revisiting the within-person self-efficacy and performance relation

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Revisiting the Within-PersonSelf-Efficacy and PerformanceRelationErin M. Richard , James M. Diefendorff & James H.MartinPublished online: 13 Nov 2009.

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Revisiting the Within-PersonSelf-Efficacy and Performance Relation

Erin M. RichardLouisiana State University

James M. DiefendorffUniversity of Colorado at Denver

James H. MartinUniversity of Missouri—Rolla

In response to recent debate regarding the direction of the relation between self-effi-cacy and performance (Bandura & Locke, 2003; Vancouver, Thompson, Tischner, &Putka, 2002; Vancouver, Thompson, & Williams, 2001), this investigation examinesthe within-person relations between self-efficacy and performance over time in 2 dif-ferent learning contexts. Study 1 examines the relation using exam performance in aclassroom context, and Study 2 examines the relation using a computerized learningtask in a lab setting. Both studies find a significant, positive within-person relationbetween performance and subsequent self-efficacy. However, both studies fail to findthe positive relation between self-efficacy and subsequent performance predicted bysocial cognitive theory. Future research directions aimed at resolving the debate arediscussed.

Recently, there has been debate in the literature over the causal relation betweenself-efficacy and performance. Self-efficacy, a construct referring to an individ-ual’s belief in his or her ability to perform a task (Gist & Mitchell, 1992), emergedfrom social cognitive theory (Bandura, 1977, 1986), and has been associated withperformance in a variety of work settings (see Gist, 1987; Gist & Mitchell, 1992).In a recent meta-analysis, Stajkovic and Luthans (1998) found a weighted averagecorrelation of .38 between self-efficacy and performance. However, the causal di-

HUMAN PERFORMANCE, 19(1), 67–87Copyright © 2006, Lawrence Erlbaum Associates, Inc.

Correspondence should be sent to Erin M. Richard, Florida Institute of Technology, 150 W. Univer-sity Boulevard, Melbourne, FL 32901. E-mail: [email protected]

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rection of this relation is unclear. Vancouver and colleagues (Vancouver, Thomp-son, Tischner, & Putka, 2002; Vancouver, Thompson, & Williams, 2001) recentlysuggested that the positive relation exhibited in the literature might be due more tothe influence of performance on self-efficacy than the influence of self-efficacy onperformance. Furthermore, Vancouver et al. (2002) and Vancouver et al. (2001)demonstrated a situation where performance was positively related to subsequentself-efficacy, but self-efficacy was negatively related to subsequent performance.The purpose of this investigation is to further explore the self-efficacy and perfor-mance relation using the same design and statistical procedures employed by Van-couver and colleagues (2001, 2002), but applied to different performance contexts.

SELF-EFFICACY AND PERFORMANCE

The positive effect of performance on self-efficacy is widely accepted (Stajkovic& Luthans, 1998). Bandura (1997) noted that past performance is a major determi-nant of a person’s self-efficacy, such that successful performance on a task leads toincreased self-efficacy, and failure leads to decreased self-efficacy. Consistentwith this idea, several studies have found that past performance is a significant pre-dictor of future self-efficacy (e.g., Mitchell, 1997; Mitchell, Hopper, Daniels,George-Falvy, & James, 1994).

Bandura (1997) also stated that self-efficacy positively impacts subsequent per-formance; however, this effect has been subject to more scrutiny. Several mecha-nisms have been proposed through which self-efficacy might positively affect per-formance. Specifically, high self-efficacy is thought to cause individuals to sethigher goals, thus improving subsequent performance (Bandura, 1997; Bandura &Wood, 1989; Bouffard-Bouchard, 1990). In addition, the goal setting literature hasargued that self-efficacy increases goal commitment, which has a positive effect onperformance (Locke & Latham, 1990). Still others have suggested that self-effi-cacy improves performance by controlling distractions and disruptive thoughts,thereby focusing the individual’s attention on the task at hand (Ackerman, Kanfer,& Goff, 1995; Kanfer & Ackerman, 1996). Finally, self-efficacy has been found tolead to greater persistence in the face of obstacles or failure (e.g., Cervone &Peake, 1986; Jacobs, Prentice-Dunn, & Rogers, 1984) and better use of strategies(e.g., Bandura & Wood, 1989; Bouffard-Bouchard, 1990).

In contrast to these ideas, some researchers have argued that self-efficacymight simply be a reflection of past performance, rather than a generative moti-vational belief impacting future performance (Heggestad & Kanfer, in press;Mitchell, 1997). Indeed, individuals make judgments of their own efficacy basedprimarily on past performance levels (Mitchell, 1997). Research in support ofthis position has shown that self-efficacy no longer predicts future performancewhen the effects of ability or past performance are partialled out (e.g., Ackerman

68 RICHARD, DIEFENDORFF, MARTIN

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et al., 1995; Heggestad & Kanfer, 2005; Mitchell et al., 1994). Thus, an impor-tant question remains: Does high self-efficacy result in greater motivation andincreased performance, or is it primarily a reflection of high past performance?Much of the past research on this topic has been inconclusive because of the useof cross-sectional designs.

