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Scandinavian Journal of Educational Research Vol. 54, No. 2, April 2010, 125–132

Preoccupation with Failure Affects Number of Study Hours—Not Academic Achievement

Geir Scott Brunborg, Ståle Pallesen, Åge Diseth and Svein Larsen University of Bergen

Taylor and FrancisCSJE_A_464298.sgm10.1080/00313831003637899Scandinavian Journal of Education Research0031-3831 (print)/1470-1170 (online)Original Article2010Taylor & Francis5420000002010Geir ScottBrunborggeir.brunborg@psysp.uib.no

It has been claimed that perceived academic control (AC) in combination with preoccupation with failure (PWF) produces a strong motivation for success, and the interaction (AC x PWF) has been shown to predict academic achievement. In a prospective study, 442 first year psychology students completed a questionnaire about their background, study habits, AC, and PWF. The results showed a positive main effect of AC on academic achievement, but no main effect of PWF was found on academic achievement. The interaction (AC x PWF) was not related to academic achievement. There were no main effects of AC or PWF on study hours. However, the interaction effect (AC x PWF) indicated that among students scoring low on AC, those with high PWF worked more (24.4 hours per week) than those with low PWF (20.9 hours per week). Among those with high AC, no difference on study hours was found between those scoring low and high on PWF.

Keywords: academic control, preoccupation with failure, academic achievement, study hours

Ascertaining which factors are related to academic achievement in university students is important both for academic institutions and for their students. Academic context, such as students’ course experiences, as well as students’ approaches to learning, have been found to be associated with academic achievement (Diseth, 2007). Individual differences in personality in relation to academic achievement is an avenue for research that focuses more on how dispositional characteristics affect academic achievement (Diseth, 2003). The role of this research is to identify the personality traits that are involved, and how these traits can affect academic achievement. Knowledge gained from such research can contribute to understanding why university students vary in academic achievement, and may provide valuable input to guidance counselors and institutions that wish to improve academic achievement among their students.

Academic control (AC) is defined as a domain-specific form of perceived control. It describes a student’s beliefs in his/her capacity to influence achievement outcomes (Perry,

Geir Scott Brunborg, Department of Psychosocial Science, Faculty of Psychology, University of Bergen; Ståle Pallesen, Department of Psychosocial Science, Faculty of Psychology, University of Bergen; Svein Larsen, Department of Psychosocial Science, Faculty of Psychology, University of Bergen; Åge Diseth, Research Centre for Health Promotion, Faculty of Psychology, University of Bergen.

This research was supported by the Faculty of Psychology at the University of Bergen. Correspondence concerning this article should be addressed to Geir Scott Brunborg, Faculty of

Psychology, University of Bergen, Christiesgate 12, 5020 Bergen, Norway. E-mail: geir.brunborg@psysp.uib.no

126 BRUNBORG, PALLESEN, DISETH, AND LARSEN

Hladkyj, Pekrun, Chipperfield, & Chipperfield, 2005). Lately, AC has gained attention in the higher education literature. Studies have shown that AC is a significant predictor of achievement for first year university students (Howell, Watson, Powell, & Buro, 2006; Perry, Hladkyj, Perkun, & Pelletier, 2001) and also for university students throughout their undergraduate training (Perry et al., 2005). The reason for this is that high AC students may show greater persistence in their academic work, are more attentive in understanding their course materials, and participate more in study-relevant activities.

Academic control has been viewed as a relatively stable personality trait since it is based on the perceived control construct (Rotter, 1966). However, because AC is a domain- specific trait, it may be subject to influences of past experience with academic successes and failures, and therefore the state qualities of AC are recognized as well (Perry et al., 2005). A way to account for the influence of past experiences on AC is to include high school grade point average (HSGPA) in studies using AC. This is especially applicable when first- year students are used as participants.

