teacher required
the students to focus on mathematics. Scale reliabilities are presented in Table 1.
Academic buoyancy (e.g., “I'm good at dealing with setbacks – e.g., bad mark, negative
feedback on my work”; 4 items): Assessed through four items, academic buoyancy refers to
students’ ability to effectively deal with setback, challenge, stress, and pressure that occur in
the ordinary course of school life (i.e., an everyday academic resilience). For completeness,
all academic buoyancy items are presented in the Appendix. This scale is reliable from
internal consistency and test-retest perspectives (Time 1 Cronbach’s α = .80; Time 2
Cronbach’s α = .82; test-retest r = .67). Previous research using this scale has shown it to
present a sound factor structure, is reliable and normally distributed, and significantly
predicts a variety of academic outcomes among high school students (Martin & Marsh,
2006).
Self-efficacy, uncertain control, and anxiety were intact scales drawn from the
Motivation and Engagement Scale – High School (MES-HS; Martin, 2001, 2003, 2007, in
press). Martin has shown that the MES-HS has a sound factor structure, comprises reliable
and approximately normally distributed dimensions, is significantly associated with literacy,
numeracy, and achievement in mathematics and English, and is sensitive to age- and gender-
Academic Buoyancy 20
related differences in motivation. The academic engagement scales and the measure of
teacher-student relationship were drawn from Martin (in press; Martin & Marsh, 2005) and
have demonstrated reliable and sound factor structure as well as concurrent and criterion
validity (Martin, in press; Martin & Marsh, 2005).
Self-efficacy (e.g., “If I try hard, I believe I can do my schoolwork well”; 4 items; Time
1 Cronbach’s α = .77; Time 2 Cronbach’s α = .83) is students’ belief and confidence in their
ability to understand or to do well in their schoolwork, to meet challenges they face, and to
perform to the best of their ability. Uncertain control (e.g., “I'm often unsure how I can avoid
doing poorly in this subject”; 4 items; Time 1 Cronbach’s α = .80; Time 2 Cronbach’s α =
.86) assesses students’ uncertainty about how to do well or how to avoid doing poorly.
Anxiety (e.g., “When exams and assignments are coming up, I worry a lot”; 4 items; Time 1
Cronbach’s α = .78; Time 2 Cronbach’s α = .84) has two parts: feeling nervous and
worrying. Feeling nervous is the uneasy or sick feeling students get when they think about
their schoolwork, assignments, or exams. Worrying is their fear about not doing very well in
their schoolwork, assignments, or exams. Academic engagement (Time 1 Cronbach’s α =
.85; Time 2 Cronbach’s α = .87) is assessed through five factors including persistence (e.g.,
“If I can’t understand my schoolwork at first, I keep going over it until I do”; 4 items),
enjoyment of school (e.g., “I enjoy being a student”; 4 items), class participation (e.g., “I get
involved in things we do in class”; 4 items), educational aspirations (e.g., “I intend to
complete school”; 4 items), and valuing of school (e.g., "Learning at school is important to
me"; 4 items). Teacher-student relationships (e.g., “I get along well with my teacher”; 4
items; Time 1 Cronbach’s α = .89; Time 2 Cronbach’s α = .88) assesses students’
perceptions of how they get on with their teacher and their teacher’s regard for them.
Academic Buoyancy 21
Construct Validity of Academic Buoyancy Using External Datasets
Although not part of the present study’s substantive focus, it is important to establish that
academic buoyancy is indeed a valid measure to use in the proposed models. Researchers in
psychometric psychology have increasingly emphasized the need to both develop and
evaluate frameworks and instruments within a construct validation framework (e.g., see
Marsh, 1997, 2002). Accordingly, using a construct validity approach, Martin (in press) has
shown that the academic buoyancy measure is significantly associated with persistence and
negatively associated with disengagement. Martin and Marsh (2006) have shown that
academic buoyancy predicts class participation over and above motivation and engagement
factors underpinning academic buoyancy. In terms of other ‘objective’ measures, work in
progress by Martin and Marsh amongst a sample of 3,450 students from six Australian high
schools (mean age = 14.47, SD = 1.59; 34% females, 66% males) shows academic buoyancy
significantly correlated with homework completion (p<0.001), absenteeism (negatively;
p<0.001), literacy (p<0.001), and numeracy (p<0.05). In other work ‘in progress’, Martin and
Marsh conducted a study of teachers who rated their classrooms (144 classrooms) on a
number of dimensions – including academic buoyancy – and found that the construct was
significantly associated with academic achievement (p<0.001), persistence in the face of
difficulty (p<0.001), disengagement (negatively; p<0.001), and student participation in class
(p<0.001). Moreover, after controlling for prior achievement and ability, the correlations
between academic buoyancy and persistence in the face of difficulty, disengagement, and
participation in class were somewhat attenuated but remained significant.
Just as ability and achievement can inform ‘educational capital’ dimensions relevant to
academic buoyancy, so too can dimensions such as ethnicity. Using the external dataset
identified above (N=3,450), ‘in progress’ work by Martin and Marsh found no significant
difference in mean levels of academic buoyancy between non-English speaking background
Academic Buoyancy 22
students (N=620) and English-speaking background students and no significant difference in
mean levels of academic buoyancy between Aboriginal students (N=53; a very disadvantaged
minority in Australia) and non-Aboriginal students.
