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. Author manuscript; available in PMC: 2020 Aug 21.
Published in final edited form as: Contemp Educ Psychol. 2018 Sep 26;55:120–128. doi: 10.1016/j.cedpsych.2018.09.007

Fostering Grit: Perceived School Goal-Structure Predicts Growth in Grit and Grades

Daeun Park 1, Alisa Yu 2, Rebecca N Baelen 3, Eli Tsukayama 4, Angela L Duckworth 5
PMCID: PMC7441845  NIHMSID: NIHMS1509219  PMID: 32831457

Abstract

Grit, the inclination to pursue long-term goals with passion and perseverance, predicts academic achievement and professional success, but how to encourage grit in students remains an open question. The goal of the current study was to understand how perceptions of school culture influence the development of grit in middle school students. We conducted a year-long, prospective, longitudinal study (N= 1,277) examining grit, perceived goal structures (mastery vs. performance), and academic achievement. In cross-sectional analyses, we found that students who perceived their schools as more mastery goal-oriented were grittier and earned higher report card grades. In contrast, students who perceived their schools as more performance goal-oriented were less gritty and earned lower report card grades. In longitudinal analyses, changes in perceived mastery school goal structure predicted changes in grit over the school year, which in turn predicted changes in grades. Changes in perceived performance school goal structure, in contrast, did not reliably predict changes in grit. These findings suggest that school environments that emphasize the value of learning for learning’s sake may encourage children to sustain interest in and effort toward long-term goals.

Schools seek to foster an array of competencies that promote success within and beyond the classroom. This mission accords with empirical evidence affirming the importance of attributes other than cognitive ability (Dweck, Walton, & Cohen, 2014; Farrington et al., 2012; Heckman, Humphries, & Kautz, 2014; Levin, 2013; Willingham, 1985). Recently, grit—the tendency to sustain passion and perseverance for long-term goals—has emerged as an important predictor of overcoming challenges (Duckworth, Peterson, Matthews, & Kelly, 2007; Eskreis-Winkler, Duckworth, Shulman, & Beal, 2014; Sheldon, Jose, Kashdan, & Jarden, 2015). Nonetheless, little is known about why some students are grittier than others. In the current investigation of more than one thousand eighth graders, we examined the influence of perceived school goal structures (mastery vs. performance) on grit and academic achievement.

Grit

Grit is conceptualized as an inclination to sustain interest in and effort toward personally meaningful and challenging goals over time (Duckworth et al., 2007). Grit is not positively associated with IQ, but it reliably predicts academic outcomes, including high school and college graduation (Duckworth et al., 2007; Eskreis-Winkler et al., 2014) and persistence at the United States Military Academy at West Point (Duckworth et al., 2007). Among adults, grit predicts goal attainment (Sheldon et al., 2015), persistence and effectiveness in teaching (Robertson-Kraft & Duckworth, 2014), lower turnover in sales (Eskreis-Winkler et al., 2014), and the attainment and successful completion of Special Forces military training (Eskreis-Winkler et al., 2014).

Grit has several motivational and behavioral correlates. Motivationally, grittier individuals are more likely to engage in attention-absorbing activities and to seek meaning and purpose (Hill, Burrow, & Bronk, 2016; Von Culin, Tsukayama, & Duckworth, 2014). Grittier individuals also tend to believe that their abilities are malleable rather than fixed (Hochanadel & Finamore, 2015; West et al., 2016), demonstrate higher levels of self-efficacy (Muenks, Wigfield, Yang, & O’Neal, 2017), and look for specific and changeable causes when confronted with adversity (Duckworth, Quinn, & Seligman, 2009). Behaviorally, grittier individuals engage in more self-regulated learning (Wolters & Hussain, 2015) and deliberate practice, an effortful and challenging goal-directed type of practice (Ericsson, Krampe, & Tesch-Romer, 1993) that emphasizes improving upon specific skills or weaknesses (Duckworth, Kirby, Tsukayama, Berstein, & Ericsson, 2011; Duckworth et al., 2007).

