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Clarifying Achievement Goals and Their Impact

Journal of Personality and Social Psychology

September 2003
85(3):541-53

DOI:10.1037/0022-3514.85.3.541

Source
PubMed

Authors:

Heidi Grant

Columbia University

The study of achievement goals has illuminated basic motivational processes, though controversy surrounds their nature and impact. In 5 studies, including a longitudinal study in a difficult premed course, the authors show that the impact of learning and performance goals depends on how they are operationalized. Active learning goals predicted active coping, sustained motivation, and higher achievement in the face of challenge. Among performance goals, ability-linked goals predicted withdrawal and poorer performance in the face of challenge (but provided a "boost" to performance when students met with success); normative goals did not predict decrements in motivation or performance; and outcome goals (wanting a good grade) were in fact equally related to learning goals and ability goals. Ways in which the findings address discrepancies in the literature are discussed.

Principal-Component Factor Analysis With Item Loadings

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Processing style mediates the effects of learning goals on course grade. Values are standardized regression coefficients. a Standardized regression coefficient controlling for Scholastic Aptitude Test, perceived chemistry ability, past chemistry course experience, and gender. *p .05. **p .01.

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Clarifying Achievement Goals and Their Impact

Heidi Grant and Carol S. Dweck

Columbia University

The study of achievement goals has illuminated basic motivational processes, though controversy

surrounds their nature and impact. In 5 studies, including a longitudinal study in a difficult premed

course, the authors show that the impact of learning and performance goals depends on how they are

operationalized. Active learning goals predicted active coping, sustained motivation, and higher achieve-

ment in the face of challenge. Among performance goals, ability-linked goals predicted withdrawal and

poorer performance in the face of challenge (but provided a “boost” to performance when students met

with success); normative goals did not predict decrements in motivation or performance; and outcome

goals (wanting a good grade) were in fact equally related to learning goals and ability goals. Ways in

which the findings address discrepancies in the literature are discussed.

Considerable evidence suggests that much of achievement mo-

tivation (e.g., intrinsic interest, strategy use, and persistence) can

be understood in terms of the different goals individuals bring to

the achievement context (see Ames, 1992; Ames & Archer, 1988;

Butler, 1987, 1993; Dweck & Elliott, 1983; Dweck & Leggett,

1988; Elliott & Dweck, 1988; Harackiewicz, Barron, Carter,

Lehto, & Elliot, 1997; Kaplan & Maehr, 1999; Middleton &

Midgely, 1997; Nicholls, 1984; Pintrich, 2000a; Rawsthorne &

Elliot, 1999; Utman, 1997). However, there are some disagree-

ments and some conflicting findings on the nature of these rela-

tions. Specifically, researchers disagree on how to best define and

operationalize the major classes of goals, and on the precise impact

of these goals on motivation and achievement.

In the original goal models, two classes of goals were identi-

fied—performance goals, where the purpose is to validate one’s

ability or avoid demonstrating a lack of ability, and learning goals,

where the aim is to acquire new knowledge or skills (i.e., to

increase one’s ability; see Dweck & Elliott, 1983). Different

researchers have used different labels for these two classes of

goals—performance goals have also been called ego-involved

goals (e.g., Nicholls, 1984) or ability goals (e.g., Ames, 1992), and

learning goals have also been called mastery goals (e.g., Ames,

1992; Butler, 1993; Elliot & Harackiewicz, 1996; Meece & Holt,

1993) or task goals (e.g., Middleton & Midgely, 1997; Nicholls,

1984).

These two classes of goals were then linked to motivation and

performance in achievement situations. Performance goals, with

their emphasis on outcomes as measures of ability, were shown to

produce a vulnerability to helplessness and debilitation after a

setback or negative feedback, particularly in cases where current

perceptions of ability were low (Ames & Archer, 1988; Butler,

1993; Elliott & Dweck, 1988; Jagacinski & Nicholls, 1987; Meece,

Blumenfeld, & Hoyle, 1988). That is, when the goal is to validate

ability and individuals do not believe they can accomplish this,

motivation and performance tend to suffer. Learning goals, with

their emphasis on understanding and growth, were shown to fa-

cilitate persistence and mastery-oriented behaviors in the face of

obstacles, even when perceptions of current ability might be low

(Ames & Archer, 1988; Butler, 1993, Elliott & Dweck, 1988;

Jagacinski & Nicholls, 1987; Utman, 1997).

Performance and learning goals have also been shown to predict

real-world performance, including exam grades, course grades,

and achievement test scores, controlling for past performance

(Dweck & Sorich, 1999; Greene & Miller, 1996; Kaplan & Maehr,

1999; Meece & Holt, 1993; Midgely & Urdan, 1995; Roeser,

Midgely, & Urdan, 1996). In addition, goal effects obtain both

when the goals have been experimentally manipulated (Butler,

1987; Elliott & Dweck, 1988; Graham & Golen, 1991), and when

students’ naturally existing goals have been assessed (Ames &

Archer, 1988; Bouffard, Boisvert, Verzeau, & Larouche, 1995;

Midgely, Anderman, & Hicks, 1995; Miller, Behrens, Greene, &

Newman, 1993; Pintrich & DeGroot, 1990; Pintrich & Garcia,

1991). The fact that induced goals have been found to have strong

impact is important for two reasons. First, it means that goals can

have a causal role in producing achievement patterns. Second, it

means that learning environments can be constructed in ways that

enhance achievement (Ames, 1992; Maehr & Midgley, 1991;

Roeser et al., 1996).

Despite early agreement regarding the effects of performance

and learning goals on motivation and performance, recent research

has revealed a more complicated picture. Some researchers have

questioned whether learning goals affect performance at all, sug-

Heidi Grant and Carol S. Dweck, Department of Psychology, Columbia

University.

This article is based on a doctoral dissertation submitted to Columbia

University by Heidi Grant under the supervision of Carol S. Dweck. It

was supported by National Institute of Mental Health Grant F31-

MH12706-01 to Heidi Grant. We thank Dean Kathleen McDermott and

Professor Leonard Fine and the Department of Chemistry at Columbia

University for their support and assistance with this project. We are also

grateful for the comments and suggestions given by the dissertation com-

mittee: Geraldine Downey, E. Tory Higgins, Harvey Hornstein, and Gab-

riele Oettingen. Finally, we thank Andrew Eliot, Judith Harackiewicz, and

Corwin Senko for their insightful comments on an earlier version of this

article.

Correspondence concerning this article should be addressed to Heidi

Grant, who is now at the Department of Psychology, New York University,

6 Washington Place, 7th Floor, New York, New York 10003. E-mail:

hgp1@nyu.edu

2003, Vol. 85, No. 3, 541–553 0022-3514/03/$12.00 DOI: 10.1037/0022-3514.85.3.541

541

gesting that they chiefly influence intrinsic motivation (e.g., Bar-

ron & Harackiewicz, 2001; Elliot & Church, 1997; Harackiewicz

et al., 1997; Harackiewicz, Barron, Tauer, Carter, & Elliot, 2000).

Some have argued that performance goals predict higher, not

lower, grades, and do not affect intrinsic motivation (e.g., Barron

& Harackiewicz, 2001; Elliot & Church, 1997; cf. Rawsthorne &

Elliot, 1999).

We propose that looking at the ways in which performance and

learning goals have been defined or operationalized can help

account for the discrepant findings that have been obtained by

different researchers. To test this proposal, items were created to

measure the different forms of goals that have been prominently

represented in existing research. Five studies explore the relation-

ships among these goals, their ability to predict intrinsic motiva-

tion and performance under highly challenging or difficult circum-

stances, and the mechanisms through which they may bring about

those effects. We begin by describing the important dimensions

along which the operationalizations of performance and learning

goals vary in current achievement goal research, and describing

how each of these dimensions is represented in the following

studies.

What Is a Performance Goal and What Is Its Effect?

Achievement goal researchers have already made one important

distinction among performance goals—namely, the distinction be-

tween performance approach goals (where the focus is on attain-

ing success) and performance avoidance goals (where the focus is

on the avoidance of failure; Elliot, 1999; Elliot & Church, 1997;

Elliot & Harackiewicz, 1996; Middleton & Midgely, 1997; Pin-

trich, 2000a). In general, this program of research has suggested

that it is the avoidance form of performance goals that predict

lower intrinsic motivation and performance, with approach goals

often relating positively to performance.