Taken together, across a number of datasets using classroom achievement, literacy,
numeracy, teacher ratings, and other reports of participation and disengagement, the
academic buoyancy measure can be considered a construct that is valid from multiple
‘objective’ perspectives. Moreover, in other data on ethnicity it appears that academic
buoyancy is a construct relevant to all stakeholders – and might therefore constitute a
measure that is different from resilience in that buoyancy is an attribute applicable across
stakeholders whilst resilience is an attribute that demarcates more specific groups. In sum,
academic buoyancy is deemed a defensible measure to use in the present study.
Confirmatory Factor Analysis and Structural Equation Modeling
Confirmatory factor analysis (CFA) and structural equation modeling (SEM), performed with
LISREL version 8.72 (Jöreskog & Sörbom, 2005), were used to test the hypothesized
models. Our primary analyses comprised three steps. The first is a test of the central
measurement model using CFA. The second is a test of the hypothesized longitudinal model
using SEM. The third is a test of the same longitudinal model but with gender and age
included as predictors of Time 1 factors. Maximum likelihood was the method of estimation
used for the models. In evaluating goodness of fit of alternative models, the root mean
square error of approximation (RMSEA) is emphasized. Although the RMSEA is apparently
the most widely endorsed criterion of fit, also presented are the non-normed fit index (NNFI),
the comparative fit index (CFI), the χ2 test statistic, and an evaluation of parameter estimates.
For RMSEAs, values at or less than .05 and .08 are taken to reflect a close and reasonable fit
respectively (see Jöreskog & Sörbom, 1993; Marsh, Balla & Hau, 1996; Schumacker &
Lomax, 1996). The NNFI and CFI vary along a 0 to 1 continuum in which values at or
Academic Buoyancy 23
greater than .90 and .95 are typically taken to reflect acceptable and excellent fits to the data
respectively (McDonald & Marsh, 1990).
For large-scale studies, the inevitable missing data is a potentially important problem,
particularly when the amount of missing data exceeds 5% (e.g., Graham & Hoffer, 2000). A
growing body of research has emphasized potential problems with traditional pairwise,
listwise, and mean substitution approaches to missing data (e.g., Brown, 1994; Graham &
Hoffer, 2000; Little & Rubin, 1987), leading to the implementation of the Expectation
Maximization Algorithm, the most widely recommended approach to imputation for missing
data, as operationalized using missing value analysis in LISREL. Only students with both
Time 1 and 2 responses were included in the analyses, yielding relatively little missing data
which were subsequently handled with the EM Algorithm. Only 3% and 5% of the data were
missing at Time 1 and Time 2 respectively, and so the EM Algorithm was considered an
appropriate procedure.
Test-retest parameters in CFA and SEM
In CFA and SEM, longitudinal data pose statistical problems particular to their structure. If
Time 1 and Time 2 constructs are measured using the same (or parallel) items then
measurement errors associated with matching Time 1 and Time 2 items are likely to be
correlated and the failure to take into account such correlated uniquenesses will bias
parameter estimates. The implications of correlated uniquenesses are well known in
longitudinal SEM studies in which the same items are completed by the same participants on
multiple occasions (see Jöreskog, 1979; Marsh, Roche, Pajares, & Miller, 1997). Thus in the
CFA and SEM used to test the factor structure and hypothesized model in the present study,
not only are factors correlated, but the parallel item uniquenesses are also correlated.
Preliminary Multilevel Modeling
Academic Buoyancy 24
One question to be resolved before analyzing the data concerns the level/s at which to carry
out the analyses. In general, it is inappropriate to pool responses of individuals without
regard to groups unless it can be shown that the groups do not differ significantly from each
other (for further discussion, see Goldstein, 2003; Marsh & Hau, 2003; Rasbash, Steele,
Browne, & Prosser, 2004; Raudenbush & Bryk, 2002). Hence, before moving into the central
elements of the analyses, it was considered critical to examine the relative variance in the
measures explained at student, class, and school levels. Given this was a preliminary phase of
the analysis, only Time 1 data were analyzed. For the present investigation, the data were
conceptualized as a three-level model, consisting of student at the first level, class at the
second level, and school at the third level. The multilevel analyses were conducted using
MLwiN version 2.00 (Rasbash et al., 2004).
In these preliminary analyses, a baseline variance components model (Rasbash et al.,
2004) or intercept-only model (Hox, 1998) was used. Findings showed that on all measures,
the bulk of variance is accounted for at the student level. That is, there is greater variation
from student to student than there is from class to class or school to school. Of the measures
in the study, only three yielded statistically significant class-level variance (enjoyment of the
subject, educational aspirations, and teacher-student relationship) and none yielded
significant school-level variance. Of particular importance to this study, there was no
significant class- or school-level variance in academic buoyancy. Given that: (a) the central
measure of academic buoyancy yielded primarily student-level variance and very little class-
and school-level variance, (b) most of the other measures yielded primarily student-level
variance, and (c) only three measures yielded statistically significant class-level variance,
subsequent analyses were conducted at the student level only.
