research paper for personal growth

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• Published: 07 August 2019

A national experiment reveals where a growth mindset improves achievement

• David S. Yeager 1 ,
• Paul Hanselman 2 ,
• Gregory M. Walton 3 ,
• Jared S. Murray 1 ,
• Robert Crosnoe 1 ,
• Chandra Muller 1 ,
• Elizabeth Tipton 4 ,
• Barbara Schneider 5 ,
• Chris S. Hulleman 6 ,
• Cintia P. Hinojosa 7 ,
• David Paunesku 8 ,
• Carissa Romero 9 ,
• Kate Flint 10 ,
• Alice Roberts 10 ,
• Jill Trott 10 ,
• Ronaldo Iachan 10 ,
• Jenny Buontempo 1 ,
• Sophia Man Yang 1 ,
• Carlos M. Carvalho 1 ,
• P. Richard Hahn 11 ,
• Maithreyi Gopalan 12 ,
• Pratik Mhatre 1 ,
• Ronald Ferguson 13 ,
• Angela L. Duckworth 14 &
• Carol S. Dweck 3  

Nature volume  573 ,  pages 364–369 ( 2019 ) Cite this article

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A global priority for the behavioural sciences is to develop cost-effective, scalable interventions that could improve the academic outcomes of adolescents at a population level, but no such interventions have so far been evaluated in a population-generalizable sample. Here we show that a short (less than one hour), online growth mindset intervention—which teaches that intellectual abilities can be developed—improved grades among lower-achieving students and increased overall enrolment to advanced mathematics courses in a nationally representative sample of students in secondary education in the United States. Notably, the study identified school contexts that sustained the effects of the growth mindset intervention: the intervention changed grades when peer norms aligned with the messages of the intervention. Confidence in the conclusions of this study comes from independent data collection and processing, pre-registration of analyses, and corroboration of results by a blinded Bayesian analysis.

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About 20% of students in the United States will not finish high school on time 1 . These students are at a high risk of poverty, poor health and early mortality in the current global economy 2 , 3 , 4 . Indeed, a Lancet commission concluded that improving secondary education outcomes for adolescents “presents the single best investment for health and wellbeing” 5 .

The transition to secondary school represents an important period of flexibility in the educational trajectories of adolescents 6 . In the United States, the grades of students tend to decrease during the transition to the ninth grade (age 14–15 years, UK year 10), and often do not recover 7 . When such students underperform in or opt out of rigorous coursework, they are far less likely to leave secondary school prepared for college or university or for advanced courses in college or university 8 , 9 . In this way, early problems in the transition to secondary school can compound over time into large differences in human capital in adulthood.

One way to improve academic success across the transition to secondary school is through social–psychological interventions, which change how adolescents think or feel about themselves and their schoolwork and thereby encourage students to take advantage of learning opportunities in school 10 , 11 . The specific intervention evaluated here—a growth mindset of intelligence intervention—addresses the beliefs of adolescents about the nature of intelligence, leading students to see intellectual abilities not as fixed but as capable of growth in response to dedicated effort, trying new strategies and seeking help when appropriate 12 , 13 , 14 , 15 , 16 . This can be especially important in a society that conveys a fixed mindset (a view that intelligence is fixed), which can imply that feeling challenged and having to put in effort means that one is not naturally talented and is unlikely to succeed 12 .

The growth mindset intervention communicates a memorable metaphor: that the brain is like a muscle that grows stronger and smarter when it undergoes rigorous learning experiences 14 . Adolescents hear the metaphor in the context of the neuroscience of learning, they reflect on ways to strengthen their brains through schoolwork, and they internalize the message by teaching it to a future first-year ninth grade student who is struggling at the start of the year. The intervention can lead to sustained academic improvement through self-reinforcing cycles of motivation and learning-oriented behaviour. For example, a growth mindset can motivate students to take on more rigorous learning experiences and to persist when encountering difficulties. Their behaviour may then be reinforced by the school context, such as more positive and learning-oriented responses from peers or instructors 10 , 17 .

Initial intervention studies with adolescents taught a growth mindset in multi-session (for example, eight classroom sessions 15 ), interactive workshops delivered by highly trained adults; however, these were not readily scalable. Subsequent growth mindset interventions were briefer and self-administered online, although lower effect sizes were, of course, expected. Nonetheless, previous randomized evaluations, including a pre-registered replication, found that online growth mindset interventions improved grades for the targeted group of students in secondary education who previously showed lower achievement 13 , 16 , 18 . These findings are important because previously low-achieving students are the group that shows the steepest decline in grades during the transition to secondary school 19 , and these findings are consistent with theory because a growth mindset should be most beneficial for students confronting challenges 20 .

Here we report the results of the National Study of Learning Mindsets, which examined the effects of a short, online growth mindset intervention in a nationally representative sample of high schools in the United States (Fig. 1 ). With this unique dataset we tested the hypotheses that the intervention would improve grades among lower-achieving students and overall uptake of advanced courses in this national sample.

Between August and November 2015, 82% of schools delivered the intervention; the remaining 18% delivered the intervention in January or February of 2016. Asterisk indicates that the median number of days between sessions 1 and 2 among schools implementing the intervention in the autumn was 21 days; for spring-implementing schools it was 27 days. The coin-tossing symbol indicates that random assignment was made during session 1. The tick symbol indicates that a comprehensive analysis plan was pre-registered at https://osf.io/tn6g4 . The blind-eye symbol indicates that, first, teachers and researchers were kept blinded to students’ random assignment to condition, and, second, the Bayesian, machine-learning robustness tests were conducted by analysts who at the time were blinded to study hypotheses and to the identities of the variables.

A focus on heterogeneity

The study was also designed with the purpose of understanding for whom and under what conditions the growth mindset intervention improves grades. That is, it examined potential sources of cross-site treatment effect heterogeneity. One reason why understanding heterogeneity of effects is important is because most interventions that are effective in initial efficacy trials go on to show weaker or no effects when they are scaled up in effectiveness trials that deliver treatments under everyday conditions to more heterogeneous samples 21 , 22 , 23 . Without clear evidence about why average effect sizes differ in later-conducted studies—evidence that could be acquired from a systematic investigation of effect heterogeneity—researchers may prematurely discard interventions that yield low average effects but could provide meaningful and replicable benefits at scale for targeted groups 21 , 23 .

Further, analyses of treatment effect heterogeneity can reveal critical evidence about contextual mechanisms that sustain intervention effects. If school contexts differ in the availability of the resources or experiences needed to sustain the offered belief change and enhanced motivation following an intervention, then the effects of the intervention should differ across these school contexts as well 10 , 11 .

Sociological theory highlights two broad dimensions of school contexts that might sustain or impede belief change and enhanced motivation among students treated by a growth mindset intervention 6 . First, schools with the least ‘formal’ resources, such as high-quality curricula and instruction, may not offer the learning opportunities for students to be able to capitalize on the intervention, while those with the most resources may not need the intervention. Second, some schools may not have the ‘informal’ resources needed to sustain the intervention effect, such as peer norms that support students when they take on challenges and persist in the face of intellectual difficulty. We hypothesized that both of these dimensions would significantly moderate growth mindset intervention effects.

Historically, the scientific methods used to answer questions about the heterogeneity of intervention effects have been underdeveloped and underused 21 , 24 , 25 . Common problems in the literature are: (1) imprecise site-level impact estimates (because of cluster-level random assignment); (2) inconsistent fidelity to intervention protocols across sites (which can obscure the workings of the cross-site moderators of interest); (3) non-representative sampling of sites (which causes site selection bias 22 , 26 ); and (4) multiple post hoc tests for the sources of treatment effect size heterogeneity (which increases the probability of false discoveries 24 ).

We overcame all of these problems in a single study. We randomized students to condition within schools and consistently had high fidelity of implementation across sites (see  Supplementary Information section 5 ). We addressed site selection bias by contracting a professional research company, which recruited a sample of schools that generalized to the entire population of ninth-grade students attending regular US public schools 27 (that is, schools that run on government funds; see  Supplementary Information section 3 ). Next, the study used analysis methods that avoided false conclusions about subgroup effects, by generating a limited number of moderation hypotheses (two), pre-registering a limited number of statistical tests and conducting a blinded Bayesian analysis that can provide rigorous confirmation of the results (Fig. 1 ).

Expected effect sizes

In this kind of study, it is important to ask what size of effect would be meaningful. As a leading educational economist concluded, “in real-world settings, a fifth of a standard deviation [0.20 s.d.] is a large effect” 28 . This statement is justified by the ‘best evidence synthesis’ movement 29 , which recommends the use of empirical benchmarks, not from laboratory studies, but from the highest-quality field research on factors affecting objective educational outcomes 30 , 31 . A standardized mean difference effect size of 0.20 s.d. is considered ‘large’ because it is: (1) roughly how much improvement results from a year of classroom learning for ninth-grade students, as shown by standardized tests 30 ; (2) at the high end of estimates for the effect of having a very high-quality teacher (versus an average teacher) for one year 32 ; and (3) at the uppermost end of empirical distributions of real-world effect sizes from diverse randomized trials that target adolescents 31 . Notably, the highly-cited ‘nudges’ studied by behavioural economists and others, when aimed at influencing real-world outcomes that unfold over time (such as college enrolment or energy conservation 33 ) rather than one-time choices, rarely, if ever, exceed 0.20 s.d. and typically have much smaller effect sizes.

Returning to educational benchmarks, 0.20 s.d. and 0.23 s.d. were the two largest effects observed in a recent cohort analysis of the results of all of the pre-registered, randomized trials that evaluated promising interventions for secondary schools funded as part of the US federal government’s i3 initiative 34 (the median effect for these promising interventions was 0.03 s.d.; see  Supplementary Information section 11 ). The interventions in the i3 initiative typically targeted lower-achieving students or schools, involved training teachers or changing curricula, consumed considerable classroom time, and cost several thousand US dollars per student. Moreover, they were all conducted in non-representative samples of convenience that can overestimate effects. Therefore, it would be noteworthy if a short, low-cost, scalable growth mindset intervention, conducted in a nationally representative sample, could achieve a meaningful proportion of the largest effects seen for past traditional interventions, within the targeted, pre-registered group of lower-achieving students.

Defining the primary outcome and student subgroup

The primary outcome was the post-intervention grade point average (GPA) in core ninth-grade classes (mathematics, science, English or language arts, and social studies), obtained from administrative data sources of the schools (as described in the pre-analysis plan found in the  Supplementary Information section 13 and at https://osf.io 35 ). Following the pre-registered analysis plan, we report results for the targeted group of n  = 6,320 students who were lower-achieving relative to peers in the same school. This group is typically targeted by comprehensive programmes evaluated in randomized trials in education, as there is an urgent need to improve their educational trajectories. The justification for predicting effects in the lower-achieving group is that (1) this group benefitted in previous growth mindset trials; (2) lower-achieving students may be undergoing more academic difficulties and therefore may benefit more from a growth mindset that alters the interpretation of these difficulties; and (3) students who already have a high GPA may have less room to improve their GPAs. We defined students as relatively lower-achieving if they were earning GPAs at or below the school-specific median in the term before random assignment or, if they were missing prior GPA data, if they were below the school-specific median on academic variables used to impute prior GPA (as described in the analysis plan). Supplementary analyses for the sample overall can be found in Extended Data Table 1 , and robustness analyses for the definition of lower-achieving students are included in Extended Data Fig. 1 ( Supplementary Information section 7 ).

Average effects on mindset

Among lower-achieving adolescents, the growth mindset intervention reduced the prevalence of fixed mindset beliefs relative to the control condition, reported at the end of the second treatment session, unstandardized B  = −0.38 (95% confidence interval = −0.31, −0.46), standard error of the regression coefficient (s.e.) = 0.04, n  = 5,650 students, k  = 65 schools, t  = −10.14, P  < 0.001, standardized mean difference effect size of 0.33.