Another variable that has been emphasized within higher education is preoccupation with failure (PWF). Preoccupation with failure is viewed as a stable personality trait, and measures of PWF can be used to rate the degree to which individuals focus on failure feed- back, and the amount of time they spend brooding on things that may or may not go wrong in their lives (Kuhl, 1994). Preoccupation with failure is a subscale of action control, which describes individual differences in self-regulation, and classifies individuals on a continuum ranging from a state- to an action-oriented focus for dealing with preoccupation with failure. In higher education research, PWF has gained interest because students’ worries about performing sub-optimally may affect their study effort and academic achievement. Results from studies looking at PFW and academic achievement, however, have been somewhat inconsistent. For example, results from a classroom simulation study found that high PWF students performed more poorly than low PWF students (Menec, Perry, & Struthers, 1995). Prospective surveys of university students have shown both a positive relationship between PWF and academic achievement (Perry et al., 2001), and no direct relationship (Perry et al., 2005). An interaction effect of AC and PWF on academic achievement has also been reported (Perry et al., 2001, 2005). These studies report that the combination of high AC and high PWF was associated with better grades than other combinations of AC and PWF, whilst students with low PWF generally did poorly throughout their college careers.

Researchers have also looked at the relationship between number of study hours per week and academic achievement among university students. In higher education there seems to be no clear association between a high number of study hours per week and greater academic achievement. A few studies have found a significant, albeit small, relationship between number of study hours and academic results (Kuhl & Hu, 1999; Schuman, Walsh, Olson, & Etheridge, 1985) but most studies have not demonstrated any direct relationship between these two variables (Cavell & Woehr, 1994; Plant, Ericsson, Hill, & Asberg, 2005; Woehr & Cavell, 1993). For example, Plant et al. (2005) found that study hours only predicted grades when the quality of study and previous academic performance (i.e. HSGPA and SAT scores) were taken into consideration. Therefore, there is reason to believe that the factors that impact on students’ number of study hours may not be the same factors that impact on academic achievement.

Finally, studies have found a relationship between prior scholastic achievement (e.g. high school grades) and achievement in higher education (Britton & Tesser, 1991; Larose, Bernier, & Tarabulsy, 2005; Ting & Robinson, 1998; Woehr & Cavell, 1993), also when

ACADEMIC CONTROL AND PREOCCUPATION WITH FAILURE 127

controling for the effect of the number of study hours (Diseth, 2007). Therefore, HSGPA should be included as a covariate when investigating different predictors of academic achievement. In the present study HSGPA is also included since it may account for some of the variance in the situational aspects of AC.

Because previous findings have been inconclusive, the first goal of the present study was to investigate whether AC and PWF as well as the interaction (AC x PWF) could predict grades in higher education when controlling for HSGPA. Secondly, we wanted to investigate whether AC and PWF and the interaction (AC x PWF) was associated with the number of study hours.

Methods

Participants

A total of 482 first year psychology students at the University of Bergen, Norway, were recruited to participate in the study. Of these, 40 students had to be excluded for at least one of three reasons: (1) failure to give their correct student identification number so that the exam grade could not be collected; (2) not being registered for the course; and (3) not taking the exam. An analysis of differences between attrition students and students who took the final exam is reported elsewhere (Brunborg, Pallesen, Diseth, & Larsen, 2007). A total of 442 students (341 women, 101 men) with a mean age of 21.8 years (SD = 4.6) participated. All measures were compiled in a questionnaire adminis- tered to the participants. Means and standard deviations among the study variables are presented in Table 1.

Instruments

Perceived academic control measure.

Academic control was measured using the perceived academic control measure (Perry et al., 2001), which consists of eight items that measure different aspects of feeling of control over one’s academic achievement. Example items are “I have a great deal of control over my academic performance in my psychology course,” and “The more effort I put into my courses, the better I do in them.” Participants indicated their agreement on a five-point scale ranging from 1 = strongly disagree to 5 = strongly agree. Internal consistency (Cronbach’s alpha) for the scale in the present study was .73.

Table 1

Means, Standard Deviations and Correlations of the Study Variables

Variable Mean SD 1 2 3 4

1. AC 31.65 4.08 –

2. PWF 18.56 2.84 −.21* – 3. HSGPA 4.63 0.40 −.02 .16* – 4. Academic achievement 3.25 1.41 .21* .01 .22* –

5. Study hours 24.52 11.02 −.02 .05 −.17* .37*

Notes: * p < .05, AC = Academic control, PWF = preoccupation with failure, HSGPA = high school grade point average.

128 BRUNBORG, PALLESEN, DISETH, AND LARSEN

Action control questionnaire.

