mood states, self-set goals, self-efficacy and performance in academic examinations

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Personality and Individual Differences 42 (2007) 573–583

Mood states, self-set goals, self-efficacy and performancein academic examinations

Richard C. Thelwell a,*, Andrew M. Lane b, Neil J.V. Weston a

a Department of Sport and Exercise Science, University of Portsmouth, Spinnaker Building,

Cambridge Road, Portsmouth PO1 2ER, UKb School of Sport, Performing Arts and Leisure, University of Wolverhampton,

Gorway Road, Walsall WS1 3BD, UK

Received 7 October 2005; received in revised form 5 July 2006; accepted 26 July 2006Available online 5 October 2006

Abstract

The present study investigated relationships between mood, performance goals, and both written andoral examination performance. Fifty-seven undergraduate students completed a mood measure thatassessed the subscales of anger, calmness, confusion, depression, fatigue, happiness, tension and vigor, indi-cated the grade set as a goal for the examinations, and rated their confidence to achieve this goal. Thesemeasures were completed approximately 30 min before each examination. Structural equation modellingresults indicated that mood states, self-efficacy and self-set goals predicted 20% of oral examination perfor-mance and 7% of written examination performance. In both samples, findings indicate that positive moodstates are associated with self-efficacy to achieve self-set goals. We suggest that future research should lookat the extent to which intervention strategies designed to enhance mood states are associated with enhancedperformance.� 2006 Elsevier Ltd. All rights reserved.

Keywords: Emotion; Performance; Self-efficacy; Cognition; Applied psychology

0191-8869/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.doi:10.1016/j.paid.2006.07.024

* Corresponding author. Tel.: +44 23 92 845164.E-mail address: [email protected] (R.C. Thelwell).

mailto:[email protected]

574 R.C. Thelwell et al. / Personality and Individual Differences 42 (2007) 573–583

1. Introduction

There has been an increase in the amount of empirical research suggesting psychological statessuch as mood to be predictive of performance, especially in situations that have a high degree ofpersonal importance. Defined as ‘‘a set of feelings, ephemeral in nature, varying in intensity andduration, and usually involving more than one emotion (Lane & Terry, 2000, p. 16), mood hasbeen reported to influence individuals in a number of situations that have included athletic com-petition (Beedie, Terry, & Lane, 2000) and academic examinations (Catanzaro, 1996; Lane,Whyte, Terry, & Nevill, 2005; Totterdell & Leach, 2001). A theoretical position forwarded to ex-plain such effects is the notion that the moods experienced by the individual serve in an informa-tional manner. Thus the ‘mood as information’ hypothesis suggests that the affective content ofmood states provide information on personal resources to cope with task demands, and it is be-lieved that the predictive effects of mood states are especially salient when the outcome of theactivity is uncertain (Bless, 2001; Gendolla & Krusken, 2002). With this in mind, it is likely thatnegative or unpleasant moods will associate with a difficult or problematic situation, where infor-mation regarding the self, task and coping strategies are negatively phrased. Should this occur, itis also likely that the negative moods will relate to low self-efficacy (Bandura, 1990). In contrast,positive moods are more likely to provide functional information for the individual regarding thesituation, and relate to high self-efficacy.

Acknowledging how ‘situations’ often influence mood states held by the individual, it is impor-tant to be aware that when the situation is one of importance, mood states may influence perfor-mance in both a positive or negative manner. Whilst individuals may wish to experience positivemoods prior to a difficult or uncertain event, it is often the case that individuals will experiencenegative moods due to the discrepancy between the demands of the task and the resources thatthe individual has at their disposal to cope with the situation (Carver & Scheier, 1990; Martin& Tesser, 1996). As a result, individuals who experience negative moods may attend to specificinformation in greater detail to reduce the discrepancy that may be present for the task (Cervone,Kopp, Schaumann, & Scott, 1994). If that is the case then it may be the goals set within the taskthat are more appropriate to examine, rather than global performance. For example, in an exam-ination, a student may have a very negative mood due to perceiving the task demands to outweightheir personal resources needed to achieve a high grade. As a result, they may use the negativemood state to help them mobilize their effort in a functional manner towards the goal of achievinga threshold pass. Alternatively, the negative mood may act in a dysfunctional manner and disableany mobilization of effort due to the attainability of the goal (task demands) being too high (Gen-dolla & Krusken, 2002). Thus, as argued by Bandura (1990), individuals with low self-efficacyexpectations before doing a personally important task could result in experiencing feelings ofdespondency, especially if they anticipate failure.