However, as discussed below, the positive and negative effects

of performance approach goals have typically been found when

performance goals are operationalized in particular ways, and the

positive and negative effects of different types of performance

approach goals have not been systematically explored. Thus our

purpose in this article is to distinguish among approach forms of

performance goals, and we propose that they take at least three

distinct forms: (a) goals that are linked to validating an aspect of

self (e.g., one’s ability), (b) goals that are explicitly normative in

nature, and (c) goals that are simply focused on obtaining positive

outcomes (i.e., doing well). It is the first form that was linked to

impairment in the earlier models, but it has tended to be the second

two forms that have been linked to more positive outcomes in

recent work. Let us take a closer look at these different forms of

approach goals.

For some researchers, the essence of a performance goal is

seeking to validate one’s ability (operationalized either by sug-

gesting to participants that their performance on a task measures

the extent to which they possess a valued ability, or by assessing

the extent to which they generally strive to validate their ability).

Debilitation occurs when outcomes indicate a lack of ability, but

performance maintenance or enhancement can occur when success

is expected (Ames, 1992; Elliott & Dweck, 1988; see Dweck &

Leggett, 1988). It should be noted that debilitation here requires

the presence of challenges, setbacks, or failure—an easy task or

course is not expected to produce debilitation, even in the presence

of strong performance goals. To represent this view, we developed

ability goal items (e.g., “It is important to me to validate that I am

smart.”).

For others, the essence of a performance goal is a normative

comparison (i.e., wanting to perform better than others), and a goal

that is nonnormative (e.g., using an absolute standard such as a

perfect score, or tying absolute performance to self-worth) is not

considered to be a performance goal (Elliot, 1999; Elliot &

Church, 1997; Elliot & Harackiewicz, 1996; Maehr & Midgely,

1991; Pintrich, 2000b). Here, performance goals are often opera-

tionalized by informing participants that their performance on a

task will be evaluated normatively, or by measuring their agree-

ment with statements such as “It is important to me to do well

compared to others in this class” (Elliot & Church, 1997).

The issue of whether normative performance goals are empiri-

cally distinct from performance goals that do not contain a nor-

mative standard has not been systematically addressed in the

achievement goal literature. Yet it is an important question, be-

cause to some theorists, as noted, the presence of normative

comparison is the essence of a performance goal (Elliot & Harac-

kiewicz, 1996; see Rawsthorne & Elliot, 1999), and to others, a

potentially interesting but nonessential aspect of a performance

goal (Elliott & Dweck, 1988). It would be interesting to find that

normative and nonnormative performance goals do indeed differ,

particularly if these differences could illuminate discrepancies in

the reported effects of performance goals on motivation and per-

formance. The following studies contain both normative and non-

normative versions of performance goals. An example of an ex-

plicitly normative goal would be the following: “One of my major

goals in school is to feel that I am more intelligent than other

students.” In contrast, the goal item, “It is important to me to

validate that I am intelligent, ” is not explicitly normative.

Sometimes goal items used to measure performance-goal orien-

tation simply ask the participant about wanting to do well on a

task, such as wanting to earn a high grade in a course. For people

who are focused on doing well, negative outcomes do not neces-

sarily indicate a lack of ability (i.e., holding this type of goal does

imply a particular causal attribution for success or failure). We

refer to the goal of wanting to do well on a particular task as an

outcome goal, and it, too, is represented in our studies (e.g., “It is

important to me to get good grades in my classes.”). A closely

related construct is “competence valuation,” or the degree to which

a task is perceived to be important (Elliot & McGregor, 2001),

which has been found to relate positively to intrinsic motivation

and performance (Barron & Harackiewicz, 2001). We find this

type of goal particularly interesting, because “wanting to do well”

can also be an important part of a learning goal framework. In

other words, a person with a learning goal may care very much

about doing well on a task, but perhaps for different reasons (i.e.,

in order to maximize learning, as an indicator of successful learn-

ing, or for instrumental reasons). Later, we address the question of

whether outcome goals are best understood as performance goals.

What Is a Learning Goal? When Is It Helpful?

There is generally less controversy and more agreement with

respect to the nature of learning goals. As noted learning goals,

task goals, and mastery goals have often been regarded as concep-

542

GRANT AND DWECK

tually equivalent (Ames, 1992; Linnenbrink & Pintrich, 2000).

Yet, potentially important differences among operationalizations

do exist. For some (Ames, 1992; Elliot & Church, 1997; Elliott &

Dweck, 1988; Harackiewicz et al., 1997; Middleton & Midgley,

1997), a learning goal is an active striving toward development and

growth of competence, and is operationalized by emphasizing the

importance and benefits of learning some new knowledge or skill

to the participant, or by asking participants to indicate the extent to

which learning and developing new skills are major academic

goals. However, the terms “task goals” and “mastery goals” do not

put an explicit emphasis on learning; thus, we thought it important

to test the extent to which the desire to learn may be similar or

different from the desire to master challenges. As a result, we

included items measuring two forms of learning goals. An example

of a learning goal without an explicit challenge-mastery compo-

nent is “I strive to constantly learn and improve in my courses.” An

example of an explicit challenge-mastery item is “It is very im-

portant to me to feel that my coursework offers me real

challenges.”

It should be reiterated that, despite the substantial agreement

among researchers with respect to the concept of a learning goal,

the data with respect to the influence of learning goals on moti-

vation and performance are not without inconsistencies. Typically,

those who adopt learning goals are found to engage in deeper,

more self-regulated learning strategies, have higher intrinsic mo-

tivation, and perform better, particularly in the face of challenge or

setbacks (Ames, 1992; Dweck & Leggett, 1988; Kaplan & Midg-

ley, 1997; Pintrich, 2000a; Pintrich & Garcia, 1991; Utman, 1997;

see also Barron & Harackiewicz, 2000). However, recently, sev-

eral studies have failed to find enhanced performance outcomes

resulting from learning goals (although enhanced intrinsic moti-

vation was found; Elliot & Church, 1997; Elliot, McGregor, &

Gable, 1999).

Conditions Under Which Goal Effects Are Tested

The effects of learning and performance goals on motivation

and achievement have been tested under a wide variety of circum-

stances—with students working on interesting “NINA” puzzles

(Elliot & Harackiewicz, 1996), performing a concept-formation

task (Elliott & Dweck, 1988), solving math problems (designed to

be highly challenging in one condition; Barron & Harackiewicz,

2001; cf. Middleton & Midgely, 1997), or taking an intermediate-

level psychology course (Elliot & Church, 1997). Importantly,

these tasks may have varied with respect to the degree of difficulty

or challenge encountered by the participant, and the degree to

which performance on the task had importance or meaning to the

participant. We feel that conditions where the degree of difficulty

is substantial for a large number of participants and the outcome is

highly important are more likely to reveal goal effects on motiva-

tion, coping, and achievement, and have tried to use such condi-

tions in the studies reported here.

In summary, there have been major differences in the ways

goals have been operationalized, and it is not surprising that the

data are inconsistent with respect to how and when performance

and learning goals affect motivation and achievement. In the

following studies, we attempted to illuminate these issues. In three

studies, we developed and tested a set of items to tap different

forms of learning and performance goals. In the fourth study, to

gain an initial sense of the patterns associated with each goal type,

we presented students with scenarios depicting important aca-

demic setbacks and examined how the different goals predicted

intrinsic motivation and coping. In the fifth study, the different

goals were used to predict intrinsic motivation, study strategies,

and performance in an important and challenging course.

Study 1

Given the number of goals we hoped to measure and compare

(e.g., ability goals, outcome goals, normative outcome goals, nor-

mative ability goals, learning goals, and challenge-mastery goals),

we wanted to use the fewest possible items to measure each type

of goal while still maintaining high reliability. It was felt that using

relatively few items would minimize the frustration and confusion

participants might experience when required to answer many sim-

ilarly worded items. Thus, 10 items for each of type of goal were

created and carefully tested with 560 participants, and the most

reliable three items were selected for each goal. In three prelimi-

nary studies (Studies 1–3) reported below, the items used to

measure each type of goal achieved good reliability and validity, as

demonstrated by the relatively high alphas for each group of items,

the high correlation of each group of items with scales assessing

conceptually similar variables, and the high test–retest correlations

(the Appendix contains the complete list of items). For each goal

item, participants were asked to rate their agreement on a 7-point

Likert-type scale ranging from 1 (strongly disagree)to7(strongly

agree).

