meta, analytical, effect, economic, inequality, well
Buttrick, N. R. & Oishi, S. The psychological consequences of income inequality. Soc. Pers. Psychol. Compass 11, e12304 (2017).
Google Scholar
Wilkinson, R. G. & Pickett, K. E. The enemy between us: the psychological and social costs of inequality. Eur. J. Soc. Psychol. 47, 11–24 (2017).
Google Scholar
Rodríguez-Bailón, R., Sánchez-Rodríguez, Á, García-Sánchez, E., Petkanopoulou, K. & Willis, G. B. Inequality is in the air: contextual psychosocial effects of power and social class. Curr. Opin. Psychol. 33, 120–125 (2020).
Google Scholar
Wienk, M. N., Buttrick, N. R. & Oishi, S. The social psychology of economic inequality, redistribution, and subjective well-being. Eur. Rev. Soc. Psychol. 33, 45–80 (2022).
Google Scholar
Carr, P. ‘How the other half lives’?: taking a critical approach to the social psychology of economic inequality and extreme wealth. Soc. Pers. Psychol. Compass 17, e12743 (2023).
Google Scholar
Fusar-Poli, P. et al. Preventive psychiatry: a blueprint for improving the mental health of young people. World Psychiatry 20, 200–221 (2021).
Google Scholar
Heinz, A., Zhao, X. & Liu, S. Implications of the association of social exclusion with mental health. JAMA Psychiatry 77, 113–114 (2020).
Google Scholar
Herrman, H. et al. Time for united action on depression: a Lancet–World Psychiatric Association Commission. Lancet 399, 957–1022 (2022).
Google Scholar
Lengfelder, C. Exploring Dynamics of Inequality in Human Development. Background Paper No. 3-2019 (United Nations Development Programme, 2019).
Occhipinti, J. A. et al. The influence of economic policies on social environments and mental health. Bull. World Health Organ. 102, 323–329 (2024).
Google Scholar
Saunders, P. & Evans, N. Beware False Prophets (Centre for Independent Studies, 2011).
Snowdon, C. The Spirit Level Delusion: Fact-checking the Left’s New Theory of Everything. (Little Dice, 2010).
Pinker, S. Enlightenment Now: The Case for Reason, Science, Humanism, and Progress (Penguin UK, 2018).
Hirschman, A. O. & Rothschild, M. The changing tolerance for income inequality in the course of economic development. Q. J. Econ. 87, 544–566 (1973).
Google Scholar
Cheung, F. Can income inequality be associated with positive outcomes? Hope mediates the positive inequality–happiness link in rural China. Soc. Psychol. Pers. Sci. 7, 320–330 (2016).
Google Scholar
Sommet, N. & Elliot, A. J. A competitiveness-based theoretical framework to study the psychology of income inequality. Curr. Dir. Psychol. Sci. 32, 318–327 (2023).
Google Scholar
Rodgers, M. A. & Pustejovsky, J. E. Evaluating meta-analytic methods to detect selective reporting in the presence of dependent effect sizes. Psychol. Methods 26, 141–160 (2020).
Google Scholar
Voracek, M., Kossmeier, M. & Tran, U. S. Which data to meta-analyze, and how?. Z. Psychol. 227, 64–82 (2019).
Van Lissa, C. J. in Small sample size solutions. A Guide for Applied Researchers and Practitioners (eds van de Schoot, R. & Miočević, M) 186–202 (Routledge, 2020).
Atkinson, A. B. On the measurement of inequality. J. Econ. Theory 2, 244–263 (1970).
Google Scholar
Chancel, L., Piketty, T., Saez, E. & Zucman, G. World Inequality Report 2022 (Harvard Univ. Press, 2022).
United Nations. Inequality—Bridging the Divide (United Nations, 2020).
Yang, Y. & Konrath, S. A systematic review and meta-analysis of the relationship between economic inequality and prosocial behaviour. Nat. Hum. Behav. 7, 1899–1916 (2023).
Google Scholar
Shimonovich, M. et al. Causal assessment of income inequality on self-rated health and all-cause mortality: a systematic review and meta-analysis. Milbank Q. 102, 141–182 (2024).
Google Scholar
Pazzona, M. Revisiting the income inequality–crime puzzle. World Dev. 176, 106520 (2024).