Academic Buoyancy 25
Preliminary Multigroup CFA and Tests of Invariance
Another issue to address before proceeding to the central analyses, concerns the justification
of pooling data across gender and year groups. Inadequate attention has been given to gender
and grade-level differences in the factor structure of motivation and the question, for
example, of whether a given instrument measures the same components with equal validity
for males and females and for students in different grade levels. Such concerns about factor
structure invariance are most appropriately evaluated by using CFA to determine whether—
and how—the structure of a given instrument varies according to gender and age or grade
level (see Byrne & Shavelson, 1987; Hattie, 1992). Typically, the minimal condition of
factorial invariance is the invariance of the factor loadings relating items to their latent
constructs (Marsh, 1993b), but the invariance of other parameters are of interest as well.
Invariance across boys and girls
The first multigroup CFA examined the factor structure for boys and girls and allowed all
factor loadings, uniquenesses, and correlations to be freely estimated. This is the least
restrictive model. This model yielded an acceptable fit to the data (NNFI = .94,
RMSEA=.07). The present study examined the comparative fit indices for four other models
across boys and girls. The first model holds the factor loadings invariant across boys and
girls (NNFI = .93, RMSEA=.07); the second holds both factor loadings and uniquenesses
invariant (NNFI = .92, RMSEA=.08); the third holds the factor loadings and correlations
invariant (NNFI = .92, RMSEA=.08); and the fourth holds the factor loadings, the
uniquenesses, and the correlations invariant (NNFI = .91, RMSEA=.08). These fit indices
indicate that when successive elements of the factor structure are held invariant across
gender, the fit indices are predominantly comparable. However, the application of
recommended criteria for evidence of lack of invariance (i.e., a change of 0.01 in fit
indices—see Cheung & Rensvold, 2002) indicates that total invariance has not been achieved
Academic Buoyancy 26
but that this appears to be limited to uniquenesses and not factor loadings and correlations
which are of greater import to this study and for tests of invariance more generally (Marsh,
1993b).
Invariance across Junior and Middle high school
Invariance tests were then conducted for junior (Year 8) and middle (Year 10) high school
students. The first multigroup CFA allowed all factor loadings, uniquenesses, and
correlations to be freely estimated. This is the least restrictive model and yielded a good fit to
the data (NNFI = .97; RMSEA = .05). Again, the present study examined the comparative fit
indices for four other models across junior and middle high school students. The first model
holds the factor loadings invariant across boys and girls (NNFI = .97, RMSEA=.05); the
second holds both factor loadings and uniquenesses invariant (NNFI = .97, RMSEA=.05);
the third holds the factor loadings and correlations invariant (NNFI = .97, RMSEA=.05); and
the fourth holds the factor loadings, the uniquenesses, and the correlations invariant (NNFI =
.97, RMSEA=.05). These fit indices indicate that when successive elements of the factor
structure are held invariant across year level, the fit indices are essentially the same with all
models yielding NNFIs of .97 and RMSEAs of .05. Indeed, the application of recommended
criteria for evidence of lack of invariance (i.e., a change of 0.01 in fit indices—see Cheung &
Rensvold, 2002) indicates that there is relative invariance across all models.
Taken together, these data suggest that in terms of underlying constructs and the
composition of and relationships amongst these constructs, boys and girls and then junior and
middle high school students are not substantially different and that the data can be pooled
across gender and also across junior and middle high school students.
Results
Our primary analyses comprised three steps. The first is a test of the central measurement
model using CFA. The second is a test of the hypothesized longitudinal model using SEM.
Academic Buoyancy 27
The third is a test of the same longitudinal model but with gender and age included as
predictors of Time 1 factors.
Confirmatory Factor Analysis
The measurement model tested using CFA comprised all six factors (academic buoyancy,
self-efficacy, control, anxiety, engagement, teacher-student relationships) and both gender
and age (with uniquenesses fixed to zero as they are ‘observed’ variables). In this
measurement model, all factors were allowed to covary. Gender and age were included at this
step because (a) their associated correlations are important to assess when interpreting effects
in later longitudinal models and (b) their inclusion in a CFA would not affect other factor
loadings and correlations (for completeness, the fit indices for a CFA without gender and age
are: χ2 = 7390.44, df = 2964, p < 0.001, NNFI = .97, CFI = .97, RMSEA = .05). This CFA
yielded a good fit to the data (χ2 = 7571.54, df = 3100, p < 0.001, NNFI = .97, CFI = .97,
RMSEA = .05). Factor loadings are presented in Table 1. The loadings across Times 1 and 2
are acceptable. Reliabilities (Cronbach’s α) presented in Table 1 are also acceptable.
Correlations are presented in Table 2. Of particular interest are the test-retest correlations and
the correlations between academic buoyancy and the hypothesized predictor factors and both
gender and age. Taken together, there are high test-retest correlations with all correlations
ranging from .66 to .82 and a mean correlation of approximately .70. Amongst the higher
correlates with Time 1 academic buoyancy are Time 1 anxiety (negative), uncertain control
(negative), and engagement. Indeed, Time 1 anxiety and uncertain control are relatively
strong correlates with Time 2 academic buoyancy as well. Notwithstanding this, Time 1 self-
efficacy and teacher-student relationships are also significantly correlated with Time 1 and 2
academic buoyancy. Among the higher correlates with Time 2 academic buoyancy are Time
2 anxiety (negative), uncertain control (negative), and engagement. Nevertheless, Time 2
self-efficacy and teacher-student relationships are also significantly correlated with Time 2
Academic Buoyancy 28
academic buoyancy. Gender and age are significantly associated with academic buoyancy
such that males and younger students are significantly higher in academic buoyancy at Times
1 and 2.