Preoccupation with failure was measured using the action control questionnaire (Kuhl & Hu, 1999) with item modifications suggested by Perry et al. (2001). It consists of 12 items that all have a forced choice format, and participants indicate which of two alternatives they agree with. Example items for the scale are: “When I have lost something that is very valu- able to me and I can’t find it anywhere: (a) I have a hard time concentrating on something else, (b) I put it out of my mind for a little while”; and “When I have to solve a difficult problem: (a) It takes me a long time to adjust myself to it, (b) It bothers me for a while, but then I don’t think about it anymore.” Internal consistency (Kuder-Richardson 20) for the scale in the present study was .73.

High school grade point average.

Questions (self-report) about HSGPA were also included in the questionnaire. The Norwegian high school grades range from 1 (lowest) to 6 (highest). The high school tran- script provides an average grade with two decimals. Most students receive extra points depending on the combination of high school courses and various post high school activities (e.g. military service) that they later add to their average grade to make up a sum, which can be used for application into higher education. Participants were asked to enter the average grade without the extra points so that the students could be compared on high school achievement.

Study hours.

Study hours were measured in the questionnaire by asking participants to write the aver- age number of hours they spent on study activities per week, including lectures, tutorials, private seminars, and self-study.

Academic achievement.

Academic achievement was based upon the final exam grade for the “Introduction to Psychology” course. A list of results and corresponding student identification numbers were obtained from the university administration. Grades were given in accordance with the European Credit Transfer System, which ranges from A (excellent) to F (fail). In contrast to the American scale, this scale has no minus and plus increments, hence grades were coded in the following way: A = 6, B = 5, …, F = 1.

The participants also reported their sex and age in the questionnaire.

Procedure

The questionnaires were distributed during five lectures for the course “Introduction to Psychology”. At the first two lectures, the participants spent ten minutes of the first half of the lecture completing the questionnaires. At the last three lectures, questionnaires were distributed to those who had not already participated, along with a pre-paid envelope with the instruction to mail the questionnaire back to the university. To reach those students who were absent from the lectures, questionnaires were mailed to those who had not already

ACADEMIC CONTROL AND PREOCCUPATION WITH FAILURE 129

participated. A letter accompanying the questionnaire assured participants’ anonymity and option to withdraw from the survey. To assure a prospective design, all questionnaires were collected at least one month prior to the exam. Participation was voluntary and participants were not paid. The total population comprised a total of 724 students. Of these, 482 returned the questionnaires, thus the response rate was 66.6%. The study was approved by the Norwegian Social Science Data Services.

Statistics

Means and standard deviations were calculated for each variable measured with interval and ratio scales. For these variables the interrelationships were calculated by the Pearson product moment correlation coefficient. Academic control and PWF were dichotomized into high and low scores by a median split procedure. In order to investigate the relationship between AC and PWF on exam grade and number of study hours per week, a 2 × 2 factorial ANCOVA was conducted. The first factor comprised AC (low/high) and the second factor comprised PWF (high low). In these analyses HSGPA was entered as a covariate. To calcu- late effect sizes for main effects and interactions, partial eta-squared (ηp

2) was used. According to Cohen’s (1988) guidelines, small, medium, and large effects measured in ηp

are around .01, .09, and .25, respectively.

Results

Bivariate correlations among the study variables are presented in Table 1. Academic control was positively correlated with academic achievement, but no correlation between PWF and academic achievement was found. Neither AC nor PWF were correlated with number of study hours. High school grade point average was positively correlated with academic achievement and negatively correlated with number of study hours. Number of study hours was positively correlated with academic achievement.

A factorial ANCOVA was conducted for academic achievement. The results from the ANCOVA showed that HSGPA was a significant covariate for academic achievements, F(1, 421) = 21.01, p < .001, ηp

2 = .05. A significant main effect of AC on academic achieve- ment was found, F(1, 421) = 15.96, p < .001, ηp

2 = .04. For students scoring low on AC, mean grade was 2.88 (SD = 1.57), and for students scoring high on AC, mean grade was 3.58 (SD = 1.49). However, no main effect of PWF on academic achievement was found, F(1, 421) = 0.03, p > .05. The interaction (AC x PWF) on academic achievement was not significant, F(1, 421) = 0.12, p > .05. Effect sizes for both HSGPA and AC on academic achievement are considered to be small (Cohen, 1988).

