Learning and Individual Differences 27 (2013) 157–162

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Learning and Individual Differences

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Do clusters of test anxiety and academic buoyancy differentially predictacademic performance?

David W. Putwain a,⁎, Anthony L. Daly b

a Faculty of Education, Edge Hill University, United Kingdomb School of Education, University of South Australia, Australia

⁎ Corresponding author at: Department of Psychology,Road, Ormskirk, Lancashire L39 4QP, United Kingdom. Te1695 579997.

E-mail address: putwaind@edgehill.ac.uk (D.W. Putwa

1041-6080/$ – see front matter © 2013 Elsevier Inc. All rihttp://dx.doi.org/10.1016/j.lindif.2013.07.010

a b s t r a c t

a r t i c l e i n f o

Article history:Received 27 February 2013Received in revised form 28 June 2013Accepted 21 July 2013

Keywords:Test anxietyAcademic buoyancyAcademic performanceCluster analysis

In this study we adopted a person-centred approach to examine whether students could be identified in distinctclusters on the basis of their test anxiety and academic buoyancy scores, andwhether students' academic perfor-mance differed accordingly. We performed a cluster analysis on a sample of 469 secondary school studentspreparing for high-stakes examinations and we identified five empirically-distinct clusters. Three correspondedto a continuum of high test anxiety/low academic buoyancy, mid test anxiety/mid academic buoyancy and lowtest anxiety/high academic buoyancy. Two clusters corresponded to students with mid-high test anxiety andmid-high academic buoyancy. Academic performance was highest for students in clusters of low test anxiety/high academic buoyancy or mid test anxiety/ high academic buoyancy. Performance was lowest for students inclusters of high test anxiety/ low academic buoyancy. These findings show how academic buoyancy may lowerthreat appraisal in some students and show a performance protective role in others.

© 2013 Elsevier Inc. All rights reserved.

1. Introduction

Research examining the relationships between test anxiety, aca-demic performance and other psychoeducational constructs typicallyfollows a variable-centred approach. In this study a person-centred ap-proach was utilised to examine whether distinct clusters of studentscould be identified on the basis of their test anxiety and academic buoy-ancy (one's capacity to withstand academic challenge and pressure),and whether academic performance differs by cluster.

1.1. Test anxiety and the appraisal of evaluative threat

Test anxiety refers to the tendency to appraise performance-evaluative situations, such as examinations, as threatening (Spielberger& Vagg, 1995). According to the self-referent executive function (S-REF)model of evaluation anxiety (Zeidner & Matthews, 2005), the appraisalof a threat is controlled by processes that integrate self-knowledge(e.g., competence beliefs) with possible responses to the threat situation(e.g., effort withdrawal vs. effort expenditure). Appraisal processes aretriggered by thoughts regarding a situational threat, such as beingreminded of a forthcoming examination. Ways of coping with the threat,the possible plans for action and their consequences, draw upon and

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interact with self-knowledge beliefs such as avoidant motivations(e.g., avoiding situations where ones' competence may be exposed) andnegative competence beliefs (e.g., an expectation of failure).

Metacognitions, beliefs and knowledge concerning one's own cogni-tive system, may heighten the appraisal of threat. For example, a beliefthat worrying is an effective way to cope with threat may result inincreased test anxiety. Students who respond to failure with self-handicapping strategies, for example by withdrawing effort or avoidingopportunities to develop skills, become more attentive to threats, andincrease their negative beliefs, thereby leading to an increased appraisalof threat. The outcome of a threat appraisal is an increase in state anxi-ety and performance-interfering cognitions. Test anxiety in the S-REFmodel, therefore, is distributed across several distinct processes andnot isolated to particular components or elements.

Evidence supports the influence of various elements of the S-REFmodel on threat appraisal, including the roles of coping (Matthews,Hillyard, & Campbell, 1999; Putwain, Symes, Connors, & Douglas-Osborn, 2012), metacognition (Matthews et al., 1999; Spada &Moneta, 2012), negative self-beliefs (Putwain & Daniels, 2010;Putwain & Symes, 2012), avoidant motivation (Putwain, Woods, &Symes, 2010), bias for threat (Putwain, Langdale, Woods, & Nicholson,2011) and self-handicapping (Gadbois & Sturgeon, 2011). State anxietyand worrisome thoughts have been shown to reduce working memorycapacity (Owens, Stevenson, & Hadwin, 2012; Owens, Stevenson,Norgate, & Hadwin, 2008). Highly test anxious persons do not performaswell as their low test anxious counterparts on time-pressured assess-ments such as tests and examinations, when ability or prior educationalachievement is controlled for (Chapell et al., 2005; Hembree, 1988;McDonald, 2001).