Structural Equation Modeling Examining a Longitudinal Process Model
It was of interest to examine the central hypothesized model (see Figure 1) in which (a) Time
1 demographics, self-efficacy, engagement, anxiety, uncertain control, and teacher-student
relationship predict Time 1 academic buoyancy and also their Time 2 counterparts, (b) Time
1 academic buoyancy predicts Time 2 self-efficacy, engagement, anxiety, uncertain control,
teacher-student relationship, and academic buoyancy, and (c) Time 2 self-efficacy,
engagement, anxiety, uncertain control, and teacher-student relationship predict Time 2
academic buoyancy. As a first step in this process, we examined a longitudinal model in
which gender and age were not included. This model fit the data well (χ2 = 8021.40, df =
2998, p < 0.001, NNFI = .96, CFI = .97, RMSEA = .05). The second step in analyses was to
conduct the same analysis but with gender and age (with correlated errors) as predictors of all
Time 1 constructs. This model also fit the data well (χ2 = 8225.67, df = 3146, p < 0.001,
NNFI = .96, CFI = .97, RMSEA = .05). Parameter estimates for both sets of analyses are
presented in Figure 2. Statistically significant paths in this figure at p<0.05 are denoted by *
and all other paths represented are significant at p<0.1 (paths not meeting these criteria are
not presented in Figure 2, but were nonetheless retained in the model that was analyzed – see
Table 2 for all path coefficients).
Results show that younger students are more academically buoyant than older students
and that females (compared with males) and older students (compared with younger students)
are significantly higher in anxiety. Time 1 self-efficacy, academic engagement, and anxiety
significantly predict Time 1 academic buoyancy, with anxiety explaining the bulk of the
variance in Time 1 academic buoyancy (as indicated by the highest proportion of variance
Academic Buoyancy 29
explained in the total effects in LISREL). Time 1 academic buoyancy predicts Time 2
anxiety even after controlling for the substantial effect of T1 anxiety. At Time 2, self-
efficacy, academic engagement, teacher-student relationships and anxiety significantly
predict Time 2 academic buoyancy even after controlling for the substantial effect of T1
buoyancy, with anxiety again explaining the bulk of the variance in academic buoyancy
(again, as indicated by the highest proportion of variance explained in the total effects in
LISREL). Importantly, these four predictors explain variance in Time 2 academic buoyancy
over and above that explained by academic buoyancy at Time 1.
Discussion
The present study sought to develop academic buoyancy as a construct reflecting everyday
academic resilience within a positive psychology context and was defined as students’ ability
to successfully deal with academic setbacks and challenges that are typical of the ordinary
course of school life (e.g., poor grades, competing deadlines, exam pressure, difficult
schoolwork). Multilevel modeling found that the bulk of variance in academic buoyancy was
explained at the student level. Confirmatory factor analysis and structural equation modeling
showed that: (a) Time 1 anxiety (negatively), self-efficacy, and academic engagement
significantly predict Time 1 academic buoyancy; (b) Time 2 anxiety (negatively), self-
efficacy, academic engagement, and teacher-student relationships explain variance in Time 2
academic buoyancy over and above that explained by academic buoyancy at Time 1; and (c)
of the significant predictors, anxiety explains the bulk of variance in academic buoyancy.
The Relative Salience of Predictors
The present findings align with Masten’s conclusion that “recent studies continue to
corroborate the importance of a relatively small set of global factors associated with
resilience. These include connections to competent and caring adults in the family and
community, cognitive and self-regulation skills, positive views of self, and motivation to be
Academic Buoyancy 30
effective in the environment” (2001, p. 234). Hence, although not addressing traditional
resilience in this study, our construction of ‘everyday resilience’ or ‘buoyancy’, maps onto
broader conclusions in the traditional domain.
Notwithstanding the correlational nature of the data, one of the striking features of the
study is the relative salience of anxiety in the model—explaining by far the bulk of variance
in the context of the other predictor factors. This is something of a new finding in that it does
not appear that anxiety has been considered in previous resilience-related research and
suggests a powerful factor in explaining students’ academic buoyancy. In fact, the substantial
relation between gender and academic buoyancy is almost entirely mediated by anxiety
(Figure 2). Indeed, this might shed further light on previous work finding females reporting
higher levels of academic hassles and emotion-focused coping (Zeidner, 1994). Although,
there may be a tendency for females to be more prepared to admit to their anxiety than males,
we suggest that even in this context the size of the difference is substantial. Moreover, it
would be hypothesized that if males were substantially less inclined to admit to ‘weakness’,
then their scores on academic buoyancy would be substantially lower than females’ and this
was clearly not the case with a non-significant beta path between gender and academic
buoyancy. The findings regarding anxiety suggest some clear and somewhat new direction
for intervention regarding academic buoyancy in that it seems anxiety should be a key target
for such intervention.