The next goal of the study was to discern how AC and PWF would relate to number of study hours, with HSGPA as a covariate in an ANCOVA. First, HSGPA was a significant covariate for study hours, F(1, 421) = 13.69, p < .001, ηp

2 = .03. Neither of the main effects, for AC (F[1, 421] = 3.42, p > .05) or PWF (F[1, 421] = 0.40, p > .05) were significant. However, the interaction effect (AC x PWF) on study hours was significant, F (1, 421) = 6.99, p < .001, ηp

2 = .02. The interaction effect indicated that PWF was unrelated to number of study hours among students who scored high on AC. However, as shown in Figure 1, among students scoring low on AC, those with high PWF worked more (25.2 hours per week) than those with low PWF (21.0 hours per week). Effect sizes for both HSGPA and the AC × PWF interaction on study hours are considered to be small (Cohen, 1988).

130 BRUNBORG, PALLESEN, DISETH, AND LARSEN

Figure 1. Mean number of study hours for the four combinations (low and high) of AC and PWF.

Discussion

The results from the present study showed a main effect of AC on academic achieve- ment. This is in accordance with previous findings (Howell et al., 2006; Perry et al., 2005) suggesting that students with high beliefs in their capacities to influence academic achieve- ment prior to the examination may receive higher grades compared to students with low such beliefs. Students with high AC may have greater mastery of their studies and may be able to obtain more knowledge and skills from their study efforts, and hence achieve better grades.

No main effect of PWF on academic achievement was found. This supports previous findings (Perry et al., 2005) that PWF is unrelated to academic achievement when no medi- ating variables are considered. Surprisingly, the results also failed to support previous find- ings of an interaction between AC and PWF on academic achievement, i.e. that students high on AC and high on PWF receive better grades compared to other students (Perry et al., 2001, 2005).

No main effect of AC on number of study hours was found. High or low levels of AC do not seem to be associated with an increase or decrease in number of study hours. Hence, the results do not support that the relationship between AC and academic achievement is mediated by number of study hours. There was no main effect of PWF on number of study hours either. High PWF does not seem to be related to increased study effort and low PWF does not seem to be related to decreased study effort.

In the present study we did, however, find a significant interaction effect of AC and PWF on students’ number of study hours. Among students with low AC, students low on PWF worked fewer hours per week compared to students high on PWF. One possi- ble interpretation of this finding is that the fear of performing poorly is an important source of motivation for students who lack perceived control over their academic achievements.

Figure 1. Mean number of study hours for the four combinations (low and high) of AC and PWF.

ACADEMIC CONTROL AND PREOCCUPATION WITH FAILURE 131

High school grade point average was used as a covariate in the analyses to account for both the effect of prior scholastic attainment on academic achievement, and because it can be related to the situational aspects of AC. Academic control has been viewed as a stable trait with state qualities, and by statistically controlling for HSGPA the effect of prior scho- lastic attainment on AC can be reduced. However, more studies may be required to deter- mine if AC and PWF are stable traits, and to determine the degree to which situational aspects can influence scores on the measures.

Although previous studies have suggested that academic achievement is mainly a func- tion of how students’ efforts are exerted, not only how much effort is exerted (Diseth, 2007; Plant et al., 2005), number of study hours was highly correlated with academic achievement in the present study. It is possible that achievement in introductory psychology courses is more strongly related to study hours compared to other academic disciplines or higher academic levels; however, studies making such comparisons are needed to investigate if this is indeed the case.

The present study used a prospective design to investigate the relationship between AC, PWF, and academic achievement. The measure of academic achievement was not based on self-report, but actual grades obtained from the university administration after the self- report part of the data collection was completed. This is a strength because respondents could not have been affected by their exam results when completing the questionnaires.

A limitation in the study is that HSGPA and study hours were collected using self- report. Students could hypothetically have answered untruthfully, but there was no obvious benefit in doing so. With HSGPA this could have been avoided, for example, by requesting an authorized copy of high school academic records. However, previous research has shown a correlation of .85 between self-reported and transcript-based GPAs (Schuman et al., 1985), which indicates high reliability of self-reported HSGPA.

In summary, this study found a main effect of AC on academic achievement, but failed to find a main effect of PWF on academic achievement, or an interaction affect of AC and PWF on academic achievement. No main effects of AC or PWF on number of study hours were found; however, an interaction effect of AC and PWF on study hours was found, suggesting that among students low on AC, those with low PWF study for fewer hours than their high-PWF counterparts.

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