Google Scholar
Wilkinson, R. & Pickett, K. The Spirit Level: Why Equality is Better for Everyone (Penguin UK, 2010).
Kawachi, I. & Kennedy, B. P. The Health of Nations: Why Inequality is Harmful to Your Health (New Press, 2006).
Alesina, A., Di Tella, R. & MacCulloch, R. Inequality and happiness: are Europeans and Americans different? J. Pub. Econ. 88, 2009–2042 (2004).
Google Scholar
Layte, R. The association between income inequality and mental health: testing status anxiety, social capital, and neo-materialist explanations. Eur. Sociol. Rev. 28, 498–511 (2012).
Google Scholar
Abdel-Khalek, A. M. Measuring happiness with a single-item scale. Soc. Behav. Pers. 34, 139–150 (2006).
Google Scholar
Oishi, S., Kesebir, S. & Diener, E. Income inequality and happiness. Psychol. Sci. 22, 1095–1100 (2011).
Google Scholar
Napier, J. L. & Jost, J. T. Why are conservatives happier than liberals? Psychol. Sci. 19, 565–572 (2008).
Google Scholar
Diener, E., Diener, M. & Diener, C. Factors predicting the subjective well-being of nations. J. Pers. Soc. Psychol. 69, 851–864 (1995).
Google Scholar
Wilkinson, R. Comment: income, inequality, and social cohesion. Am. J. Public Health 87, 1504–1506 (1997).
Google Scholar
Peters, K. et al. The language of inequality: evidence economic inequality increases wealth category. Pers. Soc. Psychol. Bull. 48, 1204–1219 (2022).
Google Scholar
Kim, Y. & Sommet, N. Income is a stronger predictor of subjective social class in more economically unequal places. Pers. Soc. Psychol. Bull. 51, 1173–1186 (2025).
Google Scholar
Sommet, N., Elliot, A. J., Jamieson, J. P. & Butera, F. Income inequality, perceived competitiveness, and approach-avoidance motivation. J. Pers. 87, 767–784 (2019).
Google Scholar
Sánchez-Rodríguez, Á, Willis, G. B., Jetten, J. & Rodríguez-Bailón, R. Economic inequality enhances inferences that the normative climate is individualistic and competitive. Eur. J. Soc. Psychol. 49, 1114–1127 (2019).
Google Scholar
Davidai, S. Economic inequality fosters the belief that success is zero-sum. Pers. Soc. Psychol. Bull. 51, 1030–1046 (2023).
Murayama, K. & Elliot, A. J. The competition–performance relation: a meta-analytic review and test of the opposing processes model of competition and performance. Psychol. Bull. 138, 1035–1070 (2012).
Google Scholar
Sommet, N. & Elliot, A. J. The effects of US county and state income inequality on self-reported happiness and health are equivalent to zero. Qual. Life Res. 31, 1999–2009 (2022).
Google Scholar
Burns, J. K., Tomita, A. & Kapadia, A. S. Income inequality and schizophrenia: increased schizophrenia incidence in countries with high levels of income inequality. Int. J. Soc. Psychiatry 60, 185–196 (2014).
Google Scholar
Tibber, M. S., Walji, F., Kirkbride, J. B. & Huddy, V. The association between income inequality and adult mental health at the subnational level—a systematic review. Soc. Psychiatry Psychiatr. Epidemiol. 57, 1–24 (2022).
Google Scholar
Ngamaba, K. H., Panagioti, M. & Armitage, C. J. Income inequality and subjective well-being: a systematic review and meta-analysis. Qual. Life Res. 27, 577–596 (2018).
Google Scholar
Patel, V. et al. Income inequality and depression: a systematic review and meta-analysis of the association and a scoping review of mechanisms. World Psychiatry 17, 76–89 (2018).
Google Scholar
Ribeiro, W. S. et al. Income inequality and mental illness-related morbidity and resilience: a systematic review and meta-analysis. Lancet Psychiatry 4, 554–562 (2017).
Google Scholar
Wilkinson, R. & Pickett, K. Inequality and mental illness. Lancet Psychiatry 4, 512–513 (2017).
Google Scholar
Vergés, A. In Etiopathogenic Theories and Models in Depression (eds Jiménez, J. P. et al.) 223–241 (Springer, 2021).