Method

Participants

A total of 451 participants (218 men and 233 women) recruited from the

Columbia University student population were paid $5 for their participa-

tion. Fifty-seven percent of participants were Caucasian, 9% African

American, 19% Asian, 8% Latino, and 7% were “other” or unidentified.

Procedure

Participants were asked to complete a goal inventory containing the

three items for each of the types of goals along with several unrelated

measures. The goal items were presented in random order. Participants in

this initial study (and all subsequently reported studies) read and signed

consent forms that informed them about the procedure, the information that

would be asked of them, and their rights as research participants. They

were reminded that they were free to leave the study at any time without

penalty.

Results

Exploratory Factor Analysis

Principal-components analysis, using varimax rotation and eig-

envalues greater than 1, yielded four factors (accounting for 24%,

20%, 16%, and 12% of the total variance, respectively). Factor 1

contains all normative items (both normative ability and normative

outcome). Factor 2 contains both the learning and challenge-

mastery items. Factor 3 contains all of the nonnormative outcome

goal items. Factor 4 contains all nonnormative ability goal items.

543

CLARIFYING ACHIEVEMENT GOALS

This analysis was repeated using oblimin rotation, which yielded

nearly identical results (see Table 1).

These analyses revealed that, consistent with our expectations,

learning goals (Factor 2), outcome goals (Factor 3), and ability

goals (Factor 4) are distinct constructs. As we will see, learning

and ability goals are the major classes of predictive goals in

subsequent studies. In addition, participants clearly distinguished

between normative and nonnormative forms of performance goals,

suggesting that this distinction is a meaningful and potentially

important one. Normative goals loaded together (Factor 1), sug-

gesting that participants who endorsed them did not distinguish

strongly between normative outcome and normative ability goals.

Similarly, challenge-mastery goals did not distinguish themselves

reliably from learning goals. Thus, four groups or classes of goals

emerged in this study, and we focus on these groups throughout the

remainder of the article: learning (comprised of both learning and

challenge-mastery items), outcome, ability, and normative (com-

prised of both normative outcome and normative ability items).

Internal Consistency

Although Cronbach’s alpha is dependent on the length of the

measure (i.e., number of items in a scale), our goal measures

nonetheless achieve substantial alphas. The alphas for each of the

four subsets of the goal items (ranging from .81 to .92) had an

average of .86, consistent with unidimensionality for each set of

items.

Confirmatory Factor Analysis (CFA)

CFA was conducted on the achievement goal items using

EQS 5.7 (Bentler & Wu, 1995). Solutions were generated on the

basis of maximum-likelihood estimation. Seven models were

tested. For each model, we calculated multiple indices of fit:

chi-square, comparative fit index (CFI), normed fit index (NFI),

nonnormed fit index (NNFI), root-mean-square error of approxi-

mation (RMSEA), and Akaike Information Criterion (AIC), a

comparison statistic for nonhierarchical models. The results from

these analyses indicated that two models provided a good fit to the

data: Model A,

␹

(123, N ⫽ 451) ⫽ 490.23, CFI ⫽ .93, NFI ⫽ .91,

NNFI ⫽ .92, RMSEA ⫽ .08, AIC ⫽ 244; Model B,

␹

(120, N ⫽

451) ⫽ 395.76, CFI ⫽ .95, NFI ⫽ .93; NNFI ⫽ .94, RMSEA ⫽

.07, AIC ⫽ 155). Although Model B (a six-factor model) does

provide a slightly better fit, Model A—a hierarchical model with

four primary factors (i.e., an ability goal factor, an outcome goal

factor, a normative factor comprised of normative ability and

normative outcome factors, and a learning factor comprised of

learning and challenge-mastery factors)—is consistent with the

results from the two principal-components analyses, as well as

with the pattern of item intercorrelations and scale alphas. Thus, a

four-factor goal model, consisting of learning goals, outcome

goals, ability performance goals, and normative performance

goals, received the most consistent support and provided the best

overall fit to the data.

Correlations Among Classes of Goals

All four goal indices were positively correlated. Outcome goals

(wanting to do well) appear to accompany the valuing of any

achievement goals, whether those goals pertain to learning (r ⫽

.37, p ⬍ .001), to validating one’s ability (r ⫽ .53, p ⬍ .001), or

to outperforming others (r ⫽ .34, p ⬍ .001). Learning goals were

positively related to outcome goals, as noted, as well as ability

goals (r ⫽ .41, p ⬍ .001) and normative goals (r ⫽ .17, p ⬍ .001).

Finally, ability goals and normative goals were strongly correlated

(r ⫽ .52, p ⬍ .001). Although it appears that individuals who value

achievement may value many aspects of it, we will see that clearly

distinct and unique patterns are associated with each type of goal.

Table 1

Principal-Component Factor Analysis With Item Loadings

Goal item type Item no.

Varimax rotation

12 3 4

Learning 1 .72 (.74)

2 .70 (.68)

3 .66 (.62) .51 (⫺.44)

Challenge-mastery 1 .84 (.87)

2 .80 (.82)

3 .74 (.75)

Outcome 1 .81 (⫺.79)

2 .74 (⫺.71)

3 .87 (⫺.86)

Ability 1 .50 (⫺.46)

2 .79 (⫺.80)

3 .81 (⫺.83)

Normative outcome 1 .81 (.85)

2 .81 (.86)

3 .83 (.86)

Normative ability 1 .82 (.78)

2 .81 (.77)

3 .85 (.82)

Note. All loadings above .40 are shown. Oblimin rotation values are shown in parentheses.

544

GRANT AND DWECK

Analysis by Gender

Tests for mean difference in goal ratings by gender revealed

several significant differences, though the pattern of differences

varied across studies. Because these differences did not replicate

across studies, there is little reason to believe that any were

representative of the general population. It is important to note that

there were no interactive effects of goal and gender in any of the

studies. In other words, performance and learning goals exerted the

same effects on both men and women in each study. Therefore, in

the interest of brevity, gender differences will not be discussed for

each study.

Study 2

Method

Participants

A total of 54 participants (23 men, 31 women) recruited from the

Columbia University student population were paid $10 for their

participation.

Procedure

Participants completed the goal items as part of a battery of measures,

and then completed the items again in another battery of measures ex-

actly 2 weeks later.

Results

Correlations between Time 1 and Time 2 ratings were calculated

for each goal. The correlations ranged from .69 to .88, and the

average test–retest correlation was .79. Thus, participants’ scores

were substantially consistent over time.

Study 3

Study 3 was designed to obtain construct validity for the goal

items by relating them to other goal measures. Two commonly

used measures of achievement goal orientation were chosen (i.e.,

Button, Mathieu, & Zajac, 1996, and Elliot & Church, 1997). In

particular, it was important to show that (a) our measure of

learning goals mapped onto other operationalizations of learning

goals, (b) that our measure of normative performance goals was an

accurate representation of how these goals have been measured in

the literature, and (c) that our outcome goals were equally related

to learning and performance goals in other measures, as they had

been in ours. Neither measure taps ability goals as we have defined

them.

Method

Measures

Learning and Performance Orientation Scales (Button et al., 1996).

Button et al.’s (1996) inventory is composed of two scales (Learning and

Performance), each containing eight items. In general, Button et al.’s

learning goal items capture the conceptualization proposed by Dweck and

Elliott (1983)—an emphasis on challenge-seeking, use of effort and strat-

egies, and desire to develop and grow. Button et al.’s performance items

emphasize wanting to do well and not make mistakes, though there are two

items that involve social comparison and the opinions of others.

Elliot and Church’s (1997) Achievement Goal Scale. The goal orien-

tation scale used by Elliot and his colleagues in their classroom studies

(Elliot & Church, 1997; Elliot et al., 1999) consists of three subscales with

six items each, of which we focused on two: Mastery and Performance

Approach (the third subscale is Performance Avoidance). The Mastery

items emphasize wanting to learn as much as possible and thoroughly

master new material. Performance Approach items emphasize wanting to

do better than others (i.e., normative items).

Participants

A total of 87 participants (37 men, 50 women) were recruited from the

Columbia University student community and paid $5 for their participa-

tion. Sixty percent of participants were Caucasian, 22% African American,

13% Asian, and 5% were “other” or unidentified.