Appraisal Processes

Competence Beliefs

Responses to failure

Performance InterferingCognitions

Threat Situation

AB

C

D

Fig. 1. A simplified diagram of the S-REF model of test anxiety (adapted from Zeidner &Matthews, 2005) highlighting the points (A–D) at which buoyancy may influence testanxiety processes.

158 D.W. Putwain, A.L. Daly / Learning and Individual Differences 27 (2013) 157–162

The S-REF model of test anxiety offers a sophisticated way in whichacademic buoyancy can be integrated with test anxiety processes. Thebiopsychosocial model of test anxiety (Lowe et al., 2008), for example,focuses on the proximal and distal factors that can influence the ap-praisal of testing situations as threatening. Academic buoyancy wouldbe considered an intra-individual influence, alongside other variablessuch as academic self-efficacy and social–emotional functioning. Thisapproach is useful in identifying the various antecedents of test anxietyand how they might interact, but it has less focus on the processes in-volved in test anxiety than does the S-REF model. As we outlinebelow, test anxiety in the S-REF model is distributed across several pro-cesses which academic buoyancy is theorised to influence.

Furthermore, by adopting a person-centred approach, we explorethe possibility that test anxiety and academic buoyancy may combinein different ways between students. Traditional, variable-centred ap-proaches (e.g., Putwain et al., 2012), have reported how test anxietyand academic buoyancy are inversely related, but such approaches arelimited in the extent to which they can establish a more complex inter-play between the two variables. For instance, might there be a generalpattern by which most academically buoyant students show lowertest anxiety, but that some academically buoyant students exhibithigh test anxiety. Combining a person-centred approach with theprocess-focused approach afforded by the S-REFmodel offers the possi-bility of a nuanced understanding of how test anxiety and academicbuoyancy may relate.

1.2. Academic buoyancy and the appraisal of evaluative threat

Academic buoyancy refers to a student's capacity to withstand thepressures and respond adaptively to the setbacks that are experiencedduring the routine course of schooling, college and university education(Martin & Marsh, 2009). It is predicted by the ‘five Cs’ of coordination,commitment, control, composure and confidence (Martin, Colmar,Davey, &Marsh, 2010; Martin &Marsh, 2006) and can be differentiatedfrom academic resilience both theoretically and empirically (Martin, inpress). Although academic resilience and academic buoyancy positivelycorrelate, they show a differentiated pattern of relations with negativeoutcomes: buoyancy relates more strongly with low-level outcomes(e.g., academic anxiety) and resilience with more major outcomes(e.g., disengagement from school). Academic buoyancy describes anadaptive response to typical academic challenges such as competingdeadlines, examination pressure or poor grades, whereas academic re-silience describes an adaptive response to severe challenges such aschronic underachievement, school refusal or alienation (Martin &Marsh, 2008a, 2009). Accordingly, academic buoyancy has greater rele-vance to the general school population and can be considered as a pro-active approach to managing challenges, thereby minimising the needfor resilient responses (Martin &Marsh, 2009). Academic buoyancy cor-relates inversely with general academic anxiety (Martin & Marsh,2008a; Martin et al., 2010) and test anxiety (Putwain, Daly,Chamberlain, & Sadreddini, submitted for publication; Putwain et al.,2012), and predicts future academic anxiety, when the autoregressivecorrelation with prior anxiety is controlled for (Martin, in press).

There are different points and processes in the S-REF model whereacademic buoyancy could buffer against the likelihood of a threat ap-praisal (see Fig. 1). When faced with potential evaluative threat, buoy-ant students may access positive self-knowledge and motivations thatare success- rather than failure-orientated (confidence, point A) andmaintain self-efficacy following, for example, lower than expectedgrades (control, point B). When faced with feedback that indicates fail-ure, buoyant students may make effort attributions that protectagainst the need for self-worth protection strategies, such as effortwith-drawal or self-handicapping, to maintain motivation and engagementin academic settings (coordination and commitment, point C). There-fore, buoyant students appraise evaluative situations as being less

threatening and they subsequently experience less academic and testanxiety.

