What the Meta-Analyses Say About Grit

Last Updated May 27, 2026

Meta-analyses changed the conversation about grit. Early grit research made a strong and influential claim: long-term achievement depends not only on talent, intelligence, or opportunity, but also on perseverance and passion for long-term goals. That claim helped bring sustained effort, delayed reward, and long-term commitment into the center of educational and psychological debate. But once many studies accumulated, meta-analyses began asking a more demanding question: how strong is the evidence when the full literature is synthesized?

The answer is neither simple rejection nor simple confirmation. The meta-analytic literature suggests that grit is meaningfully related to achievement, performance, retention, and academic outcomes, but usually more modestly than popular accounts imply. The most consistent finding is that perseverance of effort tends to matter more than consistency of interests. The second major finding is that grit overlaps substantially with conscientiousness and related self-regulatory traits. The third is that grit should be interpreted as one part of a wider developmental and institutional system rather than as a stand-alone explanation for success.

This deep dive examines what the meta-analyses say about grit, what they do not say, and how their findings should shape responsible interpretation. The evidence supports grit as a useful construct, but not as a master key. It points toward a more careful science of persistence: facet-level interpretation, modest effect sizes, stronger research design, attention to measurement, and serious consideration of opportunity, support, burnout, and social context.

Overview

Grit became influential because it offered a compelling explanation for long-term achievement: people succeed not only because they are talented, but because they sustain effort and commitment across time. That idea resonated in education, psychology, parenting, leadership, sport, military training, and public culture. But broad appeal is not the same as settled evidence.

Meta-analysis is the point at which a research field begins to ask whether the accumulated evidence supports the popular narrative. Instead of relying on one striking study, a meta-analysis combines results across many studies to estimate overall patterns. It asks how strongly grit is associated with performance, retention, academic achievement, conscientiousness, self-control, well-being, or other outcomes. It also asks whether the evidence is consistent, whether different facets behave differently, and whether the construct adds anything beyond older personality traits.

The meta-analytic evidence has narrowed the strongest claims about grit. It supports a relationship between grit and achievement-related outcomes, but the relationship is usually modest. It suggests that perseverance of effort is generally more predictive than consistency of interests. It shows that grit overlaps strongly with conscientiousness. It cautions against treating grit as a simple solution for educational or organizational performance.

This does not mean grit is meaningless. It means grit should be interpreted with greater precision. The best reading of the evidence is not “grit does not matter.” It is: grit matters in some ways, under some conditions, through some facets, and alongside many other predictors.

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Why meta-analyses matter for grit

Individual studies are valuable, but they are limited. One study may use a small sample, a narrow population, a specific outcome, a particular version of a scale, or a setting where grit is unusually relevant. Another study may find a weaker effect or no meaningful incremental validity. Meta-analysis helps move the field beyond selective examples.

For grit, meta-analyses matter because the public narrative became stronger than the evidence could safely support. Popular grit discourse often implied that perseverance and passion were decisive ingredients of success. Meta-analysis asks whether that claim holds when many studies are examined together.

Meta-analysis also makes heterogeneity visible. Grit may relate differently to academic grades, military retention, workplace persistence, sports outcomes, subjective well-being, and long-term project completion. It may operate differently across age groups, cultures, educational levels, and measurement instruments. It may matter more when outcomes require sustained effort and less when outcomes are driven by ability, prior preparation, institutional access, or external constraints.

Most importantly, meta-analysis can separate broad enthusiasm from disciplined inference. It does not ask whether grit is inspiring. It asks what the evidence supports.

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The Credé, Tynan, and Harms synthesis

The most important early meta-analysis of grit was the synthesis by Credé, Tynan, and Harms. It directly challenged the strongest public claims about grit. The authors examined the structure of grit and its relations with performance, retention, conscientiousness, cognitive ability, and demographic variables. Their analysis concluded that grit is related to performance and retention, but not as strongly or as uniquely as popular accounts suggested.