Before considering directions for such intervention work, there remains the question as
to why anxiety is such a powerful predictor of academic buoyancy. Indeed, the research into
the related areas of academic hassles and academic coping also finds significant associations
with anxiety on a consistent basis (Kohn et al., 1991; Lazarus, 1991; Lazarus & Folkman,
1984; Shirom, 1986; Zeidner, 1992, 1994), a noteworthy parallel to the role of anxiety in the
related domain of academic buoyancy. One reason is that anxiety may reflect a fear of failure
Academic Buoyancy 31
disposition and that students’ responses to it reflect low academic buoyancy. Passer (1983)
explored how individuals high in anxiety appraised competition and challenge. It was found
that individuals high on this dimension expected to perform less well in an upcoming
competition, worried more frequently about making mistakes, not playing well, and losing,
and expected more negative evaluation following failure than individuals who were low on
anxiety. In relation to academic buoyancy, it may be that all these outcomes could be ways
that individuals’ (low) academic buoyancy is played out. Indeed, Martin (1998) has found
that anxiety predicts quite counter-productive strategies students employ to deal with their
fear of failure, including defensive pessimism (see also Garcia et al., 1995; Norem & Cantor,
1986; Norem & Illingworth, 1993) and self-handicapping (see also Berglas, 1987; Jones &
Berglas, 1978)—and these too may be ways in which (low) academic buoyancy is manifested
in students’ academic lives.
It is also noteworthy that the present study focused on mathematics. There is a good
deal of recent data relating to mathematics anxiety amongst students (e.g., Bessant, 1995;
Pajares & Urdan, 1996; Schneider & Nevid, 1993; Vance & Watson, 1994) and this may
have rendered anxiety more salient among respondents and hence invoked anxiety as a
construct particularly relevant to the present study of academic buoyancy. It would be very
interesting to assess the role of anxiety in other school subjects. How much variance does
anxiety explain in academic buoyancy in school subjects that evince relatively lower levels
of anxiety?
In terms of intervention work on anxiety, strategies to deal with it are underpinned by
cognitive-behavioral, need achievement, and self-worth motivation theories (Atkinson 1957;
Beck, 1995; Covington, 1992; McClelland, 1965). From these perspectives, ways to reduce
anxiety at the student level include showing them how to deal more effectively with fear of
failure, helping them develop effective relaxation techniques, helping them prepare
Academic Buoyancy 32
academically and psychologically for pressure situations such as tests and exams, and
helping them deal with the stresses and anxieties associated with academic challenges and
adversities that face them. Ways to reduce fear of failure at a class level include promoting a
classroom climate of cooperation, self-improvement, and personal bests (Qin, Johnson, &
Johnson, 1995), showing that mistakes can be a springboard for success and do not reflect on
students’ worth as a person, and repositioning success so that it is seen more in terms of
personal progress and improvement than outperforming others (Covington, 1992).
Indeed, such intervention work might in the first instance be directed towards the
mathematics domain given that the present study has identified the salience of anxiety and
buoyancy in mathematics and the fact that mathematics is known to elicit relatively higher
levels of anxiety (e.g., Bessant, 1995; Pajares & Urdan, 1996; Schneider & Nevid, 1993;
Vance & Watson, 1994). In fact, mathematics has been shown to yield higher teacher-/-class-
level variance relative to English and science (Martin & Marsh, 2005) and so mathematics
educators might be ideally placed to implement techniques and strategies at the class level to
deal with mathematics anxiety.
Process-Focused Approaches to Enhancing Academic Resilience
Addressing students’ anxiety is one means of addressing their academic buoyancy. This is
what Masten and Coatsworth (1998) would be likely to identify as the process-focused
approach to dealing with risk—that is, tapping into the adaptational systems that reduce risk.
Indeed, focusing on academic buoyancy in terms of a process is cause for optimism because
it implies that students have mobility in moving out of risk and into buoyancy (Catterall,
1998). As Waxman et al. (1997) note, when enhancing students’ ability to deal with setback
there are “alterable processes or mechanisms that can be developed and fostered for all
students” (p. 137).
Academic Buoyancy 33
Another process-focused approach is to target academic buoyancy more specifically
and to harness theory and practice that have identified the processes by which intervention
and assistance can occur. In relation to this, Rutter (1987) identifies four stages in the path to
building a capacity to deal with setback and adversity as follows: reduce risk impact and
change students’ exposure to risk, reduce potential negative chain reactions following
exposure to risk, improve self-efficacy, and open/create new opportunities. Similarly,
Morales (2000) proposed a resilience cycle in which the student realistically and effectively
identifies major risk, the student then seeks out protective factors that can offset or reduce the
negative effects of the risk, the protective factors serve to propel the student to deal with the
risk, the student then sees the value of this protective factor and refines/progresses them, and
then there takes place continuous refinement and implementation of the protective factor that
sustains the student’s ability to deal with risk. Indeed, in the context of the present study’s
findings, students may activate composure (low anxiety), supportive relationships, self-
efficacy, and academic engagement as the protective mechanisms by which they deal with
perceived or actual risk. With this in mind, it is important to recognize that because risk is
often multi-faceted and cumulative (one risk factor is often accompanied and exacerbated by
others) it is important to have cumulative protection efforts, perhaps along the lines
suggested here (Yoshikawa, 1994).