Elevated test anxiety is present across several processes in the S-REFmodel, only some ofwhichwould be buffered by high academic buoyan-cy. Thus, some highly buoyant students (e.g., those with metacognitivebeliefs that focusing on worry is an effective coping approach) may stillexperience higher levels of test anxiety. During periods of evaluativethreat, buoyant students who experience higher test anxiety may beless prone to the performance-interfering influence of anxiety (compo-sure, point D) and their performance is, therefore, not adversely affected.

1.3. A person-centred analysis of test anxiety and academic buoyancy

Our conceptualising of academic buoyancy and test anxiety pro-poses that academic buoyancy may influence the multiple processeswithin the S-REF model. Some of these would be expected to lowerthe general level of threat appraisal, whereas others would be expectedto maintain levels of performance when threat appraisal was high. Onemight therefore expect tofind three types of individuals: thosewith lowthreat appraisal (low test anxiety and high academic buoyancy), thosewith high threat appraisal (high test anxiety and low academic buoyan-cy) and those with high threat appraisal, but performance-protected(high test anxiety and high academic buoyancy). Analytic approachesto test anxiety and academic buoyancy have typically followed avariable-centred approach, by examining relations between anteced-ents (e.g., avoidant motivations) and outcomes (e.g., test anxiety) incross-sectional and longitudinal designs. Such approaches are not wellsuited to establishing whether individuals or groups of individualsshare particular attributes, such as differing combinations of test anxietyand academic buoyancy (Hart, Atkins, & Fegley, 2003).

One notable exception was the use of cluster analysis by Martin andMarsh (2006) to differentiate three types of buoyant students: highbuoyancy with low anxiety and high control, high buoyancy with highanxiety and low control, and low buoyancy students with high anxietyand low control. This gives some precedence and empirical justificationfor differentiating between students using both buoyancy and anxiety.We extend this approach to examine whether students can be clusteredusing academic buoyancy and test anxiety, rather than general academicanxiety. In addition, we examine the academic performance of differentclusters, rather than merely statistically controlling for student perfor-mance. This approach offers the possibility of distinguishing betweenprofiles of students with differing combinations of test anxiety and aca-demic buoyancy (e.g., lower threat appraisal vs. performance protection).The multidimensionality of test anxiety is well-established, consisting ofcognitive concerns over performance and perceptions of physiologicalarousal (e.g., Morris, Davis, & Hutchings, 1981; Spielberger, Gonzalez,Taylor, Algaze, & Anton, 1978). These components were measured inthis study using worry and tension scales, respectively (see Sarason,

159D.W. Putwain, A.L. Daly / Learning and Individual Differences 27 (2013) 157–162

1984, 1988). The cognitive and affective–physiological components oftest anxiety show differing relations with motivations and competencebeliefs (e.g., Putwain, Connors, & Symes, 2010; Putwain, Woods, et al.,2010), and performance outcomes (e.g., Hembree, 1988). Includingthese different components of test anxiety in a person-centred analysisrather than treating test anxiety as a single, unidimensional, variablealso allows for the possibility that academic buoyancy may show differ-ent differentiated clusters or differentiated patterns with academic per-formance and the worry and tension components of test anxiety.

1.4. Aims of the present study

The aims of this studywere twofold. First, we examinedwhether ad-olescent students following a programme of study in England for theirschool leaving qualifications showed distinct clusters of academic buoy-ancy and test anxiety. From the literature discussed above, it washypothesised that students may be differentiated into clusters of:(i) high test anxiety/low academic buoyancy, (ii) low test anxiety/high academic buoyancy, and (iii) high test anxiety/high academicbuoyancy. Second, we examined the academic performance of the stu-dent clusters. It was hypothesised that academic performance wouldbe highest in the low test anxiety/high academic buoyancy cluster andlowest in the high test anxiety/low academic buoyancy cluster. Giventhe buffering influence of high academic buoyancy, the performanceof the high test anxiety/high academic buoyancy cluster was predictedto be higher than that of the high test anxiety/low academic buoyancycluster. This person-centred approach offers the possibility of under-standing the differingways inwhich test anxiety and academic buoyan-cy may combine for different students.