Their central contribution was not simply that grit has modest effects. It was that grit appears psychometrically and conceptually less tidy than the public narrative implied. The higher-order grit construct was questioned, the overlap with conscientiousness was substantial, and perseverance of effort emerged as more useful than consistency of interests for many criterion outcomes.

This finding changed the responsible interpretation of grit. Instead of treating grit as a single superior character trait, researchers were pushed to examine the facets separately. The phrase “grit predicts success” became too broad. A better question became: does perseverance of effort predict a specific outcome after accounting for conscientiousness, prior achievement, cognitive ability, and context?

The Credé synthesis is therefore best understood as a refinement, not a demolition. It did not prove that effort and persistence do not matter. It showed that the construct of grit needs a more modest and precise interpretation.

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Grit and academic achievement meta-analyses

Academic achievement became one of the most important domains for grit research because schools are environments where long-term effort matters. Students must study, practice, revise, recover from mistakes, tolerate slow progress, and maintain goals over time. It is therefore plausible that grit would relate to academic outcomes.

Meta-analytic work on grit and academic achievement generally finds positive associations, but the effects are weak to moderate rather than large. Lam and Zhou’s systematic review and meta-analysis of K–12 and higher education found positive associations between overall grit and academic achievement, with perseverance of effort showing the largest effect relative to total grit and consistency of interest. A later cross-cultural meta-analysis reported a generally weak-to-moderate association between overall grit and academic achievement, with perseverance again stronger than consistency.

These findings are important because they neither dismiss grit nor justify overclaiming. Grit may support academic outcomes, especially through effort. But academic achievement is also shaped by prior knowledge, instruction, socioeconomic conditions, school quality, health, family support, peer culture, disability accommodations, discrimination, and assessment systems.

The meta-analytic lesson for education is clear: grit may be relevant, but it should not become a substitute for good teaching, adequate support, or structural fairness.

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Perseverance of effort versus consistency of interests

The clearest meta-analytic message is that perseverance of effort and consistency of interests should not be treated as interchangeable. Perseverance of effort is usually the stronger and more useful facet when predicting performance or achievement-related outcomes. Consistency of interests is conceptually important but empirically weaker in many analyses.

This makes sense. Many achievement outcomes depend directly on sustained action. Studying, practicing, revising, training, completing assignments, continuing after failure, and persisting through difficulty all belong to the effort dimension. Consistency of interests may help effort accumulate over time, but it is less directly connected to immediate performance indicators.

Consistency is also developmentally complicated. People need exploration. Students may change majors. Early-career adults may test roles. Researchers may revise questions. Artists may change forms. Workers may leave harmful institutions. A scale that treats changing interests as low grit may confuse adaptive development with deficiency.

The meta-analytic conclusion should therefore be facet-sensitive: perseverance of effort deserves serious attention, while consistency of interests should be interpreted with developmental and contextual care.

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Grit and conscientiousness

Meta-analyses repeatedly raise the same question: is grit truly distinct from conscientiousness? Conscientiousness is a broad Big Five personality trait involving responsibility, diligence, industriousness, dependability, organization, and goal-directed behavior. Grit, especially perseverance of effort, sits close to this trait family.

The overlap matters because psychology already had concepts for diligence and long-term effort before grit became popular. If grit mainly repackages conscientiousness in more motivational language, then researchers should not claim that grit is a wholly new predictor. The correct question is incremental validity: does grit explain meaningful variance beyond conscientiousness and other established predictors?

The answer is often modest. Grit may add some conceptual value by emphasizing long-term goals, perseverance, and durable commitment. But the meta-analytic evidence does not support treating grit as radically separate from older personality constructs.

For practice, this means grit should not be used as a fashionable replacement for more established constructs. It can be useful, but it belongs inside a larger personality and self-regulation framework.

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Performance, retention, and persistence outcomes

Grit is often expected to matter most where outcomes require persistence over time. Performance, retention, and persistence outcomes are therefore central to the meta-analytic literature. These include staying in demanding programs, completing tasks, maintaining effort, performing in difficult training environments, and continuing toward long-term objectives.