Average effects on core course GPAs

In line with our first major prediction, lower-achieving adolescents earned higher GPAs in core classes at the end of the ninth grade when assigned to the growth mindset intervention, B =  0.10 grade points (95% confidence interval = 0.04, 0.16), s.e. = 0.03, n  = 6,320, k  = 65, t  = 3.51, P  = 0.001, standardized mean difference effect size of 0.11, relative to comparable students in the control condition. This conclusion is robust to alternative model specifications that deviate from the pre-registered model (Extended Data Fig. 1 ).

To map the growth mindset intervention effect onto a policy-relevant indicator of high school success, we analysed poor performance rates, defined as the percentage of adolescents who earned a GPA below 2.0 on a four-point scale (that is, a ‘D’ or an ‘F’; as described in the pre-analysis plan). Poor performance rates are relevant because recent changes in US federal laws (the Every Student Succeeds Act 36 ), have led many states to adopt reductions in the poor performance rates in the ninth grade as a key metric for school accountability. More than three million ninth-grade students attend regular US public schools each year, and half are lower-achieving according to our definition. The model estimates that 5.3% (95% confidence interval = −1.7, −9.0), s.e. = 1.8, t  = 2.95, P  = 0.005 of 1.5 million students in the United States per year would be prevented from being ‘off track’ for graduation by the brief and low-cost growth mindset intervention, representing a reduction from 46% to 41%, which is a relative risk reduction of 11% (that is, 0.05/0.46).

Average effects on mathematics and science GPAs

A secondary analysis focused on the outcome of GPAs in only mathematics and science (as described in the analysis plan). Mathematics and science are relevant because a popular belief in the United States links mathematics and science learning to ‘raw’ or ‘innate’ abilities 37 —a view that the growth mindset intervention seeks to correct. In addition, success in mathematics and science strongly predicts long-term economic welfare and well-being 38 . Analyses of outcomes for mathematics and science supported the same conclusions ( B  = 0.10 for mathematics and science GPAs compared to B  = 0.10 for core GPAs; Extended Data Tables 1 – 3 ).

Quantifying heterogeneity

The intervention was expected to homogeneously change the mindsets of students across schools—as this would indicate high fidelity of implementation—however, it was expected to heterogeneously change lower-achieving students’ GPAs, as this would indicate potential school differences in the contextual mechanisms that sustain an initial treatment effect. As predicted, a mixed-effects model found no significant variability in the treatment effect on self-reported mindsets across schools (unstandardized \(\hat{\tau }=0.08\) , Q 64  = 57.2, P  = 0.714), whereas significant variability was found in the effect on GPAs among lower-achieving students across schools (unstandardized \(\hat{\tau }=0.09\) , Q 64  = 85.5, P  = 0.038) 39 (Extended Data Fig. 2 ).

Moderation by school achievement level

First, we tested competing hypotheses about whether the formal resources of the school explained the heterogeneity of effects. Before analysing the data, we expected that in schools that are unable to provide high-quality learning opportunities (the lowest-achieving schools), treated students might not sustain a desire to learn. But we also expected that other schools (the highest-achieving schools) might have such ample resources to prevent failure such that a growth mindset intervention would not add much.

The heterogeneity analyses found support for the latter expectation, but not the former. Treatment effects on ninth-grade GPAs among lower-achieving students were smaller in schools with higher achievement levels, intervention × school achievement level (continuous) interaction, unstandardized B  = −0.07 (95% confidence interval = 0.02, 0.13), s.e. = 0.03, z  = −2.76, n  = 6,320, k  = 65, P  = 0.006, standardized β  = −0.25. In follow-up analyses with categorical indicators for school achievement, medium-achieving schools (middle 50%) showed larger effects than higher-achieving schools (top 25%). Low-achieving schools (bottom 25%) did not significantly differ from medium-achieving schools (Extended Data Table 2 ); however, this non-significant difference should be interpreted cautiously, owing to wide confidence intervals for the subgroup of lowest-achieving schools.

Moderation by peer norms

Second, we examined whether students might be discouraged from acting on their enhanced growth mindset when they attend schools in which peer norms were unsupportive of challenge-seeking, whereas peer norms that support challenge-seeking might function to sustain the effects of the intervention over time. We measured peer norms by administering a behavioural challenge-seeking task (the ‘make-a-math-worksheet’ task) at the end of the second intervention session (Fig. 1 ) and aggregating the values of the control group to the school level.

The pre-registered mixed-effects model yielded a positive and significant intervention × behavioural challenge-seeking norms interaction for GPA among the targeted group of lower-achieving adolescents, such that the intervention produced a greater difference in end-of-year GPAs relative to the control group when the behavioural norm that surrounded students was supportive of the growth mindset belief system, B  = 0.11 (95% confidence interval = 0.01, 0.21), s.e. = 0.05, z  = 2.18, n  = 6,320, k  = 65, P  = 0.029, β  = 0.23. The same conclusion was supported in a secondary analysis of only mathematics and science GPAs (Extended Data Table 2 ).

Subgroup effect sizes

Putting together the two pre-registered moderators (school achievement level and school norms), the conditional average treatment effect (CATEs) on core GPAs within low- and medium-achieving schools (combined) was 0.14 grade points when the school was in the third quartile of behavioural norms and 0.18 grade points when the school was in the fourth and highest quartile of behavioural norms, as shown in Fig. 2 . For mathematics and science grades, the CATEs ranged from 0.16 to 0.25 grade points in the same subgroups of low- and medium-achieving schools with more supportive behavioural norms (for results separating low- and medium-achieving schools, see Fig. 2c, d and Extended Data Table 3 ). We also found that even the high-achieving schools showed meaningful treatment effects among their lower achievers on mathematics and science GPAs when they had norms that supported challenge seeking—0.08 and 0.11 grade points for the third and fourth quartiles of school norms, respectively, in the high-achieving schools ( P  = 0.002; Extended Data Table 3 ).

a , c , Treatment effects on core course grade point averages (GPAs). b , d , Treatment effects on GPAs of only mathematics and science. a , b , The CATEs represent the estimated subgroup treatment effects from the pre-registered linear mixed-effects model, with survey weights, when fixing the racial/ethnic composition of the schools to the population median to remove any potential confounding effect of that variable on moderation hypothesis tests. Achievement levels: low, 25th percentile or lower; middle, 25th–75th percentile; high, 75th percentile or higher, which follows the categories set in the sampling plan and in the pre-registration. Norms indicate the behavioural challenge-seeking norms, as measured by the responses of the control group to the make-a-math-worksheet task after session 2. c , d , Box plots represent unconditional treatment effects (one for each school) estimated in the pre-registered linear mixed-effects regression model with no school-level moderators, as specified for research question 3 in the pre-analysis plan and described in the  Supplementary Information section 7.4 . The distribution of the school-level treatment effects was re-scaled to the cross-site standard deviation, in accordance with standard practice. Dark lines correspond to the median school in a subgroup and the boxes correspond to the middle 75% of the distribution (the interquartile range). Supportive schools are defined as above the population median (third and fourth quartiles); unsupportive schools are defined as those below the population median (first and second quartiles). n  = 6,320 students in k  = 65 schools.

Source data

Bayesian robustness analysis.

A team of statisticians, at the time blind to study hypotheses, re-analysed the dataset using a conservative Bayesian machine-learning algorithm, called Bayesian causal forest (BCF). BCF has been shown by both its creators and other leading statisticians in open head-to-head competitions to be the most effective of the state-of-the-art methods for identifying systematic sources of treatment effect heterogeneity, while avoiding false positives 40 , 41 .

The BCF analysis assigned a near-certain posterior probability that the population-average treatment effect (PATE) among lower-achieving students was positive and greater than zero, P PATE > 0  ≥ 0.999, providing strong evidence of positive average treatment effects. BCF also found stronger CATEs in schools with positive challenge-seeking norms, and weaker effects in the highest-achieving schools (Extended Data Fig. 3 and Supplementary Information section 8 ), providing strong correspondence with the primary analyses.

Advanced mathematics course enrolment in tenth grade

The intervention showed weaker benefits on ninth-grade GPAs in high-achieving schools. However, students in these schools may benefit in other ways. An analysis of enrolment in rigorous mathematics courses in the year after the intervention examined this possibility. The enrolment data were gathered with these analyses in mind but since the analyses were not pre-registered, they are exploratory.

Course enrolment decisions are potentially relevant to all students, both lower- and higher-achieving, so we explored them in the full cohort. We found that the growth mindset intervention increased the likelihood of students taking advanced mathematics (algebra II or higher) in tenth grade by 3 percentage points (95% confidence interval = 0.01, 0.04), s.e. = 0.01, n  = 6,690, k  = 41, t  = 3.18, P  = 0.001, from a rate of 33% in the control condition to a rate of 36% in the intervention condition, corresponding to a 9% relative increase. Notably, we discovered a positive intervention × school achievement level (continuous) interaction, ( B  = 0.04 (95% confidence interval = 0.00, 0.08), s.e. = 0.02, z  = 2.26, P  = 0.024, the opposite of what we found for core course GPAs. Within the highest-achieving 25% of schools, the intervention increased the rate at which students took advanced mathematics in tenth grade by 4 percentage points ( t  = 2.37, P  = 0.018). In the lower 75% of schools—where we found stronger effects on GPA—the increase in the rate at which students took advanced mathematics courses was smaller: 2 percentage points ( t  = 2.00, P  = 0.045). Thus an exclusive focus on GPA would have obscured intervention benefits among students attending higher-achieving schools.

The National Study of Learning Mindsets showed that a low-cost treatment, delivered in less than an hour, attained a substantial proportion of the effects on grades of the most effective rigorously evaluated adolescent interventions of any cost or duration in the literature within the pre-registered group of lower-achieving students. Moreover, the intervention produced gains in the consequential outcome of advanced mathematics course-taking for students overall, which is meaningful because the rigor of mathematics courses taken in high school strongly predicts later educational attainment 8 , 9 , and educational attainment is one of the leading predictors of longevity and health 38 , 42 . The finding that the growth mindset intervention could redirect critical academic outcomes to such an extent—with no training of teachers; in an effectiveness trial conducted in a population-generalizable sample; with data collected by an independent research company using repeatable procedures; with data processed by a second independent research company; and while adhering to a comprehensive pre-registered analysis plan—is a major advance.

Furthermore, the evidence about the kinds of schools where the growth mindset treatment effect on grades was sustained, and where it was not, has important implications for future interventions. We might have expected that the intervention would compensate for unsupportive school norms, and that students who already had supportive peer norms would not need the intervention as much. Instead, it was when the peer norm supported the adoption of intellectual challenges that the intervention promoted sustained benefits in the form of higher grades.

Perhaps students in unsupportive peer climates risked paying a social price for taking on intellectual challenges in front of peers who thought it undesirable to do so. Sustained change may therefore require both a high-quality seed (an adaptive belief system conveyed by a compelling intervention) and conductive soil in which that seed can grow (a context congruent with the proffered belief system). A limitation of our moderation results, of course, is that we cannot draw causal conclusions about the effects of the school norm, as the norms were measured, not manipulated. It is encouraging that a Bayesian analysis, reported in the Supplementary Information section 8, yielded evidence consistent with a causal interpretation of the school norms variable. The present research therefore sets the stage for a new era of experimental research that seeks to enhance both students’ mindsets and the school environments that support student learning.

We emphasize that not all forms of growth mindset interventions can be expected to increase grades or advanced course-taking, even in the targeted subgroups 11 , 12 . New growth mindset interventions that go beyond the module and population tested here will need to be subjected to rigorous development and validation processes, as the current programme was 13 .