Yields of the Present Study in Relation to ‘Buoyancy’ and ‘Resilience’
At the outset of the study we proposed that buoyancy is quite distinct from resilience. We
posited the two differed in definitional terms, in terms of the samples to which they relate, in
relation to operational aspects, in terms of methodological elements, and in terms of the
interventions that respond to them. The present data support the notion that there is a form of
‘everyday resilience’ that is not related to ‘acute’ and ‘chronic’ adversities that are seen as
‘major assaults’ on the developmental processes relevant to a relative minority of students
Academic Buoyancy 34
(see Garmezy, 1981; Lindstroem, 2001; Luthar & Cicchetti, 2000; Masten, 2001; Werner,
2000) but in fact is relevant to the many individuals who are faced with setbacks, challenges,
and pressures that are part of the ordinary course of life. That is, the data seem to support an
everyday resilience or buoyancy that is relevant to the many who must negotiate the ups and
downs of everyday life as distinct from acute and chronic adversities relevant to traditional
constructions of resilience.
The data also resolves a challenge presented by Martin and Marsh (2006) who had
previously studied more everyday academic resilience across the full range of school
students. Their challenge was that traditional definitions of resilience were confined to the
relative few who experienced extreme adversity and yet the reality was that multitudes of
students face less extreme but nonetheless problematic setbacks and challenges as part of
everyday life at school. This study, then, bridges the gap between traditional treatments of
academic resilience of acute, chronic, intense, and sustained adversity experienced by the
relative few (e.g., Garmezy, 1981; Lindstroem, 2001; Luthar & Cicchetti, 2000; Masten,
2001; Werner, 2000) and Martin and Marsh’s extension of the concept to address all
students.
The data also support the notion that buoyancy and resilience can be demarcated on two
primary dimensions: differences of degree and differences of kind. In terms of differences of
degree, in contrast to the more extreme adversities relevant to academic resilience, academic
buoyancy targets the more typical experience of isolated poor grades and ‘patches’ of poor
performance, to ‘typical’ stress levels and daily pressures, and to threats to confidence as a
result of a poor grade. In terms of differences of kind, in contrast to the more adverse types of
adversities relevant to academic resilience, academic buoyancy is relevant more to low-level
stress and confidence, to dips in motivation and engagement, and to dealing with negative
feedback on schoolwork.
Academic Buoyancy 35
Whilst differentiating buoyancy from resilience, it is also useful to revisit
academic hassles and academic coping. It was proposed at the outset of the
investigation that the hassle-related research and the coping research can be integrated
under the buoyancy concept. Specifically, it was suggested that academic buoyancy as
operationalized in the present study brings together key elements of the hassle and
coping research domains in that it: (a) explicitly addresses students’ problem-focused
coping in response to (b) their everyday academic hassles, stressors, and strains. The
data seemed to support this integration in that some key buoyancy findings are
mirrored in the academic hassle and coping research. For example, anxiety is a salient
factor in students’ academic hassles and academic coping (Kohn et al., 1991; Lazarus,
1991; Lazarus & Folkman, 1984; Shirom, 1986; Zeidner, 1992, 1994). Similarly,
gender effects in the present study are mirrored in academic hassles and coping
research (Zeidner, 1994).
Taken together, the present study demonstrates that on a number of bases there is merit
and justification in demarcating buoyancy and resilience. The data show that the two are
conceptually distinct, relate to different (but overlapping for some students) samples, and are
assessed in specifically unique ways in terms of the respective instrumentation.
Limitations of the Present Study and Future Directions
The present study provides an enhanced understanding of the processes involved in academic
buoyancy and the key factors that underpin it. There are, however, a number of potential
limitations important to consider when interpreting findings and which provide some
direction for further research.
The data presented in this study are all self-reported. This raises issues of validity,
reporting biases, veracity of recall, and impression management. Although this is a logical
and defensible methodology in its own right given the substantive focus, it is important to
Academic Buoyancy 36
conduct research that examines the same constructs using data derived from additional
sources such as, for example, that from teachers and parents. Furthermore, the relationship
between academic buoyancy and actual achievement and related data over time would further
delineate the processes and impacts relevant to it. Achievement data would also be
appropriate given that academic buoyancy is relevant to the development of competence. In
addition to achievement data, it is important to conduct similar research across more
heterogeneous samples that are able to provide further information about the interface
between academic buoyancy and economic background and ethnicity to name but two
potentially relevant factors. Notwithstanding this, in Method we did demonstrate that across a
number of datasets using classroom achievement, literacy, numeracy, teacher ratings, and
other reports of participation and disengagement, the academic buoyancy measure can be
considered a construct that is valid from multiple ‘objective’ perspectives. On this basis it
was deemed a defensible measure in the present study.
It is also important to recognize that the measures relate to mathematics and so the
extent to which these findings extend to other school subject domains requires further study.
It may be that the more focused the study of academic buoyancy is on specific school
subjects the more differentiated the findings will be from one subject to another. Indeed,
Masten and Coatsworth (1998) suggest that global approaches to enhancing a capacity to
deal with setback and adversity will not be as effective at targeting such a capacity on more
specific dimensions. This view is echoed by others who report that targeted intervention and
support are likely to be more effective than global support hoping to affect specific
dimensions (Weisz, Weiss, Han, Granger, & Morton, 1995). Hence, future research should
test these constructs in the context of other academic domains. Also in relation to the
measure of academic buoyancy, it is important to note that this study was not a construct-
validation or instrument-development study. Rather, it had more of a substantive focus.