Though related to other psychological constructs, grit differs theoretically and empirically. Conceptually, grit belongs to the Big Five conscientiousness family (Duckworth et al., 2007; Schmidt, Nagy, Fleckenstein, Mollier, & Retelsdorf, 2018), a broad family of characteristics that also encompasses self-control, responsibility, traditionalism, and orderliness (Roberts, Chernyshenko, & Stark, 2005). Grit is distinct from other conscientiousness facets in its emphasis on “long-term stamina rather than short-term intensity” of effort and interest (p. 1089; Duckworth et al., 2007). For example, self-control enables individuals to resist momentary temptations, but grit specifies the “pursuit of superordinate goals of enduring significance.” Accordingly, Duckworth and Gross (2014) have pointed out that “measures of self-control are generally more predictive of everyday measures of adaptive functioning (e.g., grades, physical health). Grit, on the other hand, predicts retention at West Point and performance in the National Spelling Bee” (p.2).

Surprisingly little is known about how contextual influences encourage or discourage grit. Since grit entails sustained effort and goal commitment over time, environments that encourage students to struggle, grow, and learn may support the development of long-term interests and persistence. In contrast, environments that emphasize demonstrating ability, outperforming others, and avoiding mistakes may lead students to abandon interest in and persistence toward long-term goals. To better understand the influence of learning environments on grit, we drew upon achievement goal theory, a model that describes qualitatively different motivations for engaging in academic behavior (Midgley et al., 1998; Elliot & Hulleman, 2017).

Achievement Goal Structure

Early achievement goal theory distinguished mastery versus performance goal orientations (Ames, 1992; Dweck & Leggett, 1988; Midgley et al., 1998). A mastery goal orientation focuses on acquiring and improving new skills for their own sake, whereas a performance goal orientation encourages demonstrating ability in relation to others. When given a choice, students with a mastery goal orientation tend to choose challenging tasks because these tasks provide opportunities to learn and grow. Students with a mastery goal orientation tend to respond adaptively when confronted with obstacles—they demonstrate greater positive affect, greater sustained effort, and better problem-solving strategy formulation. In contrast, students with a performance goal orientation prefer to engage in easier tasks that ensure success. Those with a performance goal orientation tend to attribute failure to low ability and respond maladaptively to obstacles, exhibiting greater negative affect, lower effort, and impaired performance (Ames, 1984; Dweck & Leggett, 1988; Elliot & Dweck, 1988; Maehr, 1989).1

While aforementioned goal orientations describe achievement goals at a personal level, classroom goal structure refers to goal-related messages made salient by the policies, practices, and communication strategies that teachers employ with students (Ames, 1992; Ames & Archer, 1988; Kaplan, Middleton, Urdan, & Midgley, 2002; Maehr & Midgley, 1996). Paralleling the personal achievement goal literature, the classroom goal structure literature distinguishes between mastery and performance goal structure. Teachers in performance-structured classrooms tend to emphasize outperforming others, post students’ grades publicly, or group students based on grades. In contrast, teachers in mastery-structured classrooms tend to emphasize skill development, allow students to retake tests until they understand all materials, and group students based on interests (Meece, Anderman, & Anderman, 2006; Midgley et al., 1998). When students perceive their classrooms to be mastery-structured, they tend to attribute failure to lack of effort, value practice, invest greater effort, and persist at academic tasks (Ames & Archer, 1988; Wentzel, 1997; Wolters, 2004). Conversely, when students perceive their classrooms to be performance-structured, they tend to attribute failure to lack of fixed ability, are more likely to procrastinate, and are less likely to persist (Ames & Archer, 1988; Wolters, 2004).

Schools, too, have distinct cultures. As with classroom goal structure, school-level culture has been associated with students’ motivational and behavioral outcomes (Maehr & Midgley, 1991). For the most part, prior work suggests that schools promoting mastery goals are associated with better student outcomes. When students perceive that their schools have a mastery goal structure, they tend to believe they can master skills so long as they don’t give up (Kaplan & Maehr, 1999). Furthermore, students are more likely to concentrate, exert effort, persist, and have higher academic self-efficacy (Gonida, Voulala, & Kiosseoglou, 2009; Roeser, Midgley, & Urdan, 1996). Interestingly, evidence about the effect of a performance goal in school culture is mixed. One study found that perceiving school as performance-oriented predicted greater levels of negative affect (e.g., boredom, anger, frustration) and more disruptive behaviors (Kaplan & Maehr, 1999), but other studies have found no reliable association between performance goal structure and academic engagement (Gonida et al., 2009) or self-efficacy (Midgley, Anderman, & Hicks, 1995).

Although prior studies have provided initial evidence that perceived school goal structures influence effort and academic achievement (Wang & Degol, 2016), most have been cross-sectional in design (Gonida et al., 2009; Kaplan & Maehr, 1999; Midgley et al., 1995; Roeser et al., 19962). To our knowledge, only one published study has examined associations between perceived school goal structures and changes in student motivation and grades over time. Specifically, Roeser and Eccles (1988) found in a sample of eighth graders that perceived mastery school goal structure covaried positively with academic self-concept, motivation, and report card grades, even when controlling for identical measures from the prior year.