Taking the theoretical suggestions for the relationships between mood states, self-efficacy andperformance forward, recent research (e.g., Lane et al., 2005) has found that pre-examinationmood states are not only predictive of performance, but are also related to the difficulty ofself-set goals, and self-efficacy estimates to achieve these goals. Specifically, Lane et al. reportedsignificant positive intercorrelations between vigor, self-efficacy, self-set goals and examinationperformance among a sample of 50 undergraduate students for a practical examination. Whilstan insight to the potential influence of depressed mood on mood states, goals, and performance

Mood states

Goals

Self-efficacy

Performance

Fig. 1. Hypothesized model for relationships between mood states, self-set goals, self-efficacy and performance.

R.C. Thelwell et al. / Personality and Individual Differences 42 (2007) 573–583 575

within an academic context was provided, Lane et al. indicated that before the findings could beused in applied settings, replication was needed. They also commented that future research shouldseek to explore the predictive effectiveness of mood on different forms of assessment, given thattheir findings were limited to practical forms of summative assessment. To develop the knowledgeand understanding to this area of research, it is therefore appropriate to gain a more detailedunderstanding to whether similar findings are evident within alternative forms of summativeassessment.

The aim of the present study is to further the research by Lane et al. (2005) to incorporate alter-native forms of academic assessment. The two forms of assessment included (a) the traditionalwritten examination, and, (b) the contemporary oral (viva-style) examination. For both formsof examination, the purpose was to examine relationships between pre-examination mood states,goals, self-efficacy, and actual examination performance. The present study tested the notion thatmood states predicted cognitive states (goals and self-efficacy) and that mood states and cognitivestates predict performance. The hypothesized model is depicted in Fig. 1. It should be noted thatFig. 1 represents the starting point to developing a theoretical framework. The present studysought to operationalize mood, using subcomponents of the construct, thereby assess specificaffective states such as anger, depression, etc., rather than a single measure of mood that com-bined specific states under a general heading. This approach to assessing mood is consistent withthe notion that the affective content of mood provides information and it is this information thatinfluences performance.

2. Methods

2.1. Participants

Volunteer participants were 57 (Male n = 32, Female n = 25: Age range 18–28 years) under-graduate students studying for a degree in Sports Science at the University of the first author.

2.2. Measures

Mood was measured using the Brunel Mood Scale-32 (BRUMS-32) that has been recentlydeveloped (see Lane & Jarrett, 2005) from two previously validated scales (Matthews, Jones, &


Chamberlain, 1990; Terry, Lane, Lane, & Keohane, 1999; Terry, Lane, & Fogarty, 2003). The ori-ginal Brunel Mood Scale (BRUMS: Terry et al., 1999, 2003) is a 24-item mood state scale basedon the Profile of Mood States (McNair, Lorr, & Droppleman, 1971). The BRUMS assesses themood states of anger, confusion, depression, fatigue, tension, and vigor. The current 32-item scalewas formed by adding the items that assess the subscales of happiness and calmness from theUWIST (Matthews et al., 1990) to the 24-items from the BRUMS. The argument for includinga greater number of positive mood states was in response to the frequently cited limitation thatthe POMS has an excessively negative orientation. To this end, it has been suggested in recent lit-erature (e.g., Hanin, 2000) that positive mood dimensions, such as happiness and calmness, mayalso influence performance. With the addition of the two new subscales, the BRUMS-32 is sug-gested to provide a more balanced assessment of positive mood and negative mood, and it hasalso been employed in recent mood-related research (e.g., Thelwell, Weston, Lane, & Greenlees,2006).