2. Method

2.1. Participants

Participants were 469 English secondary school students (male n =234, female n = 235) following a programme of study leading towardsthe school leaving qualification, the General Certificate of Secondary Ed-ucation (GCSE),with amean age of 15.0 years (SD = 0.73). Participantswere drawn from 11 coeducational state secondary schools in theNorthof England who were participating in a wider study to test anxiety andcoping with evaluative stress (N = 3225). All students were selectedfor this analysis for which GCSE performance data was available.These schools represented a diverse socio-demographic body of stu-dents and were located in some of the most and least deprived regionsof England (ranging from 73rd most deprived borough to the 32,290thmost deprived borough out of a total of 32,482—see McLennan et al.,2011). The proportion of students in participating schools eligible forfree school meals, as a proxy indicator for low income, was 14.3% (com-pared to an average of 15.9% in all English schools) and the proportionfor which English was not their first language was 12% (compared toan average of 12.3% in all English schools).

2.2. Measures

Academic buoyancywasmeasured using the unidimensional 4-itemscale developed by Martin and Marsh (2008a, 2008b). Participantsresponded to items (e.g., ‘I think I'm good at dealing with schoolworkpressures’) on 5-point scale (1 = Strongly disagree, 3 = Neitheragree nor disagree, and 5 = Strongly agree). Item scoreswere averagedto give a total score between 1 and 5. The reliability and validity of thisscale have been established in previous research, including adolescentstudents in the UK (Martin & Marsh, 2008a, 2008b, 2009; Putwainet al., 2012), and the internal reliability coefficient for the presentstudy was acceptable (Cronbach's α = .76).

Test anxiety was measured using the worry (6 items) and tension(5 items) scales from the Revised Test Anxiety Scale (Benson, Moulin-

Julian, Schwarzer, Seipp, & El-Zahhar, 1992; Hagtvet & Benson, 1997).Exemplar items include ‘I worry a great deal before taking an importantexam’ (worry) and ‘During exams I feel very tense’ (tension). Partici-pants responded to items on a 4-point scale (1 = Almost never, 2 =Sometimes, 3 = Often, and 4 = Almost always) which were averagedacross items to give a total score between 1 and 4. The reliability and va-lidity of these scales have been established in previous research (e.g.,Hagtvet & Benson, 1997), including with adolescent students in theUK; Putwain, Connors, et al., 2010; Putwain, Woods, et al., 2010), andthe internal reliability coefficients for the present studywere acceptable(Cronbach's α worry = .74, tension = .84).

Academic performance was measured using participants' aggregat-ed mean grade in three key GCSE subjects: Math, English and Science.GCSEs are graded on an eight-point scale of A*–G in a government-regulated process that combines norm and criterion referencing (seeStringer, 2012), with C representing a pass grade. As raw test scoresare typically unavailable, the GCSE grades were converted to a numeri-cal scale (A* = 8, A = 7, G = 1) for analysis, a common practice insimilar research with GCSE performance (e.g., Daly & Pinot de Moira,2010).

2.3. Procedure

Self-report measures for text anxiety and academic buoyancy werecollected in February of the academic year, three to four months beforestudents sat GCSE exams, which were scheduled over May and June.Students completed measures during a period of the school day thatwas scheduled for completing administrative tasks. Measures werepresented in a counterbalanced order. Institutional consentwas provid-ed by theHead Teacher of each participating school and individualwrit-ten consent, which included allowing access to student GCSE grades,was provided by each participating student.

3. Results

Cluster analysis, an exploratory multivariate data reduction tech-nique, was used to place students into relatively homogenous groupsbased on their test anxiety and academic buoyancy scores bymaximisingsimilarities within students belonging to a particular cluster and dissim-ilarities between students belonging to different clusters. We performeda hierarchical cluster analysis on academic buoyancy and the two testanxiety scales (worry and tension) using Ward's method. This approachis based on analysis of variance to calculate the sumof squared deviationsfrom themean of a cluster. Each case is initially treated as its own clusterand then merged with other clusters, which minimises any increasein the sum of squared deviations to minimise within-cluster varianceand emphasise between-cluster differences (Aldenderfer & Blashfield,1984). As academic buoyancy and test anxiety were measured using dif-ferent scale metrics, we standardised all three scales prior to the analysisusing z-score transformations, whereby the original distribution istransformed to a mean of 0 and standard deviation of 1. As this transfor-mation may result in some loss of meaning, we present untransformedscale scores where relevant to aid interpretation (e.g., Cohen & Cohen,1983). Results from the cluster analysis produced an agglomerationschedule, which provided a number of solutions equal to the number ofcases. Table 1 reports the agglomeration schedule for the final ten clus-ters and the change in coefficients at each stage.