Though many researchers continue to espouse the positive effects of self-effi-cacy (e.g., Chen, Gully, Whiteman, & Kilcullen, 2000; Phillips & Gully, 1997;VandeWalle, Cron, & Slocum, 2001), recent studies have suggested that self-effi-cacy may not always have positive effects on subsequent performance. For exam-ple, a recent meta-analysis by Boyer et al. (2000) examined only studies whereself-efficacy was manipulated (ensuring that the causal arrow went in the self-effi-cacy to performance direction). Results showed that 13.6% of studies reportednegative or null effects of manipulated self-efficacy on performance. Vancouver etal. (2002) and Vancouver et al. (2001) have thus argued that the positive correlationfound between self-efficacy and performance may be due to performance’s influ-ence on self-efficacy, not self-efficacy’s influence on performance, and that “anover-reliance on cross-sectional, correlational designs” has masked the complexityof the relation (Vancouver et al., 2001, p. 605).

To examine the direction of the self-efficacy and performance relation, Vancou-ver and colleagues (2002) and Vancouver and colleagues (2001) conducted a seriesof studies looking at the within-person relation between self-efficacy and perfor-mance, using a longitudinal research design. These studies used the Mastermind(Cadaco, Inc., Chicago, IL) game as the performance task, which requires individ-uals to guess the correct arrangement of colored squares in as few steps as possible.Examining the within-person self-efficacy and performance relation over time,Vancouver et al. (2001, 2002) found that performance was positively related tosubsequent self-efficacy, but self-efficacy was negatively related to subsequentperformance. They interpreted this negative self-efficacy effect as being the resultof a kind of complacency: high self-efficacy leads to overconfidence and with-drawal of cognitive effort, resulting in lower subsequent performance.

Vancouver et al. (2001) interpreted these findings to be consistent with controltheory (CT) models of human behavior (Carver & Scheier, 1998). According toCT, humans are motivated to reduce discrepancies between their perceived currentstate and their goal (or desired state). Therefore, the amount of attention, persis-tence, and effort devoted to a task is theorized to be a function of the amount of dis-crepancy detected. In CT, if a person perceives that he or she has fallen short of agoal, that person devotes resources to the goal until there is no longer a discrepancysensed. Conversely, if a person perceives that the goal has been surpassed, he orshe may reduce the amount of resources being put forth and direct resources to-ward other goals.

Individuals working on the Mastermind game did not receive feedback until af-ter committing to a guess; therefore, they had to infer how they were doing from

SELF-EFFICACY AND PERFORMANCE 69

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limited information, which may have included self-efficacy beliefs. Thus, Vancou-ver et al. (2002) reasoned that high self-efficacy (resulting from high performanceon a previous trial) caused individuals to feel that there was less discrepancy be-tween their current state and their goal, resulting in the expenditure of less cogni-tive resources on the current task trial (i.e., committing to a guess without thinkingit through) and decreased performance.

In defense of the positive effects of self-efficacy, Bandura and Locke (2003)critiqued several aspects of the Vancouver et al. (2002) and Vancouver et al. (2001)studies, including the nature of the Mastermind task. They argued that the patternof findings in these studies was an artifact of the task, rather than a result of anydetrimental effects associated with high self-efficacy. They took issue with theguessing game nature of the task and the fact that each trial was independent of ev-ery other trial. Because there was nothing learnable or transferable from one trial toanother (i.e., the correct solution changes with each trial), they argued that self-ef-ficacy beliefs could not develop over time and could not have a generative, motiva-tional effect on behavior. In addition, Bandura and Locke argued that when the taskinvolves guesswork, ratings of self-efficacy are basically meaningless. Thus, theyrecommended that a meaningful test of the within-person relation between self-ef-ficacy and performance should use a task that allows for continuous improvementin these variables over time, rather than several unrelated trials of the same activity.

In response to this debate, the purpose of this investigation is to further examinethe within-person self-efficacy and performance relation in contexts that allow indi-viduals to continually strive for improvement over time. The following two studies,therefore, used the same statistical procedures employed by Vancouver and col-leagues (2001,2002) toexamine thewithin-personrelationsofself-efficacyandper-formance in two different learning contexts. These contexts were selected in re-sponse to Bandura and Locke’s (2003) call for examining these relations in taskswhere knowledge is transferred across trials and performance and self-efficacy candevelop across time. Because of the existence of adequate support for both sides ofthe issue, we made no directional hypotheses as to the relation between self-efficacyandsubsequentperformance.Rather,wedecided toexplore this relationbyapplyingVancouver and colleagues’ (2002) and Vancouver and colleagues’ (2001) analytictechniques using tasks that conform to the parameters suggested by Bandura andLocke (2003). Our purposes were to explore two main issues: First, does self-effi-cacy have an independent, causal influence on performance when examined in thecontext of learning? If so, is the nature of that relation positive or negative?