Current Study

In the current investigation, we tested the hypothesis that school goal structures can either help or hinder the tendency to pursue sustained interest in and effort toward long-term goals. To examine this possibility, we followed more than one thousand middle school students across a full academic year. In both the fall and spring, we collected the following data: students’ self-reported perceptions of their school’s goal structures, grit reflected in students’ self-report questionnaires as well as ratings by teachers in four core academic subjects, and report card grades from school records. To analyze our data, we fit a series of path models, each assessing hypothesized relationships between goal structure, grit, and GPA, as well as alternative reverse-mediation models.

Method

Participants

A sample of N= 1,277 eighth graders (Mage = 14.77) and 57 teachers in seven middle schools in Pennsylvania, California, and Texas participated in the study. According to school records, approximately 53% were African American, 28% were Caucasian, 12% were Hispanic, 6% were Asian, and 1% were of other ethnic backgrounds. About 49% were female, and 64% qualified for free or reduced-price meals.

Procedure

Participants were recruited into the study through IRB-approved out-opt consent forms given by school administrators. Students were eligible to participate if their parents consented (i.e., did not opt-out) to the study and if students themselves also assented. About 86% of eligible students consented and participated in the study. Before establishing school partnerships, we made sure that each school site had access to the resources/technology necessary to administer the survey. In the fall (November) and spring (May/June), all participants took the survey on school laptops. We obtained data from school records on report card grades, gender, ethnicity, and free/reduced-priced meal status.

Measures

Grit.

In the fall and spring, students completed five items assessing sustained passion (e.g., “I stuck with a project or activity for more than a few weeks”) and sustained perseverance (e.g., “I tried very hard even though I failed”) using a 5-point Likert scale (1 = never, 5 = always) adapted from Park, Tsukayama, Goodwin, Patrick, and Duckworth (2017). (See Appendix A.) The observed alphas were .74 and .79 in the fall and spring, respectively.

Separately, we asked teachers of four major subject areas—English, social studies, science, and math—to rate each student on grit in the fall and spring. To minimize the tediousness of providing student ratings, we followed the protocol used in Park et al. (2017) and asked teachers to provide a single global rating of grit for each student. Teachers reviewed the same grit items that the students completed about themselves, and were asked to rate each student using a 5-point Likert scale (1 = never, 5 = always). The intraclass correlation coefficients were .83 and .86 in the fall and spring, respectively, and thus we averaged teachers’ ratings for each student at each time point to create a teacher rating of grit for fall and spring respectively for each student.

Following Eid and Diener (2006) and Duckworth and Seligman (2005), we created composite scores from self and teacher ratings to increase validity and reliability. The correlations between student and teacher ratings were r = .29, p < .001 in the fall and r = .26, p < .001 in the spring. This compares favorably to meta-analytic correlations between child self-report and informant ratings (r = .22; Achenbach, McConaughy, & Howell, 1987).3 Following Nunnally (1978),4 we found that the reliability of these composites scores was .78 in the fall and .79 in the spring.

Perceived school goal structures.

In the fall and spring, students completed six items about mastery goal structure and five items measuring performance goal structure in their schools, all drawn from Roeser et al. (1996). The mastery goal structure items examined the extent to which students perceived their school as emphasizing academic improvement and mastery (e.g., “This school was a place where mistakes were okay as long as we were learning”). The performance goal structure items examined the extent to which students perceived school as emphasizing academic performance, competition, and social comparisons (e.g., “This school was a place where only a few kids got praised for their school work”). All items were endorsed on a 5-point Likert scale (1 = never, 5 = always). The observed alphas for mastery goal structure were .78 and .87 in the fall and spring, respectively. The observed alphas for performance goal structure were .83 and .87 in the fall and spring, respectively.

Grade Point Average (GPA).

We collected fall and spring semester report card grades from school records. In order to standardize grading systems across schools, we standardized GPA within each school, then standardized scores across schools (M= 0, SD = 1; see Park et al., 2017 for a similar method).

Demographics.

We collected data on gender, ethnicity, and free or reduced-price lunch status from school records.