The largest change in coefficientswas observedwhenmoving fromaone- to a two-cluster solution. However, relatively large changes werealso observed when moving from two- to three- , from three- to four-, and from four- to five-cluster solutions. Thus, we considered four pos-sible solutionswith two, three, four andfive factors. For ease of interpre-tation, the various solutions are described with respect to theiruntransformed scores, relative to the scale range. The two-factor solu-tion produced clusters of: (a) high anxiety/low buoyancy and (b) lowanxiety/high buoyancy, which differed significantly on all three

Table 1The agglomeration schedule for the last ten clusters.

Number ofclusters

Agglomeration coefficient(rounded)

Change in coefficientto next level (%)

10 310 6.99 333 7.48 356 7.27 381 7.06 421 9.55 479 16.14 556 15.43 657 19.72 818 41.71 1403 –

160 D.W. Putwain, A.L. Daly / Learning and Individual Differences 27 (2013) 157–162

variables (ps all b .001).1 The three-factor solution produced an addi-tional cluster of (c) mid anxiety/mid buoyancy. All three clusters dif-fered significantly on all three variables (ps all b .001). The four-factorsolution produced an additional cluster of mid-high test anxiety/midbuoyancy. All four clusters differed significantly on all three variables(ps all b .001), with the exception of worry and tension between clus-ters 3 and 4 (p N .05). Lastly, the five-factor solution produced an addi-tional cluster of (e) mid anxiety/high buoyancy (ps all b .001, with theexception of worry and tension between clusters 3 and 5).

In balancing parsimony and explanatory power, we chose the five-factor solution based on the following criteria: (i) it yielded a relativelylarge change in agglomeration coefficients, (ii) statistically significantdifferences were identified between clusters,2 and (iii) it differentiatedbetween clusters in a manner that was theoretically and empiricallymeaningful. The two- and three-factor solutions were able to accountfor the hypothesised role of buoyancy on threat appraisals (points A, B,and C in Fig. 1). The four- and five-factor solutions allowed for the possi-bility that some students may have combinations of test anxiety and ac-ademic buoyancy which relate in other ways, thereby allowing theopportunity to examine the protective or buffering role of buoyancy onacademic performance (point D in Fig. 1). In particular, the five-factorsolution offered the most differentiated test of this hypothesis. Table 2reports the descriptive statistics for the five clusters on worry, tensionand buoyancy. Although we did not utilise GCSE score in our interpreta-tion of clusters, it is included in Table 2 for expediency.

One-way, between-participants ANOVAs showed significant differ-ences between clusters in worry, F(4, 464) = 140.64, p b .001, ηp

2 =.55; tension, F(4, 464) = 344.39, p b .001, ηp

2 = .75; academic buoyan-cy, F(4, 464) = 210.86, p b .001, ηp

2 = .65; and GCSE score, F(4,464) = 4.85, p = .001, ηp

2 = .04. Post-hoc tests with Bonferroni cor-rections showed significant differences (all ps b .001) inworry betweenall clusters, with the exception of clusters 3 and 5 (p N .05); significantdifferences between all clusters in tension (all ps b .001, except clusters1 and 4, p = .03, and clusters 3 and 5, p N .05); and significant differ-ences (all p's b .001) in academic buoyancy between all clusters. Signif-icant differences in GCSE score were observed between clusters 1 and2 (p = .004), clusters 1 and 3 (p = .009), and clusters 1 and 5 (p =.002). All other comparisons were non-significant (p N .05). To aid in-terpretation, all scores are graphed in Fig. 2.

4. Discussion

This study had two aims, the first of which was to examine whethera sample of students could be groupedmeaningfully into clusters basedon their test anxiety and academic buoyancy scores. The second was toexplorewhether the clusters would exhibit distinct patterns of academ-ic performance. The cluster analysis identified a range of possible

1 Tests of difference used t-tests for the two-factor solution and a one-way ANOVAwithBonferroni correction on post-hoc tests for all other solutions.