Academic Buoyancy 37
Future work might further investigate the measurement of academic buoyancy, particularly
from multidimensional perspectives.
Another reason to study the present constructs in the context of other school subjects is
because there are elements of mathematics that may influence findings in ways particular to
mathematics and not to other subjects. Gender is a salient consideration in this respect. For
example, Martin (2004) has previously found that on the very scales assessed in this study,
girls are significantly higher in engagement but also significantly higher in general anxiety.
Similarly, a good deal of research has shown that females experience higher levels of anxiety
in mathematics-based subjects than males (e.g., see Bradley & Wygant, 1998; Flessati &
Jamieson, 1991; Martin & Marsh, 2005). Although the present study formally built gender
into the central model and thus tested its role in a comprehensive and appropriate way, there
is a need to test the same model in other school subjects to explore the generalizability of the
findings presented here or to better understand how and in what ways mathematics may
differ from other subjects.
In addition to the school-subject specificity of the academic buoyancy scale, the nature
of the items themselves also warrant some further comment. It would be helpful to better
understand the specific nature of challenges facing school students. This might encompass
the dimensionality of potential challenges. For example, exams for some students pose a
greater challenge than ongoing submitted assessment tasks. It might also encompass the
frequency of potential challenges. For example, isolated setbacks are easier to deal with than
more frequent setbacks. It might also encompass the degree of the potential challenges. For
example, a poor grade on a daily quiz is likely to be less stressful than a poor grade on a
major exam or standardized test. Indeed, lessons learnt from the hassle-related research
would have students also rate the frequency and extent to which their challenges occur and
the extent to which they are distressing or aversive to them (Seidman et al., 1995; Zeidner,
Academic Buoyancy 38
1990, 1992). It would also be helpful to better understand the extent to which the items and
phrasing are meaningful across distinct contexts. For example, to what extent is the item, “I
don’t let study stress get on top of me” meaningful in different cultural or international
contexts?
It is also likely that the concept and construct of academic buoyancy is relevant to the
‘educational capital’ (and by inference, its inverse of educational risk) that students bring to
their academic lives. For example, ability, prior achievement, SES, race, and prior adverse
experiences (educational or otherwise) are likely to be relevant issues – particularly given the
long line of research into traditional resilience that identifies these factors as influential. The
present study did not directly assess these factors and so future work is needed here. We did
identify in Method other data showing that even after controlling for teacher-rated student
ability, the link between academic buoyancy and other outcomes remained substantial and
significant, yet it is noteworthy that the although the associations remained significant, they
were attenuated once ability was included as a covariate, thus lending support for the need
for further research in this area.
Two issues related to the central model are also important to note. First, while the study
is longitudinal, most of the pathways were freely estimated at the one time point. Thus,
‘causal’ statements regarding the proposed predictors and consequences are not advanced in
relation to the ‘static’ data. Related to this is that the data were all correlational and such data
pose constraints that make it difficult to definitively conclude that X really does explain Y.
Second, the central model was based primarily on conceptual rationale and in this sense
analyses were essentially confirmatory rather than exploratory. While the data lend
themselves to testing a variety of models, we adopted the position recommended by Jöreskog
and Sörbom (1993). As detailed earlier, this position holds that model testing should be based
on a priori conceptual rationale rather than being generated by the data themselves. This
Academic Buoyancy 39
approach is, we consider, a strength of the study. Ultimately, however, the true test of the
utility of our conceptualization of buoyancy is whether experimental interventions that target
the key predictors of academic buoyancy actually make a meaningful difference.
Finally, it is probable that academic buoyancy is a necessary but not sufficient
condition for academic resilience. That is, resilient students are likely to also be buoyant. We
proposed at the outset that the two can be distinguished in terms of the degree to which they
are different. This implies something of a hierarchy and so further research needs to examine
this, not only in terms of hierarchical structural equation models (e.g., see Marsh &
Shavelson, 1985) but also from an item-response theory (Rasch, 1966; Waugh & Addison,
1997) perspective. Moreover, in facilitating students’ resilience to more dramatic adverse
academic and life events it is important to help them deal with ongoing challenges and
demands that present themselves – that is, develop their buoyancy. Indeed, if developing
resilience is in part about helping individuals offset risk (Martin & Marsh, 2006) then
buoyancy may be the first part of this.
Conclusion
The proposed yields of the present study are multifold. It has: (a) provided a first step in
exploring the concept of academic buoyancy, a concept reflecting more of an everyday
academic resilience and that is distinct from the more traditional resilience construct; (b)
shed light on the factors giving rise to academic buoyancy; (c) assessed these issues using a
model that captured both predictors and academic buoyancy across two time points, thus
extending previous research which often examines related issues in cross-sectional designs;
(d) identified the salient role of anxiety in the buoyancy process, a factor previously
unrecognized; (e) explicitly drawn together individual and school dimensions in the one
longitudinal model to assess their relative salience in the buoyancy process; and, (f) provided
psychometrically strong measures of the key components underlying academic buoyancy.
Academic Buoyancy 40
Taken together, then, the findings of the present investigation hold not only substantive and
methodological implications for researchers studying academic buoyancy, but are also
relevant to practitioners operating in contexts in which individuals are required to effectively
deal with setback, adversity, and challenge in the academic setting.