Procedure

Participants completed our goal items, along with the Learning and

Performance Orientation Scales (Button et al., 1996), and the Achievement

Goals Scale used by Elliot and his colleagues (Elliot & Church, 1997). The

three measures were presented in three different orders across participants.

There were no discernable effects of order.

Results

As expected, the learning goal items were highly positively

correlated with Button et al.’s (1996) Learning scale (r ⫽ .72, p ⬍

.001) and Elliot and Church’s (1997) Mastery scale (r ⫽ .76, p ⬍

.001). This suggests that the items are valid indices of a learning

orientation.

In Study 1, outcome goal items, with their focus on the value of

doing well, were compatible with learning goals, ability goals, and

normative goals. They were also positively correlated with both

Button et al.’s (1996) Learning (r ⫽ .37, p ⬍ .01) and Performance

(r ⫽ .45, p ⬍ .001) Scales, as well as with Elliot and Church’s

(1997) Mastery (r ⫽ .41, p ⬍ .001) and Performance Approach

(r ⫽ .30, p ⬍ .01) scales. This is further evidence of the hybrid

nature of outcome goals.

Ability goal items were positively correlated with Button et al.’s

(1996) Performance Scale (r ⫽ .45, p ⬍ .001) and Elliot and

Church’s (1997) Performance Approach scale (r ⫽ .46, p ⬍ .001),

but only moderately, because neither of those scales focus on

ability validation.

As predicted, normative goal items were highly correlated with

Elliot and Church’s (1997; normative) Performance Approach

scale (r ⫽ .83). Ability and outcome goal items were significantly

less correlated with this scale (outcome r ⫽ .30 vs. normative r ⫽

.83, t[85] ⫽ 4.01, p ⬍ .001; Ability r ⫽ .46 vs. Normative r ⫽ .83,

t[85] ⫽ 1.98, p ⬍ .05).

In summary, a comparison of these three measures yielded

evidence for the construct validity of our goal items. High corre-

lations with conceptually similar subscales in the Button et al.

(1996) and Elliot and Church (1997) measures can be taken as

evidence that the items are tapping into the right goal constructs.

Study 4

We believe that it is important to look at goal effects when

individuals experience major setbacks or failure on highly valued

545

CLARIFYING ACHIEVEMENT GOALS

tasks, because it is under these conditions that we would expect

goal effects on motivation, coping, and achievement to be maxi-

mal. Studies 4 and 5 were designed to look at how each of the

different goals we identified predicts indices of intrinsic motiva-

tion, mastery-oriented coping, and performance, after a significant

or sustained difficulty or setback, by means of hypothetical failure

scenarios (Study 4), reports of habitual coping style (Study 4), and

a very challenging premed college course (Study 5).

We also included measurements of some of the affective and

cognitive variables that comprise the psychological processes that

accompany goal pursuit. Much recent achievement goal work pays

little attention to the psychological concomitants of goals: attribu-

tions, beliefs, and contingency of self-worth (Molden & Dweck,

2000). By including these measures, we hoped to capture a richer

motivational picture of performance and learning goal processes.

In Study 4, two scenarios were generated in which the par-

ticipant encounters failure in an important achievement setting

(adapted from Zhao & Dweck, 1997). The use of hypothetical

scenarios was used here as a first step in relating the different goals

to the variety of cognitive, affective, and behavioral variables

involved in coping with difficulty in achievement situations.

Participants in Study 4 also completed a measure of chronic

coping style (COPE; Carver, Scheier, & Weintraub, 1989), so that

we might look at the relationship between goal orientation and

participants’ own personal history of coping with setbacks. Thus,

the first part of Study 4 asks participants to indicate how they

would respond to a situation if it occurred, and the second part of

Study 4 asks them to reflect on past situations that have actually

occurred.

Method

Participants

A total of 92 participants (40 men, 52 women) were recruited for pay

from the Columbia University community. Sixty-one percent of partici-

pants were Caucasian, 21% African American, 12% Asian, and 6% were

“other” or unidentified. They received $5 for their participation.

Procedure

Participants completed the goal items, and then, after a 5-min word-

completion filler task, they received one of two randomly assigned sce-

narios, shown in previous work to elicit motivational differences (see Zhao

& Dweck, 1997). The scenario asked them to read about a failure experi-

ence in a college classroom (either getting a bad grade on an important

essay in a key course or doing poorly on the Graduate Record Examination

when they strongly wished to go to graduate school), and to imagine it

happening to them. These two scenarios were vividly written and were

selected to represent situations that they could easily personally relate to

(i.e., doing poorly on an essay in a course in your major, and doing poorly

on a test in science class). Here is an example:

Imagine that during your second semester at Columbia, you take an

important course in your major, in which students are required to read

their essays out loud to the entire class. This happens several times

throughout the semester. The time comes for the first readings. By the

time it’s your turn, most of the students have already presented their

essays. All of them did pretty well, and you know that their essays got

good grades from the professor. But when you read your essay to the

class, the professor and the other students don’t seem to like your

presentation very much, and later you find out that the grade he gave

you was a C⫺.

Participants were then asked to indicate what they would think, how they

would feel, and how they would behave after the failure by rating their

degree of agreement with a series of statements. These statements include

items assessing loss of intrinsic motivation (e.g., “I’d probably feel less

interested in the subject”), help-seeking (e.g., “I would seek help from my

professor or my classmates”), planning (e.g., “I’d start planning how to do

better on the next presentation”), and time and energy withdrawal (e.g., “I

would devote less time and energy to the class”), as well as attributions for

the failure (e.g., “I would feel like I wasn’t smart enough”), loss of

self-worth (e.g., “I would feel like a loser”), and rumination (e.g., “I would

dwell on my failure”). Responses were made by circling a number on a

7-point scale ranging from 1 (not at all true of me) to7(very true of me).

After a second 5-min word-completion filler task, participants were

asked to complete the Ways of Coping Scale (COPE; Carver et al., 1989).

This scale measures the ways in which individuals have coped with

difficulties when they have arisen. Subscales include Active Cop-

ing, Planning, Positive Reinterpretation, Denial, and Behavioral

Disengagement.

Results

For each of the analyses conducted, scenario version (1 or 2)

was entered as a predictor, and no effect for scenario version was

found. Therefore, all analyses reported were conducted collapsing

across scenario version. Each of the four goal types (learning,

outcome, ability, and normative) was entered as a predictor in a

series of simultaneous regressions that included all two-way inter-

actions. There were no significant two-way interactions, so these

terms were dropped in subsequent analyses. Thus, the effects of

each goal on the variables of interest control for any effects of the

other three classes of goals. In this way, we could determine what,

if any, were the unique effects of each class of goal on our

achievement variables.

Intrinsic Motivation

Table 2 depicts the unique relationship between each type of

goal and an index of loss of intrinsic motivation, created by adding

together responses from the following three items (

␣

⫽ .89): “I’d

probably feel less interested in the subject,”“I probably wouldn’t

enjoy the course as much as before,” and “I wouldn’t really be

excited about the course anymore.”

As can be seen, learning goals were negatively related to de-

creases in intrinsic motivation, whereas outcome and ability goals

were significantly correlated with decreases in intrinsic motiva-

tion. Of interest, normative goals did not predict loss of intrinsic

motivation. This finding is worth noting, in that the program of

research that has most consistently found that approach forms of

performance goals do not negatively influence intrinsic motivation

has used a normative definition of performance goal (e.g., Elliot &

Church, 1997). Also, Epstein and Harackiewicz (1992) have found

that students high in achievement motivation who were assigned

competitive goals (which are inherently normative) experienced

increased interest in a task when they were given a failure expec-

tancy. This finding suggests that competitive strivings may buffer

individuals when they experience difficulty or failure, in ways that

ability-focused strivings do not.

546

GRANT AND DWECK

Behavioral Coping

Endorsement of several possible behavioral responses by goal

type is also displayed in Table 2. Consistent with the maintenance

of intrinsic motivation, learning goals predicted planning (one

item: “I’d start planning how to do better on the next presenta-

tion”), and negatively predicted withdrawal of time and energy

(one item: “I would devote less time and energy to the class”).

Ability goals, in contrast, positively predicted withdrawal of time

and energy.