Finally, this study offers lessons for the science of adolescent behaviour change. Beliefs—and particularly beliefs that affect how students make sense of ongoing challenges—are important during high-stakes developmental turning points such as pubertal maturation 43 , 44 or the transition to secondary school 6 . Indeed, new interventions in the future should address the interpretation of other challenges that adolescents experience, including social and interpersonal difficulties, to affect outcomes (such as depression) that thus far have proven difficult to address 43 . And the combined importance of belief change and school environments in our study underscores the need for interdisciplinary research to understand the numerous influences on adolescents’ developmental trajectories.

Ethics approval

Approval for this study was obtained from the Institutional Review Board at Stanford University (30387), ICF (FWA00000845), and the University of Texas at Austin (#2016-03-0042). In most schools this experiment was conducted as a programme evaluation carried out at the request of the participating school district 45 . When required by school districts, parents were informed of the programme evaluation in advance and given the opportunity to withdraw their children from the study. Informed student assent was obtained from all participants.

Participants

Data came from the National Study of Learning Mindsets 45 , which is a stratified random sample of 65 regular public schools in the United States that included 12,490 ninth-grade adolescents who were individually randomized to condition. The number of schools invited to participate was determined by a power analysis to detect reasonable estimates of cross-site heterogeneity; as many of the invited schools as possible were recruited into the study. Grades were obtained from the schools of the students, and analyses focused on the lower-achieving subgroup of students (those below the within-school median). The sample reflected the diversity of young people in the United States: 11% self-reported being black/African-American, 4% Asian-American, 24% Latino/Latina, 43% white and 18% another race or ethnicity; 29% reported that their mother had a bachelor’s degree or higher. To prevent deductive disclosure for potentially-small subgroups of students, and consistent with best practices for other public-use datasets, the policies for the National Study of Learning Mindsets require analysts to round all sample sizes to the nearest 10, so this was done here.

Data collection

To ensure that the study procedures were repeatable by third parties and therefore scalable, and to increase the independence of the results, two different professional research companies, who were not involved in developing the materials or study hypotheses, were contracted. One company (ICF) drew the sample, recruited schools, arranged for treatment delivery, supervised and implemented the data collection protocol, obtained administrative data, and cleaned and merged data. They did this work blind to the treatment conditions of the students. This company worked in concert with a technology vendor (PERTS), which delivered the intervention, executed random assignment, tracked student response rates, scheduled make-up sessions and kept all parties blind to condition assignment. A second professional research company (MDRC) processed the data merged by ICF and produced an analytic grades file, blind to the consequences of their decisions for the estimated treatment effects, as described in  Supplementary Information section 12 . Those data were shared with the authors of this paper, who analysed the data following a pre-registered analysis plan (see  Supplementary Information section 13 ; MDRC will later produce its own independent report using its processed data, and retained the right to deviate from our pre-analysis plan).

Selection of schools was stratified by school achievement and minority composition. A simple random sample would not have yielded sufficient numbers of rare types of schools, such as high-minority schools with medium or high levels of achievement. This was because school achievement level—one of the two candidate moderators—was strongly associated with school racial/ethnic composition 46 (percentage of Black/African-American or Hispanic/Latino/Latina students, r  = −0.66).

A total of 139 schools were selected without replacement from a sampling frame of roughly 12,000 regular US public high schools, which serve the vast majority of students in the United States. Regular US public schools exclude charter or private schools, schools serving speciality populations such as students with physical disabilities, alternative schools, schools that have fewer than 25 ninth-grade students enrolled and schools in which ninth grade is not the lowest grade in the school.

Of the 139 schools, 65 schools agreed, participated and provided student records. Another 11 schools agreed and participated but did not provide student grades or course-taking records; therefore, the data of their students are not analysed here. School nonresponse did not appear to compromise representativeness. We calculated the Tipton generalizability index 47 , a measure of similarity between an analytic sample and the overall sampling frame, along eight student demographic and school achievement benchmarks obtained from official government sources 27 . The index ranges from 0 to 1, with a value of 0.90 corresponding to essentially a random sample. The National Study of Learning Mindsets showed a Tipton generalizability index of 0.98, which is very high (see  Supplementary Information section 3 ).

Within schools, the average student response rate for eligible students was 92% and the median school had a response rate of 98% (see definitions in  Supplementary Information section 5 ). This response rate was obtained by extensive efforts to recruit students into make-up sessions if students were absent and it was aided by a software system, developed by the technology vendor (PERTS), that kept track of student participation. A high within-school response rate was important because lower-achieving students, our target group, are typically more likely to be absent.

Growth mindset intervention content

In preparing the intervention to be scalable, we revised past growth mindset interventions to focus on the perspectives, concerns and reading levels of ninth-grade students in the United States, through an intensive research and development process that involved interviews, focus groups and randomized pilot experiments with thousands of adolescents 13 .

The control condition, focusing on brain functions, was similar to the growth mindset intervention, but did not address beliefs about intelligence. Screenshots from both interventions can be found in  Supplementary Information section 4 , and a detailed description of the general intervention content has previously been published 13 . The intervention consisted of two self-administered online sessions that lasted approximately 25 min each and occurred roughly 20 days apart during regular school hours (Fig. 1 ).

The growth mindset intervention aimed to reduce the negative effort beliefs of students (the belief that having to try hard or ask for help means you lack ability), fixed-trait attributions (the attribution that failure stems from low ability) and performance avoidance goals (the goal of never looking stupid). These are the documented mediators of the negative effect of a fixed mindset on grades 12 , 15 , 48 and the growth mindset intervention aims to reduce them. The intervention did not only contradict these beliefs but also used a series of interesting and guided exercises to reduce their credibility.

The first session of the intervention covered the basic idea of a growth mindset—that an individual’s intellectual abilities can be developed in response to effort, taking on challenging work, improving one’s learning strategies, and asking for appropriate help. The second session invited students to deepen their understanding of this idea and its application in their lives. Notably, students were not told outright that they should work hard or employ particular study or learning strategies. Rather, effort and strategy revision were described as general behaviours through which students could develop their abilities and thereby achieve their goals.

The materials presented here sought to make the ideas compelling and help adolescents to put them into practice. It therefore featured stories from both older students and admired adults about a growth mindset, and interactive sections in which students reflected on their own learning in school and how a growth mindset could help a struggling ninth-grade student next year. The intervention style is described in greater detail in a paper reporting the pilot study for the present research 13 and in a recent review article 12 .

Among these features, our intervention mentioned effort as one means to develop intellectual ability. Although we cannot isolate the effect of the growth mindset message from a message about effort alone, it is unlikely that the mere mention of effort to high school students would be sufficient to increase grades and challenge seeking. In part this is because adolescents often already receive a great deal of pressure from adults to try hard in school.

Intervention delivery and fidelity

The intervention and control sessions were delivered as early in the school year as possible, to increase the opportunity to set in motion a positive self-reinforcing cycle. In total 82% of students received the intervention in the autumn semester before the Thanksgiving holiday in the United States (that is, before late November) and the rest received the intervention in January or February; see  Supplementary Information section 5 for more detail. The computer software of the technology vendor randomly assigned adolescents to intervention or control materials. Students also answered various survey questions. All parties were blind to condition assignment, and students and teachers were not told the purpose of the study to prevent expectancy effects.

The data collection procedures yielded high implementation fidelity across the participating schools, according to metrics listed in the pre-registered analysis plan. In the median school, treated students viewed 97% of screens and wrote a response for 96% of open-ended questions. In addition, in the median school 91% students reported that most or all of their peers worked carefully and quietly on the materials. Fidelity statistics are reported in full in  Supplementary Information section 5.6 ; Extended Data Table 2 shows that the treatment effect heterogeneity conclusions were unchanged when controlling for the interaction of treatment and school-level fidelity as intended.

Self-reported fixed mindset

Students indicated how much they agreed with three statements such as “You have a certain amount of intelligence, and you really can’t do much to change it” (1, strongly disagree; 6, strongly agree). Higher values corresponded to a more fixed mindset; the pre-analysis plan predicted that the intervention would reduce these self-reports.

GPAs . Schools provided the grades of each student in each course for the eight and ninth grade. Decisions about which courses counted for which content area were made independently by a research company (MDRC; see  Supplementary Information section 12 ). The GPAs are a theoretically relevant outcome because grades are commonly understood to reflect sustained motivation, rather than only prior knowledge. It is also a practically relevant outcome because, as noted, GPA is a strong predictor of adult educational attainment, health and well-being, even when controlling for high school test scores 38 .

School achievement level

The school achievement level moderator was a latent variable that was derived from publicly available indicators of the performance of the school on state and national tests and related factors 45 , 46 , standardized to have mean = 0 and s.d. = 1 in the population of the more than 12,000 US public schools.

Behavioural challenge-seeking norms of the schools

The challenge-seeking norm of each school was assessed through a behavioural measure called the make-a-math-worksheet task 13 . Students completed the task towards the end of the second session, after having completed the intervention or control content. They chose from mathematical problems that were described either as challenging and offering the chance to learn a lot or as easy and not leading to much learning. Students were told that they could complete the problems at the end of the session if there was time. The school norm was estimated by taking the average number of challenging mathematical problems that adolescents in the control condition attending a given school chose to work on. Evidence for the validity of the challenge-seeking norm is presented in the  Supplementary Information section 10 .

Norms of self-reported mindset of the schools

A parallel analysis focused on norms for self-reported mindsets in each school, defined as the average fixed mindset self-reports (described above) of students before random assignment. The private beliefs of peers were thought to be less likely to be visible and therefore less likely to induce conformity and moderate treatment effects, relative to peer behaviours 49 ; hence self-reported beliefs were not expected to be significant moderators. Self-reported mindset norms did not yield significant moderation (see Extended Data Table 2 ).

Course enrolment to advanced mathematics

We analysed data from 41 schools who provided data that allowed us to calculate rates at which students took an advanced mathematics course (that is, algebra II or higher) in tenth grade, the school year after the intervention. Six additional schools provided tenth grade course-taking data but did not differentiate among mathematics courses. We expected average effects of the treatment on challenging course taking in tenth grade to be small because not all students were eligible for advanced mathematics and not all schools allow students to change course pathways. However, some students might have made their way into more advanced mathematics classes or remained in an advanced pathway rather than dropping to an easier pathway. These challenge-seeking decisions are potentially relevant to both lower- and higher-achieving students, so we explored them in the full sample of students in the 41 included schools.

Analysis methods

We used intention-to-treat analyses; this means that data were analysed for all students who were randomized to an experimental condition and whose outcome data could be linked. A complier average causal effects analysis yielded the same conclusions but had slightly larger effect sizes (see  Supplementary Information section 9 ). Here we report only the more conservative intention-to-treat effect sizes. Standardized effect sizes reported here were standardized mean difference effect sizes and were calculated by dividing the treatment effect coefficients by the raw standard deviation of the control group for the outcome, which is the typical effect size estimate in education evaluation experiments. Frequentist P values reported throughout are always from two-tailed hypothesis tests.

Model for average treatment effects

Analyses to estimate average treatment effects for an individual person used a cluster-robust fixed-effects linear regression model with school as fixed effect that incorporated weights provided by statisticians from ICF, with cluster defined as the primary sampling unit. Coefficients were therefore generalizable to the population of inference, which is students attending regular public schools in the United States. For the t distribution, the degrees of freedom is 46, which is equal to the number of clusters (or primary sampling units, which was 51) minus the number of sampling strata (which was 5) 45 .