STUDY 1

Study 1 examines the intra-individual relation between self-efficacy and perfor-mance in a classroom setting. Because Vancouver et al.’s (2002) and Vancouver etal.’s (2001) studies were conducted in the laboratory, a question remains about the


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effects of self-efficacy on performance when real-life consequences for behaviorare present (Bandura & Locke, 2003). The classroom setting was chosen becausestudents are concerned about test performance and learning can occur over time.

Method

Participants

One hundred and two students in two undergraduate statistics classes served asparticipants. Nineteen participants were excluded because of missing or extremedata on one or more occasions, reducing the sample size to 83 (79.1% women),with a mean age of 19.9 years.

Measures

Performance. Performance was assessed with four exams consisting of mul-tiple-choice questions about statistical concepts (e.g., variance, statistical power)and computation items (e.g., computing standard deviation, conducting t tests).The tests were administered during the 4th, 8th, 12th, and 16th weeks of the twoclasses. Each exam was worth 100 points.

Self-efficacy. Self-efficacy was assessed with six items asking individuals torate their level of confidence (1 = no confidence at all; 5 = complete confidence;based on Saks, 1995) in performing several behaviors necessary for successful per-formance on the exams (see Appendix A). Wood and Locke (1987) used a simi-larly broad measure, noting that self-efficacy for academic test performance is acomplex judgment made up of perceptions of ability to perform several actionsneeded for successful exam performance (e.g., studying, note taking, memoriz-ing). Though traditional measures of self-efficacy separate self-efficacy strengthand magnitude, research has demonstrated that Likert-type measures of self-effi-cacy have similar reliability, equivalent levels of predictive validity, and similarfactor structures as the more traditional methods (e.g., Maurer & Andrews, 2000;Maurer & Pierce, 1998; Mudgett & Quinones, 1997). Several studies have suc-cessfully used the Likert method to measure self-efficacy (e.g., Chen, Casper, &Cortina, 2001; Chen et al., 2000; Diefendorff, 2004; Maurer, Mitchell, & Barbeite,2002; Wang & Netemeyer, 2002). In addition, simplified Likert-type measures ofself-efficacy, like the one used here, require 70% less responses from participantsthan traditional strength and magnitude measures (Maurer & Andrews, 2000).

Procedure

Participants completed self-efficacy questionnaires one week prior to each ofthe four tests. Performance feedback was given to participants within 1 week ofeach exam.


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Results

Table 1 reports the means, standard deviations, scale reliabilities, and inter-correlations among the study variables. Consistent with the between-person find-ings reported in Stajkovic and Luthans (1998), self-efficacy and performance werepositively correlated, ranging from .28 to .40 across the four tests. The correlationbetween average self-efficacy and average performance (across tests) was r = .49.Repeated measures analysis of variance (ANOVA) indicated a main effect of trialon both self-efficacy, F(3, 255) = 11.11, p < .001, η2 = .12, and performance, F(3,255) = 8.69, p < .01, η2 = .09, indicating that both of these variables decreased sig-nificantly over time (see the means in Table 1). These trends are not uncommon foracademic tasks where learning is cumulative and exams increase in difficulty.

Hierarchical linear modeling (HLM; Bryk & Raudenbush, 1992) was used totest the within-person relations between self-efficacy and subsequent performanceand performance and subsequent self-efficacy. A concern with testing within-per-son relations between these variables in a learning context is that time (i.e., testnumber) represents a nonrandom trend in the data. Thus, time represents a system-atic source of variance that needs to be included in the regression to appropriatelymodel the dependent variable (Beal & Weiss, 2003). For example, a trend in thedata (i.e., gradually increasing or decreasing) could result in a positive relation be-tween self-efficacy and performance that is spurious, rather than the result of anycausal mechanisms. As demonstrated, we found that both self-efficacy and perfor-mance decreased across trials, suggesting that the course content became more dif-ficult and self-efficacy declined . Thus, a positive relation could be observed due tothe fact that both variables move in the same direction over trials, rather than due toany causal effect of one variable on the other. Controlling for trend (i.e., test num-ber) in the analyses will rule out this possibility (Beal & Weiss, 2003). Table 2 re-


TABLE 1Study 1 Intercorrelations, Reliabilities, and Descriptive Statistics

Variable 1 2 3 4 5 6 7 8

1. Self-efficacy 1 (.83)2. Self-efficacy 2 .76** (.89)3. Self-efficacy 3 .64** .66** (.88)4. Self-efficacy 4 .57** .57** .72** (.89)5. Performance 1 .34** .48** .41** .31**6. Performance 2 .34** .39** .51** .29** .63**7. Performance 3 .22* .29** .28** .40** .58** .53**8. Performance 4 .28* .34** .33** .40** .71** .63** .68**M 4.18 4.06 3.86 3.85 80.73 77.05 75.02 75.78SD .58 .74 .77 .82 12.53 13.66 12.70 13.09

Note. Available internal consistency reliabilities are reported in parentheses in the diagonals.*p < .05. **p < .01.