Outcome goals were the only goals that were positively related

to help-seeking (one item: “I would seek help from my professor

or my classmates”). Help-seeking may be perceived as a good way

to obtain the good grades that those who endorse outcome goals

clearly value.

Attributions

Turning to the psychological processes that accompany goal

pursuit, learning goals (

␤

⫽ .56, p ⬍ .001) were predictive of

effort-based attributions for failure (one item: “I think that if I

work harder, I can do better”), whereas ability goals (

␤

⫽ .22, p ⬍

.05) and outcome goals (

␤

⫽ .36, p ⬍ .01), in contrast, were

predictive of ability-based attributions (one item: “I feel like I’m

just not good at this subject”). Learning goals were negatively

related to making ability attributions for poor performance (

␤

⫽

⫺.37, p ⬍ .01).

These results are consistent with prior research, which found

attributions to low ability to be associated with drops in intrinsic

motivation and helplessness, whereas attributions to effort were

associated with intrinsic motivation maintenance and mastery-

oriented coping (e.g., Mueller & Dweck, 1998).

Again, normative goals were not reliable predictors of negative

ability attributions. Taken together with the finding that these goals

do not reliably predict loss of intrinsic motivation, the data begin

to suggest that normative performance goals may be a hardier form

of performance goal (i.e., one that does not tend to lead to “help-

less” forms of coping and behavior). This is again consistent with

Elliot and colleagues’ findings (see Elliot & Church, 1997; Elliot

et al., 1999) that (normative) performance approach goals do not

lead to lower motivation and performance.

Loss of Self-Worth

Loss of self-worth is akin to a negative ability attribution, but it

is more global. It, too, can often accompany helpless motivational

and behavioral responses to a setback (e.g., Covington, 1992;

Crocker & Wolfe, 2001). A composite index of self-worth loss was

created by adding together responses from the following three

items: “I would feel like a loser,”“I would feel like a failure,” and

“I’d think less of myself as a person” (

␣

⫽ .84). Consistent with

results thus far, ability (

␤

⫽ .56, p ⬍ .001) goals were positively

correlated with loss of self-worth.

Rumination

The tendency to ruminate on one’s setbacks has been associated

with helplessness. A composite index of ruminating and dwelling

on the failure was created by adding together responses from the

following two items: “I would dwell on how poorly I did” and “I

would replay it all over and over again in my mind” (

␣

⫽ .92).

Rumination was fairly strongly related to ability goals (

␤

⫽ .47,

p ⬍ .001). Thus, those goals that tend to lead to ability attributions

and negative self-evaluation also predict dwelling on the negative

outcome and its meaning.

The results from the hypothetical failure scenarios revealed a

consistent pattern among the motivational and coping variables.

Learning goals predicted active, engaged responding, whereas

ability goals predicted self-denigration and withdrawal. Outcome

goals were associated with a hybrid response pattern (i.e., low

ability attributions and decreased intrinsic motivation as well as

help-seeking). Finally, normative goals were not reliable predic-

tors of mastery-oriented or helpless responding.

Chronic Coping Style

We now turn to the question of whether different goals predict

different reported histories of coping with setbacks in past achieve-

ment situations. Different styles of chronic coping were measured

by the Ways of Coping Scale (Carver et al., 1989), which asks

participants to indicate the extent to which they have typically

engaged in various coping strategies.

Consistent with the responses to the failure scenarios, learning

goals predicted active coping (

␤

⫽ .38, p ⬍ .01) and planning

(

␤

⫽ .33, p ⬍ .01). They were also predictive of positive reinter-

pretation of a setback (

␤

⫽ .30, p ⬍ .05) and negatively related to

denial (

␤

⫽⫺.36, p ⬍ .01), behavioral disengagement (

␤

⫽⫺.35,

p ⬍ .01), and mental disengagement (

␤

⫽⫺.28, p ⬍ .05).

Ability goals negatively predicted positive reinterpretation of a

setback (

␤

⫽⫺.30, p ⬍ .05). Of interest, normative goals were

significant predictors of denial after a setback (

␤

⫽ .25, p ⬍ .05)

and behavioral disengagement (

␤

⫽ .28, p ⬍ .01). The finding for

denial perhaps suggests that competitive striving might keep indi-

Table 2

Goals and Responses to Failure

Goal

Loss of intrinsic

motivation

Withdrawal of time

and effort Help-seeking Planning

Learning ⫺.39*** ⫺.40*** .17 .57***

Outcome .29** .00 .36** .03

Ability .40*** .32** ⫺.02 ⫺.02

Normative ⫺.11 ⫺.02 ⫺.16 ⫺.16

Note. Values are standardized regression coefficients.

** p ⬍ .01. *** p ⬍ .001.

547

CLARIFYING ACHIEVEMENT GOALS

viduals from recognizing a poor performance when they produce

one. This may provide some explanation for the consistent finding

that normative goals did not predict negative cognitive, affective,

and behavioral responding to a hypothetical setback (e.g., loss of

intrinsic motivation, low ability attributions, loss of self-worth,

rumination) as strongly or consistently as nonnormative ability

goals.

In summary, learning goals were associated with active coping,

and a wide range of positive, mastery-oriented indicators. Learning

goals appear to be a powerful predictor of behaviors that will

preserve intrinsic motivation and performance in the face of dif-

ficulty. In contrast, ability goals were associated with a loss of

motivation and common indices of helplessness. Outcome goals

(which are related to both learning goals and ability goals) also

predicted a loss of motivation and low ability attributions for

failure, but predicted proactive behaviors as well (e.g., help-

seeking). Taken together, these results suggest that valuing doing

well is not in itself a good predictor of responses to failure—rather,

the goals that accompany valuing doing well (learning or validat-

ing ability) seem responsible for much of the “action.” Normative

goals were not among the performance goals that related strongly

or consistently to the variables measured, suggesting that under

some circumstances, competitive performance goal items may not

predict maladaptive cognitions, affect, or coping when other types

of performance goals (i.e., ability goals) do.

Study 5

Study 5 differed from Study 4 in several ways. First, the goal

items were used to predict motivation and performance in a “real-

world” setting, specifically for freshman and sophomore under-

graduates taking an important and often career-defining course.

Study 5 also differed from many past course-taking studies in the

level of sustained challenge or difficulty encountered by partici-

pants (and, as explained below, in our special attention to students

who encountered successive setbacks over the course of the se-

mester). For this reason, we would expect to see more facilitative

effects of learning goals on motivation and performance, as well as

the debilitating effects of performance goals.

Aside from being a real-world study, Study 5 differed from

Study 4 in another important way. Study 4 presented students with

a fait accompli—a defined failure—and therefore, perhaps did not

allow us to see the potentially beneficial effects of performance

goals for people experiencing challenge but not failure. Study 5

allowed us to monitor students throughout the semester, by looking

in on students as they began this new, important, and challenging

course. Here we might find that for students who are doing well,

ability goals will provide a boost over time, whereas for students

who are encountering difficulty, ability goals will predict further

impairment. In other words, Study 5 allowed us to see goal effects

as they played out over time—both their facilitative effects and

their detrimental effects.

Most potential premed, engineering, and science majors at Co-

lumbia University enroll in General Chemistry in the Fall of their

freshman year. The permission and support of the Columbia Uni-

versity Provost, Deans of the College of Arts and Sciences, and

General Chemistry instructors were granted to conduct an inten-

sive study of these students throughout the semester. Surveys

tracked students’ intrinsic motivation and performance at several

points throughout the semester, and grades were obtained from the

Chemistry Department with permission of the students.

Method

Participants

Participants were 85% freshmen, 50% female and 50% male. The

number of participants in each wave of the study varied between 78 and

128, depending on class/recitation attendance. In the largest sample, par-

ticipants were 59% Caucasian, 7% African American, 26% Asian, and 8%

Latino. The average grade on any exam in this course was a C⫹, suggest-

ing that this was a course in which many participants experienced difficulty

or setbacks. For the smaller samples, we tested to ensure that the partici-

pants were entirely representative of the larger sample and that no system-

atic attrition had occurred. Thus, although attendance (and hence partici-

pation in the study) varied over the waves of the study, no significant

differences among samples at the different time points were found in terms

of gender, ethnicity, goal endorsement, or grades.