The evidence suggests that grit is related to these outcomes, but the relationship is not strong enough to treat grit as a stand-alone explanation. In retention contexts, for example, grit may help explain who continues, but continuation is also shaped by fit, support, health, institutional climate, economic constraints, and whether the setting deserves continued participation.

This distinction is ethically important. Retention is not always good. Staying in a harmful program, exploitative workplace, abusive institution, or misaligned path should not automatically be celebrated. A person may leave because they lack support, but they may also leave because leaving is wise.

Meta-analyses can estimate average associations between grit and retention-like outcomes. They cannot determine whether persistence in a specific context is healthy, just, or meaningful.

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Passion, perseverance, and interaction effects

Some meta-analytic work argues that grit should not be reduced to perseverance alone. Jachimowicz and colleagues emphasized that passion may be a key component of grit because perseverance may predict performance more positively when joined to strong passion for the goal. This line of work challenges a purely additive interpretation.

The idea is conceptually important. Perseverance without passion may become mechanical endurance. Passion without perseverance may remain aspiration. Together, they may create a more powerful pattern: effort that is sustained because the goal remains meaningful.

However, this argument does not erase the earlier meta-analytic caution. It suggests that the relation among facets may be more complex than simple averaging. Passion may matter less as an independent predictor and more as a moderator of perseverance. In other words, passion may change the meaning of effort rather than merely add another quantity to a total score.

This is a useful refinement because it moves grit research away from one-number interpretation. It asks how effort and interest interact, under what conditions, and for which outcomes.

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Effect sizes and practical significance

One of the most important lessons from meta-analysis is that statistical significance is not the same as practical significance. With large samples, small effects can become statistically significant. The practical question is whether those effects are large enough to guide policy, intervention, selection, or institutional design.

For grit, the meta-analytic effects are often positive but modest. That means grit may matter, but it should not be treated as a decisive predictor. A modest correlation may be meaningful in research, especially across large populations, but it is not strong enough to justify high-stakes individual decisions.

Practical significance also depends on comparison. If prior achievement, academic self-efficacy, socioeconomic status, instruction, attendance, cognitive ability, conscientiousness, or school quality predict outcomes more strongly, then grit should not be given disproportionate attention. It may still be useful, but only within a balanced model.

Effect sizes also need ethical interpretation. A small average association can become dangerous if institutions exaggerate it into a character judgment. The evidence supports humility.

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Measurement problems in the meta-analytic evidence

Meta-analyses are only as strong as the studies they synthesize. If the primary studies use self-report measures, cross-sectional designs, inconsistent outcomes, different grit scales, or limited controls, the meta-analysis inherits those limitations.

Grit measurement raises several issues. The most common grit scales are self-report instruments. Respondents may overstate or understate persistence depending on social desirability, self-image, comparison group, mood, recent success or failure, and the setting where the measure is administered. Short scales are efficient, but they may also reduce nuance.

Another issue is whether studies use total grit scores or facet scores. If total scores are used, perseverance of effort and consistency of interests are averaged together. This can obscure which facet is responsible for any association with outcomes. Because meta-analyses often depend on the measures and reporting practices of the original studies, inconsistent facet reporting becomes a field-wide limitation.

The strongest future research will use longitudinal designs, transparent measurement, preregistered models where appropriate, facet-level analysis, and careful controls for related traits and structural conditions.

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Moderators, culture, and educational level

Meta-analyses also ask whether the grit-outcome relationship varies across conditions. These conditions are moderators: features of samples, measures, cultures, developmental stages, or outcomes that change the strength of associations.

Educational level may matter because grit has different meanings at different ages. In childhood and adolescence, changing interests may reflect normal exploration. In higher education, consistency of interests may reflect specialization, but students may still be forming identity and direction. In adult work, persistence may be shaped by role fit, labor conditions, autonomy, and institutional support.