Guolo, A. & Varin, C. Random-effects meta-analysis: the number of studies matters. Stat. Methods Med. Res. 26, 1500–1518 (2017).
Google Scholar
Metelli, S. & Chaimani, A. Challenges in meta-analyses with observational studies. BMJ Mental Health 23, 83–87 (2020).
Sterne, J. A. C., Gavaghan, D. & Egger, M. Publication and related bias in meta-analysis: power of statistical tests and prevalence in the literature. J. Clin. Epidemiol. 53, 1119–1129 (2000).
Google Scholar
Kenny, D. A. & Judd, C. M. The unappreciated heterogeneity of effect sizes: implications for power, precision, planning of research, and replication. Psychol. Methods 24, 578–589 (2019).
Google Scholar
Moreau, D. & Gamble, B. Conducting a meta-analysis in the age of open science: tools, tips, and practical recommendations. Psychol. Methods 27, 426–432 (2022).
Google Scholar
Oishi, S., Cha, Y., Komiya, A. & Ono, H. Money and happiness: the income–happiness correlation is higher when income inequality is higher. PNAS Nexus 1, pgac224 (2022).
Google Scholar
Bor, J., Cohen, G. H. & Galea, S. Population health in an era of rising income inequality: USA, 1980–2015. Lancet 389, 1475–1490 (2017).
Google Scholar
Gravelle, H. How much of the relation between population mortality and unequal distribution of income is a statistical artefact? BMJ 316, 382–385 (1998).
Google Scholar
Shimonovich, M., Pearce, A., Thomson, H., McCartney, G. & Katikireddi, S. V. Assessing the causal relationship between income inequality and mortality and self-rated health: protocol for systematic review and meta-analysis. Syst. Rev. 11, 20 (2022).
Google Scholar
Kondo, N. et al. Income inequality, mortality, and self rated health: meta-analysis of multilevel studies. BMJ 339, b4471 (2009).
Google Scholar
Duncan, D. & Sabirianova Peter, K. Unequal inequalities: do progressive taxes reduce income inequality?. Int. Tax Public Finan. 23, 762–783 (2016).
Google Scholar
Murad, M. H., Wang, Z., Chu, H. & Lin, L. When continuous outcomes are measured using different scales: guide for meta-analysis and interpretation. BMJ 364, k4817 (2019).
Google Scholar
Carpenter, C. J. Meta-analyzing apples and oranges: how to make applesauce instead of fruit salad. Hum. Commun. Res. 46, 322–333 (2019).
Google Scholar
Schröder, M. Income inequality and life satisfaction: unrelated between countries, associated within countries over time. J. Happiness Stud. 19, 1021–1043 (2018).
Google Scholar
Johnston, C. D. & Newman, B. J. Economic inequality and US public policy mood across space and time. Am. Politics Res. 44, 164–191 (2016).
Google Scholar
De Maio, F. G. Income inequality measures. J. Epidemiol. Community Health 61, 849–852 (2007).
Google Scholar
Stanley, T. D., Carter, E. C. & Doucouliagos, H. What meta-analyses reveal about the replicability of psychological research. Psychol. Bull. 144, 1325–1346 (2018).
Google Scholar
IntHout, J., Ioannidis, J. P., Borm, G. F. & Goeman, J. J. Small studies are more heterogeneous than large ones: a meta-meta-analysis. J. Clin. Epidemiol. 68, 860–869 (2015).
Google Scholar
Igelström, E., Campbell, M., Craig, P. & Katikireddi, S. V. Cochrane’s risk of bias tool for non-randomized studies (ROBINS-I) is frequently misapplied: a methodological systematic review. J. Clin. Epidemiol. 140, 22–32 (2021).
Google Scholar
Onofrio, B. M., Sjölander, A., Lahey, B. B., Lichtenstein, P. & Öberg, A. S. Accounting for confounding in observational studies. Annu. Rev. Clin. Psychol. 16, 25–48 (2020).
Google Scholar
Lynch, J. W., Smith, G. D., Kaplan, G. A. & House, J. S. Income inequality and mortality: importance to health of individual income, psychosocial environment, or material conditions. BMJ 320, 1200–1204 (2000).
Google Scholar
Jachimowicz, J. M. et al. Higher economic inequality intensifies the financial hardship of people living in poverty by fraying the community buffer. Nat. Hum. Behav. 4, 702–712 (2020).