2 With the exception of worry and tension between clusters 3 and 5.

solutions, ranging from two to five clusters. We proposed that thefive-factor solution provided a parsimonious explanation of the datawith empirically-distinct clusters in terms of test anxiety, academicbuoyancy and academic performance. Furthermore, this solution pro-vides support for the ways in which academic buoyancy is theorisedto interact with test anxiety within the S-REF model. Three clusters(1–3) clearly represent the inverse relationship between test anxietyand academic buoyancy and two clusters (4 & 5) represent studentswith higher test anxiety and academic buoyancy. Our novel analysiscomplements other typologies of students based on dimensions of testanxiety and fear of failure within variable-centred approaches (e.g.,Covington, 1992; Martin, 2010; Zeidner, 1998).

The first cluster represented high anxiety/low academic buoyancyand had the lowest academic performance, the second cluster repre-sented low anxiety/high academic buoyancy and had the highestacademic performance, and the third cluster represented mid anxiety/mid academic buoyancy. This finding is consistent with previousresearch utilising variable-centred approaches that has demonstratedan inverse correlation between test anxiety and academic buoyancy(Putwain et al., 2012) and supports the assertion, based on the S-REFmodel, that perceived threat appraisals will be lower when academicbuoyancy is higher. Thus, academic buoyancy may protect againsttest anxiety in academically challenging circumstances, such asexperiencing failure and managing examination pressure. Positiveself-knowledge beliefs may influence students' thoughts that failurecan be overcome and that adaptive attributions may prevent a debil-itating cycle of effort withdrawal and avoidance. These three clusterscontained fewer students than clusters 4 and 5, which we will dis-cuss next.

The fourth cluster represented mid-high test anxiety/mid buoyancyand the fifth cluster represented mid anxiety/high buoyancy. This sup-ports the hypothesis that, in addition to the general inverse trend be-tween test anxiety and academic buoyancy, some buoyant studentsmay nevertheless still become test anxious. We propose that theremay be some elements of the S-REF model, such as metacognitive be-liefs that, unlike self-knowledge beliefs and person–situation interac-tions, are not directly influenced by academic buoyancy. As such,students may appraise performance-evaluative situations as threaten-ing, not because of poor self-beliefs or maladaptive person–situation in-teractions per se, but simply because of a tendency to worry. Academicbuoyancy, however, is not redundant for such students, as it may play arole inmaintaining composure during the phase immediately precedingand during an examination or test, thereby buffering against a cata-strophic reaction (see Putwain, 2009; Putwain, Connors, et al., 2010;Putwain, Woods, et al., 2010). The findings presented here supportthis interpretation. Students in cluster 5 performed as well as those incluster 2 at GCSE, even though they reported higher test anxiety, anddid significantly better than those in cluster 1. Students in cluster 4,with mid-high test anxiety, did not perform quite as well as students inclusters 2, 3 and 5, but the difference was not statistically significant. To-gether, clusters 4 and 5 represented the majority of students, highlight-ing the heterogeneity of educational performance that may be evidentin students who report themselves as moderately test anxious.

In terms of academic performance, it is not possible to determinewhether cluster 2, 3 or 5 has the most adaptive profile (all students inthese three clusters obtained a mean pass grade). It would thereforeseem, for performance, that it does not matter if test anxiety is mid orlow, as long as academic buoyancy is high. The least adaptive profile iscluster 1,when high test anxiety is accompanied by low academic buoy-ancy and where students obtained amean fail grade. This type of analy-sis provides a useful companion to the variable-centred approaches thattypically report a small inverse correlation between academic buoyancyand test anxiety, particularly the cognitive (worry) component(e.g., Chapell et al., 2005;Hembree, 1988). It suggests that itmay be use-ful in variable-centred approaches to consider academic buoyancy as apossible moderator of the test anxiety and academic performance

Table 2Descriptive statistics for the five clusters (n = 469).