Each of the eight subscales within the BRUMS-32 has four items. Examples of Anger items in-clude ‘‘Annoyed’’ and ‘‘Bitter’’, Confusion items include ‘‘Muddled’’ and ‘‘Confused’’, Depressionitems include ‘‘Miserable’’ and ‘‘Unhappy’’, Fatigue items include ‘‘Exhausted’’ and ‘‘Sleepy’’,Tension items include ‘‘Nervous’’ and ‘‘Worried’’, and Vigor items include ‘‘Active’’ and ‘‘Lively’’.Calmness items include ‘‘Restful’’ and ‘‘Composed’’ and Happiness items include ‘‘Contented’’and ‘‘Satisfied’’. All items within the BRUMS-32 are rated on a 5-point scale where a responseof 0 equals ‘‘not at all’’ and 4 equals ‘‘extremely’’. Alpha coefficients for each subscale in the presentstudy were over .70.

2.3. Academic goals and goal-confidence

Prior to each of the examinations, participants were asked to indicate the grade that they set asa goal for the examination. They were also asked to rate the confidence that they have for achieve-ment of the goal. Goal-confidence was rated on a 9-point scale where 1 = ‘‘no confidence at all’’,and 9 = ‘‘very confident’’.

2.4. Examination performance

The two methods for assessing examination performance included traditional written examina-tion performance and a more contemporary oral examination. The written examination (‘‘Intro-duction to Sport and Exercise Psychology’’) was 1.5 h in length and comprised two sections wheresection one had 45 multiple-choice questions (not negatively marked) that had to be answered.Section two had a total of 5 essay titles where students were required to answer 1. The overallgrade for the examination took into account performance in both sections. In accord with theUniversity’s assessment policy, on completion of the examination, all students’ work was markedand a total of 10% of the examination scripts were subjected to blind second marking by a subjectspecialist. All double marked work was then stored and made available for the Subject ExternalExaminer to ensure that the assessment policy had been adhered to, and that appropriate markshad been awarded.

The oral assessment (‘‘Introduction to Sports Physiology’’) was a new form of assessment with-in the curriculum (see Oakley, 2004 for a full review). Each student was required to answer a total


of six questions within their oral examination, where a total of three learning outcomes (two ques-tions per learning outcome) were assessed. To enable variety in the questions posed, the unit coor-dinator was required to prepare a question-bank of 10–20 questions for each learning outcome. Apro-forma marking sheet was used to grade student responses to each question on a 0–5 scale,using set marking criteria that enabled an overall mark to be given.

Although two members of staff examined the students in the oral assessment (thus providingdouble marking), having gained consent from the students, some of the oral assessments were taperecorded to enable commentary from the Subject External Examiner to ensure that the assessmentpolicy had been adhered to and that students were awarded appropriate grades. For both forms ofassessment, confirmed student grades were made available following the Examination Board. Themarking system employed ranges from 0% to 100% where marks of 70% + equated to a first classpass, marks between 60% and 69% equated to an upper second class pass, marks between 50% and59% equated to a lower second class pass, marks between 40% and 49% equated to a third classpass, and, marks of 39% or below equated to a fail.

2.5. Experimental procedures

Prior to the study commencing, participants completed written informed consent forms havingbeen provided with detailed participant information sheets outlining the nature and methodologyof the study. Having ensured confidentiality, participants completed a BRUMS-32, and indicatedtheir percentage goal for the examination (0–100) and confidence in their ability to achieve thegoal (rated on a 9-point scale where 1 = no confidence and 9 = very confident) approximately30 min prior to their written and oral examinations (that were over one week apart). Data werechecked for missing data before collecting. Participants were asked if missing data was intended oraccidental. Of the few cases of missing data, all were due to missing items and participants com-pleted the missing items. Also prior to the summative assessments, all students had completed for-mative assessments for each topic area. The study had ethical approval from the institution of thefirst author.