Procedure

General Chemistry is a lecture course that is structured around three

midterms and a final exam. Data were collected from participants at four

points during the semester: twice 2–3 weeks before the first midterm, once

immediately after the first midterm, and again 2 weeks before the final

exam. Data were collected in the last 15–20 min of class or recitation. The

measures were presented (along with other measures in a packet of ques-

tionnaires) in the following sequence:

Session 1 (2–3 weeks before first midterm): goal items, demographic

information

Session 2 (1 week after Session 1): intrinsic motivation, perception of

chemistry ability

Session 3 ( after first midterm): general study strategies (from Elliot

et al., 1999)

Session 4 (before final exam): intrinsic motivation

Consistent with the results of Study 4, we predicted that learning goals

would be positively related to intrinsic motivation and grades (despite the

lack of the influence of learning goals on performance found in previous

studies in what may have been less academically strenuous or personally

relevant contexts). We expected ability goals to be associated with lower

performance after multiple setbacks, as suggested by Dweck and Leggett

(1988), but not necessarily with lower performance overall. In fact, we

expected that students who were doing well in the course might experience

a “boost” from holding strong ability goals.

Results

Perceived Ability

If different types of goals are systematically related to different

levels of perceived ability, then it is possible that the effects of

goals obtained in this study are simply due to this confounding

factor. To rule out this explanation, perceived level of ability in

chemistry was measured at the beginning of the course (one item:

“Compared to other students in this course, please rate your ability

in chemistry” on a 10-point scale ranging from top 10% to lower

10%). Perception of ability in chemistry was related to overall

course grade (r ⫽ .27, p ⬍ .01). It was also related to intrinsic

548

GRANT AND DWECK

motivation at the beginning (r ⫽ .26, p ⬍ .01) and at the end (r ⫽

.31, p ⬍ .05) of the course.

Correlations between perceived ability in chemistry and goal

type revealed that normative goals were significantly positively

related to perceived ability (r ⫽ .38, p ⬍ .001). In other words,

people with normative goals tended to believe that their ability was

high relative to others. This could help account for the resilience

(or, better put, lack of negative consequences) associated with

normative goals in Study 4. Outcome goals were also positively

related to perceived ability (r ⫽ .21, p ⬍ .05), whereas learning

and ability goals were unrelated to perceptions of chemistry

ability.

Intrinsic Motivation

In a set of linear regressions, we looked at the relationship

between goal type and intrinsic motivation, measured by enjoy-

ment of and interest in the course (two items,

␣

⫽ .88). These data

were collected at the beginning of the course and again before the

final exam. The regressions controlled for perceived ability

(though an essentially identical pattern emerged when perceived

ability was not included in the analysis) and for the effects of each

of the other three goal indices. We also included gender in our

initial analyses, but gender did not predict intrinsic motivation and

was dropped from subsequent analyses predicting intrinsic

motivation.

In this highly difficult course, learning goals predicted higher

intrinsic motivation at the beginning (

␤

⫽ .23), t(128) ⫽ 2.34, p ⬍

.05, and at the end (

␤

⫽ .22), t(78) ⫽ 2.02, p ⬍ .05, of the course.

This is consistent with the findings of Elliot, Harackiewicz, and

their colleagues (Elliot & Church, 1997; Elliot & McGregor, 1998;

Harackiewicz & Elliot, 1998) that learning goals positively predict

intrinsic motivation. There were no other significant predictors of

intrinsic motivation.

Grades

We looked at the relationship between each goal type and

students’ total grades, controlling for Scholastic Aptitude Test

(SAT) score, perceived ability in chemistry, number of prior

courses in chemistry, and gender, as well as the effects of other

goal indices. Gender predicted total grade (

␤

⫽⫺.19), t(126) ⫽

⫺3.03, p ⬍ .01, such that men tended to have higher grades than

women. In addition, we looked at the extent to which each goal

type predicted improvement from Exam 1 to the final exam,

controlling for performance on Exam 1 (the interaction of each

goal with performance on Exam 1 was also included as a

predictor).

Total Course Grade

Consistent with the pattern of effort attribution and mastery-

oriented coping associated with learning goals in Study 4, learning

goals positively predicted course grade (

␤

⫽ .20), t(120) ⫽ 2.42,

p ⬍ .05. No other goals were significant predictors of course

grade. The fact that learning goals emerged as a significant pre-

dictor of performance supplements the findings of Elliot, Harac-

kiewicz, and their colleagues (e.g., Elliot & Church, 1997;

Harackiewicz et al., 1997), who have suggested that performance

goals, and not learning goals, predict course performance. This

result could imply that when a course involves sustained challenge,

learning goals do positively affect course performance.

Improvement in Grade From Exam 1 to Final Exam

Learning goals also significantly predicted grade improvement

(

␤

⫽ .25), t(122) ⫽ 2.94, p ⬍ .01, and were the only goals to do

so.

Final Exam Grade

Earlier, we had predicted that ability goals would have a nega-

tive effect on performance for those students who had experienced

prolonged setbacks. To address this question, we looked at how

goals predicted performance on the final exam for those students

who had performed poorly throughout the semester. We simulta-

neously regressed each goal type, the average of students’ Exam 1,

2, and 3 grades (our index of past performance), and the interaction

of goal type with average exam grades, onto final exam grades. We

predicted a significant interaction for ability goals, such that stu-

dents who had done poorly throughout the semester (i.e., those

with low average exam grades) would suffer for holding strong

ability goals, whereas those who had done well throughout the

semester might receive a boost on the final.

As predicted, there was a significant interaction between ability

goals and average grade on Exams 1, 2, and 3 (

␤

⫽ .52),

t(71) ⫽ 2.23, p ⬍ .05. Figure 1 illustrates this effect. We have

plotted data for participants who were either one standard devia-

tion above or below the mean endorsement of ability goals (see

Jaccard, Turrisi, & Wan, 1990). Participants were further separated

into high- and low-course performance groups (based on a median

split of performance on exams prior to final). As shown, partici-

pants with low prefinal grades score lower on the final exam if

they are high rather than low in ability goals. In contrast, partici-

pants with higher prefinal grades earn better scores on the final

Figure 1. Final exam grade predicted by past performance and ability

goals.

549

CLARIFYING ACHIEVEMENT GOALS

exam if they are high rather than low in ability goals. This finding

suggests that when setbacks are repeated, ability goals predict poor

performance, but may indeed provide a boost when an individual

is doing well (see Elliott & Dweck, 1988).

Study Strategies

To further understand the differences we found in performance,

we looked at three study strategies (deep processing, surface

processing, and disorganized processing) that were adapted from a

scale used by Elliot et al. (1999), and were assessed immediately

after students took the first exam. The tendency to engage in deep

processing was significantly correlated with grade in the course

(r ⫽ .29, p ⬍ .01). Disorganized processing was negatively related

to course grade (r ⫽⫺.36, p ⬍ .001). Surface processing was

unrelated to course grade (r ⫽ .08, ns).

Outcome goals predicted surface processing of course material

(r ⫽ .29, p ⬍ .01), and learning goals predicted deeper processing

of course material (r ⫽ .31, p ⬍ .01). In contrast, normative goals

were negatively related to deep processing (r ⫽⫺.21, p ⬍ .05),

suggesting that one drawback associated with a competitive goal

might be the absence of deep analysis of issues or material.

Mediational Analyses for Learning Goal Effects on

Course Grade

The significant correlation between learning goals and deep

processing (r ⫽ .31), as well as the correlation between deep

processing and course grade (r ⫽ .29), suggested processing style

as a possible mediator of the effect of learning goals on course

grade. Consistent with this hypothesis, the relationship between

learning goals and course grade (controlling as we had earlier for

SAT score, perceived ability in chemistry, past chemistry course

experience, and gender), when controlling for extent of deep

processing, is not significant (

␤

⫽⫺.06, ns), whereas deep pro-

cessing remains a significant predictor of course grade (

␤

⫽ .43,

p ⬍ .05; see Table 3 and Figure 2).

General Discussion

Items measuring different types of performance and learning

goals were created and used in five studies to help to shed light on

several important, unresolved issues in current achievement goal

research. Studies 1–3 yielded evidence for four types of goals:

learning goals, outcome goals (wanting to do well), ability-linked

performance goals, and normative performance goals. Individuals’

responses in these three preliminary studies and two more com-

prehensive studies suggested answers to a number of the funda-

mental questions posed in the literature.