Model for the heterogeneity of effects

To examine cross-school heterogeneity in the treatment effect among lower-achieving students, we estimated multilevel mixed effects models (level 1, students; level 2, schools) with fixed intercepts for schools and a random slope that varied across schools, following current recommended practices 39 . The model included school-centred student-level covariates (prior performance and demographics; see the  Supplementary Information section 7 ) to make site-level estimates as precise as possible. This analysis controlled for school-level average student racial/ethnic composition and its interaction with the treatment status variable to account for confounding of student body racial/ethnic composition with school achievement levels. Student body racial/ethnic composition interactions were never significant at P  < 0.05 and so we do not discuss them further (but they were always included in the models, as pre-registered).

A final pre-registered robustness analysis was conducted to reduce the influence of two possible sources of bias: awareness of study hypotheses when conducting analyses and misspecification of the regression model (see the  Supplemental Information, section 13, p. 12 ). Statisticians who were not involved in the study design and unaware of the moderation hypotheses re-analysed a blinded dataset that masked the identities of the variables. They did so using an algorithm that has emerged as a leading approach for understanding moderators of treatments: BCF 40 . The BCF algorithm uses machine learning tools to discover (or rule out) higher-order interactions and nonlinear relations among covariates and moderators. It is conservative because it uses regularization and strong prior distributions to prevent false discoveries. Evidence for the robustness of the moderation analysis in our pre-registered model comes from correspondence with the estimated moderator effects of BCF in the part of the distribution where there are the most schools (that is, in the middle of the distribution), because this is where the BCF algorithm is designed to have confidence in its estimates (Extended Data Fig. 3 ).

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this paper.

Data availability

Technical documentation for the National Study of Learning Mindsets is available from ICPSR at the University of Michigan ( https://doi.org/10.3886/ICPSR37353.v1 ). Aggregate data are available at https://osf.io/r82dw/ . Student-level data are protected by data sharing agreements with the participating districts; de-identified data can be accessed by researchers who agree to terms of data use, including required training and approvals from the University of Texas Institutional Review Board and analysis on a secure server. To request access to data, researchers should contact [email protected]. The pre-registered analysis plan can be found at https://osf.io/tn6g4 . The intervention module will not be commercialized and will be available at no cost to all secondary schools in the United States or Canada that wish to use it via https://www.perts.net/ . Selections from the intervention materials are included in the Supplementary Information. Researchers wishing to access full intervention materials should contact [email protected] and must agree to terms of use, including non-commercialization of the intervention.

Code availability

Syntax can be found at https://osf.io/r82dw/ or by contacting [email protected].

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Acknowledgements

This manuscript uses data from the National Study of Learning Mindsets (principal investigator, D.Y.; co-investigators: R.C., C.S.D., C.M., B.S. and G.M.W.; https://doi.org/10.3886/ICPSR37353.v1 ). The programme and surveys were administered using systems and processes developed by the Project for Education Research That Scales (PERTS ( https://www.perts.net/ ); principal investigator, D.P.). Data collection was carried out by an independent contractor, ICF (project directors, K.F. and A.R.). Planning meetings were hosted by the Mindset Scholars Network at the Center for Advanced Study in the Behavioral Sciences (CASBS) with support from a grant from Raikes Foundation to CASBS (principal investigator, M. Levi), and the study received assistance or advice from M. Shankar, T. Brock, C. Bryan, C. Macrander, T. Wilson, E. Konar, E. Horng, J. Axt, T. Rogers, A. Gelman, H. Bloom and M. Weiss. We are grateful for feedback on a preprint from L. Quay, D. Bailey, J. Harackiewicz, R. Dahl, A. Suleiman and M. Greenberg. Funding was provided by the Raikes Foundation, the William T. Grant Foundation, the Spencer Foundation, the Bezos Family Foundation, the Character Laboratory, the Houston Endowment, the Yidan Prize for Education Research, the National Science Foundation under grant number HRD 1761179, a personal gift from A. Duckworth and the President and Dean of Humanities and Social Sciences at Stanford University. Preparation of the manuscript and the development of the analytical approach were supported by National Institute of Child Health and Human Development (10.13039/100000071 R01HD084772), P2C-HD042849 (to the Population Research Center (PRC) at The University of Texas at Austin). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health and the National Science Foundation.

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Contributions

D.S.Y. conceived the study and led the design, analysis and writing; C.S.D. was involved in every phase of the study, particularly the conception of the study, the study design, the preparation of intervention materials, the interpretation of analyses and the writing of the manuscript; G.M.W. co-conceived the study, contributed to intervention material design and assisted with the interpretation of analyses and writing of the manuscript. P.H. contributed to the design of the study and the conceptualization of the moderators, co-developed the analysis plan, carried out statistical analyses, developed the Supplementary Information and assisted with the writing of the manuscript; R.C. and C.M. contributed to study design, analysis and writing of the paper, especially with respect to sociological theory about context effects; E.T., R.I., D.S.Y. and B.S. developed the school sampling plan; C.R. co-developed the intervention content; D.P. and PERTS developed the intervention delivery and survey data collection software; K.F., A.R., J.T., and R.I. led data collection and independently cleaned and merged raw data prior to access by the analysts; J.S.M., C.M.C. and P.R.H. executed a blinded analysis of the data and contributed to the writing of the paper; C.S.H., A.L.D. and D.S.Y. co-developed the behavioural norms measure; M.G., P.M., J.B. and S.Y.H. contributed to data analysis; R.F. contributed to study design.

Corresponding authors

Correspondence to David S. Yeager or Paul Hanselman .

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Competing interests.

The authors declare no competing interests for this study. Several authors have disseminated growth mindset research to public audiences and have complied with their institutional financial interest disclosure requirements; currently no financial conflicts of interest have been identified. Specifically, D.P. is the co-founder and executive director at PERTS, an institute at Stanford University that offers free growth mindset interventions and measures to schools, and authors D.S.Y, C.S.D., G.W., A.L.D., D.P., and C.H. have disseminated findings from research to K-12 schools, universities, non-profit entities, or private entities via paid or unpaid speaking appearances or consulting. None of the authors has a financial relationship with any entity that sells growth mindset products or services.

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Peer review information Nature thanks Eric Grodsky, Luke Miratrix and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended data fig. 1 the finding that the growth mindset effect on gpa is positive among lower-achieving students is robust to deviations from the pre-registered statistical model..

a , b , Each estimate represents an unstandardized treatment effect on GPA (on a 0 to 4.3 scale) estimated in separate fixed-effects regression models with school as a fixed effect. Most of the alternative specifications were known to produce less-valid tests of the hypothesis, but some of them required fewer subjective judgments and so it was informative to show that the main conclusion of a positive treatment effect was supported even with a suboptimal model specification. Examples include revising the core GPA outcome to include non-core classes such as speech, debate or electives (because this does not involve coding of core classes; see ‘Includes Non-Core Courses’), or revising the post-treatment marking period to include pre-treatment data in cases in which schools implemented the intervention in the Spring (because this does not involve coding pre- and post-treatment making periods; see ‘Includes Some Pre-Treatment GPA’). a , The effects of changing just one or two model specifications at a time while leaving the rest of the pre-registered model specifications the same. Open circles represent the pre-registered definition of lower-achieving students (below the school-specific median), and filled dots represent the alternative definition of lower-achieving students (below the school-specific median and below a 3.0 GPA out of 4.3). b , A histogram of all possible combinations of the alternative model specifications that shows that effects are uniformly positive. Note that the treatment effect estimates on the far left of b are from clearly less-valid models; for example, they insufficiently control for prior achievement, they drop participants with missing data, they do not use survey weights (so results are not representative and therefore do not answer our research questions). Panels a and b both show that even exercising all of these degrees of freedom in a way that could obscure true treatment effects still yields positive point estimates. Further explanations of why the alternatives were not selected for the pre-registration are included in  Supplementary Information section 7.3 .

Extended Data Fig. 2 The growth mindset intervention effect in a given school is almost always positive, although there is significant heterogeneity across schools.

a , b , Mindset treatment effects on for core course GPAs ( a ) and mathematics/science GPAs ( b ). Estimates were generated using the pre-registered linear mixed-effects model (see  Supplementary Information section 7 , RQ3). Note that the treatment effect at any individual school is likely to have a very wide confidence interval even when there is a true positive effect, owing to small sample sizes for each school on its own. Therefore, as with any multi-site trial, effects of individual schools are not expected to be significantly different from zero even though the average treatment effect is significantly different from zero. The plotted treatment effects were estimated in an unconditional model with no cross-level interactions (that is, without consideration of the potential moderators) and so the points are shrunken towards the sample mean. Thus, these plotted estimates do not correspond to the estimated CATEs reported in the paper or in Extended Data Table 3 .

Extended Data Fig. 3 A BCF analysis reproduces the same pattern of moderation by norms as the pre-registered linear mixed-effects model

The BCF analysis uses a nonparametric Bayesian model designed to shrink effect sizes to see if any effect can update a relatively strong prior centered on null effects and biased toward low degrees of treatment effect moderation. a , b , Data points correspond to school-level treatment effects estimated by the pre-registered linear mixed-effects model ( a ) or the BCF model ( b ). Treatment effects refers to the difference between the treatment and control groups in terms of mathematics/science GPAs at the end of ninth grade in a school, adjusting for pre-random-assignment covariates and including survey weights. The models included three school-level moderators of the student-level randomized treatment: the achievement level (categorical, dummies for low and high, medium group as the reference category in the linear model), the behavioural growth mindset norms (continuous) and the percentage of racial or ethnic minority students (continuous) of the schools. School-level treatment effects include the fitted values plus the model-estimated, school-specific random effect. Challenge-seeking behavioural norm refers to the average number of challenging mathematics problems (out of 8) chosen by students in the control group in a given school. N , the number of lower-achieving students in a school. Percent minority, the percentage of students who identify as black, African-American, Hispanic, Latino or indigenous American, split at the school-level population median (26% of the student body of the school). The dashed lines represent the estimated intercept and slope for the linear trend of the estimated treatment effects in norms. b , The coloured lines represent LOESS smoothing curves for the trend in  norms of the estimated treatment effects, fitted to the estimated school-level treatment effects within achievement groups and weighted by school sample size. The area between thevertical lines is the interquartile range (IQR) of norms, where neither model is extrapolating. The two models agree broadly about average effects, particularly within the IQR of norms, while BCF estimated somewhat lower degrees of heterogeneity and extrapolates in a fundamentally different fashion at the extremes of norms (since it is a nonlinear model).Recall BCF is designed to shrink toward an overall effect size of zero, and to shrink CATEs of similar schools towards one another, in order to avoid over-fitting the data. Unlike the preregistered linear models, BCF was specified with no prior hypotheses about the functional form of moderation (nonlinearities and/or interactions between multiple moderators and treatment) so this shrinkage is necessary to obtain stable estimates of treatment effects. However, it does lead to smaller estimates of effect sizes and a lower estimated degree of moderation relative to the preregistered linear mixed effects model.

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Yeager, D.S., Hanselman, P., Walton, G.M. et al. A national experiment reveals where a growth mindset improves achievement. Nature 573 , 364–369 (2019). https://doi.org/10.1038/s41586-019-1466-y

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Philosophers and psychological scientists converge on the idea that wisdom involves certain aspects of thinking (e.g., intellectual humility, recognition of uncertainty and change, consideration of the broader context at hand and perspectives of others, integration of these perspectives/compromise) enabling application of knowledge to life challenges. How does wise thinking change across various contexts people encounter in their lives? Empirical evidence indicates that people’s ability to think wisely varies dramatically across experiential contexts they encounter over the lifespan. Moreover, wise thinking varies from one situation to another, with self-focused contexts inhibiting wise thinking. Experiments can show ways to buffer reasoning against bias in cases where self-interests are unavoidable. Specifically, an ego-decentering cognitive mindset enables wise thinking about personally meaningful issues. It appears that experiential, situational and cultural factors are even more powerful in shaping wisdom than previously imagined. Focus on such contextual factors can shed new light on the processes underlying wise thought and its development, helps to integrate different approaches to studying wisdom and have implications for measurement and development of wisdom-enhancing interventions.