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TABLE 2Study 1 Hierarchical Linear Modeling Results

Analyses Without Trendas a Covariate Analyses Including Trend as a Covariate

Fixed Effects Coefficient SECoefficient at

Step 1SE atStep 1

Coefficient atStep 2

SE atStep 2

Posttrial self-efficacy as dependent variableIntercept, γ00 3.93936** .07549 3.93936** .07549 3.93936** .07549Trial number, γ10 — — –.04482* .01597 –.01673 .01489Performance, γ20 .01661** .00331 — — .01658** .00350

Performance as dependent variableIntercept, γ00 77.37385** 1.1962** 77.37385** 1.19624 77.37385** 1.19625Trial number, γ10 — — –1.62862* .36936 –1.59875** .37379Pretrial self-efficacy, γ20 1.24202 1.1872 — — –.23275 1.1677

Note. Coefficients relevant to the research questions of interest are represented in bold-faced type.*p < .01. **p < .001.

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ports the results of analyses both with and without controlling for trend. However,based on the recommendations of Beal and Weiss (2003), we focus on those analy-ses which include test number as a covariate. The level-1 equations, which modelwithin-person relations, were as follows:

Step 1:

Performance = β0j + β1j (Trial number) + rij (1)

Self-efficacy = β0j + β1j (Trial number) + rij (2)

Step 2:

Performance = β0j + β1j (Trial number) + β2j (Past Self-efficacy) + rij (3)

Self-efficacy = β0j + β1j (Trial number) + β2j (Past Performance) + rij (4)

where β0j is the level-1 intercept, β1j and β2j are the level-1 slopes, and rij is thelevel-1 error term. The level-2 equations modeled the level-1 slopes across partici-pants and were as follows:

β0j= γ00 + uj (5)

β1j = γ10 + uj (6)

β2j = γ20 + uj (7)

where γ00, γ10, and γ20 represent the level-2 intercepts and uj is the level-2 error termin the slope. A significant, positive γ20 (in Step 2) would indicate a positive relationbetween the variables of interest (independent of any common effects of trial num-ber). Results are reported in Table 2.

At Step 1, trial number was negatively related to both self-efficacy and perfor-mance. Consistent with the ANOVAs, these negative relations indicate that the ex-ams got more difficult over time, and self-efficacy decreased. At Step 2 (i.e., aftercontrolling for trial number), prior performance was a significant, positive predictorof self-efficacy (γ20 = .01658, p < .001), accounting for 21.9% of the within-personvariance in self-efficacy. These results are consistent with Vancouver et al.’s (2002)and Vancouver et al.’s (2001) findings. In addition, after controlling for trial, self-ef-ficacy was negatively, yet not significantly related to subsequent performance (γ20 =–.23275, ns). This effect accounted for 2.10% of the variance in performance.

The variances of the level-2 error terms, uj , were inspected to determine if therewere individual differences in these relations. The variances for both of the level-2error terms were nonsignificant, indicating that individuals did not significantlydiffer regarding the within-person relation between self-efficacy and subsequentperformance, χ2(79, N = 80) = 68.17, ns, and between performance and subsequentself-efficacy, χ2(82, N = 83) = 90.47, ns.


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Discussion

Between-subject results demonstrated the typical positive relations betweenself-efficacy and performance (Stajkovic & Luthans, 1998). Counter to SocialCognitive Theory predictions, however, the within-person self-efficacy and per-formance relation did not support a positive effect of self-efficacy on perfor-mance. The within-person design revealed that performance was positively re-lated to subsequent self-efficacy, but self-efficacy was negatively, though notsignificantly, related to subsequent performance.

Because the participants in Study 1 were tested only four times during the se-mester, a limitation of this study is the small number of within-person observa-tions (four). Thus, we cannot rule out low power as a possible explanation forthe nonsignificant negative effect. For these reasons, we report effect size as wellas the dichotomous significance test, as recommended by Hallahan andRosenthal (2000). It is important to note that, although nonsignificant in this par-ticular study, the .02 effect size is equal to a negative self-efficacy and perfor-mance effect reported in Study 2 of Vancouver et al. (2001). However, evenwhen interpreted most conservatively, these findings demonstrate that, in a taskinvolving both learning over time and real-world consequences for behavior(both of which are conditions Bandura & Locke suggested were needed to ap-propriately test this within-person relation), self-efficacy did not have a positiveeffect on subsequent performance.