Analytic Strategy

Because the middle school students in our study changed classrooms during the day and were rated by multiple teachers, there was no practical way to conduct analyses at the classroom level. Also, our sample only included seven schools—too few to conduct multilevel modeling with schools as a level. Therefore, we dummy coded each school and included these dummy codes (McNeish & Stapleton, 2016) along with socioeconomic status and demographic variables in subsequent analyses.

Previous theoretical and empirical work suggests that achievement goal structures influence academic achievement indirectly through psychologically proximal outcomes (Dupeyrat & Mariné, 2005; Park et al., 2017; Roeser et al., 1996). Following recommendations by Rucker, Preacher, Tormala, and Petty (2011) and Hayes (2009) to test the significance of indirect effects in mediation analyses, we examined the indirect effect of perceived school goal structures on GPA through grit using the procedure. To test our hypotheses, we conducted path analyses in Mplus (Muthén & Muthén, 2017). The average missing rate from all variables included in the current analyses was about 5% of data, and it varied by variables (0%−18.8%); thus, we used full information maximum likelihood (FIML) estimation because it is less biased and more efficient than traditional missing data techniques (Collins, Schafer, & Kam, 2001; Enders & Bandalos, 2001; Little & Rubin, 2014; Peters & Enders, 2002). In all analyses, we controlled for demographics (i.e., gender, ethnicity, free-reduced lunch status, school affiliation).

We fit three main models. Conceptually, all models tested the effect of goal structures on grit and academic achievement, but empirically, models differed in which time points were examined. In Model 1, we examined whether perceived school goal structures predicted students’ grit, which in turn predicted GPA using cross-sectional data in the fall. In Model 2, we examined whether fall perceived goal structures predicted changes (from fall to spring) in grit and GPA. In Model 3, we examined rank-order changes in perceived school goal structures, grit, and GPA, respectively. To provide greater empirical evidence for the directionality of our proposed model, we tested a series of alternative models. In three alternative models, we fit reverse-mediation models corresponding to each of the main models, respectively, to explore the possibility of reciprocal causation.

Results

Table 1 shows descriptive statistics and zero-order correlations for all variables. Overall, absolute levels of grit decreased from the fall (M= 3.60) to spring semester (M= 3.46), χ2(1) = 93.89, p <.001.5 Similarly, absolute levels of perceived mastery goal structure decreased (Mfall = 4.02, Mspring= 3.63; χ2(l) = 212.69, p < .001) and perceived performance goal structure increased (Mfall= 2.46, Mspring = 2.93; χ2(1) = 198.50,p <.001) for the same period. These findings were consistent with previous research, which showed a downward trajectory of academic motivation, engagement, and achievement during adolescence (Blackwell, Trzesniewski, & Dweck, 2007; Midgley & Edelin, 1998; Roeser, Eccles, & Sameroff, 2000).

Table 1.

Means, standard deviations, and correlations among all measures.

Measures M SD 1 2 3 4 5 6 7 8
Fall
1 Grit 3.60 0.68
2 Mastery goal-structure 4.02 0.70 0.26>***
3 Performance goal-structure 2.46 0.98 −0.20*** −0.32***
4 Standardized GPA 0.00 1.00 0.64*** 0.10** −0.11***
Spring
5 Grit 3.46 0.77 0.76*** 0.24*** −0.20*** 0.65***
6 Mastery goal-structure 3.63 0.90 0.22*** 0.49*** −0.25*** 0.11** 0.31***
7 Performance goal-structure 2.93 1.06 −0.20*** −0.27*** 0.50*** −0.12*** −0.24*** −0.44***
8 Standardized GPA 0.00 1.00 0.58*** 0.08* −0.11** 0.78*** 0.68*** 0.12*** −0.15***
Demographics
7 Female 49% 0.14*** 0.01 −0.02 0.20*** 0.14*** −0.03 −0.02 0.20***
8 African American 53% −0.17*** 0.04 0.12*** −0.15*** −0.21*** 0.03 0.12*** −0.14***
9 Caucasian 28% 0.07* −0.05 −0.13*** 0.07* 0.09** −0.08* −0.05 0.06*
10 Hispanic 12% 0.05 0.03 0.03 −0.02 0.08** 0.06* −0.09** 0.01
11 Asian 6% 0.16 <0.01 −0.04 0.20*** 0.16*** 0.01 −0.04 0.16***
12 Multiracial 1% <−0.01 −0.06 0.02 0.01 −0.02 −0.06* 0.03 <0.01
13 Free Reduced Lunch 64% −0.13*** −0.02 0.10** −0.11*** −0.15*** 0.01 0.02 −0.12***
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*

p < .05.