First, are there different types of learning goals? What is the

relationship of learning goals to intrinsic motivation and perfor-

mance? We looked at two types of learning goals: striving to learn

and develop versus seeking to master challenges. These two goals

were highly correlated and loaded together in two principal-

components analyses, so the items were combined into a single

learning goal measure. Although we did not find evidence in our

studies for separating these two types of learning goals, they may

still differ importantly from the “task goals” found in past research

that are often operationalized in ways that contain neither striving

to learn nor challenge-seeking.

Studies 4 and 5 provided evidence for the positive effects of

learning goals on both intrinsic motivation and performance, con-

sistent with the early research on achievement goals (see, e.g.,

Ames, 1992; Ames & Archer, 1988; Butler, 1987; Dweck & Leg-

gett, 1988; Elliott & Dweck, 1988; Meece et al., 1988; Nicholls,

1984). Individuals who endorse learning goals should be more

likely to see negative outcomes as information about ways to

improve the learning process, rather than as indicators of stable

low ability. As expected, in response to a major hypothetical

failure (Study 4), learning goals predicted a wide range of positive,

mastery-oriented indicators—including sustained intrinsic motiva-

tion, planning, and persistence. Participants with strong learning

goals also reported a history of having used more mastery-oriented

coping methods (e.g., active coping, planning) in response to past

setbacks.

In Study 5, in an important and difficult college course, learning

goals predicted better processing of course material, higher intrin-

sic motivation, higher grades, and greater improvement over time.

Further analysis suggested that the relationship between learning

goals and course grades was mediated by the tendency to engage

in deeper processing of course material. The impact of learning

goals on performance may be seen chiefly when a high degree of

challenge is present, when a task is personally important, or when

the processing of complex, difficult material is necessary. A po-

tentially important topic for future research is the role that these

factors play in the presence or absence of learning-goal effects on

performance.

Turning to other questions posed earlier, is wanting to do well

different from wanting to prove your ability? When might perfor-

Table 3

Summary of Learning and Ability Goal Effects From Studies 4 and 5

Goal Study 4 Study 5

Learning No decrease in intrinsic motivation

Less time and effort withdrawal

Effort attributions

Planning

Seeking positive reinterpretation and growth

Higher intrinsic motivation at beginning and

end of course

Higher grades

Greater improvement over time

Deeper processing

Ability Lower intrinsic motivation Lower grades after repeated poor performance

Loss of self-worth Higher grades after repeated good performance

Low ability attributions

Time and effort withdrawal

Rumination

550

GRANT AND DWECK

mance goals predict vulnerability, and when might they prove

beneficial to intrinsic motivation and/or performance? Individuals

who endorse ability goals (i.e., seek to validate their ability) should

be more likely to see negative outcomes as indicative of a lack of

ability. Consistent with this prediction, ability goals were associ-

ated with common indices of helplessness after a significant hy-

pothetical failure in Study 4. These goals predicted attributions to

low ability, loss of self-worth, rumination about the setback, and

loss of intrinsic motivation. In Study 5, consistent with the results

of Study 4, after multiple setbacks, ability goals predicted lower

grades. Thus, ability goals tend to predict a pattern of negative

affect and cognition, as well as poorer subsequent performance,

after a significant setback or a series of setbacks. These findings

are also consistent with the early work on achievement goals

(Ames & Archer, 1988; Butler, 1993; Elliott & Dweck, 1988;

Jagacinski & Nicholls, 1987; Meece et al., 1988; see also Midgley,

Kaplan, & Middleton, 2001). However, ability goals do not appear

to have negative effects on performance when one is still “in the

running” (i.e., when success is still possible), or when one is doing

well, and may in these cases sometimes even boost performance

because so much is on the line.

Why do the negative effects of ability goals occur? Dweck and

Leggett (1988) suggested several potential cognitive and affective

mechanisms of debilitation for individuals who hold ability goals

in the face of difficulty. These include the loss of belief in the

efficacy of effort (i.e., “My ability is so low, no amount of effort

could help me”), defensive withdrawal of effort (either as a form

of self-handicapping or as a response to the belief that the need to

put in effort confirms that one has low ability), and interference of

negative affect with concentration or test performance. Another

possibility is that students with ability goals may withdraw effort

strategically when they are doing poorly to redirect the resources

to courses where they have a better chance at getting a good grade.

Although these data do not test specifically for this possibility, the

pattern of negative attributions, rumination, and loss of self-worth

associated with ability goals suggest that withdrawal is not a solely

cool-headed strategic process.

Outcome goals had surprisingly few effects. Although corre-

lated with many key outcomes, these effects were almost always

due to the association of outcome goals with either learning goals

(e.g., for active coping and effort attributions) or ability goals (e.g.,

for loss of self worth and rumination). These effects did not

survive simultaneous regression analyses that controlled for the

influence of learning, ability, and normative goals. Taken together,

these results suggest that those researchers interested in studying

the unique effects of performance goals would do better not to

operationalize them this way, as outcome goals (wanting to do

well) can clearly be as much a part of a learning framework as a

performance framework. In fact, doing well can be a means of

assessing the acquisition and mastery of new skills and knowledge

or of demonstrating ability.

Finally, do normative and nonnormative performance goals

produce different effects? Unlike (nonnormative) ability goals,

normative performance goals did not predict any of the affective,

cognitive, or behavioral variables measured in Study 4, with the

exception of the tendency on the COPE scale (Carver et al., 1989)

to report engaging in denial and behavioral disengagement after

experiencing an academic setback. In other words, wanting to

outperform others might lead you to be reluctant to perceive your

performance as a failure. In Study 5, normative goals, unlike

ability goals, did not predict vulnerable performance, and in fact,

were associated with higher levels of perceived ability. As men-

tioned earlier, the absence of a relationship between competitive

goals and helplessness is worth noting, in that those researchers

who have most consistently found that performance goals do not

negatively influence intrinsic motivation and performance have

used a normative definition of performance goal (e.g., Elliot &

Church, 1997). Further research is warranted to explore the roles

that perceived ability and denial may play in this protective func-

tion. Moreover, it is striking that although deep processing medi-

ates the beneficial effects of learning goals on grades, the negative

relationship between normative goals and deep processing did not

seem to predict poorer grades. If the lower level of deep processing

was not a hindrance in this setting, it is very likely that competitive

zeal could have positive effects in the many settings in which deep

processing is not required (Kanfer & Ackerman, 2000).

Because ability performance goals and normative performance

goals appear to behave so differently, it would seem important for

researchers to include both types of performance goals in future

studies. In this way, we could continue to gain knowledge about

when, why, and for whom each has costs and benefits.

It should be noted that there are several differences between

these studies and many past studies of goal effects. First, the

present studies used a measure of general goal orientation (i.e., the

extent to which students typically felt oriented toward particular

goals in their courses), whereas many past studies have used goal

inventories that were specific to the task at hand, or to the course

the student was currently taking. Although it is not certain how this

difference might have affected our results and their interpretation,

the field of achievement motivation might benefit from research

that addressed differences in general versus specific goal measure-

ment. Next, our participants, attending a highly selective univer-

sity, may have differed somewhat from the typical student in ways

that could increase or decrease the impact of particular goals. Also,

somewhat larger sample sizes in some other studies may have

yielded significant effects for certain performance goals that were

not significant in our studies. Finally, as noted above, the course

that our participants were enrolled in (Study 5) appeared to require

deep processing in order to do well, and it is possible that this

factor heightened the impact of learning goals. Nonetheless, our

findings make sense both in view of much previous research and

in view of the different meanings that various goals have for the

individual.

Indeed, in this article we have taken great care to consider the

meaning that particular goals may have for the individual and to

Figure 2. Processing style mediates the effects of learning goals on

course grade. Values are standardized regression coefficients.

Standard

ized regression coefficient controlling for Scholastic Aptitude Test, per-

ceived chemistry ability, past chemistry course experience, and gender.

*p ⬍ .05. **p ⬍ .01.

551

CLARIFYING ACHIEVEMENT GOALS

consider goal effects in that light. For example, in addressing the

effects of ability-linked goals on behavior or performance, we

pointed to the inferences that students with ability-linked goals

draw from setbacks. In addressing the effects of outcome goals, we

noted that wanting to do well, far from being a pure performance

goal, may be equally linked to learning and performance goals. In

thinking about learning goals, we stressed the element of active

striving rather than a simple focus on the task or the absence of

performance goal concerns. Thus, for each goal type, we tried to

spell out the impact it might have in the face of achievement

outcomes and why. We hope that our findings have shown the

importance of conceptualizing the psychological processes that

accompany different types of goals, and of matching operational-

izations to these conceptions. When thought of in this way, it

becomes clearer when and why different goals—even ones that

have typically been classified under the same name—will have

different effects.