STUDY 2

For Study 2, we brought the question back into a laboratory setting, examining therelation between self-efficacy and performance for individuals learning a com-puter-based task. The task was a chemical reactor simulation, in which participantsattempted to control the temperature of the reactor. The goal of the task was to keepthe temperature of the reactor at a specific level by learning the underlying relationbetween fuel and temperature. The relation between fuel and temperature re-mained the same throughout the game; therefore, participants were able to transferlearning between rounds, becoming progressively better at the task, in accordancewith Bandura and Locke’s (2003) recommendations.

Method

Participants

One hundred seventy-nine students between ages 18 and 32 (M age = 20.24;70% women) participated in exchange for extra credit in their psychology courses.


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Task

Participants performed a computerized process control task in which theylearned to control the temperature of a simulated chemical reactor. The purpose ofthe task was to maintain the temperature of the reactor at 6,000 degrees by input-ting fuel pellets into the reactor. The relation between temperature and fuel was de-termined by an equation unknown to the participant; therefore, the task was to dis-cover the relation (done through trial and error) and keep the temperature as closeto 6,000 degrees as possible.

During the task, a graph on the left-hand side of the screen plotted the amountof fuel that individuals input, and a graph on the right-hand side plotted the reac-tor temperature (output). Participants chose the amount of fuel to input (choicesranged from 1,000 to 12,000 pellets, in increments of 1,000) to bring the temper-ature as close to 6,000 degrees as possible. Each trial (or screen) began with thecomputer randomly generating the initial temperature, followed by 10 partici-pant responses (fuel pellet inputs). After the 10 responses, a new trial beganwith the computer randomly generating a new initial temperature, and theparticipant making 10 more responses. Participants completed six rounds of 20trials each.

Measures

Self-efficacy. The strength of a participant’s self-efficacy for each perfor-mance round was assessed with four items, rated on a 7-point scale from 1(strongly disagree) to 7 (strongly agree), see Appendix B. As in Study 1, theseitems were modeled after those in Wood and Locke (1987).

Performance. After each participant response (fuel pellet input), the com-puter applied the fuel input amount to the underlying equation to generate a cur-rent reactor temperature. Performance was defined as the absolute distance be-tween the current reactor temperature and the target temperature of 6,000degrees. Performance scores were calculated for each round by taking the aver-age performance across 20 trials (screens), each of which had 10 responses (fora total of 200 responses in each round). Performance scores were then re-verse-coded (each score subtracted from 6,000) so that higher scores indicatebetter performance.

Procedure

Initially, participants received task instructions and performed 10 practicescreens to familiarize themselves with the task. Prior to the initial experimentalround, they completed the self-efficacy questionnaire. Participants then performeda total of six experimental rounds. After each round, they received manipulated


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normative feedback1 and completed the self-efficacy questionnaire (with the ex-ception of the final round). After the final round, participants were debriefed andawarded credit. The experimental session lasted between 1.5 and 2 hr.

Results

Table 3 reports means, standard deviations, and intercorrelations among the studyvariables. The between-subject correlations between self-efficacy and perfor-mance ranged from .03 (first performance episode) to .25 (last performance epi-sode). The correlation between average self-efficacy and average performance(across trials) was r = .17. Repeated measures ANOVAs indicated a main effect oftrial on both self-efficacy, F(5, 920) = 24.24, p < .001, η2 = .12, and performance,F(5, 835) = 100.23, p < .001, η2 = .38, with both variables increasing significantlyover time (see Table 3).

As in Study 1, trial number was entered as a covariate in the HLM analyses tocontrol for trend. In addition, the nature of the skill acquisition task suggests thatthe trend in the data might be curvilinear, consistent with the power law of practice(Anderson, 1990). Thus, a squared trial number term was also entered to controlfor the curvilinear trend. The level-1 equations were as follows:

Step 1:

Performance = β0j +β1j (Trial number) +β2j (Trial Number)2 + rij (8)

Self-efficacy = β0j +β1j (Trial number) +β2j (Trial Number)2 + rij (9)

Step 2:

Performance = β0j +β1j (Trial number) +β2j (Trial Number)2

+β3j (Pre-trial Self-efficacy) + rij (10)

Self-efficacy = β0j +β1j (Trial number) +β2j (Trial Number)2

+β3j (Past Performance) + rij (11)

whereβ0j is the level-1 intercept, β1j, β2j, andβ3j are the level-1 slopes, and rij is thelevel-1 error term. The level-2 equations modeled level-1 slopes across partici-pants and were as follows:


1Because this study was part of a broader investigation, a normative feedback manipulation (posi-tive vs. negative false normative feedback) was included after each round. Participants in the positivefeedback condition were told that their performance was better than roughly 90% of all participants.Participants in the negative feedback condition were told that their performance was better than roughly10% of all participants. The actual feedback numbers varied around the 90% or 10% value acrossrounds to appear plausible. Because χ2 tests of the full model revealed no significant level-2 variance(i.e., no between-person differences in the level-1 relations), the feedback manipulation was not exam-ined as a moderator of the self-efficacy and performance relations.