Cluster 1(n = 29, 61.8%)

Cluster 2(n = 89, 18.9%)

Cluster 3(n = 96, 20.4%)

Cluster 4(n = 121, 25.7%)

Cluster 5(n = 134, 28.8%)

High anxiety/lowbuoyancy

Low anxiety/highbuoyancy

Mid anxiety/midbuoyancy

Mid-high anxiety/midbuoyancy

Mid anxiety/highbuoyancy

M SD z M SD z M SD z M SD z M SD z

Worry 3.27 0.41 1.45 1.65 0.32 −1.23 2.30 0.43 −0.10 2.87 0.39 0.82 2.24 0.51 −0.20Tension 3.64 0.32 1.31 1.51 0.35 −1.38 2.50 0.57 −0.12 3.38 0.35 0.99 2.47 0.32 −0.17Academic buoyancy 1.42 0.34 −1.99 3.94 0.57 0.93 2.52 0.51 −0.72 2.86 0.58 −0.32 3.64 0.43 0.58GCSE score 4.24 1.37 −0.64 5.31 1.28 0.12 5.23 1.43 0.06 4.91 1.55 −0.17 5.33 1.35 0.13

Note: GCSE score 5 = C pass grade.

161D.W. Putwain, A.L. Daly / Learning and Individual Differences 27 (2013) 157–162

relationship. While our study has shown that clusters differ in theiracademic performance, it was limited in the sense that academic perfor-mance was the only outcome variable. It would enhance the under-standing and interpretation of clusters if future research includedsome of the processes that we have suggested may account for differ-ences between clusters, such as self-knowledge beliefs (e.g., avoidantmotivations and perceived self-competence), person–situation interac-tions (e.g., attributions and academic self-handicapping), and executiveprocesses (e.g., metacognitive beliefs).

4.1. Implications and limitations

The educational implication of our research is that theremay beben-efits for academic performance in havinghigh academic buoyancy, irrel-evant of whether test anxiety is high or low. Martin and Marsh (2006)suggest several ways in which teachers and educational support staffcan build antecedents of academic buoyancy. These include developingacademic self-confidence by individualising tasks where possible, en-hancing self-regulation through planning and persistence, encouragingeffort attributions through feedback, and reducing fear of failure by en-couraging feedback that does not emphasise comparison with peers.The attraction of such approaches is that they can be incorporated intoroutine teaching and learning activities by regular instructional andsupport staff, without the need for specialist intervention, althoughthese approaches could undoubtedly be delivered in the latter fashion.Furthermore, the cluster analysis highlights how it may be importantto consider test anxiety in relation to other salient variables (in thiscase, academic buoyancy) in identifying students most at risk of lowerperformance.

When considering our findings, it would be prudent to bear in mindthe limitations of using cluster analysis, in that it is a purely descriptivetechnique which can result in non-unique solutions (Johnson &Wichern, 1998; Romesburg, 1984). Although we chose a five-factor

Note: Columns for GCSE score with a ** indicate a significant difference to Cluster 1 (p< .01)

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Sta

nd

ard

ised

(z)

Sco

res Worry Tension Academic Buoyancy GCSE Score

** ****

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5

Fig. 2. Standardised worry, tension, academic buoyancy and GCSE scores for the five clus-ters. Note: Columns for GCSE score with an ** indicate a significant difference to cluster 1(p b .01).

solution theremay, in fact, not be a single ‘correct’ solution.We justifiedour interpretation theoretically (in relation to the hypothesised pointsat which academic buoyancy could relate to test anxiety in Fig. 1) andstatistically, using both internal and external criteria (the change in ag-glomeration coefficients and the significant differences between testanxiety and academic buoyancy variables, respectively). Although wehave provided clear support to our interpretation, we accept thatother scholars may have theoretical or statistical reasons to disagree.

4.2. Conclusion

Our study identified five possible clusters of students on dimensionsof test anxiety (worry and tension) and academic buoyancy. Three ofthe clusters corresponded to an inverse relationship between test anxi-ety and academic buoyancy. The remaining two clusters, which repre-sented 54.5% of students, corresponded to students with higher testanxiety and academic buoyancy. Thus, academic buoyancy may reducethe initial threat appraisal of performance-evaluative situations (as seenin clusters 1–3) in some students, but other students may still experi-ence mid to high test anxiety with higher buoyancy. The academic per-formance of students differed between clusters. Notably, the bestperformance was found in students with high academic buoyancy andwhen test anxiety was either low (reflecting a lower threat appraisal)or mid (reflecting a buffering effect on performance). Future researchmaywish to consider inmore detail themechanisms bywhich academ-ic buoyancy is proposed to influence test anxiety appraisal processes(e.g., the role of beliefs and attributions in determining effort expendi-ture vs. withdrawal following failure) and how academic buoyancy stu-dents may protect performance against the debilitating effects of testanxiety (e.g., enhanced self-regulation).

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