Culture may also matter, though findings are complex. The meaning of perseverance, passion, duty, achievement, family obligation, and long-term goal pursuit may differ across cultural settings. A construct developed in one cultural context should not be assumed to function identically everywhere. Measurement invariance, translation, and cultural interpretation matter.

Moderator evidence points toward a more contextual science of grit. The question is not only whether grit matters on average. It is when, for whom, under what conditions, and through which facet.

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What meta-analyses do not say

Meta-analyses are powerful, but they do not answer every question. They estimate patterns in the available literature. They do not directly observe the full complexity of human development.

A meta-analysis does not tell whether one person should persist in a particular goal. It does not determine whether leaving a school, workplace, relationship, career, or project is wise. It does not measure whether an institution deserves continued commitment. It does not decide whether a specific form of persistence is healthy or harmful.

Meta-analyses also cannot fully correct for structural omissions in primary studies. If the original studies undermeasure poverty, racism, disability, caregiving demands, school quality, health, trauma, or institutional support, the meta-analysis cannot magically restore those missing variables. It can only synthesize what was measured.

This matters because grit can become a language of blame. The meta-analytic evidence does not justify telling individuals that their outcomes are mainly the result of grit. It supports a more careful conclusion: persistence is one variable in a larger system of person, practice, support, opportunity, and power.

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Interpretive lessons for researchers and practitioners

The meta-analytic literature points toward several practical lessons. First, do not overstate grit. It is related to achievement-related outcomes, but the effect is usually modest. Second, separate the facets. Perseverance of effort and consistency of interests do not always behave the same way. Third, include controls. Grit should be studied alongside conscientiousness, prior achievement, cognitive ability, socioeconomic conditions, support, and burnout.

Fourth, avoid high-stakes use. The evidence does not support using grit scores for admissions, hiring, placement, discipline, promotion, or ranking. Fifth, attend to context. Persistence depends on institutions, resources, safety, feedback, and meaningful opportunity. Sixth, design environments rather than blaming individuals. Schools and organizations should support effort through better feedback, humane workloads, recovery, belonging, and clear pathways.

The strongest interpretation is developmental and institutional. Grit is not a private virtue floating above the world. It is a pattern of effort and commitment shaped by context. Meta-analysis makes that clear by showing that grit matters, but not enough to carry the explanatory burden alone.

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A mathematical lens on meta-analysis

A meta-analysis often begins by converting study findings into comparable effect sizes. For correlations, a common transformation is Fisher’s \(z\):

Each study is weighted, often by the inverse of its variance:

The pooled effect can then be estimated as a weighted mean:

Facet-level grit analysis can be represented through separate effect-size syntheses:

This mathematical distinction is the heart of the grit debate. If the two facet correlations differ, then a total grit score may not be the most informative unit of analysis.

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Python workflow: simulating a grit meta-analysis

The following Python workflow simulates study-level correlations for overall grit, perseverance of effort, and consistency of interests. It uses Fisher’s z transformation to estimate pooled random-effects-style summaries. The data are synthetic and intended for article support and research-method demonstration only.

# Python workflow: simulating a grit meta-analysis
# Synthetic data for article support and research-method demonstration only.
# Do not use this as a substitute for a real systematic review or meta-analysis.

import numpy as np
import pandas as pd

rng = np.random.default_rng(42)

# Simulate study-level metadata
k = 60
sample_size = rng.integers(150, 2500, size=k)

# Simulate study-level correlations with academic or performance outcomes
# These values are illustrative and not extracted from real studies.
r_total_grit = np.clip(rng.normal(0.18, 0.08, size=k), -0.10, 0.45)
r_perseverance = np.clip(rng.normal(0.22, 0.08, size=k), -0.10, 0.50)
r_consistency = np.clip(rng.normal(0.08, 0.07, size=k), -0.15, 0.35)

df = pd.DataFrame({
    "study_id": [f"study_{i+1:02d}" for i in range(k)],
    "sample_size": sample_size,
    "r_total_grit": r_total_grit,
    "r_perseverance": r_perseverance,
    "r_consistency": r_consistency
})