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TABLE 3Study 2 Intercorrelations, Reliabilities, and Descriptive Statistics

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13

1. Condition2. Self-efficacy 1 .08 (.76)3. Self-efficacy 2 .44** .56** (.85)4. Self-efficacy 3 .56** .46** .79** (.88)5. Self-efficacy 4 .60** .33** .70** .85** (.90)6. Self-efficacy 5 .66** .29** .72** .83** .85** (.93)7. Self-efficacy 6 .68** .28** .67** .82** .85** .92** (.95)8. Perform 1 .02 .03 .06 .06 .10 .08 .08 (.83)9. Perform 2 .08 –.14 .05 .13 .22** .20** .19* .60** (.86)

10. Perform 3 .10 –.13 .07 .19* .27** .25** .28** .50** .81** (.91)11. Perform 4 .01 –.17* –.02 .12 .18* .19* .21** .48** .74** .86** (.92)12. Perform 5 –.01 –.14 –.01 .13 .18* .20** .25** .41** .65** .80** .87** (.93)13. Perform 6 .01 –.13 .03 .15* .18* .20** .25** .41** .60** .74** .83** .91** (.94)M 2.75 3.36 3.53 3.56 3.54 3.53 2913 3202 3362 3516 3642 3679SD 1.05 1.36 1.51 1.65 1.76 1.88 485 573 685 720 744 800

Note. Available internal consistency reliabilities are reported in parentheses in the diagonals.*p < .05. **p < .01.

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β0j= γ00 + uj (12)

β1j = γ10 + uj (13)

β2j = γ20 + uj (14)

β3j =γ30 + uj (15)

where γ0, γ10, γ20, and γ30 represent the level-2 intercepts and uj is the level-2 errorterm in the slope. A significant, positive γ30 (in Step 2) would indicate a positive re-lation between the variables of interest. Results of these analyses are reported inTable 4.

At Step 1, with both the self-efficacy and performance as the dependent vari-able, trial number had a positive effect and squared trial number had a negative ef-fect. These coefficients indicate a trend that is slightly convex downward, meaningthat both self-efficacy and performance increased over time and tended to level offtoward the end of the experiment. This trend is consistent with the power law ofpractice (Anderson, 1990). At Step 2, prior performance was significantly and pos-itively related to self-efficacy (γ30 = .00016, p < .01), accounting for .93% of thewithin-person variance in self-efficacy. However, self-efficacy was not signifi-cantly related to subsequent performance (γ30 = 21.039, ns) after controlling fortrend and accounted for only .3% of the variance in subsequent performance. Thevariances of the level-2 error terms, uj, revealed no individual differences in the re-lation between self-efficacy and subsequent performance, (χ2(169, N = 170) =165.35, ns, or in the relation between performance and subsequent self-efficacy,(χ2(133, N = 134) = 150.05, ns, indicating that the relation between self-efficacyand performance were similar across persons.

Discussion

The purpose of this study was to examine the direction of the within-person rela-tions between self-efficacy and performance in a laboratory task that permittedlearning over trials. Consistent with the predictions of SCT, results showed posi-tive relations between performance and subsequent self-efficacy. However, wefailed to find a significant positive relation between self-efficacy and subsequentperformance. Note that this study followed the prescriptions put forth by Banduraand Locke (2003) by introducing a learning task in which there is continuous im-provement and transfer of knowledge between trials. As recommended in their cri-tique, the task was one in which self-efficacy and performance could increase overtime, yet the positive relation hypothesized by SCT still was not found.


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80

TABLE 4Study 2 Hierarchical Linear Modeling Results

Analyses Without Trendas a Covariate Analyses Including Trend as a Covariate

Fixed Effects Coefficient SECoefficient at

Step 1 SE at Step 1Coefficient at

Step 2 SE at Step 2

Posttrial self-efficacy as dependent variableIntercept, γ00 3.49020*** .11330 3.49020*** .11330 3.49020*** .11330Trial number, γ10 — — .02030 .01381 –.00954 .01392(Trial number)2, γ20 — — –.02232* .00865 –.01553 .00813.00813Performance, γ30 .000193** .00007 — — .00016** .00006

Performance as dependent variableIntercept, γ00 3370.85*** 43.20 3370.85*** 43.198 3370.85*** 43.198Trial number, γ10 145.56*** 10.986 144.09*** 10.999(Trial number)2, γ20 –25.15*** 4.673 –24.114*** 4.7186Pretrial self-efficacy, γ30 131.04*** 25.20 — — 21.039 14.052

Note. Coefficients relevant to the research questions of interest are represented in bold-faced type.*p < .05. **p < .01. ***p < .001.