Google Scholar
Cheung, F. & Lucas, R. E. Income inequality is associated with stronger social comparison effects: the effect of relative income on life satisfaction. J. Pers. Soc. Psychol. 110, 332–441 (2016).
Google Scholar
Louie, P., Wu, C., Shahidi, F. V. & Siddiqi, A. Inflation hardship, gender, and mental health. SSM Popul. Health 23, 101452 (2023).
Google Scholar
Schünemann, H. J. et al. in Cochrane Handbook for Systematic Reviews of Interventions (eds J. P. T. Higgins et al.) 375–402 (John Wiley & Sons, 2019).
Zeng, L. et al. GRADE guidelines 32: GRADE offers guidance on choosing targets of GRADE certainty of evidence ratings. J. Clin. Epidemiol. 137, 163–175 (2021).
Google Scholar
Higgins, J. P. T. et al. A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E). Environ. Int. 186, 108602 (2024).
Google Scholar
Cheng, H. G. & Phillips, M. R. Secondary analysis of existing data: opportunities and implementation. Shanghai Arch. Psychiatry 26, 371–375 (2014).
Google Scholar
Lloyd’s Register Foundation. Lloyd’s Register Foundation World Risk Poll Methodology (Lloyd’s Register Foundation, 2021).
Lustig, N. The “Missing Rich” in Household Surveys: Causes and Correction Approaches. Working Paper Series 75 (ECINEQ, 2020).
Davidai, S., Goya-Tocchetto, D. & Lawson, M. A. Economic segregation is associated with reduced concerns about economic inequality. Nat. Commun. 15, 5655 (2024).
Google Scholar
Willis, G. B., García-Sánchez, E., Sánchez-Rodríguez, Á, García-Castro, J. D. & Rodríguez-Bailón, R. The psychosocial effects of economic inequality depend on its perception. Nat. Rev. Psychol. 1, 301–309 (2022).
Google Scholar
Metz, N. & Burdina, M. Neighbourhood income inequality and property crime. Urban Stud. 55, 133–150 (2018).
Google Scholar
Mamunuru, S. M., Shrivastava, A. & Jayadev, A. Social networks and experienced inequality. J. Econ. Behav. Org. 229, 106799 (2025).
Google Scholar
Blesch, K., Hauser, O. P. & Jachimowicz, J. M. Measuring inequality beyond the Gini coefficient may clarify conflicting findings. Nat. Hum. Behav. 6, 1525–1536 (2022).
Google Scholar
Starmans, C., Sheskin, M. & Bloom, P. Why people prefer unequal societies. Nat. Hum. Behav. 1, 0082 (2017).
Google Scholar
Sareen, J., Afifi, T. O., McMillan, K. A. & Asmundson, G. J. Relationship between household income and mental disorders: findings from a population-based longitudinal study. Arch. Gen. Psychiatry 68, 419–427 (2011).
Google Scholar
Thomson, R. M. et al. How do income changes impact on mental health and wellbeing for working-age adults? A systematic review and meta-analysis. Lancet Public Health 7, e515–e528 (2022).
Google Scholar
Ridley, M., Rao, G., Schilbach, F. & Patel, V. Poverty, depression, and anxiety: causal evidence and mechanisms. Science 370, eaay0214 (2020).
Google Scholar
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & Group, P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6, e1000097 (2009).
Google Scholar
Higgins, J. P. et al. Cochrane Handbook for Systematic Reviews of Interventions (John Wiley & Sons, 2019).
Lakens, D., Hilgard, J. & Staaks, J. On the reproducibility of meta-analyses: six practical recommendations. BMC Psychol. 4, 24 (2016).
Google Scholar
Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71 (2021).
Google Scholar
Morgan, R. L., Whaley, P., Thayer, K. A. & Schünemann, H. J. Identifying the PECO: a framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ. Int. 121, 1027–1031 (2018).
Google Scholar
Nishi, A., Shirado, H., Rand, D. G. & Christakis, N. A. Inequality and visibility of wealth in experimental social networks. Nature 526, 426–429 (2015).
Google Scholar
Gao, L., Sun, B., Du, Z. & Lv, G. How wealth inequality affects happiness: the perspective of social comparison. Front. Psychol. 13, 829707 (2022).