def fisher_z(r):
    return 0.5 * np.log((1 + r) / (1 - r))

def inverse_fisher_z(z):
    return (np.exp(2 * z) - 1) / (np.exp(2 * z) + 1)

def fixed_effect_meta_r(r_values, n_values):
    z_values = fisher_z(r_values)
    variances = 1 / (n_values - 3)
    weights = 1 / variances
    pooled_z = np.sum(weights * z_values) / np.sum(weights)
    pooled_se = np.sqrt(1 / np.sum(weights))
    lower_z = pooled_z - 1.96 * pooled_se
    upper_z = pooled_z + 1.96 * pooled_se
    return {
        "pooled_r": inverse_fisher_z(pooled_z),
        "ci_lower": inverse_fisher_z(lower_z),
        "ci_upper": inverse_fisher_z(upper_z)
    }

summary = pd.DataFrame([
    {"facet": "total_grit", **fixed_effect_meta_r(df["r_total_grit"], df["sample_size"])},
    {"facet": "perseverance_of_effort", **fixed_effect_meta_r(df["r_perseverance"], df["sample_size"])},
    {"facet": "consistency_of_interests", **fixed_effect_meta_r(df["r_consistency"], df["sample_size"])}
])

print("Synthetic study-level data:")
print(df.head())

print("\nSynthetic pooled correlations:")
print(summary.round(3))

print("\nInterpretation:")
print(
    "In this synthetic example, perseverance of effort has the largest pooled "
    "association with the outcome, while consistency of interests is weaker. "
    "This mirrors a common interpretive lesson from the grit meta-analytic literature: "
    "facet-level evidence is more informative than a single total grit score."
)

This workflow illustrates how a meta-analysis can change interpretation. Instead of asking only whether grit matters, the analysis asks which component matters, how much, and with what uncertainty.

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R workflow: random-effects synthesis of grit correlations

The following R workflow simulates study-level correlations and uses Fisher’s z transformation to compute a simple DerSimonian-Laird-style random-effects synthesis. It is intentionally compact and educational. A real meta-analysis would require systematic search, inclusion criteria, coding, risk-of-bias assessment, moderator analysis, publication-bias checks, and transparent reproducibility.

# R workflow: random-effects synthesis of grit correlations
# Synthetic data for article support and research-method demonstration only.
# Do not use this as a substitute for a real systematic review or meta-analysis.

set.seed(42)

k <- 60
sample_size <- sample(150:2500, k, replace = TRUE)

r_total_grit <- pmin(pmax(rnorm(k, mean = 0.18, sd = 0.08), -0.10), 0.45)
r_perseverance <- pmin(pmax(rnorm(k, mean = 0.22, sd = 0.08), -0.10), 0.50)
r_consistency <- pmin(pmax(rnorm(k, mean = 0.08, sd = 0.07), -0.15), 0.35)

df <- data.frame(
  study_id = paste0("study_", sprintf("%02d", 1:k)),
  sample_size,
  r_total_grit,
  r_perseverance,
  r_consistency
)

fisher_z <- function(r) {
  0.5 * log((1 + r) / (1 - r))
}

inverse_fisher_z <- function(z) {
  (exp(2 * z) - 1) / (exp(2 * z) + 1)
}

random_effects_meta_r <- function(r_values, n_values) {
  z_values <- fisher_z(r_values)
  vi <- 1 / (n_values - 3)

  wi_fixed <- 1 / vi
  z_fixed <- sum(wi_fixed * z_values) / sum(wi_fixed)

  q <- sum(wi_fixed * (z_values - z_fixed)^2)
  df_q <- length(z_values) - 1
  c_value <- sum(wi_fixed) - (sum(wi_fixed^2) / sum(wi_fixed))
  tau2 <- max(0, (q - df_q) / c_value)

  wi_random <- 1 / (vi + tau2)
  z_random <- sum(wi_random * z_values) / sum(wi_random)
  se_random <- sqrt(1 / sum(wi_random))