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GENERAL DISCUSSION

Consistent with Vancouver et al. (2002) and Vancouver et al. (2001), the two stud-ies in this investigation used longitudinal, within-person designs to tease out the di-rection of the self-efficacy and performance relation. Both studies found that per-formance had a positive effect on subsequent self-efficacy, a finding anticipated bySCT. However, both studies failed to find a significant positive relation betweenself-efficacy and subsequent performance. Though we did not replicate the signifi-cant negative relation found by Vancouver and colleagues (2001, 2002), the lack ofa significant positive effect does appear to further challenge the long-standing as-sumption that self-efficacy always has a positive, motivational effect on subse-quent performance.

Recall that the purpose of this investigation was two fold: First, we hoped to de-termine whether self-efficacy had an independent, causal effect on performance.Both of our studies found that this was not the case. Thus, the second purpose (un-covering whether that effect was positive or negative) appears to be moot in thecontexts examined here. However, the demonstration of two new contexts wherethe relation between self-efficacy and subsequent performance is not positive doesnot invalidate the many past studies that have found positive results. On the con-trary, the findings of this investigation serve to highlight the importance of pin-pointing the boundary conditions for the positive effects of self-efficacy espousedby SCT.

In this investigation, we chose to use different tasks than that used by Vancouverand colleagues (2002) and Vancouver et al. (2001). The choice of tasks other thanthe Mastermind game allowed an examination of the relations in two new settings,and therefore contributed to the generalizability of findings. Most importantly,however, the use of learning tasks allowed a test of the self-efficacy and subsequentperformance relation under conditions recommended by Bandura and Locke(2003) in their critique of the Vancouver et al. (2002) and Vancouver et al. (2001)studies . The fact that no significant positive relation was found under these condi-tions is intriguing, and it indicates that there are numerous questions left to be an-swered in this debate.

Limitations

A potential limitation of this study involves the nature of our self-efficacy mea-sures. Specifically, our items asked participants to rate their confidence in theirability (see Appendixes A and B), which could have produced a self-report mea-sure of ability, rather than self-efficacy2. In retrospect, it would have been better tohave participants rate their confidence in their capability, which is more suggestive


2We thank an anonymous reviewer for these ideas.

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of motivation. Nevertheless, we did find significant within-person variability inthis construct, suggesting that it may be tapping self-efficacy rather than ability.Because this investigation used two new tasks, an additional limitation of this re-search is that it is difficult to pinpoint the exact cause of the different results acrossthe studies or to provide a direct comparison of the findings. Thus, it is importantthat future researchers direct their efforts toward systematically examining theconditions under which the within-person self-efficacy and performance relationwill be positive, negative, or nonsignificant. To serve as a catalyst for such re-search, in the next section we present some ideas about factors that could be re-sponsible for the differing relations.

Suggestions for Future Research

Future research should examine moderators of the within-person self-efficacy andperformance relation. Possible moderators include the presence or absence of asuperordinate goal and whether the task emphasizes the goal-setting or goal-striv-ing phase of motivation (Diefendorff, 2004). One reason the studies presented heredid not find a negative relation between self-efficacy and subsequent performancecould be because participants had superordinate learning goals that were not pres-ent in the Vancouver studies. Because the correct pattern changed for each trial ofMastermind, participants were not striving to learn anything across trials. How-ever, participants in our studies, because they had superordinate learning goals,may have been less likely to decrease their effort or become complacent in re-sponse to high self-efficacy. This argument is similar to Kluger and DiNisi’s(1996) explanation of the seemingly conflicting effects of feedback on perfor-mance. Using a control theory perspective, they argued that positive feedbackcauses a decrease in effort, unless the person sees the opportunity to obtain highergoals. It could be that self-efficacy has a negative within-person effect on subse-quent performance unless there are higher-order goals present to discourage orcancel out such complacency. Study 2 of Vancouver et al. (2001) provided someempirical support for this possibility. Their results showed an overall negativewithin-person relation between self-efficacy and subsequent performance; how-ever, for those who were assigned a difficult goal (find a solution by the fourth try),one measure of self-efficacy was positively related to subsequent performance. Inaddition, Cervone and Wood (1995) found that, in an organizational simulation,there was no main effect of self-efficacy on performance. Rather, self-efficacy in-teracted with goal condition, such that self-efficacy predicted performance onlywhen subjects were given an overall goal (try to complete weekly orders in a timethat does not exceed 25% greater than the standard) and specific feedback aboutthat goal.