Google Scholar
Proctor, C. in Encyclopedia of Quality of Life and Well-Being Research (ed. Michalos, A. C.) 6437–6441 (Springer, 2014).
Sorochan, J. & O’Neill, M. in Encyclopedia of Quality of Life and Well-Being Research (ed. Michalos, A. C.) 3995–3998 (Springer, 2014).
NCBI. Mental Disorders 2021. National Library of Medicine https://www.ncbi.nlm.nih.gov/mesh/68001523 (accessed April 2021).
Diez Roux, A. V. A glossary for multilevel analysis. J. Epidemiol. Community Health 56, 588 (2002).
Google Scholar
Inist-CNRS. OpenGrey. DANS https://doi.org/10.17026/dans-xtf-47w5 (2021).
Kim, S.-W. & Gil, J.-M. Research paper classification systems based on TF-IDF and LDA schemes. Hum.-centric Comput. Inf. Sci. 9, 30 (2019).
Google Scholar
Belur, J., Tompson, L., Thornton, A. & Simon, M. Interrater reliability in systematic review methodology: exploring variation in coder decision-making. Sociol. Methods Res. 50, 837–865 (2021).
Google Scholar
Topuz, S. G. The relationship between income inequality and economic growth: are transmission channels effective? Soc. Indic. Res. 162, 1177–1231 (2022).
Google Scholar
Buttrick, N. R., Heintzelman, S. J. & Oishi, S. Inequality and well-being. Curr. Opin. Psychol. 18, 15–20 (2017).
Google Scholar
Helliwell, J. F., Huang, H. & Wang, S. in World Happiness Report Vol. 2 (eds Helliwell, J. F. et al.) 11–46 (Sustainable Development Solutions Network, 2019).
Rohrer, J. M. Thinking clearly about correlations and causation: graphical causal models for observational data. Adv. Methods Pract. Psychol. Sci. 1, 27–42 (2018).
Google Scholar
Mdingi, K. & Ho, S.-Y. Literature review on income inequality and economic growth. MethodsX 8, 101402 (2021).
Google Scholar
Solt, F. Measuring income inequality across countries and over time: the standardized world income inequality database. Soc. Sci. Q. 101, 1183–1199 (2020).
Google Scholar
Kawachi, I. & Kennedy, B. P. The relationship of income inequality to mortality: does the choice of indicator matter? Soc. Sci. Med. 45, 1121–1127 (1997).
Google Scholar
Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Methods 1, 97–111 (2010).
Google Scholar
Van den Noortgate, W., López-López, J. A., Marín-Martínez, F. & Sánchez-Meca, J. Meta-analysis of multiple outcomes: a multilevel approach. Behav. Res. Methods 47, 1274–1294 (2015).
Google Scholar
Fernández-Castilla, B. et al. A demonstration and evaluation of the use of cross-classified random-effects models for meta-analysis. Behav. Res. Methods 51, 1286–1304 (2019).
Google Scholar
Cheung, M. W. A guide to conducting a meta-analysis with non-independent effect sizes. Neuropsychol. Rev. 29, 387–396 (2019).
Google Scholar
Hansen, C., Steinmetz, H. & Block, J. How to conduct a meta-analysis in eight steps: a practical guide. Manag. Rev. Q. 72, 1–19 (2022).
Google Scholar
Viechtbauer, W. & Cheung, M. W.-L. Outlier and influence diagnostics for meta-analysis. Res. Synth. Methods 1, 112–125 (2010).
Google Scholar
Cook, R. D. & Weisberg, S. Residuals and Influence in Regression (Chapman and Hall, 1982).
Neter, J., Kutner, M. H., Nachtsheim, C. J. & Wasserman, W. Applied Linear Statistical Models 4th edn (Irwin, 1996).
Sheather, S. A Modern Approach to Regression with R (Springer Science & Business Media, 2009).
Altman, N. & Krzywinski, M. Analyzing outliers: influential or nuisance? Nat. Methods 13, 281–282 (2016).
Google Scholar
Rogers, J. L., Howard, K. I. & Vessey, J. T. Using significance tests to evaluate equivalence between two experimental groups. Psychol. Bull. 113, 553–565 (1993).
Google Scholar
Rosenthal, J. A. Qualitative descriptors of strength of association and effect size. J. Soc. Serv. Res. 21, 37–59 (1996).