**

p < .01.

***

p < .001.

Main Models

Model 1 was cross-sectional and examined whether fall school goal structure predicted fall grit and fall GPA. By the time we collected fall data, most students in our sample had been in the same middle schools for two years. This suggested that students already might have formed ideas about their schools’ goal structures. Thus, prior to longitudinal models, we examined concurrent relationships of perceived school goal structures, grit, and GPA. Figure 1 represents the cross-sectional model with significant path coefficients. The model fit the data well—χ2(2) = 5.53, p = .06, CFI = .997, SRMR = .004, RMSEA = .037, 90% confidence interval (CI) = [.000, .076]. In the fall, students who perceived their school as mastery goal structured were grittier, β = .22, p < .001, while those who perceived their school as performance-structured were less gritty, β = −.12, p < .001. Grit related positively to GPA, β = .61, p < .001, indicating that students who are high in grit, reported by themselves and teachers, tended to attain higher GPAs. This model explained approximately 17% and 48% of variance in fall grit and fall GPA, respectively. The indirect effects for both mastery (indirect effect = .14, SE = .02, p < .001) and performance goal structures (indirect effect = −.07, SE = .02, p < .001) on GPA through grit were significant.

Figure 1.

Figure 1.

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Standardized path coefficients for path model, demonstrating that fall school goal structures predict level of fall grit, which in turn predicts fall GPA. Gender, ethnicity, school affiliation, and free reduced lunch were included as covariates in the model but are not shown.

*p < .05. **p < .01. ***p < .001.

In Model 2, we examined whether perceived school goal structures in the fall predicted changes in grit, which in turn predicted changes in GPA across the school year (Figure 2).6 As indicated by the fit indices—χ2(2) = 6.55,p =.038, CFI = .998, TLI = .968, RMSEA = .042, 90% CI = [.009, .080], the model fit the data well. Mastery school goal structure predicted grit, β = .05, p = .022, which in turn predicted increases in GPA across the school year, β = .36, p < .001. This model explained approximately 65% and 67% of variance in spring grit and spring GPA, respectively. In contrast, performance goal structure was not a significant predictor of changes in grit, β = −.03, p>. 10, although the relationship was in the predicted negative direction. In sum, the indirect effect on growth in GPA through growth in grit was significant for mastery goal structure (indirect effect = .02, SE = .01, p = .024), but not for performance goal structure (indirect effect = −.01, SE = .01, p > .10).

Figure 2.

Figure 2.

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Standardized path coefficients for path model demonstrating that fall school goal structures predict changes in grit, which in turn predicts changes in GPA. Gender, ethnicity, and school affiliation were included as covariates in the model but not shown. Correlational paths among exogenous variables were included in the model but not shown.

Finally, Model 3 examined whether changes in perceived school goal structures predicted changes in grit, which in turn predicted changes in GPA across the school year (Figure 3). This longitudinal model fit the data well, χ2(10) = 24.26, p < .01, CFI = .995, SRMR =.011, RMSEA = .033, 90% CI = [.017, .051], and produced very similar results to Model 2. Changes in perceptions of school as mastery goal structured predicted changes in grit, β = .15, p < .001, which in turn predicted higher GPA across the school year, β = .36, p < .001. This model explained approximately 66% of spring grit and spring GPA. In other words, although grit decreased across the school year, seeing school as increasingly mastery goal structured buffered against this decline. In contrast, changes in performance goal structure did not reliably predict changes in grit, β = −.01 p> .50. Grit mediated the effect of growth in GPA for mastery goal structure (indirect effect = .054, SE = .009, p < .001), but not for performance goal structure (indirect effect = −.004, SE = .008, p > .50).

Figure 3.

Figure 3.

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Standardized path coefficients for path model demonstrating that changes in school goal structures predict changes in grit, which in turn predicts changes in GPA. Gender, ethnicity, school affiliation, and free reduced lunch, and were included as covariates in the model but are not shown. Correlational paths among exogenous variables were included in the model but not shown.

*p < .05. **p < .01. ***p < .001.

Alternative Models

First, we conducted a reverse mediation analysis of Model 1. This model examined whether high achievement leads students and teachers to report higher grit scores. Specifically, fall GPA served as the mediator and fall grit served as the dependent variable. This alternative model did not fit the data as well as Model 1—χ2(2) = 67.31,p <.001, CFI = .938, SRMR = .021, RMSEA = .160, 90% CI = [.128, .194]. Furthermore, the Bayesian information criterion (BIC) for this alternative model was greater than for Model 1 (13,104 vs. 13,043). BIC differences greater than 10 are considered very strong evidence in favor of the model with the smaller value (Franken, Laceulle, Aken, & Ormel, 2017; Raftery, 1995). Thus, we concluded that our hypothesized model fits the data better than the alternative reverse mediation model.