2.6. Data analysis

A repeated measures multivariate analysis of variance (MANOVA) was employed to examinethe differences in pre-examination mood, the performance goal set pre-examination, the confi-dence to achieve the performance goal, and, the actual examination performance before the writ-ten examination, and oral examination.

Structural equation modelling (SEM) techniques were used to assess the strength and directionof hypothesized relationships between mood states, cognitive states and performance in the twoexamination conditions. Multisample structural equation modelling was used to assess the extentto which relationships differed by examination type. Consistent with the data analysis strategy ap-proach used by Scott-Lennox and Scott-Lennox (1995), a fully recursive model with equality con-straints on all hypothesized relationships was used. Moderation is inferred when relationshipsdiffer between examination type. Structural equation modelling was used to analyse relationshipsbetween variables within the correlation matrix in each examination condition. Whilst it is possi-ble to use a correlation matrix to examine the strength and direction of relationships and compare


relationships in the two examination conditions, a limitation is that it provides unnecessary re-sults, which can detract from the main focus of the study.

3. Results

Data were checked for normality before data analysis commenced. Results indicated thatwritten examination (Kolmogorov–Smirnov = .07, p = .02), oral examination (Kolmogorov–Smirnov = .10, p = .02) passed the test of normality. Anger (Oral – Kolmogorov–Smirnov = .36,p < .01; Written – Kolmogorov–Smirnov = .31, p < .01); Confusion (Oral – Kolmogorov–Smirnov = .17, p < .05; Written – Kolmogorov–Smirnov = .18, p < .01); Calmness (Oral –Kolmogorov–Smirnov = .12, p < .05; Written – Kolmogorov–Smirnov = .10, p < .05); Depression(Oral – Kolmogorov–Smirnov = .30, p < .01; Written – Kolmogorov–Smirnov = .28, p < .01);Fatigue (Oral – Kolmogorov–Smirnov = .16, p < .01; Written – Kolmogorov–Smirnov = .18,p < .01); Happiness (Oral – Kolmogorov–Smirnov = .16, p < .01; Written – Kolmogorov–Smirnov = .12, p < .05); Tension (Oral – Kolmogorov–Smirnov = .17, p < .01; Written –Kolmogorov–Smirnov = .15, p < .05); Vigor (Oral – Kolmogorov–Smirnov = .14, p < .01;Written – Kolmogorov–Smirnov = .10, p = < .05) failed the normality test. As hypotheses weremultivariate in nature, and as both MANOVA and Structural Equation Modelling using theSatorra-Bentler estimation method are robust to violations of normality, normality tests did notalter the decision on which test to use.

Repeated measures MANOVA results indicate that there was no multivariate effect betweenpsychological state variables by examination type (Wilks’ k11,41 = .71, p = .16, g2 = .29). Despiteno multivariate effect, univariate results indicate that participants produced significantly highergrades in the oral examination (see Table 1). Results indicated that there was no significant cor-relation between written examination performance and oral examination performance (r = .26,p = .06). Structural equation modelling was used to analyse relationships between variables within

Table 1Repeated measures comparison of mood scores, goals, and goal-confidence between the oral and written examinations

Written Oral F p g2

M SD M SD

Anger 0.26 0.46 0.20 0.36 1.46 0.23 0.03Confusion 1.10 0.95 1.11 0.96 0.42 0.52 0.01Calmness 1.52 0.68 1.53 0.62 0.00 0.97 0.00Depression 0.53 0.83 0.44 0.75 1.06 0.31 0.02Fatigue 1.00 0.95 0.96 0.91 0.28 0.60 0.01Happiness 1.69 0.74 1.64 0.70 0.23 0.63 0.00Tension 2.08 1.07 2.04 1.08 0.10 0.76 0.00Vigor 1.90 0.82 1.94 0.79 0.08 0.78 0.00Self-efficacy 5.69 1.31 5.79 1.16 0.01 0.92 0.00Goal 58.08 11.08 56.35 9.61 2.15 0.15 0.04Performance 56.42 11.08 62.06 13.43 7.34 0.01 0.13