  lower_z <- z_random - 1.96 * se_random
  upper_z <- z_random + 1.96 * se_random

  data.frame(
    pooled_r = inverse_fisher_z(z_random),
    ci_lower = inverse_fisher_z(lower_z),
    ci_upper = inverse_fisher_z(upper_z),
    tau2 = tau2,
    q = q
  )
}

summary <- rbind(
  cbind(facet = "total_grit", random_effects_meta_r(df$r_total_grit, df$sample_size)),
  cbind(facet = "perseverance_of_effort", random_effects_meta_r(df$r_perseverance, df$sample_size)),
  cbind(facet = "consistency_of_interests", random_effects_meta_r(df$r_consistency, df$sample_size))
)

print(head(df))
print(round(summary, 3))

cat("
Interpretation:
This synthetic workflow shows how separate pooled estimates can be produced
for total grit, perseverance of effort, and consistency of interests. A real
meta-analysis would require systematic study retrieval, transparent coding,
risk-of-bias assessment, moderator analysis, and publication-bias checks.
")

This workflow reinforces the main methodological lesson: meta-analysis is not only about producing one average effect. It is about clarifying which construct, which facet, which outcome, and which context are being synthesized.

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GitHub Repository

The companion GitHub repository provides a reproducible research-code structure for the Grit knowledge series, including article-specific workflows, synthetic data examples, documentation, and multi-language modeling assets.

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Conclusion

The meta-analyses say that grit matters, but not in the simple way popular culture often suggests. Grit is positively related to achievement, performance, and persistence-related outcomes, but the effects are usually modest. Grit overlaps substantially with conscientiousness. Perseverance of effort is generally more predictive than consistency of interests. Total grit scores can hide important facet-level differences. Measurement matters.

The strongest conclusion is not that grit should be abandoned. It is that grit should be interpreted more carefully. The construct is useful when it helps researchers and practitioners study sustained effort, long-term goal pursuit, and the developmental conditions of persistence. It becomes misleading when treated as a master explanation for success or as a moral label for individuals.

For education, the meta-analytic evidence argues against grit slogans and in favor of better learning environments: feedback, revision, support, belonging, goal clarity, and humane challenge. For organizations, it argues against glorifying endurance and in favor of designing work that makes sustained effort meaningful and sustainable. For researchers, it argues for facet-level models, stronger controls, longitudinal designs, and careful attention to context.

Grit remains worth studying, but the evidence asks for humility. Perseverance can matter. Passion can matter. Long-term goals can matter. But they matter inside lives shaped by instruction, opportunity, health, institutions, culture, resources, and power. The science of grit becomes stronger when it stops trying to explain everything and begins explaining precisely what it can.

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Related articles

• What Is Grit?
• Angela Duckworth and the Modern Science of Grit
• Perseverance and Passion for Long-Term Goals
• Grit in Positive Psychology
• The Original Grit Scale and What It Measures
• The Short Grit Scale and the Problem of Measurement
• Perseverance of Effort Versus Consistency of Interests
• Grit and Conscientiousness: Overlap, Distinction, and Debate
• Grit, Burnout, and the Risks of Overpersistence
• Grit and Academic Achievement

Further reading

• Duckworth, A.L. (2016) Grit: The Power of Passion and Perseverance. New York: Scribner.
• Peterson, C. and Seligman, M.E.P. (2004) Character Strengths and Virtues: A Handbook and Classification. New York: Oxford University Press.
• Seligman, M.E.P. (2011) Flourish: A Visionary New Understanding of Happiness and Well-being. New York: Free Press.
• National Research Council (2012) Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century. Washington, DC: The National Academies Press. Available at: https://www.nationalacademies.org/publications/13398/education-for-life-and-work-developing-transferable-knowledge-and-skills-in-the-21st-century
• University of Chicago Consortium on School Research (2015) Foundations for Young Adult Success: A Developmental Framework. Available at: https://consortium.uchicago.edu/publications/foundations-young-adult-success-developmental-framework
• Harrer, M., Cuijpers, P., Furukawa, T.A. and Ebert, D.D. (2021) Doing Meta-Analysis with R: A Hands-On Guide. Boca Raton: Chapman & Hall/CRC. Available at: https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/