Next, we tested the reverse mediation of Model 2, allowing spring GPA to serve as the mediator and spring grit as the dependent variable. Thus, fall perceived school goal structures predicted changes in GPA, which in turn predicted changes in grit. The analyses produced worse fit indices—χ2(2) = 14.77, p <.001, CFI = .994, TLI = .909, RMSEA = .071, 90% CI = [.040, .106] compared to Model 2. Furthermore, the Bayesian information criterion (BIC) for this alternative model was larger than the hypothesized model (17,051 vs. 17,043), indicating a better fit in Model 2. Although BIC differences greater than 10 are considered very strong evidence in favor of the model with the smaller values, a difference greater than 2 is still considered positive evidence for a better model (Franken et al., 2017; Raftery, 1995).

Last, to explore the possibility of reciprocal causation for Model 3, we conducted a reverse mediation analysis with spring GPA as the mediator and spring grit as the dependent variable. Thus, changes in perceived school goal structures predicted changes in GPA, which in turn predicted changes in grit. This alternative model produced inferior fit indices—χ2(10) = 74.51,p < .001, CFI = .978, SRMR= .019, RMSEA = .071, 90% CI = [.056, .087], Furthermore, the Bayesian information criterion for this alternative model was bigger than for Model 3 (22,448 vs. 22,397), indicating a better fit for the Model 3.

Discussion

If you lined up a thousand students from the most to the least gritty at the beginning of the year, and then did the same at the end, what would you find? Many students would hold the same position, but others would change places. What determines who gets grittier over time? How are these rank-order changes explained by the school context, and what are the consequences of these rank-order changes for objective measures of academic achievement? We attempted to illuminate these questions in the current investigation.

In both concurrent and longitudinal models, when students perceived their schools as emphasizing mastery, they were more likely to demonstrate greater passion and perseverance for long-term goals, and this in turn predicted earning higher report card grades. One explanation for our findings is that educators implicitly or explicitly signal that they value effort and goal perseverance, which subsequently leads students to adopt these beliefs and exhibit more grit themselves. Another possibility is that supportive and nurturing learning environments, such as those fostered in mastery goal structures, help establish high-quality relationships between teachers and students. This relationship provides a secure base for students to develop their interests and move beyond their comfort zones. Indeed, prior research has shown that effective teaching resembles effective parenting. That is, when teachers exhibit the characteristics of good parents—high expectations, nurturance, and fairness—they encourage positive motivational, social, and cognitive outcomes (Baumrind, 1991; Wentzel, 2002).

In contrast, the effects of performance goal structure were less clear. Cross-sectional analyses of adolescents in our sample indicated that students who perceived their schools as performance-focused were less gritty, but the longitudinal model showed a nonsignificant relationship between changes in performance goal orientation and changes in grit. The latter result mirrors previous studies showing that performance goal structure relates more weakly to student motivational outcomes than mastery goal structure (Ames & Archer, 1988; Urdan & Midgley, 2003). We can only speculate as to why performance goal structure may be less detrimental than mastery goal structure is beneficial. In the current study, we can rule out the possibility of attenuation due to restriction on range, because the observed variance of performance goal structure exceeded that of mastery goal structure. Likewise, it seems unlikely that attenuation due to lack of reliability can explain these asymmetric findings: the observed alpha for our performance goal structure measure exceeded that of mastery goal structure.

One explanation for these asymmetric findings is that a mastery-oriented school culture benefits all students, but the perception of school as performance-focused is only detrimental for some. One previous study found that children who approach learning with a performance goal display helplessness after failure only if they previously experienced low performance (Elliott & Dweck, 1988). In our study, however, we did not find that analyses were moderated by grit or GPA measured in the fall (results available upon request). Another possibility is that we might have uncovered more striking relationships in our sample had we measured two subtypes of performance goal structures: performance-approach and performance-avoidance goal structures. Performance-approach goals involve striving to demonstrate one’s ability, whereas avoidance goals involve striving not to demonstrate one’s incompetence (Elliot, 1999). Unfortunately, we are unaware of any validated scale distinguishing between approach and avoidance aspects of school goal structure. Thus, future measurement research investigating the subtypes of school goal structure (approach vs. avoidance goal structure) may address relatively weaker effects of performance school goal structure on student outcomes.