Journal of Adult Development

Monika Ardelt

This study examined whether (a) older adults are wiser than college students, (b) college-educated older adults are wiser than current college students, and (c) wise older adults show evidence of personal growth. Using a sample of 477 undergraduate college students and 178 older adults (age 52+), results showed that college students tended to score as high on the self-administered three-dimensional wisdom scale (3D-WS) as older adults. However, college-educated older adults tended to score significantly higher on the reflective and affective dimensions of wisdom and the overall score of the 3D-WS and than did current college students. Qualitative evidence suggests that many older adults, particularly in the top 20% of wisdom scorers, grew wiser with age by learning from life experiences. The results indicate that wisdom might increase with age for individuals with the opportunity and motivation to pursue its development.

Gordon Asmundson

Journal of Personality

Courtney Raspin

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The Effect of Trait Self-Awareness, Self-Reflection, and Perceptions of Choice Meaningfulness on Indicators of Social Identity within a Decision-Making Context

Noam dishon.

1 Department of Psychological Sciences, Faculty of Health, Arts and Design, Swinburne University of Technology, Melbourne, VIC, Australia

Julian A. Oldmeadow

Christine critchley.

2 Department of Statistics, Data Science and Epidemiology, Faculty of Health, Arts and Design, Swinburne University of Technology, Melbourne, VIC, Australia

Jordy Kaufman

Theorists operating from within a narrative identity framework have suggested that self-reflective reasoning plays a central role in the development of the self. Typically, however, narrative identity researchers have investigated this relationship using correlational rather than experimental methods. In the present study, leveraging on a classic research paradigm from within the social identity literature we developed an experiment to test the extent to which self-reflection might have a causal impact on the self-concept within a decision-making context. In a minimal group paradigm participants were prompted to reflect on their painting choices either before or after allocating points to in-group∖ out-group members. As anticipated, self-reflection augmented social identification, but only when participants felt their choices were personally meaningful. Participants who reasoned about their choices and felt they were subjectively meaningful showed stronger similarity and liking for in-group members compared to those who did not reflect on their choices or found them to be subjectively meaningless. Hence, reflecting on and finding meaning in one’s choices may be an important step in linking behavior with in-group identification and thus the self-concept in turn. The absence of any effects on in-group favoritism (a third indicator of social identification measured) as well as implications of the study’s findings for self-perception, cognitive dissonance and social identity processes are also discussed.

Introduction

Psychological scientists have approached the issue of self and identity from a range of different positions. For example, some social and cultural psychologists have investigated self and identity using a social identity theory framework whereas other personality and developmental psychologists have pursued an approach informed by narrative identity theory (see, Tajfel and Turner, 1986 ; McAdams, 2001 ; Pasupathi et al., 2007 ; Miramontez et al., 2008 ). In the present paper, we synthesize aspects of both identity projects by utilizing an experimental paradigm associated with social identity theory (i.e., the minimal group paradigm), to investigate whether self-reflective reasoning, a cognitive process theorized to be central to narrative identity development, can have a causal effect on the self and identity. We also explore if such an effect could be impacted by the level of meaningfulness one associates with their self-reflective reasoning and modulated by individual differences in trait self-awareness.

Identity from a Narrative Identity Framework

McAdams (1985 , 2001 ) model of narrative identity postulates that our sense of identity is inextricably linked with the creation of a life story. According to this model, self-narratives have two primary functions. They facilitate our sense of self-continuity across time and they help us give context and meaning to the events of our lives so that we can make sense of who we are ( McAdams and McLean, 2013 ). Self-narratives, as McAdams and McLean (2013) , state facilitate meaning making because they allow the narrator to draw “…a semantic conclusion about the self from the episodic information that the story conveys” (pp. 236). Within the narrative identity literature the process of self-reflection coupled with the extraction of self-relevant meaning is referred to as autobiographical reasoning and it is theorized to be an essential cognitive process in narrative identity development and construction ( Singer et al., 2013 ). However, as Adler et al. (2016) note, within the narrative identity literature investigators have typically employed correlational research designs thereby rendering it difficult to draw causal conclusions. Adler et al. (2016) develop this idea further stating that given this paucity of experimental work “increasing methodological sophistication and variety in the study of narrative identity with an eye toward drawing causal inferences is vital” (pp. 29).

Self-Reflection, Meaning and the Self

Although research from within the narrative identity literature demonstrating a causal link between self-reflection and identity development remains scarce, several other lines of converging research also suggest that self-reflection should play an important role in self-concept development. For example within the clinical psychology literature, reflective functioning has been used to describe a persons ability to reflect on experiences, draw inferences about behavior from these reflections, and then use those inferences to construct and develop representations of the self ( Katznelson, 2014 ). Research which has investigated reflective functioning has demonstrated that changes in reflective functioning are linked to self-concept change. For example, in research with persons affected by borderline personality disorder, (a condition which is characterized by an unstable sense of self) Levy et al. (2006) found that improvements in reflective functioning were associated with improvements in self-representations and a more integrated sense of self.

Another reason for thinking that self-reflection should represent an important mechanism in self-concept construction and development comes from research which has utilized the self-referential memory paradigm. In a typical self-referential memory paradigm study, different word categories (i.e., traits and adjectives verse semantically and orthographically related words) are presented to participants who are instructed to remember them at exposure and then asked to recall them at a later time ( Rogers et al., 1977 ). The self-reference effect describes the tendency for participants to retrieve traits and adjectives that are self-related more successfully than words that are semantically or orthographically related ( Symons and Johnson, 1997 ). Schizophrenia is another condition of which an unstable sense of self represents a core feature (see Sass and Parnas, 2003 ), and research has demonstrated that persons affected by schizophrenia tend to display weaker self-reference effects compared to healthy controls which researchers have interpreted as an indication of reduced self-reflective capacity ( Harvey et al., 2011 ).

There are also several reasons for thinking that meaning-making tendencies should play an important role in self-concept construction and development in addition to the emphasis placed upon this process by narrative identity theorists as noted previously. Firstly, in a theoretical sense, influential thinkers such as Erikson (1963) , Frankl (1969) , and Bruner (1990) , have all argued strongly for the idea that meaning is likely to play an important role in self and identity development. At the same time, research from within the organizational psychology literature has demonstrated empirically that perceptions of meaningfulness are associated with a range of self-related outcomes. Psychological empowerment captures an employees cognitive-motivational stance toward their work and is comprised of four dimensions, impact , competence , autonomy , and of particular pertinence given the current investigation, meaning which reflects the degree to which one perceives their work as being personally meaningful ( Spreitzer, 1995 ; Holdsworth and Cartwright, 2003 ). The importance of perceptions of meaningfulness within the context of psychological empowerment is further highlighted by Spreitzer et al. ( 1997 , pp. 681) who argue that the dimension of meaning “serves as the ‘engine’ of empowerment.” Research exploring psychological empowerment at an individual factor level has noted that differences in meaning are positively associated with several self-related outcomes such as self-esteem and self-efficacy ( McAllister, 2016 ).

In our own research we have found that individual differences in trait self-awareness are associated with perceptions of choice meaningfulness within a decision-making context (Dishon et al., under review). Based on pre-existing literature which has explored self-awareness more generally (e.g., Morin, 2011 ) we defined trait self-awareness as individual differences in the capacity to access knowledge, insight and understanding of internal self-related experiences. We found that participants with higher levels of trait self-awareness perceived significantly more meaning in a series of minor experimentally induced choices compared to those with lower levels of trait self-awareness. Moreover, this difference remained irrespective of whether or not participants were told that their choices were diagnostic of important personal characteristics. We concluded from this research that individuals high in trait self-awareness are more likely to reflect on their choices and more likely to find them meaningful than individuals low in trait self-awareness. Extending on this work and drawing upon the literature previously presented, in the present paper we propose and explore a theoretical model (see Figure ​ Figure1 1 ) that articulates how self-reflection and perceptions of meaningfulness might affect the self within a choice context.

Self-reflection model.

Overview of the Self-Reflection Model

The assumptions underpinning this model are that when one is presented with a potential trigger event such as (but not limited to) a choice or behavior, the self will be affected (i.e., the choice/behavior will inform the self) as a consequence of (a) whether or not self-reflection takes place, and (b) the degree to which the choice is perceived to be personally meaningful. Moreover, (c) whether or not reflection takes place may be determined by individual or situational factors. For example, individuals with higher levels of trait self-awareness may be more predisposed to engage in self-reflective reasoning, whereas for others, situational cues such as an unexpected occurrence or a prompt from a third party might act as the catalyst for self-reflective reasoning. Several predictions arise from the model.

Prediction 1: If self-reflective reasoning does occur and the choice or behavior is perceived to be highly meaningful, then self-perception will occur (by which we mean the self-concept will be modified or changed as result of the behavior or action).

Prediction 2: If self-reflective reasoning does occur and the level of personal meaning associated with the choice or behavior is perceived to be low, its affect on the self will be weak or absent.

Prediction 3: If no self-reflective reasoning occurs there will be a weak effect on the self through an automatic self-perception process. Rather than predict no effect on the self in the absence of self-reflection, we allow for the possibility of an automatic or implicit self-perception process to occur because research has demonstrated that the self-concept can be impacted even in the absence of explicit reasoning. For example, in one demonstration of this type of effect, Klimmt et al. (2010) observed that exposing participants to different types of characters in video games led to automatic shifts in self-perception as measured in a follow up Implicit Association Test.

Prediction 4: Individuals high in trait self-awareness will be more likely to engage in self-reflective reasoning than individuals low in trait self-awareness 1 .

Prediction 5: Individuals low in trait self-awareness will engage in self-reflective reasoning only if prompted, or if some other situational cue triggers self-reflection.

Although narrative identity researchers have primarily looked at self-reflective reasoning in the context of autobiographical memories (see, Pasupathi, 2015 ), in the present study we sought to initially test the veracity of our self-reflection model on a smaller scale in a relatively minimal decision-making context. We did so for several reasons. First, decision-making lends itself well to experimental testing ( Carroll and Johnson, 1990 ). This is important because as noted earlier, to date, research investigating the relationship between self-reflective reasoning and the self has largely been correlational by design and attempts to test this possibility experimentally have been insufficient ( Adler et al., 2016 ). Second, consumer decision-making research has suggested that self-narratives often arise in every day decision-making contexts ( Phillips et al., 1995 ) and some narrative identity scholars have argued that day-to-day narratives which might not be overtly autobiographical nevertheless remain tightly linked to self and identity ( Bamberg, 2011 ; Pasupathi, 2015 ). Third, behaviorist and cognitive theories (i.e., self-perception theory and cognitive dissonance theory) suggest that the self is often informed by after-the-fact explanations for behaviors or post hoc reasoning for choices ( Brehm, 1956 ; Festinger, 1957 ; Bem, 1972 ). Another reason for thinking that self-reflection could impact self-perception stems from research by Wilson et al. (1993) which demonstrated that self-reflection can impact attitudes and post-choice satisfaction within a decision-making context.

Identity from a Social Identity Framework

From the view of social identity theory, our sense of identity is heavily influenced by the social groups that we belong to ( Tajfel and Turner, 1986 ). Social identity as originally conceptualized by Tajfel (1981) refers to “…that part of an individual’s self-concept which derives from his knowledge of his membership of a social group” (p. 255). According to the theory, we come to identify with certain social groups based upon the extent to which we think we share similarities with other group members. Then, in order to maintain a positive sense of our social identity we try to ensure that our group (the in-group) is favored over other out-groups. One way of doing this is by favoring one’s in-group and discriminating against the out-group. Within the social identity literature, the extent to which we feel similar to, like, or favor other in-group members is indicative of the extent to which our identification with that group has been incorporated into our self-concept ( Hogg, 1992 , 1993 ; Ellemers et al., 1999 ; Leach et al., 2008 ). The minimal group paradigm which facilitates the measurement of in-group favoritism and out-group discrimination is one way of measuring the extent to which group membership has been incorporated into the self-concept and therefore had an effect on social identity ( Otten, 2016 ).