Another possible reason for differences across studies could have to do withwhether the task focuses on goal setting (choosing between goals) or goal striving


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(striving within a goal episode; Kanfer, 1996; Vancouver et al., 2002). Specifically,the positive, motivational influence of self-efficacy on subsequent performancemay primarily occur in situations where individuals must choose between coursesof action (i.e., choose whether or not to engage in or withdraw from an activity;choose between goal levels), rather than in situations where the goal or course ofaction has already been chosen. Many studies that have found a positive, motiva-tional effect of self-efficacy examine situations where self-efficacy leads a personto choose to engage or withdraw from a task2 (e.g., deciding whether to approach asnake, Bandura & Adams, 1977; Bandura, Reese, & Adams, 1982; reporting intentto use the “broken record technique” of persuasion, Prentice-Dunn & Jacobs,1986; deciding whether to withdraw from a difficult or impossible task, Cervone &Peake, 1986; Jacobs et al., 1984; deciding how long to leave one’s hand in cold wa-ter, Litt, 1988; occupation choice and preparation, Betz & Hackett, 1997; Lent,Brown, & Hackett, 1994). In addition, several other studies constrain engagement,yet still allow participants to set the level of the goal they will pursue; thus, the in-fluence of self-efficacy on performance in these cases might be considered a func-tion of self-efficacy’s effect on goals (e.g., Bandura & Wood, 1989). For example,Bouffard-Bouchard (1990) found that individuals in a high self-efficacy group sethigher personal objectives than individuals in a low self-efficacy group. In ourStudy 2, the reactor control task enabled only goal-striving as task engagement andgoal choice were constrained (subjects were instructed to keep the temperature be-tween 5,000 and 7,000). Thus, self-efficacy may primarily benefit performanceunder goal-setting conditions.

Once individuals are engaged in a task (i.e., goal striving), the direction of therelation between self-efficacy and performance might become a function of thebroader person-situation context. Self-efficacy may become simply a reflection ofexperience (e.g., past performance), rather than a factor exerting a causal effect onperformance. In highly routine or easy tasks, however, self-efficacy might exert anegative effect on performance through complacency (e.g., decreased customerservice in fast food jobs, Waldersee & Luthans, 1994; learning from television,Salomon, 1984; see footnote 2). If we consider exam preparation to be a somewhatroutine activity for college students (who have extensive practice with studying),these ideas might explain our negative (though nonsignificant) findings in Study 1.Similarly, a negative effect might also be expected in nonlearning contexts, such asthe Mastermind task. Although Mastermind might not be considered routine oreasy, the fact that participants are not attempting to learn any overall patterns mayreduce the amount of cognitive resources needed for the task. This may have pro-duced a complacency effect similar to that found in routine or easy tasks.

Another direction for future research is to examine mediators of the self-effi-cacy and subsequent performance relation, such as trial-by-trial goals or effort, tounderstand the mechanisms through which these differing relations exist. In addi-tion, both sides of the issue assume that the relation between self-efficacy and per-


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formance is mediated by a kind of effort (i.e., increased effort through goals inSCT; decreased effort as a result of complacency in the Vancouver et al. (2001) andVancouver et al. (2002), studies); however, effective methods of assessing effortneed to be created and studies need to be done that empirically describe the rolethat effort plays in these relations. For example, future studies using the classroomcontext could have students report the amount of study time as an indicator of ef-fort. Finally, other possible mediators of the self-efficacy–performance relationthat have been put forth in past work, such as attention and persistence in face ofobstacles and failures, should be examined in future research.

In conclusion, this article has attempted to shed new light on the debate over thecausal relation between self-efficacy and performance. Using two new perfor-mance contexts, we found nonpositive within-person relations of self-efficacywith subsequent performance. Consideration of these findings, along with those ofVancouver and colleagues (2001, 2002) suggests that the lack of a positive self-ef-ficacy and performance relation should not be attributed to quirky or unusual tasks.Rather, research should be aimed at identifying the substantive underlying causesof positive, negative, and null relations between self-efficacy and performance. Weby no means claim to settle the debate over the causal status of self-efficacy in onearticle; however, we do hope that we have sparked new discussion by providingtwo more contexts which seem to challenge the idea that self-efficacy always has apositive, motivational effect on performance. In addition, we have outlined severalideas about future research in this area, which we hope will spark new energy to-ward resolving this debate.

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APPENDIX ASelf-Efficacy Items—Study 1

1. I feel confident in my ability to learn statistics.2. I feel confident in my ability to understand the concepts in statistics.3. I feel confident in my ability to use formulas and solve mathematical prob-

lems in statistics.4. I feel confident in my ability to read and understand the textbook.5. I feel confident in my ability to understand lecture and take accurate notes.6. I feel confident in my ability to complete homework assignments.


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APPENDIX BSelf-Efficacy Items—Study 2

1. I feel confident in my ability to maintain the reactor temperature between5000 and 7000 degrees on the next performance block.

2. I am not confident in my ability to add the correct amount of fuel pellets onthe next performance block (reverse scored).

3. I feel confident in my ability to perform well on the next block.4. I feel unsure of my ability to control the temperature of the reactor (reverse

scored).


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revisiting the within-person self-efficacy and performance relation

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