Limitations and Future Directions

In addition to the aforementioned issues regarding the measurement of subtypes within performance goal structure, the current study’s limitations also suggest promising avenues for future research. First, we did not measure students’ personal goal orientations and cannot rule out the possibility that measured school goal structures were mere proxies for personal achievement goal orientations. However, a previous study has shown that personal and perceived school goal structures are not perfectly correlated (r = .33 for performance goal and r = .48 for mastery goal; Roeser et al., 1996). Also, although prior work has indicated that environmental goal structure may influence student outcomes through personal goal orientations (Anderman & Anderman, 1999; Friedel, Cortina, Turner, & Midgley, 2007; Gonida et al., 2009; Kaplan et al., 2002; Urdan, Midgley, & Anderman, 1998), a growing body of evidence suggests that environmental goal structure can also influence student outcomes independent of personal goal orientation (Anderman & Anderman, 1999; Kaplan, Gheen, & Midgley, 2002; Murayama & Elliot, 2009; Wolters, 2004). Additionally, perceived goal structures predict academic outcomes even after accounting for students’ own goal orientations (Karabenick, 2004). Relatedly, it may be that measured grit in our data is simply a proxy for personal achievement goal orientation. Against this explanation, prior studies (Akin & Arslan, 2014; Muenks et al., 2017) have indicated a small to moderate correlation between personal goal orientations and both components of grit, |rs| = .03 to .55, and that perseverance predicts student grades after accounting for personal achievement goals (Muenks et al., 2017). Nevertheless, we encourage future research to measure all three constructs in one study, not only to establish their independent effects but also to explore potential interactions.

Another limitation is that we did not assess teachers’ or expert observers’ perceptions of school goal structure. Converging evidence employing these alternative measures of school goal structure would provide a more definitive test of the influence of school culture on student grit. Consistent with our current findings, our prediction is that school goal structure assessed by teachers and observers would also influence student perceptions of school goal structure, which in turn influence grit and academic achievement.

Next, though data in our investigation were hierarchical (i.e., students were nested in classrooms that were nested in schools), we were not able to conduct hierarchical linear modeling (HLM) to account for between-classroom variance, because students changed classrooms throughout the day. Likewise, because there were only seven schools in our sample, we were not able to use HLM to examine between-school variance. A much larger sample of schools would allow for modeling random-school effects.

Finally, we do not want to overstate the role of grit in determining report card grades. Related competencies, including self-control and conscientiousness, can have an even stronger influence on academic achievement in middle school (Duckworth & Gross, 2014; Duckworth & Seligman, 2017; Eskreis-Winkler et al., 2016; Muenks et al., 2017; Vedel & Poropat, 2017). Future research on goal structure would benefit from including a wider array of individual differences. Likewise, studies employing more comprehensive measures of grit would provide a conceptual replication and extension of the current investigation. To limit survey length, we did not administer measures of grit with separate subscales (Duckworth & Quinn, 2009) assessing passion and perseverance. Thus, additional research is needed to examine how goal structures may be differentially related to the facets of passion and perseverance.

How might schools convey that it is learning, rather than performing, that matters most? Ames (1992) suggested providing diverse tasks, involving students in decision-making processes, praising effort with detailed explanations, grouping students based on their interests, providing self-referenced standards, and being flexible with time allocated to learning tasks. Of course, cues outside the classroom also matter. For example, school-wide awards for achievement may encourage performance orientation in students, whereas awards for growth and sustained pursuit of intrinsic interests may foster mastery orientation. Likewise, a recent review article (Haimovitz & Dweck, 2017) suggests that creating school-wide cultural norms that celebrate struggle, mistakes, and shared responsibility for learning may help children pursue deeper learning and growth.

In sum, the current findings suggest the importance of school cultures that emphasize personal growth and effort. An environment that facilitates learning and mastery may lay the foundation for students to develop their passion and perseverance for their future goals, which in turn will help them to flourish in school and beyond.

Highlights.

  • Little is known about how school cultures help or hinder grit, the pursuit of long-term goals with passion and perseverance.

  • In a large sample of adolescents, perceiving the school culture to be mastery-structured predicted rank-order increases in grit and, in turn, rank-order increases in report card grades.

  • The effect of performance-structured aspects of school culture on grit and grades were less clear.