In conclusion, we have found evidence to suggest that a careful

examination of different types of performance and learning goals

can indeed begin to clarify current controversies in the field. These

studies have shown that learning goals do exert a positive influ-

ence on both intrinsic motivation and performance when individ-

uals encounter prolonged challenge or setbacks. In addition, al-

though performance goals that are focused on validating ability

can have beneficial effects on performance when individuals are

meeting with success, these same goals can predict impaired

motivation and performance after setbacks.

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Appendix

Achievement Goal Inventory Items

Outcome Goal Items (

␣

⫽ .85)

It is very important to me to do well in my courses.

I really want to get good grades in my classes.

A major goal I have in my courses is to perform really well.

Ability Goal Items (

␣

⫽ .81)

It is important to me to confirm my intelligence through my schoolwork.

In school I am focused on demonstrating my intellectual ability.

One of my important goals is to validate my intelligence through my

schoolwork.

Normative Goal Items (

␣

⫽ .92)

Normative Outcome

It is very important to me to do well in my courses compared to others.

I try to do better in my classes than other students.

A major goal I have in my courses is to get higher grades than the other

students.

Normative Ability

It is very important to me to confirm that I am more intelligent than other

students.

When I take a course in school, it is very important for me to validate

that I am smarter than other students.

In school I am focused on demonstrating that I am smarter than other

students.

Learning Goal Items (

␣

⫽ .86)

Learning

I strive to constantly learn and improve in my courses.

In school I am always seeking opportunities to develop new skills and

acquire new knowledge.

In my classes I focus on developing my abilities and acquiring new ones.

Challenge-Mastery

I seek out courses that I will find challenging.

I really enjoy facing challenges, and I seek out opportunities to do so in

my courses.

It is very important to me to feel that my coursework offers me real

challenges.

Received February 6, 2002

Revision received January 16, 2003

Accepted January 21, 2003 䡲

553

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This study investigates the influence of instructor mindset on student perceptions. Due to the need for increasing science, technology, engineering, and mathematics (STEM) student retention and the issue of underrepresentation of minority groups in STEM disciplines, we also examined whether instructor mindset impacts underrepresented students more than represented students in the context of STEM education. Undergraduate student participants (N = 273) reviewed growth and fixed mindset syllabi and responded to a questionnaire to assess perceptions about their likely response and the professor. The significant main effect of faculty mindset revealed that students anticipated better grades and reported a more positive view of the instructor, greater self-efficacy, and higher expectations the professor would treat them fairly after reading the growth mindset syllabus than after reading the fixed mindset syllabus. Females reported lower expected grades, less self-efficacy, and lower expectations of fair treatment than males after reading the fixed syllabus; there were no gender differences after reading the growth mindset syllabus. The results of underrepresented racial/ethnicity group analyses were less clear cut. Our findings, alongside similar research, suggest that students have more positive perceptions of their ability to succeed when an instructor endorses a growth mindset than when an instructor endorses a fixed mindset. Implications include interventions to enhance growth-mindset orientation among instructors.

Assessing Coping Strategies: A Theoretically Based Approach

Article

Full-text available

Feb 1989

We developed a multidimensional coping inventory to assess the different ways in which people respond to stress. Five scales (of four items each) measure conceptually distinct aspects of problem-focused coping (active coping, planning, suppression of competing activities, restraint coping, seeking of instrumental social support); five scales measure aspects of what might be viewed as emotion-focused coping (seeking of emotional social support, positive reinterpretation, acceptance, denial, turning to religion); and three scales measure coping responses that arguably are less useful (focus on and venting of emotions, behavioral disengagement, mental disengagement). Study 1 reports the development of scale items. Study 2 reports correlations between the various coping scales and several theoretically relevant personality measures in an effort to provide preliminary information about the inventory's convergent and discriminant validity. Study 3 uses the inventory to assess coping responses among a group of undergraduates who were attempting to cope with a specific stressful episode. This study also allowed an initial examination of associations between dispositional and situational coping tendencies.

Achievement goals and optimal motivation: A multiple goals approach

Article

Full-text available

Jan 2000

Short-term and Long-term Consequences of Achievement Goals: Predicting Interest and Performance over Time

Article

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Jun 2000

Why do some students excel in their college classes and develop interest in an academic discipline? The authors examined both the short-term and long-term consequences of students' achievement goals in an introductory psychology course. Mastery goals positively predicted subsequent interest in the course, but not course grades. Performance goals positively predicted grades, but not interest. Three semesters later, the authors obtained measures of continued interest in the discipline and long-term performance. Mastery goals predicted subsequent enrollment in psychology courses, whereas performance goals predicted long-term academic performance. These positive and complementary effects of mastery and performance goals on different measures of academic success are consistent with a multiple-goals perspective in which both goals can have beneficial consequences in college education.

Predictors and consequences of achievement goals in the college classroom: Maintaining interest and making the grade

Article

Full-text available

Dec 1997
J PERS SOC PSYCHOL

The authors investigated personality predictors of achievement goals in an introductory psychology class, as well as the consequences of these goals for the motivation and performance of 311 undergraduates. Two dimensions of achievement motivation (workmastery and competitive orientations; J. T. Spence & R. L. Helmreich, 1983) predicted the goals endorsed. Individuals high in workmastery were more likely to adopt mastery goals and less likely to adopt work avoidance goals, whereas competitive individuals were more likely to endorse performance and work avoidance goals. Students adopting mastery goals were more interested in the class, but students adopting performance goals achieved higher levels of performance. These results suggest that both mastery and performance goals can lead to important positive outcomes in college classes.

Perceptions of the school psychological environment and early adolescents' self appraisals and academic engagement: The mediating role of goals and belonging

Article

Jan 1996

Test anxiety and the hierarchical model of approach and avoidance achievement motivation

Article

Apr 1999
J PERS SOC PSYCHOL

This research was designed to incorporate the test anxiety (TA) construct into the hierarchical model of approach and avoidance achievement motivation. Hypotheses regarding state and trait TA were tested in 2 studies, and the results provided strong support for the predictions. State TA (specifically, worry) was documented as a mediator of the negative relationship between performance-avoidance goals and exam performance. The positive relationship between performance-approach goals and exam performance was shown to be independent of TA processes. A series of analyses documented the conceptual and functional convergence of trait TA and fear of failure (FOF), and further validation of the proposed integration was obtained by testing trait TA/FOF and state TA together in the same model. Mastery goals were positively and performance-avoidance goals negatively related to long-term retention.

Student goal orientation and self-regulation in the college classroom

Article

Jan 1991

Mastery-oriented thinking

Article

Jan 1999

Students' Goal Orientations and Cognitive Engagement in Classroom Activities

Article

Dec 1988

We used structural equation analysis to test the validity of a goal mediational model for conceptualizing the influence of individual and situational variables on students' cognitive engagement in science activities. Fifth- and sixth-grade students (N = 275) from 10 classrooms completed a set of questionnaires designed to assess their goal orientations and their use of high-level or effort-minimizing learning strategies while completing six different science activities. Results indicate that students who placed greater emphasis on task-mastery goals reported more active cognitive engagement. In contrast, students oriented toward gaining social recognition, pleasing the teacher, or avoiding work reported a lower level of cognitive engagement. The relative strength of these goals was related to differences in students' intrinsic motivation and attitudes toward science. Our analyses also suggested that these variables exerted a greater influence in small-group than in whole-class activities.

Motivational and Self-Regulated Learning Components of Classroom Academic Performance

Article

Mar 1990

A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self-regulation, and use of learning strategies was administered, and performance data were obtained from work on classroom assignments. Self-efficacy and intrinsic value were positively related to cognitive engagement and performance. Regression analyses revealed that, depending on the outcome measure, self-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Intrinsic value did not have a direct influence on performance but was strongly related to self-regulation and cognitive strategy use, regardless of prior achievement level. The implications of individual differences in motivational orientation for cognitive engagement and self-regulation in the classroom are discussed.

Clarifying Achievement Goals and Their Impact

Abstract and Figures

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