In a typical minimal group paradigm experiment, participants are randomly allocated to a group and then asked to concurrently distribute resources to in-group and out-group members on allocation matrices specifically designed to measure allocation strategies that favor the in-group and∖or discriminate against an out-group ( Tajfel et al., 1971 ). Research in the field has consistently demonstrated that even when people are led to believe that their assignment to a group is for a trivial reason, such as their preferences for abstract artwork, they still tend to allocate resources more favorably to in-group members ( Otten, 2016 ). Whilst researchers have often been interested in using this methodology to investigate topics such as prejudice and discrimination, the allocation of resources within a minimal group paradigm environment need not be used exclusively for this end ( Bourhis et al., 1994 ). The allocation of resources within a minimal group paradigm context can also serve as a subtle and discreet measure of the degree to which group membership has been incorporated into the self-concept and one’s sense of social identity more generally ( Otten, 2016 ). Another way that social identity researchers have measured the extent to which commitment to a group can impact one’s self-concept and sense of identity is by measuring self-reported liking of, and similarity with, other anonymous in-group members (e.g., Hogg, 1992 , 1993 ; Ellemers et al., 1999 ; Leach et al., 2008 ). Ellemers et al. (1999) research is also important in the context of the current study because it demonstrates that social identification is more strongly affected when people are able to self-select into a group (as opposed to being assigned a group) and it would seem reasonable to think that self-reflective reasoning is a process that could be quite important for self-selection decisions.

The Current Study

In recent research in our lab we investigated the connection between self-reflective reasoning within a decision-making context and the self. We found that the degree of personal meaning that was given to a trivial choice was associated with individual differences in trait self-awareness (Dishon et al., under review). In the present study we sought to extend this research by investigating further if the cognitive process of engaging in self-reflective reasoning could affect one’s sense of identity. We also sought to explore whether an effect of this kind might be impacted by the extent to which one felt as though their reasoning had been personally meaningful and also moderated by individual differences in trait self-awareness. To test this model we developed an experiment that utilized and extended upon traditional minimal group paradigm work. Participants were randomly assigned to either an experimental or control condition. In the experimental condition participants were prompted to engage in self-reflective reasoning immediately after making painting choices whereas in the control condition participants went on to allocate resources immediately after selecting paintings. We used in-group∖out-group allocation strategies as one dependent measure of identity and we also used similarity and liking ratings with in-group∖out-group members as additional dependent measures of identity.

Based on the proposed model we hypothesized that participants who are relatively high in trait self-awareness would be more likely to spontaneously self-reflect on their choices and therefore be relatively unaffected by the self-reflection prompt manipulation. As such it was expected that for these participants, self-perception would be related to the perceived meaningfulness of their painting choices more so than condition. We also expected that participants who are relatively low in trait self-awareness would be less likely to spontaneously self-reflect on their choices and therefore more greatly affected by the self-reflection prompt manipulation. As such it was expected that for these participants, self-perception would be related to the perceived meaningfulness of their painting choices only in the experimental condition (i.e., when they have been prompted to self-reflect.)

Materials and Methods

Participants.

Two hundred and six undergraduate psychology students voluntarily participated in the study in exchange for course credit. During the procedure, a manipulation check was administered to ensure that participants had attended to feedback regarding group allocation (the details of which are explained further in the Procedure section below). The responses of 32 participants who failed the manipulation check were discarded leaving a remaining pool of 174 participants (139 female, 35 male) with a mean age of 33.06 years ( SD = 11.78). The difference in failure rates between conditions was not significant ( p = 0.518). Ethical approval for the study was provided by Swinburne University’s Human Research Ethics Committee (SUHREC).

Effects of the experimental manipulation on identity were inferred by, (a) the extent to which participants incorporated their in-group identification into their self-concept and measured by participant’s in-group favoritism when distributing resources to in-group∖out-group members on Tajfel matrices and, (b) participant’s self-identification with in-group∖out-group members which was assessed by measuring their liking of, and perceived similarity with, in-group∖out-group members.

Tajfel matrices

Tajfel matrices consist of six matrices in which participants are asked to allocate resources concurrently to an in-group member and out-group member along a spectrum of pre-determined in-group to out-group ratios. The six matrices comprise three pairs (one of each pair is a reversed version of the original).

There are four main allocation strategies that can be measured with Tajfel matrices. Parity is an allocation strategy whereby the participant distributes an equal amount of resources to both in-group and out-group recipients. Maximum In-Group Profit is an allocation strategy that sees the greatest possible amount of resources awarded to the in-group recipient irrespective of what is awarded to the out-group recipient. Maximum Difference reflects a strategy that optimizes the differential allocation of resources between recipients in favor of the in-group recipient at the expense, however, of absolute in-group profit. Maximum Joint Profit reflects a strategy in which overall allocation of resources is maximized across both in-group and out-group.

The matrices facilitated the calculation of pull scores which reflected participants’ gravitation toward particular allocation strategies. Matrix pair A compared the pull of Maximum In-Group Profit and Maximum Difference (i.e., in-group favoritism) against Maximum Joint Profit. Matrix pair B compared the pull of Maximum Difference against Maximum In-Group Profit and Maximum Joint Profit. Matrix C compared the pull of Parity against Maximum In-Group Profit and Maximum Difference [See Bourhis et al. ( 1994 ) for a comprehensive and in-depth account of the procedure involved in Tajfel matrix preparation, administration, and calculation].

Following a similar procedure to Grieve and Hogg (1999) we then conducted a factor analysis of the pull scores using principal axis factoring with promax rotation to examine the possibility of computing an overall in-group favoritism score. This revealed a single in-group favoritism factor which explained 48.9% of the variance (all loadings ≥ 0.63). The items were then summed and averaged to produced an overall measure of in-group favoritism with higher scores representing greater in-group favoritism (Cronbach’s α = 0.74).

In-group self-identification

As other researchers have done previously (e.g., Hains et al., 1997 ; Grieve and Hogg, 1999 ), participants’ liking of, and perceived similarity with, in-group∖out-group members were recorded to measure their level of self-identification with their in-group. To do so, after being presented with pairs of de-identified paintings by Paul Klee and Wassily Kandinsky and receiving feedback that their choices indicated a preference for the work of Klee irrespective of their actual choices, (see the Procedure section below for a more detailed account of the process involved,) participants were asked to imagine themselves meeting two people, one who had a preference for Klee and the other who had a preference for Kandinsky. Participants then rated on a seven-point scale which of these two people they thought they were most similar to in general (Q1), in artistic preferences (Q2), in painting preferences (Q3), in academic ability (Q4), and in political opinions (Q5). Using the same scenario, participants were also asked to rate who they thought they would like more (Q6), who they thought they would get along with more (Q7), and who they would like to meet more (Q8). Responses on questions 1–5 were summed and averaged to calculate an overall similarity score with higher scores representing a greater level of similarity with an in-group member (Cronbach’s α = 0.75). Response for questions 6–8 were summed and averaged to calculate an overall liking score with higher scores representing a greater level of liking for an in-group member (Cronbach’s α = 0.82).

Meaningfulness

Meaningfulness associated with self-reflective reasoning was measured by providing participants with a five-item Subjective Meaningfulness Scale which included items such as “I feel as though my choices were genuine” and, “My choices were meaningless.” Participants were asked to indicate their level of agreement with each statement on a 5-point scale (1 = strongly disagree, 5 = strongly agree). Responses were coded so that higher scores indicated greater level of meaningfulness. A factor analysis using principal axis factoring and promax rotation revealed that all five items loaded on a single factor which explained 32.6% of the variance (all loadings ≥ 0.43). Scores were then summed and averaged and an overall meaningfulness score was calculated (Cronbach’s α = 0.69; Guttman’s Lambda 2 = 0.70).

Trait Self-Awareness

Trait Self-Awareness was operationalized as function of participants’ scores on the Sense of Self Scale (SOSS; Flury and Ickes, 2007 ) which is a single factor 12-item measure designed to assess sense of self and self-understanding (Cronbach’s α = 0.86) and the Self-Reflection and Insight Scale (SRIS; Grant et al., 2002 ) which is a two factor 20-item measure of self-reflection and insight (Cronbach’s α = 0.88). In the present sample, using principal axis factoring and promax rotation, both measures retained their original factor structures with the SOSS exhibiting a single factor which accounted for 36.3% of the variance and the SRIS exhibiting two factors which accounted for a combined 53.6% of the variance (Factor 1 = 34.3%, Factor 2 = 19.3%). Both measures were scored so that higher scores indicated stronger sense of self and greater levels of self-reflection and insight and both measures were significantly correlated ( r = 0.35, p < 0.001). Scores on these scales were then summed to create an overall trait self-awareness score with higher scores representing greater levels of trait self-awareness (Cronbach’s α across the total 32-items = 0.77; principal axis factoring with promax rotation revealed three factors accounting for 50.6% of the variance [Factor 1 = 24.6%, Factor 2 = 21.8%, Factor 3 = 3.9%]).

Six pairs of images of paintings by Paul Klee and Wassily Kandinsky were utilized as the painting stimuli.

The experiment was administered online. Once consent to participate was provided, participants were informed they would be required to choose their preferred painting from six pairs of paintings which were then presented sequentially. All paintings were presented without the artists’ names attached to any of the works. After making their painting selections participants were randomly assigned to one of two conditions (a reasoning pre resource allocation, similarity and liking ratings condition or, a reasoning post resource allocation, similarity and liking ratings condition). Participants in both conditions were presented with all the same stimuli and experiences except the order of exposure was manipulated slightly between conditions as outlined below.

In the reasoning pre condition, after the initial painting selection phase, participants took part in the self-reflective reasoning phase. In the self-reflective reasoning phase participants were presented with and asked to reflect on a 15-item list of potential reasons for their painting selections and then presented with an open text box and asked to reflect further in their own words about their reasons for their painting choices. Following this participants were presented with and completed the subjective meaningfulness measure. Then although they remained unware to it at the time, irrespective of their actual choices participants were informed that their choices indicated that they preferred the works of Paul Klee 2 . Participants were then presented with instructions pertaining to the completion of the Tajfel matrices before moving on to complete them. Following this, participants were presented with the in-group∖out-group similarity and liking measure. Participants then completed the trait self-awareness measures before recording their gender (female, male, or other) and age. A manipulation check was then conducted whereby participants were asked to indicate who they had previously been informed that their painting choices indicated they preferred the works of (possible response were, Paul Klee, Wassily Kandinsky, or Don’t remember). Participants were then presented with a debriefing statement, informed the experiment was over and thanked for their participation.

In the reasoning post condition, the order of exposure was manipulated so that after making painting selections, participants were told their choices indicated a preference for Paul Klee 3 and were administered with the matrices and in-group∖out-group similarity and liking measures before the self-reflective reasoning phase. After completing the choice reasoning phase and the subjective meaningfulness measure, participants in this condition were also then presented with the same trait self-awareness 4 measures, demographic questions, manipulation check and debriefing as their counterparts in the alternate condition.

Outlier Analysis

Three multivariate outliers (1 in the control and 2 in the self-reflection condition) were detected and removed from the analysis thereby leaving a total sample of 171 (86 in the control condition and 85 in the self-reflection condition).

Descriptive Statistics

Descriptive statistics for trait self-awareness, choice meaningfulness, in-group similarity, in-group liking, and in-group favoritism as a function of self-reflection condition are presented in Table ​ Table1 1 .