Acknowledgement

This research was made possible by the Templeton Foundation and the National Institute on Aging (R24-AG048081–01). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Appendix A

Grit Scale

Response scale (5): Never — Rarely — Sometimes — Often — Always

  1. I finished whatever I started.

  2. I stuck with a project or activity for more than a few weeks.

  3. I tried very hard even though I failed.

  4. I stayed committed to my goals, even if they took a long time to complete.

  5. I kept working hard even if I felt like quitting.

Appendix B

Perceived School Goal structures

Response scale (5): Never—Rarely—Sometimes—Often—Always

Mastery Goal structure

During the past month, this school was a place where…

  1. teachers believed all students could learn.

  2. understanding the work was more important than getting the right answers.

  3. mistakes are okay as long as we were learning.

  4. teachers thought how much you learned was more important than test scores or grades.

  5. teachers wanted students to really understand their work, not just memorize it.

  6. trying hard counted a lot in this school.

Performance Goal structure

During the past month, this school was a place where…

  1. teachers treated kids who got good grades better than other kids.

  2. only a few kids got praised for their school work.

  3. teachers only cared about the smart kids.

  4. adults had given up on some of its students.

  5. special privileges were given to students who got the highest grades.

Footnotes

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1

Later, this dichotomous model was extended to a trichotomous model by differentiating performance goal orientation further into performance-approach and performance-avoidance (Elliot & Church, 1997; Elliot & Harackiewicz, 1996): (a) mastery goal, an orientation to develop skills and master tasks; (b) performance-approach goal, an orientation to demonstrate competence by outperforming others; and (c) performance-avoidance goal, an orientation to avoid underperforming others. Empirical evidence has consistently suggested that mastery goal is related to positive outcomes. Performance-avoidance goal has mostly been related to negative outcomes, such as procrastination, worry, help-avoidance, low intrinsic motivation, and poor performance, while performance-approach goal has been associated with some positive outcomes, such as effort, persistence, and high performance, and some negative outcomes, such as help-avoidance and negative affect (for reviews, see Midgely, Kaplan, & Middlton, 2001; Elliot & Hulleman, 2017).

2

In Roeser et al. (1996), although students’ prior achievement goal and achievement were included as covariates, covariates for a primary independent variable (i.e., school goal structure) and a dependent variable (i.e., psychological outcome) were not included in the models.

3

We used different raters (i.e., teachers paired with the children themselves) to assess grit primarily because a multisource measurement approach increases reliability and validity, with the multiple sources contributing complementary information about the behavior or trait of interest (Roberts, Walton, & Viechtbauer, 2006; Duckworth & Yeager, 2015; Park et al., 2017; Tsukayama, Duckworth, & Kim, 2013). We ran a confirmatory factor analysis (CFA) to validate the constructs of grit and goal structures. For grit, we ran a second-order CFA specifying teacher- and student-rated grit as separate sub-constructs. The CFA fit the data well, χ2(492) = f 093.305, p< . 001, CF1 = .959, RMSEA = 0.031, 95% Cl = [0.029, 0.034], The second-order grit factor loadings ranged from .37 to .76 (avg. = .56), the first-order grit factor loadings ranged from .42 to .74 (avg. = .63), and the first-order goal structure factor loadings ranged from .48 to .85 (average = .70); all factor loadings were highly significant (all ps < .001). We also ran separate models with student- and teacher-rated scales. In the student-rated grit model, results were replicated (i.e., mastery goal structure predicted changes in grit), β = .21 ,P< .01, and in the teacher-rated grit model, the mastery-grit path was marginally significant but in the predicted direction, β = .03, p < .10.

4

We used the following formula for the reliability of linear combinations from Nunnally (1978): ∑σi2−∑riiσi2σy2, where σi2 is the variance of variable i, σy2· is the variance of the linear combination, and rii is the reliability of variable i.

5

This test is conceptually equivalent to a paired-sample t-test for mean differences. Because we used structural equation modeling in order to use full information maximum likelihood (FIML) to account for missing data, the test statistic is a Wald test (in a chi-square scale) rather than a t-test.

6

Controlling for prior levels of a variable enables examination of changes. Because levels at time 1 are statistically held constant, differences at time 2 are differences in change (Fleeson, 2007). For a more mathematical explanation, see Kessler and Greenberg (1981).

Contributor Information

Daeun Park, Chungbuk National University.

Alisa Yu, Stanford University.

Rebecca N. Baelen, University of Pennsylvania

Eli Tsukayama, University of Hawaii – West Oahu.

Angela L. Duckworth, University of Pennsylvania

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