Google Scholar
Lakens, D., Scheel, A. M. & Isager, P. M. Equivalence testing for psychological research: a tutorial. Adv. Methods Pract. Psychol. Sci. 1, 259–269 (2018).
Google Scholar
Harrer, M., Cuijpers, P., Furukawa, T. A. & Ebert, D. D. Doing Meta-Analysis with R: A Hands-On Guide 1st edn (Chapman & Hall/CRC, 2021).
Konstantopoulos, S. Fixed effects and variance components estimation in three-level meta-analysis. Res. Synth. Methods 2, 61–76 (2011).
Google Scholar
Joanna, I., John, P. A. I., Maroeska, M. R. & Jelle, J. G. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open 6, e010247 (2016).
Google Scholar
Peters, J. L., Sutton, A. J., Jones, D. R., Abrams, K. R. & Rushton, L. Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry. J. Clin. Epidemiol. 61, 991–996 (2008).
Google Scholar
Langan, D., Higgins, J. P. T., Gregory, W. & Sutton, A. J. Graphical augmentations to the funnel plot assess the impact of additional evidence on a meta-analysis. J. Clin. Epidemiol. 65, 511–519 (2012).
Google Scholar
Palmer, T. M., Sutton, A. J., Peters, J. L. & Moreno, S. G. Contour-enhanced funnel plots for meta-analysis. Stata J. 8, 242–254 (2008).
Google Scholar
Kossmeier, M., Tran, U. S. & Voracek, M. Visualizing meta-analytic data with R package metaviz. R package version 0.3.1 (2020).
Stanley, T. D. & Doucouliagos, H. Meta-regression approximations to reduce publication selection bias. Res. Synth. Methods 5, 60–78 (2014).
Google Scholar
Henmi, M. & Copas, J. B. Confidence intervals for random effects meta-analysis and robustness to publication bias. Stat. Med. 29, 2969–2983 (2010).
Google Scholar
Iyengar, S. & Greenhouse, J. B. Selection models and the file drawer problem. Stat. Sci. 3, 109–117 (1988).
Carter, E. C., Schönbrodt, F. D., Gervais, W. M. & Hilgard, J. Correcting for bias in psychology: a comparison of meta-analytic methods. Adv. Methods Pract. Psychol. Sci. 2, 115–144 (2019).
Google Scholar
Vevea, J. L. & Hedges, L. V. A general linear model for estimating effect size in the presence of publication bias. Psychometrika 60, 419–435 (1995).
Google Scholar
Coburn, K. M. & Vevea, J. L. Estimating weight-function models for publication bias (version 2.0.2). https://CRAN.R-project.org/package=weightr (2012).
Coburn, K. M. & Vevea, J. L. Publication bias as a function of study characteristics. Psychol. Methods 20, 310–330 (2015).
Google Scholar
Simonsohn, U., Nelson, L. D. & Simmons, J. P. P-curve: a key to the file-drawer. J. Exp. Psychol. 143, 534–547 (2014).
Google Scholar
Bishop, D. V. M. & Thompson, P. A. Problems in using p-curve analysis and text-mining to detect rate of p-hacking and evidential value. PeerJ 4, e1715 (2016).
Google Scholar
Brunner, J. & Schimmack, U. Estimating population mean power under conditions of heterogeneity and selection for significance. Meta-Psychology https://doi.org/10.15626/MP.2018.874 (2020).
van Aert, R. C. M., Wicherts, J. M. & van Assen, M. A. L. M. Conducting meta-analyses based on p values: reservations and recommendations for applying p-uniform and p-curve. Persp. Psychol. Sci. 11, 713–729 (2016).
Google Scholar
Bartoš, F. & Schimmack, U. Z-curve 2.0: estimating replication rates and discovery rates. Meta-Psychology https://doi.org/10.15626/MP.2021.2720 (2022).
Simonsohn, U., Simmons, J. P. & Nelson, L. D. Specification curve analysis. Nat. Hum. Behav. 4, 1208–1214 (2020).
Google Scholar
Moeyaert, M. et al. Methods for dealing with multiple outcomes in meta-analysis: a comparison between averaging effect sizes, robust variance estimation and multilevel meta-analysis. Int. J. Soc. Res. Method. 20, 559–572 (2017).