Academic Buoyancy 41
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Academic Buoyancy 51
Appendix
Academic Buoyancy Items
(Time 1 Cronbach’s α = .80; Time 2 Cronbach’s α = .82; Test-retest r = .67)
“I'm good at dealing with setbacks at school (e.g., negative feedback on my work, poor
results)”.
“I don't let study stress get on top of me”.
“I think I'm good at dealing with schoolwork pressures”.
“I don't let a bad mark affect my confidence”.
Academic Buoyancy 52
Figure 1. Hypothesized academic buoyancy process model
T1 Self-efficacy
T1 Engagement
T1 Anxiety
T1 Control
T1 Teach-student
relationship
T1 Academic
Buoyancy
T2 Self-efficacy
T2 Engagement
T2 Anxiety
T2 Control
T2 Teach-student
relationship
T2 Academic
Buoyancy
Gender
Age
Academic Buoyancy 53
Table 1
Factor Loadings (Time 1/Time 2)
Self effic
(SE)
Anxiety
(ANX)
Uncert
control
(UC)
Teach-
Stu
r’ship
(RSHIP)
Engage
(ENG)
Gender
Age
Ac
Buoyancy
(ACBOY)
67/75
68/76
59/67
78/80
78/81
69/72
57/69
74/78
67/73
68/74
75/85
75/80
78/81
84/80
80/79
84/81
82/86
73/78
91/88
93/91
69/72
100
100
67/71
67/69
77/78
72/74
77/83
78/84
80/86
89/88
85/87
-
-
80/82
Note 1. Engagement loadings are higher order loadings, comprised of five first order factors.
Note 2. Decimals omitted
Note 3. All loadings significant at p<0.05
Academic Buoyancy 54
Table 2
Factor Correlations from CFA and Beta Coefficients from SEM
TIME 1
TIME 2
SE1
ANX1
UC1
RSHIP1
ENG1
ACBOY1
SE2
ANX2
UC2
RSHIP2
ENG2
ACBOY2
GENDER
AGE
Correlations from First Order CFA
Self-Efficacy (SE1)
-
Anxiety (ANX1)
10
-
Uncert control (UC1)
-34
45
-
Tch-stu r’ship (RSHIP1)
44
-01
-25
-
Engage (ENG1)
68
09
-29
67
-
Ac Buoyancy (ACBOY1)
29
-65
-49
27
30
-
Self-Efficacy (SE2)
66
12
-29
40
56
16
-
Anxiety (ANX2)
05
72
29
-03
09
-53
15
-
Uncert control (UC2)
-27
34
67
-22
-27
-36
-29
46
-
Tch-stu r’ship (RSHIP2)
39
06
-17
67
52
16
51
07
-23
-
Engage (ENG2)
53
09
-23
51
82
18
70
16
-26
69
-
Ac Buoyancy (ACBOY2)
21
-48
-38
21
21
67
27
-59
-43
28
32
-
Gender
-03
-27
-01
-05
-07
21
-05
-26
-01
-10
-08
18
-
Age
01
11
03
-04
-06
-14
-09
06
04
01
-11
-18
05
-
All Beta Path Coefficients (with columns ‘predicting’ rows) see Figure 2 for statistically significant parameters
Self-Efficacy (SE1)
-03
-01
Anxiety (ANX1)
-29
12
Uncert control (UC1)
-01
04
Tch-stu r’ship (RSHIP1)
-05
-03
Engage (ENG1)
-06
-05
Ac Buoyancy (ACBOY1)
25
-65
-06
06
11
06
-06
Self-Efficacy (SE2)
65
-01
Anxiety (ANX2)
65
-10
Uncert control (UC2)
66
-04
Tch-stu r’ship (RSHIP2)
66
-01
Engage (ENG2)
77
Ac Buoyancy (ACBOY2)
35
14
-46
-01
08
16
Note. Decimals omitted; Test-retest correlations in bold; Significant beta coefficients presented in Figure 2.
Academic Buoyancy 55
Figure 2. Longitudinal academic buoyancy process model (χ2 = 8225.67, df = 3146, NNFI = .96, CFI = .97, RMSEA = .05, * p<0.05)
Note 1. Although all hypothesized paths are tested in the model, only paths significant at p<0.1 are presented in the figuresee Table 2 for all path coefficients in model.
Note 2. Path coefficients for SEM model without gender and age in analysis are in brackets ( ) - χ2 = 8021.40, df = 2998, NNFI = .96, CFI = .97, RMSEA = .05
T1 Self-efficacy
T1 Engagement
T1 Anxiety
T1 Control
T1 Teach-student
relationship
T1 Academic
Buoyancy
T2 Self-efficacy
T2 Engagement
T2 Anxiety
T2 Control
T2 Teach-student
relationship
T2 Academic
Buoyancy
-.29*
.12*
-.06
.25(.25)*
.11(12)*
-.65(-.68)*
.65(.66)*
.77(.77)*
.65(.64)*
.66(.66)*
.66(.66)*
.14(.14)*
.16(.16)*
-.46(.46)*
.08(.08)*
.35(.35)*
-.10
Gender
(1=FM; 2=M)
Age
Academic Buoyancy 56
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