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References

• Credé, M. (2018) ‘What shall we do about grit? A critical review of what we know and what we don’t know’, Educational Researcher, 47(9), pp. 606–611. Available at: https://doi.org/10.3102/0013189X18801322
• Credé, M., Tynan, M.C. and Harms, P.D. (2017) ‘Much ado about grit: A meta-analytic synthesis of the grit literature’, Journal of Personality and Social Psychology, 113(3), pp. 492–511. Available at: https://doi.org/10.1037/pspp0000102
• Datu, J.A.D. (2021) ‘Beyond passion and perseverance: Review and future research initiatives on the science of grit’, Frontiers in Psychology, 11, 545526. Available at: https://doi.org/10.3389/fpsyg.2020.545526
• Datu, J.A.D., Valdez, J.P.M. and King, R.B. (2016) ‘Perseverance counts but consistency does not! Validating the Short Grit Scale in a collectivist setting’, Current Psychology, 35, pp. 121–130. Available at: https://doi.org/10.1007/s12144-015-9374-2
• Duckworth, A.L. (n.d.) Research. Available at: https://angeladuckworth.com/research/
• Duckworth, A.L., Peterson, C., Matthews, M.D. and Kelly, D.R. (2007) ‘Grit: Perseverance and passion for long-term goals’, Journal of Personality and Social Psychology, 92(6), pp. 1087–1101. Available at: https://doi.org/10.1037/0022-3514.92.6.1087
• Duckworth, A.L. and Quinn, P.D. (2009) ‘Development and validation of the Short Grit Scale (Grit–S)’, Journal of Personality Assessment, 91(2), pp. 166–174. Available at: https://doi.org/10.1080/00223890802634290
• Duckworth, A.L. and Yeager, D.S. (2015) ‘Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes’, Educational Researcher, 44(4), pp. 237–251. Available at: https://doi.org/10.3102/0013189X15584327
• Gonzalez, O., Canning, J.R., Smyth, H. and MacKinnon, D.P. (2020) ‘A psychometric evaluation of the Short Grit Scale’, European Journal of Psychological Assessment, 36(4), pp. 646–657. Available at: https://doi.org/10.1027/1015-5759/a000535
• Jachimowicz, J.M., Wihler, A., Bailey, E.R. and Galinsky, A.D. (2018) ‘Why grit requires perseverance and passion to positively predict performance’, Proceedings of the National Academy of Sciences, 115(40), pp. 9980–9985. Available at: https://doi.org/10.1073/pnas.1803561115
• Lam, K.K.L. and Zhou, M. (2019) ‘Examining the relationship between grit and academic achievement within K–12 and higher education: A systematic review’, Psychology in the Schools, 56(10), pp. 1654–1686. Available at: https://doi.org/10.1002/pits.22302
• Lam, K.K.L. and Zhou, M. (2022) ‘Grit and academic achievement: A comparative cross-cultural meta-analysis’, Journal of Educational Psychology, 114(3), pp. 597–621. Available at: https://doi.org/10.1037/edu0000699
• National Research Council (2012) Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century. Washington, DC: The National Academies Press. Available at: https://www.nationalacademies.org/publications/13398/education-for-life-and-work-developing-transferable-knowledge-and-skills-in-the-21st-century
• Roberts, B.W., Kuncel, N.R., Shiner, R., Caspi, A. and Goldberg, L.R. (2007) ‘The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes’, Perspectives on Psychological Science, 2(4), pp. 313–345. Available at: https://doi.org/10.1111/j.1745-6916.2007.00047.x
• University of Chicago Consortium on School Research (2015) Foundations for Young Adult Success: A Developmental Framework. Chicago: University of Chicago Consortium on School Research. Available at: https://consortium.uchicago.edu/publications/foundations-young-adult-success-developmental-framework

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