Wilks’ k11,41 = .71, p = .16, g2 = .29


the correlation matrix in each examination condition, where results indicated some support forthe hypothesized model in the oral examination condition (v2 = 12.98, p < .01; Comparative FitIndex: CFI = .97; Root Mean Squared Error of Approximation: RMSEA = .033) and the writtenexamination (v2 = 8.21; CFI = .98; RMSEA = .04). It should be noted that the v2 was significant,suggesting that revisions to the model were needed. As Figs. 2 and 3 indicate, mood, self-efficacy,and self-set goals predicted 20% of performance variation for the oral examination and 7% of thevariance in the written examination, respectively. Predictive paths for the oral examination indi-cated that successful performance was associated with setting a difficult goal, high self-efficacy andlow vigor coupled with low confusion, depression and tension. For relationships between moodstates and self-set goals, results indicated that setting a difficult goal for the oral examinationwas associated with feeling calm and happy coupled with low tension and fatigue. Self-efficacyestimates toward performance in the oral examination, was associated with feeling calm and hap-py coupled with low depression, tension and fatigue.

For written performance, results indicated confusion and tension significantly hampered per-formance. A difficult goal was associated with tension and vigor. Self-efficacy was associated withlow confusion, depression and tension and higher scores for calmness.

Results indicated some support for the multisample analysis (v2 = 41.71, df = 28, p > .01;CFA = .98; RMSEA = .067). The v2 value was not significant at the .01 level, but was significantat the .05 level. With multisample CFA, the statistics derive from the Lagrange multiplier test(LMT), which tests the equality of relationships between samples (in this instance assessment for-mat). LMT results indicate that there were no significant differences in the strength of relation-ships between the two assessments. It is important to recognize that although some variables

Anger

Confusion

Fatigue

Tension

Vigor

Performance:Oral R2 = .20

.26

Calmness

Depression

Happiness

GoalOral R2 =.46

Self-efficacyOral R2 =.43

.24

-.32*

.28

-.41

.30-.38

.37

.31

-.38

.55

-.34

.44

.28

-.10-.01

-.01

Fig. 2. Paths predicting oral examination performance from self-efficacy, goals and mood states (*p < .05).

Anger

Confusion

Fatigue

Tension

Vigor

Performance:

Written R2

= .07

Calmness

Depression

Happiness

Goal

Written R2

=.39

Self-efficacy

Written R2

= .37

.30

-.38

-.38

.24

-.33

-.23

.26

.28

.23

-.08

-.10

-.04

Fig. 3. Paths predicting written examination performance from self-efficacy, goals and mood states.


were significant predictors in one assessment and not the other, relationships were generally weakto moderate. An acknowledged limitation of the data analysis strategy used in the present study isthat it was under-powered, suggesting a large sample size was desirable.

4. Discussion

The present study examined relationships between mood and two forms of examination perfor-mance by testing the notion that mood states would predict cognitive states prior to examinations,which in turn, would predict performance. Although there were two forms of examinations withinthe present study, there was little difference between the findings for each. In fact, other than thedifference in the amount of variance that the mood and cognitive states collectively contributed tobetween the examinations, the predictive values of mood states on cognitive states, and cognitivestates on performance were similar. In short, the findings generally supported the hypothesizedmodel depicted in Fig. 1 where higher positive moods (e.g., happiness) and lower negative moods(e.g., tension) are suggested to be associated with higher self-efficacy and goal difficulty, which inturn were associated with higher performance levels. This is of particular interest given that po-sitive moods are often associated with a positive perception of the situation and that self-efficacy iscontinually reported to be a major predictor of performance in a variety of settings (Bandura,1990; Wise & Trunnell, 2001). Also, given that positive moods often suggest the individual to havethe appropriate personal resources to cope with the task in hand, it is important to report that in


the main, they were associated with setting a higher goal, thus supporting the notion that the indi-viduals perceived themselves to have the requisite resources to achieve in situations where the out-come is uncertain (Gendolla & Krusken, 2002).