Google Scholar
Gleser, L. J. & Olkin, I. in The Handbook of Research Synthesis (eds Cooper, H. & Hedges, L. V.) 339–355 (Russell Sage Foundation, 1994).
De Dominicis, L., Florax, R. J. G. M. & De Groot, H. L. F. A meta-analysis on the relationship between income inequality and economic growth. Scot. J. Polit. Econ. 55, 654–682 (2008).
Google Scholar
McNeish, D. & Kelley, K. Fixed effects models versus mixed effects models for clustered data: reviewing the approaches, disentangling the differences, and making recommendations. Psychol. Methods 24, 20–35 (2019).
Google Scholar
Borenstein, M. & Hedges, L. V. in The Handbook of Research Synthesis and Meta-Analysis 3rd edn (eds Cooper, H., Hedges, L. V. & Valentine, J. C.) 207–244 (Russell Sage Foundation, 2019).
Pastor, D. A. & Lazowski, R. A. On the multilevel nature of meta-analysis: a tutorial, comparison of software programs, and discussion of analytic choices. Multivar. Behav. Res. 53, 74–89 (2018).
Google Scholar
Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Mem. Lang. 68, 255–278 (2013).
Google Scholar
Koch, J. & Leimbach, M. SSP economic growth projections: Major changes of key drivers in integrated assessment modelling. Ecol. Econ. 206, 107751 (2023).
Google Scholar
Anyaegbu, G. Using the OECD equivalence scale in taxes and benefits analysis. Econ. Labour Mark. Rev. 4, 49–54 (2010).
Google Scholar
Kahneman, D. & Deaton, A. High income improves evaluation of life but not emotional well-being. Proc. Natl Acad. Sci. USA 107, 16489–16493 (2010).
Google Scholar
Gallup. Worldwide Research Methodology and Codebook (Gallup, 2021).
Turon, H. et al. Agreement between a single-item measure of anxiety and depression and the Hospital Anxiety and Depression Scale: a cross-sectional study. PLoS ONE 14, e0210111 (2019).
Google Scholar
World Bank Group. GNI per capita, Atlas method (current US$). World Bank https://data.worldbank.org/indicator/NY.GNP.PCAP.CD (2024).
Allison, P. D. Fixed Effects Regression Models (SAGE, 2009).
Brüderl, J. & Ludwig, V. in The SAGE Handbook of Regression Analysis and Causal Inference (eds Best H. & Wolf C.) 327–357 (2015).
Wooldridge, J. M. Econometric Analysis of Cross Section and Panel Data (MIT Press, 2010).
Falkenström, F., Solomonov, N. & Rubel, J. To detrend, or not to detrend, that is the question? The effects of detrending on cross-lagged effects in panel models. Psychol. Methods https://doi.org/10.1037/met0000632 (2023).
Giesselmann, M. & Schmidt-Catran, A. W. Interactions in fixed effects regression models. Sociol. Methods Res. 51, 1100–1127 (2022).
Google Scholar
World Bank Group. Indicators. World Bank https://data.worldbank.org/indicator (2024).
Van Lissa, C. J. Doing meta-analysis in R and exploring heterogeneity using metaforest. GitHub https://cjvanlissa.github.io/Doing-Meta-Analysis-in-R/ (2019).
Strobl, C., Malley, J. & Tutz, G. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol. Methods 14, 323 (2009).
Google Scholar
Fife, D. A. & D’Onofrio, J. Common, uncommon, and novel applications of random forest in psychological research. Behav. Res. Methods 55, 2447–2466 (2023).
Google Scholar
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
Google Scholar
Cramer, A. O. et al. Hidden multiplicity in exploratory multiway ANOVA: prevalence and remedies. Psychon. Bull. Rev. 23, 640–647 (2016).
Google Scholar
Diener, E., Inglehart, R. & Tay, L. Theory and validity of life satisfaction scales. Soc. Indic. Res. 112, 497–527 (2013).
Google Scholar
World Bank Group. Inflation, GDP deflator: linked series (annual %). World Bank https://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG.AD (2024).
■ مصدر الخبر الأصلي
نشر لأول مرة على: www.nature.com
تاريخ النشر: 2025-11-26 02:00:00
الكاتب: Nicolas Sommet
تنويه من موقع “yalebnan.org”:
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بتاريخ: 2025-11-26 02:00:00.
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