Means and standard deviations for trait self-awareness, choice meaningfulness, similarity, liking, and in-group favoritism by self-reflection condition.

Effect of Experimental Manipulation on IV’s

We conducted between groups analyses to investigate if the self-reflection and control groups differed on the IV’s of choice meaningfulness and trait self-awareness as a function of the self-reflection manipulation. Independent samples t -test’s revealed that there was no significant difference in choice meaningfulness ( p = 0.365) or trait self-awareness ( p = 0.218) between conditions thereby demonstrating the IV’s were robust to the self-reflection manipulation.

Multiple-Sample Path Analysis

We ran a multiple-sample path analysis using the structural equation modeling program MPLUS (v 7.4) to investigate if the main effects of meaningfulness and trait self-awareness as well as the interaction effects (i.e., choice meaningfulness × trait self-awareness) on the DV’s differed between the experimental self-reflection and control non-self-reflection conditions. The model tested three exogenous/independent variables all predicting the three endogenous/dependent variables, in-group favoritism, similarity and liking. The exogenous variables were the main effects of trait self-awareness and choice meaningfulness, and a trait self-awareness × choice meaningfulness interaction.

Because the parameter to case ratio was under the required minimum of 1 parameter to 5 cases (1:4.75 or 36:171) as suggested by Kline (2011) , we discreetly tested each section of the model. In other words, three independent models with each of the three endogenous dependant variables were examined separately thereby ensuring that the parameter to case ratio was sufficient (i.e., 1:17.1 or 10:171). In all models the Satorra–Bentler robust estimator was used to account for multivariate non-normality, and all parameters were free across the self-reflection and control conditions. Chi-square Wald tests were utilized on a fully unconstrained model to test significant differences in the effects across conditions given the expectation that there would be differences in regression weights across groups ( Muthén and Muthén, 1998–2017 ). There were no significant differences in the results of these separate models and the full model 5 . Given this, the parameters for the full model are presented in Table ​ Table2 2 . Because the model was saturated with zero degrees of freedom fit indices are not reported.

Unstandardized regression weights and Wald Tests for the multi-sample path analysis.

Main Effects of Choice Meaningfulness and Trait Self-Awareness

The results in Table ​ Table2 2 reveal that there was a significant main effect for choice meaningfulness in both the control and self-reflection conditions for similarity, however, Wald tests reveal that the difference in effects between conditions was not significant. This suggests that higher choice meaningfulness scores were associated with higher similarity scores in both the self-reflection and control conditions. The results in Table ​ Table2 2 also demonstrate that there was a significant main effect of choice meaningfulness for liking in the self-reflection condition whereas the main effect of choice meaningfulness for liking in the control condition was not significant. The Wald test demonstrates that this difference in effects between conditions was significant, suggesting that higher choice meaningfulness scores were associated with higher liking scores in the self-reflection condition only. Whilst there was also a significant main effect of trait self-awareness on liking in the self-reflection condition, the Wald test demonstrates that this was not significantly different from the non-significant main effect of trait self-awareness in the control condition.

Trait Self-Awareness × Choice Meaningfulness Interaction Effects

As seen in Table ​ Table2 2 , for liking, the interaction between trait self-awareness and choice meaningfulness was only significant in the self-reflection condition and as the significant Wald test demonstrates, the strength of this interaction effect was also significantly different between the control and self-reflection conditions (see Figure ​ Figure2 2 ).

Predicted liking scores by trait self-awareness and choice meaningfulness groups across conditions. Groups were defined as 1 standard deviation above (high) and below (low) the mean (moderate).

The results in Figure ​ Figure2 2 suggest that for the self-reflection group the relationship between choice meaningfulness and liking strengthens as trait self-awareness scores decrease. That is, higher choice meaningfulness scores appear to be strongly associated with higher liking scores for those with lower trait self-awareness scores. This demonstrates a stronger impact of the self-reflection manipulation on participants lower in trait self-awareness and a reduction in the impact of the manipulation as trait self-awareness levels increase. Parallel trends were observed for similarity though the Wald test was only marginally significant.

The present study explored whether engaging in self-reflective reasoning could affect in-group identification and thereby demonstrate an effect of self-reflection on indicators of social identity and the self-concept. The possibility that such an effect could be impacted by the perceived level of meaningfulness associated with reasoning, and modulated by individual differences in trait self-awareness was also explored. Based on previous research, we developed a model which predicted that participants with higher levels of trait self-awareness would be minimally affected by the self-reflection manipulation. It was therefore hypothesized that for these participants self-perception would be related to the perceived meaningfulness of their painting choices more so than condition. The model further predicted that the self-reflection manipulation would have a greater impact on participants lower in trait self-awareness. Consequently, it was further anticipated that for these participants, self-perception would be related to perceived meaningfulness of their painting choices only in the experimental condition (i.e., when they were prompted to self-reflect). Participants’ in-group similarity and liking ratings (but not in-group favoritism allocations) supported these predictions and provided general support for the theoretical model proposed earlier.

Considering first the main effects of meaningfulness across conditions, the data demonstrated that whilst greater levels of meaning were associated with greater in-group similarity scores in both conditions, greater levels of meaning were only associated with in-group liking scores in the self-reflection condition. Taken together these results suggest that compared to the control condition, in the self-reflection condition stronger perceptions of meaningfulness led to stronger in-group identification. This is in line with predictions 1 and 2 relating to the self-reflection pathway in the theoretical model. Additionally, in line with prediction 3 of the theoretical model relating to the no self-reflection pathway, the effect of meaning on in-group perceptions in the control condition was smaller relative to the self-reflection condition. Moreover, the fact that there was no interaction between trait self-awareness and choice meaningfulness in the control group is suggestive of the possibility that in this condition, the main effect of meaning on in-group liking was the product of automatic or implicit processing because it occurred in the absence of any situational prompting and was not also impacted by pre-existing individual dispositions toward spontaneous self-reflection.

At the same time, the interaction between trait self-awareness and choice meaningfulness in the experimental condition indicates that the relationship between perceptions of choice meaningfulness and in-group liking strengthens as trait self-awareness levels decrease. This result suggests that the situational self-reflection prompt exhibited a stronger impact on participants who were less inclined (in terms of individual disposition) to engage in spontaneous self-reflection, and had less of an impact on participants with greater levels of individual disposition toward spontaneous self-reflection. This provides support for the hypothesis presented earlier and is also in line with predictions 4 and 5 of the theoretical model. A similar trend was also noted for in-group similarity, however, the difference in the strength of the effect between conditions was only marginally significant.

Limitations and Future Directions

One aspect of the study that could be viewed as both a limitation and strength is the way in which identity was measured. In the present study utilizing an already established experimental paradigm we developed a subtle way of testing the effect of self-reflective reasoning on identity. However, the cognitive process we were investigating was a process theorized from a narrative identity theory perspective, and the methodology used was born out of the social identity theory literature. On the one hand this approach represented a strength of the design in that it facilitated a discreet measurement of the effect of self-reflective reasoning on identity. At the same time, however, there are differences in the way that identity is conceptualized across both projects. To provide a stronger test of the hypothesis that self-reflective reasoning can affect narrative identity, experimental work with a more traditional dependent measure of narrative identity would be useful.

Another limitation was the way in which trait self-awareness was operationalized. In the present study trait self-awareness was operationalized as a function of participants’ scores on the SOSS and the SRIS. Whilst we had good reason to combine and operationalize these measures as a means of measuring trait self-awareness, future research aimed at the development of a dedicated measure of trait self-awareness would be worthwhile.

Though we were able to ensure that the modeling we conducted was sufficiently powered the present study could have benefited from a larger sample size. In the present study the parameter to cases ratio for the overall model was less then recommended (e.g., Kline, 2011 ). Therefore as outlined in the results section, to ensure that our modeling was sufficiently powered we initially computed three discreet models, one for each dependent variable. Although there was no significant difference in the outcomes between the individual and the combined models, in future to avoid the necessity of running independent models for each dependent variable it would be beneficial to recruit a larger sample which meets the parameter to cases ratio for the entire model in the first instance.

It is also possible that the deception that we engaged in (i.e., providing all participants with feedback that they preferred the work of Klee, irrespective of their actual choices) could have raised suspicions amongst participants who may have actually had some pre-existing knowledge of Klee and∖or Kandinsky (i.e., the artists whose works were used as the choice stimuli). Whilst we did include a manipulation check to ensure that the deception had had its intended effect, in future research, to address this issue more comprehensively it would be beneficial if participants were also directly questioned about their pre-existing knowledge of the artists whose works are used as the choice stimuli. Another way that this issue could be controlled for in the future would be to use the works of unknown artists as the choice stimuli.

Implications

The results of the present study may be of value to researchers who are interested in the developmental trajectory of narrative identity and autobiographical reasoning. Previous research looking at the development of narrative identity has suggested that the ability to cultivate a life-story tends to arise on average by about 14 years of age and that this is preceded by autobiographical reasoning for memorable life events which tends to first arise between the ages of nine and ten ( Bohn and Berntsen, 2008 ). Little is known, however, about the antecedents to the onset of autobiographical reasoning processes. Whilst it could be the case that development of autobiographical reasoning processes occurs in a stepwise fashion with little preceding them, the results of this study which demonstrate that reasoning about a trivial choice can effect the self and identity, beg the question that perhaps autobiographical reasoning processes develop as a continuous extension of more basic self-reflective reasoning processes which develop earlier in childhood. Perhaps it is the practice of more basic self-reflective reasoning which lays the cognitive foundations for, and facilitates the development of, more advanced autobiographical reasoning. One piece of recent research which dovetails with this idea comes from Bryan et al. (2014) who found in their work that children between the ages of three and six are already engaging in everyday decision making behaviors that are motivated by their developing sense of self and identity.

The fact that we observed significant effects for in-group identification and no effects on in-group favoritism has implications for researchers interested in intergroup discrimination and self-categorization. Specifically, the effects that we observed for in-group liking and in-group similarity suggest that self-categorization is likely to be influenced by both self-reflection and the level of subjective meaningfulness associated with choices or behaviors on which self-categorization is based. The absence of any effect on in-group favoritism suggests that intergroup discrimination is unlikely to be substantially impacted by self-reflection or choice meaningfulness. Research which has investigated positive-negative asymmetry within a minimal group paradigm context may help explain the discrepancy in effects between the attitudinal and behavioral measures. Positive-negative asymmetry research (see, Buhl, 1999 ; Mummendey et al., 2000 ) has demonstrated that group members tend to display stronger in-group preferences on positive stimuli compared to negative stimuli (i.e., evaluations of well regarded attributes such as creativity or intelligence, verses allocations of aversive noise). Given this research, one possibility that exists then is that in the current study, the attitudinal in-group liking and similarity measures which required participants to evaluate group members along positive dimensions were perceived more favorably compared to the behavioral measure of in-group favoritism which required participants to make allocation choices that had the potential to disadvantage out-group members.

Another possible explanation, however, for the absence of an effect on in-group favoritism could be due to aspects of the wider cultural climate within which participants were located at the time. Specifically, when this experiment took place Australia remained in the midst of a nation-wide debate regarding the legalization of same-sex marriage. Within the context of this debate university students have had strong messages of social justice and fairness directed at them at a cultural level. For example, the National Union of Students, which is the nations peak student representative body strongly advocated for students to support marriage equality ( Barlow, 2017 ). Given the cultural climate and the strong messages of social justice and fairness directed at students during the period in which this experiment took place, it is possible that in the allocation matrix tasks participants felt more compelled to engage in resource allocations which emphasized parity rather than discrimination. At the same time, in-group similarity and in-group liking ratings may have remained relatively immune to the impact of these cultural messages because perceptions of in-group identification do not necessarily equate to out-group discrimination and therefore do not have the same kinds of implications for one’s sense of fairness or social justice.