Despite the present study furthering that of Lane et al. (2005) to alternative methods of sum-mative assessment, there is a fundamental difference between the studies. For example, whilst thepresent study examined the extent to which mood states related to cognitive states pre-examina-tion, and in turn, how they related to examination performance, Lane et al. examined the extent towhich depressed mood influenced the mood–performance relationship in accord with the concep-tual model of mood forwarded by Lane and Terry (2000). Despite the differences in the aims ofthe studies, there are some consistencies between the findings reported across them. Firstly, Laneet al. reported that irrespective of whether individuals experienced depressed mood, positive moodstates influenced cognitive states pre-examination and successful examination performance. Thissupports the finding within the present study that positive mood states appear to be related to theattainment of high levels of self-efficacy and the setting of difficult goals. Secondly, both studieshighlight the potential concerns associated with negative moods, and in particular to how theymay provide negative information on the forthcoming task (Gendolla & Krusken, 2002), whichmay lead to lower levels of self-efficacy being experienced, and lower, or inappropriate goals beingset prior to the examination (Comunian, 1989; Lane, 2001).

Given the consistent findings across the present study and that conducted by Lane et al. (2005),we suggest that whilst the findings could be used to inform practice, practitioners need to be awareof some of the limitations across the studies. Firstly, although the present study extended the workof Lane et al. to an alternative sample and method of assessment, it remains that the results acrossboth studies provided associative, rather than causative links. Secondly, the two studies employedcross-sectional research designs, which reduce the applied impact of the findings due to a lack ofintra-individual examination of the mood, cognitive state, and performance relationships. As aconsequence, we suggest that future research should follow a more applied research design andexamine mood states associated with functional cognitive states and successful performance levelsvia idiographic methods. This notion has been reinforced further by Hanin (2000) who suggestedemotion–performance relationships to be highly individualised and as a result, worthy of intra-individual study. On this point however, the present study did not seek to intervene among par-ticipants reporting negative mood states and low levels of self-efficacy and dysfunctional goalsprior to examinations. Instead, it sought to test mood–performance relationships in the two exam-inations rather than seeking to conduct an intervention to alleviate symptoms of inappropriatemoods and other unpleasant cognitive states. Therefore, future research should seek to identifyindividual mood-profiles associated with high levels of self-efficacy, functional goals and examina-tion success across different types of examinations, and develop interventions that can be used tobring about modifications in mood states so that participants are in their ideal cognitive state andperformance states before examinations.

A second, but related line of further research should investigate relationships between moodstate changes before, during and after taking an important examination. This may be even moresalient given that it is possible for mood states to change upon commencing the examination, thusmaking the relationship between pre-examination mood and performance questionable. Also,researchers may wish to consider examining the relationships between mood throughout perfor-mance and emotional intelligence (Goleman, 1995; Schutte et al., 1998), especially seeing that


recent research (e.g., Parker, Summerfeldt, Hogan, & Majeski, 2004) has found links betweenemotional intelligence and academic performance. Should such work be conducted, it would beexpected that individuals who have high levels of emotional intelligence would be aware of thepotentially facilitating effects of mood states and be able to regulate their mood to appropriatestates to maximize performance.

In conclusion, results of the present study found mood states, cognitive states and performanceto have some relationship. Irrespective of examination type, positive mood states were associatedwith more facilitative cognitive states and performance, with negative mood states hampering per-formance. Finally, future research examining the relationships between mood states and perfor-mance is required from an idiographic approach.

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mood states, self-set goals, self-efficacy and performance in academic examinations

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