The present study may also have some implications for researchers whose work is informed by self-perception and cognitive dissonance theories. The results of the present study suggest that the application of self-reflection theory could be useful in some contexts in which cognitive dissonance and self-perception theories are not well positioned to explain the effect of choice or behavior on the self. According to self-perception theory ( Bem, 1972 ), after-the-fact explanations for behavior are generally limited to attributions about the internal (dispositional) or external (situational) cause of a behavior and are also only likely to occur in circumstances in which there is a weak or non-existent pre-existing explanation for the behavior. From the view of cognitive dissonance theory ( Festinger, 1957 ), post hoc reasoning about choices is limited to choices that induce dissonance and are motivated by a desire to reduce dissonance. Our model, however, suggests that choice or behavior is likely to effect the self as a consequence of whether it was actually perceived to be personally meaningful and that this needn’t be exclusive to dissonance inducing choices, nor to behaviors for which one does not have a pre-existing explanation.

Within the narrative identity literature, reflecting on life events in a personally meaningful way has been conceptualized as one of the key psychological mechanisms underpinning our sense of identity. To date, however, research on this issue has been largely correlational with little causal evidence available to confirm or disconfirm this claim. In the present study we sought to test experimentally if this cognitive process theorized to be so vital for identity development, could have a causal effect on self and identity. We also sought to explore the possibility that such an effect could be impacted by the level of meaningfulness associated with self-reflective reasoning, and modulated by individual differences in trait self-awareness. The results of this study largely supported our hypothesis and the proposed model from which those predictions were derived. For participants who were high in trait self-awareness, being prompted to engage in self-reflective reasoning mattered little. For this group of participants, in-group liking and similarity was related to perceptions of subjective meaningfulness relatively equally across conditions. At the same time, however, for participants low in trait self-awareness, being prompted to engage in self-reflection mattered a great deal. For these participants, subjective meaningfulness moderated in-group liking and similarity only when they had been prompted to engage in self-reflection. Overall the results of this study provide evidence to suggest that engaging in self-reflective reasoning can affect the self and identity and that this effect is impacted by both choices meaningfulness and individual differences in trait self-awareness.

Author Contributions

ND, JAO, CC, and JK all contributed to the paper and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding. ND was supported by an Australian Government funded Research Training Program Scholarship.

1 Even though we predict that those high in trait self-awareness will be more likely to engage in self-reflection, given the magnitude of the relationship between trait self-awareness and perceptions of choice meaningfulness noted in our previous study (Dishon et al., under review) there is still scope for those high in trait self-awareness to find their choices meaningless.

2 Participants remained unware of this deception until they were debriefed at the end of the study.

3 As was the case in the reasoning pre condition, participants in this condition also remained unaware to the fact that they had received this feedback irrespective of their actual choices up until they were debriefed at the end of the study.

4 Even though individual differences in trait self-awareness represent a starting point in the theoretical model, in both conditions the trait self-awareness measure was administered after the self-reflection manipulation and in-group∖out-group ratings had taken place because we wanted to avoid potentially priming self-reflection processes in participants prior to their exposure to the self-reflection manipulation.

5 We also unpacked the in-group favoritism factor and ran the model on each of the individual pull scores. There was no significant difference between these models and the models using the in-group favoritism factor.

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Research paper: growth mindset and personal growth.

Personal growth may become an important topic for each of us at any moment in time. It shows when we realize the need to live our life with purpose, authenticity, and showing concern towards others.

Personal growth can be referred also as self-improvement and self-growth.

It is a process that requires an open mind, motivation, the desire to improve, to learn, but also the willingness to get out of the current comfort zone, and even to feel sometimes uncomfortable. It also implies to strive to make changes.

Personal growth is possible if we are willing to develop a positive mindset, a growth mindset.

According to the Merriam Webster dictionary, one definition of growth is“progressive development” and mindset is defined as“a mental attitude or inclination”.

The logical conclusion is that having a growth mindset means having a mental attitude that leads to progressive development.

The growth mindset has its roots in the Mindset Theory. At the base of the theory sits the idea that our beliefs influence the way we respond to life’s situations. The theory premises start from the way we assess our capabilities, talents, and intelligence and their impact on our lives and success. (Murphy & Dweck, 2016).

Over 30 years ago, ProfessorCarol Dweck and her colleagues studied the behavior of thousands of children on learning and they defined terms as fixed mindset and growth mindset to describe the underlying beliefs about learning and intelligence. The study demonstrated that understanding the need of putting in extra effort and time in our endeavors can lead to higher achievement.

Fixed Mindset

A fixed mindset describes people who believe that intelligence, talent, or natural-born skills are enough to lead to success and these cannot be improved.

People with fixed mindsets may avoid challenges, give up easily, and ignore useful feedback.

Growth Mindset

A growth mindset describes people who believe that can get better at something or succeed by the dedication of time, effort, and energy. People with a growth mindset embrace challenges, overcome obstacles, learn from feedback, and seek out inspiration, that can contribute to their success.

The difference between the two mindsets is that one view doesn’t support change, while the other support opportunities for improvement and can lead to change.

Understanding the benefit of cultivating a growth mindset can be very powerful and willingness to put in extra time and effort can lead to achieving desired goals. This can contribute to a fuller, more meaningful life.

The dominant mindset can show on handling a task, a project, or a situation.

We can choose to tell ourselves that we cannot handle it, we do not have enough abilities, we are not trained to do that we will find excuses not to handle it. We can choose also to see it as an opportunity to learn something new, to improve our skills, to search for resources and support to handle the topic, and finally to grow.

The growth mindset can lead us in acquiring new skills, new knowledge, and step into new areas of expertise.

How can the growth mindset be cultivated?

Personal growth is a continuing process. Cultivating a growth mindset can be very empowering and can boost our confidence to try new ways, determine us to concentrate our efforts on desired outcomes, and move us one step forward towards achieving what is meaningful to us.

By knowing precisely what we want to achieve, we can think about what skills can we use, what else do we need to learn, when is the right time to start; we can estimate how much time do we need and where do we want to concentrate our efforts. We would be able to deal with problems and challenges, to spot the distractions or barriers that might stop us or lead us astray. We move on the path to growth, learning, self-esteem, and confidence.

Self-reflection:

• Define for yourself what your purpose encompasses;
• Explore the things you are passionate about;
• Cultivate your self-awareness: more aware of your strengths and weaknesses;
• Reflect on your former experiences: understand and apply the learnings;
• Replace judgment with acceptance: pay attention to your words and thoughts;
• Create a new perspective and trust your skills and abilities;
• Commit to embracing challenges: prepare yourself for success and risk;
• Estimate realistically the time and effort your goal will take;
• Cultivate your determination and perseverance: put in hard work, weight the obstacles, think about the measures in place to overcome them;
• Value the learning process over the result;
• Reflect on your learning regularly;
• Take ownership of your attitude.

Coaching Application

Coaching serves us to explore a new path or to navigate through new circumstances.

The coaching space is set for us to explore the many perspectives that we experience in life, to enhance our skills and resources, to discover the answers within ourselves. 

During the coaching journey, you can explore how you look at the world, what inspires you; you can achieve clarity and you can understand what ideas or thoughts might be blocking our progress.

You will begin to see and think through things in a clearer and more balanced way:

• Where am I now?
• Where do I want to be?
• How can I get there?

ICA Coaching Power Tools for personal growth empowerment

Reframing is an important part of the coaching process. It’s about creating a shift and seeing new perspectives. We can look at a situation, analyze it, discern, and re-frame perspectives. However, a coach is an objective observer to our situation and can help the client, to identify unhelpful perspectives, and to support him to re-frame them. (ICA Reframing perspectives)

During the coaching process, the client is supported to lighten his current perspective, he will be able to step back, take some distance, and gain greater clarity on the situation. The clients present in the moment, free of judgments that can bind him to old ways of thinking. Discussing the worst-case scenario enables shifting the view from significance to lightness. (ICA Lightness vs Significance)

We can create a powerful platform for sustainable personal and professional growth if we start from a self-trust perspective. Often, we give space to doubt, as we let the judgments about ourselves prevail and hold us back. To trust ourselves we need to know what our beliefs are, our values, and our purpose. The coach is partnering with the client to explore and find ways in which they can create a perspective of trust. (ICA Trust vs Doubt)

Blame keeps us from fully enjoying and engaging in our lives. When we view responsibility as a privilege, instead of as a burden, we awaken many possibilities for change and growth. Using a powerful question, the coach can support the client to consider his role in the situation, and thus taking a big step towards becoming responsible. (ICA Responsibility vs Blame)

Action is the foundation needed to push us forward to achieve our goals. It is the path to growth, learning, self-esteem, and confidence. Negative judgments about ourselves can pull us back and keeps us stuck, choosing to delay instead to act. The coach can support the client to explore a different point of view and thus creating a new way of looking at the situation. By using powerful questioning, the coach can support the client to move from a dis-empowering to an empowering perspective. The client can decide to turn concerns into action. (ICA Action vs Delay)

Exploring our underlying beliefs and what we are truly committed to, we can realize that we have choices. Following through on commitments and act upon, instead of only trying gives us the desire to change. When we are determined to commit to our values, our vision, we are more likely to view these as learning opportunities and keep moving forward. In the process, we will build confidence and self-trust. Working with the client, the coach will need to support him in understanding what he wants to commit to. The coach’s role is to support the client to see the importance of aligning his own beliefs with the desired pathway forward. (ICA Commitment vs Trying)

Some thought-provoking questions to support the client in moving forward:

• What is it that you're deeply passionate about?
• Which are the areas that interest you?
• What do others see in you?
• What is your reaction when faced with a demand or a challenge?
• If you would consider your goal without worrying, what would you do?
• What might keep you from getting where you want to go?
• If all the obstacles would be removed how would that change your view?
• What can you do about the part you can/ cannot change?
• Let’s just assume for a moment that the worst thing that could happen has already happened what will you do now?
• Which option do you want to pursue?
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College as a Growth Opportunity: Assessing Personal Growth Initiative and Self-determination Theory

• Research Paper
• Published: 20 September 2020
• Volume 22 , pages 2143–2163, ( 2021 )

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• Ingrid K. Weigold   ORCID: orcid.org/0000-0002-0086-0102 1 ,
• Arne Weigold 2 ,
• Shu Ling 1 &
• Migyeong Jang 1  

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Although college presents a time for personal and vocational development, little research has examined individuals’ intentional use of college as a growth opportunity. Consequently, the current study assessed relationships among personal growth initiative (an individual’s active desire to grow in personally relevant domains), basic needs satisfaction at college, and positive outcomes in samples of students from a large, public, predominantly White institution ( n  = 818) and a small, private, minority-majority college ( n  = 195). Using structural equation modeling, we examined a hypothesized model in which personal growth initiative was indirectly related to the two outcomes (psychological well-being and vocational commitment) through the satisfaction of the basic needs of autonomy, competence, and relatedness; this was compared to a reverse model. The hypothesized model was a better fit for the data in the university sample, and both models had similar fit for the college sample. Tests of indirect effects using the hypothesized model showed evidence of mediation, with similarities and differences between the samples. Finally, the two models were invariant across institutions at the structural level. PGI and basic needs satisfaction explained over half of the variance in psychological well-being and approximately one-quarter of the variance in vocational commitment in both samples.

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Weigold, I.K., Weigold, A., Ling, S. et al. College as a Growth Opportunity: Assessing Personal Growth Initiative and Self-determination Theory. J Happiness Stud 22 , 2143–2163 (2021). https://doi.org/10.1007/s10902-020-00312-x

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Accepted : 11 September 2020

Published : 20 September 2020

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DOI : https://doi.org/10.1007/s10902-020-00312-x

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